---
title: "Olympic Games"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
social: menu
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(treemapify)
library(gganimate)
data<-read.csv("../athlete_events.csv")
region<-read.csv("../noc_regions.csv")
```
Sport Events {data-icon="fa-globe"}
=====================================
Column {data-width=400}
-------
### Olympic Games Sport Events{data-height=400}
### Olympic Games Sport Events{data-height=600}
```{r}
# show the change of sport event
fig_dat1<-data %>% filter(Year!=1906) %>%
group_by(Year,Season) %>%
summarise(sports=n_distinct(Sport),Sport_events=n_distinct(Event))%>%
mutate(Sport=Season, Event=Season)
fig_dat1 %>% ggplot(aes(x=Year,y=Sport_events)) +
geom_line(aes(color=Event)) +
geom_point(aes(color=Event)) +
geom_bar(aes(x=Year, y=sports*1.5, fill=Sport),stat = "identity") +
scale_fill_manual(values=c("red","blue")) +
scale_colour_manual(values = c("red","blue")) +
facet_grid(Sport~., scale="free_y") +
labs(x="Year of Olympic Games") +
scale_x_continuous(breaks = seq(1890 , 2016, 12)) +
scale_y_continuous(name = "Number of Event",
sec.axis = sec_axis(~./1.5, name = "Number of Sport"))
```
Column {.tabset data-width=600}
-------
### 2016 Rio Olympic Summer Games
```{r}
fig_dat2<-data %>% filter(Year==2016 & Season=="Summer") %>% group_by(Sport) %>% summarise(Sport_events=n_distinct(Event))
fig_dat2 %>% ggplot(aes(area=Sport_events, fill=Sport, label=paste(Sport, Sport_events))) +
geom_treemap() +
geom_treemap_text(colour = "white",
place = "centre",
size = 12,
reflow = TRUE) +
theme(legend.position = "none") +
labs(title="Events number under 34 sports for 2016 Rio Summer Olympic Games")
```
### 2014 Sochi Olympic Winter Games
```{r}
fig_dat3<-data %>% filter(Year==2014 & Season=="Winter") %>% group_by(Sport) %>% summarise(Sport_events=n_distinct(Event))
fig_dat3 %>% ggplot(aes(area=Sport_events, fill=Sport, label=paste(Sport, Sport_events))) +
geom_treemap() +
geom_treemap_text(colour = "white",
place = "centre",
size = 12) +
theme(legend.position = "none") +
labs(title="Events number under 15 sports for 2014 Sochi Winter Olympic Games")
```
Athletes {data-icon="fa-swimmer"}
=====================================
Column {.tabset data-width=500}
-------
### Summer Olympic Games Athletes
```{r}
# show the number of athlete
fig_dat4<-data %>%
filter(Season=="Summer" & Year!=1906) %>%
group_by(Year,Sex) %>%
summarise(Athletes=n_distinct(ID))
fig4<-fig_dat4 %>% ggplot(aes(x=Year,y=Athletes, fill=Sex)) +
geom_bar(stat="identity",position = "stack") +
labs(x="Year of Olympic Games", y="Number of Athletes") +
scale_x_continuous(breaks = seq(1896 , 2016, 20))
ggplotly(fig4)
```
### Winter Olympic Games Athletes
```{r}
# show the number of athlete
fig_dat5<-data %>%
filter(Season=="Winter") %>%
group_by(Year,Sex) %>%
summarise(Athletes=n_distinct(ID))
fig5<-fig_dat5 %>% ggplot(aes(x=Year,y=Athletes, group=Sex, fill=Sex)) +
geom_bar(stat="identity",position = "stack") +
labs(x="Year of Olympic Games", y="Number of Athletes") +
scale_x_continuous(breaks = seq(1896 , 2016, 20))
ggplotly(fig5)
```
Column {.tabset data-width=500}
-------
### Summer Olympic Games
```{r}
sub_data1<-data %>% filter(Medal=='Gold' & Year!=1906) %>%
group_by(Year,Season, NOC) %>%
summarise(Gold_Medal_count=n_distinct(Event))
sub_data2<-data %>% filter(Year!=1906) %>%
group_by(Year,Season, NOC) %>%
summarise(athletes_count=n_distinct(ID))
fig_dat6<-merge(sub_data2,sub_data1, by=c("Year","Season","NOC"))
fig_dat6 %>% filter(Season=="Summer") %>%
ggplot(aes(x=athletes_count, y=Gold_Medal_count)) +
geom_point() +
labs(x="Number of athletes", y="Number of gold medal") +
facet_wrap(Year~.)
