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