tor (to-R) helps you to import multiple files at once. For example:
- Run
list_rds()to import all .csv files from your working directory into a list. - Run
load_csv()to import all .csv files from your working directory into your global environment.
Installation
Install tor from CRAN with:
install.packages("tor")
Or install the development version from GitHub with:
# install.packages("devtools") devtools::install_github("maurolepore/tor")
Example
list_*(): Import multiple files from a directory into a list
All functions default to importing files from the working directory.
dir() #> [1] "_pkgdown.yml" "codecov.yml" "cran-comments.md" "csv1.csv" #> [5] "csv2.csv" "DESCRIPTION" "inst" "LICENSE.md" #> [9] "man" "NAMESPACE" "NEWS.md" "R" #> [13] "README.md" "README.Rmd" "tests" "tor.Rproj" #> [17] "vignettes" list_csv() #> Parsed with column specification: #> cols( #> x = col_double() #> ) #> Parsed with column specification: #> cols( #> y = col_character() #> ) #> $csv1 #> # A tibble: 2 x 1 #> x #> <dbl> #> 1 1 #> 2 2 #> #> $csv2 #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b
Often you will specify a path to read from.
# Helpes create paths to examples tor_example() #> [1] "csv" "mixed" "rdata" "rds" "tsv" (path_rds <- tor_example("rds")) #> [1] "/home/mauro/R/x86_64-pc-linux-gnu-library/4.0/tor/extdata/rds" dir(path_rds) #> [1] "rds1.rds" "rds2.rds" list_rds(path_rds) #> $rds1 #> # A tibble: 2 x 1 #> x #> <dbl> #> 1 1 #> 2 2 #> #> $rds2 #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b
You may read all files with a particular extension.
path_mixed <- tor_example("mixed") dir(path_mixed) #> [1] "csv.csv" "lower_rdata.rdata" "rda.rda" #> [4] "upper_rdata.RData" list_rdata(path_mixed) #> $lower_rdata #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b #> #> $rda #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b #> #> $upper_rdata #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b
Or you may read specific files matching a pattern.
list_rdata(path_mixed, regexp = "[.]RData", ignore.case = FALSE) #> $upper_rdata #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b
list_any() is the most flexible function. You supply the function to read with.
(path_csv <- tor_example("csv")) #> [1] "/home/mauro/R/x86_64-pc-linux-gnu-library/4.0/tor/extdata/csv" dir(path_csv) #> [1] "csv1.csv" "csv2.csv" list_any(path_csv, read.csv) #> $csv1 #> # A tibble: 2 x 1 #> x #> <int> #> 1 1 #> 2 2 #> #> $csv2 #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b
It understands lambda functions and formulas (powered by rlang).
# Use the pipe (%>%) library(magrittr) (path_rdata <- tor_example("rdata")) #> [1] "/home/mauro/R/x86_64-pc-linux-gnu-library/4.0/tor/extdata/rdata" dir(path_rdata) #> [1] "rdata1.rdata" "rdata2.rdata" path_rdata %>% list_any(function(x) get(load(x))) #> $rdata1 #> # A tibble: 2 x 1 #> x #> <dbl> #> 1 1 #> 2 2 #> #> $rdata2 #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b # Same path_rdata %>% list_any(~get(load(.x))) #> $rdata1 #> # A tibble: 2 x 1 #> x #> <dbl> #> 1 1 #> 2 2 #> #> $rdata2 #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b
Pass additional arguments via ... or inside the lambda function.
path_csv %>% list_any(readr::read_csv, skip = 1) #> Parsed with column specification: #> cols( #> `1` = col_double() #> ) #> Parsed with column specification: #> cols( #> a = col_character() #> ) #> $csv1 #> # A tibble: 1 x 1 #> `1` #> <dbl> #> 1 2 #> #> $csv2 #> # A tibble: 1 x 1 #> a #> <chr> #> 1 b path_csv %>% list_any(~read.csv(., stringsAsFactors = FALSE)) #> $csv1 #> # A tibble: 2 x 1 #> x #> <int> #> 1 1 #> 2 2 #> #> $csv2 #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b
It also provides the arguments regexp, ignore.case, and invert to pick specific files in a directory (powered by fs).
path_mixed <- tor_example("mixed") dir(path_mixed) #> [1] "csv.csv" "lower_rdata.rdata" "rda.rda" #> [4] "upper_rdata.RData" path_mixed %>% list_any(~get(load(.)), "[.]Rdata$", ignore.case = TRUE) #> $lower_rdata #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b #> #> $upper_rdata #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b path_mixed %>% list_any(~get(load(.)), regexp = "[.]csv$", invert = TRUE) #> $lower_rdata #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b #> #> $rda #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b #> #> $upper_rdata #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b
load_*(): Load multiple files from a directory into an environment
All functions default to importing files from the working directory and into the global environment.
# The working directory contains .csv files dir() #> [1] "_pkgdown.yml" "codecov.yml" "cran-comments.md" "csv1.csv" #> [5] "csv2.csv" "DESCRIPTION" "inst" "LICENSE.md" #> [9] "man" "NAMESPACE" "NEWS.md" "R" #> [13] "README.md" "README.Rmd" "tests" "tor.Rproj" #> [17] "vignettes" load_csv() #> Parsed with column specification: #> cols( #> x = col_double() #> ) #> Parsed with column specification: #> cols( #> y = col_character() #> ) # Each file is now available as a dataframe in the global environment csv1 #> # A tibble: 2 x 1 #> x #> <dbl> #> 1 1 #> 2 2 csv2 #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b rm(list = ls())
You may import files from a specific path.
(path_mixed <- tor_example("mixed")) #> [1] "/home/mauro/R/x86_64-pc-linux-gnu-library/4.0/tor/extdata/mixed" dir(path_mixed) #> [1] "csv.csv" "lower_rdata.rdata" "rda.rda" #> [4] "upper_rdata.RData" load_rdata(path_mixed) ls() #> [1] "lower_rdata" "path_mixed" "rda" "upper_rdata" rda #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b
You may import files into a specific environment.
e <- new.env() ls(e) #> character(0) load_rdata(path_mixed, envir = e) ls(e) #> [1] "lower_rdata" "rda" "upper_rdata"
For more flexibility use load_any() with a function able to read one file of the format you want to import.
dir() #> [1] "_pkgdown.yml" "codecov.yml" "cran-comments.md" "csv1.csv" #> [5] "csv2.csv" "DESCRIPTION" "inst" "LICENSE.md" #> [9] "man" "NAMESPACE" "NEWS.md" "R" #> [13] "README.md" "README.Rmd" "tests" "tor.Rproj" #> [17] "vignettes" load_any(".", .f = readr::read_csv, regexp = "[.]csv$") #> Parsed with column specification: #> cols( #> x = col_double() #> ) #> Parsed with column specification: #> cols( #> y = col_character() #> ) # The data is now available in the global environment csv1 #> # A tibble: 2 x 1 #> x #> <dbl> #> 1 1 #> 2 2 csv2 #> # A tibble: 2 x 1 #> y #> <chr> #> 1 a #> 2 b
