Homework 3

Do not change anything in the following chunk

You will be working on olympic_gymnasts dataset. Do not change the 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 it df.

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

Question 2: From df create df2 that only have year of 2008 2012, and 2016

df2 <- df |>
  filter(year == 2008 | year == 2012 | year == 2016)

Question 3 Group by these three years (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 × 2
##    year mean_age
##   <dbl>    <dbl>
## 1  2008     21.6
## 2  2012     21.9
## 3  2016     22.2

Question 4 Use 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)

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

Question 5 This question is open ended. Create a question that requires you to use at least two verbs. Create a code that answers your question. Then below the chunk, reflect on your question choice and coding procedure

Question: What is the average age of medalists in 2008, 2012, and 2016?

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

## # A tibble: 3 × 2
##    year mean_age
##   <dbl>    <dbl>
## 1  2008     21.1
## 2  2012     20.8
## 3  2016     21.8

Discussion: I chose this question because I wanted to compare the average age of gymnasts who won medals in the three years. I filtered the data to keep only medalists, grouped them by year, and then found the mean age for each year.