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Bike Share blog post
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| library(ggplot2) | |
| library(scales) | |
| library(reshape) | |
| ## Get the data from here: https://archive.ics.uci.edu/ml/machine-learning-databases/00275/ | |
| data <- read.csv('day.csv') | |
| data$workingday <- as.factor(data$workingday) | |
| data$weathersit <- as.factor(data$weathersit) | |
| data$weekday <- as.factor(data$weekday) | |
| data$mnth <- as.factor(data$mnth) | |
| data$season <- as.factor(data$season) | |
| data$holiday <- as.factor(data$holiday) | |
| data$diff <- data$reg - data$cas | |
| ## Melt the data for ggplot. Could also use melt() from reshape package. I didn't | |
| seasons <- split(data,data$season) | |
| d1 = data.frame(dff1 = seasons[[1]]$diff,s=1) | |
| d2 = data.frame(dff1 = seasons[[2]]$diff,s=2) | |
| d3 = data.frame(dff1 = seasons[[3]]$diff,s=3) | |
| d4 = data.frame(dff1 = seasons[[4]]$diff,s=4) | |
| dall <- rbind(d1,d2,d3,d4) | |
| ## Loop through the seasons and save a .png of the density charts to later convert into a .gif | |
| season = c("Summer","Spring","Fall","Winter") | |
| cumulative_season <- as.character() | |
| cumulative_count <- as.numeric() | |
| for(i in seq_along(season)){ | |
| cumulative_count <- c(cumulative_count,i) | |
| cumulative_season <- append(cumulative_season,season[i]) | |
| png(paste("plot",i,".png",sep='')) | |
| p<-ggplot(dall[dall$s%in%cumulative_count,], | |
| aes(fill = as.factor(s),x=as.numeric(dff1)))+ | |
| geom_density(alpha=0.3,show_guide=T)+ | |
| scale_y_continuous(limits=c(0,0.0005),labels=comma)+ | |
| scale_x_continuous(limits=c(-500,7000)) + | |
| xlab("Difference between Registered and Casual Riders")+ | |
| ylab("Density of Days")+ | |
| ggtitle("Distribution of the Difference\n of Registered - Casual riders by Season")+ | |
| scale_fill_discrete(labels=cumulative_season, | |
| name="Season") | |
| print(p) | |
| dev.off() | |
| } | |
| ## Use ImageMagick to convert to .gif | |
| system("convert -delay 80 *.png plot1.gif") | |
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| library(ggplot2) | |
| library(scales) | |
| library(reshape) | |
| data <- read.csv('day.csv') | |
| data$temps <- cut(data$atemp,breaks=seq(0,1.1,0.01)) | |
| data$workingday <- as.factor(data$workingday) | |
| data$weathersit <- as.factor(data$weathersit) | |
| data$weekday <- as.factor(data$weekday) | |
| data$mnth <- as.factor(data$mnth) | |
| data$season <- as.factor(data$season) | |
| data$holiday <- as.factor(data$holiday) | |
| agg <- aggregate(data$cas,list(data$temps),sum) | |
| names(agg) <- c("Group","cas") | |
| agg$reg <- aggregate(data$reg,list(data$temps),sum)$x | |
| ## Calculate proportions to use in chart | |
| agg$casprop <- agg$cas/sum(agg$cas) | |
| agg$regprop <- agg$reg/sum(agg$reg) | |
| plotdf <- agg[,c(1,4,5)] | |
| ## Melt data for ggplot | |
| mdf <- melt(plotdf) | |
| mdf$Group <- as.numeric(mdf$Group) | |
| ggplot(mdf,aes(x=Group,y=value,color=variable))+ | |
| geom_area(aes(fill=variable),stat='identity',position='fill',alpha=0.6,color='black')+ | |
| scale_y_continuous(labels=percent,limits=c(0,1))+ | |
| scale_x_continuous(limits=c(5,85),breaks=seq(5,85,10))+ | |
| xlab("Temperature (Farenheit")+ | |
| ylab("Proportion of Rider type")+ | |
| ggtitle("Proportion of Rider Type using Bike Share\n as Temperature Increases")+ | |
| scale_fill_discrete(name = "Rider Type",labels=c("Casual","Registered")) |
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