newdata is an R package to generate new data frames by varying some
variables while holding the others constant.
By default, all specified variables vary across their range while all other variables are held constant at a reference value. The user can specify the length of each sequence, require that only observed values and combinations are used and add new variables. Types, classes, factor levels and time zones are always preserved.
Consider the following observed ‘old’ data frame.
library(newdata)
newdata::old_data
#> # A tibble: 3 × 9
#> lgl int dbl chr fct ord dte dtt hms
#> <lgl> <int> <dbl> <chr> <fct> <ord> <date> <dttm> <time>
#> 1 TRUE 1 1 most most most 1970-01-02 1969-12-31 16:00:01 00'01"
#> 2 FALSE 4 4.5 most most most 1970-01-05 1969-12-31 16:00:04 00'04"
#> 3 NA 6 8.2 a rarity a rari… a ra… 1970-01-07 1969-12-31 16:00:06 00'06"By default all variables are set to a reference value.
xnew_data(old_data)
#> # A tibble: 1 × 9
#> lgl int dbl chr fct ord dte dtt hms
#> <lgl> <int> <dbl> <chr> <fct> <ord> <date> <dttm> <time>
#> 1 FALSE 3 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"Specifying a variable causes it to vary sequentially across its range.
xnew_data(old_data, int)
#> # A tibble: 6 × 9
#> lgl int dbl chr fct ord dte dtt hms
#> <lgl> <int> <dbl> <chr> <fct> <ord> <date> <dttm> <time>
#> 1 FALSE 1 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 2 FALSE 2 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 3 FALSE 3 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 4 FALSE 4 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 5 FALSE 5 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 6 FALSE 6 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"The user can specify the length of each sequence.
xnew_data(old_data, xnew_seq(int, length_out = 3))
#> # A tibble: 3 × 9
#> lgl int dbl chr fct ord dte dtt hms
#> <lgl> <int> <dbl> <chr> <fct> <ord> <date> <dttm> <time>
#> 1 FALSE 1 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 2 FALSE 3 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 3 FALSE 6 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"The user can also indicate whether only observed values should be used in the sequence.
xnew_data(old_data, xnew_seq(int, length_out = 3, obs_only = TRUE))
#> # A tibble: 3 × 9
#> lgl int dbl chr fct ord dte dtt hms
#> <lgl> <int> <dbl> <chr> <fct> <ord> <date> <dttm> <time>
#> 1 FALSE 1 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 2 FALSE 4 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 3 FALSE 6 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"The xobs_only() function can be used to filter out unobserved values
after the sequence has been generated.
xnew_data(old_data, xobs_only(xnew_seq(int, length_out = 3)))
#> # A tibble: 2 × 9
#> lgl int dbl chr fct ord dte dtt hms
#> <lgl> <int> <dbl> <chr> <fct> <ord> <date> <dttm> <time>
#> 1 FALSE 1 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 2 FALSE 6 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"When two or more variables are specified all combinations are used.
xnew_data(old_data, int, fct)
#> # A tibble: 18 × 9
#> lgl int dbl chr fct ord dte dtt hms
#> <lgl> <int> <dbl> <chr> <fct> <ord> <date> <dttm> <time>
#> 1 FALSE 1 4.57 most not obs a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 2 FALSE 1 4.57 most a rarity a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 3 FALSE 1 4.57 most most a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 4 FALSE 2 4.57 most not obs a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 5 FALSE 2 4.57 most a rarity a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 6 FALSE 2 4.57 most most a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 7 FALSE 3 4.57 most not obs a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 8 FALSE 3 4.57 most a rarity a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 9 FALSE 3 4.57 most most a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 10 FALSE 4 4.57 most not obs a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 11 FALSE 4 4.57 most a rarity a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 12 FALSE 4 4.57 most most a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 13 FALSE 5 4.57 most not obs a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 14 FALSE 5 4.57 most a rarity a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 15 FALSE 5 4.57 most most a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 16 FALSE 6 4.57 most not obs a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 17 FALSE 6 4.57 most a rarity a rar… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 18 FALSE 6 4.57 most most a rar… 1970-01-04 1969-12-31 16:00:03 00'03"To only get observed combinations use xobs_only()
xnew_data(old_data, xobs_only(int, fct))
#> # A tibble: 3 × 9
#> lgl int dbl chr fct ord dte dtt hms
#> <lgl> <int> <dbl> <chr> <fct> <ord> <date> <dttm> <time>
#> 1 FALSE 1 4.57 most most a rari… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 2 FALSE 4 4.57 most most a rari… 1970-01-04 1969-12-31 16:00:03 00'03"
#> 3 FALSE 6 4.57 most a rarity a rari… 1970-01-04 1969-12-31 16:00:03 00'03"Adding a new variable is simple.
xnew_data(old_data, extra = c(TRUE, FALSE))
#> # A tibble: 2 × 10
#> lgl int dbl chr fct ord dte dtt hms
#> <lgl> <int> <dbl> <chr> <fct> <ord> <date> <dttm> <time>
#> 1 FALSE 3 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> 2 FALSE 3 4.57 most not obs a rarity 1970-01-04 1969-12-31 16:00:03 00'03"
#> # ℹ 1 more variable: extra <lgl>Casting variables to be the same class as the original is achieved as follows.
xnew_data(old_data, xcast(lgl = 1, int = 7, dbl = 10L, fct = "a rarity", hms = "00:00:02"))
#> # A tibble: 1 × 9
#> lgl int dbl chr fct ord dte dtt hms
#> <lgl> <int> <dbl> <chr> <fct> <ord> <date> <dttm> <time>
#> 1 TRUE 7 10 most a rarity a rari… 1970-01-04 1969-12-31 16:00:03 00'02"To install the latest release version from CRAN.
install.packages("newdata")To install the latest development version from r-universe.
install.packages("newdata", repos = c("https://poissonconsulting.r-universe.dev", "https://cloud.r-project.org"))or from GitHub
# install.packages("remotes")
remotes::install_github("poissonconsulting/newdata")Please report any issues.
Pull requests are always welcome.
Please note that the newdata project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.