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Philippine Tropical Cyclones Data

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panukatan/bagyo

Project Status: Active – The project has reached a stable, usable state and is being actively developed.Lifecycle: stableCRAN statuscran checksCRANCRANCRANR-CMD-checkR-hubtest-coverageCodecov test coverageCodeFactorDOIDOI

Oceans and seas significantly impact continental weather, withevaporation from the sea surface driving cloud formation andprecipitation. Tropical cyclones, warm-core low-pressure systems, formover warm oceans where temperatures exceed 26°C, precipitated by therelease of latent heat from condensation. These cyclones, known byvarious names depending on the region, have organised circulations anddevelop primarily in tropical and subtropical waters, except in regionswith cooler sea surface temperatures and high vertical wind shears. Theyreach peak intensity over warm tropical waters and weaken upon landfall,often causing extensive damage before dissipating.

The Philippines frequently experiences tropical cyclones (calledbagyo - pronounced /baɡˈjo/, [bɐɡˈjo] - in the Filipinolanguage) because of its geographical position. These cyclones typicallybring heavy rainfall, leading to widespread flooding, as well as strongwinds that cause significant damage to human life, crops, and property.Data on cyclones are collected and curated by thePhilippineAtmospheric, Geophysical, and Astronomical Services Administration(PAGASA).

This package contains Philippine tropical cyclones data from 2017 to2020 in a machine-readable format. It is hoped that this data packageprovides an interesting and unique dataset for data exploration andvisualisation as an adjunct to the traditionalirisdataset and to the currentpalmerpenguinsdataset.

Installation

You can installbagyo fromCRAN with:

install.packages("bagyo")

You can install the development version ofbagyo from thepanukatanr-universe with:

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

Once thebagyo package has been installed, thebagyo dataset can beloaded into R as follows:

library(bagyo)data(package="bagyo")bagyo#> # A tibble: 101 × 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 91 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      27          995.       39.3#> 2 Tropical Storm           29          987.       58.8#> 3 Severe Tropical Storm    17          979.       72.6#> 4 Typhoon                  23          943.       99.1#> 5 Super Typhoon             5          907       113

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    45.29630 hours#> 2 Tropical Storm         61.03448 hours#> 3 Severe Tropical Storm  81.29412 hours#> 4 Typhoon               110.04348 hours#> 5 Super Typhoon         115.60000 hours

bagyo is great to visualise

Citation

If you find thebagyo package useful please cite using the suggestedcitation provided by a call to thecitation() 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 0.1.1,#>   <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 0.1.1},#>     url = {https://panukatan.io/bagyo/},#>     doi = {10.5281/zenodo.10972235},#>   }

Community guidelines

Feedback, bug reports and feature requests are welcome; file issues orseek supporthere. If youwould like to contribute to the package, please see ourcontributingguidelines.

This project is released with aContributor Code ofConduct. Byparticipating in this project you agree to abide by its terms.



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