diff --git a/index.html b/index.html
index 0843247..980561f 100644
--- a/index.html
+++ b/index.html
@@ -75,9 +75,8 @@
 <div class="section level2">
 <h2 id="about-the-package">About the package<a class="anchor" aria-label="anchor" href="#about-the-package"></a>
 </h2>
-<p><code>bulkreadr</code> is an R package designed to simplify and streamline the process of reading and processing large volumes of data. With a collection of functions tailored for bulk data operations, the package allows users to efficiently read multiple sheets from Microsoft Excel/Google Sheets workbooks and multiple CSV files from a directory. It returns the data as organized data frames, making it convenient for further analysis and manipulation.</p>
-<p>Whether dealing with extensive data sets or batch processing tasks, “bulkreadr” empowers users to effortlessly handle data in bulk, saving time and effort in data preparation workflows.</p>
-<p>Additionally, the package seamlessly works with labelled data from SPSS and Stata. For a quick video tutorial, I gave a talk at the International Association of Statistical Computing webinar. The recorded session is available <a href="https://isi-web.org/webinar/iasc-bulkreadr-ultimate-tool-reading-data-bulk" class="external-link">here</a> and the webinar resources <a href="https://github.com/gbganalyst/bulkreadr-webinar" class="external-link">here</a>.</p>
+<p><code>bulkreadr</code> is a robust and user-friendly R package designed to optimize the process of importing and processing large datasets. It offers streamlined functionality for reading multiple sheets from Microsoft Excel or Google Sheets workbooks, as well as handling multiple CSV files from a directory with ease.</p>
+<p>In addition, <code>bulkreadr</code> provides seamless compatibility with labelled data from software such as <code>SPSS</code> and <code>Stata</code>, automatically converting coded values into their respective labels upon import. This significantly reduces manual effort, making it an invaluable resource for data analysts and researchers looking to save time and optimize their workflows. For a quick video tutorial, I gave a talk at the International Association of Statistical Computing webinar. The recorded session is available <a href="https://isi-web.org/webinar/iasc-bulkreadr-ultimate-tool-reading-data-bulk" class="external-link">here</a> and the webinar resources <a href="https://github.com/gbganalyst/bulkreadr-webinar" class="external-link">here</a>.</p>
 </div>
 <div class="section level2">
 <h2 id="the-concept-map">The Concept Map<a class="anchor" aria-label="anchor" href="#the-concept-map"></a>
diff --git a/pkgdown.yml b/pkgdown.yml
index fc82c39..20d54fb 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   bulkreadr: bulkreadr.html
   labelled-data: labelled-data.html
   other-functions: other-functions.html
-last_built: 2024-09-10T12:36Z
+last_built: 2024-09-10T13:55Z
 urls:
   reference: https://gbganalyst.github.io/bulkreadr/reference
   article: https://gbganalyst.github.io/bulkreadr/articles
diff --git a/search.json b/search.json
index b1ab4c9..e975c6f 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://gbganalyst.github.io/bulkreadr/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 bulkreadr authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"about-the-package","dir":"Articles","previous_headings":"","what":"About the package","title":"Introduction to bulkreadr","text":"bulkreadr R package designed simplify streamline process reading processing large volumes data. collection functions tailored bulk data operations, package allows users efficiently read multiple sheets Microsoft Excel/Google Sheets workbooks multiple CSV files directory. returns data organized data frames, making convenient analysis manipulation. Whether dealing extensive data sets batch processing tasks, “bulkreadr” empowers users effortlessly handle data bulk, saving time effort data preparation workflows.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Introduction to bulkreadr","text":"can install bulkreadr package CRAN : development version GitHub ","code":"install.packages(\"bulkreadr\") if(!require(\"devtools\")){  install.packages(\"devtools\") }  devtools::install_github(\"gbganalyst/bulkreadr\")"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"how-to-load-the-package","dir":"Articles","previous_headings":"","what":"How to load the package","title":"Introduction to bulkreadr","text":"Now installed bulkreadr package, can simply load using:","code":"library(bulkreadr)"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"functions-in-bulkreadr-package","dir":"Articles","previous_headings":"","what":"Functions in bulkreadr package","title":"Introduction to bulkreadr","text":"section provides concise overview different functions available bulkreadr package importing bulk data R.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"note","dir":"Articles","previous_headings":"","what":"Note","title":"Introduction to bulkreadr","text":"majority functions within package, utilize data stored system file bulkreadr, can accessed using system.file() function. wish utilize data stored local directory, please ensure set appropriate file path prior using functions provided bulkreadr package.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"read_excel_workbook","dir":"Articles","previous_headings":"","what":"read_excel_workbook()","title":"Introduction to bulkreadr","text":"read_excel_workbook() reads data sheets Excel workbook return appended dataframe.","code":"# path to the xls/xlsx file.  path <- system.file(\"extdata\", \"Diamonds.xlsx\", package = \"bulkreadr\")  # read the sheets  read_excel_workbook(path = path) #> # A tibble: 260 × 9 #>   carat color clarity depth table price     x     y     z #>   <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1  2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #> 2  0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #> 3  1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #> 4  0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #> 5  0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #> # ℹ 255 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"read_excel_files_from_dir","dir":"Articles","previous_headings":"","what":"read_excel_files_from_dir()","title":"Introduction to bulkreadr","text":"read_excel_files_from_dir() reads Excel workbooks \"~/data\" directory returns appended dataframe.","code":"# path to the directory containing the xls/xlsx files.  directory <- system.file(\"xlsxfolder\",  package = \"bulkreadr\")  # import the workbooks  read_excel_files_from_dir(dir_path = directory) #> # A tibble: 260 × 10 #>   cut   carat color clarity depth table price     x     y     z #>   <chr> <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Fair   2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #> 2 Fair   0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #> 3 Fair   1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #> 4 Fair   0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #> 5 Fair   0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #> # ℹ 255 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"read_csv_files_from_dir","dir":"Articles","previous_headings":"","what":"read_csv_files_from_dir()","title":"Introduction to bulkreadr","text":"read_csv_files_from_dir() reads csv files \"~/data\" directory returns appended dataframe. resulting dataframe order CSV files directory.","code":"# path to the directory containing the CSV files.  directory <- system.file(\"csvfolder\",  package = \"bulkreadr\")  # import the csv files  read_csv_files_from_dir(dir_path = directory) #> # A tibble: 260 × 10 #>   cut   carat color clarity depth table price     x     y     z #>   <chr> <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Fair   2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #> 2 Fair   0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #> 3 Fair   1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #> 4 Fair   0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #> 5 Fair   0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #> # ℹ 255 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"read_gsheets","dir":"Articles","previous_headings":"","what":"read_gsheets()","title":"Introduction to bulkreadr","text":"read_gsheets() function imports data multiple sheets Google Sheets spreadsheet appends resulting dataframes sheet together create single dataframe. function powerful tool data analysis, allows easily combine data multiple sheets single dataset.","code":"# Google Sheet ID or the link to the sheet  sheet_id <- \"1izO0mHu3L9AMySQUXGDn9GPs1n-VwGFSEoAKGhqVQh0\"  # read all the sheets  read_gsheets(ss = sheet_id) #> # A tibble: 260 × 9 #>   carat color clarity depth table price     x     y     z #>   <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1  2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #> 2  0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #> 3  1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #> 4  0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #> 5  0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #> # ℹ 255 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"what-is-labelled-data-in-r","dir":"Articles","previous_headings":"","what":"What is labelled data in R?","title":"Introduction to labelled data","text":"Labelled data SPSS Stata refers datasets variable (column) values assigned meaningful labels. labels provide context, descriptions categories, making data easier understand analyze. instance, variable representing gender might numerical codes (1, 2) labels (“Male”, “Female”). feature enhances data analysis allowing researchers work descriptive labels instead deciphering codes numeric values, facilitating clearer interpretation communication statistical results. R ecosystem, packages like foreign haven, facilitates importation labelled data software like SPSS Stata, ensuring smooth transition R. bulkreadr package extends functionality leveraging haven streamline process. automatically converts labelled data R’s factor data type, eliminating need manual recoding. enhancement significantly improves efficiency data analysis workflow within R environment.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"note","dir":"Articles","previous_headings":"What is labelled data in R?","what":"Note","title":"Introduction to labelled data","text":"majority functions within package, utilize data stored system file bulkreadr, can accessed using system.file() function. wish utilize data stored local directory, please ensure set appropriate file path prior using functions provided bulkreadr package.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"read_spss_data","dir":"Articles","previous_headings":"What is labelled data in R?","what":"read_spss_data()","title":"Introduction to labelled data","text":"read_spss_data() designed seamlessly import data SPSS data (.sav .zsav) files. converts labelled variables factors, crucial step enhances ease data manipulation analysis within R programming environment. Read SPSS data file without converting variable labels column names Read SPSS data file convert variable labels column names","code":"library(bulkreadr)  file_path <- system.file(\"extdata\", \"Wages.sav\", package = \"bulkreadr\")  data <- read_spss_data(file = file_path)  data #> # A tibble: 400 × 9 #>      id  educ south                  sex   exper  wage occup marr        ed      #>   <dbl> <dbl> <fct>                  <fct> <dbl> <dbl> <fct> <fct>       <fct>   #> 1     3    12 does not live in South Male     17  7.5  Other Married     High s… #> 2     4    13 does not live in South Male      9 13.1  Other Not married Some c… #> 3     5    10 lives in South         Male     27  4.45 Other Not married Less t… #> 4    12     9 lives in South         Male     30  6.25 Other Not married Less t… #> 5    13     9 lives in South         Male     29 20.0  Other Married     Less t… #> # ℹ 395 more rows data <- read_spss_data(file = file_path, label = TRUE)  data #> # A tibble: 400 × 9 #>   `Worker ID` `Number of years of education` `Live in south`        Gender #>         <dbl>                          <dbl> <fct>                  <fct>  #> 1           3                             12 does not live in South Male   #> 2           4                             13 does not live in South Male   #> 3           5                             10 lives in South         Male   #> 4          12                              9 lives in South         Male   #> 5          13                              9 lives in South         Male   #> # ℹ 395 more rows #> # ℹ 5 more variables: `Number of years of work experience` <dbl>, #> #   `Wage (dollars per hour)` <dbl>, Occupation <fct>, `Marital status` <fct>, #> #   `Highest education level` <fct>"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"read_stata_data","dir":"Articles","previous_headings":"What is labelled data in R?","what":"read_stata_data()","title":"Introduction to labelled data","text":"read_stata_data() reads Stata data file (.dta) R data frame, converting labeled variables factors. Read Stata data file without converting variable labels column names Read Stata data file convert variable labels column names","code":"file_path <- system.file(\"extdata\", \"Wages.dta\", package = \"bulkreadr\")  data <- read_stata_data(file = file_path)  data #> # A tibble: 400 × 9 #>      id  educ south                  sex   exper  wage occup marr        ed      #>   <dbl> <dbl> <fct>                  <fct> <dbl> <dbl> <fct> <fct>       <fct>   #> 1     3    12 does not live in South Male     17  7.5  Other Married     High s… #> 2     4    13 does not live in South Male      9 13.1  Other Not married Some c… #> 3     5    10 lives in South         Male     27  4.45 Other Not married Less t… #> 4    12     9 lives in South         Male     30  6.25 Other Not married Less t… #> 5    13     9 lives in South         Male     29 20.0  Other Married     Less t… #> # ℹ 395 more rows data <- read_stata_data(file = file_path, label = TRUE)  data #> # A tibble: 400 × 9 #>   `Worker ID` `Number of years of education` `Live in south`        Gender #>         <dbl>                          <dbl> <fct>                  <fct>  #> 1           3                             12 does not live in South Male   #> 2           4                             13 does not live in South Male   #> 3           5                             10 lives in South         Male   #> 4          12                              9 lives in South         Male   #> 5          13                              9 lives in South         Male   #> # ℹ 395 more rows #> # ℹ 5 more variables: `Number of years of work experience` <dbl>, #> #   `Wage (dollars per hour)` <dbl>, Occupation <fct>, `Marital status` <fct>, #> #   `Highest education level` <fct>"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"generate_dictionary","dir":"Articles","previous_headings":"What is labelled data in R?","what":"generate_dictionary()","title":"Introduction to labelled data","text":"generate_dictionary() creates data dictionary specified data frame. function particularly useful understanding documenting structure dataset, similar data dictionaries Stata SPSS.","code":"# Creating a data dictionary from an SPSS file  file_path <- system.file(\"extdata\", \"Wages.sav\", package = \"bulkreadr\")  wage_data <- read_spss_data(file = file_path)  generate_dictionary(wage_data) #> # A tibble: 9 × 6 #>   position variable description                     `column type` missing levels #>      <int> <chr>    <chr>                           <chr>           <int> <name> #> 1        1 id       Worker ID                       dbl                 0 <NULL> #> 2        2 educ     Number of years of education    dbl                 0 <NULL> #> 3        3 south    Live in south                   fct                 0 <chr>  #> 4        4 sex      Gender                          fct                 0 <chr>  #> 5        5 exper    Number of years of work experi… dbl                 0 <NULL> #> # ℹ 4 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"look_for","dir":"Articles","previous_headings":"What is labelled data in R?","what":"look_for()","title":"Introduction to labelled data","text":"look_for() function designed emulate functionality Stata lookfor command R. provides powerful tool searching large datasets, specifically targeting variable names, variable label descriptions, factor levels, value labels. function handy users working extensive complex datasets, enabling quickly efficiently locate variables interest.","code":"# Look for a single keyword.  