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 rowsQuestion 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.
df3 <- df2 %>% #New df here
group_by(year) %>% #Column first
summarise(mean = mean(age, na.rm = TRUE))
df3## # A tibble: 3 × 2
## year mean
## <dbl> <dbl>
## 1 2008 21.6
## 2 2012 21.9
## 3 2016 22.2Question 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)
olympic_year <- olympic_gymnasts %>%
group_by(year) %>%
mutate(mean_age = mean(age, na.rm = TRUE))
olympic_year## # A tibble: 25,528 × 17
## # Groups: year [29]
## 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 17 Paavo J… M 28 175 64 Finl… FIN 1948… 1948 Summer Lond…
## 2 17 Paavo J… M 28 175 64 Finl… FIN 1948… 1948 Summer Lond…
## 3 17 Paavo J… M 28 175 64 Finl… FIN 1948… 1948 Summer Lond…
## 4 17 Paavo J… M 28 175 64 Finl… FIN 1948… 1948 Summer Lond…
## 5 17 Paavo J… M 28 175 64 Finl… FIN 1948… 1948 Summer Lond…
## 6 17 Paavo J… M 28 175 64 Finl… FIN 1948… 1948 Summer Lond…
## 7 17 Paavo J… M 28 175 64 Finl… FIN 1948… 1948 Summer Lond…
## 8 17 Paavo J… M 28 175 64 Finl… FIN 1948… 1948 Summer Lond…
## 9 17 Paavo J… M 32 175 64 Finl… FIN 1952… 1952 Summer Hels…
## 10 17 Paavo J… M 32 175 64 Finl… FIN 1952… 1952 Summer Hels…
## # ℹ 25,518 more rows
## # ℹ 5 more variables: sport <chr>, event <chr>, medal <chr>, medalist <lgl>,
## # mean_age <dbl>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
# Who are the gymnasts that are from Team USA with the events they competed in?
team <- olympic_gymnasts |>
select(name, games, team, event) |>
filter(team == "United States")
team## # A tibble: 1,830 × 4
## name games team event
## <chr> <chr> <chr> <chr>
## 1 James Kanati Allen 1968 Summer United States Gymnastics Men's Individ…
## 2 James Kanati Allen 1968 Summer United States Gymnastics Men's Team Al…
## 3 James Kanati Allen 1968 Summer United States Gymnastics Men's Floor E…
## 4 James Kanati Allen 1968 Summer United States Gymnastics Men's Horse V…
## 5 James Kanati Allen 1968 Summer United States Gymnastics Men's Paralle…
## 6 James Kanati Allen 1968 Summer United States Gymnastics Men's Horizon…
## 7 James Kanati Allen 1968 Summer United States Gymnastics Men's Rings
## 8 James Kanati Allen 1968 Summer United States Gymnastics Men's Pommell…
## 9 William Peter Andelfinger 1904 Summer United States Gymnastics Men's Individ…
## 10 William Peter Andelfinger 1904 Summer United States Gymnastics Men's Individ…
## # ℹ 1,820 more rowsDiscussion: If I know how to make it so data doesn’t repeat the same name even though the dataset including the different events or olympic games they’d competed in, I’d like to incorporate that to this. I like that I was able to narrow down on the results I can get from a large dataset and work from there, instead of having to look at literally the whole thing.