mirror of https://github.com/msberends/AMR.git
206 lines
9.9 KiB
R
206 lines
9.9 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2020 Berends MS, Luz CF et al. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.github.io/AMR. #
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# ==================================================================== #
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# Read and format data ----------------------------------------------------
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library(tidyverse)
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library(maps)
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library(httr)
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GET_df <- function(ip) {
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ip <- paste0("https://ipinfo.io/", ip, "?token=", ipinfo_token)
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result <- ip %>% GET()
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stop_for_status(result)
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result %>%
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content(type = "text", encoding = "UTF-8") %>%
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jsonlite::fromJSON(flatten = TRUE) %>%
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as_tibble()
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}
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# get website analytics
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source("data-raw/country_analysis_url_token.R")
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url_json <- paste0(country_analysis_url,
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"/index.php?&module=API&token_auth=",
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country_analysis_token,
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"&method=Live.getLastVisitsDetails&idSite=3&language=en&expanded=1&date=2018-01-01,2028-01-01&period=range&filter_limit=-1&format=JSON&segment=&translateColumnNames=1")
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data_json <- jsonlite::read_json(url_json)
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data <- tibble(
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timestamp_server = as.POSIXct(sapply(data_json, function(x) x$serverTimestamp), origin = "1970-01-01"),
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ipaddress = sapply(data_json, function(x) x$visitIp))
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rm(data_json)
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# add country data based on IP address and ipinfo.io API
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unique_ip <- unique(data$ipaddress)
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ip_tbl <- GET_df(unique_ip[1])
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p <- AMR:::progress_estimated(n = length(unique_ip) - 1, min_time = 0)
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for (i in 2:length(unique_ip)) {
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p$tick()
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ip_tbl <- ip_tbl %>%
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bind_rows(GET_df(unique_ip[i]))
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}
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close(p)
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ip_tbl.bak <- ip_tbl
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# add long and lat
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ip_tbl <- ip_tbl %>%
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separate(loc, into = c("y", "x"), sep = ",", remove = FALSE, convert = TRUE)
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# Plot world map ----------------------------------------------------------
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countries_geometry <- sf::st_as_sf(map('world', plot = FALSE, fill = TRUE)) %>%
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mutate(countries_code = countrycode::countrycode(ID,
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origin = 'country.name',
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destination = 'iso2c',
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custom_match = c("Ascension Island" = "GB", # Great Britain
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"Azores" = "PT", # Portugal
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"Barbuda" = "GB", # Great Britain
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"Bonaire" = "BQ", # Bonaire, Saint Eustatius and Saba
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"Canary Islands" = "ES", # Spain
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"Chagos Archipelago" = "MU", # Mauritius
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"Grenadines" = "VC", # Saint Vincent and the Grenadines
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"Heard Island" = "AU", # Australia
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"Kosovo" = "XK",
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"Madeira Islands" = "PT", # Portugal
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"Micronesia" = "FM",
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"Saba" = "BQ", # Bonaire, Saint Eustatius and Saba
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"Saint Martin" = "MF",
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"Siachen Glacier" = "IN", # India
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"Sint Eustatius" = "BQ" # Bonaire, Saint Eustatius and Saba
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)),
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included = as.integer(countries_code %in% ip_tbl$country),
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not_antarctica = as.integer(ID != "Antarctica"),
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countries_name = ifelse(included == 1, as.character(ID), NA))
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# add countries not in the list
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countries_missing <- unique(ip_tbl$country[!ip_tbl$country %in% countries_geometry$countries_code])
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for (i in seq_len(length(countries_missing))) {
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countries_geometry <- countries_geometry %>%
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rbind(countries_geometry %>%
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filter(ID == "Netherlands") %>%
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mutate(ID = countrycode::countrycode(countries_missing[i],
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origin = 'iso2c',
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destination = 'country.name'),
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countries_code = countries_missing[i],
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included = 1,
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not_antarctica = 1,
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countries_name = countrycode::countrycode(countries_missing[i],
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origin = 'iso2c',
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destination = 'country.name')))
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}
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# how many?
