tor

The goal of tor (to-R) is to make importing data into R ridiculously easy. It helps you to import multiple files from a single directory into R in a simple, intuitive way. list_csv() creates a list of all “*.csv” files in your working directory; and load_rdata() loads all “*.rdata” files into your global environment (see all functions in Reference). tor does nothing you can’t do with functions from base R but it makes a frequent task less painful. It has few dependencies and works well with the tidyverse.

Installation

# install.packages("devtools")
devtools::install_github("maurolepore/tor")

Example

library(tor)

All functions list whatever they read, and default to reading from the working directory.

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

dir()
#>  [1] "_pkgdown.yml"     "cran-comments.md" "csv1.csv"        
#>  [4] "csv2.csv"         "datasets"         "DESCRIPTION"     
#>  [7] "docs"             "inst"             "LICENSE.md"      
#> [10] "man"              "NAMESPACE"        "NEWS.md"         
#> [13] "R"                "README.md"        "README.Rmd"      
#> [16] "tests"            "tmp.R"            "tor.Rproj"       
#> [19] "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/R-3.5.2/library/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/R-3.5.2/library/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/R-3.5.2/library/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 load from the working directory.

# The working directory contains .csv files
dir()
#>  [1] "_pkgdown.yml"     "cran-comments.md" "csv1.csv"        
#>  [4] "csv2.csv"         "datasets"         "DESCRIPTION"     
#>  [7] "docs"             "inst"             "LICENSE.md"      
#> [10] "man"              "NAMESPACE"        "NEWS.md"         
#> [13] "R"                "README.md"        "README.Rmd"      
#> [16] "tests"            "tmp.R"            "tor.Rproj"       
#> [19] "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

You may load from a path.

rm(list = ls())
ls()
#> character(0)

(path_mixed <- tor_example("mixed"))
#> [1] "C:/Users/LeporeM/Documents/R/R-3.5.2/library/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

Related projects

There are great packages to read and write data, for example rio and io. tor does less than the alternatives, but it is much smaller and more flexible.