How to do group by in R using dplyr?
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# How to do group by in R using dplyr?

This recipe helps you do group by in R using dplyr

0

## Recipe Objective

Aggregation is one of the fundamental techniques in data manipulation that a data scientist should know. In R, we have dplyr package which is an add-on package most widely used to carry out data manipulation tasks. To carry out the task of aggregation, dplyr package provides us with group_by() function. ​

The group_by() function groups multiple rows of the dataframe based on a categorical column. When combined with summarise() function, it gives us a way to calculate mean, sum, count, minimium or maximum using in-built functions for the specified variables.

There are two ways in which we can use group_by() function :

1. Using dplyr pipe operator (%>%)
2. Using summarise_at()

In this recipe, we will learn how to use group_by() fuction by dplyr package in R. ​

## Step 1: Loading the required library and Creating a DataFrame

Creating a STUDENT dataframe with Name and marks of two subjects in 3 Trimester exams. ​

``` # data manipulation library(dplyr) library(tidyverse) STUDENT = data.frame(Name = c("Ram","Ram", "Ram", "Shyam", "Shyam", "Shyam", "Jessica", "Jessica", "Jessica"), Science_Marks = c(55, 60, 65, 80, 70, 75, 45, 65, 70), Math_Marks = c(70, 75, 73, 50, 53, 55, 65, 78, 75), Trimester = c(1, 2, 3, 1, 2, 3, 1, 2, 3)) glimpse(STUDENT) ```
```Rows: 9
Columns: 4
\$ Name           Ram, Ram, Ram, Shyam, Shyam, Shyam, Jessica, Jessica,...
\$ Science_Marks  55, 60, 65, 80, 70, 75, 45, 65, 70
\$ Math_Marks     70, 75, 73, 50, 53, 55, 65, 78, 75
\$ Trimester      1, 2, 3, 1, 2, 3, 1, 2, 3
```

## Step 2: Application of group_by Function

Syntax: group_by(x, ...) ​

where: ​

1. x = dataframe
2. ... = variables by which grouping needs to take place
``` # to check the variois arguements of the function ?group_by() ```

Query 1: To find the average marks for each student in a year (Trimester 1, 2 and 3) ​

Approach 1: Using pipe operator (%>%) ​

``` # first grouping the columns by student names and then carrying out summarise function on it STUDENT %>% group_by(Name) %>% summarise_at(vars(c(Science_Marks, Math_Marks)), funs(mean(.))) ```
```Name	Science_Marks	Math_Marks
Jessica	60		72.66667
Ram	60		72.66667
Shyam	75		52.66667
```

Approach 2: Using summarise_at() ​

``` summarise_at(group_by(STUDENT,Name), vars(c(Science_Marks, Math_Marks)), funs(mean(.))) ```
```Name	Science_Marks	Math_Marks
Jessica	60		72.66667
Ram	60		72.66667
Shyam	75		52.66667
```

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