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countries_geometry %>% filter(included == 1) %>% nrow()
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countries_geometry$countries_name <- gsub("UK", "United Kingdom", countries_geometry$countries_name, fixed = TRUE)
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countries_geometry$countries_name <- gsub("USA", "United States", countries_geometry$countries_name, fixed = TRUE)
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countries_plot <- ggplot(countries_geometry) +
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geom_sf(aes(fill = included, colour = not_antarctica),
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size = 0.25,
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show.legend = FALSE) +
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theme_minimal() +
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theme(panel.grid = element_blank(),
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axis.title = element_blank(),
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axis.text = element_blank()) +
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scale_fill_gradient(low = "white", high = "#128f7645") +
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# this makes the border Antarctica turn white (invisible):
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scale_colour_gradient(low = "white", high = "#128f76")
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countries_plot_mini <- countries_plot
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countries_plot_mini$data <- countries_plot_mini$data %>% filter(ID != "Antarctica")
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# countries_plot_mini <- countries_plot_mini + scale_colour_gradient(low = "#81899B", high = "#81899B")
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countries_plot_big <- countries_plot +
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labs(title = tools::toTitleCase("Countries the AMR package for R was downloaded from"),
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subtitle = paste0("Between March 2018 (first release) and ",
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format(Sys.Date(), "%B %Y")),
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caption = "Source: https://cran-logs.rstudio.com") +
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theme(plot.title = element_text(size = 16, hjust = 0.5),
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plot.subtitle = element_text(size = 12, hjust = 0.5)) +
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geom_text(aes(x = -170,
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y = -75,
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label = stringr::str_wrap(paste0("Countries (n = ",
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length(countries_name[!is.na(countries_name)]), "): ",
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paste(sort(countries_name[!is.na(countries_name)]), collapse = ", ")),
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200)),
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hjust = 0,
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size = 4)
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# main website page
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ggsave("pkgdown/logos/countries.png",
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width = 6,
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height = 2.5,
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units = "in",
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dpi = 100,
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plot = countries_plot_mini,
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scale = 1)
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# when clicked - a high res enlargement
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ggsave("pkgdown/logos/countries_large.png",
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width = 11,
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height = 6,
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units = "in",
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dpi = 300,
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plot = countries_plot_big,
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scale = 1.5)
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# Gibberish ---------------------------------------------------------------
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data %>%
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left_join(ip_tbl, by = c("ipaddress" = "ip")) %>%
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group_by(country = countrycode::countrycode(country,
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origin = 'iso2c',
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destination = 'country.name')) %>%
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summarise(first = min(timestamp_server)) %>%
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arrange(desc(first)) %>%
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mutate(frame = case_when(first <= as.POSIXct("2019-06-30") ~ "Q1-Q2 2019",
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first <= as.POSIXct("2019-12-31") ~ "Q3-Q4 2019",
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TRUE ~ "Q1-Q2 2020")) %>%
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View()
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#
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# p1 <- data %>%
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# group_by(country) %>%
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# summarise(first = min(timestamp_server)) %>%
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# arrange(first) %>%
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# mutate(n = row_number()) %>%
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# ggplot(aes(x = first, y = n)) +
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# geom_line() +
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# geom_point(aes(x = max(first), y = max(n)), size = 3) +
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# scale_x_datetime(date_breaks = "2 months", date_labels = "%B %Y") +
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# labs(x = NULL, y = "Number of countries")
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#
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# package_releases <- read_html("https://cran.r-project.org/src/contrib/Archive/AMR/") %>%
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# rvest::html_table() %>%
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# .[[1]] %>%
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# as_tibble(.name_repair = "unique") %>%
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# filter(`Last modified` != "") %>%
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# transmute(version = gsub("[^0-9.]", "",
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# gsub(".tar.gz", "", Name)),
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# datetime = as.POSIXct(`Last modified`)) %>%
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# # add current
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# bind_rows(tibble(version = as.character(packageVersion("AMR")),
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# datetime = as.POSIXct(packageDate("AMR")))) %>%
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# # remove the ones not plottable
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# filter(datetime > min(p1$data$first))
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#
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# p1 + geom_linerange(data = package_releases, aes(x = datetime, ymin = 0, ymax = 80), colour = "red", inherit.aes = FALSE)
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#
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