Lab 02
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.csv 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.tsvanddata/gapminder.rdsto 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
#> # 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.
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.
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
#> # 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
#> # 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