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)
df

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

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

Question 3 Group by these three years (2008,2012, and 2016) and summarize the mean of the age in each group.

df3 <- df2 |>
  group_by(year) |>
  summarise(mean_age = mean(age, na.rm = TRUE))
df3

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) |>
  summarise(mean_age = mean(age, na.rm = TRUE))
oly_year

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

# Your R code here
most_gold_events <- olympic_gymnasts |>
  filter(medal == "Gold") |>
  group_by(event) |>
  summarise(total_gold = n()) |>
  arrange(desc(total_gold)) |>
  head(10)
  
most_gold_events

Discussion: Enter your discussion of results here.

My question was which gymnastic event got the most gold and if they were distributed evenly or not. I needed to filter gold medals using the filter function to specifically get “Gold”. I grouped all the data with its event type and got the total gold count for each. Then I rearranged the data in descending order so the higher gold count is on top so it’s easier to understand. From doing all of this I concluded that the Team’s All-Around event generally got the most gold compared to other events.