look_for(wage_data, \"south\") #>  pos variable label         col_type missing values                 #>  3   south    Live in south fct      0       does not live in South #>                                              lives in South  look_for(wage_data, \"^s\") #>  pos variable label         col_type missing values                 #>  3   south    Live in south fct      0       does not live in South #>                                              lives in South         #>  4   sex      Gender        fct      0       Male                   #>                                              Female"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/other-functions.html","id":"pull_out","dir":"Articles","previous_headings":"","what":"pull_out()","title":"Other functions in bulkreadr","text":"pull_out() similar [. acts vectors, matrices, arrays lists extract replace parts. pleasant use magrittr (⁠%>%⁠) base(|>) operators.","code":"library(bulkreadr) library(dplyr)  top_10_richest_nig <- c(\"Aliko Dangote\", \"Mike Adenuga\", \"Femi Otedola\", \"Arthur Eze\", \"Abdulsamad Rabiu\", \"Cletus Ibeto\", \"Orji Uzor Kalu\", \"ABC Orjiakor\", \"Jimoh Ibrahim\", \"Tony Elumelu\")  top_10_richest_nig %>%    pull_out(c(1, 5, 2)) #> [1] \"Aliko Dangote\"    \"Abdulsamad Rabiu\" \"Mike Adenuga\" top_10_richest_nig %>%    pull_out(-c(1, 5, 2)) #> [1] \"Femi Otedola\"   \"Arthur Eze\"     \"Cletus Ibeto\"   \"Orji Uzor Kalu\" #> [5] \"ABC Orjiakor\"   \"Jimoh Ibrahim\"  \"Tony Elumelu\""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/other-functions.html","id":"convert_to_date","dir":"Articles","previous_headings":"","what":"convert_to_date()","title":"Other functions in bulkreadr","text":"convert_to_date() parses input vector POSIXct date-time object. also powerful convert excel date number like 42370 date value like 2016-01-01.","code":"## ** heterogeneous dates **  dates <- c(   44869, \"22.09.2022\", NA, \"02/27/92\", \"01-19-2022\",   \"13-01-  2022\", \"2023\", \"2023-2\", 41750.2, 41751.99,   \"11 07 2023\", \"2023-4\"   )  # Convert to POSIXct or Date object  convert_to_date(dates) #>  [1] \"2022-11-04\" \"2022-09-22\" NA           \"1992-02-27\" \"2022-01-19\" #>  [6] \"2022-01-13\" \"2023-01-01\" \"2023-02-01\" \"2014-04-21\" \"2014-04-22\" #> [11] \"2023-07-11\" \"2023-04-01\"  # It can also convert date time object to date object   convert_to_date(lubridate::now()) #> [1] \"2024-09-10\""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/other-functions.html","id":"inspect_na","dir":"Articles","previous_headings":"","what":"inspect_na()","title":"Other functions in bulkreadr","text":"inspect_na() summarizes rate missingness column data frame. grouped data frame, rate missingness summarized separately group. Grouped dataframe summary","code":"# dataframe summary  inspect_na(airquality) #> # A tibble: 6 × 3 #>   col_name   cnt  pcnt #>   <chr>    <int> <dbl> #> 1 Ozone       37 24.2  #> 2 Solar.R      7  4.58 #> 3 Wind         0  0    #> 4 Temp         0  0    #> 5 Month        0  0    #> # ℹ 1 more row airquality %>%    group_by(Month) %>%    inspect_na() #> # A tibble: 25 × 4 #> # Groups:   Month [5] #>   Month col_name   cnt  pcnt #>   <int> <chr>    <int> <dbl> #> 1     5 Ozone        5  16.1 #> 2     5 Solar.R      4  12.9 #> 3     5 Wind         0   0   #> 4     5 Temp         0   0   #> 5     5 Day          0   0   #> # ℹ 20 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/other-functions.html","id":"fill_missing_values","dir":"Articles","previous_headings":"","what":"fill_missing_values()","title":"Other functions in bulkreadr","text":"fill_missing_values() efficient function addresses missing values data frame. uses imputation function, also known column-based imputation, impute missing values. supports various imputation methods continuous variables, including minimum, maximum, mean, median, harmonic mean, geometric mean. categorical variables, missing values replaced mode column. approach ensures accurate consistent replacements derived individual columns, resulting complete reliable dataset improved analysis decision-making. Impute using mean method continuous variables Impute using geometric mean continuous variables specify variables Petal_Length Petal_Width","code":"df <- tibble::tibble(   Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5),   Sepal.Width = c(4.1, 3.6, 3, 3, 2.9, 2.5, 2.4),   Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7),   Petal_Width = c(NA, 0.2, 1.2, 0.2, 1.3, 1.8, NA),   Species = c(\"setosa\", NA, \"versicolor\", \"setosa\",     NA, \"virginica\", \"setosa\"   ) ) df #> # A tibble: 7 × 5 #>   Sepal_Length Sepal.Width Petal_Length Petal_Width Species    #>          <dbl>       <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2         4.1          1.5        NA   setosa     #> 2          5           3.6          1.4         0.2 NA         #> 3          5.7         3            4.2         1.2 versicolor #> 4         NA           3            1.4         0.2 setosa     #> 5          6.2         2.9         NA           1.3 NA         #> # ℹ 2 more rows #' df <- tibble::tibble( #' Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5), #' Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7), #' Petal_Width = c(NA, 0.2, 1.2, 0.2, 1.3, 1.8, NA), #' Species = c(\"setosa\", NA, \"versicolor\", \"setosa\", #'            NA, \"virginica\", \"setosa\") #' ) result_df_mean <- fill_missing_values(df, method = \"mean\")  result_df_mean #> # A tibble: 7 × 5 #>   Sepal_Length Sepal.Width Petal_Length Petal_Width Species    #>          <dbl>       <dbl>        <dbl>       <dbl> <chr>      #> 1         5.2          4.1          1.5        0.94 setosa     #> 2         5            3.6          1.4        0.2  setosa     #> 3         5.7          3            4.2        1.2  versicolor #> 4         5.72         3            1.4        0.2  setosa     #> 5         6.2          2.9          3          1.3  setosa     #> # ℹ 2 more rows result_df_geomean <- fill_missing_values(df, selected_variables = c (\"Petal_Length\", \"Petal_Width\"), method = \"geometric\")  result_df_geomean #> # A tibble: 7 × 5 #>   Sepal_Length Sepal.Width Petal_Length Petal_Width Species    #>          <dbl>       <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2         4.1         1.5        0.732 setosa     #> 2          5           3.6         1.4        0.2   NA         #> 3          5.7         3           4.2        1.2   versicolor #> 4         NA           3           1.4        0.2   setosa     #> 5          6.2         2.9         2.22       1.3   NA         #> # ℹ 2 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/other-functions.html","id":"impute-missing-values-nas-in-a-grouped-data-frame","dir":"Articles","previous_headings":"fill_missing_values()","what":"Impute missing values (NAs) in a grouped data frame","title":"Other functions in bulkreadr","text":"can use fill_missing_values() grouped data frame using grouping map functions. example :","code":"sample_iris <- tibble::tibble( Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5), Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7), Petal_Width = c(0.3, 0.2, 1.2, 0.2, 1.3, 1.8, NA), Species = c(\"setosa\", \"setosa\", \"versicolor\", \"setosa\",           \"virginica\", \"virginica\", \"setosa\") ) sample_iris #> # A tibble: 7 × 4 #>   Sepal_Length Petal_Length Petal_Width Species    #>          <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2          1.5         0.3 setosa     #> 2          5            1.4         0.2 setosa     #> 3          5.7          4.2         1.2 versicolor #> 4         NA            1.4         0.2 setosa     #> 5          6.2         NA           1.3 virginica  #> # ℹ 2 more rows sample_iris %>%   group_by(Species) %>%   group_split() %>%   map_df(fill_missing_values, method = \"median\") #> # A tibble: 7 × 4 #>   Sepal_Length Petal_Length Petal_Width Species    #>          <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2          1.5         0.3 setosa     #> 2          5            1.4         0.2 setosa     #> 3          5.2          1.4         0.2 setosa     #> 4          5.5          3.7         0.2 setosa     #> 5          5.7          4.2         1.2 versicolor #> # ℹ 2 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Ezekiel Ogundepo. Author, maintainer. Ernest Fokoue. Contributor. Golibe Ezeechesi. Contributor. Fatimo Adebanjo. Contributor. Isaac Ajao. Contributor.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ogundepo E (2024). bulkreadr: Ultimate Tool Reading Data Bulk. R package version 1.1.1, https://gbganalyst.github.io/bulkreadr/, https://github.com/gbganalyst/bulkreadr.","code":"@Manual{,   title = {bulkreadr: The Ultimate Tool for Reading Data in Bulk},   author = {Ezekiel Ogundepo},   year = {2024},   note = {R package version 1.1.1, https://gbganalyst.github.io/bulkreadr/},   url = {https://github.com/gbganalyst/bulkreadr}, }"},{"path":"https://gbganalyst.github.io/bulkreadr/index.html","id":"bulkreadr-","dir":"","previous_headings":"","what":"The Ultimate Tool for Reading Data in Bulk","title":"The Ultimate Tool for Reading Data in Bulk","text":"Ultimate Tool Reading Data Bulk","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/index.html","id":"about-the-package","dir":"","previous_headings":"","what":"About the package","title":"The Ultimate Tool for Reading Data in Bulk","text":"bulkreadr R package designed simplify streamline process reading processing large volumes data. collection functions tailored bulk data operations, package allows users efficiently read multiple sheets Microsoft Excel/Google Sheets workbooks multiple CSV files directory. returns data organized data frames, making convenient analysis manipulation. Whether dealing extensive data sets batch processing tasks, “bulkreadr” empowers users effortlessly handle data bulk, saving time effort data preparation workflows. Additionally, package seamlessly works labelled data SPSS Stata. quick video tutorial, gave talk International Association Statistical Computing webinar. recorded session available webinar resources .","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"The Ultimate Tool for Reading Data in Bulk","text":"can install bulkreadr package CRAN : development version GitHub ","code":"install.packages(\"bulkreadr\") if(!require(\"devtools\")){  install.packages(\"devtools\") }  devtools::install_github(\"gbganalyst/bulkreadr\")"},{"path":"https://gbganalyst.github.io/bulkreadr/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"The Ultimate Tool for Reading Data in Bulk","text":"Now installed bulkreadr package, can simply load using: get started bulkreadr, read docs.","code":"library(bulkreadr)"},{"path":"https://gbganalyst.github.io/bulkreadr/index.html","id":"context","dir":"","previous_headings":"","what":"Context","title":"The Ultimate Tool for Reading Data in Bulk","text":"bulkreadr designed integrate augment capabilities established packages readxl, readr, googlesheets4, offering enhanced functionality reading bulk data within R programming environment. readxl tidyverse package reading Excel files (xls xlsx) R data frame. readr tidyverse package reading delimited files (e.g., csv tsv) R data frame. googlesheets4 package interact Google Sheets Sheets API v4 https://developers.google.com/sheets/api.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/aliases.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or replace parts of an object — pull_out","title":"Extract or replace parts of an object — pull_out","text":"pull_out() similar [. acts vectors, matrices, arrays lists extract replace parts. pleasant use magrittr (%>%) base (|>) operators.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/aliases.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or replace parts of an object — pull_out","text":"pull_out() return object class input object.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/aliases.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or replace parts of an object — pull_out","text":"","code":"good_choice <- letters %>%   pull_out(c(5, 2, 1, 4))  good_choice #> [1] \"e\" \"b\" \"a\" \"d\"  iris %>%   pull_out(, 1:4) %>%   head() #>   Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1          5.1         3.5          1.4         0.2 #> 2          4.9         3.0          1.4         0.2 #> 3          4.7         3.2          1.3         0.2 #> 4          4.6         3.1          1.5         0.2 #> 5          5.0         3.6          1.4         0.2 #> 6          5.4         3.9          1.7         0.4"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/bulkreadr-package.html","id":null,"dir":"Reference","previous_headings":"","what":"bulkreadr: The Ultimate Tool for Reading Data in Bulk — bulkreadr-package","title":"bulkreadr: The Ultimate Tool for Reading Data in Bulk — bulkreadr-package","text":"Designed simplify streamline process reading processing large volumes data R, package offers collection functions tailored bulk data operations. enables users efficiently read multiple sheets Microsoft Excel Google Sheets workbooks, well various CSV files directory. data returned organized data frames, facilitating analysis manipulation. Ideal handling extensive data sets batch processing tasks, bulkreadr empowers users manage data bulk effortlessly, saving time effort data preparation workflows. Additionally, package seamlessly works labelled data SPSS Stata.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/bulkreadr-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"bulkreadr: The Ultimate Tool for Reading Data in Bulk — bulkreadr-package","text":"Maintainer: Ezekiel Ogundepo gbganalyst@gmail.com (ORCID) contributors: Ernest Fokoue epfeqa@rit.edu (ORCID) [contributor] Golibe Ezeechesi golibe.ezeechesi@gmail.com [contributor] Fatimo Adebanjo adebanjofatimo2000@gmail.com [contributor] Isaac Ajao isaacoluwaseyiajao@gmail.com [contributor]","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/convert_to_date.html","id":null,"dir":"Reference","previous_headings":"","what":"User friendly date parsing function — convert_to_date","title":"User friendly date parsing function — convert_to_date","text":"convert_to_date() parses input vector POSIXct date object. also powerful convert excel date number like 42370 date value like 2016-01-01.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/convert_to_date.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User friendly date parsing function — convert_to_date","text":"","code":"convert_to_date(date_num_char, tz = \"UTC\")"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/convert_to_date.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User friendly date parsing function — convert_to_date","text":"date_num_char character numeric vector dates tz Time zone indicator. NULL (default), Date object returned. Otherwise POSIXct time zone attribute set tz.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/convert_to_date.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"User friendly date parsing function — convert_to_date","text":"vector class Date","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/convert_to_date.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"User friendly date parsing function — convert_to_date","text":"","code":"## ** heterogeneous dates **  dates <- c(   44869, \"22.09.2022\", NA, \"02/27/92\", \"01-19-2022\",   \"13-01-  2022\", \"2023\", \"2023-2\", 41750.2, 41751.99,   \"11 07 2023\", \"2023-4\" )  convert_to_date(dates) #>  [1] \"2022-11-04\" \"2022-09-22\" NA           \"1992-02-27\" \"2022-01-19\" #>  [6] \"2022-01-13\" \"2023-01-01\" \"2023-02-01\" \"2014-04-21\" \"2014-04-22\" #> [11] \"2023-07-11\" \"2023-04-01\""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/fill_missing_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Fill missing values in a data frame — fill_missing_values","title":"Fill missing values in a data frame — fill_missing_values","text":"fill_missing_values() efficient function addresses missing values data frame. uses imputation function, also known column-based imputation, impute missing values. continuous variables, supports various methods imputation, including minimum, maximum, mean, median, harmonic mean, geometric mean. categorical variables, missing values replaced mode column. approach ensures accurate consistent replacements derived individual columns, resulting complete reliable dataset improved analysis decision-making.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/fill_missing_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fill missing values in a data frame — fill_missing_values","text":"","code":"fill_missing_values(   df,   selected_variables = NULL,   method = c(\"mean\", \"min\", \"max\", \"median\", \"harmonic\", \"geometric\") )"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/fill_missing_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fill missing values in a data frame — fill_missing_values","text":"df dataframe process missing value imputation. selected_variables optional vector variable names within df missing values imputed. NULL (default), imputation applied variables data frame. Variables must quoted. method character string specifying imputation method continuous variables. Supported methods \"min\", \"max\", \"mean\", \"median\", \"harmonic\", \"geometric\". default method \"mean\". categorical variables, mode always used.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/fill_missing_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fill missing values in a data frame — fill_missing_values","text":"data frame missing values imputed according specified method.