X-Git-Url: https://code.communitydata.science/covid19.git/blobdiff_plain/6493361fbd95f44a3b27131f4f79329d40e61c90..784458f2067287871dcfeefe8348fcc71a230244:/wikipedia/analysis/pageview_example.R

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+### 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)
+
+
+