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, team, year, medalist)

head(df)

## # A tibble: 6 × 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

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

df2 <- olympic_gymnasts |>
  select(name, sex, age, team, year, medalist) |>
  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

head(df2)

## # A tibble: 6 × 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

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

q3 <- df2 |>
  group_by(year) |>
  summarize(mean = mean(age))
q3

## # A tibble: 3 × 2
##    year  mean
##   <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 = mean(age))

head(oly_year)

## # A tibble: 6 × 2
##    year  mean
##   <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

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

What is the ratio between the gold medals of males versus females?

# Your R code here

gold_medals <- olympic_gymnasts |>
  select(sex, medal) |>
  filter(medal == "Gold") |>
  group_by(sex) |>
  count(medal)
gold_medals

## # A tibble: 2 × 3
## # Groups:   sex [2]
##   sex   medal     n
##   <chr> <chr> <int>
## 1 F     Gold    234
## 2 M     Gold    551

Discussion: I chose this question as I was curious myself on what the ratio between gold medals of males versus females is. I wanted to see how different the values would be for each gender. I named this tibble gold_medals as I am primarily focusing on the amount of gold medals each gender of gymnasts obtained at the Olympics. I selected the two variables of the Olympic gymnast data set that were needed in the scenario of the question. Then I filtered the medal selection to gold as well as grouped by sex and counted the medals. Using this code, I answered my question by observing that Male gymnasts had more than double the amount of gold medals than female gymnasts in the Olympics. Males had 551 gold medals compared to the females having 234 gold medals.