breaking down funtion exercise into meaningful steps

materials/sections/r-practice-function-cleaning-data.qmd

@@ -31,7 +31,7 @@ library(dplyr)
 library(janitor)
 ```
 
-3. Load the species table using the following code. This code scrapes a table from a url and uses some cleaning and wrangling functions to get the table into our Environement in the format we want.
+3. Load the species table using the following code. This code scrapes a table from a url and uses some cleaning and wrangling functions to get the table into our Environment in the format we want.
 
 ```{r}
 webpage <- rvest::read_html("https://www.pwrc.usgs.gov/BBL/Bander_Portal/login/speclist.php")
@@ -43,12 +43,14 @@ species <- tbls[[1]] %>%
     janitor::clean_names() %>% 
     select(alpha_code, common_name) %>% 
     mutate(alpha_code = tolower(alpha_code))
+
+head(species, 3)
 ```
 
 4.  Obtain data from the the Arctic Data Center [Utqiaġvik shorebird breeding ecology study, Utqiaġvik](https://doi.org/10.18739/A23R0PT35). Download the following files:
 
-    -   `Utqiagvik_adult_shorebird_banding.csv`
-    -   `Utqiagvik_chick_shorebird_banding.csv`
+    -   `Utqiagvik_predator_surveys.csv`
+    -   `Utqiagvik_nest_data.csv`
 
 **Note:** It's up to you on how you want to download and load the data! You can either use the download links (obtain by right-clicking the "Download" button and select "Copy Link Address" for each data entity) or manually download the data and then upload the files to RStudio server.
 
@@ -66,51 +68,122 @@ This is a handy package that requires a moderate amount of knowledge of `html` t
 
 ## Write a function that will translate species codes into common names.
 
-<!--
-EDIT FROM HERE
 
 ::: callout-note
 ### Read and explore data
+Read in each data file and store the data frame as `shorebird_adult` and `shorebird_chick` accordingly. After reading the data, insert a new chunk or in the console, explore the data using any function we have used during the lessons (eg. `colname()`, `glimpse()`)
 
-   fsfsfsf
 ::: 
 
+```{r}
+#| code-summary: "Answer"
+
+## When reading from a file in your data folder in your Rpoj
+nest_data <-  read_csv("data/Utqiagvik_nest_data.csv")
+
+predator_survey <- read_csv("data/	Utqiagvik_predator_surveys.csv")
+
+
+## When reading using the url
+nest_data <-  read_csv("https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3A982bd2fc-4edf-4da7-96ef-0d11b853102d")
+
+predator_survey <- read_csv("https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3A9ffec04c-7e2d-41dd-9e88-b6c2e8c4375e")
+
+## Exploring the data (these functions can also be used to explore nest_data) 
+
+colnames(predator_survey)
+glimpse(predator_survey)
+unique(predator_survey$species)
+summary(predator_survey)
+
+```
 
 
 ::: callout-note
-## Write functions to clean species code
+## How would you translate species codes into common names for one of the data frmes?
+
+Before thinking of how to write a function, first discuss what are you trying to achieve and how would you get there. Write and run the code that would allow you to combine the `species` data frame with the `predator_survey` so that the outcome data frame has the species code and common names.
 
- -   **Hint**: The fastest way to do this involves adding a column to the `data.frame`. Your function will have two arguments
-    -   **Optional Extra Challenge**: For a little extra challenge, try to incorporate an `if` statement that looks for `NA` values in the common name field you are adding. What other conditionals might you include to make your function smarter?
+**Hint:** `joins`
+
 :::
 
+```{r}
+#| code-summary: "Answer"
+
+predator_comm_names <- left_join(predator_survey,
+                                 species,
+                                 by = c("species" = "alpha_code"))
+
+```
+
+
 
-### Visual schematic of data
+::: callout-note
+## Write a functions to add species common name to any data frame.
+How can you generalize the code from the previous question and make it into a function?
+
+The idea is that you can use this function in any data frame that has a `species` column with the Bird Banding Laboratory Species Code.
+
+:::
 
-Make this:
 
-    year common_name         pred_count
-    2003 Glaucous Gull               54
-    2003 Parasitic Jaeger             2
-    2003 Pomarine Jaeger              6
-    2004 Glaucous Gull               69
-    2004 Long-tailed Jaeger          13
+```{r}
+#| code-summary: "Answer"
 
-And then make this:
+assign_species_name <- function(df, species){
+    return_df <- left_join(df, species, by = c("species" = "alpha_code"))
+    return(return_df)
+}
 
 ```
-common_name             year total_predated pred_count
-American Golden-Plover  2003              4         62
-American Golden-Plover  2004             22         93
-American Golden-Plover  2005              0         72
-American Golden-Plover  2006              0        193
-American Golden-Plover  2007             12        154
-American Golden-Plover  2008             22        328
-American Golden-Plover  2009             92        443
+
+
+::: callout-note
+## Document your funtion inserting Roxygen skeleton and adding the necesary description.
+Place the cursor inside your function, In the top menu go to Code > Insert Roxygen skeleton. Document parameters, return and write one example. 
+
+:::
+
+```{r}
+#| code-summary: "Answer"
+
+#' Title
+#'
+#' @param df A data frame containing BBL species codes in column `species`
+#' @param species A data frame defining BBL species codes with columns `alpha_code` and `common_name` 
+#'
+#' @return A data frame with original data df, plus the common name of species
+#' @export
+#'
+#' @examples `*provide example*`
+
+
+assign_species_name <- function(df, species){
+    return_df <- left_join(df, species, by = c("species" = "alpha_code"))
+    return(return_df)
+}
+
 ```
 
-REMOVE ARROW BELOW
--->
+
+::: callout-note
+## Use your function to clean names of each data frame
+
+<!--Finalize this question-->
+
+:::
+
+
+
+::: callout-note
+## Challenge
+
+**Optional Extra Challenge**: For a little extra challenge, try to incorporate an `if` statement that looks for `NA` values in the common name field you are adding. What other conditionals might you include to make your function smarter?
+:::
+
+
+
 
 ::: {.callout-caution icon="false"}
 

materials/session_17.qmd

@@ -3,10 +3,15 @@ title: "Practice Session II"
 title-block-banner: true
 execute:
   eval: false
+format:
+  html:
+    code-link: true
+    code-fold: true
+    code-summary: "Answer"
+    code-overflow: wrap
 ---
 
 
 
-
 {{< include /sections/r-practice-function-cleaning-data.qmd >}}