class: center, middle, inverse, title-slide # An overview of dplyr ## Daryn Ramsden ### thisisdaryn at gmail dot com ### last updated: 2021-03-06 --- ### The data we will be using ```r #install.packages("palmerpenguins") library(palmerpenguins) ``` <div data-pagedtable="false"> <script data-pagedtable-source type="application/json"> 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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 flipper_length_mm body_mass_g <fct> <fct> <dbl> <dbl> <int> <int> 1 Chinstrap Dream 46.5 17.9 192 3500 2 Chinstrap Dream 50 19.5 196 3900 3 Chinstrap Dream 51.3 19.2 193 3650 4 Chinstrap Dream 45.4 18.7 188 3525 5 Chinstrap Dream 52.7 19.8 197 3725 6 Chinstrap Dream 45.2 17.8 198 3950 7 Chinstrap Dream 46.1 18.2 178 3250 8 Chinstrap Dream 51.3 18.2 197 3750 9 Chinstrap Dream 46 18.9 195 4150 10 Chinstrap Dream 51.3 19.9 198 3700 # … with 58 more rows, and 2 more variables: 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 flipper_length_mm body_mass_g <fct> <fct> <dbl> <dbl> <int> <int> 1 Adelie Torgersen 39.2 19.6 195 4675 2 Adelie Torgersen 42 20.2 190 4250 3 Adelie Torgersen 34.6 21.1 198 4400 4 Adelie Torgersen 42.5 20.7 197 4500 5 Adelie Torgersen 46 21.5 194 4200 6 Adelie Dream 39.2 21.1 196 4150 7 Adelie Dream 39.8 19.1 184 4650 8 Adelie Dream 44.1 19.7 196 4400 9 Adelie Dream 39.6 18.8 190 4600 10 Adelie Dream 42.3 21.2 191 4150 # … with 167 more rows, and 2 more variables: 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_mm body_mass_g year <dbl> <dbl> <int> <int> <int> 1 39.1 18.7 181 3750 2007 2 39.5 17.4 186 3800 2007 3 40.3 18 195 3250 2007 4 NA NA NA NA 2007 5 36.7 19.3 193 3450 2007 6 39.3 20.6 190 3650 2007 7 38.9 17.8 181 3625 2007 8 39.2 19.6 195 4675 2007 9 34.1 18.1 193 3475 2007 10 42 20.2 190 4250 2007 # … with 334 more rows ``` --- ### `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_… body_mass_g sex <fct> <fct> <dbl> <dbl> <int> <int> <fct> 1 Adelie Torge… 39.1 18.7 181 3750 male 2 Adelie Torge… 39.5 17.4 186 3800 fema… 3 Adelie Torge… 40.3 18 195 3250 fema… 4 Adelie Torge… NA NA NA NA <NA> 5 Adelie Torge… 36.7 19.3 193 3450 fema… 6 Adelie Torge… 39.3 20.6 190 3650 male # … with 2 more variables: 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 ``` <div data-pagedtable="false"> <script data-pagedtable-source type="application/json"> 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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 ``` <div data-pagedtable="false"> <script data-pagedtable-source type="application/json"> 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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_… body_mass_g sex <fct> <fct> <dbl> <dbl> <int> <int> <fct> 1 Adelie Torge… 39.1 18.7 181 3750 male 2 Adelie Torge… 39.5 17.4 186 3800 fema… 3 Adelie Torge… 40.3 18 195 3250 fema… 4 Adelie Torge… NA NA NA NA <NA> 5 Adelie Torge… 36.7 19.3 193 3450 fema… 6 Adelie Torge… 39.3 20.6 190 3650 male # … with 1 more variable: 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 flipper_length_mm body_mass_g <fct> <fct> <dbl> <dbl> <int> <int> 1 Adelie Torgersen 39.1 18.7 181 3750 2 Adelie Torgersen 39.5 17.4 186 