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<- olympic_gymnasts |>
  filter(year%in%c(2008,2012,2016))
df2
## # A tibble: 2,703 × 16
##       id name     sex     age height weight team  noc   games  year season city 
##    <dbl> <chr>    <chr> <dbl>  <dbl>  <dbl> <chr> <chr> <chr> <dbl> <chr>  <chr>
##  1    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  2    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  3    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  4    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  5    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  6    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  7   396 Katja A… F        25    165     55 Germ… GER   2008…  2008 Summer Beij…
##  8   396 Katja A… F        25    165     55 Germ… GER   2008…  2008 Summer Beij…
##  9   396 Katja A… F        25    165     55 Germ… GER   2008…  2008 Summer Beij…
## 10   396 Katja A… F        25    165     55 Germ… GER   2008…  2008 Summer Beij…
## # ℹ 2,693 more rows
## # ℹ 4 more variables: sport <chr>, event <chr>, medal <chr>, medalist <lgl>

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(avg_age = mean(age))
## # A tibble: 3 × 2
##    year avg_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(avrge_age=mean(age))
oly_year
## # A tibble: 29 × 2
##     year avrge_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

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
  df2 |>
    filter(year %in% c(2008, 2012, 2016)) |>
    group_by(year) |>
    mutate(age_difference = age - mean(age))
## # A tibble: 2,703 × 17
## # Groups:   year [3]
##       id name     sex     age height weight team  noc   games  year season city 
##    <dbl> <chr>    <chr> <dbl>  <dbl>  <dbl> <chr> <chr> <chr> <dbl> <chr>  <chr>
##  1    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  2    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  3    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  4    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  5    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  6    51 Nstor A… M        23    167     64 Spain ESP   2016…  2016 Summer Rio …
##  7   396 Katja A… F        25    165     55 Germ… GER   2008…  2008 Summer Beij…
##  8   396 Katja A… F        25    165     55 Germ… GER   2008…  2008 Summer Beij…
##  9   396 Katja A… F        25    165     55 Germ… GER   2008…  2008 Summer Beij…
## 10   396 Katja A… F        25    165     55 Germ… GER   2008…  2008 Summer Beij…
## # ℹ 2,693 more rows
## # ℹ 5 more variables: sport <chr>, event <chr>, medal <chr>, medalist <lgl>,
## #   age_difference <dbl>

Discussion: Enter your discussion of results here. I chose this question because it goes beyond basic filtering and summarizing. We’re able to contextualize each gymnast’s age, which could be useful for spotting unusually young or experienced competitors.