HDS 2.3-2.4

library(palmerpenguins)
library(tidyverse)

Begin by loading the tidyverse and palmerpenguins packages above.

Take a glimpse of the penguins data and determine which of the variables are categorical and which are quantitative:

glimpse(penguins)

Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

Make a list of the categorical variables: -species -island -sex

Make a list of the quantitative variables: -bill length -bill depth -flipper length -body mass -year

Summarizing Variables

Create a code chunk that summarizes the number of penguins by species:

library(dplyr)

penguins %>%
  group_by(species) %>%
  summarize(count = n())

# A tibble: 3 × 2
  species   count
  <fct>     <int>
1 Adelie      152
2 Chinstrap    68
3 Gentoo      124

Create a code chunk that summarizes the number of penguins by island:

library(dplyr)

penguins %>%
  group_by(island) %>%
  summarize(count = n())

# A tibble: 3 × 2
  island    count
  <fct>     <int>
1 Biscoe      168
2 Dream       124
3 Torgersen    52

Create a code chunk that summarizes the number of penguins by species and island simultaneously:

library(dplyr)

penguins %>%
  group_by(species, island) %>%
  summarize(count = n())

`summarise()` has grouped output by 'species'. You can override using the
`.groups` argument.

# A tibble: 5 × 3
# Groups:   species [3]
  species   island    count
  <fct>     <fct>     <int>
1 Adelie    Biscoe       44
2 Adelie    Dream        56
3 Adelie    Torgersen    52
4 Chinstrap Dream        68
5 Gentoo    Biscoe      124

Create a code chunk that summarizes body_mass_g. It should produce a data frame with the mean, median, minimum, maximum, and standard deviation of the body_mass_g:

library(dplyr)
penguins %>%
  summarize(
    mean_body_mass = mean(body_mass_g, na.rm = TRUE),
    median_body_mass = median(body_mass_g, na.rm = TRUE),
    min_body_mass = min(body_mass_g, na.rm = TRUE),
    max_body_mass = max(body_mass_g, na.rm = TRUE),
    sd_body_mass = sd(body_mass_g, na.rm = TRUE)
  )

# A tibble: 1 × 5
  mean_body_mass median_body_mass min_body_mass max_body_mass sd_body_mass
           <dbl>            <dbl>         <int>         <int>        <dbl>
1          4202.             4050          2700          6300         802.