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

diff --git a/wikipedia_views/analysis/pageview_example.R b/wikipedia_views/analysis/pageview_example.R
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--- a/wikipedia_views/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)
-
-
-