3800 3 Adelie Torgersen 40.3 18 195 3250 4 Adelie Torgersen NA NA NA NA 5 Adelie Torgersen 36.7 19.3 193 3450 6 Adelie Torgersen 39.3 20.6 190 3650 7 Adelie Torgersen 38.9 17.8 181 3625 8 Adelie Torgersen 39.2 19.6 195 4675 9 Adelie Torgersen 34.1 18.1 193 3475 10 Adelie Torgersen 42 20.2 190 4250 # … with 334 more rows, and 3 more variables: 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_mm body_mass_g sex <fct> <fct> <dbl> <dbl> <int> <int> <fct> 1 Adelie Torgersen 39.1 18.7 181 3750 male 2 Adelie Torgersen 39.5 17.4 186 3800 female 3 Adelie Torgersen 40.3 18 195 3250 female 4 Adelie Torgersen NA NA NA NA <NA> 5 Adelie Torgersen 36.7 19.3 193 3450 female 6 Adelie Torgersen 39.3 20.6 190 3650 male 7 Adelie Torgersen 38.9 17.8 181 3625 female 8 Adelie Torgersen 39.2 19.6 195 4675 male 9 Adelie Torgersen 34.1 18.1 193 3475 <NA> 10 Adelie Torgersen 42 20.2 190 4250 <NA> # … with 334 more rows, and 1 more variable: 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) ``` ``` # A tibble: 344 x 8 SPECIES ISLAND BILL_LENGTH_MM BILL_DEPTH_MM FLIPPER_LENGTH_MM BODY_MASS_G <fct> <fct> <dbl> <dbl> <int> <int> 1 Adelie Torgersen 39.1 18.7 181 3750 2 Adelie Torgersen 39.5 17.4 186 3800 3 Adelie Torgersen 40.3 18 195 3250 4 Adelie Torgersen NA NA NA NA 5 Adelie Torgersen 36.7 19.3 193 3450 6 Adelie Torgersen 39.3 20.6 190 3650 7 Adelie Torgersen 38.9 17.8 181 3625 8 Adelie Torgersen 39.2 19.6 195 4675 9 Adelie Torgersen 34.1 18.1 193 3475 10 Adelie Torgersen 42 20.2 190 4250 # … with 334 more rows, and 2 more variables: SEX <fct>, YEAR <int> ``` --- ## `rename_with` ```r penguins %>% rename_with(toupper, where(is.numeric)) ``` ``` # A tibble: 344 x 8 species island BILL_LENGTH_MM BILL_DEPTH_MM FLIPPER_LENGTH_MM BODY_MASS_G <fct> <fct> <dbl> <dbl> <int> <int> 1 Adelie Torgersen 39.1 18.7 181 3750 2 Adelie Torgersen 39.5 17.4 186 3800 3 Adelie Torgersen 40.3 18 195 3250 4 Adelie Torgersen NA NA NA NA 5 Adelie Torgersen 36.7 19.3 193 3450 6 Adelie Torgersen 39.3 20.6 190 3650 7 Adelie Torgersen 38.9 17.8 181 3625 8 Adelie Torgersen 39.2 19.6 195 4675 9 Adelie Torgersen 34.1 18.1 193 3475 10 Adelie Torgersen 42 20.2 190 4250 # … with 334 more rows, and 2 more variables: sex <fct>, YEAR <int> ``` --- ## `relocate` ### A function * (**default**) move selected variables to the front * move selected columns before a specified location * move selected columns after a specified location --- ## `relocate` examples Example: bring all the factor variables to the front ```r penguins %>% relocate(where(is.factor)) ``` --- ## `relocate` examples Example: bring all the factor variables to the front ```r penguins %>% relocate(where(is.factor)) ``` ``` # A tibble: 344 x 8 species island sex bill_length_mm bill_depth_mm flipper_length_mm <fct> <fct> <fct> <dbl> <dbl> <int> 1 Adelie Torgersen male 39.1 18.7 181 2 Adelie Torgersen female 39.5 17.4 186 3 Adelie Torgersen female 40.3 18 195 4 Adelie Torgersen <NA> NA NA NA 5 Adelie Torgersen female 36.7 19.3 193 6 Adelie Torgersen male 39.3 20.6 190 7 Adelie Torgersen female 38.9 17.8 181 8 Adelie Torgersen male 39.2 19.6 195 9 Adelie Torgersen <NA> 34.1 18.1 193 10 Adelie Torgersen <NA> 