Name:
Andrew ID:
Collaborated with:
This lab is to be done in class (completed outside of class time if need be). You can collaborate with your classmates, but you must identify their names above, and you must submit your own lab as an knitted PDF file on Gradescope, by Friday 9pm, this week.
This week’s agenda: practicing how to use the map
family of functions from purrr, and how to perform basic
computations on data frames using dplyr.
Below we install tidyverse which gives us the packages
we need (purrr and dplyr) needed to complete
this lab. We also install the repurrrsive package which has
the Game of Thrones data set that we’ll use for the first couple of
questions. Since this may be the first time installing packages for some
of you, we’ll show you how. If you already have these packages
installed, then you can of course skip this part. Note: do not
remove eval=FALSE from the above code chunk, just run
the lines below in your console. You can also select “Tools” –>
“Install Packages” from the RStudio menu.
install.packages("tidyverse")
install.packages("repurrrsive")Now we’ll load these packages. Note: the code chunk below will cause errors if you try to knit this file without installing the packages first.
Post edit. Loading the tidyverse
package in its entirety includes plyr, and this can cause
namespace issues with the dplyr package. Better to just
load only what you need.
library(purrr)
library(dplyr)
library(repurrrsive)Below we inspect a data set on the 30 characters from Game of Thrones
from the repurrrsive package. It’s stored in a list called
got_chars, which is automatically loaded into your R
session when you load the `repurrrsive`` package.
class(got_chars)## [1] "list"length(got_chars)## [1] 30names(got_chars[[1]])## [1] "url" "id" "name" "gender" "culture"
## [6] "born" "died" "alive" "titles" "aliases"
## [11] "father" "mother" "spouse" "allegiances" "books"
## [16] "povBooks" "tvSeries" "playedBy"got_chars[[1]]$name## [1] "Theon Greyjoy"got_chars[[1]]$aliases## [1] "Prince of Fools" "Theon Turncloak" "Reek" "Theon Kinslayer"1a. Using the map functions from the
purrr package, extract the names of the characters in
got_chars so that you produce a character vector of length
30. Do this four different ways: (i) using map(), defining
a custom function on-the-fly, and casting the resulting list into an
appropriate data structure; (ii) using one of the map_***()
functions, but still defining a custom function on-the-fly; (iii) using
one of the map_***() functions, and using one of
`[`() or `[[`() functions, as well as an
additional argument; (iv) using one of the map_***()
functions, and passing a string instead of a function (relying on its
ability to define an appropriate extractor accordingly).
Store each of the results in a different vector and check that they are all identical.
# YOUR CODE GOES HERE# YOUR CODE GOES HERE1c. Run the code below in your console. What does it do?
1:5 %in% 3:6Using the logic you can infer about the %in% operator
(you can also read its help file), craft a single line of code to
compute a Boolean vector of length 6 that checks whether the first Game
of Thrones character, stored in got_chars[[1]], has
appeared in each of the 6 TV seasons. Print the result to the
console.
# YOUR CODE GOES HERE1d. Run the two lines of code below in their console. What do they do?
rbind(1:5, 6:10, 11:15)
do.call(rbind, list(1:5, 6:10, 11:15))Using the logic you can infer about the do.call()
function (you can also read its help file), as well as the logic from
the last question, complete the following task. Using
map(), a custom-defined function, as well as some
post-processing of its results, produce a matrix that has dimension 30 x
6, with each column representing a TV season, and each row a character.
The matrix should have a value of TRUE in position (i,j) if
character i was in season j, and FALSE otherwise. Print the
first 6 rows of the result to the console.
# YOUR CODE GOES HEREmap_df() and no post-processing. The
result will now be a data frame (not a matrix). Print the first 6 rows
of the result to the console. Hint: map_dfr() will throw an
error if it can’t infer column names.# YOUR CODE GOES HEREmap_dfr(), create a data
frame of dimension 30 x 5, whose columns represent, for each Game of
Thrones character, their name, birth date, death date, gender, and
culture. Store it as got_df and print the last 3 rows to
the console.# YOUR CODE GOES HEREgot_df, show that you can
compute whether each character is alive or not, and compare this to what
is stored in got_chars, demonstrating that the two ways of
checking whether each character is alive lead to equal results.# YOUR CODE GOES HEREfilter(), print the subset
of the rows of got_df that correspond to Ironborn
characters. Then print the subset that correspond to female
Northmen.# YOUR CODE GOES HEREgot_df. Print the results to the
console. Hint: what happens if you pass table() two
arguments?# YOUR CODE GOES HEREgroup_by() and
summarize() on got_df, compute how many
characters in each culture have died. Which culture—aside from the
unknown category represented by ““—has the most deaths?# YOUR CODE GOES HEREThis is data set from the Rio Olympics data set that we saw in Lab 3.
