# Data frame Manipulation with dplyr

## Overview

Teaching: 30 min
Exercises: 10 min
Questions
• How can I manipulate dataframes without repeating myself?

Objectives
• To be able to use the six main dataframe manipulation ‘verbs’ with pipes in `dplyr`.

• To understand how `group_by()` and `summarize()` can be combined to summarize datasets.

• Be able to analyze a subset of data using logical filtering.

Manipulation of dataframes means many things to many researchers, we often select certain observations (rows) or variables (columns), we often group the data by a certain variable(s), or we even calculate summary statistics. We can do these operations using the normal base R operations:

``````mean(gapminder[gapminder\$continent == "Africa", "gdpPercap"])
``````
`````` 2193.755
``````
``````mean(gapminder[gapminder\$continent == "Americas", "gdpPercap"])
``````
`````` 7136.11
``````
``````mean(gapminder[gapminder\$continent == "Asia", "gdpPercap"])
``````
`````` 7902.15
``````

But this isn’t very efficient, and can become tedious quickly because there is a fair bit of repetition. Repeating yourself will cost you time, both now and later, and potentially introduce some nasty bugs.

## The `dplyr` package

Luckily, the `dplyr` package provides a number of very useful functions for manipulating dataframes in a way that will reduce the above repetition, reduce the probability of making errors, and probably even save you some typing. As an added bonus, you might even find the `dplyr` grammar easier to read.

Here we’re going to cover 6 of the most commonly used functions as well as using pipes (`%>%`) to combine them.

1. `select()`
2. `filter()`
3. `group_by()`
4. `summarize()`
5. `mutate()`

If you have have not installed this package earlier, please do so:

``````install.packages('dplyr')
``````

``````library("dplyr")
``````

## Using `select()`

If, for example, we wanted to move forward with only a few of the variables in our dataframe we could use the `select()` function. This will keep only the variables you select.

``````year_country_gdp <- select(gapminder, year, country, gdpPercap)
`````` If we open up `year_country_gdp` we’ll see that it only contains the year, country and gdpPercap. Above we used ‘normal’ grammar, but the strengths of `dplyr` lie in combining several functions using pipes. Since the pipes grammar is unlike anything we’ve seen in R before, let’s repeat what we’ve done above using pipes.

``````year_country_gdp <- gapminder %>% select(year,country,gdpPercap)
``````

To help you understand why we wrote that in that way, let’s walk through it step by step. First we summon the `gapminder` data frame and pass it on, using the pipe symbol `%>%`, to the next step, which is the `select()` function. In this case we don’t specify which data object we use in the `select()` function since in gets that from the previous pipe. Fun Fact: You may have encountered pipes before in the shell. In R, a pipe symbol is `%>%` while in the shell it is `|` but the concept is the same!

## Using `filter()`

If we now wanted to move forward with the above, but only with European countries, we can combine `select` and `filter`

``````year_country_gdp_euro <- gapminder %>%
filter(continent == "Europe") %>%
select(year, country, gdpPercap)
``````

## Challenge 1

Write a single command (which can span multiple lines and includes pipes) that will produce a dataframe that has the African values for `lifeExp`, `country` and `year`, but not for other Continents. How many rows does your dataframe have and why?

## Solution to Challenge 1

``````year_country_lifeExp_Africa <- gapminder %>%
filter(continent=="Africa") %>%
select(year,country,lifeExp)
``````

As with last time, first we pass the gapminder dataframe to the `filter()` function, then we pass the filtered version of the gapminder data frame to the `select()` function. Note: The order of operations is very important in this case. If we used ‘select’ first, filter would not be able to find the variable continent since we would have removed it in the previous step.

