52 lines
1.6 KiB
R
52 lines
1.6 KiB
R
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### COVID-19 Digital Observatory
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###
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### Minimal example analysis file using revisions data.
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###
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### Note: The revisions data files can get quite big and it may not make
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### sense to try to load them into memory in R. This example file
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### loads the first 1,000 rows of a sample file.
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library(tidyverse)
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library(lubridate)
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library(scales)
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### Import and cleanup first 1000 rows of 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_revisions-20200401.tsv")
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revisions <- read.table(DataURL, sep="\t", header=TRUE,
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stringsAsFactors=FALSE,
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nrows=1000)
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revisions$timestamp <- as_datetime(revisions$timestamp)
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### Group edit by editor to see edit counts
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user.edit.counts <- revisions %>%
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group_by(user, title) %>% # for the example, there's only one
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# article title
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summarize(appearances = n()) %>%
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arrange(-appearances)
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### Export that as a little table
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write.table(user.edit.counts,
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file="output/user_edit_counts-first1000.csv", sep=",",
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row.names=FALSE)
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### A simple time series of edits
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plot <- revisions %>%
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arrange(timestamp) %>%
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mutate(count = as.numeric(rownames(revisions))) %>%
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ggplot(aes(x=timestamp, y=count)) +
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geom_jitter(alpha=0.1) +
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xlab("Date") +
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ylab("Total edits (cumulative)") +
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ggtitle(paste("Revisions to article: ",
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head(revisions$title,1), sep="")) +
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theme_minimal()
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ggsave("output/first_1000_edit_timeseries.png", plot)
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