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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>An overview of dplyr</title>
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class: center, middle, inverse, title-slide
# An overview of dplyr
## Daryn Ramsden
### thisisdaryn at gmail dot com
### last updated: 2020-10-21
---
### The data we will be using
```r
#install.packages("palmerpenguins")
library(palmerpenguins)
```
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</script>
</div>
---
### What do these variables represent?
Data were collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network.
* *species*: *Adelie*, *Chinstrap* or *Gentoo*
* *island*: *Biscoe*, *Dream* or *Torgersen* (factor)
* *bill_length_mm*: bill length mm (numeric)
* *bill_depth_mm*: bill depth in mm (numeric)
* *flipper_length_mm*: flipper length in mm (numeric)
* *body_mass_g*: body mass in grams (numeric)
* *sex*: *male* or *female* (factor)
* *year*: 2007, 2008 or 2009
---
## dplyr: a package for data manipulation
The data you get is almost in the form you want
--
`dplyr` is an R package that encapsulates many common data manipulation tasks
--
Sometimes you want to:
--
* keep only some of the rows
--
* keep only some of the columns
--
* adds new columns
--
* sort data
--
* provide summary statistics
--
`dplyr` has functions for each of these (and many others)
---
## Using `dplyr`
#### How do you install `dplyr`?
```r
install.packages("dplyr")
# or install.packages("tidyverse)
```
#### How do you use `dplyr`?
```r
library(dplyr)
# or library(tidyverse)
```
---
## Key single table verbs/functions
* Working with rows:
* `filter`: keep only some of the rows based on column values
* `slice`: keep some of the rows based on their location
* `arrange`: sort data
* Working with columns:
* `select`: keep only some of the columns
* `mutate` adds new columns
* `rename` change the name of specified columns
* `relocate` changes the order of the columns
* Groups of rows:
* `summarise` (and `group_by`): provide summary statistics
---
## `filter`
#### a function for specifying which rows to keep
Example 1: How do we get all penguins of the Chinstrap species?
---
## `filter`
#### a function for specifying which rows to keep
Example 1: How do we get all penguins of the Chinstrap species?
```r
chinstrap <- filter(penguins, species == "Chinstrap")
```
---
## `filter`
#### a function for specifying which rows to keep
Example 1: How do we get all penguins of the Chinstrap species?
```r
chinstrap <- filter(penguins, species == "Chinstrap")
chinstrap
```
```
# A tibble: 68 x 8
species island bill_length_mm bill_depth_mm
<fct> <fct> <dbl> <dbl>
1 Chinst… Dream 46.5 17.9
2 Chinst… Dream 50 19.5
3 Chinst… Dream 51.3 19.2
4 Chinst… Dream 45.4 18.7
5 Chinst… Dream 52.7 19.8
6 Chinst… Dream 45.2 17.8
7 Chinst… Dream 46.1 18.2
8 Chinst… Dream 51.3 18.2
9 Chinst… Dream 46 18.9
10 Chinst… Dream 51.3 19.9
# … with 58 more rows, and 4 more variables:
# flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
# year <int>
```
---
## `filter`
#### a function for specifying which rows to keep
Example 2: How do we get penguins that are 4 kg or greater?
---
## `filter`
#### a function for specifying which rows to keep
Example 2: How do we get penguins that are 4 kg or greater?
```r
penguins_4k <- filter(penguins, body_mass_g >= 4000)
```
---
## `filter`
#### a function for specifying which rows to keep
Example 2: How do we get penguins that are 4 kg or greater?
```r
penguins_4k <- filter(penguins, body_mass_g >= 4000)
penguins_4k
```
```
# A tibble: 177 x 8
species island bill_length_mm bill_depth_mm
<fct> <fct> <dbl> <dbl>
1 Adelie Torge… 39.2 19.6
2 Adelie Torge… 42 20.2
3 Adelie Torge… 34.6 21.1
4 Adelie Torge… 42.5 20.7
5 Adelie Torge… 46 21.5
6 Adelie Dream 39.2 21.1
7 Adelie Dream 39.8 19.1
8 Adelie Dream 44.1 19.7
9 Adelie Dream 39.6 18.8
10 Adelie Dream 42.3 21.2
# … with 167 more rows, and 4 more variables:
# flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
# year <int>
```
---
### Assessment
How many penguins were found on Torgersen island (<i>Torgersen</i>)?
---
### Assessment
How many penguins were found on Torgersen island (<i>Torgersen</i>)?
