library(ggplot2)
# Enter the data in such a way to match the Plot
data<-
rbind(
c("C",0.97),
c("C++",0.84),
c("Go",3.75),
c("Rust",0.93),
c("Julia",1.32),
c("Python",163),
c("Lua",113),
c("Swift",1.4),
c("Intel Fortran",1.42),
c("Haskell GHC",1.51),
c("C#",3.14),
c("Chapel",3.34),
c("F#",3.77),
c("Pascal",3.86),
c("Ada",4.02),
c("Node JS",4.03),
c("LISP",4.09),
c("Java",4.15),
c("OCaml",7.53),
c("Dart",8.72),
c("Racket",10.45),
c("PHP",24.89),
c("Erlang",44),
c("Ruby",262)
)
data<-apply(data,2,rev)
data<-as.data.frame(data)
data[,2]<-as.numeric(data[,2])
data[,2]<-log(data[,2]+1)
# Make a Data Frame
df<-as.data.frame(data)
names(df)<-c("Language","Time")
df$Language<-factor(seq_len(nrow(df)),labels=df$Language)
df$Time<-as.numeric(df$Time)
# If you want it ordered in descending uncomment below
# df[rank(df$Time), ]
# Make sure the languages keep the order they were given in
# I want them in a specific order
df$Language<-factor(sort(df$Time),labels=df$Language)
factor(df$Language,ordered=TRUE,levels=rank(df$Time))
# Identify clusters with heirarchical clustering
times<-df$Time;names(times)<-df$Language
hc.av<-hclust(dist(times),method="average")
plot(hc.av)
df$Category<-factor(cutree(tree=hc.av,k=4))
levels(df$Category)<-rev(levels(df$Category))
# Build the Plot
p<-ggplot(df,aes(x=Language,y=Time,fill=Category))+
geom_col(col='black')+
labs(y="Log Time",
title="Comparison of Programming Language",subtitle="Log Time to Produce 16002 Mandelbrot")+
guides(fill=guide_legend("Performance\nCategory"))+
theme_classic()+
theme(axis.text.x=element_text(angle=90,vjust=0.5,hjust=1))+
coord_flip()
ggsave("/tmp/barplot_language_speeds.svg")
p