42 20.2 190 # … with 334 more rows, and 2 more variables: body_mass_g <int>, year <int> ``` --- ## `relocate` examples Example: relocate all factor variables after *body_mass_g* ```r penguins %>% relocate(contains("bill"), .after = body_mass_g) ``` ``` # A tibble: 344 x 8 species island flipper_length_mm body_mass_g bill_length_mm bill_depth_mm <fct> <fct> <int> <int> <dbl> <dbl> 1 Adelie Torgersen 181 3750 39.1 18.7 2 Adelie Torgersen 186 3800 39.5 17.4 3 Adelie Torgersen 195 3250 40.3 18 4 Adelie Torgersen NA NA NA NA 5 Adelie Torgersen 193 3450 36.7 19.3 6 Adelie Torgersen 190 3650 39.3 20.6 7 Adelie Torgersen 181 3625 38.9 17.8 8 Adelie Torgersen 195 4675 39.2 19.6 9 Adelie Torgersen 193 3475 34.1 18.1 10 Adelie Torgersen 190 4250 42 20.2 # … with 334 more rows, and 2 more variables: sex <fct>, year <int> ``` --- ### `across`: a really useful new function What if you wanted the average value - per group - of each numeric column? Annoying way: ```r penguins %>% group_by(species) %>% summarise(avg_bill_length = mean(bill_length_mm, na.rm = TRUE), avg_bill_depth = mean(bill_depth_mm, na.rm = TRUE), avg_fl_length_mm = mean(flipper_length_mm, na.rm = TRUE), avg_body_mass_g = mean(body_mass_g, na.rm = TRUE)) ``` --- ### `across`: a really useful new function What if you wanted the average value - per group - of each numeric column? Annoying way: ```r penguins %>% group_by(species) %>% summarise(avg_bill_length = mean(bill_length_mm, na.rm = TRUE), avg_bill_depth = mean(bill_depth_mm, na.rm = TRUE), avg_fl_length_mm = mean(flipper_length_mm, na.rm = TRUE), avg_body_mass_g = mean(body_mass_g, na.rm = TRUE)) ``` ``` # A tibble: 3 x 5 species avg_bill_length avg_bill_depth avg_fl_length_mm avg_body_mass_g <fct> <dbl> <dbl> <dbl> <dbl> 1 Adelie 38.8 18.3 190. 3701. 2 Chinstrap 48.8 18.4 196. 3733. 3 Gentoo 47.5 15.0 217. 5076. ``` --- ### `across`: a really useful new function What if you wanted the average value - per group - of each numeric column? Neater/better way: ```r penguins %>% group_by(species) %>% summarise(across(where(is.numeric) & !contains("year"), mean, na.rm = TRUE)) ``` --- ### `across`: a really useful new function What if you wanted the average value - per group - of each numeric column? Neater/better way: ```r penguins %>% group_by(species) %>% summarise(across(where(is.numeric) & !contains("year"), mean, na.rm = TRUE)) ``` ``` # A tibble: 3 x 5 species bill_length_mm bill_depth_mm flipper_length_mm body_mass_g <fct> <dbl> <dbl> <dbl> <dbl> 1 Adelie 38.8 18.3 190. 3701. 2 Chinstrap 48.8 18.4 196. 3733. 3 Gentoo 47.5 15.0 217. 5076. ``` --- ### `across`: a closer look `across` has two primary arguments: * <tt>.cols</tt> selects the columns you want to operate on * <tt>.fns</tt> is a function or list of functions that you want to apply * can be a `purrr` style formula --- ### multiple summaries with `across` Example: For each island, what is the average of all numeric variables and the count of all factor variables? ```r penguins %>% group_by(island) %>% summarise( across(where(is.numeric), mean, na.rm = TRUE), across(where(is.factor), n_distinct), n = n(), ) ``` ``` # A tibble: 3 x 9 island bill_length_mm bill_depth_mm flipper_length_… body_mass_g year species <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <int> 1 Biscoe 45.3 15.9 210. 