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R-CMD-check

denguedatahub

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/

Installation

You can install the development version of denguedatahub from GitHub with:

install.packages("denguedatahub")
# install.packages("devtools")
devtools::install_github("thiyangt/denguedatahub")

Example

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 2023-01-16

Sri Lanka Weekly Dengue Cases

head(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

country_weekly <- srilanka_weekly_data |>
  group_by(year, week, start.date) %>%
  summarise(total_cases = sum(cases, na.rm = TRUE), .groups = 'drop') |>
  arrange(start.date)

country_weekly <- country_weekly |> 
  mutate(
         yearweek = yearweek(start.date)) |>
  distinct(yearweek, .keep_all = TRUE) 

country_weekly_tsibble <- country_weekly |>
  as_tsibble(index = yearweek)

p1 <- ggplot(country_weekly_tsibble, aes(x = yearweek, y = total_cases)) +
  geom_line() +
  scale_x_yearweek(date_breaks = "1 year", date_labels = "%Y") +
  labs(
    x = "Year",
    y = "Weekly Dengue Cases"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(face = "bold")
  )
p1


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)
  )

World

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.