revisions to reflect updated example filename and clean comments in R code
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@ -1,11 +1,11 @@
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"article","project","timestamp","views"
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"2019–20_coronavirus_pandemic","en.wikipedia","2020033100",831879
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"2020_coronavirus_pandemic_in_India","en.wikipedia","2020033100",323123
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"2019–20_coronavirus_pandemic_by_country_and_territory","en.wikipedia","2020033100",315572
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"2020_coronavirus_pandemic_in_the_United_States","en.wikipedia","2020033100",290535
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"Coronavirus_disease_2019","en.wikipedia","2020033100",211391
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"2020_coronavirus_pandemic_in_Italy","en.wikipedia","2020033100",209908
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"Coronavirus","en.wikipedia","2020033100",188921
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"USNS_Comfort_(T-AH-20)","en.wikipedia","2020033100",150422
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"USNS_Comfort_(T-AH-20)","en.wikipedia","2020033100",150422
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"WrestleMania_36","en.wikipedia","2020033100",137637
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"Charles,_Prince_of_Wales","en.wikipedia","2020010100",32880
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"Tom_Hanks","en.wikipedia","2020010100",23586
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"Boris_Johnson","en.wikipedia","2020010100",12974
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"Eurovision_Song_Contest_2020","en.wikipedia","2020010100",7901
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"Mike_Pence","en.wikipedia","2020010100",4088
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"Olga_Kurylenko","en.wikipedia","2020010100",3653
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"WrestleMania_36","en.wikipedia","2020010100",3484
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"World_Health_Organization","en.wikipedia","2020010100",3002
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"Severe_acute_respiratory_syndrome","en.wikipedia","2020010100",2037
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"Centers_for_Disease_Control_and_Prevention","en.wikipedia","2020010100",909
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@ -9,34 +9,22 @@ library(scales)
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### Import and cleanup one datafile from the observatory
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DataURL <-
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url("https://covid19.communitydata.science/datasets/wikipedia/digobs_covid19-wikipedia-enwiki_dailyviews-20200401.tsv")
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url("https://covid19.communitydata.science/datasets/wikipedia/digobs_covid19-wikipedia-enwiki_dailyviews-20200101.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$timestamp <- fct_explicit_na(views$timestamp)
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views$timestamp <- fct_explicit_na(as.character(views$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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