Moja global Chile data pre-processing project setup
The following section describes how to set up the project on your local machine. The following are minimum requirements:
Setting up the project
The following instructions describe how to install all the required tools to do live data pre-processing on a Windows 10 system.
After installing, R Studio and R programming language, start the R Studio IDE and follow these steps:
Version Controlto checkout a project from a version control repository.
Gitto clone the project from our GitHub repository.
Add the repository URL:
https://github.com/moja-global/GCBM.Chile.Data_Preprocessing. Select the subdirectory as per your needs and click on
After the repository is cloned and a workspace is initialized, download the Input data required for the data pre-processing. You can download it from the GitHub releases:
Visit the GitHub releases on the GCBM Chile Data Preprocessing repository and check out the latest release.
Scroll down to find the
Input_Files.rarwhich contains the dataset used by the pre-processing algorithm.
Input_Files.rarand download it on your local machine. Extract the
Input_Filesdirectory on the root of the cloned GCBM Chile Data Preprocessing repository.
Optional: You can download the
Output_Files.raras well for reproducibility and checking the results of the pre-processing algorithm.
Ensure the following directory structure for the project:
├── Input_Files │ ├── Growth # Excel spreadsheet with growth data │ ├── LUC # Trazabilidad (Land use) data │ ├── SOC # Soil Organic carbon data | └── Temperature # Temperature raw data (NetCDF) ├── Output_Files │ ├── input_database │ └── layers │ └── raw ├── disturbances │ ├── environment | └── inventory ├── Processing_codes ├── README.md ├── docs/ ├── run_all.R └── ...
Running the project
With the initial setup complete, you are now ready to run the pre-processing algorithm code.
From the R Studio IDE, select the
run_all.Rfile and click on
Runto run all the pre-processing code.
Optionally, choose the individual files in the
Preprocessing_Codesto run them singularly. Make sure to install the R packages as suggested by the R Studio.
With the repository and tools set up on your workstation, you can now either edit existing code or prepare local datasets for a GCBM run using R.