library(tidyverse)Reading Data from File
We load the tidyverse package to begin.
An external comma-separated values (csv) can be read into R using read_csv() function from the readr package, a core package of the tidyverse. The first argument of the function is the most imporant: the path to the file. You can think about the path as the address of the file: the file called students.csv and that it lives in the data folder.
students <- read_csv("data/students.csv")he code above will work if you have the students.csv file in a data folder in your project. You can download the students.csv file from https://pos.it/r4ds-students-csv or you can read it directly from that URL with:
students <- read_csv("https://pos.it/r4ds-students-csv")When you run read_csv(), it prints out a message telling you the number of rows and columns of data, the delimiter that was used, and the column specifications (names of columns organized by the type of data the column contains). It also prints out some information about retrieving the full column specification and how to quiet this message.
Practical advice
Once you read data in, the first step usually involves transforming it in some way to make it easier to work with in the rest of your analysis. Let’s take another look at the students data with that in mind.
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 In the favourite.food column, there are a bunch of food items, and then the character string N/A, which should have been a real NA that R will recognize as “not available”. This is something we can address using the na argument. By default, read_csv() only recognizes empty strings (““) in this dataset as NAs, we want it to also recognize the character string”N/A”.
students <- read_csv("https://pos.it/r4ds-students-csv", na = c("N/A", ""))
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 <NA> 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 You might also notice that the Student ID and Full Name columns are surrounded by backticks. That’s because they contain spaces, breaking R’s usual rules for variable names; they’re non-syntactic names. To refer to these variables, you need to surround them with backticks, `:
students |>
rename(
student_id = `Student ID`,
full_name = `Full Name`
)# 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 <NA> 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 An alternative approach is to use janitor::clean_names() to use some heuristics to turn them all into snake case at once.
students |> janitor::clean_names()# A tibble: 6 × 5
student_id full_name favourite_food meal_plan 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 <NA> 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 Another common task after reading in data is to consider variable types. For example, meal_plan is a categorical variable with a known set of possible values, which in R should be represented as a factor:
students |>
janitor::clean_names() |>
mutate(meal_plan = factor(meal_plan))# A tibble: 6 × 5
student_id full_name favourite_food meal_plan age
<dbl> <chr> <chr> <fct> <chr>
1 1 Sunil Huffmann Strawberry yoghurt Lunch only 4
2 2 Barclay Lynn French fries Lunch only 5
3 3 Jayendra Lyne <NA> 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 Note that the values in the meal_plan variable have stayed the same, but the type of variable denoted underneath the variable name has changed from character (
Before you analyze these data, you’ll probably want to fix the age and id columns. Currently, age is a character variable because one of the observations is typed out as five instead of a numeric 5.
students <- students |>
janitor::clean_names() |>
mutate(
meal_plan = factor(meal_plan),
age = parse_number(if_else(age == "five", "5", age))
)A new function here is if_else(), which has three arguments. The first argument test should be a logical vector. The result will contain the value of the second argument, yes, when test is TRUE, and the value of the third argument, no, when it is FALSE. Here we’re saying if age is the character string “five”, make it “5”, and if not leave it as age.
Other arguments
There are a couple of other important arguments that we need to mention, and they’ll be easier to demonstrate if we first show you a handy trick: read_csv() can read text strings that you’ve created and formatted like a CSV file:
read_csv(
"a,b,c
1,2,3
4,5,6"
)# A tibble: 2 × 3
a b c
<dbl> <dbl> <dbl>
1 1 2 3
2 4 5 6Usually, read_csv() uses the first line of the data for the column names, which is a very common convention. But it’s not uncommon for a few lines of metadata to be included at the top of the file. You can use skip = n to skip the first n lines or use comment = “#” to drop all lines that start with (e.g.) #:
read_csv(
"The first line of metadata
The second line of metadata
x,y,z
1,2,3",
skip = 2
)# A tibble: 1 × 3
x y z
<dbl> <dbl> <dbl>
1 1 2 3read_csv(
"# A comment I want to skip
x,y,z
1,2,3",
comment = "#"
)# A tibble: 1 × 3
x y z
<dbl> <dbl> <dbl>
1 1 2 3In other cases, the data might not have column names. You can use col_names = FALSE to tell read_csv() not to treat the first row as headings and instead label them sequentially from X1 to Xn:
read_csv(
"1,2,3
4,5,6",
col_names = FALSE
)# A tibble: 2 × 3
X1 X2 X3
<dbl> <dbl> <dbl>
1 1 2 3
2 4 5 6Alternatively, you can pass col_names a character vector which will be used as the column names:
read_csv(
"1,2,3
4,5,6",
col_names = c("x", "y", "z")
)# A tibble: 2 × 3
x y z
<dbl> <dbl> <dbl>
1 1 2 3
2 4 5 6These arguments are all you need to know to read the majority of CSV files that you’ll encounter in practice. (For the rest, you’ll need to carefully inspect your .csv file and read the documentation for read_csv()’s many other arguments.)
Other file types
Once you’ve mastered read_csv(), using readr’s other functions is straightforward; it’s just a matter of knowing which function to reach for:
read_csv2() reads semicolon-separated files. These use ; instead of , to separate fields and are common in countries that use , as the decimal marker.
read_tsv() reads tab-delimited files.
read_delim() reads in files with any delimiter, attempting to automatically guess the delimiter if you don’t specify it.
read_fwf() reads fixed-width files. You can specify fields by their widths with fwf_widths() or by their positions with fwf_positions().
read_table() reads a common variation of fixed-width files where columns are separated by white space.
read_log() reads Apache-style log files.