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/fill_missing_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fill missing values in a data frame — fill_missing_values","text":"","code":"library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union  # Assuming 'df' is the dataframe you want to process  df <- tibble::tibble( Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5), Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7), Petal_Width = c(NA, 0.2, 1.2, 0.2, 1.3, 1.8, NA), Species = c(\"setosa\", NA, \"versicolor\", \"setosa\",            NA, \"virginica\", \"setosa\") )  # Impute using the mean method for continuous variables  result_df_mean <- fill_missing_values(df, method = \"mean\")  result_df_mean #> # A tibble: 7 × 4 #>   Sepal_Length Petal_Length Petal_Width Species    #>          <dbl>        <dbl>       <dbl> <chr>      #> 1         5.2           1.5        0.94 setosa     #> 2         5             1.4        0.2  setosa     #> 3         5.7           4.2        1.2  versicolor #> 4         5.72          1.4        0.2  setosa     #> 5         6.2           3          1.3  setosa     #> 6         6.7           5.8        1.8  virginica  #> 7         5.5           3.7        0.94 setosa      # Impute using the geometric mean for continuous variables and specify # variables `Petal_Length` and `Petal_Width`.  result_df_geomean <- fill_missing_values(df, selected_variables = c (\"Petal_Length\", \"Petal_Width\"), method = \"geometric\")  result_df_geomean #> # A tibble: 7 × 4 #>   Sepal_Length Petal_Length Petal_Width Species    #>          <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2         1.5        0.732 setosa     #> 2          5           1.4        0.2   NA         #> 3          5.7         4.2        1.2   versicolor #> 4         NA           1.4        0.2   setosa     #> 5          6.2         2.22       1.3   NA         #> 6          6.7         5.8        1.8   virginica  #> 7          5.5         3.7        0.732 setosa      # Impute missing values (NAs) in a grouped data frame  # You can do that by using the following:  sample_iris <- tibble::tibble( Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5), Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7), Petal_Width = c(0.3, 0.2, 1.2, 0.2, 1.3, 1.8, NA), Species = c(\"setosa\", \"setosa\", \"versicolor\", \"setosa\",            \"virginica\", \"virginica\", \"setosa\") )  sample_iris %>% group_by(Species) %>% group_split() %>% map_df(fill_missing_values, method = \"median\") #> # A tibble: 7 × 4 #>   Sepal_Length Petal_Length Petal_Width Species    #>          <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2          1.5         0.3 setosa     #> 2          5            1.4         0.2 setosa     #> 3          5.2          1.4         0.2 setosa     #> 4          5.5          3.7         0.2 setosa     #> 5          5.7          4.2         1.2 versicolor #> 6          6.2          5.8         1.3 virginica  #> 7          6.7          5.8         1.8 virginica"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a data dictionary from labelled data — generate_dictionary","title":"Create a data dictionary from labelled data — generate_dictionary","text":"generate_dictionary() creates data dictionary specified data frame. function particularly useful understanding documenting structure dataset, similar data dictionaries Stata SPSS.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a data dictionary from labelled data — generate_dictionary","text":"","code":"generate_dictionary(data)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a data dictionary from labelled data — generate_dictionary","text":"data data frame survey object","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a data dictionary from labelled data — generate_dictionary","text":"tibble representing data dictionary. row corresponds variable original data frame, providing detailed information variable's characteristics.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a data dictionary from labelled data — generate_dictionary","text":"function returns tibble (modern version R's data frame) following columns: position: integer vector indicating column position data frame. variable: character vector containing names variables (columns). description: character vector human-readable description variable. column type: character vector specifying data type (e.g., numeric, character) variable. missing: integer vector indicating count missing values variable. levels: list vector containing levels categorical variables, applicable.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a data dictionary from labelled data — generate_dictionary","text":"","code":"# Creating a data dictionary from an SPSS file  file_path <- system.file(\"extdata\", \"Wages.sav\", package = \"bulkreadr\")  wage_data <- read_spss_data(file = file_path)  generate_dictionary(wage_data) #> # A tibble: 9 × 6 #>   position variable description                     `column type` missing levels #>      <int> <chr>    <chr>                           <chr>           <int> <name> #> 1        1 id       Worker ID                       dbl                 0 <NULL> #> 2        2 educ     Number of years of education    dbl                 0 <NULL> #> 3        3 south    Live in south                   fct                 0 <chr>  #> 4        4 sex      Gender                          fct                 0 <chr>  #> 5        5 exper    Number of years of work experi… dbl                 0 <NULL> #> 6        6 wage     Wage (dollars per hour)         dbl                 0 <NULL> #> 7        7 occup    Occupation                      fct                 0 <chr>  #> 8        8 marr     Marital status                  fct                 0 <chr>  #> 9        9 ed       Highest education level         fct                 0 <chr>"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize missingness in data frame columns — inspect_na","title":"Summarize missingness in data frame columns — inspect_na","text":"inspect_na() summarizes rate missingness column data frame. grouped data frame, rate missingness summarized separately group.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize missingness in data frame columns — inspect_na","text":"","code":"inspect_na(df)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize missingness in data frame columns — inspect_na","text":"df data frame","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize missingness in data frame columns — inspect_na","text":"tibble summarizing count percentage columnwise missingness data frame.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summarize missingness in data frame columns — inspect_na","text":"tibble returned contains columns: col_name, character vector containing column names df1. cnt, integer vector containing number missing values column. pcnt, percentage records columns missing.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize missingness in data frame columns — inspect_na","text":"","code":"library(dplyr)  # dataframe summary  inspect_na(airquality) #> # A tibble: 6 × 3 #>   col_name   cnt  pcnt #>   <chr>    <int> <dbl> #> 1 Ozone       37 24.2  #> 2 Solar.R      7  4.58 #> 3 Wind         0  0    #> 4 Temp         0  0    #> 5 Month        0  0    #> 6 Day          0  0     # grouped dataframe summary  airquality %>% group_by(Month) %>%  inspect_na() #> # A tibble: 25 × 4 #> # Groups:   Month [5] #>    Month col_name   cnt  pcnt #>    <int> <chr>    <int> <dbl> #>  1     5 Ozone        5  16.1 #>  2     5 Solar.R      4  12.9 #>  3     5 Wind         0   0   #>  4     5 Temp         0   0   #>  5     5 Day          0   0   #>  6     6 Ozone       21  70   #>  7     6 Solar.R      0   0   #>  8     6 Wind         0   0   #>  9     6 Temp         0   0   #> 10     6 Day          0   0   #> # ℹ 15 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/look_for.html","id":null,"dir":"Reference","previous_headings":"","what":"Look for keywords variable names and descriptions in labelled data — look_for","title":"Look for keywords variable names and descriptions in labelled data — look_for","text":"look_for() function designed emulate functionality Stata lookfor command R. provides powerful tool searching large datasets, specifically targeting variable names, variable label descriptions, factor levels, value labels. function handy users working extensive complex datasets, enabling quickly efficiently locate variables interest.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/look_for.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Look for keywords variable names and descriptions in labelled data — look_for","text":"","code":"look_for(   data,   ...,   labels = TRUE,   values = TRUE,   ignore.case = TRUE,   details = c(\"basic\", \"none\", \"full\") )"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/look_for.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Look for keywords variable names and descriptions in labelled data — look_for","text":"data data frame survey object ... optional list keywords, character string (several character strings), can formatted regular expression suitable base::grep() pattern, vector keywords; displays variables specified labels whether search variable labels (descriptions); TRUE default values whether search within values (factor levels value labels); TRUE default ignore.case whether make keywords case sensitive; TRUE default (case ignored matching) details add details variable (full details time consuming big data frames, FALSE equivalent \"none\" TRUE \"full\")","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/look_for.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Look for keywords variable names and descriptions in labelled data — look_for","text":"tibble data frame featuring variable position, name description (exists) original data frame.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/look_for.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Look for keywords variable names and descriptions in labelled data — look_for","text":"","code":"look_for(iris) #>  pos variable     label col_type missing values     #>  1   Sepal.Length —     dbl      0                  #>  2   Sepal.Width  —     dbl      0                  #>  3   Petal.Length —     dbl      0                  #>  4   Petal.Width  —     dbl      0                  #>  5   Species      —     fct      0       setosa     #>                                          versicolor #>                                          virginica   # Look for a single keyword.  look_for(iris, \"petal\") #>  pos variable     label col_type missing values #>  3   Petal.Length —     dbl      0              #>  4   Petal.Width  —     dbl      0               look_for(iris, \"s\") #>  pos variable     label col_type missing values     #>  1   Sepal.Length —     dbl      0                  #>  2   Sepal.Width  —     dbl      0                  #>  5   Species      —     fct      0       setosa     #>                                          versicolor #>                                          virginica"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"result calling rhs(lhs).","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_csv_files_from_dir.html","id":null,"dir":"Reference","previous_headings":"","what":"Reads all CSV files from a directory — read_csv_files_from_dir","title":"Reads all CSV files from a directory — read_csv_files_from_dir","text":"read_csv_files_from_dir reads csv files \"~/data\" directory returns appended dataframe. resulting dataframe order CSV files directory.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_csv_files_from_dir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reads all CSV files from a directory — read_csv_files_from_dir","text":"","code":"read_csv_files_from_dir(dir_path = \".\", col_types = NULL, .id = NULL)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_csv_files_from_dir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reads all CSV files from a directory — read_csv_files_from_dir","text":"dir_path Path directory containing CSV files. col_types One NULL, cols() specification, string. See vignette(\"readr\") details. NULL, column types inferred guess_max rows input, interspersed throughout file. convenient (fast), robust. guessed types wrong, need increase guess_max supply correct types . Column specifications created list() cols() must contain one column specification column. want read subset columns, use cols_only(). Alternatively, can use compact string representation character represents one column: c = character = integer n = number d = double l = logical f = factor D = date T = date time t = time ? = guess _ - = skip default, reading file without column specification print message showing readr guessed . remove message, set show_col_types = FALSE set options(readr.show_col_types = FALSE). .id name column store file path. useful reading multiple input files data file paths, data collection date. NULL (default) extra column created.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_csv_files_from_dir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reads all CSV files from a directory — read_csv_files_from_dir","text":"tibble. column type mismatch data frames row binding, error occur. R combine columns different types. example, combine column integers column characters.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_csv_files_from_dir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reads all CSV files from a directory — read_csv_files_from_dir","text":"","code":"directory <- system.file(\"csvfolder\",  package = \"bulkreadr\")  read_csv_files_from_dir(dir_path = directory, .id = \"cut\") #> # A tibble: 260 × 10 #>    cut                   carat color clarity depth table price     x     y     z #>    <chr>                 <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 /home/runner/work/_t…  2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #>  2 /home/runner/work/_t…  0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #>  3 /home/runner/work/_t…  1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #>  4 /home/runner/work/_t…  0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #>  5 /home/runner/work/_t…  0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #>  6 /home/runner/work/_t…  0.5  E     VS2      64.9    56  1397  5.01  4.95  3.23 #>  7 /home/runner/work/_t…  1    E     SI1      65.1    61  4435  6.15  6.08  3.98 #>  8 /home/runner/work/_t…  1.09 J     VS2      64.6    58  3443  6.48  6.41  4.16 #>  9 /home/runner/work/_t…  0.98 H     SI2      67.9    60  2777  6.05  5.97  4.08 #> 10 /home/runner/work/_t…  0.7  F     SI1      65.3    54  1974  5.58  5.54  3.63 #> # ℹ 250 more rows  # Column types mismatch error -------------------------------------- # If the `read_csv_files_from_dir()` function complains about a data type mismatch, # then set the `col_types` argument to `\"c\"`. # This will make all the column types in the resulting dataframe be characters."},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_files_from_dir.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","title":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","text":"read_excel_files_from_dir() reads Excel workbooks \"~/data\" directory returns appended dataframe.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_files_from_dir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","text":"","code":"read_excel_files_from_dir(dir_path, col_types = NULL, .id = NULL)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_files_from_dir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","text":"dir_path Path directory containing xls/xlsx files. col_types Either NULL guess spreadsheet character vector containing one entry per column options: \"skip\", \"guess\", \"logical\", \"numeric\", \"date\", \"text\" \"list\". exactly one col_type specified, recycled. content cell skipped column never read column appear data frame output. list cell loads column list length 1 vectors, typed using type guessing logic col_types = NULL, cell--cell basis. .id name optional identifier column. Provide string create output column identifies input. column use names available, otherwise use positions.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_files_from_dir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","text":"tibble. column type mismatch data frames row binding, error occur. R combine columns different types. example, combine column integers column characters.