tor (to-R) helps you to import multiple files at once. For example:
list_rds()
to import all .csv files from your working
directory into a list.load_csv()
to import all .csv files from your working
directory into your global environment.Install tor from CRAN with:
install.packages("tor")
Or install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("maurolepore/tor")
library(tor)
list_*()
: Import multiple files from a directory into a listAll 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/3.6/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/3.6/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] "/home/mauro/R/x86_64-pc-linux-gnu-library/3.6/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
#>
#> $upper_rdata
#> # A tibble: 2 x 1
#> y
#> <chr>
#> 1 a
#> 2 b
load_*()
: Load multiple files from a directory into an environmentAll functions default to importing files from the working directory and into the global environment.
# The working directory contains .csv files
dir()
#> [1] "_pkgdown.yml" "codecov.yml" "cran-comments.md" "csv1.csv"
#> [5] "csv2.csv" "DESCRIPTION" "inst" "LICENSE.md"
#> [9] "man" "NAMESPACE" "NEWS.md" "R"
#> [13] "README.md" "README.Rmd" "tests" "tor.Rproj"
#> [17] "vignettes"
load_csv()
#> Parsed with column specification:
#> cols(
#> x = col_double()
#> )
#> Parsed with column specification:
#> cols(
#> y = col_character()
#> )
# Each file is now available as a dataframe in the global environment
csv1
#> # A tibble: 2 x 1
#> x
#> <dbl>
#> 1 1
#> 2 2
csv2
#> # A tibble: 2 x 1
#> y
#> <chr>
#> 1 a
#> 2 b
rm(list = ls())
You may import files from a specific path
.
(path_mixed <- tor_example("mixed"))
#> [1] "/home/mauro/R/x86_64-pc-linux-gnu-library/3.6/tor/extdata/mixed"
dir(path_mixed)
#> [1] "csv.csv" "lower_rdata.rdata" "rda.rda"
#> [4] "upper_rdata.RData"
load_rdata(path_mixed)
ls()
#> [1] "lower_rdata" "path_mixed" "rda" "upper_rdata"
rda
#> # A tibble: 2 x 1
#> y
#> <chr>
#> 1 a
#> 2 b
You may import files into a specific envir
onment.
e <- new.env()
ls(e)
#> character(0)
load_rdata(path_mixed, envir = e)
ls(e)
#> [1] "lower_rdata" "rda" "upper_rdata"
For more flexibility use load_any()
with a function able to read one
file of the format you want to import.
dir()
#> [1] "_pkgdown.yml" "codecov.yml" "cran-comments.md" "csv1.csv"
#> [5] "csv2.csv" "DESCRIPTION" "inst" "LICENSE.md"
#> [9] "man" "NAMESPACE" "NEWS.md" "R"
#> [13] "README.md" "README.Rmd" "tests" "tor.Rproj"
#> [17] "vignettes"
load_any(".", .f = readr::read_csv, regexp = "[.]csv$")
#> 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
Two great packages to read and write data are rio and io.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.