AMR/data-raw/country_analysis.R

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