About the dataset

Palmer penguins. Data were collected on 344 penguins living on three islands (Torgersen, Biscoe, and Dream) in the Palmer Archipelago, Antarctica by Dr. Kristen Gorman. In addition to which island each penguin lived on, the data contains information on the species of the penguin (Adelie, Chinstrap, and Gentoo), its bill length, bill depth, flipper length, its body mass, and sex of the penguin (male or female).

The palmerpenguins package contains two datasets: penguins and penguins_raw. We will be using penguins.

data(package = 'palmerpenguins')

head(penguins)

## # A tibble: 6 × 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
## # ℹ 2 more variables: sex <fct>, year <int>

?penguins

penguins <- penguins

You should see an object called “penguins”, which is a Dataset with 344 observations and 8 variables. mm=millimeters. g=grams.

The dataset contains the following fields:

• species: penguin species
• island: island of observation
• bill_length_mm: bill length in millimetres
• bill_depth_mm: bill depth in millimetres
• flipper_length_mm: flipper length in millimetres
• body_mass_g: body mass in grams
• sex: penguin sex
• year: year of observation

Mapping data

What if we want to show the relationship between three variables in the same graph? We can employ color!

Let’s take a look:

ggplot(data = penguins) + 
  geom_jitter(mapping = aes(x = bill_depth_mm, 
                            y = bill_length_mm, 
                            color = island))

Island is categorical character variable with a discrete range of possible values whereas, body mass is a continuous numeric variable in which any number of potential values can exist between known values. To represent this, R uses a color bar with a continuous gradient.

To answer the question: Which species of penguin is found in all three islands?

We will add another type of aesthetic called shape, for categorical data like species.

ggplot(data = penguins) + 
  geom_jitter(mapping = aes(x = bill_depth_mm, 
                            y = bill_length_mm, 
                            color = island,
                            shape = species))

Can you spot the difference?

ggplot(data = penguins) + 
  geom_point(mapping = aes(x = bill_depth_mm, 
                           y = bill_length_mm),
             color = "blue")

Important point: Values set outside aesthetics will apply to the entire geom or plot!

Geometrical objects

Next, we will consider different options for geoms. Using different geom_ functions user can highlight different aspects of data.

A useful geom function is geom_boxplot(). It adds a layer with the “box and whiskers” plot illustrating the distribution of values within categories. The following chart breaks down bill length by island, where the box represents first and third quartile (the 25th and 75th percentiles), the middle bar signifies the median value and the whiskers extent to cover 95% confidence interval. Outliers (outside of the 95% confidence interval range) are shown separately.

ggplot(data = penguins) + 
  geom_boxplot(mapping = aes(x = species, 
                             y = bill_length_mm))

Layers can be added on top of each other. In the following graph we will place the boxplots over jittered points to see the distribution of outliers more clearly. We can map two aesthetic properties to the same variable. Here we will also use different color for each island.

ggplot(data = penguins) + 
  geom_jitter(mapping = aes(x = species, 
                            y = bill_length_mm, 
                            color = species)) +
  geom_boxplot(mapping = aes(x = species,
                             y = bill_length_mm))

Now, this was slightly inefficient due to duplication of code - we had to specify the same mappings for two layers. To avoid it, you can move common arguments of geom_ functions to the main ggplot() function. In this case every layer will “inherit” the same arguments, specified in the “parent” function.

ggplot(data = penguins,
       mapping = aes(x = island, 
                     y = bill_length_mm)) + 
  geom_jitter(aes(color = island)) +
  geom_boxplot(alpha = .6) 

#alpha takes a value from 0 (transparent) to 1 (solid).

You can still add layer-specific mappings or other arguments by specifying them within individual geoms. Here, we’ve set the transparency of the boxplot to .6, so we can see the points behind it, and also mapped color to island in the points. It is recommended to build each layer separately and then moving common arguments up to the “parent” function.

We can use linear models to highlight differences in dependency between bill length and body mass by island. Notice that we added a separate argument to the geom_smooth() function to specify the type of model we want ggplot2 to built using the data (linear model). The geom_smooth() function has also helpfully provided confidence intervals, indicating “goodness of fit” for each model (shaded gray area). For more information on statistical models, please refer to help (by typing ?geom_smooth)

ggplot(data = penguins, 
       mapping = aes(x = bill_depth_mm, 
                     y = bill_length_mm)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm")

ggplot(data = penguins, 
       mapping = aes(x = bill_depth_mm, 
                     y = bill_length_mm)) +
  geom_point(mapping = aes(color = species),
             alpha = 0.5) +
  geom_smooth(method = "lm")

ggplot(data = penguins, 
       mapping = aes(x = bill_depth_mm, 
                     y = bill_length_mm,
                     color = species)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm")

Sub-plots (plot panels)

Often, we’d like to create the same set of plots, but as distinctly different subplots. This way, we don’t need to map so many aesthetics (it can end up being really messy).

