Introduction
Various application programs such as R and Microsoft Excel support importing and exporting data in tabular format. A CSV file contains data in the form of rows and columns that is data in the form of a table, A CSV file comprises several rows of data, and every piece of information within that row is separated by commas.
In this article, I will discuss how to read comma-separated data values in R and store these values in a data frame.
Reading comma-separated values
The read.csv function can be used to read data from comma-separated values (CSV) files. If a CSV file contains a header row, then to read data from such files, we can use the following syntax.
df1 <- read.csv("filename")
To read data from a CSV file that includes a row header, we can include a new argument named header and change its value as follows.
> df1 <- read.csv("filename", header=FALSE)
The above syntax will generate the following output.
> df1 = read.csv("bank.csv", header = TRUE)
> df1
age job marital education default balance housing loan contact day month duration campaign pdays previous
1 59 admin. married secondary no 2343 yes no unknown 5 may 1042 1 -1 0
2 56 admin. married secondary no 45 no no unknown 5 may 1467 1 -1 0
3 41 technician married secondary no 1270 yes no unknown 5 may 1389 1 -1 0
4 55 services married secondary no 2476 yes no unknown 5 may 579 1 -1 0
5 54 admin. married tertiary no 184 no no unknown 5 may 673 2 -1 0
if we import data from a CSV file named bank.csv containing data frames with 5 rows and 15 columns. On top, there is a row header that consists of the names of columns,
To read data from a CSV file that has been imported using the syntax mentioned above, we are using the read.csv function. The read.csv function builds up a data frame. Data frame is one of the ways through which we can represent any data in R which is available in the form of rows and columns that are in the form of tables.
The function in the below code implies that the CSV file already contains a header row.
> data = read.csv("bank.csv")
> data
age job marital education default balance housing loan contact day month duration campaign pdays previous
1 59 admin. married secondary no 2343 yes no unknown 5 may 1042 1 -1 0
2 56 admin. married secondary no 45 no no unknown 5 may 1467 1 -1 0
3 41 technician married secondary no 1270 yes no unknown 5 may 1389 1 -1 0
4 55 services married secondary no 2476 yes no unknown 5 may 579 1 -1 0
5 54 admin. married tertiary no 184 no no unknown 5 may 673 2 -1 0
6 42 management single tertiary no 0 yes yes unknown 5 may 562 2 -1 0
7 56 management married tertiary no 830 yes yes unknown 6 may 1201 1 -1 0
8 60 retired divorced secondary no 545 yes no unknown 6 may 1030 1 -1 0
9 37 technician married secondary no 1 yes no unknown 6 may 608 1 -1 0
10 28 services single secondary no 5090 yes no unknown 6 may 1297 3 -1 0
11 38 admin. single secondary no 100 yes no unknown 7 may 786 1 -1 0
12 30 blue-collar married secondary no 309 yes no unknown 7 may 1574 2 -1 0
13 29 management married tertiary no 199 yes yes unknown 7 may 1689 4 -1 0
14 46 blue-collar single tertiary no 460 yes no unknown 7 may 1102 2 -1 0
15 31 technician single tertiary no 703 yes no unknown 8 may 943 2 -1 0
16 35 management divorced tertiary no 3837 yes no unknown 8 may 1084 1 -1 0
17 32 blue-collar single primary no 611 yes no unknown 8 may 541 3 -1 0
18 49 services married secondary no -8 yes no unknown 8 may 1119 1 -1 0
19 41 admin. married secondary no 55 yes no unknown 8 may 1120 2 -1 0
20 49 admin. divorced secondary no 168 yes yes unknown 8 may 513 1 -1 0
21 28 admin. divorced secondary no 785 yes no unknown 8 may 442 2 -1 0
22 43 management single tertiary no 2067 yes no unknown 8 may 756 1 -1 0
23 43 management divorced tertiary no 388 yes no unknown 8 may 2087 2 -1 0
24 43 blue-collar married primary no -192 yes no unknown 8 may 1120 2 -1 0
25 37 unemployed single secondary no 381 yes no unknown 8 may 985 2 -1 0
26 35 blue-collar single secondary no 40 yes no unknown 9 may 617 4 -1 0
27 31 technician single tertiary no 22 yes no unknown 9 may 483 3 -1 0
28 43 blue-collar single secondary no 3 yes no unknown 9 may 929 3 -1 0
29 31 admin. married secondary no 307 yes no unknown 9 may 538 1 -1 0
30 28 blue-collar single secondary no 759 yes no unknown 9 may 710 1 -1 0
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