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