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read.table() function in R is often used when a delimited ASCII file (e.g. text file or csv file) is to be imported to generate a data frame. The basic syntax is
df <- read.table(input, options) Where
input is usually a csv or txt file from working directory, and a common list of options are :
header: specify whether the first line in the file will be made as variable names;
sep: specify the delimiter symbol in the file, default is sep=” “, denoting any white space character;
row.names: parameters specifying row identifier;
col.names: parameters specifying column identifier;
colClasses: a vector specifying the classes for each column, e,g, ‘character’, ‘numeric’, etc.
Look at an example csv file University-Fullname-full located in the current working directory, it contains 20 students’ testing scores for mathemtatics, physics, chemistry, as well as their demographic informations.
Full contents of the data file can be found here:
https://rdatacode.com/wp-admin/post.php?post=192&action=edit
We can use read.table() to import this file into a new data frame in R.
# to set working directory
> setwd("d:\\RStatistics-Tutorial")
#use read.table() to read a csv file to create a data frame
#use comma as delimiter, first line of the file used as
# variable names, and set a row identifier
> grade <- read.table("University-Fullname-full.csv", header=TRUE, row.names="StudentID", sep=",")
#to show the new data frame
> grade
Fullname Race Gender Country Age Math Physics Chemistry Date
1 James Zhang A Male US 23 73 70 87 10/31/2008
2 Wilson Li E Female UK 26 95 76 83 3/16/2008
3 Richard Nuan Ye A Male UK 35 77 83 92 5/22/2008
4 Mary Deng E Female US 21 60 99 84 1/24/2009
5 Jason Wilson A Male UK 19 77 89 93 7/30/2009
6 Jennifer Hopkin A Female UK 43 79 64 83 4/5/2009
7 Kari Gjendem E Female US 37 87 99 67 11/24/2008
8 Wenche Dale E Female US 28 95 87 93 10/2/2008
9 Jane Larsen A Female US 19 73 92 84 6/5/2009
10 Steinar Hansen A Male US 25 66 93 65 8/1/2008
11 Michael Chen A Male UK 42 83 90 77 10/24/2008
12 Josef Curton E Male US 32 71 63 96 11/8/2009
13 Jennifer Jones E Male US 27 79 76 82 10/29/2008
14 Gary Grant E Female UK 35 90 78 92 10/24/2008
15 Phil Yao A Male UK 21 69 69 83 10/15/2008
16 Nora Spears E Female US 29 79 83 76 3/11/2009
17 Goril Nordmann A Female UK 36 91 79 69 5/24/2008
18 Lisa Bondvik E Female US 39 65 73 87 7/9/2009
19 Guri Olsen E Female US 24 87 72 89 8/12/2009
20 Martin Jones A Male US 25 82 73 62 3/27/2008
#show the structure information of the data frame
> str(grade)
'data.frame': 20 obs. of 9 variables:
$ Fullname : chr "James Zhang" "Wilson Li" "Richard Nuan Ye" "Mary Deng" ...
$ Race : chr "A" "E" "A" "E" ...
$ Gender : chr "Male" "Female" "Male" "Female" ...
$ Country : chr "US" "UK" "UK" "US" ...
$ Age : int 23 26 35 21 19 43 37 28 19 25 ...
$ Math : int 73 95 77 60 77 79 87 95 73 66 ...
$ Physics : int 70 76 83 99 89 64 99 87 92 93 ...
$ Chemistry: int 87 83 92 84 93 83 67 93 84 65 ...
$ Date : chr "10/31/2008" "3/16/2008" "5/22/2008" "1/24/2009" ...
> Option colClasses can be used to definitely setting the variable type simutaneouly when importing the csv file.
#set column variable type in read.table()
> vartype<-c("character", "character", "character", "character", "character", "numeric","numeric", "numeric","numeric","character")
> grade <- read.table("University-Fullname-full.csv", colClasses=vartype, header=TRUE, sep=",")
> #grade
> str(grade)
'data.frame': 20 obs. of 10 variables:
$ StudentID: chr "1" "2" "3" "4" ...
$ Fullname : chr "James Zhang" "Wilson Li" "Richard Nuan Ye" "Mary Deng" ...
$ Race : chr "A" "E" "A" "E" ...
$ Gender : chr "Male" "Female" "Male" "Female" ...
$ Country : chr "US" "UK" "UK" "US" ...
$ Age : num 23 26 35 21 19 43 37 28 19 25 ...
$ Math : num 73 95 77 60 77 79 87 95 73 66 ...
$ Physics : num 70 76 83 99 89 64 99 87 92 93 ...
$ Chemistry: num 87 83 92 84 93 83 67 93 84 65 ...
$ Date : chr "10/31/2008" "3/16/2008" "5/22/2008" "1/24/2009" ...
> You can also watch our videos on YouTube to get more illustrative experience about data creation.
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