How to do group by sum in R? By using `aggregate()`

from R base or `group_by()`

function along with the `summarise()`

from the dplyr package you can do the group by on dataframe on a specific column and get the sum of a column for each group.

Using the group_by() function from the dplyr package is an efficient approach hence, I will cover this first and then use the aggregate() function from the R base to group by sum on single and multiple columns.

## 1. Quick Examples

Following are quick examples of how to perform group by sum.

```
# Create Data Frame
df = read.csv('/Users/admin/apps/github/r-examples/resources/emp.csv')
df
# Load dplyr
library(dplyr)
# Group by sum using dplyr
# returns tibble table
agg_tbl <- df %>% group_by(department) %>%
summarise(sum_salary=sum(salary),
.groups = 'drop')
# Convert tibble to df
df2 <- agg_tbl %>% as.data.frame()
# Group by sum of multiple columns
df2 <- df %>% group_by(department,state) %>%
summarise(sum_salary=sum(salary),
sum_bonus= sum(bonus),
.groups = 'drop') %>%
as.data.frame()
# Group by sum of multiple columns
df2 <- df %>% group_by(department,state) %>%
summarise(across(c(salary, bonus),sum),
.groups = 'drop') %>%
as.data.frame()
# Sum on all columns
num_df<- df[,c("department","state","age","salary","bonus")]
df2 <- num_df %>% group_by(department, state) %>%
summarise(across(everything(), sum),
.groups = 'drop') %>%
as.data.frame()
# Group by sum using R Base aggregate()
agg_df <- aggregate(df$salary, by=list(df$department), FUN=sum)
# R Base aggregate() on multiple columns
agg_df <- aggregate(df$salary, by=list(df$department,df$state), FUN=sum)
```

Let’s create a DataFrame by reading a CSV file.

```
# Read CSV file into DataFrame
df = read.csv('/Users/admin/apps/github/r-examples/resources/emp.csv')
df
```

Yields below output.

## 2. Group By Sum in R using dplyr

You can use `group_by()`

function along with the `summarise()`

from dplyr package to find the group by sum in R DataFrame, `group_by()`

returns the grouped_df ( A grouped Data Frame) and use summarise() on grouped df results to get the group by sum.

To use these functions first, you have to install dplyr first using install.packages(‘dplyr’) and load it using `library(dplyr)`

. I will use dplyr infix operator `%>%`

across all our examples as the result of group_by() function goes as input to summarise() function.

```
# Load dplyr
library(dplyr)
# Group by sum using dplyr
agg_tbl <- df %>% group_by(department) %>%
summarise(sum_salary = sum(salary),
.groups = 'drop')
agg_tbl
# Convert tibble to df
df2 <- agg_tbl %>% as.data.frame()
df2
```

Yields below output. The above example does the group by on `department`

column using `group_by()`

and gets the sum of `salary`

for each department using `summarise()`

.

Note that `group_by()`

and `summarise()`

function returns tibble, if you want DataFrame you should convert tibble to dataframe by using `as.data.frame()`

.

## 3. Group By Sum of Multiple Columns in R

By using the dplyr group_by() perform group on department and state columns (multiple columns) and get the sum of `salary`

and `bonus`

for each department & state combination.

```
# Group by sum of multiple columns
df2 <- df %>% group_by(department,state) %>%
summarise(sum_salary=sum(salary),
sum_bonus= sum(bonus),
.groups = 'drop') %>%
as.data.frame()
df2
```

Yields below output.

You can also use across() with the vector of elements you wanted to apply summarise on.

```
# Group by sum of multiple columns using across()
df2 <- df %>% group_by(department,state) %>%
summarise(across(c(salary, bonus),sum),
.groups = 'drop') %>%
as.data.frame()
df2
```

## 4. Sum on All Columns Except Group by Columns

Finally, let’s see how to apply the aggregate function sum on all columns of the DataFrame except grouping columns. While doing this make sure your dataframe has only numeric columns plus grouping columns. Having non-numeric on summarise returns an error.

This example does the group by on `department`

and `state`

columns, summarises on all columns except grouping columns, and apply the `sum`

function on all summarised columns.

```
# Sum on all columns except group by columns
num_df<- df[,c("department","state","age","salary","bonus")]
df2 <- num_df %>% group_by(department, state) %>%
summarise(across(everything(), sum),
.groups = 'drop') %>%
as.data.frame()
df2
```

## 4. Group By Sum using R base aggregate()

R base provides an `aggregate()`

function to perform the grouping on the dataframe, let’s use this to perform a groupby on the `department`

column and get the sum of `salary`

for each department.

```
# Group by sum using R Base aggregate()
agg_df <- aggregate(df$salary, by=list(df$department), FUN=sum)
agg_df
```

Yields below output.

## 5. R Base aggregate() on Multiple Columns

The following example also uses the `aggregate()`

function to group rows based on `department`

and `state`

columns and uses the `sum`

function to get the sum of `salary`

for each department & state combination.

```
# R Base aggregate() on multiple columns
agg_df <- aggregate(df$salary, by=list(df$department,df$state), FUN=sum)
agg_df
```

Yields below output.

## Conclusion

In this article, I have explained how to do group by sum in R by using group_by() function from the dplyr package and aggregate function from the R base. Between these two, dplyr functions perform efficiently when you are dealing with larger datasets.