```r
torgersen<- filter(penguins, island == "Torgersen")
dim(torgersen)
```
```
[1] 52 8
```
--
Also could have used:
```r
torgersen<- penguins %>% filter(island == "Torgersen")
dim(torgersen)
```
```
[1] 52 8
```
---
## `select`
#### A function/verb for specifying which columns to keep
As of dplyr 1.0 there are 5 ways to use select
1. By **position**
2. By **name**
3. by **function of name**
4. by **type**
5. by combination of the above using logical operators (`|`, `&`, `!`)
---
### `select` by position
Example: select columns 1, 3 and 5 from `penguins`
--
```r
penguins %>% select(1, 3, 5)
```
---
### `select` by position
Example: select columns 1, 3 and 5 from `penguins`
```r
penguins %>% select(1, 3, 5)
```
```
# A tibble: 344 x 3
species bill_length_mm flipper_length_mm
<fct> <dbl> <int>
1 Adelie 39.1 181
2 Adelie 39.5 186
3 Adelie 40.3 195
4 Adelie NA NA
5 Adelie 36.7 193
6 Adelie 39.3 190
7 Adelie 38.9 181
8 Adelie 39.2 195
9 Adelie 34.1 193
10 Adelie 42 190
# … with 334 more rows
```
---
### `select` by name
Example: select *species*, *island* and *body_mass_g*
--
```r
penguins %>% select(species, island, body_mass_g)
```
---
### `select` by name
Example: select *species*, *island* and *body_mass_g*
```r
penguins %>% select(species, island, body_mass_g)
```
```
# A tibble: 344 x 3
species island body_mass_g
<fct> <fct> <int>
1 Adelie Torgersen 3750
2 Adelie Torgersen 3800
3 Adelie Torgersen 3250
4 Adelie Torgersen NA
5 Adelie Torgersen 3450
6 Adelie Torgersen 3650
7 Adelie Torgersen 3625
8 Adelie Torgersen 4675
9 Adelie Torgersen 3475
10 Adelie Torgersen 4250
# … with 334 more rows
```
---
### `select` by a function of column names
`select` can be used in conjunction with other useful functions such as:
* `starts_with`
* `ends_with`
* `contains`
* `matches`
---
### `select` by a function of column names
Example: Choose all columns that contain "mm":
```r
penguins_mm <- penguins %>% select(contains("mm"))
```
---
### `select` by a function of column names
Example: Choose all columns that contain "mm":
```r
penguins_mm <- penguins %>% select(contains("mm"))
penguins_mm
```
```
# A tibble: 344 x 3
bill_length_mm bill_depth_mm flipper_length_mm
<dbl> <dbl> <int>
1 39.1 18.7 181
2 39.5 17.4 186
3 40.3 18 195
4 NA NA NA
5 36.7 19.3 193
6 39.3 20.6 190
7 38.9 17.8 181
8 39.2 19.6 195
9 34.1 18.1 193
10 42 20.2 190
# … with 334 more rows
```
---
### `select` by a function of column names
Example: How to choose all columns starting with "bill":
```r
bills_df <- penguins %>% select(starts_with("bill"))
```
---
### `select` by a function of column names
Example: How to choose all columns starting with "bill":
```r
bills_df <- penguins %>% select(starts_with("bill"))
bills_df
```
```
# A tibble: 344 x 2
bill_length_mm bill_depth_mm
<dbl> <dbl>
1 39.1 18.7
2 39.5 17.4
3 40.3 18
4 NA NA
5 36.7 19.3
6 39.3 20.6
7 38.9 17.8
8 39.2 19.6
9 34.1 18.1
10 42 20.2
# … with 334 more rows
```
---
### `select` by type
Example: choose all numeric columns:
```r
penguins %>% select(where(is.numeric))
```
---
### `select` by type
Example: choose all numeric columns:
```r
penguins %>% select(where(is.numeric))
```
```
# A tibble: 344 x 5
bill_length_mm bill_depth_mm flipper_length_… body_mass_g
<dbl> <dbl> <int> <int>
1 39.1 18.7 181 3750
2 39.5 17.4 186 3800
3 40.3 18 195 3250
4 NA NA NA NA
5 36.7 19.3 193 3450
6 39.3 20.6 190 3650
7 38.9 17.8 181 3625
8 39.2 19.6 195 4675
9 34.1 18.1 193 3475
