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sose2021/r/w05/L05-exerc.R

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+library(tidyverse)
+
+
+# --- load athlete data
+
+# We are only interested in following disciplines
+std_runs <- c("100 metres", "200 metres", "400 metres", "800 metres", 
+              "1,500 metres", "5,000 metres", "10,000 metres", "Marathon")
+
+read_csv("athlete_events.csv") %>% 
+  filter(Sport == "Athletics") %>% 
+  select(Name, Sex, Age, Height, Weight, Year, Event, Medal) %>% 
+  mutate(
+    Event = str_remove(Event, "Athletics Women's "),
+    Event = str_remove(Event, "Athletics Men's ")) %>% 
+  filter(Event %in% std_runs) ->
+  athletes
+
+
+# --- Top Medalists
+
+athletes %>% 
+    pivot_wider(names_from = Medal, values_from = Medal) %>%
+    mutate(across(c(Gold, Bronze, Silver), ~ !is.na(.))) %>%
+    group_by(Name) %>%
+    summarize(across(where(is.logical), sum)) %>%
+    arrange(-Gold, -Silver, -Bronze) %>%
+    transmute(Name, Gold, Silver, Bronze) ->
+    medal_ranking
+
+
+# --- Age distribution among Women's Marathon participants
+
+medal_color <- c(Bronze = "#6A3805", Silver = "#B4B4B4", Gold = "#AF9500")
+
+athletes %>% 
+    filter(Event == "Marathon", Sex == "F", Year > 1980) %>%
+    mutate(Year = as.factor(Year)) ->
+    d
+
+ggplot(d, aes(x = Year, y = Age)) +
+    geom_boxplot(na.rm=T) +
+    geom_point(data = drop_na(d), mapping = aes(color = Medal)) +
+    scale_color_manual(values = medal_color) +
+    ggtitle("Age distribution among Women's Marathon participants")
+
+# --- Change in height of male runners
+
+athletes %>% 
+    mutate(Event = factor(Event, levels=std_runs)) %>% # use factor for ordering
+    filter(Sex == "M", Year > 1900) %>%
+    group_by(Event, Year) %>%
+    summarize(MeanHeight = mean(Height, na.rm=T)) %>%
+    ggplot(aes(x = Year, y = MeanHeight, color = Event)) +
+        geom_point() + 
+        ggtitle("Men's runs - mean across participants") +
+        geom_smooth(se = FALSE)
+
+# --- Medalist times
+
+# Times are given as strings with inconsistent format.
+# Need custom function for conversion in seconds
+str2sec <- function(s) { 
+  s %>% 
+    str_split(":|h|-") %>% 
+    sapply(function(x) {
+      v <- as.double(x)
+      v3 <- c(0,0,0)
+      v3[(4-length(v)):3] <- v
+      v3[1] * 3600 + v3[2] * 60 + v3[3]
+    })
+}
+
+# We are only interested in following disciplines
+std_runs <- c("100M", "200M", "400M", "800M", "1500M", "5000M", "10000M", "Marathon")
+
+read_csv("results.csv") %>% 
+  mutate(
+    Event = str_remove(Event, c(" Men")),
+    Event = str_remove(Event, c(" Women"))) %>% 
+  filter(Event %in% std_runs) %>% 
+  mutate(Result = str2sec(Result)) %>% 
+  drop_na() ->
+  runs
+
+medal_color <- c(B = "#6A3805", S = "#B4B4B4", G = "#AF9500")
+
+ggplot(runs, aes(x = Year, y = Result, shape = Gender)) +
+    facet_wrap(vars(factor(Event, levels = std_runs)), scales = "free_y") +
+    geom_point(aes(color = Medal)) +
+    scale_color_manual(values = medal_color) +
+    ggtitle("Times of medal winners in different running disciplines") +
+    xlab("Year") +
+    ylab("Time in seconds") +
+    geom_smooth(se = T)