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_files_from_dir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","text":"","code":"directory <- system.file(\"xlsxfolder\",  package = \"bulkreadr\")  read_excel_files_from_dir(dir_path = directory, .id = \"cut\") #> # A tibble: 260 × 10 #>    cut                   carat color clarity depth table price     x     y     z #>    <chr>                 <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 /home/runner/work/_t…  2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #>  2 /home/runner/work/_t…  0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #>  3 /home/runner/work/_t…  1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #>  4 /home/runner/work/_t…  0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #>  5 /home/runner/work/_t…  0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #>  6 /home/runner/work/_t…  0.5  E     VS2      64.9    56  1397  5.01  4.95  3.23 #>  7 /home/runner/work/_t…  1    E     SI1      65.1    61  4435  6.15  6.08  3.98 #>  8 /home/runner/work/_t…  1.09 J     VS2      64.6    58  3443  6.48  6.41  4.16 #>  9 /home/runner/work/_t…  0.98 H     SI2      67.9    60  2777  6.05  5.97  4.08 #> 10 /home/runner/work/_t…  0.7  F     SI1      65.3    54  1974  5.58  5.54  3.63 #> # ℹ 250 more rows  # Column types mismatch error -------------------------------------- # If the `read_excel_files_from_dir()` function complains about a data type mismatch, # then set the `col_types` argument to `\"text\"`. # This will make all the column types in the resulting dataframe be characters."},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_workbook.html","id":null,"dir":"Reference","previous_headings":"","what":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","title":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","text":"read_excel_workbook() reads data sheets Excel workbook return appended dataframe.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_workbook.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","text":"","code":"read_excel_workbook(path, col_types = NULL, .id = NULL)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_workbook.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","text":"path Path xls/xlsx file. col_types Either NULL guess spreadsheet character vector containing one entry per column options: \"skip\", \"guess\", \"logical\", \"numeric\", \"date\", \"text\" \"list\". exactly one col_type specified, recycled. content cell skipped column never read column appear data frame output. list cell loads column list length 1 vectors, typed using type guessing logic col_types = NULL, cell--cell basis. .id name optional identifier column. Provide string create output column identifies input. column use names available, otherwise use positions.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_workbook.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","text":"tibble. column type mismatch data frames row binding, error occur. R combine columns different types. example, combine column integers column characters.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_workbook.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","text":"","code":"path <- system.file(\"extdata\", \"Diamonds.xlsx\", package = \"bulkreadr\", mustWork = TRUE)  read_excel_workbook(path = path, .id = \"Year\") #> # A tibble: 260 × 10 #>    Year  carat color clarity depth table price     x     y     z #>    <chr> <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 Fair   2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #>  2 Fair   0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #>  3 Fair   1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #>  4 Fair   0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #>  5 Fair   0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #>  6 Fair   0.5  E     VS2      64.9    56  1397  5.01  4.95  3.23 #>  7 Fair   1    E     SI1      65.1    61  4435  6.15  6.08  3.98 #>  8 Fair   1.09 J     VS2      64.6    58  3443  6.48  6.41  4.16 #>  9 Fair   0.98 H     SI2      67.9    60  2777  6.05  5.97  4.08 #> 10 Fair   0.7  F     SI1      65.3    54  1974  5.58  5.54  3.63 #> # ℹ 250 more rows   # Column types mismatch error -------------------------------------- # If the `read_excel_workbook()` function complains about a data type mismatch, # then set the `col_types` argument to `\"text\"`. # This will make all the column types in the resulting DataFrame be characters."},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_gsheets.html","id":null,"dir":"Reference","previous_headings":"","what":"Import data from multiple sheets in Google Sheets — read_gsheets","title":"Import data from multiple sheets in Google Sheets — read_gsheets","text":"read_gsheets() function imports data multiple sheets Google Sheets spreadsheet appends resulting dataframes sheet together create single dataframe. function powerful tool data analysis, allows easily combine data multiple sheets single dataset.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_gsheets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import data from multiple sheets in Google Sheets — read_gsheets","text":"","code":"read_gsheets(ss, col_types = NULL, .id = NULL)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_gsheets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import data from multiple sheets in Google Sheets — read_gsheets","text":"ss Something identifies Google Sheet: file id string drive_id URL can recover id one-row dribble, googledrive represents Drive files instance googlesheets4_spreadsheet, gs4_get() returns Processed as_sheets_id(). col_types Column types. Either NULL guess spreadsheet string readr-style shortcodes, one character code per column. exactly one col_type specified, recycled. See Column Specification . .id name optional identifier column. Provide string create output column identifies input. column use names available, otherwise use positions.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_gsheets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import data from multiple sheets in Google Sheets — read_gsheets","text":"tibble. column type mismatch data frames row binding, error occur. R combine columns different types. example, combine column integers column characters.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_gsheets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import data from multiple sheets in Google Sheets — read_gsheets","text":"","code":"if (FALSE) { # googlesheets4::gs4_has_token()  sheet_id <- \"1izO0mHu3L9AMySQUXGDn9GPs1n-VwGFSEoAKGhqVQh0\"  read_gsheets(ss = sheet_id, .id = \"sheet.name\")  # Column types mismatch error -------------------------------------- # If the `read_gsheets()` function complains about a data type mismatch, # then set the `col_types` argument to `\"c\"`. # This will make all the column types in the resulting dataframe be characters.  # For example, } if (FALSE) { # googlesheets4::gs4_has_token() sheet_id <- \"1rrjKAV05POre9lDVtHtZePTa8VROf1onVO47cHnhrTU\"  try(read_gsheets(ss = sheet_id)) # error, column types mismatch  read_gsheets(ss = sheet_id, col_types = \"c\")  }"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_spss_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Read SPSS data file — read_spss_data","title":"Read SPSS data file — read_spss_data","text":"read_spss_data() designed seamlessly import data SPSS data (.sav  .zsav) files. converts labelled variables factors, crucial step enhances ease data manipulation analysis within R programming environment.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_spss_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read SPSS data file — read_spss_data","text":"","code":"read_spss_data(file, label = FALSE)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_spss_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read SPSS data file — read_spss_data","text":"file path SPSS data file. label Logical indicating whether use variable labels column names (default FALSE).","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_spss_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read SPSS data file — read_spss_data","text":"tibble containing data SPSS file.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_spss_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read SPSS data file — read_spss_data","text":"","code":"# Read an SPSS data file without converting variable labels as column names  file_path <- system.file(\"extdata\", \"Wages.sav\", package = \"bulkreadr\")  data <- read_spss_data(file = file_path)  data #> # A tibble: 400 × 9 #>       id  educ south                  sex   exper  wage occup marr        ed     #>    <dbl> <dbl> <fct>                  <fct> <dbl> <dbl> <fct> <fct>       <fct>  #>  1     3    12 does not live in South Male     17  7.5  Other Married     High … #>  2     4    13 does not live in South Male      9 13.1  Other Not married Some … #>  3     5    10 lives in South         Male     27  4.45 Other Not married Less … #>  4    12     9 lives in South         Male     30  6.25 Other Not married Less … #>  5    13     9 lives in South         Male     29 20.0  Other Married     Less … #>  6    14    12 does not live in South Male     37  7.3  Other Married     High … #>  7    17    11 does not live in South Male     16  3.65 Other Not married Less … #>  8    20    12 does not live in South Male      9  3.75 Other Not married High … #>  9    21    11 lives in South         Male     14  4.5  Other Married     Less … #> 10    23     6 lives in South         Male     45  5.75 Other Married     Less … #> # ℹ 390 more rows  # Read an SPSS data file and convert variable labels as column names  data <- read_spss_data(file = file_path, label = TRUE)  data #> # A tibble: 400 × 9 #>    `Worker ID` `Number of years of education` `Live in south`        Gender #>          <dbl>                          <dbl> <fct>                  <fct>  #>  1           3                             12 does not live in South Male   #>  2           4                             13 does not live in South Male   #>  3           5                             10 lives in South         Male   #>  4          12                              9 lives in South         Male   #>  5          13                              9 lives in South         Male   #>  6          14                             12 does not live in South Male   #>  7          17                             11 does not live in South Male   #>  8          20                             12 does not live in South Male   #>  9          21                             11 lives in South         Male   #> 10          23                              6 lives in South         Male   #> # ℹ 390 more rows #> # ℹ 5 more variables: `Number of years of work experience` <dbl>, #> #   `Wage (dollars per hour)` <dbl>, Occupation <fct>, `Marital status` <fct>, #> #   `Highest education level` <fct>"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_stata_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Stata data file — read_stata_data","title":"Read Stata data file — read_stata_data","text":"Read Stata data file","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_stata_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Stata data file — read_stata_data","text":"","code":"read_stata_data(file, label = FALSE)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_stata_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read Stata data file — read_stata_data","text":"file path Stata data file. label Logical indicating whether use variable labels column names (default FALSE).","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_stata_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Stata data file — read_stata_data","text":"data frame containing Stata data, labeled variables converted factors.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_stata_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Stata data file — read_stata_data","text":"","code":"# Read Stata data file without converting variable labels as column names  file_path <- system.file(\"extdata\", \"Wages.dta\", package = \"bulkreadr\")  data <- read_stata_data(file = file_path)  data #> # A tibble: 400 × 9 #>       id  educ south                  sex   exper  wage occup marr        ed     #>    <dbl> <dbl> <fct>                  <fct> <dbl> <dbl> <fct> <fct>       <fct>  #>  1     3    12 does not live in South Male     17  7.5  Other Married     High … #>  2     4    13 does not live in South Male      9 13.1  Other Not married Some … #>  3     5    10 lives in South         Male     27  4.45 Other Not married Less … #>  4    12     9 lives in South         Male     30  6.25 Other Not married Less … #>  5    13     9 lives in South         Male     29 20.0  Other Married     Less … #>  6    14    12 does not live in South Male     37  7.3  Other Married     High … #>  7    17    11 does not live in South Male     16  3.65 Other Not married Less … #>  8    20    12 does not live in South Male      9  3.75 Other Not married High … #>  9    21    11 lives in South         Male     14  4.5  Other Married     Less … #> 10    23     6 lives in South         Male     45  5.75 Other Married     Less … #> # ℹ 390 more rows  # Read Stata data file and convert variable labels as column names  data <- read_stata_data(file = file_path, label = TRUE)  data #> # A tibble: 400 × 9 #>    `Worker ID` `Number of years of education` `Live in south`        Gender #>          <dbl>                          <dbl> <fct>                  <fct>  #>  1           3                             12 does not live in South Male   #>  2           4                             13 does not live in South Male   #>  3           5                             10 lives in South         Male   #>  4          12                              9 lives in South         Male   #>  5          13                              9 lives in South         Male   #>  6          14                             12 does not live in South Male   #>  7          17                             11 does not live in South Male   #>  8          20                             12 does not live in South Male   #>  9          21                             11 lives in South         Male   #> 10          23                              6 lives in South         Male   #> # ℹ 390 more rows #> # ℹ 5 more variables: `Number of years of work experience` <dbl>, #> #   `Wage (dollars per hour)` <dbl>, Occupation <fct>, `Marital status` <fct>, #> #   `Highest education level` <fct>"},{"path":"https://gbganalyst.github.io/bulkreadr/news/index.html","id":"bulkreadr-111-2024-03-01","dir":"Changelog","previous_headings":"","what":"bulkreadr 1.1.1 (2024-03-01)","title":"bulkreadr 1.1.1 (2024-03-01)","text":"CRAN release: 2024-05-26 pleased announce release bulkreadr version 1.1.1. version introduces significant enhancements features aimed improving functionality user experience package. Enhanced fill_missing_values() Functionality: fill_missing_values() function significantly improved support various imputation methods, empowering users handle missing data greater precision flexibility. addition previously supported “mean” imputation method, function now accommodates following strategies: Minimum Value (Min): Imputes missing entries minimum value observed within respective column. Maximum Value (Max): Fills missing data points maximum value found column. Mean: Continues offer average value imputation, replacing missing values mean available data column. Median: Imputes missing entries employing median value column, providing robust alternative mean imputation, especially presence outliers. Harmonic Mean: Offers sophisticated option imputing missing values using harmonic mean, ideal data distributions approach representative. Geometric Mean: Completes enhanced range imputation methods allowing replacement missing values geometric mean, suited datasets product values interest. enhancements designed provide users comprehensive toolkit data imputation, ensuring fill_missing_values() can effectively tailored meet unique demands diverse datasets analysis requirements. Package Website Launch: better serve users provide detailed documentation, launched official bulkreadr package website. website offers comprehensive guides, function references, examples help users maximize package’s potential. Visit us https://gbganalyst.github.io/bulkreadr information. believe updates significantly enhance data analysis workflows look forward feedback.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/news/index.html","id":"bulkreadr-110-2023-11-13","dir":"Changelog","previous_headings":"","what":"bulkreadr 1.1.0 (2023-11-13)","title":"bulkreadr 1.1.0 (2023-11-13)","text":"CRAN release: 2023-11-16 update includes following new features: generate_dictionary(): function designed automatically create comprehensive data dictionary labelled datasets. generated dictionary provides detailed insights variable, aiding better data understanding management. look_for(): enhances capability efficiently search within labelled datasets. allows users quickly find variable names descriptions searching specific keywords. feature streamlines data exploration analysis, particularly large datasets extensive variables. enhancements aim improve user experience data management exploration within bulkreadr. hope new features assist users effectively navigating understanding labelled datasets.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/news/index.html","id":"bulkreadr-100-2023-09-20","dir":"Changelog","previous_headings":"","what":"bulkreadr 1.0.0 (2023-09-20)","title":"bulkreadr 1.0.0 (2023-09-20)","text":"CRAN release: 2023-09-26 update includes following new features improvements: Developed read_stata_data() import Stata data file (.dta) R data frame, converting labeled variables factors. Reduced dependency packages optimize efficiency.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/news/index.html","id":"bulkreadr-020-2023-09-11","dir":"Changelog","previous_headings":"","what":"bulkreadr 0.2.0 (2023-09-11)","title":"bulkreadr 0.2.0 (2023-09-11)","text":"CRAN release: 2023-09-15 update includes following new features improvements: Developed bulkreadr vignette Developed read_spss_data() seamlessly import data SPSS data (.sav .zsav) files converting labelled variables factors, crucial step enhances ease data manipulation analysis within R programming environment. Added unit tests","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/news/index.html","id":"bulkreadr-010-2023-07-24","dir":"Changelog","previous_headings":"","what":"bulkreadr 0.1.0 (2023-07-24)","title":"bulkreadr 0.1.0 (2023-07-24)","text":"CRAN release: 2023-08-15 update includes following new features improvements: Improved error handling adding meaningful error messages functions within bulkreadr package. make easier users identify troubleshoot issues may arise use package. Added package-level documentation. user can now use ?bulkreadr::bulkreadr basic package-level documentation. Added inspect_na() summarize missingness data frame columns fill_missing_values() impute missing values dataframe.","code":""}]
