Packages
R is an open-source language, and developers contribute functionality
to R via packages. In this lab we will work with three packages:
datasauRus which contains the dataset, and
tidyverse which is a collection of packages for doing data
analysis in a “tidy” way.
Load these packages by running the following in the Console.
library(tidyverse)
library(datasauRus)
If you haven’t installed these packages yet and R complains, then you
can install these packages by running the following command. (Note that
R package names are case-sensitive)
install.packages(c("tidyverse", "datasauRus"))
Note that the packages are also loaded with the same commands in your
R Markdown document.
Data
The data frame we will be working with today is called
datasaurus_dozen and it’s in the datasauRus
package. Actually, this single data frame contains 12 datasets, designed
to show us why data visualisation is important and how summary
statistics alone can be misleading. The different datasets are maked by
the dataset variable.
To find out more about the dataset, type the following in your
Console: ?datasaurus_dozen. A question mark before the name
of an object will always bring up its help file. This command must be
ran in the Console.
- Based on the help file, how many rows and how many columns does the
datasaurus_dozen file have? What are the variables included
in the data frame? Add your responses to your lab report.
Let’s take a look at what these datasets are. To do so we can make a
frequency table of the dataset variable:
datasaurus_dozen %>%
count(dataset)
## # A tibble: 13 × 2
## dataset n
## <chr> <int>
## 1 away 142
## 2 bullseye 142
## 3 circle 142
## 4 dino 142
## 5 dots 142
## 6 h_lines 142
## 7 high_lines 142
## 8 slant_down 142
## 9 slant_up 142
## 10 star 142
## 11 v_lines 142
## 12 wide_lines 142
## 13 x_shape 142
The original Datasaurus (dino) was created by Alberto
Cairo in this
great blog post. The other Dozen were generated using simulated
annealing and the process is described in the paper Same Stats,
Different Graphs: Generating Datasets with Varied Appearance and
Identical Statistics through Simulated Annealing by Justin Matejka
and George Fitzmaurice. In the paper, the authors simulate a variety of
datasets that the same summary statistics to the Datasaurus but have
very different distributions.
Data visualization and summary
- Plot
y vs. x for the dino
dataset. Then, calculate the correlation coefficient between
x and y for this dataset.
Below is the code you will need to complete this exercise. Basically,
the answer is already given, but you need to include relevant bits in
your Rmd document and successfully knit it and view the results.
Start with the datasaurus_dozen and pipe it into the
filter function to filter for observations where
dataset == "dino". Store the resulting filtered data frame
as a new data frame called dino_data.
dino_data <- datasaurus_dozen %>%
filter(dataset == "dino")
There is a lot going on here, so let’s slow down and unpack it a
bit.
First, the pipe operator: %>%, takes what comes
before it and sends it as the first argument to what comes after it. So
here, we’re saying filter the datasaurus_dozen
data frame for observations where dataset == "dino".
Second, the assignment operator: <-, assigns the name
dino_data to the filtered data frame.
Next, we need to visualize these data. We will use the
ggplot function for this. Its first argument is the data
you’re visualizing. Next we define the aesthetic mappings.
In other words, the columns of the data that get mapped to certain
aesthetic features of the plot, e.g. the x axis will
represent the variable called x and the y axis
will represent the variable called y. Then, we add another
layer to this plot where we define which geometric shapes
we want to use to represent each observation in the data. In this case
we want these to be points,m hence geom_point.
ggplot(data = dino_data, mapping = aes(x = x, y = y)) +
geom_point()
If this seems like a lot, it is. And you will learn about the
philosophy of building data visualizations in layer in detail in the
upcoming class. For now, follow along with the code that is
provided.
For the second part of this exercises, we need to calculate a summary
statistic: the correlation coefficient. Correlation coefficient, often
referred to as \(r\) in statistics,
measures the linear association between two variables. You will see that
some of the pairs of variables we plot do not have a linear relationship
between them. This is exactly why we want to visualize first: visualize
to assess the form of the relationship, and calculate \(r\) only if relevant. In this case,
calculating a correlation coefficient really doesn’t make sense since
the relationship between x and y is definitely
not linear – it’s dinosaurial!
But, for illustrative purposes, let’s calculate correlation
coefficient between x and y.
Start with dino_data and calculate a summary statistic
that we will call r as the correlation between
x and y.
dino_data %>%
summarize(r = cor(x, y))
Plot y vs. x for the star
dataset. You can (and should) reuse code we introduced above, just
replace the dataset name with the desired dataset. Then, calculate the
correlation coefficient between x and y for
this dataset. How does this value compare to the r of
dino?
Plot y vs. x for the
circle dataset. You can (and should) reuse code we
introduced above, just replace the dataset name with the desired
dataset. Then, calculate the correlation coefficient between
x and y for this dataset. How does this value
compare to the r of dino?
Facet by the dataset variable, placing the plots in a 3 column grid,
and don’t add a legend.
- Finally, let’s plot all datasets at once. In order to do this we
will make use of facetting.
ggplot(datasaurus_dozen, aes(x = x, y = y, color = dataset))+
geom_point()+
facet_wrap(~ dataset, ncol = 3) +
theme(legend.position = "none")
And we can use the group_by function to generate all
correlation coefficients.
datasaurus_dozen %>%
group_by(dataset) %>%
summarize(r = cor(x, y))
You’re done with the data analysis exercises, but we’d like you to do
two more things:
Click on the gear icon in on top of the R Markdown document, and
select “Output Options…” in the dropdown menu. In the pop up dialogue
box go to the Figures tab and change the height and width of the
figures, and hit OK when done. Then, knit your document and see how you
like the new sizes. Change and knit again and again until you’re happy
with the figure sizes. Note that these values get saved in the YAML.
You can also use different figure sizes for different figures. To do
so click on the gear icon within the chunk where you want to make a
change. Changing the figure sizes added new options to these chunks:
fig.width and fig.height. You can change them
by defining different values directly in your R Markdown document as
well.
- Change the look of your report:
Once again click on the gear icon in on top of the R Markdown
document, and select “Output Options…” in the dropdown menu. In the
General tab of the pop up dialogue box try out different syntax
highlighting and theme options. Hit OK and knit your document to see how
it looks. Play around with these until you’re happy with the look.
