An R package for providing historical (30-year) average monthly climatological data (minimum temperature, maximum temperature, and precipitation) for municipalities in Brazil. Data are retrieved from the Climatempo platform through automated web scraping. The package provides flexible tools for location search and structured climate data extraction, supporting applications in ecology, environmental science, and data science.
This package is intended for research and educational purposes, and users should ensure compliance with the terms of use of the data source.
You can install the development version of climabr from GitHub:
# Install development version from GitHub
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_github("jrz-s/climabr")climabr enables users to:
library(climabr)
# Retrieve climate list for Aracaju (Sergipe, Brazil)
df <- search_locations(state = "se", city = "aracaju")
climate_list(df)
# Retrieve climate data for Aracaju (Sergipe, Brazil)
climate_data(state = "se", city = "aracaju")The package supports robust matching of user inputs:
A typical workflow consists of:
search_locations()climate_data()The main function returns a tidy tibble with the following variables:
region: Geographic region state: State
abbreviation city_name: City name site: Site
identifier (optional) month: Month (1–12)
tmin: Minimum temperature (°C) tmax: Maximum
temperature (°C) precip: Precipitation (mm)
This package performs automated web scraping from the Climatempo website to facilitate access to publicly available climatological data. It is designed to support research, educational, and non-commercial applications by providing a structured and reproducible approach to retrieving climate information.
Users are responsible for ensuring that their use of this package complies with the terms of service and policies of the data provider.
When using this package, users should:
The authors are not affiliated with, endorsed by, or sponsored by Climatempo.
This package is provided “as is” without warranty of any kind. Its functionality may change over time as the source website evolves, and users should be aware that web scraping workflows are inherently subject to structural changes in the data source.
Zárate-Salazar, Jhonatan Rafael, PhD
Postdoctoral Researcher
Graduate Program in Ecology and Conservation
Federal University of
Sergipe (UFS), São Cristóvão, Sergipe, Brazil
📧 Email:
rzaratesalazar@gmail.com
🔗 ORCID: https://orcid.org/0000-0001-9251-5340
💻 GitHub: https://github.com/jrz-s
🎓 CV lattes: http://lattes.cnpq.br/5635448203792516
Benites-Alfaro, Omar E., MSc
Data Analytst
Universidad
Catolica Sedes Sapientiae, Lima, Peru
Oliveira, Eduardo V. da Silva, PhD
Professor, Department of
Ecology
Federal University of Sergipe, São Cristóvão, Sergipe,
Brazil
Gouveia, Sidney Feitosa, PhD
Professor, Department of Ecology
Federal University of Sergipe, São Cristóvão, Sergipe, Brazil