Creating data frames using read.table() and read.csv() in R

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R language provides several useful functions for importing delimited files and creating data frames. These delimited files are often stored in local computer directory, with extensions ‘txt’, ‘csv’, ‘dat’. The mostly widely used functions for importing these files in R are read.table() and read.csv().

1. read.table()

By specifying different delimiter in the function, read.table() can accommodate importing data files with type ‘txt, ‘csv’, ‘dat’ and more. The basic form of the function is :

df <- read.table(f, options)

Where:

f is a delimited file,

options includes the following characteristics:
header: whether the the first line in the file is used for variable names
sep: The delimiter in the file to separate data values.
sep=”” – spaces, tabs, new line used for delimiting
sep=”,” – comma used for delimiting
sep=”\t” – tab used for delimiting

row.names: specifying one or more variables to represent row identifiers
col.names: specify a character vector containing the variable names
na.strings – value codes which will be converted to “NA” in data frame
stringsAsFactors – whether character variables should be converted to factors, default is FALSE.

In the next lines, we show a csv file containing student exam scores, and it is stored in the computer’s local directory.

StudentID,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

The following code shows how to import this csv file and generate a data frame, by setting the delimiter as ‘,’, and the first row of the csv file as data frame’s column variable labels.

# to set working directory
> setwd("d:\\RStatistics-Tutorial")   
> 
#use read.table() to read a csv file to create a data frame
> grade <- read.table("University-Fullname-full.csv",  header=TRUE, row.names="StudentID", sep=",")
> grade
          Fullname Race Gender Country Age Math Physics Chemistry
1      James Zhang    A   Male      US  23   73      70        87
2        Wilson Li    E Female      UK  26   95      76        83
3  Richard Nuan Ye    A   Male      UK  35   77      83        92
4        Mary Deng    E Female      US  21   60      99        84
5     Jason Wilson    A   Male      UK  19   77      89        93
6  Jennifer Hopkin    A Female      UK  43   79      64        83
7     Kari Gjendem    E Female      US  37   87      99        67
8      Wenche Dale    E Female      US  28   95      87        93
9      Jane Larsen    A Female      US  19   73      92        84
10  Steinar Hansen    A   Male      US  25   66      93        65
11    Michael Chen    A   Male      UK  42   83      90        77
12    Josef Curton    E   Male      US  32   71      63        96
13  Jennifer Jones    E   Male      US  27   79      76        82
14      Gary Grant    E Female      UK  35   90      78        92
15        Phil Yao    A   Male      UK  21   69      69        83
16     Nora Spears    E Female      US  29   79      83        76
17  Goril Nordmann    A Female      UK  36   91      79        69
18    Lisa Bondvik    E Female      US  39   65      73        87
19      Guri Olsen    E Female      US  24   87      72        89
20    Martin Jones    A   Male      US  25   82      73        62
         Date
1  10/31/2008
2   3/16/2008
3   5/22/2008
4   1/24/2009
5   7/30/2009
6    4/5/2009
7  11/24/2008
8   10/2/2008
9    6/5/2009
10   8/1/2008
11 10/24/2008
12  11/8/2009
13 10/29/2008
14 10/24/2008
15 10/15/2008
16  3/11/2009
17  5/24/2008
18   7/9/2009
19  8/12/2009
20  3/27/2008

#show the structure 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" ...

2. read.csv()

You can also use read.csv() to import a csv file and generate a data frame.

The basic form of the function is

read.csv(f)

Where f is a csv file stored on computer, and the effect is same as using read.table() with header =TRUE, and sep=”,”

The next code example shows the reading same file using read.csv()

# to set working directory
> setwd("d:\\RStatistics-Tutorial")   
> 
#using read.csv() to import a csv file and generate a data #frame
> grade <- read.csv("University-Fullname-full.csv")
> grade
   StudentID        Fullname Race Gender Country Age Math Physics
1          1     James Zhang    A   Male      US  23   73      70
2          2       Wilson Li    E Female      UK  26   95      76
3          3 Richard Nuan Ye    A   Male      UK  35   77      83
4          4       Mary Deng    E Female      US  21   60      99
5          5    Jason Wilson    A   Male      UK  19   77      89
6          6 Jennifer Hopkin    A Female      UK  43   79      64
7          7    Kari Gjendem    E Female      US  37   87      99
8          8     Wenche Dale    E Female      US  28   95      87
9          9     Jane Larsen    A Female      US  19   73      92
10        10  Steinar Hansen    A   Male      US  25   66      93
11        11    Michael Chen    A   Male      UK  42   83      90
12        12    Josef Curton    E   Male      US  32   71      63
13        13  Jennifer Jones    E   Male      US  27   79      76
14        14      Gary Grant    E Female      UK  35   90      78
15        15        Phil Yao    A   Male      UK  21   69      69
16        16     Nora Spears    E Female      US  29   79      83
17        17  Goril Nordmann    A Female      UK  36   91      79
18        18    Lisa Bondvik    E Female      US  39   65      73
19        19      Guri Olsen    E Female      US  24   87      72
20        20    Martin Jones    A   Male      US  25   82      73
   Chemistry       Date
1         87 10/31/2008
2         83  3/16/2008
3         92  5/22/2008
4         84  1/24/2009
5         93  7/30/2009
6         83   4/5/2009
7         67 11/24/2008
8         93  10/2/2008
9         84   6/5/2009
10        65   8/1/2008
11        77 10/24/2008
12        96  11/8/2009
13        82 10/29/2008
14        92 10/24/2008
15        83 10/15/2008
16        76  3/11/2009
17        69  5/24/2008
18        87   7/9/2009
19        89  8/12/2009
20        62  3/27/2008
> 

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