### COVID-19 Digital Observatory ### 2020-03-28 ### ### Minimal example analysis file using pageview data library(tidyverse) library(ggplot2) library(scales) ### Import and cleanup data DataURL <- url("https://github.com/CommunityDataScienceCollective/COVID-19_Digital_Observatory/raw/master/wikipedia_views/data/dailyviews2020032600.tsv") views <- read.table(DataURL, sep="\t", header=TRUE, stringsAsFactors=FALSE) ### Alternatively, uncomment and run if working locally with full git ### tree ### ### Identify data source directory and file ## DataDir <- ("../data/") ## DataFile <- ("dailyviews2020032600.tsv") ## related.searches.top <- read.table(paste(DataDir,DataFile, sep=""), ## sep="\t", header=TRUE, ## stringsAsFactors=FALSE) ### Cleanup and do the grouping with functions from the Tidyverse ### (see https://www.tidyverse.org for more info) views <- views[,c("article", "project", "timestamp", "views")] views$timestamp <- factor(views$timestamp) ### Sorts and groups at the same time views.by.proj.date <- arrange(group_by(views, project, timestamp), desc(views)) ### Export just the top 10 by pageviews write.table(head(views.by.proj.date, 10), file="output/top10_views_by_project_date.csv", sep=",", row.names=FALSE) ### A simple visualization p <- ggplot(data=views.by.proj.date, aes(views)) ## Density plot with log-transformed axis p + geom_density() + scale_x_log10(labels=comma)