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", "year","medalist")
df

## # A tibble: 25,528 × 5
##    name                    sex     age  year medalist
##    <chr>                   <chr> <dbl> <dbl> <lgl>   
##  1 Paavo Johannes Aaltonen M        28  1948 TRUE    
##  2 Paavo Johannes Aaltonen M        28  1948 TRUE    
##  3 Paavo Johannes Aaltonen M        28  1948 FALSE   
##  4 Paavo Johannes Aaltonen M        28  1948 TRUE    
##  5 Paavo Johannes Aaltonen M        28  1948 FALSE   
##  6 Paavo Johannes Aaltonen M        28  1948 FALSE   
##  7 Paavo Johannes Aaltonen M        28  1948 FALSE   
##  8 Paavo Johannes Aaltonen M        28  1948 TRUE    
##  9 Paavo Johannes Aaltonen M        32  1952 FALSE   
## 10 Paavo Johannes Aaltonen M        32  1952 TRUE    
## # ℹ 25,518 more rows

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

df2 <- df[df$year %in% c(2008, 2012, 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))

## # 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))
min(oly_year)

## [1] 19.86606

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. Take find all the average ages in the olympic_gymnasts dataset and arrange them from descending

olympic_gymnasts |> group_by(year) |> summarize(mean_age = mean(age)) |> arrange(desc(mean_age))

## # A tibble: 29 × 2
##     year mean_age
##    <dbl>    <dbl>
##  1  1948     27.8
##  2  1924     27.6
##  3  1920     26.7
##  4  1936     25.8
##  5  1928     25.6
##  6  1952     25.3
##  7  1904     25.1
##  8  1956     24.8
##  9  1906     24.7
## 10  1896     24.3
## # ℹ 19 more rows

Discussion: Enter your discussion of results here. Results: The highest mean age was for the year 1948, with an average of 27.83. The lowest mean age was from the year 1988, which had an average of 19.87.