By the end of this lab you will effectively visualize numeric and categorical data.
1. Download the lab template by pasting the code below in your console:
download.file("https://sta101.github.io/static/labs/lab02_template.Rmd",
destfile = "lab02.rmd") 2. Under the “Files” tab on the right hand side, click on
lab02.rmd to open the lab template.
3. Complete the exercises below using the space provided.
Be sure to update the YAML at the top of the document to include your name and the date.
Today we will use our standard
library(tidyverse)Today, we will be working with data from the first three full seasons
of the NC Courage, a highly successful National Women’s Soccer League
(NWSL) team located near Duke in Cary, NC. The Courage moved to the
Triangle from Western New York in 2017 and had three epic seasons in NC,
culminating in winning the championship game that was held at their
stadium in Cary in 2019! (Data for this lab was sourced from the
nwslR package on Github, and verified with the NC Courage
website by Meredith Brown in a previous semester.)
courage = read_csv("https://sta101.github.io/static/labs/data/courage.csv")How many observations are there in the courage data frame? What
does each observation in the courage data frame represent?
Write your answer as narrative under “Exercise 1”. You do not need to
create a new code chunk here.
How many games did the Courage win, lose and tie between 2017 and 2019 seasons? Show your code in the space provided under “Exercise 2” and write your response as narrative.
How many points do the Courage typically win by (on average)? Use
the code chunk below to get started. In the code below,
abs() is the absolute value function. Why do we want to use
this absolute value function here? Write your answers as narrative (but
keep the code chunk to show your work).
Hint: we are only interested in games the Courage win.
courage %>%
mutate(win_pts = abs(home_pts - away_pts)) %>%
filter(___) %>%
summarize(___)courage2 = courage %>%
mutate(courage_pts = ifelse(home_team == "NC", home_pts, away_pts))
courage2 %>%
group_by(___) %>%
summarize(___)In the code chunk above we use ifelse logic to mutate a
new column.
home_team == "NC" finds all rows where the home team is
NC Courage.courage_pts column with home_pts otherwise
(else) Courage must be the away team and we populate
courage_pts with away_pts.Create a bar plot of Courage wins, losses and ties. Change the x-axis label to “Game outcome” and give the graph an informative title.
Next we’ll investigate visually whether or not NC Courage has a
home-field advantage. To start, let’s build on the courage2
data frame. Add a new column called home_courage that takes
the value “home” if Courage is the home team and “away” if Courage is
the away team. Hint: use the ifelse logic from exercise 4.
Next, add another new column, total_pts that shows the sum
total of points scored by both teams in a game. Finally add a third new
column opponent_pts that shows the total points scored by
the opposing team. Save your entire data frame as
courage3.
Using your courage3 data frame from the previous
exercise, create a scatter plot of opponent_pts (y) vs
courage_pts (x). Color the scatter plot by whether the
Courage are home or away. Add
geom_jitter as well as the line \(y=x\) using the code below. Finally,
facet your plot by season. Be sure to include informative
titles for the plot, axes and legend. What does the line represent? What
does it mean for a point to fall above the line? Below the line? Write
your answer in your lab narrative.
# data-here %>%
ggplot(...code here...) +
# insert faceting/labels code here +
geom_jitter(width = 0.1, height = 0.1) +
geom_abline(slope=1, intercept=0)How many home games did Courage play in the 2017-2019 seasons?
How many away games? Hint: use courage3 to answer this
question. Be sure to show your work (code) and answer the question
(narrative).
What proportion of home games do Courage win?
What proportion of away games do Courage win/? Hint:
use courage3 and count as part of your
solution. Do you think this is evidence of a home-field advantage? Why
or why not?
If we want to formally test whether or not the Courage have a home-field advantage, then we must first define what this means! In your own words, what do you think a home-field advantage means? (while there is a right answer, this part is graded for completion, so don’t worry too much about answering this in exactly the right way.
Now that you’ve defined what it means to have a home field advantage, define what it means to not have a home-field advantage.
We’ll pick-up here in the next class.
Reminder: For all assignments in this course there is a “formatting” component to the grade. To receive full points for “formatting”, you must:
1. Have your name (and team name if appropriate) at the top of the knitted document
2. Pipes %>% and ggplot layers + should
be followed by a newline (see formatting above)
3. Your code should be under the 80 character code limit. (You shouldn’t have to scroll to see all your code on the knitted document).
4. All exercises and corresponding pages should be linked on gradescope.
These necessary “tidyverse” style choices are good general practice and will help make your code more legible. For a more extensive list of recommended guidelines, click here.
In this class, we will submit .pdf documents to
Gradescope. Once you are fully satisfied with your lab, Knit to .pdf to
create a .pdf document. You may notice that the formatting/theme of the
report has changed – this is expected. Remember – you must turn in a
.pdf file to the Gradescope page before the submission deadline for
credit. To submit your assignment:
Go to http://www.gradescope.com and click Log in in the top
right corner. - Click School Credentials,
Duke NetID and log in using your NetID
credentials.
Click on your STA 101 course.
Click on the assignment, and you’ll be prompted to submit it.
Mark the pages associated with each exercise, 1 - 5. All of the papers of your lab should be associated with at least one question (i.e., should be “checked”). - Select the first page of your .pdf submission to be associated with the “Formatting” section.
Grading
Total: 50 pts.
Exercise 1: 2pts
Exercise 2: 4pts
Exercise 3: 6pts
Exercise 4: 4pts
Exercise 5: 6pts
Exercise 6: 6pts
Exercise 7: 8pts
Exercise 8: 3pts
Exercise 9: 3pts
Exercise 10: 1pt
Exercise 11: 1pt
Workflow and formatting: 6pts