4716. 2008. 2 2 Dream 44.2 18.3 193. 3713. 2008. 2 3 Torge… 39.0 18.4 191. 3706. 2008. 1 # … with 2 more variables: sex <int>, n <int> ``` --- ### `across` example with `filter` Example: get all rows without missing values: ```r penguins_complete <- penguins %>% filter(across(everything(), ~ !is.na(.x))) ``` -- Is that any different to? ```r penguins_complete2 <- penguins %>% filter(across(everything(), complete.cases)) ``` --- ### `across` example with `distinct` All combinations of variables meeting specified criteria using `distinct` ```r penguins %>% distinct(across(is.factor, sort = TRUE)) ``` ``` # A tibble: 13 x 3 species island sex <fct> <fct> <fct> 1 Adelie Torgersen male 2 Adelie Torgersen female 3 Adelie Torgersen <NA> 4 Adelie Biscoe female 5 Adelie Biscoe male 6 Adelie Dream female 7 Adelie Dream male 8 Adelie Dream <NA> 9 Gentoo Biscoe female 10 Gentoo Biscoe male 11 Gentoo Biscoe <NA> 12 Chinstrap Dream female 13 Chinstrap Dream male ``` --- ### `across` example with `count` Counts of all combinations of variables meeting specified criteria using `count` ```r penguins %>% count(across(is.factor, sort = TRUE)) ``` ``` # A tibble: 13 x 4 species island sex n <fct> <fct> <fct> <int> 1 Adelie Biscoe female 22 2 Adelie Biscoe male 22 3 Adelie Dream female 27 4 Adelie Dream male 28 5 Adelie Dream <NA> 1 6 Adelie Torgersen female 24 7 Adelie Torgersen male 23 8 Adelie Torgersen <NA> 5 9 Chinstrap Dream female 34 10 Chinstrap Dream male 34 11 Gentoo Biscoe female 58 12 Gentoo Biscoe male 61 13 Gentoo Biscoe <NA> 5 ``` --- ### `across` example with `mutate` Using `across` with `mutate` to rescale all numeric variables between 0 and 1 ```r rescale01 <- function(x) { rng <- range(x, na.rm = TRUE) (x - rng[1]) / (rng[2] - rng[1]) } penguins_rescaled <- penguins %>% mutate(across(where(is.numeric), rescale01)) penguins_rescaled ``` ``` # A tibble: 344 x 8 species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g <fct> <fct> <dbl> <dbl> <dbl> <dbl> 1 Adelie Torgersen 0.255 0.667 0.153 0.292 2 Adelie Torgersen 0.269 0.512 0.237 0.306 3 Adelie Torgersen 0.298 0.583 0.390 0.153 4 Adelie Torgersen NA NA NA NA 5 Adelie Torgersen 0.167 0.738 0.356 0.208 6 Adelie Torgersen 0.262 0.893 0.305 0.264 7 Adelie Torgersen 0.247 0.560 0.153 0.257 8 Adelie Torgersen 0.258 0.774 0.390 0.549 9 Adelie Torgersen 0.0727 0.595 0.356 0.215 10 Adelie Torgersen 0.360 0.845 0.305 0.431 # … with 334 more rows, and 2 more variables: sex <fct>, year <dbl> ``` --- ## Row-wise operations Question: what if we wanted to create a new column that was the average of the <i>bill_depth_mm</i> and <i>bill_length_mm</i> variables? You might try: ```r penguins %>% select(contains("bill")) %>% mutate(avg = mean(c(bill_length_mm, bill_depth_mm), na.rm = TRUE)) ``` --- ## Row-wise operations Question: what if we wanted to create a new column that was the average of the <i>bill_depth_mm</i> and <i>bill_length_mm</i> variables? You might try: ```r penguins %>% select(contains("bill")) %>% mutate(avg = mean(c(bill_length_mm, bill_depth_mm), na.rm = TRUE)) ``` ``` # A tibble: 344 x 3 bill_length_mm bill_depth_mm avg <dbl> <dbl> <dbl> 1 39.1 18.7 30.5 2 39.5 17.4 30.5 3 40.3 18 30.5 4 NA NA 30.5 5 36.7 19.3 30.5 6 39.3 20.6 30.5 7 38.9 17.8 30.5 8 39.2 19.6 30.5 9 34.1 18.1 30.5 10 42 20.2 30.5 # … with 334 more rows ``` --- ### Using `rowwise` We can use `rowwise` prior to mutate instead ```r penguins %>% select(contains("bill")) %>% rowwise() %>% mutate(avg = mean(c(bill_length_mm, bill_depth_mm), na.rm = TRUE)) ``` --- ### Using `rowwise` We can use `rowwise` prior to mutate instead ```r penguins %>% select(contains("bill")) %>% rowwise() %>% mutate(avg = mean(c(bill_length_mm, bill_depth_mm), na.rm = TRUE)) ``` ``` # A tibble: 344 x 3 # Rowwise: bill_length_mm bill_depth_mm avg <dbl> <dbl> <dbl> 1 39.1 18.7 28.9 2 39.5 17.4 28.4 3 40.3 18 29.2 4 NA NA NaN 5 36.7 19.3 28 6 39.3 20.6 30.0 7 38.9 17.8 28.4 8 39.2 19.6 29.4 9 34.1 18.1 26.1 10 42 20.2 31.1 # … with 334 more rows ``` --- ## Joins To illustrate the join functions, we will use two small data sets First, a data frame containing the populations of 8 countries (via census.gov): ```r populations <- readr::read_csv("data/populations.csv") populations ``` ``` # A tibble: 8 x 2 Country Population <chr> <dbl> 1 India 1326093247 2 United States 329877505 3 Indonesia 267026366 4 Pakistan 233500636 5 Nigeria 214028302 6 Bangladesh 162650853 7 Russia 141722205 8 Mexico 128649565 ``` --- ## Joins Next, a data frame containing the land areas of some countries (via wikipedia) ```r areas <- readr::read_csv("data/areas.csv") areas ``` ``` # A tibble: 7 x 2 Country Area <chr> <dbl> 1 Russia 16377742 2 China 9326410 3 United States 9147593 4 Brazil 8460415 5 India 2973190 6 Indonesia 1811569 7 Nigeria 910768 ``` Note that some countries are in both data frames while others are only in one. --- ### Inner joins with `inner_join` Inner joins combine tables, taking only entries that are in both: ```r inner_join(populations, areas) ``` ``` # A tibble: 5 x 3 Country Population Area <chr> <dbl> <dbl> 1 India 1326093247 2973190 2 United States 329877505 9147593 3 Indonesia 267026366 1811569 4 Nigeria 214028302 910768 5 Russia 141722205 16377742 ``` --- ### Full joins with `full_join` Full joins combine tables, taking all entries from either: ```r full_join(populations, areas) ``` ``` # A tibble: 10 x 3 Country Population Area <chr> <dbl> <dbl> 1 India 1326093247 2973190 2 United States 329877505 9147593 3 Indonesia 267026366 1811569 4 Pakistan 233500636 NA 5 Nigeria 214028302 910768 6 Bangladesh 162650853 NA 7 Russia 141722205 16377742 8 Mexico 128649565 NA 9 China NA 9326410 10 Brazil NA 8460415 ``` --- ### Left (or right) joins with `left_join` (or `right_join`) Left joins take all the rows in the first table along with any rows in the second table that match ```r left_join(populations, areas) ``` ``` # A tibble: 8 x 3 Country Population Area <chr> <dbl> <dbl> 1 India 1326093247 2973190 2 United States 329877505 9147593 3 Indonesia 267026366 1811569 4 Pakistan 233500636 NA 5 Nigeria 214028302 910768 6 Bangladesh 162650853 NA 7 Russia 141722205 16377742 8 Mexico 128649565 NA ```