```
### Winter Olympic Games
```{r}
fig_dat6 %>% filter(Season=="Winter") %>%
ggplot(aes(x=athletes_count, y=Gold_Medal_count)) +
geom_point() +
labs(x="Number of athletes", y="Number of gold medal") +
facet_wrap(Year~.)
```
Medals {data-icon="fa-medal"}
=====================================
Column {.tabset data-width=500}
-------
### Summer Olympic Games Gold Medal Leader Board
```{r}
# show the country with most gold Medal for each Summer Olympic Games
fig_dat6<-data %>% filter(Medal=='Gold') %>%
filter(Season=="Summer" & Year!=1906) %>%
group_by(Year,NOC) %>%
summarise(Gold_Medal_count=n_distinct(Event)) %>%
top_n(1, Gold_Medal_count) %>% arrange(desc(Gold_Medal_count)) %>%
merge(region,by="NOC")
fig_dat6 %>% ggplot(aes(x=factor(Year), y=Gold_Medal_count, fill=region)) +
geom_bar(stat='identity',position = position_dodge()) +
geom_text(aes(x=factor(Year), y=Gold_Medal_count/2,label=NOC),size=2, position =position_dodge(width=1)) +
geom_text(aes(x=factor(Year), y=Gold_Medal_count*1.05,label=Gold_Medal_count),size=2, position =position_dodge(width=1)) +
coord_flip() +
labs(x="Year",y="Gold Medals")
```
### Winter Olympic Games Gold Medal Leader Board
```{r}
# show the country with most gold Medal for each Summer Olympic Games
fig_dat7<-data %>% filter(Medal=='Gold') %>%
filter(Season=="Winter") %>%
group_by(Year,NOC) %>%
summarise(Gold_Medal_count=n_distinct(Event)) %>%
top_n(1, Gold_Medal_count) %>% arrange(desc(Gold_Medal_count)) %>%
merge(region,by="NOC")
fig_dat7 %>% ggplot(aes(x=factor(Year), y=Gold_Medal_count, fill=region)) +
geom_bar(stat='identity',position = position_dodge()) +
geom_text(aes(x=factor(Year), y=Gold_Medal_count/2,label=NOC),size=2, position =position_dodge(width=1)) +
geom_text(aes(x=factor(Year), y=Gold_Medal_count*1.05,label=Gold_Medal_count),size=2, position =position_dodge(width=1)) +
coord_flip() +
labs(x="Year",y="Gold Medals")
```
Column {.tabset data-width=500}
-------
### 2016 Rio Olympic Summer Games Medals Leaderboard
```{r}
fig_dat8<-data %>% filter(Year==2016 & Season=="Summer" & Medal!="NA") %>%
group_by(NOC,Medal) %>%
summarise(Medal_count=n_distinct(Event)) %>%
mutate(total_medal=sum(Medal_count)) %>%
spread(Medal, Medal_count) %>%
arrange(desc(Gold),desc(total_medal)) %>%
head(10) %>%
ungroup() %>%
mutate(gold_rank=1:10) %>%
gather("Medal","count",-c(NOC,gold_rank,total_medal)) %>%
mutate(Medal=factor(Medal, levels=c("Gold","Silver","Bronze"))) %>%
merge(region,by="NOC")
fig_dat8 %>% ggplot(aes(x= reorder(region,-gold_rank),y=count, fill=Medal)) +
geom_bar(stat = "identity", position = position_stack(reverse = T)) +
scale_fill_manual(values=c("#FFCC33","#D7D7D7","#CC6600"))+
labs(x="", y="") +
coord_flip() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
```
### 2014 Sochi Olympic Winter Games Medals Leaderboard
```{r}
fig_dat9<-data %>% filter(Year==2014 & Season=="Winter" & Medal!="NA") %>%
group_by(NOC,Medal) %>%
summarise(Medal_count=n_distinct(Event)) %>%
mutate(total_medal=sum(Medal_count)) %>%
spread(Medal, Medal_count) %>%
arrange(desc(Gold),desc(total_medal)) %>%
head(10) %>%
ungroup() %>%
mutate(gold_rank=1:10) %>%
gather("Medal","count",-c(NOC,gold_rank,total_medal)) %>%
mutate(Medal=factor(Medal, levels=c("Gold","Silver","Bronze"))) %>%
merge(region,by="NOC")
fig_dat9 %>% ggplot(aes(x= reorder(region,-gold_rank),y=count, fill=Medal)) +
geom_bar(stat = "identity", position = position_stack(reverse = T)) +
scale_fill_manual(values=c("#FFCC33","#D7D7D7","#CC6600"))+
labs(x="", y="") +
coord_flip() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
```
