# Learn R: Aggregates

Learn the basics of aggregate functions in R with dplyr, which let us calculate quantities that describe groups of data.

Start## Key Concepts

Review core concepts you need to learn to master this subject

dplyr package

dplyr package

The dplyr package provides functions that perform data manipulation operations oriented to explore and manipulate datasets. At the most basic level, the package functions refers to data manipulation “verbs” such as select, filter, mutate, arrange, summarize among others that allow to chain multiple steps in a few lines of code. The dplyr package is suitable to work with a single dataset as well as to achieve complex results in large datasets.

dplyr summarise()

dplyr summarise()

`dplyr`

‘s `summarise()`

function can collapse a data frame to a single row, summarising the specified columns by applying a summary function.

Summary functions, or functions that take a vector of values and return a single value, include: `mean()`

, `median()`

, `sd()`

(standard deviation), `var()`

(variance), `min()`

, `max()`

, `IQR()`

(interquartile range), `n_distinct()`

(number of unique values), and `sum()`

.

For example, to find the mean `population`

and `gdp`

from `countries`

data frame:

```
countries %>%
summarise(mean_pop = mean(population, na.rm = TRUE))
```

To find the mean and standard deviation of both the `population`

and `gdp`

columns from a `countries`

data frame:

```
countries %>%
summarise(mean_pop = mean(population, na.rm = TRUE),
sd_pop = sd(population, na.rm = TRUE),
mean_gdp = mean(gdp, na.rm = TRUE),
sd_gdp = sd(gdp, na.rm = TRUE))
```

dplyr group_by()

dplyr group_by()

`dplyr`

‘s `group_by()`

function can group together rows of a data frame with the same value(s) in either a specified column or multiple columns, allowing for the application of summary functions on the individual groups.

`group_by()`

changes the unit of analysis from a complete dataset to individual groups.

For example, consider a data frame `countries`

. To find the mean and standard deviation of the `population`

column grouped by `continent`

:

```
countries %>%
group_by(continent) %>%
summarise(mean_pop = mean(population, na.rm = TRUE),
sd_pop = sd(population, na.rm = TRUE))
```

To find the mean `math_score`

and `reading_score`

by `age`

and `gender`

from a `students`

data frame:

```
students %>%
group_by(age, gender) %>%
summarise(mean_math_score = mean(math_score, na.rm = TRUE),
mean_reading_score = mean(reading_score, na.rm = TRUE))
```

filter() with group_by()

filter() with group_by()

Combining `dplyr`

‘s `group_by()`

and `filter()`

functions allows for the filtering of rows of a data frame based on per-group metrics.

Grouping a data frame by a selection of columns followed by a call to `filter()`

allows for filtering a data frame based on per-group summary functions.

Given a `students`

data frame, to keep all the rows of `students`

whose per-age average `math_score`

is less than `80`

:

```
students %>%
group_by(age) %>%
filter(mean(math_score, na.rm = TRUE) > 80)
```

`group_by()`

groups the data frame by`age`

`filter()`

will keep all the rows of the data frame whose per-group (per-age) average`math_score`

is greater than`80`

groub_by() with mutate()

groub_by() with mutate()

Combining `dplyr`

‘s `group_by()`

and `mutate()`

functions allows for the creation of new variables that involve per-group metrics in their calculation.

Grouping a data frame by a selection of columns followed by a call to `mutate()`

allows for the creation of new columns based on per-group summary functions.

Given a `students`

data frame, to add a new column containing the difference between a student’s score and the mean `math_score`

for each student’s respective age group:

```
students %>%
group_by(age) %>%
mutate(diff_from_age_mean = math_score - mean(math_score, na.rm = TRUE))
```

`group_by()`

groups the data frame by`age`

`mutate()`

will add a new column`diff_from_age_mean`

which is calculated as the difference between a row’s individual`math_score`

and the`mean(math_score)`

for that row’s age-group

- 1In this lesson you will learn about aggregates in R using dplyr. An aggregate statistic is a way of creating a single number that describes a group of numbers. Common aggregate statistics include m…
- 2In this exercise, you will learn how to
*combine*all of the values from a column for a single calculation. This can be done with the help of the dplyr function summarize(), which returns a new da… - 3When we have a bunch of data, we often want to calculate aggregate statistics (mean, standard deviation, median, percentiles, etc.) over certain subsets of the data. Suppose we have a grade book w…
- 4Sometimes, we want to group by more than one column. We can do this by passing multiple column names as arguments to the group_by function. Imagine that we run a chain of stores and have data abo…
- 5While group_by() is most often used with summarize() to calculate summary statistics, it can also be used with the dplyr function filter() to filter rows of a data frame based on per-group metrics….
- 6group_by() can also be used with the dplyr function mutate() to add columns to a data frame that involve per-group metrics. Consider the same educational technology company’s enrollments table fro…
- 7This lesson introduced you to aggregates in R using dplyr. You learned: * How to calculate summary statistics with summarize() * How to perform
*aggregate*statistics over individual rows with the…

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