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
## # A tibble: 25,528 × 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 have year of 2008 2012, and 2016

df2<- df|>
  filter(year== c(2008,2012,2016))
## Warning: There was 1 warning in `filter()`.
## ℹ In argument: `year == c(2008, 2012, 2016)`.
## Caused by warning in `year == c(2008, 2012, 2016)`:
## ! longer object length is not a multiple of shorter object length
df2
## # A tibble: 886 × 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 Katja Abel                     F        25 Germany  2008 FALSE   
##  4 Denis Mikhaylovich Ablyazin    M        19 Russia   2012 TRUE    
##  5 Denis Mikhaylovich Ablyazin    M        19 Russia   2012 FALSE   
##  6 Denis Mikhaylovich Ablyazin    M        24 Russia   2016 TRUE    
##  7 Denis Mikhaylovich Ablyazin    M        24 Russia   2016 TRUE    
##  8 Andreea Roxana Acatrinei       F        16 Romania  2008 TRUE    
##  9 Jonna Eva-Maj Adlerteg         F        17 Sweden   2012 FALSE   
## 10 Kseniya Dmitriyevna Afanasyeva F        16 Russia   2008 FALSE   
## # ℹ 876 more rows

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     21.7
## 2  2012     22.0
## 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

# Using the olympic_gymnasts dataset find the Olympians who competed in the Gymnastics Women's Uneven Bars in 2016 and sort their age from youngest to oldest and name the new dataset age_women_uneven_bars

age_women_uneven_bars <- olympic_gymnasts |>
  filter(event == "Gymnastics Women's Uneven Bars", year == "2016") |>
arrange(age)

The reason I decided to pick this question was that, when I was looking at the olympic_gymnasts dataset, I noticed that one of the events was Gymnastics Women’s Uneven Bars. I decided to filter out all the events to only include Gymnastics Women’s Uneven Bars. To be more specific, I chose a random year, which ended up being 2016, so I filtered the dataset to only include that year. Finally, I arranged the ages, which allows the viewer to see the youngest to the oldest Olympic gymnasts.