tor

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tor (to-R) helps you to import multiple files at once. For example:

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

library(tor)

list_*(): Import multiple files from a directory into a list

All functions default to importing files from the working directory.

dir()
#>  [1] "_pkgdown.yml"     "cran-comments.md" "csv1.csv"        
#>  [4] "csv2.csv"         "DESCRIPTION"      "docs"            
#>  [7] "inst"             "LICENSE.md"       "man"             
#> [10] "NAMESPACE"        "NEWS.md"          "R"               
#> [13] "README.md"        "README.Rmd"       "tests"           
#> [16] "tmp.R"            "tor.Rproj"        "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] "C:/Users/LeporeM/Documents/R/win-library/3.5/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] "C:/Users/LeporeM/Documents/R/win-library/3.5/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    
#>   <fct>
#> 1 a    
#> 2 b

It understands lambda functions and formulas (powered by rlang).

# Use the pipe (%>%)
library(magrittr)

(path_rdata <- tor_example("rdata"))
#> [1] "C:/Users/LeporeM/Documents/R/win-library/3.5/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"     "cran-comments.md" "csv1.csv"        
#>  [4] "csv2.csv"         "DESCRIPTION"      "docs"            
#>  [7] "inst"             "LICENSE.md"       "man"             
#> [10] "NAMESPACE"        "NEWS.md"          "R"               
#> [13] "README.md"        "README.Rmd"       "tests"           
#> [16] "tmp.R"            "tor.Rproj"        "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] "C:/Users/LeporeM/Documents/R/win-library/3.5/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"     "cran-comments.md" "csv1.csv"        
#>  [4] "csv2.csv"         "DESCRIPTION"      "docs"            
#>  [7] "inst"             "LICENSE.md"       "man"             
#> [10] "NAMESPACE"        "NEWS.md"          "R"               
#> [13] "README.md"        "README.Rmd"       "tests"           
#> [16] "tmp.R"            "tor.Rproj"        "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

Related projects

Two great packages to read and write data are rio and io.