DATA 101 — Homework 3: Data Wrangling

Author

Student

Instructions

You will be working on the olympic_gymnasts dataset from TidyTuesday. Please DO NOT change the setup code below:

olympics <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-07-27/olympics.csv')

olympic_gymnasts <- olympics |> 
  filter(!is.na(age)) |>             # only keep athletes with known age
  filter(sport == "Gymnastics") |>   # keep only gymnasts
  mutate(
    medalist = case_when(             # add column for success in medaling
      is.na(medal) ~ FALSE,           # NA values go to FALSE
      !is.na(medal) ~ TRUE            # non-NA values (Gold, Silver, Bronze) go to TRUE
    )
  )

More information about the dataset can be found at:
https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-07-27/readme.md

Question 1: Create a subset dataset with the following columns only: name, sex, age, team, year, and medalist. Call this new dataset df.

df <- olympic_gymnasts |>
  select(name, sex, age, team, year, medalist)

df

# A tibble: 25,528 x 6
   name                    sex     age team     year medalist
   <chr>                   <chr> <dbl> <chr>   <dbl> <lgl>   
 1 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
 2 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
 3 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
 4 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
 5 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
 6 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
 7 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
 8 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
 9 Paavo Johannes Aaltonen M        32 Finland  1952 FALSE   
10 Paavo Johannes Aaltonen M        32 Finland  1952 TRUE    
# i 25,518 more rows

Question 2: From df, create df2 that only contains gymnasts from the years 2008, 2012, and 2016.

df2 <- df |>
  filter(year %in% c(2008, 2012, 2016))

df2

# A tibble: 2,703 x 6
   name              sex     age team     year medalist
   <chr>             <chr> <dbl> <chr>   <dbl> <lgl>   
 1 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
 2 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
 3 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
 4 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
 5 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
 6 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
 7 Katja Abel        F        25 Germany  2008 FALSE   
 8 Katja Abel        F        25 Germany  2008 FALSE   
 9 Katja Abel        F        25 Germany  2008 FALSE   
10 Katja Abel        F        25 Germany  2008 FALSE   
# i 2,693 more rows

Question 3: Group df2 by year (2008, 2012, and 2016) and summarize the mean of the age in each group.

df2 |>
  group_by(year) |>
  summarize(mean_age = mean(age))

# A tibble: 3 x 2
   year mean_age
  <dbl>    <dbl>
1  2008     21.6
2  2012     21.9
3  2016     22.2

Question 4: Using the full olympic_gymnasts dataset, group by year and find the mean of the age for each year. Call this dataset oly_year.
(Optional: After creating the dataset, find the minimum average age and the year it occurred).

oly_year <- olympic_gymnasts |>
  group_by(year) |>
  summarize(mean_age = mean(age))

oly_year

# A tibble: 29 x 2
    year mean_age
   <dbl>    <dbl>
 1  1896     24.3
 2  1900     22.2
 3  1904     25.1
 4  1906     24.7
 5  1908     23.2
 6  1912     24.2
 7  1920     26.7
 8  1924     27.6
 9  1928     25.6
10  1932     23.9
# i 19 more rows

# Optional: show the year with the lowest average age
oly_year |>
  slice_min(mean_age, n = 1, with_ties = FALSE)

# A tibble: 1 x 2
   year mean_age
  <dbl>    <dbl>
1  1988     19.9

Question 5 (Open-ended): Create a question that requires you to use at least two dplyr verbs (e.g., filter, select, mutate, group_by, summarize, arrange). Write the code that answers your question, and below the chunk, write a brief reflection on your question choice and findings.

My question: How does the medalist rate among gymnasts age 20 or younger compare with the rate among gymnasts older than 20?

age_medal_summary <- olympic_gymnasts |>
  mutate(age_group = if_else(age <= 20, "20 or younger", "Older than 20")) |>
  group_by(age_group) |>
  summarize(
    observations = n(),
    medalist_rate = mean(medalist)
  ) |>
  arrange(desc(medalist_rate))

age_medal_summary

# A tibble: 2 x 3
  age_group     observations medalist_rate
  <chr>                <int>         <dbl>
1 Older than 20        16676        0.0918
2 20 or younger         8852        0.0741

Reflection & Discussion:
I chose this question because age may relate to both experience and physical demands in gymnastics. I used mutate() to create two age groups, group_by() and summarize() to calculate the share of observations associated with a medal, and arrange() to make the comparison easy to read. The results show that gymnasts older than 20 had a medalist rate of 9.2%, compared with 7.4% for gymnasts age 20 or younger. This suggests that the older group had the higher medalist rate in these data. Because each row represents an Olympic event participation rather than one unique athlete, this comparison describes event-level observations and should not be interpreted as a causal effect of age.