The goal of denguedatahub is to provide the research
community with a unified dataset by collecting worldwide dengue-related
data, merged with exogenous variables helpful for a better understanding
of the spread of dengue and the reproducibility of research.
Check out the website at https://denguedatahub.netlify.app/
You can install the development version of denguedatahub from GitHub with:
install.packages("denguedatahub")# install.packages("devtools")
devtools::install_github("thiyangt/denguedatahub")This is a basic example which shows you how to solve a common problem:
library(tsibble)
#> Registered S3 method overwritten by 'tsibble':
#> method from
#> as_tibble.grouped_df dplyr
#>
#> Attaching package: 'tsibble'
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, union
library(denguedatahub)
head(level_of_risk)
#> # A tibble: 6 × 4
#> country level_of_risk region last_accessed
#> <chr> <chr> <chr> <date>
#> 1 Angola Sporadic/Uncertain Africa 2023-01-16
#> 2 Benin Sporadic/Uncertain Africa 2023-01-16
#> 3 Burkina Faso Frequent/Continuous Africa 2023-01-16
#> 4 Burundi Sporadic/Uncertain Africa 2023-01-16
#> 5 Cameroon Sporadic/Uncertain Africa 2023-01-16
#> 6 Cape Verde Sporadic/Uncertain Africa 2023-01-16head(srilanka_weekly_data)
#> # A tibble: 6 × 6
#> year week start.date end.date district cases
#> <dbl> <dbl> <chr> <chr> <chr> <dbl>
#> 1 2006 52 12/23/2006 12/29/2006 Colombo 71
#> 2 2006 52 12/23/2006 12/29/2006 Gampaha 12
#> 3 2006 52 12/23/2006 12/29/2006 Kalutara 12
#> 4 2006 52 12/23/2006 12/29/2006 Kandy 20
#> 5 2006 52 12/23/2006 12/29/2006 Matale 4
#> 6 2006 52 12/23/2006 12/29/2006 NuwaraEliya 1
library(ggplot2)
library(viridis)
#> Loading required package: viridisLite
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
ggplot(
filter(srilanka_weekly_data, year < 2019 & year > 2012),
aes(
x = week,
y = district,
fill = cut(
cases,
breaks = c(0, 50, 100, 200, Inf),
labels = c("0–50", "50–100", "100–200", ">200"),
include.lowest = TRUE,
right = FALSE
)
)
) +
geom_tile(color = "white") +
scale_fill_viridis_d(
option = "C",
name = "Dengue Cases"
) +
facet_wrap(~year, ncol = 3) +
labs(
title = "Weekly Dengue Cases by District in Sri Lanka (2012–2019)",
x = "Week Number",
y = "District"
) +
theme_minimal(base_size = 12) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1, size = 6),
axis.text.y = element_text(size = 7),
legend.position = "bottom",
strip.text = element_text(size = 9)
)
ggplot(
filter(srilanka_weekly_data, year > 2019),
aes(
x = week,
y = district,
fill = cut(
cases,
breaks = c(0, 50, 100, 200, Inf),
labels = c("0–50", "50–100", "100–200", ">200"),
include.lowest = TRUE,
right = FALSE
)
)
) +
geom_tile(color = "white") +
scale_fill_viridis_d(
option = "C",
name = "Dengue Cases"
) +
facet_wrap(~year, ncol = 3) +
labs(
title = "Weekly Dengue Cases by District in Sri Lanka (2020–2025)",
x = "Week Number",
y = "District"
) +
theme_minimal(base_size = 12) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1, size = 6),
axis.text.y = element_text(size = 7),
legend.position = "bottom",
strip.text = element_text(size = 9)
)
library(tidyverse)
world_annual |>
filter(region=="Afghanistan") |>
head()
#> long lat group order region subregion code year incidence
#> 1 74.89131 37.23164 2 12 Afghanistan <NA> AFG 1990 23371
#> 2 74.89131 37.23164 2 12 Afghanistan <NA> AFG 1991 25794
#> 3 74.89131 37.23164 2 12 Afghanistan <NA> AFG 1992 29766
#> 4 74.89131 37.23164 2 12 Afghanistan <NA> AFG 1993 32711
#> 5 74.89131 37.23164 2 12 Afghanistan <NA> AFG 1994 34268
#> 6 74.89131 37.23164 2 12 Afghanistan <NA> AFG 1995 35823
#> dengue.present
#> 1 yes
#> 2 yes
#> 3 yes
#> 4 yes
#> 5 yes
#> 6 yes