removing outdated file names

diff --git a/wikipedia/analysis/output/top10_views_by_project_date.csv b/wikipedia/analysis/output/top10_views_by_project_date.csv
deleted file mode 100644 (file)
index ce7eb5e..0000000
--- a/wikipedia/analysis/output/top10_views_by_project_date.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-"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

diff --git a/wikipedia/analysis/pageview_example.R b/wikipedia/analysis/pageview_example.R
deleted file mode 100644 (file)
index fb5359a..0000000
--- a/wikipedia/analysis/pageview_example.R
+++ /dev/null
@@ -1,52 +0,0 @@
-### COVID-19 Digital Observatory
-### 2020-03-28
-### 
-### Minimal example analysis file using pageview data
-
-library(tidyverse)
-library(scales)
-
-### Import and cleanup one datafile from the observatory
-
-DataURL <-
-    url("https://covid19.communitydata.science/datasets/wikipedia/digobs_covid19-wikipedia-enwiki_dailyviews-20200401.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)
-
-
-### 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)
-
-
-