Lets say, the last plot we made, we want to understand if there are also differences between male and female penguins.

In ggplot2, this is called a “facet”, and the function we use is called either facet_wrap or facet_grid.

ggplot(penguins, 
      aes(x = bill_depth_mm, 
          y = bill_length_mm,
          color = species)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm") +
  facet_wrap(~ sex)

The facet’s take formula arguments, meaning they contain the tilde (~).

This plot looks a little crazy though, as we have penguins with missing sex information getting their own panel, and really, it makes more sense to compare the sexes within each species rather than the other way around.

ggplot(penguins, 
      aes(x = bill_depth_mm, 
          y = bill_length_mm,
          color = sex)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm") +
  facet_wrap(~ species + island)

The {dplyr} package

The {dplyr} package provides a number of very useful functions for manipulating data sets in a way that will reduce the probability of making errors, and even save you some typing time.

We’re going to cover 6 of the most commonly used functions as well as using pipes (|>) to combine them.

1. select() (covered in this class)
2. filter() (covered next class)
3. arrange() (covered next class)
4. mutate() (covered next class)
5. group_by() (covered next class)
6. summarize() (covered next class)

Selecting columns

Let us first talk about selecting columns. In {dplyr}, the function name for selecting columns is select()! Most {tidyverse} function names for functions are inspired by English grammar, which will help us when we are writing our code.

To select data, we must first tell select which data set we are selecting from, and then give it our selection. Here, we are asking R to select() from the penguins data set the island, species and sex columns

select(penguins, island, species, sex)

## # A tibble: 344 × 3
##    island    species sex   
##    <fct>     <fct>   <fct> 
##  1 Torgersen Adelie  male  
##  2 Torgersen Adelie  female
##  3 Torgersen Adelie  female
##  4 Torgersen Adelie  <NA>  
##  5 Torgersen Adelie  female
##  6 Torgersen Adelie  male  
##  7 Torgersen Adelie  female
##  8 Torgersen Adelie  male  
##  9 Torgersen Adelie  <NA>  
## 10 Torgersen Adelie  <NA>  
## # ℹ 334 more rows

When we use select() we don’t need to use quotations, we write in the names directly.

We can also use the numeric indexes for the column, if we are 100% certain of the order of the columns:

select(penguins, 1:3, 6)

## # A tibble: 344 × 4
##    species island    bill_length_mm body_mass_g
##    <fct>   <fct>              <dbl>       <int>
##  1 Adelie  Torgersen           39.1        3750
##  2 Adelie  Torgersen           39.5        3800
##  3 Adelie  Torgersen           40.3        3250
##  4 Adelie  Torgersen           NA            NA
##  5 Adelie  Torgersen           36.7        3450
##  6 Adelie  Torgersen           39.3        3650
##  7 Adelie  Torgersen           38.9        3625
##  8 Adelie  Torgersen           39.2        4675
##  9 Adelie  Torgersen           34.1        3475
## 10 Adelie  Torgersen           42          4250
## # ℹ 334 more rows

In some cases, we want to remove columns, and not necessarily state all columns we want to keep. Select also allows for this by adding a minus (-) sign in front of the column name you don’t want.

select(penguins, -bill_length_mm, -bill_depth_mm)

## # A tibble: 344 × 6
##    species island    flipper_length_mm body_mass_g sex     year
##    <fct>   <fct>                 <int>       <int> <fct>  <int>
##  1 Adelie  Torgersen               181        3750 male    2007
##  2 Adelie  Torgersen               186        3800 female  2007
##  3 Adelie  Torgersen               195        3250 female  2007
##  4 Adelie  Torgersen                NA          NA <NA>    2007
##  5 Adelie  Torgersen               193        3450 female  2007
##  6 Adelie  Torgersen               190        3650 male    2007
##  7 Adelie  Torgersen               181        3625 female  2007
##  8 Adelie  Torgersen               195        4675 male    2007
##  9 Adelie  Torgersen               193        3475 <NA>    2007
## 10 Adelie  Torgersen               190        4250 <NA>    2007
## # ℹ 334 more rows

select(penguins, species, sex, year)

## # A tibble: 344 × 3
##    species sex     year
##    <fct>   <fct>  <int>
##  1 Adelie  male    2007
##  2 Adelie  female  2007
##  3 Adelie  female  2007
##  4 Adelie  <NA>    2007
##  5 Adelie  female  2007
##  6 Adelie  male    2007
##  7 Adelie  female  2007
##  8 Adelie  male    2007
##  9 Adelie  <NA>    2007
## 10 Adelie  <NA>    2007
## # ℹ 334 more rows

select(penguins, ends_with("mm"))

## # A tibble: 344 × 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
## # ℹ 334 more rows