+[{"path":"https://gbganalyst.github.io/bulkreadr/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 bulkreadr authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"about-the-package","dir":"Articles","previous_headings":"","what":"About the package","title":"Introduction to bulkreadr","text":"bulkreadr R package designed simplify streamline process reading processing large volumes data. collection functions tailored bulk data operations, package allows users efficiently read multiple sheets Microsoft Excel/Google Sheets workbooks multiple CSV files directory. returns data organized data frames, making convenient analysis manipulation. Whether dealing extensive data sets batch processing tasks, “bulkreadr” empowers users effortlessly handle data bulk, saving time effort data preparation workflows.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Introduction to bulkreadr","text":"can install bulkreadr package CRAN : development version GitHub ","code":"install.packages(\"bulkreadr\") if(!require(\"devtools\")){  install.packages(\"devtools\") }  devtools::install_github(\"gbganalyst/bulkreadr\")"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"how-to-load-the-package","dir":"Articles","previous_headings":"","what":"How to load the package","title":"Introduction to bulkreadr","text":"Now installed bulkreadr package, can simply load using:","code":"library(bulkreadr)"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"functions-in-bulkreadr-package","dir":"Articles","previous_headings":"","what":"Functions in bulkreadr package","title":"Introduction to bulkreadr","text":"section provides concise overview different functions available bulkreadr package importing bulk data R.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"note","dir":"Articles","previous_headings":"","what":"Note","title":"Introduction to bulkreadr","text":"majority functions within package, utilize data stored system file bulkreadr, can accessed using system.file() function. wish utilize data stored local directory, please ensure set appropriate file path prior using functions provided bulkreadr package.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"read_excel_workbook","dir":"Articles","previous_headings":"","what":"read_excel_workbook()","title":"Introduction to bulkreadr","text":"read_excel_workbook() reads data sheets Excel workbook return appended dataframe.","code":"# path to the xls/xlsx file.  path <- system.file(\"extdata\", \"Diamonds.xlsx\", package = \"bulkreadr\")  # read the sheets  read_excel_workbook(path = path) #> # A tibble: 260 × 9 #>   carat color clarity depth table price     x     y     z #>   <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1  2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #> 2  0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #> 3  1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #> 4  0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #> 5  0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #> # ℹ 255 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"read_excel_files_from_dir","dir":"Articles","previous_headings":"","what":"read_excel_files_from_dir()","title":"Introduction to bulkreadr","text":"read_excel_files_from_dir() reads Excel workbooks \"~/data\" directory returns appended dataframe.","code":"# path to the directory containing the xls/xlsx files.  directory <- system.file(\"xlsxfolder\",  package = \"bulkreadr\")  # import the workbooks  read_excel_files_from_dir(dir_path = directory) #> # A tibble: 260 × 10 #>   cut   carat color clarity depth table price     x     y     z #>   <chr> <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Fair   2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #> 2 Fair   0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #> 3 Fair   1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #> 4 Fair   0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #> 5 Fair   0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #> # ℹ 255 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"read_csv_files_from_dir","dir":"Articles","previous_headings":"","what":"read_csv_files_from_dir()","title":"Introduction to bulkreadr","text":"read_csv_files_from_dir() reads csv files \"~/data\" directory returns appended dataframe. resulting dataframe order CSV files directory.","code":"# path to the directory containing the CSV files.  directory <- system.file(\"csvfolder\",  package = \"bulkreadr\")  # import the csv files  read_csv_files_from_dir(dir_path = directory) #> # A tibble: 260 × 10 #>   cut   carat color clarity depth table price     x     y     z #>   <chr> <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Fair   2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #> 2 Fair   0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #> 3 Fair   1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #> 4 Fair   0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #> 5 Fair   0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #> # ℹ 255 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/bulkreadr.html","id":"read_gsheets","dir":"Articles","previous_headings":"","what":"read_gsheets()","title":"Introduction to bulkreadr","text":"read_gsheets() function imports data multiple sheets Google Sheets spreadsheet appends resulting dataframes sheet together create single dataframe. function powerful tool data analysis, allows easily combine data multiple sheets single dataset.","code":"# Google Sheet ID or the link to the sheet  sheet_id <- \"1izO0mHu3L9AMySQUXGDn9GPs1n-VwGFSEoAKGhqVQh0\"  # read all the sheets  read_gsheets(ss = sheet_id) #> # A tibble: 260 × 9 #>   carat color clarity depth table price     x     y     z #>   <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1  2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #> 2  0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #> 3  1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #> 4  0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #> 5  0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #> # ℹ 255 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"what-is-labelled-data-in-r","dir":"Articles","previous_headings":"","what":"What is labelled data in R?","title":"Introduction to labelled data","text":"Labelled data SPSS Stata refers datasets variable (column) values assigned meaningful labels. labels provide context, descriptions categories, making data easier understand analyze. instance, variable representing gender might numerical codes (1, 2) labels (“Male”, “Female”). feature enhances data analysis allowing researchers work descriptive labels instead deciphering codes numeric values, facilitating clearer interpretation communication statistical results. R ecosystem, packages like foreign haven, facilitates importation labelled data software like SPSS Stata, ensuring smooth transition R. bulkreadr package extends functionality leveraging haven streamline process. automatically converts labelled data R’s factor data type, eliminating need manual recoding. enhancement significantly improves efficiency data analysis workflow within R environment.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"note","dir":"Articles","previous_headings":"What is labelled data in R?","what":"Note","title":"Introduction to labelled data","text":"majority functions within package, utilize data stored system file bulkreadr, can accessed using system.file() function. wish utilize data stored local directory, please ensure set appropriate file path prior using functions provided bulkreadr package.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"read_spss_data","dir":"Articles","previous_headings":"What is labelled data in R?","what":"read_spss_data()","title":"Introduction to labelled data","text":"read_spss_data() designed seamlessly import data SPSS data (.sav .zsav) files. converts labelled variables factors, crucial step enhances ease data manipulation analysis within R programming environment. Read SPSS data file without converting variable labels column names Read SPSS data file convert variable labels column names","code":"library(bulkreadr)  file_path <- system.file(\"extdata\", \"Wages.sav\", package = \"bulkreadr\")  data <- read_spss_data(file = file_path)  data #> # A tibble: 400 × 9 #>      id  educ south                  sex   exper  wage occup marr        ed      #>   <dbl> <dbl> <fct>                  <fct> <dbl> <dbl> <fct> <fct>       <fct>   #> 1     3    12 does not live in South Male     17  7.5  Other Married     High s… #> 2     4    13 does not live in South Male      9 13.1  Other Not married Some c… #> 3     5    10 lives in South         Male     27  4.45 Other Not married Less t… #> 4    12     9 lives in South         Male     30  6.25 Other Not married Less t… #> 5    13     9 lives in South         Male     29 20.0  Other Married     Less t… #> # ℹ 395 more rows data <- read_spss_data(file = file_path, label = TRUE)  data #> # A tibble: 400 × 9 #>   `Worker ID` `Number of years of education` `Live in south`        Gender #>         <dbl>                          <dbl> <fct>                  <fct>  #> 1           3                             12 does not live in South Male   #> 2           4                             13 does not live in South Male   #> 3           5                             10 lives in South         Male   #> 4          12                              9 lives in South         Male   #> 5          13                              9 lives in South         Male   #> # ℹ 395 more rows #> # ℹ 5 more variables: `Number of years of work experience` <dbl>, #> #   `Wage (dollars per hour)` <dbl>, Occupation <fct>, `Marital status` <fct>, #> #   `Highest education level` <fct>"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"read_stata_data","dir":"Articles","previous_headings":"What is labelled data in R?","what":"read_stata_data()","title":"Introduction to labelled data","text":"read_stata_data() reads Stata data file (.dta) R data frame, converting labeled variables factors. Read Stata data file without converting variable labels column names Read Stata data file convert variable labels column names","code":"file_path <- system.file(\"extdata\", \"Wages.dta\", package = \"bulkreadr\")  data <- read_stata_data(file = file_path)  data #> # A tibble: 400 × 9 #>      id  educ south                  sex   exper  wage occup marr        ed      #>   <dbl> <dbl> <fct>                  <fct> <dbl> <dbl> <fct> <fct>       <fct>   #> 1     3    12 does not live in South Male     17  7.5  Other Married     High s… #> 2     4    13 does not live in South Male      9 13.1  Other Not married Some c… #> 3     5    10 lives in South         Male     27  4.45 Other Not married Less t… #> 4    12     9 lives in South         Male     30  6.25 Other Not married Less t… #> 5    13     9 lives in South         Male     29 20.0  Other Married     Less t… #> # ℹ 395 more rows data <- read_stata_data(file = file_path, label = TRUE)  data #> # A tibble: 400 × 9 #>   `Worker ID` `Number of years of education` `Live in south`        Gender #>         <dbl>                          <dbl> <fct>                  <fct>  #> 1           3                             12 does not live in South Male   #> 2           4                             13 does not live in South Male   #> 3           5                             10 lives in South         Male   #> 4          12                              9 lives in South         Male   #> 5          13                              9 lives in South         Male   #> # ℹ 395 more rows #> # ℹ 5 more variables: `Number of years of work experience` <dbl>, #> #   `Wage (dollars per hour)` <dbl>, Occupation <fct>, `Marital status` <fct>, #> #   `Highest education level` <fct>"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"generate_dictionary","dir":"Articles","previous_headings":"What is labelled data in R?","what":"generate_dictionary()","title":"Introduction to labelled data","text":"generate_dictionary() creates data dictionary specified data frame. function particularly useful understanding documenting structure dataset, similar data dictionaries Stata SPSS.","code":"# Creating a data dictionary from an SPSS file  file_path <- system.file(\"extdata\", \"Wages.sav\", package = \"bulkreadr\")  wage_data <- read_spss_data(file = file_path)  generate_dictionary(wage_data) #> # A tibble: 9 × 6 #>   position variable description                     `column type` missing levels #>      <int> <chr>    <chr>                           <chr>           <int> <name> #> 1        1 id       Worker ID                       dbl                 0 <NULL> #> 2        2 educ     Number of years of education    dbl                 0 <NULL> #> 3        3 south    Live in south                   fct                 0 <chr>  #> 4        4 sex      Gender                          fct                 0 <chr>  #> 5        5 exper    Number of years of work experi… dbl                 0 <NULL> #> # ℹ 4 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/labelled-data.html","id":"look_for","dir":"Articles","previous_headings":"What is labelled data in R?","what":"look_for()","title":"Introduction to labelled data","text":"look_for() function designed emulate functionality Stata lookfor command R. provides powerful tool searching large datasets, specifically targeting variable names, variable label descriptions, factor levels, value labels. function handy users working extensive complex datasets, enabling quickly efficiently locate variables interest.","code":"# Look for a single keyword.  