One of the first steps of any data analysis project is **exploratory data analysis**.

This involves exploring a dataset in three ways:

**1. Summarizing** a dataset using descriptive statistics.

**2. Visualizing** a dataset using charts.

**3. Identifying** missing values.

By performing these three actions, you can gain an understanding of how the values in a dataset are distributed and detect any problematic values before proceeding to perform a hypothesis test or perform statistical modeling.

The easiest way to perform exploratory data analysis in R is by using functions from the tidyverse packages.

The following step-by-step example shows how to use functions from these packages to perform exploratory data analysis on the **diamonds** dataset that comes built-in with the tidyverse packages.

**Step 1: Load & View the Data**

First, let’s use the **data()** function to load the **diamonds** dataset:

**library(tidyverse)
#load ***diamonds* dataset
data(diamonds)

We can take a look at the first six rows of the dataset by using the **head()** function:

**#view first six rows of diamonds dataset
head(diamonds)
carat cut color clarity depth table price x y z
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
**

**Step 2: Summarize the Data**

We can use the **summary()** function to quickly summarize each variable in the dataset:

**#summarize diamonds dataset
summary(diamonds)
carat cut color clarity depth
Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065 Min. :43.00
1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258 1st Qu.:61.00
Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194 Median :61.80
Mean :0.7979 Premium :13791 G:11292 VS1 : 8171 Mean :61.75
3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066 3rd Qu.:62.50
Max. :5.0100 I: 5422 VVS1 : 3655 Max. :79.00
J: 2808 (Other): 2531
table price x y z
Min. :43.00 Min. : 326 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710 1st Qu.: 4.720 1st Qu.: 2.910
Median :57.00 Median : 2401 Median : 5.700 Median : 5.710 Median : 3.530
Mean :57.46 Mean : 3933 Mean : 5.731 Mean : 5.735 Mean : 3.539
3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540 3rd Qu.: 6.540 3rd Qu.: 4.040
Max. :95.00 Max. :18823 Max. :10.740 Max. :58.900 Max. :31.800 **

For each of the numeric variables we can see the following information:

**Min**: The minimum value.**1st Qu**: The value of the first quartile (25th percentile).**Median**: The median value.**Mean**: The mean value.**3rd Qu**: The value of the third quartile (75th percentile).**Max**: The maximum value.

For the categorical variables in the dataset (cut, color, and clarity) we see a frequency count of each value.

For example, for the **cut** variable:

**Fair**: This value occurs 1,610 times.**Good**: This value occurs 4,906 times.**Very Good**: This value occurs 12,082 times.**Premium**: This value occurs 13,791 times.**Ideal**: This value occurs 21,551 times.

We can use the **dim()** function to get the dimensions of the dataset in terms of number of rows and number of columns:

**#display rows and columns
dim(diamonds)
[1] 53940 10
**

We can see that the dataset has **53,940 **rows and **10** columns.

**Step 3: Visualize the Data**

We can also create charts to visualize the values in the dataset.

For example, we can use the **geom_histogram()** function to create a histogram of the values for a certain variable:

**#create histogram of values for price
ggplot(data=diamonds, aes(x=price)) +
geom_histogram(fill="steelblue", color="black") +
ggtitle("Histogram of Price Values")
**

We can also use the **geom_point()** function to create a scatterplot of any pairwise combination of variables:

**#create scatterplot of carat vs. price, using cut as color variable
ggplot(data=diamonds, aes(x=carat, y=price, color=cut)) +
geom_point()
**

We can also use the **geom_boxplot()** function to create a boxplot of one variable grouped by another variable:

**#create scatterplot of price, grouped by cut
ggplot(data=diamonds, aes(x=cut, y=price)) +
geom_boxplot(fill="steelblue")
**

We can also use the **cor()** function to create a correlation matrix to view the correlation coefficient between each pairwise combination of numeric variables in the dataset:

**#create correlation matrix of (rounded to 2 decimal places)
round(cor(diamonds[c('carat', 'depth', 'table', 'price', 'x', 'y', 'z')]), 2)
carat depth table price x y z
carat 1.00 0.03 0.18 0.92 0.98 0.95 0.95
depth 0.03 1.00 -0.30 -0.01 -0.03 -0.03 0.09
table 0.18 -0.30 1.00 0.13 0.20 0.18 0.15
price 0.92 -0.01 0.13 1.00 0.88 0.87 0.86
x 0.98 -0.03 0.20 0.88 1.00 0.97 0.97
y 0.95 -0.03 0.18 0.87 0.97 1.00 0.95
z 0.95 0.09 0.15 0.86 0.97 0.95 1.00
**

**Related:** What is Considered to Be a “Strong” Correlation?

**Step 4: Identify Missing Values**

We can use the following code to count the total number of missing values in each column of the dataset:

**#count total missing values in each column
sapply(diamonds, function(x) sum(is.na(x)))
carat cut color clarity depth table price x y z
0 0 0 0 0 0 0 0 0 0**

From the output we can see that there are zero missing values in each column.

In practice, you’ll likely encounter several missing values throughout your dataset.

This function will be useful for counting the total number of missing values.

**Related:** How to Impute Missing Values in R

**Additional Resources**

The following tutorials explain how to perform other common operations in R:

How to Use length() Function in R

How to Use cat() Function in R

How to Use substring() Function in R