We’ll use the tidyverse package for much of the data wrangling and visualization. The tidyverse package should be installed by now on your computer. If not, take again a look at the Set Up tutorial. You can load the tidyverse with the following command:
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.0 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Data
The data is contained in the file lego.csv and can be found on Moodle in the folder Data files, Quarto files and Solutions to previous problem sets.
It is simulated data based on real prices for LEGO sales in 2018 in the United States. Names and hobbies have been randomly generated. The dataset contains the following variables.
Variable
Description
first_name
First name of customer
last_name
Last name of customer
age
Age of customer
phone_number
Phone number of customer
set_id
Set ID of LEGO set purchased
number
Item number of LEGO set purchased
theme
Theme of LEGO set purchased
subtheme
Sub theme of LEGO set purchased
year
Year of purchase
name
Name of LEGO set purchased
pieces
Number of pieces of LEGOs in set purchased
us_price
Price of the set purchased in US Dollars
quantity
Quantity of LEGO set(s) purchased
Solutions
Solution 1. Save the data and the starter file appropriately so the following code works.
lego <-read_csv("data/lego.csv")
Rows: 620 Columns: 13
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (7): first_name, last_name, phone_number, number, theme, subtheme, name
dbl (6): age, set_id, year, pieces, us_price, quantity
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Remark: Don’t forget to remove eval: false or change it to eval: true.
Solution 2. The three most common names (number) are Jackson (13), Jacob (11), and Joseph (11)
lego |>count(first_name) |>arrange(desc(n))
# A tibble: 211 × 2
first_name n
<chr> <int>
1 Jackson 13
2 Jacob 11
3 Joseph 11
4 Michael 10
5 Audrey 8
6 Connor 8
7 Kaitlyn 8
8 Lucas 8
9 Amanda 7
10 Joshua 7
# ℹ 201 more rows
Solution 3. Remove this text, and add your answer for Exercise 3 here. Complete the given R code. Remove the chunk option eval: false.
lego <- lego |>drop_na(subtheme)lego
# A tibble: 448 × 13
first_name last_name age phone_number set_id number theme subtheme year
<chr> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr> <dbl>
1 Kimberly Beckstead 24 216-555-2549 24701 76062 DC C… Mighty … 2018
2 Neel Garvin 35 819-555-3189 25626 70595 Ninj… Rise of… 2018
3 Chelsea Bouchard 41 <NA> 25626 70595 Ninj… Rise of… 2018
4 Bryanna Welsh 19 <NA> 24797 75138 Star… Episode… 2018
5 Bryanna Welsh 19 <NA> 24701 76062 DC C… Mighty … 2018
6 Caleb Garcia-Wide… 37 907-555-9236 25611 21127 Mine… Minifig… 2018
7 Kevin Cruz 20 947-555-7946 24767 70335 Nexo… Ultimate 2018
8 Connor Brown 36 516-555-4310 24548 71230 Dime… Fun Pack 2018
9 Connor Brown 36 516-555-4310 24913 60112 City Fire 2018
10 Connor Brown 36 516-555-4310 26034 41573 Mixe… Series 9 2018
# ℹ 438 more rows
# ℹ 4 more variables: name <chr>, pieces <dbl>, us_price <dbl>, quantity <dbl>
REMARK: Use the reduced dataset to answer the following questions.
Solution 4. The three most common themes are Star Wars (n = 75), Gear (n = 55), and Mixels (n = 55). Among these, the most common subsets are The Force Awakens (n = 17), Role-Play toys (n = 7) for Gear, and Series 9 (n = 21) for Mixels.
# A tibble: 3 × 3
# Groups: theme [3]
theme subtheme n
<chr> <chr> <int>
1 Gear Role-Play toys 7
2 Mixels Series 9 21
3 Star Wars The Force Awakens 15
Solution 5. Check.
lego <- lego |>mutate(age_group =case_when( age <=18~"18 and under", age >18& age <=25~"19 - 25", age >25& age <=35~"26 - 35", age >35& age <=50~"36 - 50", age >=51~"51 and over" ) )
Solution 6. Remove this text, and add your answer for Exercise 6 here. Complete the given R code. Remove the chunk option eval: false.
# A tibble: 5 × 2
# Groups: age_group [5]
age_group set_quantity
<chr> <dbl>
1 36 - 50 232
2 26 - 35 193
3 19 - 25 119
4 51 and over 73
5 18 and under 33
Solution 7.
lego |>filter(theme %in%c("City", "Nexo Knights", "Ninjago")) |>ggplot(aes(x = theme, fill = age_group)) +geom_bar() +scale_fill_brewer(palette ="Set1") +labs(title ="Distribution of age within each of the three popular themes",x ="Popular Lego Theme", y ="count",fill ="Age Group" )
Between the variables theme and age_group, while only weak, there is some association present. It appears, for example, that almost twice as many costumers above the age of 52 purchase Ninjago sets than City sets, although more total City sets are purchased. This stands out as a rather unique case though, as the distribution appears quite constant in each relative theme given the total sets of each theme purchased.there is some association between the variables (the themes City, Nexo Kings, and Ninjago) present. It appears, for example, that almost twice as many costumers above the age of 52 purchase Ninjago sets than City sets, although more total City sets are purchased. This appears to be a rather unique case though, as the distribution appears quite constant in each relative theme given the total sets of each theme purchased.
Solution 8. Costumers in the age group 36 - 50 spend the most on LEGOs ($7063.68)
# A tibble: 5 × 2
# Groups: age_group [5]
age_group total_spending
<chr> <dbl>
1 36 - 50 7064.
2 26 - 35 4872.
3 19 - 25 3377.
4 51 and over 1877.
5 18 and under 809.
Solution 9. 158 observation are from the age_group 36 - 50. 35 bought a set from the Star Wars or from the Ninjago theme.
#how many observation are from the age group 36 - 50?lego |>filter(age_group =="36 - 50") |>count()
# A tibble: 1 × 1
n
<int>
1 158
#how many observations from this group are from either of the themes Star Wars or Ninjago?lego |>filter(age_group =="36 - 50"& theme =="Star Wars"| age_group =="36 - 50"& theme =="Ninjago") |>count()
