library(tidyverse)Writing to a File and Data Entry
We load the tidyverse package to continue using the readr package.
Writing to a file
readr also comes with two useful functions for writing data back to disk: write_csv() and write_tsv(). The most important arguments to these functions are x (the data frame to save) and file (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.
students <- read_csv("https://pos.it/r4ds-students-csv")
write_csv(students, "students.csv")Now let’s read that csv file back in. Note that the variable type information that you just set up is lost when you save to CSV because you’re starting over with reading from a plain text file again:
students# A tibble: 6 × 5
`Student ID` `Full Name` favourite.food mealPlan AGE
<dbl> <chr> <chr> <chr> <chr>
1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4
2 2 Barclay Lynn French fries Lunch only 5
3 3 Jayendra Lyne N/A Breakfast and lunch 7
4 4 Leon Rossini Anchovies Lunch only <NA>
5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five
6 6 Güvenç Attila Ice cream Lunch only 6 write_csv(students, "students-2.csv")
read_csv("students-2.csv")# A tibble: 6 × 5
`Student ID` `Full Name` favourite.food mealPlan AGE
<dbl> <chr> <chr> <chr> <chr>
1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4
2 2 Barclay Lynn French fries Lunch only 5
3 3 Jayendra Lyne N/A Breakfast and lunch 7
4 4 Leon Rossini Anchovies Lunch only <NA>
5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five
6 6 Güvenç Attila Ice cream Lunch only 6 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 main alternative:
- 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. This means that when you reload the object, you are loading the exact same R object that you stored.
write_rds(students, "students.rds")
read_rds("students.rds")# A tibble: 6 × 5
`Student ID` `Full Name` favourite.food mealPlan AGE
<dbl> <chr> <chr> <chr> <chr>
1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4
2 2 Barclay Lynn French fries Lunch only 5
3 3 Jayendra Lyne N/A Breakfast and lunch 7
4 4 Leon Rossini Anchovies Lunch only <NA>
5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five
6 6 Güvenç Attila Ice cream Lunch only 6 - The arrow package allows you to read and write parquet files, a fast binary file format that can be shared across programming languages.
library(arrow)
write_parquet(students, "students.parquet")
read_parquet("students.parquet")# A tibble: 6 × 5
`Student ID` `Full Name` favourite.food mealPlan AGE
* <dbl> <chr> <chr> <chr> <chr>
1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4
2 2 Barclay Lynn French fries Lunch only 5
3 3 Jayendra Lyne N/A Breakfast and lunch 7
4 4 Leon Rossini Anchovies Lunch only <NA>
5 5 Chidiegwu Dunkel Pizza Breakfast and lunch five
6 6 Güvenç Attila Ice cream Lunch only 6 Parquet tends to be much faster than RDS and is usable outside of R, but does require the arrow package.
Data Entry
Sometimes you’ll need to assemble a tibble “by hand” doing a little data entry in your R script. There are two useful functions to help you do this which differ in whether you layout the tibble by columns or by rows. tibble() works by column:
tibble(
x = c(1, 2, 5),
y = c("h", "m", "g"),
z = c(0.08, 0.83, 0.60)
)# A tibble: 3 × 3
x y z
<dbl> <chr> <dbl>
1 1 h 0.08
2 2 m 0.83
3 5 g 0.6 Laying out the data by column can make it hard to see how the rows are related, so an alternative is tribble(), short for transposed tibble, which lets you lay out your data row by row. tribble() is customized for data entry in code: column headings start with ~ and entries are separated by commas. This makes it possible to lay out small amounts of data in an easy to read form:
tribble(
~x, ~y, ~z,
1, "h", 0.08,
2, "m", 0.83,
5, "g", 0.60
)# A tibble: 3 × 3
x y z
<dbl> <chr> <dbl>
1 1 h 0.08
2 2 m 0.83
3 5 g 0.6