This lab exercise is due 23:59 Monday 22 March (NZDT).

  • You should submit an R file (i.e. file extension .R) containing R code that assigns values to the appropriate symbols.
  • Your R file will be executed in order and checked against the values that have been assigned to the symbols using an automatic grading system. Marks will be fully deducted for non-identical results.
  • Intermediate steps to achieve the final results will NOT be checked.
  • Each question is worth 0.2 points.
  • You should submit your R file on Canvas.
  • Late assignments are NOT accepted unless prior arrangement for medical/compassionate reasons.

In this lab exercise, you are going to work with two data sets: user reviews for Animal Crossing - New Horizons as in user_reviews.tsv and Gapminder as in gapminder.rds. You shall use the following packages for this lab session:

library(tidyverse)
  • Make sure to include the snippet above upfront in your R file.
  • DO NOT include install.packages() in your R file.

Suppose that you have created an Rproj for this course. You need to download user_reviews.tsv here (right click, select “Download Linked File As”) to data/animal-crossing/ and gapminder.rds here to data/ under your Rproj.

  • You’re required to use relative file paths data/animal-crossing/user_reviews.tsv and data/gapminder.rds to import these data.
  • NO marks will be given for using URL links or different file paths.
  • Code below is only there to help you to verify your results. You DO NOT need to include the code like sum(good_grade), and print these objects.

Question 1

Import data/animal-crossing/user_reviews.tsv to R. You should end up with a tibble called user_reviews.

user_reviews <- read_tsv("data/animal-crossing/user_reviews.tsv")
user_reviews

#> # A tibble: 2,999 x 4
#>    grade user_name   text                                   date      
#>    <dbl> <chr>       <chr>                                  <date>    
#>  1     4 mds27272    My gf started playing before me. No o… 2020-03-20
#>  2     5 lolo2178    While the game itself is great, reall… 2020-03-20
#>  3     0 Roachant    My wife and I were looking forward to… 2020-03-20
#>  4     0 Houndf      We need equal values and opportunitie… 2020-03-20
#>  5     0 ProfessorF… BEWARE!  If you have multiple people … 2020-03-20
#>  6     0 tb726       The limitation of one island per Swit… 2020-03-20
#>  7     0 Outryder86  I was very excited for this new insta… 2020-03-20
#>  8     0 Subby89     It's 2020 and for some reason Nintend… 2020-03-20
#>  9     0 RocketRon   This is so annoying. Only one player … 2020-03-20
#> 10     0 chankills   I purchased this game for my househol… 2020-03-20
#> # … with 2,989 more rows

Question 2

Extract the grade column from the user_reviews tibble. You should end up with a double vector called user_grade.

user_grade <- user_reviews[["grade"]]
mean(user_grade)

#> [1] 4.217406

Question 3

Compare user_grade to be greater than or equal to 7. You should end up with a logical vector called good_grade.

good_grade <- user_grade >= 7
typeof(good_grade)

#> [1] "logical"

sum(good_grade)

#> [1] 1130

Question 4

Subset user_reviews given user_grade greater than or equal to 7, and the text column to be excluded. You should end up with a tibble called user_good_grade.

user_good_grade <- user_reviews[good_grade, -3]
user_good_grade

#> # A tibble: 1,130 x 3
#>    grade user_name       date      
#>    <dbl> <chr>           <date>    
#>  1    10 000PLAYER000    2020-03-20
#>  2     7 Drud            2020-03-20
#>  3     9 AlexKidd        2020-03-20
#>  4     8 ThornBattleborn 2020-03-20
#>  5     8 Skideye         2020-03-20
#>  6    10 GooMatt         2020-03-20
#>  7    10 Aladis          2020-03-20
#>  8    10 sknight1213     2020-03-20
#>  9    10 Fliff           2020-03-20
#> 10    10 dr_heartless    2020-03-20
#> # … with 1,120 more rows

Question 5

Find the function from {readr} to import an R data format data/gapminder.rds to R. You should end up with a tibble called gapminder.

gapminder <- read_rds("data/gapminder.rds")
gapminder

#> # A tibble: 1,704 x 6
#>    country     continent  year lifeExp      pop gdpPercap
#>    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
#>  1 Afghanistan Asia       1952    28.8  8425333      779.
#>  2 Afghanistan Asia       1957    30.3  9240934      821.
#>  3 Afghanistan Asia       1962    32.0 10267083      853.
#>  4 Afghanistan Asia       1967    34.0 11537966      836.
#>  5 Afghanistan Asia       1972    36.1 13079460      740.
#>  6 Afghanistan Asia       1977    38.4 14880372      786.
#>  7 Afghanistan Asia       1982    39.9 12881816      978.
#>  8 Afghanistan Asia       1987    40.8 13867957      852.
#>  9 Afghanistan Asia       1992    41.7 16317921      649.
#> 10 Afghanistan Asia       1997    41.8 22227415      635.
#> # … with 1,694 more rows