Psi Chi R - July 2024

Source: Psi Chi R

Load packages and import data

#library(tidyverse)

library(dplyr)
#library(purrr)
library(ggplot2)

dataset=read.csv('https://osf.io/download/xq8us/')

skimr::skim(dataset)

Data summary

Name	dataset
Number of rows	1344
Number of columns	8
_______________________
Column type frequency:
character	4
numeric	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Host_country	0	1	5	16	0	21	0
Host_city	0	1	4	19	0	23	0
Country_Name	0	1	3	32	0	157	0
Country_Code	0	1	0	3	86	156	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Year	0	1	1978.96	33.48	1896	1956	1988	2008	2020	▂▂▃▃▇
Gold	0	1	4.07	8.45	0	0	1	4	83	▇▁▁▁▁
Silver	0	1	4.04	7.10	0	0	2	4	78	▇▁▁▁▁
Bronze	0	1	4.39	6.84	0	1	2	5	77	▇▁▁▁▁

Level 1

-Let’s get familiar with our data. Create a table that shows the frequency of each Country in the dataset.

tab1 =dataset %>% 
  group_by(Country_Name) %>% 
  summarise(Frequency=n())

slice_sample(tab1,n=5)

Country_Name	Frequency
France	29
Switzerland	28
Belarus	7
Croatia	8
Argentina	20

Level 2

-Next, let’s look at the representation of each country’s medal status. Create atable that shows the number of times each country has won a bronze, silver, and gold medal.

tab2=dataset %>% 
  group_by(Country_Name) %>% 
  summarise(Gold = sum(Gold), Silver = sum(Silver), Bronze = sum(Bronze))

slice_sample(tab2,n=5)

Country_Name	Gold	Silver	Bronze
Sri Lanka	0	1	0
Lithuania	6	7	13
Bahrain	2	2	0
Ivory Coast	1	1	2
Germany	202	207	247

Level 3

-Is there a difference in the number of silver medals a country earns if they are competing in their home country? *Note please pick any one country you’d like to look at

home=dataset %>% 
  filter(Host_country=='United States') %>% 
  group_by(Country_Name) %>% 
  summarise(Silver = sum(Silver)) %>% 
  mutate(Percent_Win = round(Silver/sum(Silver) * 100,2))

away =dataset %>%
  filter(!Host_country=='United States') %>% 
  group_by(Country_Name) %>% 
  summarise(Silver = sum(Silver)) %>% 
  mutate(Percent_Win = round(Silver/sum(Silver) * 100,2))

shapiro.test(home$Silver)

## 
##  Shapiro-Wilk normality test
## 
## data:  home$Silver
## W = 0.30633, p-value < 2.2e-16

shapiro.test(away$Silver)

## 
##  Shapiro-Wilk normality test
## 
## data:  away$Silver
## W = 0.45531, p-value < 2.2e-16

hist(home$Silver)

hist(away$Silver)

wilcox1=wilcox.test(home$Silver,away$Silver)

#yes, there's a difference in the number of silver medals a country earns if they are competing in their home country
print(wilcox1)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  home$Silver and away$Silver
## W = 5164.5, p-value = 0.000254
## alternative hypothesis: true location shift is not equal to 0

Level 4

-Create a visualization that shows the United State’s gold medal over time.

USA_visual = dataset %>% 
  filter(Country_Name == 'United States')

USA_visual %>% 
  ggplot(aes(x=Year,y=Gold))+
  geom_line(lwd=1,col='darkred')+
  theme_bw()+
  labs(y='Number of Gold Medals',
       x= 'Years',
       title = 'The Roller Coaster of the American Quest for Gold',
       subtitle = "Examining the trajectory of the fight for the ultimate prize",fill=' ')+
  theme(plot.title = element_text(hjust = .5),
        plot.subtitle = element_text(hjust = .5))