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 %in% c(2008, 2012, 2016))
df2
## # A tibble: 2,703 × 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 by these three years (2008,2012, and 2016) and summarize the mean of the age in each group.

df2 |> 
  group_by(year) |> 
  summarise(avg= mean(age)) 
## # A tibble: 3 × 2
##    year   avg
##   <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) |>
  summarise(avg_age= mean(age)) |>
  mutate(min_avg_age= min(avg_age))
oly_year
## # A tibble: 29 × 3
##     year avg_age min_avg_age
##    <dbl>   <dbl>       <dbl>
##  1  1896    24.3        19.9
##  2  1900    22.2        19.9
##  3  1904    25.1        19.9
##  4  1906    24.7        19.9
##  5  1908    23.2        19.9
##  6  1912    24.2        19.9
##  7  1920    26.7        19.9
##  8  1924    27.6        19.9
##  9  1928    25.6        19.9
## 10  1932    23.9        19.9
## # 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

My Question Separate the gender in df and then summarize how many males and females are medalists. Name it df3

# Your R code here
df3 <- df |> group_by(sex) |> summarise(number_of_medalist= sum(medalist))
df3
## # A tibble: 2 × 2
##   sex   number_of_medalist
##   <chr>              <int>
## 1 F                    695
## 2 M                   1492

Discussion: Enter your discussion of results here.

In my question I asked to separate the gymnasts by their gender and summarize how many male and female medalists are there. So I used the group_by(sex) to separate them. Then I used summarize() and sum(medalist) inside to find the number of male and female medalists and named the column number_of_medalist.