Push the knit
button!
library(tidyverse) # contains ggplot2, dplyr, tidyr, etc
library(ggthemes)
library(ggmap)
tuberculosis
datasettb <- read_csv(here::here("data/TB_notifications_2020-07-01.csv")) %>%
dplyr::select(country, iso3, year, new_sp_m04:new_sp_fu) %>%
pivot_longer(cols=new_sp_m04:new_sp_fu, names_to="sexage", values_to="count") %>%
mutate(sexage = str_replace(sexage, "new_sp_", "")) %>%
mutate(sex=substr(sexage, 1, 1),
age=substr(sexage, 2, length(sexage))) %>%
dplyr::select(-sexage) %>%
filter(!(age %in% c("04", "014", "514", "u"))) %>%
filter(year > 1996, year < 2013) %>%
mutate(age_group = factor(age,
labels = c("15-24", "25-34", "35-44",
"45-54", "55-64", "65-"))) %>%
dplyr::select(country, year, age_group, sex, count)
# Filter Australia
tb_oz <- tb %>%
filter(country == "Australia")
# Fix names for map joining
tb_fixed <- tb %>%
mutate(region=recode(country,
"United States of America"="USA",
"United Kingdom of Great Britain and Northern Ireland"="UK",
"Russian Federation"="Russia",
"Viet Nam"="Vietnam",
"Venezuela (Bolivarian Republic of)"="Venezuela",
"Bolivia (Plurinational State of)"="Bolivia",
"Czechia"="Czech Republic",
"Iran (Islamic Republic of)"="Iran",
"Iran (Islamic Republic of)"="Laos",
"Democratic People's Republic of Korea"="North Korea",
"Republic of Korea"="South Korea",
"United Republic of Tanzania"="Tanzania",
"Congo"="Republic of Congo"))
tb_2012 <- tb_fixed %>% #<<
filter(year == 2012) %>%
group_by(region) %>%
summarise(count = sum(count))
platypus
datasetload(here::here("data/platypus.rda"))
oz_bbox <- c(112.9, # min long
-45, # min lat
159, # max long
-10) # max lat
oz_map <- get_map(location = oz_bbox, source = "osm")
# add your code here!
# add your code here!
# add your code here!