(Builds on: Data structure basics, Manipulation basics)
(Leads to: Exporting data, Google sheets, Parsing details)
So far you’ve worked with data sets that have been bundled in R packages, or have been created with tibble() or tribble(). Now it’s time to learn how to read simple flat files from disk. To do this, we’ll use functions from readr. readr is one of the core tidyverse packages, so you won’t usually load it explicitly.
library(tidyverse)
#> Loading tidyverse: ggplot2
#> Loading tidyverse: tibble
#> Loading tidyverse: tidyr
#> Loading tidyverse: readr
#> Loading tidyverse: purrr
#> Loading tidyverse: dplyr
#> Conflicts with tidy packages ----------------------------------------------
#> filter(): dplyr, stats
#> lag(): dplyr, stats
In this unit, we’re going to focus on delimited files. Delimited files have a delimiter between each value. Two types make up the majority of delimited files that you’ll see in the wild: csv (comma separated) and tsv (tab separated). We’ll focus on csv files, but everything you’ll learn applies equally to tsvs, replacing commas with tabs.
A typical csv file looks something like this:
Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species
5.1,3.5,1.4,0.2,setosa
4.9,3,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5,3.4,1.5,0.2,setosa
Note that:
Typically you can recognise a csv file by its extension: .csv. But beware! Sometimes the extension lies, and if you’re getting weird errors when reading a file, it’s a good idea to peek inside the file using readr::read_lines() and writeLines(), specifying the n_max argument to just look at the first few lines. (You’ll learn more about writeLines() when we get to strings; for now just remember it’s a useful tool for printing lines to the screen.)
"heights.csv" %>%
read_lines(n_max = 10) %>%
writeLines()
#> "earn","height","sex","ed","age","race"
#> 50000,74.4244387818035,"male",16,45,"white"
#> 60000,65.5375428255647,"female",16,58,"white"
#> 30000,63.6291977374349,"female",16,29,"white"
#> 50000,63.1085616752971,"female",16,91,"other"
#> 51000,63.4024835710879,"female",17,39,"white"
#> 9000,64.3995075440034,"female",15,26,"white"
#> 29000,61.6563258264214,"female",12,49,"white"
#> 32000,72.6985437364783,"male",17,46,"white"
#> 2000,72.0394668497611,"male",15,21,"hispanic"
This file illustrates another feature present in many csv files: some values are surrounded by quotes. Confusingly, this isn’t a guarantee that the value is a string: some csv files also surround numbers in quotes too. As you work with more csv files you’ll discover there are few hard and fast rules: for pretty much every crazy thing that you can imagine, someone has done it in a csv file somewhere.
read_csv()The workhorse for reading in csv files is called read_csv(). You give it a path to a csv file and it gives you back a tibble:
heights <- read_csv("heights.csv")
#> Parsed with column specification:
#> cols(
#> earn = col_double(),
#> height = col_double(),
#> sex = col_character(),
#> ed = col_integer(),
#> age = col_integer(),
#> race = col_character()
#> )
heights
#> # A tibble: 1,192 × 6
#> earn height sex ed age race
#> <dbl> <dbl> <chr> <int> <int> <chr>
#> 1 50000 74.42444 male 16 45 white
#> 2 60000 65.53754 female 16 58 white
#> 3 30000 63.62920 female 16 29 white
#> 4 50000 63.10856 female 16 91 other
#> 5 51000 63.40248 female 17 39 white
#> 6 9000 64.39951 female 15 26 white
#> 7 29000 61.65633 female 12 49 white
#> 8 32000 72.69854 male 17 46 white
#> 9 2000 72.03947 male 15 21 hispanic
#> 10 27000 72.23493 male 12 26 white
#> # ... with 1,182 more rows
If you are very lucky, you can point read_csv() at a file and it just works. But this is usually the exception, not the rule, and often you’ll need to tweak some arguments.
The most important arguments to read_csv() are:
col_names: usually col_names = TRUE which tells read_csv() that the first line of the file is the column names. If there aren’t any column names set col_names = FALSE or supply a character vector telling read_csv() what they should be col_names = c("x", "y", "z")
col_types: you might have noticed that when we called read_csv() above it printed out a list of column “specifications”. That describes how readr converts each column into an data structure. readr uses some pretty good heuristics to guess the type, but sometimes the heuristics fail and you’ll need to supply the truth. You’ll learn more about that later in the course
It’s fairly common to encounter csv files that have a bunch of 💩 at the top. You can use skip = n to skip the first n lines, or comment = "#" to ignore all lines that start with #.
read_csv() expects missing values to be suppled as NA. If your file uses a different convention, use na = "." to override the default.
You’ll get to practice using these arguments in the exercises.