Tidyverse: Writing to a CSV File

readr also comes with two useful functions for writing data back to disk: write_csv() and write_tsv(). Both functions increase the chances of the output file being read back in correctly by:

• Always encoding strings in UTF-8.

• Saving dates and date-times in ISO8601 format so they are easily parsed elsewhere.

If you want to export a csv file to Excel, use write_excel_csv() - this writes a special character (a "byte order mark") at the start of the file which tells Excel that you're using the UTF-8 encoding.

The most important arguments are x (the data frame to save), and path (the location to save it). You can also specify how missing values are written with na, and if you want to append to an existing file.

write_csv(challenge, "challenge.csv")

Note that the type information is lost when you save to csv:

challenge
#> # A tibble: 2,000 x 2
#>       x y         
#>   <dbl> <date>    
#> 1   404 NA        
#> 2  4172 NA        
#> 3  3004 NA        
#> 4   787 NA        
#> 5    37 NA        
#> 6  2332 NA        
#> # … with 1,994 more rows
write_csv(challenge, "challenge-2.csv")
read_csv("challenge-2.csv")
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   x = col_double(),
#>   y = col_logical()
#> )
#> # A tibble: 2,000 x 2
#>       x y    
#>   <dbl> <lgl>
#> 1   404 NA   
#> 2  4172 NA   
#> 3  3004 NA   
#> 4   787 NA   
#> 5    37 NA   
#> 6  2332 NA   
#> # … with 1,994 more rows

This makes CSVs a little unreliable for caching interim results - you need to recreate the column specification every time you load in. There are two alternatives:

1. write_rds() and read_rds() are uniform wrappers around the base functions readRDS() and saveRDS(). These store data in R's custom binary format called RDS:

   write_rds(challenge, "challenge.rds")
   read_rds("challenge.rds")
   #> # A tibble: 2,000 x 2
   #>       x y         
   #>   <dbl> <date>    
   #> 1   404 NA        
   #> 2  4172 NA        
   #> 3  3004 NA        
   #> 4   787 NA        
   #> 5    37 NA        
   #> 6  2332 NA        
   #> # … with 1,994 more rows

2. The feather package implements a fast binary file format that can be shared across programming languages:

   library(feather)
   write_feather(challenge, "challenge.feather")
   read_feather("challenge.feather")
   #> # A tibble: 2,000 x 2
   #>       x      y
   #>   <dbl> <date>
   #> 1   404   <NA>
   #> 2  4172   <NA>
   #> 3  3004   <NA>
   #> 4   787   <NA>
   #> 5    37   <NA>
   #> 6  2332   <NA>
   #> # ... with 1,994 more rows

Feather tends to be faster than RDS and is usable outside of R. RDS supports list-columns (which you'll learn about in many models); feather currently does not.

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Source: H. Wickham and G. Grolemund, https://r4ds.had.co.nz/data-import.html
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.