M. Salmon February 24, 2018
ggplot2_logs <- cranlogs::cran_downloads(packages = c("ggplot2", "dplyr"),
when = "last-month")
library("ggplot2")
ggplot(ggplot2_logs) +
geom_point(aes(date, count, col = package)) +
viridis::scale_color_viridis(discrete = TRUE)
library("gh")
library("lubridate")##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
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## date
library("ggplot2")
library("viridis")## Loading required package: viridisLite
library("dplyr")##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:lubridate':
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## The following objects are masked from 'package:base':
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library("tidyr")
library("viridis")
library("forecast")
library("janitor")
# get data, loop because pagination
stardates <- NULL
geht <- TRUE
page <- 1
while(geht){
print(page)
stars <- try(gh("/repos/robjhyndman/forecast/stargazers",
.token = Sys.getenv("GITHUB_TOKEN"),
.send_headers = c("Accept" = 'application/vnd.github.v3.star+json'),
page = page))
geht <- stars != ""
if(geht){
stardates <- c(stardates, vapply(stars, "[[", "", "starred_at"))
page <- page + 1
}
}## [1] 1
## Warning in if (geht) {: the condition has length > 1 and only the first
## element will be used
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stardates <- lubridate::ymd_hms(stardates)
# make table of counts per day
star_table <- data.frame(time = stardates)
star_table <- mutate(star_table, date = as.Date(time))
star_table <- group_by(star_table, date) %>%
summarize(n = n())
star_table <- mutate(star_table,
cum_n = cumsum(n))
complete_dates <- seq(from = min(star_table$date),
to = max(star_table$date),
by = "1 day")
# prepare ts
ts_prep <- data.frame(date = complete_dates)
ts_prep <- left_join(ts_prep, star_table,
by = "date")
ts_prep <- mutate(ts_prep, cum_n = zoo::na.locf(cum_n))
ts_stars <- xts::xts(ts_prep$cum_n, ts_prep$date)
ts_stars = ts(ts_stars, freq=365, start=c(2012, 113))
autoplot(ts_stars)
# forecast
pred <- forecast(ets(ts_stars), h = 2000)## Warning in ets(ts_stars): I can't handle data with frequency greater than
## 24. Seasonality will be ignored. Try stlf() if you need seasonal forecasts.
# plot
theme_set(theme_gray(base_size = 14))
autoplot(pred) +
ylab("Number of forecast stargazers") +
ggtitle("When will the forecast package reach 1,000 stargazers?",
subtitle = "Data accessed from the Github API via the gh package")