climplot aims to collect crucial climate data for global locations and render the Walter & Lieth climatic diagrams in a more user-friendly and personalized manner.
The main features of the package are:
- The automatic obtainment and arrangement of standardized and reliable data for drawing Walter & Lieth climatic diagrams
- The provision for more parameter to customize the plots and display comprehensive information
The package offers functions for:
- Download and arrange worldwide climate data to draw Walter & Lieth climatic diagram for global locations
- Draw the Walter & Lieth climatic diagram
- Revise the color scheme and information presentation of the diagram
To install the latest developmental version from
github and gitee, you will
need the R packages
remotes and
git2r. If you want to
install the vignettes of this package, please add
build_vignettes = TRUE.
install.packages("remotes")
#from github
remotes::install_github("auman-chan/climplot")
#from gitee
install.packages("git2r")
remotes::install_git("https://gitee.com/auman-chan/climplot.git")
#add vignettes
remotes::install_github("auman-chan/climplot", build_vignettes = TRUE)
remotes::install_git("https://gitee.com/auman-chan/climplot.git",
build_vignettes= TRUE)
#add vignettes
remotes::install_github("auman-chan/climplot", build_vignettes = TRUE)
remotes::install_git("https://gitee.com/auman-chan/climplot.git",
build_vignettes = TRUE)Information of the target locations should be ready for the extraction. It must contain five columns in the following order:
- No: Serial number of the locations
- location: Abbreviation of the locations
- lon: Longitude of the locations in decimal digit (negative numbers indicating west longitude)
- lat: Latitude of the locations in decimal digit (negative numbers indicating south latitude)
- altitude: Altitude of the locations
Other extra columns with information is allowed behind the columns above, but would be discarded in following process.
The data locdata in this package can be a example of the import
data.frame.
After preparing the climate dataset and location information, import the
data.frame to the function clim_extract:
#Example data in this package
data("locdata")
#extraction of climate data
plotdata <- clim_extract(locdata)| No | Altitude | Location | Lon | Lat | Type | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2025 | Motuo | 95.3536 | 29.3042 | prec | 10.0 | 22.0 | 38.0 | 93.0 | 114.0 | 230.0 | 241.0 | 216.0 | 187.0 | 72.0 | 12.0 | 7.0 |
| 1 | 2025 | Motuo | 95.3536 | 29.3042 | mean_temp | 10.9 | 11.6 | 15.1 | 18.6 | 21.9 | 24.0 | 24.4 | 24.6 | 23.1 | 20.1 | 16.0 | 12.4 |
| 1 | 2025 | Motuo | 95.3536 | 29.3042 | min_temp | -1.9 | -0.5 | 3.1 | 6.2 | 9.9 | 12.5 | 13.6 | 13.3 | 12.7 | 9.2 | 2.9 | -0.3 |
| 1 | 2025 | Motuo | 95.3536 | 29.3042 | max_temp | 10.9 | 11.6 | 15.1 | 18.6 | 21.9 | 24.0 | 24.4 | 24.6 | 23.1 | 20.1 | 16.0 | 12.4 |
| 2 | 1301 | Wulianshan | 100.5000 | 24.5000 | prec | 12.0 | 16.0 | 20.0 | 35.0 | 75.0 | 173.0 | 204.0 | 193.0 | 126.0 | 98.0 | 47.0 | 18.0 |
| 2 | 1301 | Wulianshan | 100.5000 | 24.5000 | mean_temp | 20.8 | 23.2 | 26.5 | 29.0 | 29.5 | 28.6 | 28.3 | 28.5 | 27.3 | 25.2 | 22.2 | 19.9 |
| 2 | 1301 | Wulianshan | 100.5000 | 24.5000 | min_temp | 5.7 | 7.4 | 10.7 | 14.4 | 17.8 | 20.4 | 20.6 | 20.1 | 18.6 | 16.0 | 11.4 | 7.1 |
| 2 | 1301 | Wulianshan | 100.5000 | 24.5000 | max_temp | 20.8 | 23.2 | 26.5 | 29.0 | 29.5 | 28.6 | 28.3 | 28.5 | 27.3 | 25.2 | 22.2 | 19.9 |
| 3 | 2082 | Wawushan | 102.9167 | 29.5000 | prec | 12.0 | 13.0 | 21.0 | 53.0 | 104.0 | 168.0 | 191.0 | 180.0 | 145.0 | 73.0 | 27.0 | 14.0 |
| 3 | 2082 | Wawushan | 102.9167 | 29.5000 | mean_temp | 4.2 | 5.9 | 10.9 | 15.6 | 18.3 | 19.8 | 22.0 | 21.8 | 17.6 | 13.7 | 9.9 | 5.9 |
| 3 | 2082 | Wawushan | 102.9167 | 29.5000 | min_temp | -4.1 | -2.8 | 1.0 | 5.7 | 9.1 | 11.9 | 14.7 | 14.3 | 11.0 | 7.0 | 2.0 | -2.2 |
| 3 | 2082 | Wawushan | 102.9167 | 29.5000 | max_temp | 4.2 | 5.9 | 10.9 | 15.6 | 18.3 | 19.8 | 22.0 | 21.8 | 17.6 | 13.7 | 9.9 | 5.9 |
The exported data.frame includes 5 kinds of information of locations(as
which in the data.frame imported), and values of 4 kinds of climate
factors across 12 months. A data.frame stores in the data plotdata of
this package, as an example of the function export.
Take the data plotdata as an example, and import them into the
function clim_plot:
data("plotdata")
loc <- subset(plotdata, No == 2)
clim_plot(loc)
In the figure above:
-
The red curve represents the annual variation of temperature, and blue one represents of precipitation variation. These two curves form two types of patches indicating humidity and aridity levels. The string-filled patches represent humid seasons while those with scattered points represent arid seasons. The polygon filled with the color same as precipitation curve indicates months with precipitation over 100mm, displaying the wet season.
-
The information on the left top includes the name, the altitude and the coordinates of the locations. The right top are the values of annual mean temperature and mean precipitation.
More examples and information, please view the help pages and the online vigenettes of this package.
Please cite climplot as follows:
Chan A (2023). climplot: climplot: Facilitate and tailor Walter & Lieth climatic diagram drawing. R package version 0.2.1, https://github.com/auman-chan/climplot.
NOTE: please also cite the ‘climatol’ package and Worldclim data.