10 42 20.2 190 4250
# … with 334 more rows, and 1 more variable: year <int>
```
---
### `select` by logical combination
Example: choose all factor variables or variables containing the word "bill"
```r
penguins %>% select(where(is.factor) | contains("bill"))
```
---
### `select` by logical combination
Example: choose all factor variables or variables containing the word "bill"
```r
penguins %>% select(where(is.factor) | contains("bill"))
```
```
# A tibble: 344 x 5
species island sex bill_length_mm bill_depth_mm
<fct> <fct> <fct> <dbl> <dbl>
1 Adelie Torgersen male 39.1 18.7
2 Adelie Torgersen female 39.5 17.4
3 Adelie Torgersen female 40.3 18
4 Adelie Torgersen <NA> NA NA
5 Adelie Torgersen female 36.7 19.3
6 Adelie Torgersen male 39.3 20.6
7 Adelie Torgersen female 38.9 17.8
8 Adelie Torgersen male 39.2 19.6
9 Adelie Torgersen <NA> 34.1 18.1
10 Adelie Torgersen <NA> 42 20.2
# … with 334 more rows
```
---
## `mutate`
#### a function to add new columns
Example: Adding a column that indicates whether a penguin has a mass greater than 4 kg
```r
penguin_extra <- penguins %>%
mutate(above_4kg= if_else(body_mass_g > 4000, TRUE, FALSE))
```
---
## `mutate`
#### a function to add new columns
Example: Adding a column that indicates whether a penguin has a mass greater than 4 kg
```r
penguin_extra <- penguins %>%
mutate(above_4kg= if_else(body_mass_g > 4000, TRUE, FALSE))
head(penguin_extra)
```
```
# A tibble: 6 x 9
species island bill_length_mm bill_depth_mm flipper_length_…
<fct> <fct> <dbl> <dbl> <int>
1 Adelie Torge… 39.1 18.7 181
2 Adelie Torge… 39.5 17.4 186
3 Adelie Torge… 40.3 18 195
4 Adelie Torge… NA NA NA
5 Adelie Torge… 36.7 19.3 193
6 Adelie Torge… 39.3 20.6 190
# … with 4 more variables: body_mass_g <int>, sex <fct>,
# year <int>, above_4kg <lgl>
```
---
## `arrange`
#### A function for sorting data
Example: Sort all penguins by body mass:
--
```r
penguins_sorted <- penguins %>% arrange(body_mass_g)
```
---
## `arrange`
#### A function for sorting data
Example: Sort all penguins by body mass:
```r
penguins_sorted <- penguins %>%
arrange(body_mass_g)
penguins_sorted
```
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</script>
</div>
---
### sorting with multiple columns using `arrange`
Example sorting by species, then by descending order of mass:
```r
penguins_sorted2 <- penguins %>%
arrange(species, desc(body_mass_g))
penguins_sorted2
```
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</script>
</div>
---
## `summarise`/`summarize`
#### A verb/function to get summary statistics.
Question: what's the mean flipper length and body mass among the Palmer penguins?
```r
penguins %>%
summarise(num_penguins = n(),
avg_mass = mean(body_mass_g, na.rm = TRUE),
avg_fl_length = mean(flipper_length_mm, na.rm = TRUE))
```
```
# A tibble: 1 x 3
num_penguins avg_mass avg_fl_length
<int> <dbl> <dbl>
1 344 4202. 201.
```
---
## `group_by`
#### A function that makes `summarise` really powerful
`group_by` creates a grouped data frame based on columns you specify
--
For example, grouping the penguins by island and species:
--
```r
gr_penguins <- penguins %>% group_by(island, species)
```
---
## `group_by`
#### A function that makes `summarise` really powerful
`group_by` creates a grouped data frame based on columns you specify
For example, grouping the penguins by island and species:
```r
gr_penguins <- penguins %>% group_by(island, species)
head(gr_penguins)