look_for(wage_data, \"south\") #>  pos variable label         col_type missing values                 #>  3   south    Live in south fct      0       does not live in South #>                                              lives in South  look_for(wage_data, \"^s\") #>  pos variable label         col_type missing values                 #>  3   south    Live in south fct      0       does not live in South #>                                              lives in South         #>  4   sex      Gender        fct      0       Male                   #>                                              Female"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/other-functions.html","id":"pull_out","dir":"Articles","previous_headings":"","what":"pull_out()","title":"Other functions in bulkreadr","text":"pull_out() similar [. acts vectors, matrices, arrays lists extract replace parts. pleasant use magrittr (⁠%>%⁠) base(|>) operators.","code":"library(bulkreadr) library(dplyr)  top_10_richest_nig <- c(\"Aliko Dangote\", \"Mike Adenuga\", \"Femi Otedola\", \"Arthur Eze\", \"Abdulsamad Rabiu\", \"Cletus Ibeto\", \"Orji Uzor Kalu\", \"ABC Orjiakor\", \"Jimoh Ibrahim\", \"Tony Elumelu\")  top_10_richest_nig %>%    pull_out(c(1, 5, 2)) #> [1] \"Aliko Dangote\"    \"Abdulsamad Rabiu\" \"Mike Adenuga\" top_10_richest_nig %>%    pull_out(-c(1, 5, 2)) #> [1] \"Femi Otedola\"   \"Arthur Eze\"     \"Cletus Ibeto\"   \"Orji Uzor Kalu\" #> [5] \"ABC Orjiakor\"   \"Jimoh Ibrahim\"  \"Tony Elumelu\""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/other-functions.html","id":"convert_to_date","dir":"Articles","previous_headings":"","what":"convert_to_date()","title":"Other functions in bulkreadr","text":"convert_to_date() parses input vector POSIXct date-time object. also powerful convert excel date number like 42370 date value like 2016-01-01.","code":"## ** heterogeneous dates **  dates <- c(   44869, \"22.09.2022\", NA, \"02/27/92\", \"01-19-2022\",   \"13-01-  2022\", \"2023\", \"2023-2\", 41750.2, 41751.99,   \"11 07 2023\", \"2023-4\"   )  # Convert to POSIXct or Date object  convert_to_date(dates) #>  [1] \"2022-11-04\" \"2022-09-22\" NA           \"1992-02-27\" \"2022-01-19\" #>  [6] \"2022-01-13\" \"2023-01-01\" \"2023-02-01\" \"2014-04-21\" \"2014-04-22\" #> [11] \"2023-07-11\" \"2023-04-01\"  # It can also convert date time object to date object   convert_to_date(lubridate::now()) #> [1] \"2024-09-10\""},{"path":"https://gbganalyst.github.io/bulkreadr/articles/other-functions.html","id":"inspect_na","dir":"Articles","previous_headings":"","what":"inspect_na()","title":"Other functions in bulkreadr","text":"inspect_na() summarizes rate missingness column data frame. grouped data frame, rate missingness summarized separately group. Grouped dataframe summary","code":"# dataframe summary  inspect_na(airquality) #> # A tibble: 6 × 3 #>   col_name   cnt  pcnt #>   <chr>    <int> <dbl> #> 1 Ozone       37 24.2  #> 2 Solar.R      7  4.58 #> 3 Wind         0  0    #> 4 Temp         0  0    #> 5 Month        0  0    #> # ℹ 1 more row airquality %>%    group_by(Month) %>%    inspect_na() #> # A tibble: 25 × 4 #> # Groups:   Month [5] #>   Month col_name   cnt  pcnt #>   <int> <chr>    <int> <dbl> #> 1     5 Ozone        5  16.1 #> 2     5 Solar.R      4  12.9 #> 3     5 Wind         0   0   #> 4     5 Temp         0   0   #> 5     5 Day          0   0   #> # ℹ 20 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/other-functions.html","id":"fill_missing_values","dir":"Articles","previous_headings":"","what":"fill_missing_values()","title":"Other functions in bulkreadr","text":"fill_missing_values() efficient function addresses missing values data frame. uses imputation function, also known column-based imputation, impute missing values. supports various imputation methods continuous variables, including minimum, maximum, mean, median, harmonic mean, geometric mean. categorical variables, missing values replaced mode column. approach ensures accurate consistent replacements derived individual columns, resulting complete reliable dataset improved analysis decision-making. Impute using mean method continuous variables Impute using geometric mean continuous variables specify variables Petal_Length Petal_Width","code":"df <- tibble::tibble(   Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5),   Sepal.Width = c(4.1, 3.6, 3, 3, 2.9, 2.5, 2.4),   Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7),   Petal_Width = c(NA, 0.2, 1.2, 0.2, 1.3, 1.8, NA),   Species = c(\"setosa\", NA, \"versicolor\", \"setosa\",     NA, \"virginica\", \"setosa\"   ) ) df #> # A tibble: 7 × 5 #>   Sepal_Length Sepal.Width Petal_Length Petal_Width Species    #>          <dbl>       <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2         4.1          1.5        NA   setosa     #> 2          5           3.6          1.4         0.2 NA         #> 3          5.7         3            4.2         1.2 versicolor #> 4         NA           3            1.4         0.2 setosa     #> 5          6.2         2.9         NA           1.3 NA         #> # ℹ 2 more rows #' df <- tibble::tibble( #' Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5), #' Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7), #' Petal_Width = c(NA, 0.2, 1.2, 0.2, 1.3, 1.8, NA), #' Species = c(\"setosa\", NA, \"versicolor\", \"setosa\", #'            NA, \"virginica\", \"setosa\") #' ) result_df_mean <- fill_missing_values(df, method = \"mean\")  result_df_mean #> # A tibble: 7 × 5 #>   Sepal_Length Sepal.Width Petal_Length Petal_Width Species    #>          <dbl>       <dbl>        <dbl>       <dbl> <chr>      #> 1         5.2          4.1          1.5        0.94 setosa     #> 2         5            3.6          1.4        0.2  setosa     #> 3         5.7          3            4.2        1.2  versicolor #> 4         5.72         3            1.4        0.2  setosa     #> 5         6.2          2.9          3          1.3  setosa     #> # ℹ 2 more rows result_df_geomean <- fill_missing_values(df, selected_variables = c (\"Petal_Length\", \"Petal_Width\"), method = \"geometric\")  result_df_geomean #> # A tibble: 7 × 5 #>   Sepal_Length Sepal.Width Petal_Length Petal_Width Species    #>          <dbl>       <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2         4.1         1.5        0.732 setosa     #> 2          5           3.6         1.4        0.2   NA         #> 3          5.7         3           4.2        1.2   versicolor #> 4         NA           3           1.4        0.2   setosa     #> 5          6.2         2.9         2.22       1.3   NA         #> # ℹ 2 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/articles/other-functions.html","id":"impute-missing-values-nas-in-a-grouped-data-frame","dir":"Articles","previous_headings":"fill_missing_values()","what":"Impute missing values (NAs) in a grouped data frame","title":"Other functions in bulkreadr","text":"can use fill_missing_values() grouped data frame using grouping map functions. example :","code":"sample_iris <- tibble::tibble( Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5), Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7), Petal_Width = c(0.3, 0.2, 1.2, 0.2, 1.3, 1.8, NA), Species = c(\"setosa\", \"setosa\", \"versicolor\", \"setosa\",           \"virginica\", \"virginica\", \"setosa\") ) sample_iris #> # A tibble: 7 × 4 #>   Sepal_Length Petal_Length Petal_Width Species    #>          <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2          1.5         0.3 setosa     #> 2          5            1.4         0.2 setosa     #> 3          5.7          4.2         1.2 versicolor #> 4         NA            1.4         0.2 setosa     #> 5          6.2         NA           1.3 virginica  #> # ℹ 2 more rows sample_iris %>%   group_by(Species) %>%   group_split() %>%   map_df(fill_missing_values, method = \"median\") #> # A tibble: 7 × 4 #>   Sepal_Length Petal_Length Petal_Width Species    #>          <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2          1.5         0.3 setosa     #> 2          5            1.4         0.2 setosa     #> 3          5.2          1.4         0.2 setosa     #> 4          5.5          3.7         0.2 setosa     #> 5          5.7          4.2         1.2 versicolor #> # ℹ 2 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Ezekiel Ogundepo. Author, maintainer. Ernest Fokoue. Contributor. Golibe Ezeechesi. Contributor. Fatimo Adebanjo. Contributor. Isaac Ajao. Contributor.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Ogundepo E (2024). bulkreadr: Ultimate Tool Reading Data Bulk. R package version 1.1.1, https://gbganalyst.github.io/bulkreadr/, https://github.com/gbganalyst/bulkreadr.","code":"@Manual{,   title = {bulkreadr: The Ultimate Tool for Reading Data in Bulk},   author = {Ezekiel Ogundepo},   year = {2024},   note = {R package version 1.1.1, https://gbganalyst.github.io/bulkreadr/},   url = {https://github.com/gbganalyst/bulkreadr}, }"},{"path":"https://gbganalyst.github.io/bulkreadr/index.html","id":"bulkreadr-","dir":"","previous_headings":"","what":"The Ultimate Tool for Reading Data in Bulk","title":"The Ultimate Tool for Reading Data in Bulk","text":"Ultimate Tool Reading Data Bulk","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/index.html","id":"about-the-package","dir":"","previous_headings":"","what":"About the package","title":"The Ultimate Tool for Reading Data in Bulk","text":"bulkreadr robust user-friendly R package designed optimize process importing processing large datasets. offers streamlined functionality reading multiple sheets Microsoft Excel Google Sheets workbooks, well handling multiple CSV files directory ease. addition, bulkreadr provides seamless compatibility labelled data software SPSS Stata, automatically converting coded values respective labels upon import. significantly reduces manual effort, making invaluable resource data analysts researchers looking save time optimize workflows. quick video tutorial, gave talk International Association Statistical Computing webinar. recorded session available webinar resources .","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"The Ultimate Tool for Reading Data in Bulk","text":"can install bulkreadr package CRAN : development version GitHub ","code":"install.packages(\"bulkreadr\") if(!require(\"devtools\")){  install.packages(\"devtools\") }  devtools::install_github(\"gbganalyst/bulkreadr\")"},{"path":"https://gbganalyst.github.io/bulkreadr/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"The Ultimate Tool for Reading Data in Bulk","text":"Now installed bulkreadr package, can simply load using: get started bulkreadr, read docs.","code":"library(bulkreadr)"},{"path":"https://gbganalyst.github.io/bulkreadr/index.html","id":"context","dir":"","previous_headings":"","what":"Context","title":"The Ultimate Tool for Reading Data in Bulk","text":"bulkreadr designed integrate augment capabilities established packages readxl, readr, googlesheets4, offering enhanced functionality reading bulk data within R programming environment. readxl tidyverse package reading Excel files (xls xlsx) R data frame. readr tidyverse package reading delimited files (e.g., csv tsv) R data frame. googlesheets4 package interact Google Sheets Sheets API v4 https://developers.google.com/sheets/api.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/aliases.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract or replace parts of an object — pull_out","title":"Extract or replace parts of an object — pull_out","text":"pull_out() similar [. acts vectors, matrices, arrays lists extract replace parts. pleasant use magrittr (%>%) base (|>) operators.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/aliases.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract or replace parts of an object — pull_out","text":"pull_out() return object class input object.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/aliases.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract or replace parts of an object — pull_out","text":"","code":"good_choice <- letters %>%   pull_out(c(5, 2, 1, 4))  good_choice #> [1] \"e\" \"b\" \"a\" \"d\"  iris %>%   pull_out(, 1:4) %>%   head() #>   Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1          5.1         3.5          1.4         0.2 #> 2          4.9         3.0          1.4         0.2 #> 3          4.7         3.2          1.3         0.2 #> 4          4.6         3.1          1.5         0.2 #> 5          5.0         3.6          1.4         0.2 #> 6          5.4         3.9          1.7         0.4"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/bulkreadr-package.html","id":null,"dir":"Reference","previous_headings":"","what":"bulkreadr: The Ultimate Tool for Reading Data in Bulk — bulkreadr-package","title":"bulkreadr: The Ultimate Tool for Reading Data in Bulk — bulkreadr-package","text":"Designed simplify streamline process reading processing large volumes data R, package offers collection functions tailored bulk data operations. enables users efficiently read multiple sheets Microsoft Excel Google Sheets workbooks, well various CSV files directory. data returned organized data frames, facilitating analysis manipulation. Ideal handling extensive data sets batch processing tasks, bulkreadr empowers users manage data bulk effortlessly, saving time effort data preparation workflows. Additionally, package seamlessly works labelled data SPSS Stata.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/bulkreadr-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"bulkreadr: The Ultimate Tool for Reading Data in Bulk — bulkreadr-package","text":"Maintainer: Ezekiel Ogundepo gbganalyst@gmail.com (ORCID) contributors: Ernest Fokoue epfeqa@rit.edu (ORCID) [contributor] Golibe Ezeechesi golibe.ezeechesi@gmail.com [contributor] Fatimo Adebanjo adebanjofatimo2000@gmail.com [contributor] Isaac Ajao isaacoluwaseyiajao@gmail.com [contributor]","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/convert_to_date.html","id":null,"dir":"Reference","previous_headings":"","what":"User friendly date parsing function — convert_to_date","title":"User friendly date parsing function — convert_to_date","text":"convert_to_date() parses input vector POSIXct date object. also powerful convert excel date number like 42370 date value like 2016-01-01.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/convert_to_date.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User friendly date parsing function — convert_to_date","text":"","code":"convert_to_date(date_num_char, tz = \"UTC\")"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/convert_to_date.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User friendly date parsing function — convert_to_date","text":"date_num_char character numeric vector dates tz Time zone indicator. NULL (default), Date object returned. Otherwise POSIXct time zone attribute set tz.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/convert_to_date.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"User friendly date parsing function — convert_to_date","text":"vector class Date","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/convert_to_date.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"User friendly date parsing function — convert_to_date","text":"","code":"## ** heterogeneous dates **  dates <- c(   44869, \"22.09.2022\", NA, \"02/27/92\", \"01-19-2022\",   \"13-01-  2022\", \"2023\", \"2023-2\", 41750.2, 41751.99,   \"11 07 2023\", \"2023-4\" )  convert_to_date(dates) #>  [1] \"2022-11-04\" \"2022-09-22\" NA           \"1992-02-27\" \"2022-01-19\" #>  [6] \"2022-01-13\" \"2023-01-01\" \"2023-02-01\" \"2014-04-21\" \"2014-04-22\" #> [11] \"2023-07-11\" \"2023-04-01\""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/fill_missing_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Fill missing values in a data frame — fill_missing_values","title":"Fill missing values in a data frame — fill_missing_values","text":"fill_missing_values() efficient function addresses missing values data frame. uses imputation function, also known column-based imputation, impute missing values. continuous variables, supports various methods imputation, including minimum, maximum, mean, median, harmonic mean, geometric mean. categorical variables, missing values replaced mode column. approach ensures accurate consistent replacements derived individual columns, resulting complete reliable dataset improved analysis decision-making.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/fill_missing_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fill missing values in a data frame — fill_missing_values","text":"","code":"fill_missing_values(   df,   selected_variables = NULL,   method = c(\"mean\", \"min\", \"max\", \"median\", \"harmonic\", \"geometric\") )"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/fill_missing_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fill missing values in a data frame — fill_missing_values","text":"df dataframe process missing value imputation. selected_variables optional vector variable names within df missing values imputed. NULL (default), imputation applied variables data frame. Variables must quoted. method character string specifying imputation method continuous variables. Supported methods \"min\", \"max\", \"mean\", \"median\", \"harmonic\", \"geometric\". default method \"mean\". categorical variables, mode always used.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/fill_missing_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fill missing values in a data frame — fill_missing_values","text":"data frame missing values imputed according specified method.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/fill_missing_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fill missing values in a data frame — fill_missing_values","text":"","code":"library(dplyr) #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union  # Assuming 'df' is the dataframe you want to process  df <- tibble::tibble( Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5), Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7), Petal_Width = c(NA, 0.2, 1.2, 0.2, 1.3, 1.8, NA), Species = c(\"setosa\", NA, \"versicolor\", \"setosa\",            NA, \"virginica\", \"setosa\") )  # Impute using the mean method for continuous variables  result_df_mean <- fill_missing_values(df, method = \"mean\")  result_df_mean #> # A tibble: 7 × 4 #>   Sepal_Length Petal_Length Petal_Width Species    #>          <dbl>        <dbl>       <dbl> <chr>      #> 1         5.2           1.5        0.94 setosa     #> 2         5             1.4        0.2  setosa     #> 3         5.7           4.2        1.2  versicolor #> 4         5.72          1.4        0.2  setosa     #> 5         6.2           3          1.3  setosa     #> 6         6.7           5.8        1.8  virginica  #> 7         5.5           3.7        0.94 setosa      # Impute using the geometric mean for continuous variables and specify # variables `Petal_Length` and `Petal_Width`.  