How to explore the penguins dataset using the explore package.
The explore package simplifies Exploratory Data Analysis (EDA). Get faster insights with less code! We will use < 10 lines of code and just 6 function names to explore penguins:
| function | package | description |
|---|---|---|
library() |
{base} | load a package |
filter() |
{dplyr} | subset rows using column values |
describe() |
{explore} | describe variables of the table |
explore() |
{explore} | explore graphically a variable |
explore_all() |
{explore} | explore all variables of the table |
explain_tree() |
{explore} | explain a target using a decision tree |
The penguins dataset comes with the palmerpenguins
package. It has 344 observations and 8 variables. (https://github.com/allisonhorst/palmerpenguins)
Furthermore, we use the packages {dplyr} for filter()
and %>% and {explore} for data exploration.
library(dplyr)
library(explore)
penguins <- use_data_penguins()
# equivalent to
# penguins <- palmerpenguins::penguinsDescribe variables
penguins %>% describe()
#> # A tibble: 8 × 8
#> variable type na na_pct unique min mean max
#> <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 species fct 0 0 3 NA NA NA
#> 2 island fct 0 0 3 NA NA NA
#> 3 bill_length_mm dbl 2 0.6 165 32.1 43.9 59.6
#> 4 bill_depth_mm dbl 2 0.6 81 13.1 17.2 21.5
#> 5 flipper_length_mm int 2 0.6 56 172 201. 231
#> 6 body_mass_g int 2 0.6 95 2700 4202. 6300
#> 7 sex fct 11 3.2 3 NA NA NA
#> 8 year int 0 0 3 2007 2008. 2009There are some NA-values (unknown values) in the data.
The variable containing the most NAs is sex. flipper_length_mm and
others contain only 2 observations with NAs.
Data cleaning
We use only penguins with known flipper length for the data exploration!
data <- penguins %>%
filter(flipper_length_mm > 0)We reduced the penguins from 344 to 342.
Explore variables
data %>%
explore_all(color = "skyblue")
Which species?
What is the relationship between all the variables and species?
data %>%
explore_all(
target = species,
color = c("darkorange", "purple", "lightseagreen"))
We already see some strong patterns in the data.
flipper_length_mm separates species Gentoo,
bill_length_mm separates species Adelie from Chinstrap. And
we see that Chinstrap and Gentoo are located on separate islands.
Now we explain species using a decision tree:
data %>% explain_tree(target = species)
We found an easy explanation how to find out the species by just using flipper_length_mm and bill_length_mm.
- If
flipper_legnth_mm >= 207, it is a Gentoo penguin (95% right) - If
flipper_length_mm < 207andbill_length_mm < 43, it is a Adelie penguin (97% right) - If
flipper_length_mm < 207andbill_length_mm >= 43, it is a Chinstrap penguin (92% right)
Now let’s take a closer look to these variables:
data %>%
explore(
flipper_length_mm, bill_length_mm,
target = species,
color = c("darkorange", "purple", "lightseagreen")
)
The plot shows a not perfect but good separation between the 3 species!