– Analysis of Penguins Dataset –
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(palmerpenguins)
library(dplyr)
data <- penguinsExploring our data with dplyr
Main functions we’ll use
arrange() filter() select() mutate() summarise() (you can also use summarize()) Reading and writing R code One thing that I really enjoy about working in R is that I can write out what I want to do in a sentence, and then translate that into code. For example, if I say:
Take the penguins dataset and then filter for all penguins that live on Torgersen island
Take the penguins dataset translates to penguins and then translates to %>% filter for all penguins that live on Torgersen island translates to filter(island == “Torgersen”) We can then take these three lines and put them together to get the following:
data %>%
filter(island == "Torgersen")## # A tibble: 52 × 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 42 more rows, and 2 more variables: sex <fct>, year <int>Applying arrange()
arrange() “arranges,” or organizes, our data in ascending order, starting from the lowest value and running to the highest (or in the case of character data, in alphabetical order).
data %>%
arrange(bill_length_mm) %>%
head()## # A tibble: 6 × 8
## species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex
## <fct> <fct> <dbl> <dbl> <int> <int> <fct>
## 1 Adelie Dream 32.1 15.5 188 3050 fema…
## 2 Adelie Dream 33.1 16.1 178 2900 fema…
## 3 Adelie Torge… 33.5 19 190 3600 fema…
## 4 Adelie Dream 34 17.1 185 3400 fema…
## 5 Adelie Torge… 34.1 18.1 193 3475 <NA>
## 6 Adelie Torge… 34.4 18.4 184 3325 fema…
## # … with 1 more variable: year <int># creating a random subset of the penguins dataset
set.seed(406)
penguins_subset <- data %>%
sample_n(12) # another dplyr function!
penguins_subset## # A tibble: 12 × 8
## species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 41.4 18.5 202 3875
## 2 Gentoo Biscoe 45.5 13.9 210 4200
## 3 Gentoo Biscoe 43.5 15.2 213 4650
## 4 Gentoo Biscoe 50.5 15.9 225 5400
## 5 Gentoo Biscoe 45.8 14.2 219 4700
## 6 Chinstrap Dream 49.3 19.9 203 4050
## 7 Adelie Biscoe 40.5 17.9 187 3200
## 8 Chinstrap Dream 45.2 16.6 191 3250
## 9 Adelie Dream 36.3 19.5 190 3800
## 10 Adelie Torgersen 39 17.1 191 3050
## 11 Adelie Biscoe 41.6 18 192 3950
## 12 Gentoo Biscoe 48.2 15.6 221 5100
## # … with 2 more variables: sex <fct>, year <int>penguins_subset %>%
mutate(body_weight_pounds = body_mass_g / 453.59237)## # A tibble: 12 × 9
## species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 41.4 18.5 202 3875
## 2 Gentoo Biscoe 45.5 13.9 210 4200
## 3 Gentoo Biscoe 43.5 15.2 213 4650
## 4 Gentoo Biscoe 50.5 15.9 225 5400
## 5 Gentoo Biscoe 45.8 14.2 219 4700
## 6 Chinstrap Dream 49.3 19.9 203 4050
## 7 Adelie Biscoe 40.5 17.9 187 3200
## 8 Chinstrap Dream 45.2 16.6 191 3250
## 9 Adelie Dream 36.3 19.5 190 3800
## 10 Adelie Torgersen 39 17.1 191 3050
## 11 Adelie Biscoe 41.6 18 192 3950
## 12 Gentoo Biscoe 48.2 15.6 221 5100
## # … with 3 more variables: sex <fct>, year <int>, body_weight_pounds <dbl>penguins_subset %>%
summarise(avg_body_mass = mean(body_mass_g))## # A tibble: 1 × 1
## avg_body_mass
## <dbl>
## 1 4102.# summarising body mass on the entire penguins dataset while removing NA values from the calculation
penguins %>%
summarise(avg_body_mass = mean(body_mass_g, na.rm = TRUE))## # A tibble: 1 × 1
## avg_body_mass
## <dbl>
## 1 4202.# now let's use the grouping function, group_by(), to look at the average body mass of penguins, in grams,
# by species
data %>%
group_by(species) %>%
summarise(avg_species_body_mass = mean(body_mass_g, na.rm = TRUE)) ## # A tibble: 3 × 2
## species avg_species_body_mass
## <fct> <dbl>
## 1 Adelie 3701.
## 2 Chinstrap 3733.
## 3 Gentoo 5076.# now let's calculate the average body mass by species AND island
data %>%
group_by(species, island) %>%
summarise(avg_species_body_mass = mean(body_mass_g, na.rm = TRUE)) ## `summarise()` has grouped output by 'species'. You can override using the `.groups` argument.## # A tibble: 5 × 3
## # Groups: species [3]
## species island avg_species_body_mass
## <fct> <fct> <dbl>
## 1 Adelie Biscoe 3710.
## 2 Adelie Dream 3688.
## 3 Adelie Torgersen 3706.
## 4 Chinstrap Dream 3733.
## 5 Gentoo Biscoe 5076.