Data Exploration with R

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This article demonstrates how to explore data with R. It is very important to explore data before starting to build a predictive model. It gives an idea about the structure of the dataset like number of continuous or categorical variables and number of observations (rows).


The snapshot of the dataset used in this tutorial is pasted below. We have five variables - Q1, Q2, Q3, Q4 and Age. The variables Q1-Q4 represents survey responses of a questionnaire. The response lies between 1 and 6. The variable Age represents age groups of the respondents. It lies between 1 to 3. 1 represents Generation Z, 2 represents Generation X and Y, 3 represents Baby Boomers.

Sample Data

Import data into R

The read.csv() function is used to import CSV file into R. The header = TRUE tells R that header is included in the data that we are going to import.
mydata <- read.csv("C:/Users/Deepanshu/Documents/Book1.csv", header=TRUE)

1. Calculate basic descriptive statistics
Data Exploration with R

To calculate summary of a particular column, say third column, you can use the following syntax :
summary( mydata[3])
To calculate summary of a particular column by its name, you can use the following syntax :
summary( mydata$Q1)

2. Lists name of variables in a dataset 

3. Calculate number of rows in a dataset

4. Calculate number of columns in a dataset

5. List structure of a dataset

6. See first 6 rows of dataset

7. First n rows of dataset

In the code below, we are selecting first 5 rows of dataset.
head(mydata, n=5) 
8. All rows but the last row
head(mydata, n= -1)
9. Last 6 rows of dataset
10. Last n rows of dataset

In the code below, we are selecting last 5 rows of dataset.
tail(mydata, n=5) 
11. All rows but the first row
tail(mydata, n= -1)
12. Number of missing values

The function below returns number of missing values in each variable of a dataset.

13. Number of missing values in a single variable
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1 Response to "Data Exploration with R"

  1. #to create the data used in this tutorial, use following command
    mydata = data.frame(Q1 = sample(1:6, 15, replace = TRUE),Q2 = sample(1:6, 15, replace = TRUE),Q3 = sample(1:6, 15, replace = TRUE), Q4 = sample(1:6, 15, replace = TRUE), Age = sample(1:3, 15, replace = TRUE))


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