## Using `group_by()` and `summarize()`

Now, we were supposed to be reducing the error prone repetitiveness of what can be done with base R, but up to now we haven’t done that since we would have to repeat the above for each continent. Instead of `filter()`, which will only pass observations that meet your criteria (in the above: `continent=="Europe"`), we can use `group_by()`, which will essentially use every unique criteria that you could have used in filter.

``````str(gapminder)
``````
``````'data.frame':	1704 obs. of  6 variables:
\$ country  : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
\$ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
\$ pop      : num  8425333 9240934 10267083 11537966 13079460 ...
\$ continent: chr  "Asia" "Asia" "Asia" "Asia" ...
\$ lifeExp  : num  28.8 30.3 32 34 36.1 ...
\$ gdpPercap: num  779 821 853 836 740 ...
``````
``````gapminder %>% group_by(continent) %>% str()
``````
``````grouped_df [1,704 × 6] (S3: grouped_df/tbl_df/tbl/data.frame)
\$ country  : chr [1:1704] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
\$ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
\$ pop      : num [1:1704] 8425333 9240934 10267083 11537966 13079460 ...
\$ continent: chr [1:1704] "Asia" "Asia" "Asia" "Asia" ...
\$ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
\$ gdpPercap: num [1:1704] 779 821 853 836 740 ...
- attr(*, "groups")= tibble [5 × 2] (S3: tbl_df/tbl/data.frame)
..\$ continent: chr [1:5] "Africa" "Americas" "Asia" "Europe" ...
..\$ .rows    : list<int> [1:5]
.. ..\$ : int [1:624] 25 26 27 28 29 30 31 32 33 34 ...
.. ..\$ : int [1:300] 49 50 51 52 53 54 55 56 57 58 ...
.. ..\$ : int [1:396] 1 2 3 4 5 6 7 8 9 10 ...
.. ..\$ : int [1:360] 13 14 15 16 17 18 19 20 21 22 ...
.. ..\$ : int [1:24] 61 62 63 64 65 66 67 68 69 70 ...
.. ..@ ptype: int(0)
..- attr(*, ".drop")= logi TRUE
``````

You will notice that the structure of the dataframe where we used `group_by()` (`grouped_df`) is not the same as the original `gapminder` (`data.frame`). A `grouped_df` can be thought of as a `list` where each item in the `list`is a `data.frame` which contains only the rows that correspond to the a particular value `continent` (at least in the example above). ## Using `summarize()`

The above was a bit on the uneventful side but `group_by()` is much more exciting in conjunction with `summarize()`. This will allow us to create new variable(s) by using functions that repeat for each of the continent-specific data frames. That is to say, using the `group_by()` function, we split our original dataframe into multiple pieces, then we can run functions (e.g. `mean()` or `sd()`) within `summarize()`.

``````gdp_bycontinents <- gapminder %>%
group_by(continent) %>%
summarize(mean_gdpPercap = mean(gdpPercap))

gdp_bycontinents
``````
``````# A tibble: 5 x 2
continent mean_gdpPercap
<chr>              <dbl>
1 Africa             2194.
2 Americas           7136.
3 Asia               7902.
4 Europe            14469.
5 Oceania           18622.
`````` That allowed us to calculate the mean gdpPercap for each continent, but it gets even better.

## Challenge 2

Calculate the average life expectancy per country. Which has the longest average life expectancy and which has the shortest average life expectancy?

## Solution to Challenge 2

``````lifeExp_bycountry <- gapminder %>%
group_by(country) %>%
summarize(mean_lifeExp=mean(lifeExp))

lifeExp_bycountry %>%
filter(mean_lifeExp == min(mean_lifeExp) | mean_lifeExp == max(mean_lifeExp))
``````
``````# A tibble: 2 x 2
country      mean_lifeExp
<chr>               <dbl>
1 Iceland              76.5
2 Sierra Leone         36.8
``````

Another way to do this is to use the `dplyr` function `arrange()`, which arranges the rows in a data frame according to the order of one or more variables from the data frame. It has similar syntax to other functions from the `dplyr` package. You can use `desc()` inside `arrange()` to sort in descending order.

``````lifeExp_bycountry %>%
arrange(mean_lifeExp) %>%
``````
``````# A tibble: 1 x 2
country      mean_lifeExp
<chr>               <dbl>
1 Sierra Leone         36.8
``````
``````lifeExp_bycountry %>%
arrange(desc(mean_lifeExp)) %>%
``````
``````# A tibble: 1 x 2
country mean_lifeExp
<chr>          <dbl>
1 Iceland         76.5
``````

