R packages developed by SpatLyu

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.title[
# R packages developed by SpatLyu
]
.author[
### Wenbo Lv
]
.date[
### 2024-10-13
]

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# R Learning materials

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.pull-left[

#### [<span style="color: #5b7b75;display: block;margin-top: 120px;"> Introduction to BaseR </span>](https://rspatial.org/intr/)

#### [<span style="color: #5b7b75;"> Advanced R </span>](https://adv-r.hadley.nz/)

#### [<span style="color: #5b7b75;"> R for Data Science (2e) & tidyverse </span>](https://r4ds.hadley.nz/)

#### [<span style="color: #5b7b75;"> Geocomputation with R </span>](https://r.geocompx.org/)

#### [<span style="color: #5b7b75;"> Spatial Data Science With Applications in R </span>](https://r-spatial.org/book/)

#### [<span style="color: #5b7b75;"> Spatial data with terra </span>](https://rspatial.org/spatial/index.html)

#### [<span style="color: #5b7b75;"> Unofficial Rcpp Api Documentation </span>](https://thecoatlessprofessor.com/programming/cpp/unofficial-rcpp-api-documentation/)

]

.pull-right[
## <span style="display: block;margin-top: 150px;">For the full version of resource recommendations, please refer to [my blog](https://spatlyu.github.io/posts/r-books/).</span>
]

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# R packages developed by Wenbo Lv and his partners

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## <span style="color: #4472c4;margin-top: 60px;">Content</span>

#### Geospatial Data

- [geocn](https://stscl.github.io/geocn/)

#### Geospatial Processing

- [qgisprocess](https://r-spatial.github.io/qgisprocess/)

- [sdsfun](https://stscl.github.io/sdsfun/)

#### Geospatial Methods

- [gdverse](https://ausgis.github.io/gdverse/)

- [geocomplexity](https://ausgis.github.io/geocomplexity/)

*And ...* 🤔

]

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# Tutorials for My Open Source Geospatial Packages

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## <span style="color: #4472c4;display: block;margin-top: 70px;"> [geocn](https://stscl.github.io/geocn/)</span>

#### Loads Spatial Data Sets of China

```r
library(sf)
library(geocn)
library(ggplot2)

albers = load_cn_alberproj()
province = load_cn_province()
province_albers = st_transform(province,albers)
ggplot(data = province_albers) +
  geom_sf(fill = 'grey90', color = 'grey') +
  theme_bw()
```

]

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<div class="center-img">
<img src="./assets/figures/fig1.png" style="margin-top: 120px; width: 70%; height: auto;" />
</div>
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<div style="margin-top: 70px;">

<pre><code>
library(sf)
library(geocn)
library(ggplot2)

world1 = load_world_country(center = "west")
ggplot(data = world1) +
  geom_sf(fill = 'grey90', color = 'grey') +
  theme_bw()
</code></pre>
<img src="./assets/figures/world1.png";width: 80%; height: auto;" />

</div>
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<pre><code>
library(sf)
library(geocn)
library(ggplot2)

world2 = load_world_country(center = "east")
ggplot(data = world2) +
  geom_sf(fill = 'grey90', color = 'grey') +
  theme_bw()
</code></pre>

<img src="./assets/figures/world2.png";width: 80%; height: auto;" />
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## <span style="color: #4472c4;display: block;margin-top: 70px;"> [qgisprocess](https://r-spatial.github.io/qgisprocess/)</span>

#### Use QGIS Processing Algorithms in R

```r
library(qgisprocess)
# Load data
road = read_sf('./data/osm.gdb/',layer='road') |> 
  st_transform(32649)
# Weighting according to road type
road |> 
  dplyr::mutate(weight = dplyr::case_match(highway,
                              c('motorway','primary') ~ 5,
                         c('motorway_link','primary_link','trunk','secondary') ~ 4,
                              c('trunk_link','secondary_link','tertiary') ~ 3,
                              c('tertiary_link','residential') ~ 2,
                              .default = 1)) -> road
qgis_run_algorithm(
  algorithm = "native:linedensity",INPUT = road,
  WEIGHT = "weight",RADIUS = 90,PIXEL_SIZE = 30) |> 
  qgis_as_terra()-> RD
```

