Read a CSV file (.csv)

You want to read a CSV file into your R session, where you can manipulate its contents. The file has the extension .csv. A CSV file is a text file that contains a table whose values are separated by commas, i.e. a Comma Separated Values file.

Step 1 - Call readr::read_csv(). read_csv() is designed to read in .csv files with , as the field separator.

Step 2 - Give read_csv() the filepath to your file as a character string. R will read the filepath as if it begins at your working directory. For example:

read_csv("my/file.csv")

Step 3 - Save the output to an object, so you can access it later.

csv_table <- read_csv("my/file.csv")

Example

We want to read in the chickens dataset which is a CSV file. We have chickens saved on our computer at /Users/bcullen/Desktop/rstudio/academySassyRecipes/recipes/basics/data/chickens.csv.

We begin by loading the readr package which contains read_csv(). Next, we pass read_csv() the filepath for our CSV file in order to read in the dataset. The file is in the data folder. The output looks like this:

library(readr)
read_csv("data/chickens.csv")

Rows: 5 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): chicken, sex, motto
dbl (1): eggs_laid

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# A tibble: 5 × 4
  chicken                 sex     eggs_laid motto                               
  <chr>                   <chr>       <dbl> <chr>                               
1 Foghorn Leghorn         rooster         0 That's a joke, ah say, that's a jok…
2 Chicken Little          hen             3 The sky is falling!                 
3 Ginger                  hen            12 Listen. We'll either die free chick…
4 Camilla the Chicken     hen             7 Bawk, buck, ba-gawk.                
5 Ernie The Giant Chicken rooster         0 Put Captain Solo in the cargo hold.

Notice that read_csv() automatically chose intelligent data types for each of the columns.

Lastly, we assign the chickens dataset read in by read_csv() to an object, chickens, so we can access it later.

chickens <- read_csv("data/chickens.csv")

Rows: 5 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): chicken, sex, motto
dbl (1): eggs_laid

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

read_csv() comes with many arguments that you can use to customize which parts of the file will be read in and how. Here are a few of the most useful:

Argument	Description
`col_names`	Should the first row be read in as column names? Defaults to `TRUE`. Can also be a character vector of column names.
`col_types`	Explicitly set the data type for each column.
`skip`	Number of rows to skip before reading any data.
`guess_max`	Maximum number of lines to read for guessing column types.

Read the help page at ?read_csv to learn more.

read_csv() and SAS

read_csv() is the equivalent of SAS’s IMPORT procedure with the DATAFILE option and DBMS = CSV. In SAS, you must specify the complete path, filename, and extension for DATAFILE, whereas when using read_csv() the path can be specified based on the working directory.

In SAS:

PROC IMPORT DATAFILE = 'filename' OUT = csv_table
  DBMS = CSV REPLACE;
RUN;

In R:

csv_table <- read_csv("my/file.csv")