Welcome to Module 2
Now that you know how to run R and store values in variables, it’s time to look at real data. We’ll use the Palmer Penguins dataset, a simple but rich dataset of penguin measurements.
By the end of this module, you will be able to:
- Load a dataset into R
- Examine its structure
- Look at basic summaries of the data
- Make a simple plot
Loading the data
The dataset comes from the palmerpenguins package. First, install the
package and then load it. You only need to install a package on a
particular machine once so don’t feel the need to run the installation
line every time you want to use a package:
# Install if you haven't already
install.packages("palmerpenguins")
# Load the package
library(palmerpenguins)
# Load the dataset
data("penguins")
You’ll notice that some lines have a hashtag at their start. Hashtags mean ‘don’t run this line’. They’re an essential tool in keeping track of what you’re doing and why you’re doing it. Future you will thank you - trust me. Annotating your code in this way also supports reproducibility if you share it with someone else. There’s nothing worse (for a given value of ‘nothing’) than being sent unformatted, non-annotated code. I once stepped on a slug, bare foot, and unformatted, non-annotated code is worse.
Note: Running library(palmerpenguins) makes the dataset available in your environment. We use the same argument to load any package.
Exploring the dataset
It is important to check any opened data before you use it so that you can understand its structure and, potentially, identify any issues. Start by taking a look at the first few rows:
head(penguins)
## # A tibble: 6 × 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
## <fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
## 2 Adelie Torgersen 39.5 17.4 186 3800 female 2007
## 3 Adelie Torgersen 40.3 18 195 3250 female 2007
## 4 Adelie Torgersen NA NA NA NA <NA> 2007
## 5 Adelie Torgersen 36.7 19.3 193 3450 female 2007
## 6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
Does this look right? Now look at the structure:
str(penguins)
## tibble [344 × 8] (S3: tbl_df/tbl/data.frame)
## $ species : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ island : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ bill_length_mm : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
## $ bill_depth_mm : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
## $ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
## $ body_mass_g : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
## $ sex : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
## $ year : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...
A word followed by parentheses (curved brackets) is a function. It means
‘take the thing inside the brackets and do something to it’. The
‘something’ in question will differ depending on the, well, function of
the function. Here, str() tells you the type of each column (numeric,
factor, etc.). This is crucial for understanding what kind of analysis
you can do. Above, head() allows us to look at the top five rows of
our data.
We should also summarise our data:
summary(penguins)
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex
## Adelie :152 Biscoe :168 Min. :32.10 Min. :13.10 Min. :172.0 Min. :2700 female:165
## Chinstrap: 68 Dream :124 1st Qu.:39.23 1st Qu.:15.60 1st Qu.:190.0 1st Qu.:3550 male :168
## Gentoo :124 Torgersen: 52 Median :44.45 Median :17.30 Median :197.0 Median :4050 NA's : 11
## Mean :43.92 Mean :17.15 Mean :200.9 Mean :4202
## 3rd Qu.:48.50 3rd Qu.:18.70 3rd Qu.:213.0 3rd Qu.:4750
## Max. :59.60 Max. :21.50 Max. :231.0 Max. :6300
## NA's :2 NA's :2 NA's :2 NA's :2
## year
## Min. :2007
## 1st Qu.:2007
## Median :2008
## Mean :2008
## 3rd Qu.:2009
## Max. :2009
##
The summary() function gives you min, max, mean and other values for
numeric columns. It shows counts for categorical columns like species
and sex. This provides a quick way to get to know your dataset.
Data classes
Every column in a dataset has a class hat describes the kind of data it holds. Understanding the class helps R know what operations you can do with those data.
Common classes:
numeric– numbers you can calculate withinteger– whole numbersfactor– categorical variables like species or sexcharacter– textlogical– TRUE/FALSE
Check the class of each column in penguins:
# Check classes of all columns
sapply(penguins, class)
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## "factor" "factor" "numeric" "numeric" "integer" "integer"
## sex year
## "factor" "integer"
Notice that species and sex are factors (categories), while
bill_length_mm and body_mass_g are numeric.This determines what
kinds of calculations or plots you can do later.
Visualising variables
Let’s plot bill length to look at the distribution:
hist(penguins$bill_length_mm,
main = "Histogram of Bill Length",
xlab = "Bill Length (mm)",
col = "skyblue",
border = "white")
If you run this code you’ll find that it doesn’t work. Why might this be? Take a look at your data (double-click penguins in your Environment, look at the structure and summary) and see if you can spot any potential issues.
The keen-eyed among you will notice that the bill_length_mm variable
has cells that contain NA values. NA is not the same as 0. A value of 0
means that the would have been a bill length of 0 mm (hopefully no
penguins were sans bill) whereas as an NA value indicates the complete
absence of data.
We can count the number of NAs in our data:
sum(is.na(penguins$bill_length_mm))
## [1] 2
Many R functions do not know how to handle an absence of data without
our instruction. We can do this a number of ways but for now we’ll use
na.omit and save the new, NA-free variable, for plotting.
bill_length <- na.omit(penguins$bill_length_mm)
hist(bill_length,
main = "Histogram of Bill Length",
xlab = "Bill Length (mm)",
col = "skyblue",
border = "white")
Note: Putting ? or ?? before a function name will search your
installed packages for any help documentation on that particular
function. You can use ?hist to see more options for histograms, for
example. Help files include descriptions of all arguments used in the
function, as well as examples of the function in use.
We can also see how bill length varies by species:
boxplot(penguins$bill_length_mm ~ penguins$species,
main = "Bill Length by Species",
ylab = "Bill Length (mm)",
col = c("lightpink","lightgreen","lightblue"))
Each box represents the distribution of bill length data for one species. Look for differences in median, spread, and outliers. Remember that this is a simple visualisation; it is not a test for significant differences. Those will come, later.
Lessons learned
- The Palmer Penguins dataset is small but has numeric and categorical data
- head(), str(), and summary() are your first tools for exploration
- Visualisations help you see patterns before analysing