Preleminary tasks

1. Launch RStudio as described here: Running RStudio and setting up your working directory

2. Prepare your data as described here: Best practices for preparing your data

R base functions for importing data

The R base function read.table() is a general function that can be used to read a file in table format. The data will be imported as a data frame.

Note that, depending on the format of your file, several variants of read.table() are available to make your life easier, including read.csv(), read.csv2(), read.delim() and read.delim2().

The simplified format of these functions are, as follow:

# Read tabular data into R
read.table(file, header = FALSE, sep = "", dec = ".")
# Read "comma separated value" files (".csv")
read.csv(file, header = TRUE, sep = ",", dec = ".", ...)
# Or use read.csv2: variant used in countries that 
# use a comma as decimal point and a semicolon as field separator.
read.csv2(file, header = TRUE, sep = ";", dec = ",", ...)
# Read TAB delimited files
read.delim(file, header = TRUE, sep = "\t", dec = ".", ...)
read.delim2(file, header = TRUE, sep = "\t", dec = ",", ...)

Reading a local file

• To import a local .txt or a .csv file, the syntax would be:

# Read a txt file, named "mtcars.txt"
my_data <- read.delim("mtcars.txt")
# Read a csv file, named "mtcars.csv"
my_data <- read.csv("mtcars.csv")

The above R code, assumes that the file “mtcars.txt” or “mtcars.csv” is in your current working directory. To know your current working directory, type the function getwd() in R console.

• It’s also possible to choose a file interactively using the function file.choose(), which I recommend if you’re a beginner in R programming:

# Read a txt file
my_data <- read.delim(file.choose())
# Read a csv file
my_data <- read.csv(file.choose())

If you use the R code above in RStudio, you will be asked to choose a file.

If your data contains column with text, R may assume that columns as a factors or grouping variables (e.g.: “good”, “good”, “bad”, “bad”, “bad”). If you don’t want your text data to be converted as factors, add stringsAsFactor = FALSE in read.delim(), read.csv() and read.table() functions. In this case, the data frame columns corresponding to string in your text file will be character.

For example:

my_data <- read.delim(file.choose(), 
                      stringsAsFactor = FALSE)

• If your field separator is for example “|”, it’s possible use the general function read.table() with additional arguments:

my_data <- read.table(file.choose(), 
                      sep ="|", header = TRUE, dec =".")

Reading a file from internet

It’s possible to use the functions read.delim(), read.csv() and read.table() to import files from the web.

my_data <- read.delim("https://www.sthda.com/upload/boxplot_format.txt")
head(my_data)

   Nom variable Group
1 IND1       10     A
2 IND2        7     A
3 IND3       20     A
4 IND4       14     A
5 IND5       14     A
6 IND6       12     A

Summary

Related articles

• Previous chapters
  • R programming basics
  • Best practices in preparing data files for importing into R
• Next chapters
  • Fast Reading of Data From txt|csv Files into R: readr package
  • Reading data From Excel Files (xls|xlsx) into R

Infos

This analysis has been performed using R (ver. 3.2.3).

Recommended for you