result_df_geomean <- fill_missing_values(df, selected_variables = c (\"Petal_Length\", \"Petal_Width\"), method = \"geometric\")  result_df_geomean #> # A tibble: 7 × 4 #>   Sepal_Length Petal_Length Petal_Width Species    #>          <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2         1.5        0.732 setosa     #> 2          5           1.4        0.2   NA         #> 3          5.7         4.2        1.2   versicolor #> 4         NA           1.4        0.2   setosa     #> 5          6.2         2.22       1.3   NA         #> 6          6.7         5.8        1.8   virginica  #> 7          5.5         3.7        0.732 setosa      # Impute missing values (NAs) in a grouped data frame  # You can do that by using the following:  sample_iris <- tibble::tibble( Sepal_Length = c(5.2, 5, 5.7, NA, 6.2, 6.7, 5.5), Petal_Length = c(1.5, 1.4, 4.2, 1.4, NA, 5.8, 3.7), Petal_Width = c(0.3, 0.2, 1.2, 0.2, 1.3, 1.8, NA), Species = c(\"setosa\", \"setosa\", \"versicolor\", \"setosa\",            \"virginica\", \"virginica\", \"setosa\") )  sample_iris %>% group_by(Species) %>% group_split() %>% map_df(fill_missing_values, method = \"median\") #> # A tibble: 7 × 4 #>   Sepal_Length Petal_Length Petal_Width Species    #>          <dbl>        <dbl>       <dbl> <chr>      #> 1          5.2          1.5         0.3 setosa     #> 2          5            1.4         0.2 setosa     #> 3          5.2          1.4         0.2 setosa     #> 4          5.5          3.7         0.2 setosa     #> 5          5.7          4.2         1.2 versicolor #> 6          6.2          5.8         1.3 virginica  #> 7          6.7          5.8         1.8 virginica"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a data dictionary from labelled data — generate_dictionary","title":"Create a data dictionary from labelled data — generate_dictionary","text":"generate_dictionary() creates data dictionary specified data frame. function particularly useful understanding documenting structure dataset, similar data dictionaries Stata SPSS.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a data dictionary from labelled data — generate_dictionary","text":"","code":"generate_dictionary(data)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a data dictionary from labelled data — generate_dictionary","text":"data data frame survey object","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a data dictionary from labelled data — generate_dictionary","text":"tibble representing data dictionary. row corresponds variable original data frame, providing detailed information variable's characteristics.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a data dictionary from labelled data — generate_dictionary","text":"function returns tibble (modern version R's data frame) following columns: position: integer vector indicating column position data frame. variable: character vector containing names variables (columns). description: character vector human-readable description variable. column type: character vector specifying data type (e.g., numeric, character) variable. missing: integer vector indicating count missing values variable. levels: list vector containing levels categorical variables, applicable.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/generate_dictionary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a data dictionary from labelled data — generate_dictionary","text":"","code":"# Creating a data dictionary from an SPSS file  file_path <- system.file(\"extdata\", \"Wages.sav\", package = \"bulkreadr\")  wage_data <- read_spss_data(file = file_path)  generate_dictionary(wage_data) #> # A tibble: 9 × 6 #>   position variable description                     `column type` missing levels #>      <int> <chr>    <chr>                           <chr>           <int> <name> #> 1        1 id       Worker ID                       dbl                 0 <NULL> #> 2        2 educ     Number of years of education    dbl                 0 <NULL> #> 3        3 south    Live in south                   fct                 0 <chr>  #> 4        4 sex      Gender                          fct                 0 <chr>  #> 5        5 exper    Number of years of work experi… dbl                 0 <NULL> #> 6        6 wage     Wage (dollars per hour)         dbl                 0 <NULL> #> 7        7 occup    Occupation                      fct                 0 <chr>  #> 8        8 marr     Marital status                  fct                 0 <chr>  #> 9        9 ed       Highest education level         fct                 0 <chr>"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize missingness in data frame columns — inspect_na","title":"Summarize missingness in data frame columns — inspect_na","text":"inspect_na() summarizes rate missingness column data frame. grouped data frame, rate missingness summarized separately group.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize missingness in data frame columns — inspect_na","text":"","code":"inspect_na(df)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize missingness in data frame columns — inspect_na","text":"df data frame","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize missingness in data frame columns — inspect_na","text":"tibble summarizing count percentage columnwise missingness data frame.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summarize missingness in data frame columns — inspect_na","text":"tibble returned contains columns: col_name, character vector containing column names df1. cnt, integer vector containing number missing values column. pcnt, percentage records columns missing.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/inspect_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize missingness in data frame columns — inspect_na","text":"","code":"library(dplyr)  # dataframe summary  inspect_na(airquality) #> # A tibble: 6 × 3 #>   col_name   cnt  pcnt #>   <chr>    <int> <dbl> #> 1 Ozone       37 24.2  #> 2 Solar.R      7  4.58 #> 3 Wind         0  0    #> 4 Temp         0  0    #> 5 Month        0  0    #> 6 Day          0  0     # grouped dataframe summary  airquality %>% group_by(Month) %>%  inspect_na() #> # A tibble: 25 × 4 #> # Groups:   Month [5] #>    Month col_name   cnt  pcnt #>    <int> <chr>    <int> <dbl> #>  1     5 Ozone        5  16.1 #>  2     5 Solar.R      4  12.9 #>  3     5 Wind         0   0   #>  4     5 Temp         0   0   #>  5     5 Day          0   0   #>  6     6 Ozone       21  70   #>  7     6 Solar.R      0   0   #>  8     6 Wind         0   0   #>  9     6 Temp         0   0   #> 10     6 Day          0   0   #> # ℹ 15 more rows"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/look_for.html","id":null,"dir":"Reference","previous_headings":"","what":"Look for keywords variable names and descriptions in labelled data — look_for","title":"Look for keywords variable names and descriptions in labelled data — look_for","text":"look_for() function designed emulate functionality Stata lookfor command R. provides powerful tool searching large datasets, specifically targeting variable names, variable label descriptions, factor levels, value labels. function handy users working extensive complex datasets, enabling quickly efficiently locate variables interest.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/look_for.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Look for keywords variable names and descriptions in labelled data — look_for","text":"","code":"look_for(   data,   ...,   labels = TRUE,   values = TRUE,   ignore.case = TRUE,   details = c(\"basic\", \"none\", \"full\") )"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/look_for.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Look for keywords variable names and descriptions in labelled data — look_for","text":"data data frame survey object ... optional list keywords, character string (several character strings), can formatted regular expression suitable base::grep() pattern, vector keywords; displays variables specified labels whether search variable labels (descriptions); TRUE default values whether search within values (factor levels value labels); TRUE default ignore.case whether make keywords case sensitive; TRUE default (case ignored matching) details add details variable (full details time consuming big data frames, FALSE equivalent \"none\" TRUE \"full\")","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/look_for.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Look for keywords variable names and descriptions in labelled data — look_for","text":"tibble data frame featuring variable position, name description (exists) original data frame.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/look_for.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Look for keywords variable names and descriptions in labelled data — look_for","text":"","code":"look_for(iris) #>  pos variable     label col_type missing values     #>  1   Sepal.Length —     dbl      0                  #>  2   Sepal.Width  —     dbl      0                  #>  3   Petal.Length —     dbl      0                  #>  4   Petal.Width  —     dbl      0                  #>  5   Species      —     fct      0       setosa     #>                                          versicolor #>                                          virginica   # Look for a single keyword.  look_for(iris, \"petal\") #>  pos variable     label col_type missing values #>  3   Petal.Length —     dbl      0              #>  4   Petal.Width  —     dbl      0               look_for(iris, \"s\") #>  pos variable     label col_type missing values     #>  1   Sepal.Length —     dbl      0                  #>  2   Sepal.Width  —     dbl      0                  #>  5   Species      —     fct      0       setosa     #>                                          versicolor #>                                          virginica"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"result calling rhs(lhs).","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_csv_files_from_dir.html","id":null,"dir":"Reference","previous_headings":"","what":"Reads all CSV files from a directory — read_csv_files_from_dir","title":"Reads all CSV files from a directory — read_csv_files_from_dir","text":"read_csv_files_from_dir reads csv files \"~/data\" directory returns appended dataframe. resulting dataframe order CSV files directory.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_csv_files_from_dir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reads all CSV files from a directory — read_csv_files_from_dir","text":"","code":"read_csv_files_from_dir(dir_path = \".\", col_types = NULL, .id = NULL)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_csv_files_from_dir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reads all CSV files from a directory — read_csv_files_from_dir","text":"dir_path Path directory containing CSV files. col_types One NULL, cols() specification, string. See vignette(\"readr\") details. NULL, column types inferred guess_max rows input, interspersed throughout file. convenient (fast), robust. guessed types wrong, need increase guess_max supply correct types . Column specifications created list() cols() must contain one column specification column. want read subset columns, use cols_only(). Alternatively, can use compact string representation character represents one column: c = character = integer n = number d = double l = logical f = factor D = date T = date time t = time ? = guess _ - = skip default, reading file without column specification print message showing readr guessed . remove message, set show_col_types = FALSE set options(readr.show_col_types = FALSE). .id name column store file path. useful reading multiple input files data file paths, data collection date. NULL (default) extra column created.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_csv_files_from_dir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reads all CSV files from a directory — read_csv_files_from_dir","text":"tibble. column type mismatch data frames row binding, error occur. R combine columns different types. example, combine column integers column characters.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_csv_files_from_dir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reads all CSV files from a directory — read_csv_files_from_dir","text":"","code":"directory <- system.file(\"csvfolder\",  package = \"bulkreadr\")  read_csv_files_from_dir(dir_path = directory, .id = \"cut\") #> # A tibble: 260 × 10 #>    cut                   carat color clarity depth table price     x     y     z #>    <chr>                 <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 /home/runner/work/_t…  2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #>  2 /home/runner/work/_t…  0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #>  3 /home/runner/work/_t…  1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #>  4 /home/runner/work/_t…  0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #>  5 /home/runner/work/_t…  0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #>  6 /home/runner/work/_t…  0.5  E     VS2      64.9    56  1397  5.01  4.95  3.23 #>  7 /home/runner/work/_t…  1    E     SI1      65.1    61  4435  6.15  6.08  3.98 #>  8 /home/runner/work/_t…  1.09 J     VS2      64.6    58  3443  6.48  6.41  4.16 #>  9 /home/runner/work/_t…  0.98 H     SI2      67.9    60  2777  6.05  5.97  4.08 #> 10 /home/runner/work/_t…  0.7  F     SI1      65.3    54  1974  5.58  5.54  3.63 #> # ℹ 250 more rows  # Column types mismatch error -------------------------------------- # If the `read_csv_files_from_dir()` function complains about a data type mismatch, # then set the `col_types` argument to `\"c\"`. # This will make all the column types in the resulting dataframe be characters."},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_files_from_dir.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","title":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","text":"read_excel_files_from_dir() reads Excel workbooks \"~/data\" directory returns appended dataframe.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_files_from_dir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","text":"","code":"read_excel_files_from_dir(dir_path, col_types = NULL, .id = NULL)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_files_from_dir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","text":"dir_path Path directory containing xls/xlsx files. col_types Either NULL guess spreadsheet character vector containing one entry per column options: \"skip\", \"guess\", \"logical\", \"numeric\", \"date\", \"text\" \"list\". exactly one col_type specified, recycled. content cell skipped column never read column appear data frame output. list cell loads column list length 1 vectors, typed using type guessing logic col_types = NULL, cell--cell basis. .id name optional identifier column. Provide string create output column identifies input. column use names available, otherwise use positions.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_files_from_dir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","text":"tibble. column type mismatch data frames row binding, error occur. R combine columns different types. example, combine column integers column characters.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_files_from_dir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Excel Workbooks data from a directory — read_excel_files_from_dir","text":"","code":"directory <- system.file(\"xlsxfolder\",  package = \"bulkreadr\")  read_excel_files_from_dir(dir_path = directory, .id = \"cut\") #> # A tibble: 260 × 10 #>    cut                   carat color clarity depth table price     x     y     z #>    <chr>                 <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 /home/runner/work/_t…  2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #>  2 /home/runner/work/_t…  0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #>  3 /home/runner/work/_t…  1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #>  4 /home/runner/work/_t…  0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #>  5 /home/runner/work/_t…  0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #>  6 /home/runner/work/_t…  0.5  E     VS2      64.9    56  1397  5.01  4.95  3.23 #>  7 /home/runner/work/_t…  1    E     SI1      65.1    61  4435  6.15  6.08  3.98 #>  8 /home/runner/work/_t…  1.09 J     VS2      64.6    58  3443  6.48  6.41  4.16 #>  9 /home/runner/work/_t…  0.98 H     SI2      67.9    60  2777  6.05  5.97  4.08 #> 10 /home/runner/work/_t…  0.7  F     SI1      65.3    54  1974  5.58  5.54  3.63 #> # ℹ 250 more rows  # Column types mismatch error -------------------------------------- # If the `read_excel_files_from_dir()` function complains about a data type mismatch, # then set the `col_types` argument to `\"text\"`. # This will make all the column types in the resulting dataframe be characters."},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_workbook.html","id":null,"dir":"Reference","previous_headings":"","what":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","title":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","text":"read_excel_workbook() reads data sheets Excel workbook return appended dataframe.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_workbook.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","text":"","code":"read_excel_workbook(path, col_types = NULL, .id = NULL)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_workbook.