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<div class="center-img">
<img src="./assets/figures/rd.png" style="margin-top: 100px; height: 50%; width: auto;" />
</div>
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## <span style="color: #4472c4;display: block;margin-top: 70px;"> [sdsfun](https://stscl.github.io/sdsfun/)</span>

#### Spatial Data Science Complementary Features

```r
library(sf)
gzma = read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
gzma_c = sdsfun::spdep_skater(gzma,8)
gzma$group = gzma_c
plot(gzma["group"])
```

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<div class="center-img">
<img src="./assets/figures/spdep_skater-1.png" style="margin-top: 100px; width: 80%; height: auto;" />
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``` r
library(sf)
library(sdsfun)
pts = read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))

wt1 = spdep_distance_swm(pts, style = 'B')
wt2 = spdep_distance_swm(pts, kernel = 'gaussian')
wt3 = spdep_distance_swm(pts, k = 3, kernel = 'gaussian')
wt4 = spdep_distance_swm(pts, k = 3, kernel = 'gaussian', bandwidth = 10000)
wt4[1:5,1:5]
```

```
##         [,1]      [,2]       [,3]       [,4]       [,5]
## 1 0.09009766 0.0000000 0.07505186 0.00000000 0.00000000
## 2 0.00000000 0.1374176 0.00000000 0.00000000 0.00000000
## 3 0.05593666 0.0000000 0.06715040 0.00000000 0.00000000
## 4 0.00000000 0.0000000 0.00000000 0.08275632 0.00000000
## 5 0.00000000 0.0000000 0.00000000 0.00000000 0.07064708
```

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``` r
library(sf)
library(sdsfun)
pts = read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))

wt1 = spdep_contiguity_swm(pts, k = 6, style = 'B')
wt2 = spdep_contiguity_swm(pts, queen = TRUE, style = 'B')
wt3 = spdep_contiguity_swm(pts, queen = FALSE, order = 2, style = 'B')
wt3[1:5,1:5]
```

```
##   [,1] [,2] [,3] [,4] [,5]
## 1    0    1    1    1    1
## 2    1    0    1    1    1
## 3    1    1    0    1    1
## 4    1    1    1    0    1
## 5    1    1    1    1    0
```

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## <span style="color: ##5b7b75;display: block;margin-top: 60px;"> [gdverse](https://ausgis.github.io/gdverse/)</span>

#### Geographical Detector Models

``` r
library(gdverse)
fd = gd(incidence ~ watershed + elevation + soiltype,
        data = NTDs,type = 'factor')
fd
```

```
## ***          Factor Detector            
## 
## | variable  | Q-statistic |   P-value   |
## |:---------:|:-----------:|:-----------:|
## | watershed |  0.6377737  | 0.000128803 |
## | elevation |  0.6067087  | 0.043382244 |
## | soiltype  |  0.3857168  | 0.372145486 |
```

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## <span style="color: ##91a5addisplay: block;margin-top: 80px;"> [geocomplexity](https://ausgis.github.io/geocomplexity/)</span>

#### Geocomplexity Mitigates Spatial Bias

```r
library(geocomplexity)
econineq = sf::read_sf(system.file('extdata/econineq.gpkg',package = 'geocomplexity'))
gc = geocd_vector(econineq)

library(ggplot2)
library(viridis)
ggplot(gc) +
   geom_sf(aes(fill = GC_Gini)) +
   scale_fill_viridis(option = "mako", direction = -1) +
   theme_bw()
```

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<div class="center-img">
<img src="./assets/figures/gc.png" style="margin-top: 120px; height: 80%; width: auto;" />
</div>
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# Thanks

### Wenbo Lv

### lyu.geosocial@gmail.com