The function `group_by()` allows us to group by multiple variables. Let’s group by `year` and `continent`.

``````gdp_bycontinents_byyear <- gapminder %>%
group_by(continent, year) %>%
summarize(mean_gdpPercap = mean(gdpPercap))
``````
```````summarise()` has grouped output by 'continent'. You can override using the `.groups` argument.
``````

That is already quite powerful, but it gets even better! You’re not limited to defining 1 new variable in `summarize()`.

``````gdp_pop_bycontinents_byyear <- gapminder %>%
group_by(continent,year) %>%
summarize(mean_gdpPercap = mean(gdpPercap),
sd_gdpPercap = sd(gdpPercap),
mean_pop = mean(pop),
sd_pop = sd(pop))
``````
```````summarise()` has grouped output by 'continent'. You can override using the `.groups` argument.
``````

## `count()` and `n()`

A very common operation is to count the number of observations for each group. The `dplyr` package comes with two related functions that help with this.

For instance, if we wanted to check the number of countries included in the dataset for the year 2002, we can use the `count()` function. It takes the name of one or more columns that contain the groups we are interested in, and we can optionally sort the results in descending order by adding `sort=TRUE`:

``````gapminder %>%
filter(year == 2002) %>%
count(continent, sort = TRUE)
``````
``````  continent  n
1    Africa 52
2      Asia 33
3    Europe 30
4  Americas 25
5   Oceania  2
``````

If we need to use the number of observations in calculations, the `n()` function is useful. For instance, if we wanted to get the standard error of the life expectancy per continent:

``````gapminder %>%
group_by(continent) %>%
summarize(se_le = sd(lifeExp)/sqrt(n()))
``````
``````# A tibble: 5 x 2
continent se_le
<chr>     <dbl>
1 Africa    0.366
2 Americas  0.540
3 Asia      0.596
4 Europe    0.286
5 Oceania   0.775
``````

You can also chain together several summary operations; in this case calculating the `minimum`, `maximum`, `mean` and `se` of each continent’s per-country life-expectancy:

``````gapminder %>%
group_by(continent) %>%
summarize(
mean_le = mean(lifeExp),
min_le = min(lifeExp),
max_le = max(lifeExp),
se_le = sd(lifeExp)/sqrt(n()))
``````
``````# A tibble: 5 x 5
continent mean_le min_le max_le se_le
<chr>       <dbl>  <dbl>  <dbl> <dbl>
1 Africa       48.9   23.6   76.4 0.366
2 Americas     64.7   37.6   80.7 0.540
3 Asia         60.1   28.8   82.6 0.596
4 Europe       71.9   43.6   81.8 0.286
5 Oceania      74.3   69.1   81.2 0.775
``````

## Using `mutate()`

We can also create new variables prior to (or even after) summarizing information using `mutate()`.

``````gdp_pop_bycontinents_byyear <- gapminder %>%
mutate(gdp_billion = gdpPercap*pop/10^9) %>%
group_by(continent, year) %>%
summarize(mean_gdpPercap = mean(gdpPercap),
sd_gdpPercap = sd(gdpPercap),
mean_pop = mean(pop),
sd_pop = sd(pop),
mean_gdp_billion = mean(gdp_billion),
sd_gdp_billion = sd(gdp_billion))
``````
```````summarise()` has grouped output by 'continent'. You can override using the `.groups` argument.
``````

## Key Points

• Use the `dplyr` package to manipulate dataframes.

• Use `select()` to choose variables from a dataframe.

• Use `filter()` to choose data based on values.

• Use `group_by()` and `summarize()` to work with subsets of data.

• Use `mutate()` to create new variables.