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","text":"path Path xls/xlsx file. col_types Either NULL guess spreadsheet character vector containing one entry per column options: \"skip\", \"guess\", \"logical\", \"numeric\", \"date\", \"text\" \"list\". exactly one col_type specified, recycled. content cell skipped column never read column appear data frame output. list cell loads column list length 1 vectors, typed using type guessing logic col_types = NULL, cell--cell basis. .id name optional identifier column. Provide string create output column identifies input. column use names available, otherwise use positions.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_workbook.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","text":"tibble. column type mismatch data frames row binding, error occur. R combine columns different types. example, combine column integers column characters.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_excel_workbook.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import data from multiple sheets of an Excel workbook — read_excel_workbook","text":"","code":"path <- system.file(\"extdata\", \"Diamonds.xlsx\", package = \"bulkreadr\", mustWork = TRUE)  read_excel_workbook(path = path, .id = \"Year\") #> # A tibble: 260 × 10 #>    Year  carat color clarity depth table price     x     y     z #>    <chr> <dbl> <chr> <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #>  1 Fair   2    I     SI1      65.9    60 13764  7.8   7.73  5.12 #>  2 Fair   0.7  H     SI1      65.2    58  2048  5.49  5.55  3.6  #>  3 Fair   1.51 E     SI1      58.4    70 11102  7.55  7.39  4.36 #>  4 Fair   0.7  D     SI2      65.5    57  1806  5.56  5.43  3.6  #>  5 Fair   0.35 F     VVS1     54.6    59  1011  4.85  4.79  2.63 #>  6 Fair   0.5  E     VS2      64.9    56  1397  5.01  4.95  3.23 #>  7 Fair   1    E     SI1      65.1    61  4435  6.15  6.08  3.98 #>  8 Fair   1.09 J     VS2      64.6    58  3443  6.48  6.41  4.16 #>  9 Fair   0.98 H     SI2      67.9    60  2777  6.05  5.97  4.08 #> 10 Fair   0.7  F     SI1      65.3    54  1974  5.58  5.54  3.63 #> # ℹ 250 more rows   # Column types mismatch error -------------------------------------- # If the `read_excel_workbook()` function complains about a data type mismatch, # then set the `col_types` argument to `\"text\"`. # This will make all the column types in the resulting DataFrame be characters."},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_gsheets.html","id":null,"dir":"Reference","previous_headings":"","what":"Import data from multiple sheets in Google Sheets — read_gsheets","title":"Import data from multiple sheets in Google Sheets — read_gsheets","text":"read_gsheets() function imports data multiple sheets Google Sheets spreadsheet appends resulting dataframes sheet together create single dataframe. function powerful tool data analysis, allows easily combine data multiple sheets single dataset.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_gsheets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import data from multiple sheets in Google Sheets — read_gsheets","text":"","code":"read_gsheets(ss, col_types = NULL, .id = NULL)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_gsheets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import data from multiple sheets in Google Sheets — read_gsheets","text":"ss Something identifies Google Sheet: file id string drive_id URL can recover id one-row dribble, googledrive represents Drive files instance googlesheets4_spreadsheet, gs4_get() returns Processed as_sheets_id(). col_types Column types. Either NULL guess spreadsheet string readr-style shortcodes, one character code per column. exactly one col_type specified, recycled. See Column Specification . .id name optional identifier column. Provide string create output column identifies input. column use names available, otherwise use positions.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_gsheets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import data from multiple sheets in Google Sheets — read_gsheets","text":"tibble. column type mismatch data frames row binding, error occur. R combine columns different types. example, combine column integers column characters.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_gsheets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import data from multiple sheets in Google Sheets — read_gsheets","text":"","code":"if (FALSE) { # googlesheets4::gs4_has_token()  sheet_id <- \"1izO0mHu3L9AMySQUXGDn9GPs1n-VwGFSEoAKGhqVQh0\"  read_gsheets(ss = sheet_id, .id = \"sheet.name\")  # Column types mismatch error -------------------------------------- # If the `read_gsheets()` function complains about a data type mismatch, # then set the `col_types` argument to `\"c\"`. # This will make all the column types in the resulting dataframe be characters.  # For example, } if (FALSE) { # googlesheets4::gs4_has_token() sheet_id <- \"1rrjKAV05POre9lDVtHtZePTa8VROf1onVO47cHnhrTU\"  try(read_gsheets(ss = sheet_id)) # error, column types mismatch  read_gsheets(ss = sheet_id, col_types = \"c\")  }"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_spss_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Read SPSS data file — read_spss_data","title":"Read SPSS data file — read_spss_data","text":"read_spss_data() designed seamlessly import data SPSS data (.sav  .zsav) files. converts labelled variables factors, crucial step enhances ease data manipulation analysis within R programming environment.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_spss_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read SPSS data file — read_spss_data","text":"","code":"read_spss_data(file, label = FALSE)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_spss_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read SPSS data file — read_spss_data","text":"file path SPSS data file. label Logical indicating whether use variable labels column names (default FALSE).","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_spss_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read SPSS data file — read_spss_data","text":"tibble containing data SPSS file.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_spss_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read SPSS data file — read_spss_data","text":"","code":"# Read an SPSS data file without converting variable labels as column names  file_path <- system.file(\"extdata\", \"Wages.sav\", package = \"bulkreadr\")  data <- read_spss_data(file = file_path)  data #> # A tibble: 400 × 9 #>       id  educ south                  sex   exper  wage occup marr        ed     #>    <dbl> <dbl> <fct>                  <fct> <dbl> <dbl> <fct> <fct>       <fct>  #>  1     3    12 does not live in South Male     17  7.5  Other Married     High … #>  2     4    13 does not live in South Male      9 13.1  Other Not married Some … #>  3     5    10 lives in South         Male     27  4.45 Other Not married Less … #>  4    12     9 lives in South         Male     30  6.25 Other Not married Less … #>  5    13     9 lives in South         Male     29 20.0  Other Married     Less … #>  6    14    12 does not live in South Male     37  7.3  Other Married     High … #>  7    17    11 does not live in South Male     16  3.65 Other Not married Less … #>  8    20    12 does not live in South Male      9  3.75 Other Not married High … #>  9    21    11 lives in South         Male     14  4.5  Other Married     Less … #> 10    23     6 lives in South         Male     45  5.75 Other Married     Less … #> # ℹ 390 more rows  # Read an SPSS data file and convert variable labels as column names  data <- read_spss_data(file = file_path, label = TRUE)  data #> # A tibble: 400 × 9 #>    `Worker ID` `Number of years of education` `Live in south`        Gender #>          <dbl>                          <dbl> <fct>                  <fct>  #>  1           3                             12 does not live in South Male   #>  2           4                             13 does not live in South Male   #>  3           5                             10 lives in South         Male   #>  4          12                              9 lives in South         Male   #>  5          13                              9 lives in South         Male   #>  6          14                             12 does not live in South Male   #>  7          17                             11 does not live in South Male   #>  8          20                             12 does not live in South Male   #>  9          21                             11 lives in South         Male   #> 10          23                              6 lives in South         Male   #> # ℹ 390 more rows #> # ℹ 5 more variables: `Number of years of work experience` <dbl>, #> #   `Wage (dollars per hour)` <dbl>, Occupation <fct>, `Marital status` <fct>, #> #   `Highest education level` <fct>"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_stata_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Read Stata data file — read_stata_data","title":"Read Stata data file — read_stata_data","text":"Read Stata data file","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_stata_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read Stata data file — read_stata_data","text":"","code":"read_stata_data(file, label = FALSE)"},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_stata_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read Stata data file — read_stata_data","text":"file path Stata data file. label Logical indicating whether use variable labels column names (default FALSE).","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_stata_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read Stata data file — read_stata_data","text":"data frame containing Stata data, labeled variables converted factors.","code":""},{"path":[]},{"path":"https://gbganalyst.github.io/bulkreadr/reference/read_stata_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read Stata data file — read_stata_data","text":"","code":"# Read Stata data file without converting variable labels as column names  file_path <- system.file(\"extdata\", \"Wages.dta\", package = \"bulkreadr\")  data <- read_stata_data(file = file_path)  data #> # A tibble: 400 × 9 #>       id  educ south                  sex   exper  wage occup marr        ed     #>    <dbl> <dbl> <fct>                  <fct> <dbl> <dbl> <fct> <fct>       <fct>  #>  1     3    12 does not live in South Male     17  7.5  Other Married     High … #>  2     4    13 does not live in South Male      9 13.1  Other Not married Some … #>  3     5    10 lives in South         Male     27  4.45 Other Not married Less … #>  4    12     9 lives in South         Male     30  6.25 Other Not married Less … #>  5    13     9 lives in South         Male     29 20.0  Other Married     Less … #>  6    14    12 does not live in South Male     37  7.3  Other Married     High … #>  7    17    11 does not live in South Male     16  3.65 Other Not married Less … #>  8    20    12 does not live in South Male      9  3.75 Other Not married High … #>  9    21    11 lives in South         Male     14  4.5  Other Married     Less … #> 10    23     6 lives in South         Male     45  5.75 Other Married     Less … #> # ℹ 390 more rows  # Read Stata data file and convert variable labels as column names  data <- read_stata_data(file = file_path, label = TRUE)  data #> # A tibble: 400 × 9 #>    `Worker ID` `Number of years of education` `Live in south`        Gender #>          <dbl>                          <dbl> <fct>                  <fct>  #>  1           3                             12 does not live in South Male   #>  2           4                             13 does not live in South Male   #>  3           5                             10 lives in South         Male   #>  4          12                              9 lives in South         Male   #>  5          13                              9 lives in South         Male   #>  6          14                             12 does not live in South Male   #>  7          17                             11 does not live in South Male   #>  8          20                             12 does not live in South Male   #>  9          21                             11 lives in South         Male   #> 10          23                              6 lives in South         Male   #> # ℹ 390 more rows #> # ℹ 5 more variables: `Number of years of work experience` <dbl>, #> #   `Wage (dollars per hour)` <dbl>, Occupation <fct>, `Marital status` <fct>, #> #   `Highest education level` <fct>"},{"path":"https://gbganalyst.github.io/bulkreadr/news/index.html","id":"bulkreadr-111-2024-03-01","dir":"Changelog","previous_headings":"","what":"bulkreadr 1.1.1 (2024-03-01)","title":"bulkreadr 1.1.1 (2024-03-01)","text":"CRAN release: 2024-05-26 pleased announce release bulkreadr version 1.1.1. version introduces significant enhancements features aimed improving functionality user experience package. Enhanced fill_missing_values() Functionality: fill_missing_values() function significantly improved support various imputation methods, empowering users handle missing data greater precision flexibility. addition previously supported “mean” imputation method, function now accommodates following strategies: Minimum Value (Min): Imputes missing entries minimum value observed within respective column. Maximum Value (Max): Fills missing data points maximum value found column. Mean: Continues offer average value imputation, replacing missing values mean available data column. Median: Imputes missing entries employing median value column, providing robust alternative mean imputation, especially presence outliers. Harmonic Mean: Offers sophisticated option imputing missing values using harmonic mean, ideal data distributions approach representative. Geometric Mean: Completes enhanced range imputation methods allowing replacement missing values geometric mean, suited datasets product values interest. enhancements designed provide users comprehensive toolkit data imputation, ensuring fill_missing_values() can effectively tailored meet unique demands diverse datasets analysis requirements. Package Website Launch: better serve users provide detailed documentation, launched official bulkreadr package website. website offers comprehensive guides, function references, examples help users maximize package’s potential. Visit us https://gbganalyst.github.io/bulkreadr information. believe updates significantly enhance data analysis workflows look forward feedback.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/news/index.html","id":"bulkreadr-110-2023-11-13","dir":"Changelog","previous_headings":"","what":"bulkreadr 1.1.0 (2023-11-13)","title":"bulkreadr 1.1.0 (2023-11-13)","text":"CRAN release: 2023-11-16 update includes following new features: generate_dictionary(): function designed automatically create comprehensive data dictionary labelled datasets. generated dictionary provides detailed insights variable, aiding better data understanding management. look_for(): enhances capability efficiently search within labelled datasets. allows users quickly find variable names descriptions searching specific keywords. feature streamlines data exploration analysis, particularly large datasets extensive variables. enhancements aim improve user experience data management exploration within bulkreadr. hope new features assist users effectively navigating understanding labelled datasets.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/news/index.html","id":"bulkreadr-100-2023-09-20","dir":"Changelog","previous_headings":"","what":"bulkreadr 1.0.0 (2023-09-20)","title":"bulkreadr 1.0.0 (2023-09-20)","text":"CRAN release: 2023-09-26 update includes following new features improvements: Developed read_stata_data() import Stata data file (.dta) R data frame, converting labeled variables factors. Reduced dependency packages optimize efficiency.","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/news/index.html","id":"bulkreadr-020-2023-09-11","dir":"Changelog","previous_headings":"","what":"bulkreadr 0.2.0 (2023-09-11)","title":"bulkreadr 0.2.0 (2023-09-11)","text":"CRAN release: 2023-09-15 update includes following new features improvements: Developed bulkreadr vignette Developed read_spss_data() seamlessly import data SPSS data (.sav .zsav) files converting labelled variables factors, crucial step enhances ease data manipulation analysis within R programming environment. Added unit tests","code":""},{"path":"https://gbganalyst.github.io/bulkreadr/news/index.html","id":"bulkreadr-010-2023-07-24","dir":"Changelog","previous_headings":"","what":"bulkreadr 0.1.0 (2023-07-24)","title":"bulkreadr 0.1.0 (2023-07-24)","text":"CRAN release: 2023-08-15 update includes following new features improvements: Improved error handling adding meaningful error messages functions within bulkreadr package. make easier users identify troubleshoot issues may arise use package. Added package-level documentation. user can now use ?bulkreadr::bulkreadr basic package-level documentation. Added inspect_na() summarize missingness data frame columns fill_missing_values() impute missing values dataframe.","code":""}]