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

Installing and loading readr

# Installing
install.packages("readr")

# Loading
library("readr")

The readr package contains functions for reading i) delimited files, ii) lines and iii) the whole file.

Functions for reading delimited files: txt|csv

The function read_delim()[in readr package] is a general function to import a data table into R. Depending on the format of your file, you can also use:

The simplified format of these functions are, as follow:

# General function
read_delim(file, delim, col_names = TRUE)

# Read comma (",") separated values
read_csv(file, col_names = TRUE)

# Read semicolon (";") separated values
# (this is common in European countries)
read_csv2(file, col_names = TRUE)
    
# Read tab separated values
read_tsv(file, col_names = TRUE)

read_csv() and read_tsv() are special case of the general function read_delim(). They’re useful for reading the most common types of flat file data, comma separated values and tab separated values, respectively.

The above mentioned functions return an object of class tbl_df which is data frame providing a nicer printing method, useful when working with large data sets.

Reading a file

# Read a txt file, named "mtcars.txt"
my_data <- read_tsv("mtcars.txt")

# Read a csv file, named "mtcars.csv"
my_data <- read_csv("mtcars.csv")

# Read a txt file
my_data <- read_tsv(file.choose())

# Read a csv file
my_data <- read_csv(file.choose())

my_data <- read_delim(file.choose(), sep = "|")

my_data <- read_tsv("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

my_data <- read_csv(file.choose())
problems(my_data)

Specify column types

There are different types of data: numeric, character, logical, …

readr tries to guess automatically the type of data contained in each column. You might see a lot of warnings in a situation where readr has guessed the column type incorrectly. To fix these problems you can use the additional arguments col_type() to specify the data type of each column.

The following column types are available:

An example is as follow (column x is an integer (i) and column treatment = “character” (c):

read_csv("my_file.csv", col_types = cols(
  x = "i", # integer column
  treatment = "c" # character column
))

Reading lines from a file

Function: read_lines().

1. Simplified format:

read_lines(file, skip = 0, n_max = -1L)

The function read_lines() returns a character vector with one element for each line.

2. Example of usage

# Demo file
my_file <- system.file("extdata/mtcars.csv", package = "readr")
# Read lines
my_data <- read_lines(my_file)
head(my_data)

[1] "\"mpg\",\"cyl\",\"disp\",\"hp\",\"drat\",\"wt\",\"qsec\",\"vs\",\"am\",\"gear\",\"carb\""
[2] "21,6,160,110,3.9,2.62,16.46,0,1,4,4"                                                     
[3] "21,6,160,110,3.9,2.875,17.02,0,1,4,4"                                                    
[4] "22.8,4,108,93,3.85,2.32,18.61,1,1,4,1"                                                   
[5] "21.4,6,258,110,3.08,3.215,19.44,1,0,3,1"                                                 
[6] "18.7,8,360,175,3.15,3.44,17.02,0,0,3,2"                                                  

Read whole file

1. Simplified format

read_file(file)

• Example of usage

# Demo file
my_file <- system.file("extdata/mtcars.csv", package = "readr")
# Read whole file
read_file(my_file)

[1] "\"mpg\",\"cyl\",\"disp\",\"hp\",\"drat\",\"wt\",\"qsec\",\"vs\",\"am\",\"gear\",\"carb\"\n21,6,160,110,3.9,2.62,16.46,0,1,4,4\n21,6,160,110,3.9,2.875,17.02,0,1,4,4\n22.8,4,108,93,3.85,2.32,18.61,1,1,4,1\n21.4,6,258,110,3.08,3.215,19.44,1,0,3,1\n18.7,8,360,175,3.15,3.44,17.02,0,0,3,2\n18.1,6,225,105,2.76,3.46,20.22,1,0,3,1\n14.3,8,360,245,3.21,3.57,15.84,0,0,3,4\n24.4,4,146.7,62,3.69,3.19,20,1,0,4,2\n22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2\n19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4\n17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4\n16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3\n17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3\n15.2,8,275.8,180,3.07,3.78,18,0,0,3,3\n10.4,8,472,205,2.93,5.25,17.98,0,0,3,4\n10.4,8,460,215,3,5.424,17.82,0,0,3,4\n14.7,8,440,230,3.23,5.345,17.42,0,0,3,4\n32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1\n30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2\n33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1\n21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1\n15.5,8,318,150,2.76,3.52,16.87,0,0,3,2\n15.2,8,304,150,3.15,3.435,17.3,0,0,3,2\n13.3,8,350,245,3.73,3.84,15.41,0,0,3,4\n19.2,8,400,175,3.08,3.845,17.05,0,0,3,2\n27.3,4,79,66,4.08,1.935,18.9,1,1,4,1\n26,4,120.3,91,4.43,2.14,16.7,0,1,5,2\n30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2\n15.8,8,351,264,4.22,3.17,14.5,0,1,5,4\n19.7,6,145,175,3.62,2.77,15.5,0,1,5,6\n15,8,301,335,3.54,3.57,14.6,0,1,5,8\n21.4,4,121,109,4.11,2.78,18.6,1,1,4,2\n"

Summary

Related articles

• Previous chapters
  • R programming basics
  • Best practices in preparing data files for importing into R
  • Reading data from txt|csv files: R base functions
• Next chapters
  • Reading data from Excel files (xls|xlsx) into R
  • Exporting data from R

Infos

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

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

Copying data from Excel and import into R

my_data <- read.table(file = "clipboard", 
                      sep = "\t", header=TRUE)

my_data <- read.table(pipe("pbpaste"), sep="\t", header = TRUE)

Importing Excel files into R using readxl package

The readxl package, developed by Hadley Wickham, can be used to easily import Excel files (xls|xlsx) into R without any external dependencies.

install.packages("readxl")

library("readxl")

# Loading
library("readxl")

# xls files
my_data <- read_excel("my_file.xls")

# xlsx files
my_data <- read_excel("my_file.xlsx")

my_data <- read_excel(file.choose())

# Specify sheet by its name
my_data <- read_excel("my_file.xlsx", sheet = "data")
  
# Specify sheet by its index
my_data <- read_excel("my_file.xlsx", sheet = 2)

my_data <- read_excel("my_file.xlsx", na = "---")

Importing Excel files using xlsx package

The xlsx package, a java-based solution, is one of the powerful R packages to read, write and format Excel files.

install.packages("xlsx")

library("xlsx")

read.xlsx(file, sheetIndex, header=TRUE)

read.xlsx2(file, sheetIndex, header=TRUE)

library("xlsx")
my_data <- read.xlsx(file.choose(), 1)  # read first sheet

Summary

Related articles

• Previous chapters
  • R programming basics
  • Best practices in preparing data files for importing into R
  • Reading data from txt|csv files: R base functions
  • Fast Reading of Data From txt|csv Files into R: readr package
• Next chapters
  • Exporting data from R

Infos

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

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).

Open your file

We suppose that you open and prepare your file with Excel as follow.

Prepare your file

1. Row and column names:

Column names should be compatible with R naming conventions. As illustrated below, our data contains some issues that should be fixed before importing:

2. Naming conventions:

3. Final file:

Our finale file should look like this:

Save your file

We recommend to save your file into .txt (tab-delimited text file) or .csv (comma separated value file) format.

If you still want to save it into xls|xlsx file formats, you can do it. We’ll learn in the next articles, how to import xls|xlsx files into R.

Related articles

• Previous chapters
  • R programming basics
• Next chapters
  • Reading data from txt|csv files: R base functions
  • 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).