The goal of mlspatial is to …
Installation
You can install the development version of mlspatial from GitHub with:
# install.packages("mlspatial") mlspatial::mlspatial("azizadeboye/mlspatial")
Example
This is a basic example which shows you how to solve a common problem:
library(mlspatial) #> Loading required package: tidyverse #> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── #> ✔ dplyr 1.1.4 ✔ readr 2.1.5 #> ✔ forcats 1.0.0 ✔ stringr 1.5.1 #> ✔ ggplot2 3.5.2 ✔ tibble 3.3.0 #> ✔ lubridate 1.9.4 ✔ tidyr 1.3.1 #> ✔ purrr 1.0.4 #> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── #> ✖ dplyr::filter() masks stats::filter() #> ✖ dplyr::lag() masks stats::lag() #> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors ## basic example code
What is special about using README.Rmd instead of just README.md?
You can include R chunks like so:
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE) library(mlspatial) library(dplyr) library(ggplot2) library(tmap) library(sf) #> Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE library(spdep) #> Loading required package: spData #> To access larger datasets in this package, install the spDataLarge #> package with: `install.packages('spDataLarge', #> repos='https://nowosad.github.io/drat/', type='source')` library(rgeoda) #> Loading required package: digest #> #> Attaching package: 'rgeoda' #> The following object is masked from 'package:spdep': #> #> skater library(gstat) library(randomForest) #> randomForest 4.7-1.2 #> Type rfNews() to see new features/changes/bug fixes. #> #> Attaching package: 'randomForest' #> The following object is masked from 'package:dplyr': #> #> combine #> The following object is masked from 'package:ggplot2': #> #> margin library(xgboost) #> #> Attaching package: 'xgboost' #> The following object is masked from 'package:dplyr': #> #> slice library(e1071) library(caret) #> Loading required package: lattice #> #> Attaching package: 'caret' #> The following object is masked from 'package:purrr': #> #> lift
# Join data mapdata <- join_data(africa_shp, panc_incidence, by = "NAME") ## OR Joining/ merging my data and shapefiles mapdata <- inner_join(africa_shp, panc_incidence, by = "NAME") ## OR mapdata <- left_join(nat, codata, by = "DISTRICT_N") str(mapdata) #> Classes 'sf' and 'data.frame': 53 obs. of 26 variables: #> $ OBJECTID : int 2 3 5 6 7 8 9 10 11 12 ... #> $ FIPS_CNTRY: chr "UV" "CV" "GA" "GH" ... #> $ ISO_2DIGIT: chr "BF" "CV" "GM" "GH" ... #> $ ISO_3DIGIT: chr "BFA" "CPV" "GMB" "GHA" ... #> $ NAME : chr "Burkina Faso" "Cabo Verde" "Gambia" "Ghana" ... #> $ COUNTRYAFF: chr "Burkina Faso" "Cabo Verde" "Gambia" "Ghana" ... #> $ CONTINENT : chr "Africa" "Africa" "Africa" "Africa" ... #> $ TOTPOP : int 20107509 560899 2051363 27499924 12413867 1792338 4689021 17885245 3758571 33986655 ... #> $ incidence : num 330.4 53.4 31.4 856.3 163.1 ... #> $ female : num 1683 362 140 4566 375 ... #> $ male : num 1869 211 197 4640 1378 ... #> $ ageb : num 669.7 93.7 68.7 2047 336.7 ... #> $ agec : num 2878 480 268 7147 1414 ... #> $ agea : num 4.597 0.265 0.718 11.888 2.13 ... #> $ fageb : num 250.3 40.2 23.1 782 59.1 ... #> $ fagec : num 1429 322 116 3775 315 ... #> $ fagea : num 3.413 0.146 0.548 8.816 1.228 ... #> $ mageb : num 419.5 53.5 45.6 1265 277.6 ... #> $ magec : num 1448 158 152 3372 1100 ... #> $ magea : num 1.184 0.12 0.17 3.073 0.902 ... #> $ yra : num 182.4 30.2 16.6 524.7 73.1 ... #> $ yrb : num 187.2 34.1 17.1 552.6 74.9 ... #> $ yrc : num 193.1 35 18 578.5 76.9 ... #> $ yrd : num 198.5 35.9 18.3 602.7 78.6 ... #> $ yre : num 204.3 36.5 18.7 621.5 79.4 ... #> $ geometry :sfc_MULTIPOLYGON of length 53; first list element: List of 1 #> ..$ :List of 1 #> .. ..$ : num [1:317, 1:2] 102188 90385 80645 74151 70224 ... #> ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg" #> - attr(*, "sf_column")= chr "geometry" #> - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ... #> ..- attr(*, "names")= chr [1:25] "OBJECTID" "FIPS_CNTRY" "ISO_2DIGIT" "ISO_3DIGIT" ...
#Visualize Pancreatic cancer Incidence by countries #Basic map with labels # quantile map p1 <- tm_shape(mapdata) + tm_fill("incidence", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"), fill.legend = tm_legend(title = "Incidence")) + tm_borders(fill_alpha = .3) + tm_compass() + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE) p2 <- tm_shape(mapdata) + tm_fill("female", fill.scale =tm_scale_intervals(values = "brewer.reds", style = "quantile"), fill.legend = tm_legend(title = "Female")) + tm_borders(fill_alpha = .3) + tm_compass() + tm_layout(legend.text.size = 0.5, legend.position = c("left", "bottom"), frame = TRUE, component.autoscale = FALSE) current.mode <- tmap_mode("plot") tmap_arrange(p1, p2, widths = c(.75, .75))
tmap_mode(current.mode)