The goal of succor is to provide helper functions for common scenarios.
Installation
You can install the development version of succor from GitHub with:
# install.packages("pak")
pak::pak("asenetcky-r-pkgs/succor")Data helpers
library(succor)
library(purrr)
suppressPackageStartupMessages(library(dplyr))
# is it a dataframe, with data?
is_dataframe_with_data(mtcars)
#> [1] TRUE
is_dataframe_with_data(mtcars[0, ])
#> [1] FALSE
# you can assert or throw errors as well
assert_dataframe_with_data(mtcars)
safe_assert <- purrr::safely(assert_dataframe_with_data)
captured <- safe_assert(mtcars[0, ])
captured$error$message
#> [1] "Assertion on '.data' failed: Must have at least 1 rows, but has 0 rows."Test and Environment Helpers
library(testthat)
#>
#> Attaching package: 'testthat'
#> The following object is masked from 'package:dplyr':
#>
#> matches
#> The following object is masked from 'package:purrr':
#>
#> is_null
# is the current session on a linux machine?
is_linux()
#> [1] FALSE
# this is useful for unit testing
testthat::test_that("test runs when on linux", {
skip_if_not_linux()
testthat::expect_true(TRUE)
})
#> Test passed 😸Miscellaneous Helpers
library(stringr)
# make a datekey for dimension wrangling
Sys.Date()
#> [1] "2025-05-16"
make_datekey(Sys.Date())
#> [1] 20250516
# Standardized timestamps
# Always the same format, length and delimiters
make_timestamp()
#> [1] "2025-05-16 14:13:39.1487"
# Mass rename dataframe column names to something more sensible
# that you can pile on you own standards after
cursed_names <-
dplyr::tibble(
COLUMN1 = 1:10,
CoLuMn2 = 1:10,
`Column 3` = 1:10,
` ColumN 4 ` = 1:10
)
colnames(cursed_names)
#> [1] "COLUMN1" "CoLuMn2" "Column 3"
#> [4] " ColumN 4 "
cursed_names |>
rename_with_stringr() |>
colnames()
#> [1] "column1" "column2" "column 3" "column 4"
cursed_names |>
rename_with_stringr() |>
dplyr::rename_with(
.fn = \(col) {
stringr::str_remove_all(col, " ")
}
) |>
colnames()
#> [1] "column1" "column2" "column3" "column4"