# Robust Geographical Detector & Robust Interaction Detector(RGD & RID)

### Set up python dependence

1. Install miniconda

2. Open the conda terminal and perform steps 3 to 6

3. Create a new conda env `gdverse`

   > `conda create -n gdverse python=3.10 -y`

4. Activate this env

   > `conda activate gdverse`

5. Install `mamba`

   > `conda install -c conda-forge mamba -y`

6. Set up python packages

   > `mamba install -c conda-forge numpy joblib pandas ruptures -y`

7. Configure `GDVERSE_PYTHON` environment variable

   - Temporarily setting up `gdverse` python dependence environment using `Sys.setenv()` in the R console or R code like `Sys.setenv(GDVERSE_PYTHON = "path/to/python/environment")`.

   - Or using `usethis::edit_r_environ()` to set long-available `gdverse` python dependencies environment path.

### Load data and package

    library(tidyverse)
    library(gdverse)

    data("ndvi")

    names(ndvi)
    ## [1] "NDVIchange"    "Climatezone"   "Mining"        "Tempchange"    "Precipitation"
    ## [6] "GDP"           "Popdensity"

### Run RGD

    ndvi_rgd = rgd(NDVIchange ~ ., data = ndvi,
                   discvar = names(select(ndvi,-c(NDVIchange,Climatezone,Mining))),
                   discnum = 3:10, cores = 12)
    ndvi_rgd
    ## ***          Robust Geographical Detector
    ##
    ## |   variable    | Q-statistic | P-value  |
    ## |:-------------:|:-----------:|:--------:|
    ## | Precipitation |  0.8933049  | 3.25e-10 |
    ## |  Climatezone  |  0.8218335  | 7.34e-10 |
    ## |  Tempchange   |  0.3874615  | 7.59e-10 |
    ## |  Popdensity   |  0.2622265  | 3.65e-10 |
    ## |      GDP      |  0.1491878  | 3.63e-10 |
    ## |    Mining     |  0.1411154  | 6.73e-10 |
    ##
    ## #### Only display the results corresponding to the maximum number of discretizations!

    plot(ndvi_rgd)

### Run RID

`RID` allows you to obtain the interactions of all possible combinations of variables. By default, `Spatial Intersection` are used to generate a new spatial partition of the interaction variables.

    ndvi_rid = rid(NDVIchange ~ ., data = ndvi,
                   discvar = names(select(ndvi,-c(NDVIchange,Climatezone,Mining))),
                   discnum = 10, cores = 12)
    ndvi_rid
    ## ***          Robust Interaction Detector
    ##
    ## |                               varibale                               |    PD     |
    ## |:--------------------------------------------------------------------:|:---------:|
    ## |       Climatezone ∩ Mining ∩ Tempchange ∩ Precipitation ∩ GDP        | 0.9684917 |
    ## | Climatezone ∩ Mining ∩ Tempchange ∩ Precipitation ∩ GDP ∩ Popdensity | 0.9628383 |
    ## |            Climatezone ∩ Tempchange ∩ Precipitation ∩ GDP            | 0.9618024 |
    ## |    Climatezone ∩ Mining ∩ Tempchange ∩ Precipitation ∩ Popdensity    | 0.9611111 |
    ## |              Mining ∩ Tempchange ∩ Precipitation ∩ GDP               | 0.9601571 |
    ##
    ##  #### Only the first five pairs of interactions are displayed! ####