downloading_paralelll.md

In allogamous/EnvRtype: Envirotyping Tools for Crop Research and Genetic Improvment

Download weather and elevation data into a faster way

last update: June 21th 2021 by Germano Costa Neto

• This tutorial aims to provide tools for speeding up the download of a wide number of environments using get_wether function. We will also mention other practical aspects that facilitate the download of weather data and its use in envirotyping.

Contents

• Case 1: More than 20 environments
• Case 2: Diverse locations across diverse countries arround the world
• Case 3 : A wide number of environments for a same given location
• Case 4: Creating multiple environment scenarios
• Case 5: Using WorldClim data base

# Case 1 : More than 20 environments - Below we give a short example using 132 environments in South America. The data set is available [here](https://github.com/allogamous/EnvRtype/blob/master/Supplementary%20Source%20and%20Data/Brazil_city.csv) or directly in R using ```read_csv``` as described below. First, the user needs to install the packages: [**foreach**](https://cran.r-project.org/web/packages/foreach/index.html) [(examples)](https://privefl.github.io/blog/a-guide-to-parallelism-in-r/), and [**doParallel**](https://cran.r-project.org/web/packages/doParallel/doParallel.pdf) [(examples)](https://www.r-bloggers.com/2016/07/lets-be-faster-and-more-parallel-in-r-with-doparallel-package/). Both will help to implement a parallelization of the ```get_weather``` function. I also put some 'errors' in the data set just to show some possible corrections for running ```get_weather``` correctly wzxhzdk:0

# Case 2 : different locations across diverse countries arround the world

# Case 3 : A wide number of environments for a same given location

# Case 4: Creating multiple environment scenarios

# Case 5: Using WorldClim data base

allogamous/EnvRtype documentation built on Nov. 1, 2024, 3:48 a.m.