R Data Visualization-Spatial data and Maps in R: Using R as a GIS
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The document provides a tutorial on using R for spatial data analysis and GIS applications, focusing on library functions for handling spatial data, raster visualization, and Google Maps integration. It includes specific examples of loading temperature and elevation data for Turkey, creating maps, and visualizing terrain attributes like slope and aspect. Additionally, it demonstrates procedures for working with temperature data from WorldClim and visualizing results using ggmap.
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![> tmin <- getData("worldclim", var = "tmin", res = 10) # this will downloa
d
> # global data on minimum temperature at 10' resolution
> tmin1 <- raster(paste(getwd(), "/wc10/tmin1.bil", sep = "")) # Tmin for
January
> fromDisk(tmin1)
[1] TRUE
> tmin1 <- tmin1/10 # Worldclim temperature data come in decimal degrees
> tmin1
class : RasterLayer
dimensions : 900, 2160, 1944000 (nrow, ncol, ncell)
resolution : 0.1666667, 0.1666667 (x, y)
extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
data source : in memory
names : tmin1
values : -54.7, 26.6 (min, max)
> plot(tmin1)](https://image.slidesharecdn.com/spatialdatainr-160920190758/85/R-Data-Visualization-Spatial-data-and-Maps-in-R-Using-R-as-a-GIS-8-320.jpg)
![> library(gtools)
> file.remove(paste(getwd(), "/wc10/", "tmin_10m_bil.zip", sep = ""))
[1] FALSE
> list.ras <- mixedsort(list.files(paste(getwd(), "/wc10/", sep = ""), full
.names = T,
+ pattern = ".bil"))
> list.ras # I have just collected a list of the files containing monthly
temperature values
list.ras <- mixedsort(list.files(paste(getwd(), "/wc10/", sep = ""), full.names =
T, pattern = ".bil")) list.ras # I have just collected a list of the files
containing monthly temperature values
>tmin.all <- stack(list.ras)
> tmin.all
class : RasterStack
dimensions : 900, 2160, 1944000, 12 (nrow, ncol, ncell, nlayers)
resolution : 0.1666667, 0.1666667 (x, y)
extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
names : tmin1, tmin2, tmin3, tmin4, tmin5, tmin6, tmin7, tmin8, tmin9
, tmin10, tmin11, tmin12](https://image.slidesharecdn.com/spatialdatainr-160920190758/85/R-Data-Visualization-Spatial-data-and-Maps-in-R-Using-R-as-a-GIS-9-320.jpg)
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ref:https://github.com/Kamaropoulos/COVID19Py
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Naive Bayes Example using R
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Python Pandas for Data Science cheatsheet
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Pandas,scipy,numpy cheatsheet
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R-ggplot2 package Examples
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treemap package in R and examples.
treemap package in R and examples.Dr. Volkan OBAN The document provides examples of using the 'treemap' package in R for visualizing data, specifically focusing on various datasets like 'gni2014' and 'business'. It includes different configurations for the treemap function, such as indexing, variable sizes, coloring schemes, and label formatting. References to the official documentation are also provided for further exploration.
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- 1. Prepared by Volkan OBAN Spatial data and Maps in R: Using R as a GIS Reference: https://pakillo.github.io/R-GIS-tutorial/ Basic packages library(sp) # classes for spatial data library(raster) # grids, rasters library(rasterVis) # raster visualization library(maptools) library(rgeos) library(dismo) library(googleVis) library(rworldmap) library(RgoogleMaps)
- 3. >mycountry <- gmap("Turkey", type = "satellite") > plot(mycountry)
- 4. >mycountry <- gmap("Turkey", type = "satellite",exp=3) > plot(mycountry)
- 5. library(RgoogleMaps) > newmap <- GetMap(center = c(41.112185,29.019965), zoom = 10, destfile = "new map.png", + maptype = "satellite")
- 6. Izmir library(RgoogleMaps) newmap <- GetMap(center = c(38.423734,27.142826), zoom = 10, destfile = "newm ap.png", + maptype = "satellite")
- 7. İstanbul Boğazı-Bosphorus: newmap1 <- GetMap(center = c(41.046018,29.033891), zoom = 10, destfile = "n ewmap1.png", maptype = "satellite")
- 8. > tmin <- getData("worldclim", var = "tmin", res = 10) # this will downloa d > # global data on minimum temperature at 10' resolution > tmin1 <- raster(paste(getwd(), "/wc10/tmin1.bil", sep = "")) # Tmin for January > fromDisk(tmin1) [1] TRUE > tmin1 <- tmin1/10 # Worldclim temperature data come in decimal degrees > tmin1 class : RasterLayer dimensions : 900, 2160, 1944000 (nrow, ncol, ncell) resolution : 0.1666667, 0.1666667 (x, y) extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax) coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs data source : in memory names : tmin1 values : -54.7, 26.6 (min, max) > plot(tmin1)
- 9. > library(gtools) > file.remove(paste(getwd(), "/wc10/", "tmin_10m_bil.zip", sep = "")) [1] FALSE > list.ras <- mixedsort(list.files(paste(getwd(), "/wc10/", sep = ""), full .names = T, + pattern = ".bil")) > list.ras # I have just collected a list of the files containing monthly temperature values list.ras <- mixedsort(list.files(paste(getwd(), "/wc10/", sep = ""), full.names = T, pattern = ".bil")) list.ras # I have just collected a list of the files containing monthly temperature values >tmin.all <- stack(list.ras) > tmin.all class : RasterStack dimensions : 900, 2160, 1944000, 12 (nrow, ncol, ncell, nlayers) resolution : 0.1666667, 0.1666667 (x, y) extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax) coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs names : tmin1, tmin2, tmin3, tmin4, tmin5, tmin6, tmin7, tmin8, tmin9 , tmin10, tmin11, tmin12
- 10. min values : -547, -525, -468, -379, -225, -170, -171, -178, -192 , -302, -449, -522 max values : 266, 273, 277, 283, 295, 312, 311, 312, 300 , 268, 267, 268 > tmin.all <- tmin.all/10 > plot(tmin.all)
- 11. > elevation <- getData("alt", country = "Turkey") > x <- terrain(elevation, opt = c("slope", "aspect"), unit = "degrees") > plot(x)
- 12. slope <- terrain(elevation, opt = "slope") aspect <- terrain(elevation, opt = "aspect") hill <- hillShade(slope, aspect, 40, 270) plot(hill, col = grey(0:100/100), legend = FALSE, main = "Türkiye") plot(elevation, col = rainbow(25, alpha = 0.35), add = TRUE)
- 13. > library(ggmap) > library(RgoogleMaps) > mapImageData1 <- get_map(location = c(lon =29.019442, lat =41.103783),col or = "color",source = "google",maptype = "satellite",zoom = 17) > > ggmap(mapImageData1,extent = "device", ylab = "Latitude",xlab = "Longitud e") İTÜ (Istanbul Technical University)