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) |>
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))
oly_year
## # A tibble: 29 × 2
## year `mean(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
#oly_year |>
# group_by(year)
# summarize(min(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
# Your R code here
olympic_gymnasts |>
filter(medalist == TRUE) |>
count(name) |>
arrange(desc(n))
## # A tibble: 1,252 × 2
## name n
## <chr> <int>
## 1 Larysa Semenivna Latynina (Diriy-) 18
## 2 Nikolay Yefimovich Andrianov 15
## 3 Borys Anfiyanovych Shakhlin 13
## 4 Takashi Ono 13
## 5 Aleksey Yuryevich Nemov 12
## 6 Sawao Kato 12
## 7 Viktor Ivanovych Chukarin 11
## 8 Vra slavsk (-Odloilov) 11
## 9 Akinori Nakayama 10
## 10 Aleksandr Nikolayevich Dityatin 10
## # ℹ 1,242 more rows
Discussion: Enter your discussion of results here.
I asked which gymnast had the most medals across the Olympics. I used filte to keep only medalists and then count to total the number of medals for each athlete and arrange in descending order to see who has the most.