In this article, you will learn about how to perform Summary Statistics in R Language on a data set and finally, you will create a data quality Report file. Let us start learning “Computing Summary Statistics in R”.

## Table of Contents

We will follow each step as a Task for better understanding. It will also help us to complete all work in sequential tasks.

### Task 1: Load and View Data Set

It is better to confirm the working directory using `getwd()`

and save your data in the working directory, or save the data in the required folder and then set the path of this folder (directory) in R using `setwd()`

function.

getwd() data <- read.csv("data.csv")

### Task 2: Calculate Measure of Frequency Metrics in R

Before calculating the frequency metrics it is better to check the data structure and some other useful information about the data, For example,

Note: here we are using `mtcars`

data set.

data <- mtcars str(data) head(data) length(data$cyl) length(unique(data$cyl)) table(data$cyl) freq <- table(data$cyl) freq <- sort(freq, descreasing = T) print(freq)

The above lines of code will tell you about the number of observations in the data set, the frequency of the cylinder variable, its unique category, and finally sorted frequency in order.

### Task 3: Calculate the Measure of Central Tendency in R

Here we will calculate some available measures of central tendencies such as mean, median, and mode. One can easily calculate the measures of central tendency in R by following the commands below:

mean(data$mpg) mean(data$mpg, na.rm = T)

median(data$mpg) median(data$mpg, na.rm = T)

Note the use of `na.rm`

argument. If there are missing values in the data then `na.rm`

should be set to true. Since the `mtcars`

data set does not contain any missing values, therefore, results for both will be the same.

There is no direct function to compute the most repeated value in the variable. However, using a combination of different functions we can calculate the mode. For example

# for continuous variable uniquevalues <- unique(data$hp) uniquevalues[which.max(tabulate(match(data$ho, uniquevalues)))]

# for categorical variable uniquevalues <- unique(data$cyl) uniquevalues[which.max(tabulate(match(data$cyl, uniquevalues)))]

### Task 4: Calculate Measure of Dispersion in R Programming

The measures of dispersion such as range, variance, and standard deviation can be computed as given below. The use of different functions for the measure of dispersion in R programming is described as follows:

min(data$disp) min(data$disp, na.rm = T) max(data$disp) max(data$disp, na.rm = T) range(data$disp, na.rm = T) var(data$disp, na.rm = T) sd(data$disp, na.rm = T)

### Task 5: Calculate Additional Quality Data Metrics

To compute more data metrics we must be aware of the data type of variables. Suppose we have numbers but its data type is set to the character. For example,

test <- as.character(1:3)

Finding the mean of such character variable (the numbers are converted to character class) will result in a warning.

mean(test) [1] NA Warning message: In mean.default(test) : argument is not numeric or logical: returning NA

Therefore, one must be aware of the data type and class of the variable for which calculations are being performed. The class of variable in R can be checked using `class() function.`

For example

class(data$hp) class(mtcars)

It may also be useful if we know the number of missing observations in the data set.

test2 <- c(NA, 2, 55, 10, NA) sum(is.na(test2)) sum(is.na(data$hp)) sum(is.na(data$hp))

Note that the data set we are using does not contain any missing values.

### Task 6: Computing Summary Statistics in R on all Columns

There are functions in R that can be applied to each column to perform certain calculations on them. For example, `apply()`

the function is used to compute the number of observations in the data set using `length`

function as an argument of `apply()`

function.

apply(data, MARGIN=2, length) sapply(data, function(x) min(x, na.rm=T))

Let us create a user-defined function that can compute the minimum, maximum, mean, total, number of missing values, unique values, and data type of each variable (column) of the data frame.

quality_data <- function(df = NULL){ if (is.null(df)) print("Please Pass a non-empty data frame") summary_tab <- do.call(data.frame, list( Min = sapply(df, function(x) min(x, na.rm = T) ), Max = sapply(df, function(x) max(x, na.rm = T) ), Mean = sapply(df, function(x) mean(x, na.rm = T) ), Total = apply(df, 2, length), NULLS = sapply(df, function(x) sum(is.na(x)) ), Unique = sapply(df, function(x) length(unique(x)) ), DataType = sapply(df, class) ) ) nums <- vapply(summary_tab, is.numeric, FUN.VALUE = logical(1)) summary_tab[, nums] <- round(summary_tab[, nums], digits = 3) return(summary_tab) } quality_data(data)

### Task 7: Generate a Quality Data Report File

df_quality <- quality_data(data) df_quality <- cbind(columns = rownames(df_quality), data.frame(df_quality, row.names = NULL) ) write.csv(df_quality, "Data Quality Report.csv", row.names = F) write.csv(df_quality, paste0("Data Quality Repor", format(Sys.time(), "%d-%m-%Y-%M%M%S"), ".csv"), row.names = F)

The `write.csv()`

function will create a file that contains all the results produced by the `quality_data()`

function.

That’s all about Calculating Descriptive Statistics in R. There are many other descriptive measures, we will learn in future posts.

To learn about importing and exporting different data files, see the post on Importing and Exporting Data in R.

Learn Basic Statistics