library(highcharter)
library(viridis)
library(tidyverse)
library(countrycode)
library(crosstalk)
library(plotly)
6 data day: OWID
Dedicate a day to exploring data from Our World in Data (OWID), applying the chosen theme to the datasets available on this platform.
6.1 Setup
6.2 Dataset preparation
<- read.csv("dataset/number-of-deaths-from-tetanus.csv")
data1 <- data1 %>%
data11 select(Year, Deaths) %>%
group_by(Year) %>%
summarise(Deaths = round((sum(Deaths))), 2)
<- data1 %>%
country_tibble1 select(Entity, Deaths) %>%
filter(Entity != 'African Region (WHO)',
!= 'East Asia & Pacific (WB)',
Entity != 'Eastern Mediterranean Region (WHO)',
Entity != 'Europe & Central Asia (WB)',
Entity != 'European Region (WHO)',
Entity != 'G20',
Entity != 'Latin America & Caribbean (WB)',
Entity != 'Middle East & North Africa (WB)',
Entity != 'North America (WB)',
Entity != 'OECD Countries',
Entity != 'Region of the Americas (WHO)',
Entity != 'South Asia (WB)',
Entity != 'South-East Asia Region (WHO)',
Entity != 'Sub-Saharan Africa (WB)',
Entity != 'Western Pacific Region (WHO)',
Entity != 'World Bank High Income',
Entity != 'World Bank Low Income',
Entity != 'World Bank Lower Middle Income',
Entity != 'World Bank Upper Middle Income',
Entity != 'World')%>%
Entity group_by(Entity) %>%
summarize(Deaths = round((sum(Deaths))), 2)
6.3 Map
highchart() %>%
hc_add_series_map(worldgeojson, country_tibble1, value = "Deaths",
joinBy = c('name','Entity')) %>%
# hc_colors(cols) %>%
# hc_colorAxis(dataClasses = color_classes(c(seq(0, 1500000, by = 250000)))) %>%
hc_colorAxis(stops = color_stops(8, c("#fee08b","#cc0000"))) %>%
hc_title(text = "Deaths from tetanus by country") %>%
hc_subtitle(text = "1990-2019") %>%
hc_tooltip(borderWidth = 1.5, headerFormat = "", valueSuffix = " deaths")
The data set used to create the dashboard can be found at:
https://ourworldindata.org/tetanus