Philippine Tropical Cyclones Data.

bagyo: Philippine Tropical Cyclones Data

Oceans and seas significantly impact continental weather, with evaporation from the sea surface driving cloud formation and precipitation. Tropical cyclones, warm-core low-pressure systems, form over warm oceans where temperatures exceed 26°C, precipitated by the release of latent heat from condensation. These cyclones, known by various names depending on the region, have organised circulations and develop primarily in tropical and subtropical waters, except in regions with cooler sea surface temperatures and high vertical wind shears. They reach peak intensity over warm tropical waters and weaken upon landfall, often causing extensive damage before dissipating.

The Philippines frequently experiences tropical cyclones (called bagyo - pronounced /baɡˈjo/, [bɐɡˈjo] - in the Filipino language) because of its geographical position. These cyclones typically bring heavy rainfall, leading to widespread flooding, as well as strong winds that cause significant damage to human life, crops, and property. Data on cyclones are collected and curated by the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA).

This package contains Philippine tropical cyclones data from 2017 to 2020 in a machine-readable format. It is hoped that this data package provides an interesting and unique dataset for data exploration and visualisation as an adjunct to the traditional iris dataset and to the current palmerpenguins dataset.

Installation

You can install bagyo from CRAN with:

install.packages("bagyo")

You can install the development version of bagyo from the panukatan r-universe with:

install.packages(
  "bagyo",
  repos = c('https://panukatan.r-universe.dev', 'https://cloud.r-project.org')
)

Once the bagyo package has been installed, the bagyo dataset can be loaded into R as follows:

library(bagyo)
data(package = "bagyo")

bagyo
#> # A tibble: 86 × 9
#>     year category_code category_name         name  rsmc_name start              
#>    <dbl> <fct>         <fct>                 <chr> <chr>     <dttm>             
#>  1  2017 TD            Tropical Depression   Auri… <NA>      2017-01-07 08:00:00
#>  2  2017 TD            Tropical Depression   Bisi… <NA>      2017-02-03 14:00:00
#>  3  2017 TD            Tropical Depression   Cris… <NA>      2017-04-14 14:00:00
#>  4  2017 TS            Tropical Storm        Dante Muifa     2017-04-26 08:00:00
#>  5  2017 STS           Severe Tropical Storm Emong Nanmadol  2017-07-02 02:00:00
#>  6  2017 TD            Tropical Depression   Fabi… Roke      2017-07-22 02:00:00
#>  7  2017 TY            Typhoon               Gorio Nesat     2017-07-25 14:00:00
#>  8  2017 TS            Tropical Storm        Huan… Haitang   2017-07-30 02:00:00
#>  9  2017 STS           Severe Tropical Storm Isang Hato      2017-08-20 08:00:00
#> 10  2017 TS            Tropical Storm        Joli… Pakhar    2017-08-24 14:00:00
#> # ℹ 76 more rows
#> # ℹ 3 more variables: end <dttm>, pressure <int>, speed <int>

Usage

bagyo is interesting to summarise

library(dplyr)

## Get cyclone category mean pressure and speed ----
bagyo |>
  group_by(category_name) |>
  summarise(
    n = n(),
    mean_pressure = mean(pressure), 
    mean_speed = mean(speed)
  )
#> # A tibble: 5 × 4
#>   category_name             n mean_pressure mean_speed
#>   <fct>                 <int>         <dbl>      <dbl>
#> 1 Tropical Depression      23          996.       39.8
#> 2 Tropical Storm           25          986.       61.6
#> 3 Severe Tropical Storm    15          978.       75  
#> 4 Typhoon                  21          941.      102. 
#> 5 Super Typhoon             2          908.      112.

bagyo is useful in learning how to work with dates

## Get cyclone category mean duration (in hours) ----
bagyo |>
  mutate(duration = end - start) |>
  group_by(category_name) |>
  summarise(mean_duration = mean(duration))
#> # A tibble: 5 × 2
#>   category_name         mean_duration  
#>   <fct>                 <drtn>         
#> 1 Tropical Depression    46.69565 hours
#> 2 Tropical Storm         57.48000 hours
#> 3 Severe Tropical Storm  79.13333 hours
#> 4 Typhoon               106.66667 hours
#> 5 Super Typhoon          77.50000 hours

bagyo is great to visualise

Citation

If you find the bagyo package useful please cite using the suggested citation provided by a call to the citation() function as follows:

citation("bagyo")
#> To cite bagyo in publications use:
#> 
#>   Ernest Guevarra (2024). _bagyo: Philippine Tropical Cyclones Data_.
#>   doi:10.5281/zenodo.10972235
#>   <https://doi.org/10.5281/zenodo.10972235>, R package version 0.1.0,
#>   <https://panukatan.io/bagyo/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {bagyo: Philippine Tropical Cyclones Data},
#>     author = {{Ernest Guevarra}},
#>     year = {2024},
#>     note = {R package version 0.1.0},
#>     url = {https://panukatan.io/bagyo/},
#>     doi = {10.5281/zenodo.10972235},
#>   }

Community guidelines

Feedback, bug reports and feature requests are welcome; file issues or seek support here. If you would like to contribute to the package, please see our contributing guidelines.

This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.