Chapter 5 Performance Tier and Gems
Experts [ 28% ] followed by Masters [ 25% ] contribute most of the Hidden Gems.Grand Masters [ 18% ] and Contributors [ 18% ] follow next. The Novice Tier and the Kaggle Team has also contributed to the Gems
<- nrow(gems_users)
TotalNoOfRows
<- c(NA,0,1,2,3,4,5)
PerformanceTier <- c("NoTier","Novice","Contributor","Expert","Master","GrandMaster","KaggleTeam")
PerformanceTier_Name
<-data.frame(PerformanceTier, PerformanceTier_Name)
df_Performance_Tier
<- inner_join(gems_users,df_Performance_Tier)
gems_user_performance
<- gems_user_performance %>%
p1 group_by(PerformanceTier_Name) %>%
summarise(Percentage = n()/TotalNoOfRows *100) %>%
arrange(desc(Percentage)) %>%
ungroup() %>%
mutate(PerformanceTier_Name = reorder(PerformanceTier_Name,Percentage))
%>%
p1 filter(!is.na(PerformanceTier_Name)) %>%
ggplot(aes(x = PerformanceTier_Name,y = Percentage, fill = (PerformanceTier_Name) )) +
geom_bar(stat='identity',colour="white") +
geom_label(aes(label = paste0("( ",round(Percentage,2)," %)",sep=""))) +
labs(x = 'Performance Tier',
y = 'Percentage',
title = 'Performance Tier and Percentage') +
coord_flip() +
theme_fivethirtyeight(base_size = 15) +
theme(legend.position = "none")