```
```
# A tibble: 6 x 8
# Groups: island, species [1]
species island bill_length_mm bill_depth_mm flipper_length_…
<fct> <fct> <dbl> <dbl> <int>
1 Adelie Torge… 39.1 18.7 181
2 Adelie Torge… 39.5 17.4 186
3 Adelie Torge… 40.3 18 195
4 Adelie Torge… NA NA NA
5 Adelie Torge… 36.7 19.3 193
6 Adelie Torge… 39.3 20.6 190
# … with 3 more variables: body_mass_g <int>, sex <fct>,
# year <int>
```
---
## How is the grouped data frame different?
--
* Extra information is added to the data frame
--
* rows that match on all the grouping variables will be in the same group
--
* rows that don't match on all the grouping variables will be in different groups
---
## `group_by` and `summarise` together
Now let's do the same summary as before with the grouped data:
--
```r
gr_penguins %>% summarise(num_penguins = n(),
avg_mass = mean(body_mass_g, na.rm = TRUE),
avg_fl_length = mean(flipper_length_mm,
na.rm = TRUE))
```
```
# A tibble: 5 x 5
# Groups: island [3]
island species num_penguins avg_mass avg_fl_length
<fct> <fct> <int> <dbl> <dbl>
1 Biscoe Adelie 44 3710. 189.
2 Biscoe Gentoo 124 5076. 217.
3 Dream Adelie 56 3688. 190.
4 Dream Chinstrap 68 3733. 196.
5 Torgersen Adelie 52 3706. 191.
```
---
### New features of `summarise`
`dplyr` 1.0 has some new features of `summarise`:
* summaries that return multiple values
* summaries that return multiple columns
---
### Summaries with multiple values
Example: using `summarise` to get the range of bill lengths for each species of penguin:
```r
penguins %>%
group_by(species) %>%
summarise(rng = range(bill_length_mm, na.rm = TRUE))
```
```
# A tibble: 6 x 2
# Groups: species [3]
species rng
<fct> <dbl>
1 Adelie 32.1
2 Adelie 46
3 Chinstrap 40.9
4 Chinstrap 58
5 Gentoo 40.9
6 Gentoo 59.6
```
---
### Summaries with multiple columns
Example: using `summarise` to find the minimum and maximum mass penguin on each island:
```r
penguins %>%
group_by(island) %>%
summarise(tibble(min_mass = min(body_mass_g, na.rm = TRUE),
max_mass = max(body_mass_g, na.rm = TRUE)))
```
```
# A tibble: 3 x 3
island min_mass max_mass
<fct> <int> <int>
1 Biscoe 2850 6300
2 Dream 2700 4800
3 Torgersen 2900 4700
```
---
### So ... a couple other things about groups
* default behavior is to remove the last level of grouping after a call to `summarise`
* grouped data can be used with other `dplyr` verbs e.g. `mutate`
* you can ungroup data using `ungroup`
---
### Example using `group_by` with `mutate`
What if we wanted to give each penguin a number within its species?
```r
numbered_penguins <- penguins %>%
group_by(species) %>%
mutate(penguin_num = 1:n())
```
---
### Example using `group_by` with `mutate`
What if we wanted to give each penguin a number within its species?
```r
numbered_penguins <- penguins %>%
group_by(species) %>%
mutate(penguin_num = 1:n())
numbered_penguins
```
```
# A tibble: 344 x 9
# Groups: species [3]
species island bill_length_mm bill_depth_mm
<fct> <fct> <dbl> <dbl>
1 Adelie Torge… 39.1 18.7
2 Adelie Torge… 39.5 17.4
3 Adelie Torge… 40.3 18
4 Adelie Torge… NA NA
5 Adelie Torge… 36.7 19.3
6 Adelie Torge… 39.3 20.6
7 Adelie Torge… 38.9 17.8
8 Adelie Torge… 39.2 19.6
9 Adelie Torge… 34.1 18.1
10 Adelie Torge… 42 20.2
# … with 334 more rows, and 5 more variables:
# flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
# year <int>, penguin_num <int>
```
---
## `rename`
#### A function/verb to rename columns
Works like `select`
Example: renaming by position
```r
penguins_different <- penguins %>% rename(bill_length = 3,
bill_depth = 4)
penguins_different
```
```
# A tibble: 344 x 8
species island bill_length bill_depth flipper_length_…
<fct> <fct> <dbl> <dbl> <int>
1 Adelie Torge… 39.1 18.7 181
2 Adelie Torge… 39.5 17.4 186
3 Adelie Torge… 40.3 18 195
4 Adelie Torge… NA NA NA
5 Adelie Torge… 36.7 19.3 193
6 Adelie Torge… 39.3 20.6 190
7 Adelie Torge… 38.9 17.8 181
8 Adelie Torge… 39.2 19.6 195
9 Adelie Torge… 34.1 18.1 193
10 Adelie Torge… 42 20.2 190
# … with 334 more rows, and 3 more variables:
# body_mass_g <int>, sex <fct>, year <int>
```
---
### `rename_with`
`rename_with` can be used with a specified transformation (and optionally with a column selection).
Example: rename all columns to be uppercase
```r
penguins %>% rename_with(toupper)
```