In the next question, we’re going to repeat some calculations from Lab 3
but using dplyr.
rio = read.csv("https://www.stat.cmu.edu/~arinaldo/Teaching/36350/F22/data/rio.csv")group_by() and
summarize(), compute how many athletes competed for each
country in the rio data frame? Print the results for the
first 10 countries to the console. Building off your here answer, use an
additional call to filter() to compute which country had
the most number of athletes and how many that was. Hint: consider using
n() from the dplyr package for the first part
here.# YOUR CODE GOES HEREgroup_by(),
summarize(), and filter(), compute which
country had the most numnber of total medals and many that was.# YOUR CODE GOES HEREgroup_by(),
summarize(), and filter(), compute which
country—among those with zero total medals—had the most number of
athletes. Hint: you will need to modify your summarize()
command to compute the number of athletes; and you might need two calls
to filter().# YOUR CODE GOES HEREgroup_by(), summarize(), and
filter(), compute the average weight of athletes in each
sport, separately for men and women, and report the two sport with the
highest average weights (one for each of men and women). Hint:
group_by() can accept more than one grouping variable.
Also, consider using na.rm=TRUE as an additional argument
to certain arithmetic summary functions so that they will not be thrown
off by NA or NaN values.# YOUR CODE GOES HEREBelow, we read two data sets of the 1000 fastest times ever recorded for the 100m sprint, in men’s and women’s track. We scraped this data from http://www.alltime-athletics.com/m_100ok.htm and http://www.alltime-athletics.com/w_100ok.htm, in early September 2021. (Interestingly, the 2nd, 3rd, 4th, 7th, and 8th fastest women’s times were all set at the most recent Tokyo Olympics, or after! Meanwhile, the top 10 men’s times are all from about a decade ago.)
sprint.m.df = read.table(
file="https://www.stat.cmu.edu/~arinaldo/Teaching/36350/F22/data/sprint.m.txt",
sep="\t", quote="", header=TRUE)
sprint.w.df = read.table(
file="https://www.stat.cmu.edu/~arinaldo/Teaching/36350/F22/data/sprint.w.txt",
sep="\t", quote="", header=TRUE)sprint.m.df and
sprint.w.df are data frames. Delete the Rank
column from each data frame, then display the first and last 3 rows of
each.# YOUR CODE GOES HERETime column and add them back as a Rank
column to sprint.m.df. Do the same for the women’s data
set. After adding back the rank columns, print out the first 10 rows of
each data frame, but only the Time, Name,
Date, and Rank columns. Hint: consider using
rank().# YOUR CODE GOES HEREsprint.m.counts. Do the
same for the women’s data set, and call the result
sprint.w.counts. What are the 5 most represented countries,
for the men, and for the women? (Interesting side note: go look up the
population of Jamaica, compared to that of the US. Pretty impressive,
eh?)# YOUR CODE GOES HEREdplyr functions, and print out again the 5 most
represented countries for men and women. (No need to save new
variables.) Hint: consider using arrange() from the
dplyr library.# YOUR CODE GOES HERE%in%
operator.# YOUR CODE GOES HEREdplyr functions, compute,
for each country, the fastest time among athletes who come from that
country. Do this for each of the men’s and women’s data sets, and
display the first 10 rows of the result.# YOUR CODE GOES HEREarrange().# YOUR CODE GOES HEREtapply() gives probably the easiest route
here. Note: your code here shouldn’t be too much more complicated than
your code in the last part.# YOUR CODE GOES HEREdplyr functions, compute,
for each country, the quadruple: name, city, country, and time,
corresponding to the athlete with the fastest time among athletes from
that country. Do this for each of the men’s and women’s data sets, and
display the first 10 rows of the result, ordered by increasing time. If
there are ties, then show all the results that correspond to the fastest
time. Hint: consider using select() from the
dplyr library.# YOUR CODE GOES HEREsplit(), followed by lapply() with a custom
function call, and then rbind() to get things in a data
frame form. Note: your code here will probably be more complicated, or
at least less intuitive, than your code in the last part.# YOUR CODE GOES HERE