Visualization is a key component to understanding and communicating your understanding to an audience. The more second nature turning your data into plots becomes, the more you can focus on the overall goals instead of being stuck on technical details.
As a freelance data analyst, I know that often times between when a project arrives at your table until it needs to be delivered is shorter than you would like, leaving limited time to consult documentation and search Stackoverflow.
This exercise set is a drilling exercise for the advanced user, but can be completed by a novice with patience and willingness to learn.
Solutions are available here.
viridis packages. Combine the three Pima data-sets from (
MASS) (used in the previous exercise set) and make a 2D density (density heat map) plot of
Using the same data, overlay a histogram of
bmi with a normal density curve using the sample mean and standard deviation.
accdeaths data-set from
MASS, make a line plot with time on the x-axis. Mark the maximum and minimum value of accidental deaths in a month with a read and blue dot, respectively. Note that the data does not come in ggplot-friendly format.
The internet surely loves cats, but most users have little idea how much a cat’s organs weigh. Using the
cats data from the
MASS package, make two 2D density plot of total weight versus hearth weight, side by side; one for each gender. In addition, add a dot for each observation.
Back to the
pima data. Make a boxplot for the
glu (glucose concentration), splitting the observations into five age groups with approximately the same number of observations.
economics data-set, make a stacked bar plot with proportions of unemployed to employed (employed or not seeking work) with the date in the x-axis.
msleep data-set, make a scatter plot (body weight versus total sleep) of all animals of the order artiodactyla. Mark the domesticated animals with a different color (from black) and annotate their names onto the graph.
msleep, make one density plot for the total sleep, colored by
vore. Play with the transparency and parameters of the density estimation.
Using the Gapminder data, (available from the
gapminder package) and data from the
rworldmap package, color countries by life expectancy in 2007. Use the
Still using the Gapminder data, make a scatter plot with the GDP per capital on a log scale on the x-axis and life expectancy on the y-axis. Map population to size and color to continent. Write a loop that makes a graph for each year and saves it with
ggsave to your hard drive, so later you can turn it into an animated graph.