Geospatial Analysis With R : Go From Beginner To Advanced

Learn the basics of the R programming language for statistical computing and graphics. Explore open source extensibility, rich packages, R Studio as an IDE, and interactive visualization.

Install and verify R and R Studio on Windows by downloading the latest R 4.3.1, running the setup, and launching R Studio to run a simple hello world example.

Discover the basic R Studio interface, including project setup, file management, and script editing. Learn to run code from the editor, view plots and environments, and access packages and tools.

Master version control with Git and GitHub for geospatial analysis with R, by creating a public repository, initializing it, and pushing commits using git add, commit, and push to main.

Explore variables in R: assign values with = or <-, name rules (dots allowed, numbers after first, underscores not allowed), check types with class, list and remove variables with rm.

Learn data types in R, from logical, numeric, integer, complex, to character and raw, with vectors, lists, matrices, arrays, factors, and data frames.

Explore arithmetic, relational, logical, and assignment operators in R, using vectors a and b to perform addition, subtraction, multiplication, division, modulo, quotient, power, and matrix multiplication with %*%.

Examine decision making in R with if, if else, and switch statements to test boolean conditions and execute code blocks for true or false outcomes, using numeric and string cases.

Explore the basics of loops in R, including for, while, and repeat loops, with practical examples that print ranges and vector elements.

Explore R functions, including built-in tools like sequence, mean, and sum, and develop user defined functions with a name, arguments, body, and return value.

Learn how R stores strings with quotes, enforce consistent quoting, and manipulate strings using paste, nchar, toupper, tolower, and substring.

Master vectors in R by creating via assignment or the c function, accessing elements by position, logical, negative, and zero indexing, and performing arithmetic with recycling and sorting.

Learn to create and manipulate lists in R with the list function, including various data types and named elements, access elements by index or name, and merge, update, convert lists.

Learn to create and manipulate matrices in R using the matrix function, byrow, and row and column names, then perform access and basic operations like addition and multiplication.

Master arrays in R, extending matrices to multiple dimensions with the array function, naming rows, columns, and matrices, and using apply to sum across array elements.

Master factors in R to categorize data using labels in limited-value columns. Create, convert, and reorder factors with the factor function, and generate levels using GL, like male/female.

Explore dataframes in R, a two-dimensional table of variables and records. Create, inspect (structure and summary), extract by rows and columns, and expand with new columns or rows.

Discover how r packages live in the library directory, extend functions and sample data, install with install.packages, load with library, and verify installed and loaded packages.

Read csv files into R, set and inspect the working directory, and write filtered results to a new csv. Subset by department and salary, and drop columns on export.

Learn to read and write Excel files in R using dedicated packages, install and load libraries, set the working directory, and read worksheets by index into a data frame.

Learn how to read json files in R with the json package, inspect their structure, install the package, and convert json data into a data frame.

learn to create pie charts, bar charts, histograms, and scatter plots in r with ggplot, mapping values and fields, and applying coord_polar and theme tweaks to enhance visuals.

Learn to create bar charts in R with ggplot2, mapping x to name and y to value, using geom_bar and coord_flip for horizontal bars, and adjusting bar width.

Create a data frame of 100 random values and plot a histogram with ggplot in R, adjusting bin width, fill color, outline, and transparency to visualize frequency.

Explore scatter plots by plotting sepal length against sepal width using the iris dataset, and learn how a scatter plot displays the relationship between two variables.

Learn to create a line chart in R with ggplot2, mapping sepal length to x and sepal width to y, and adjust color, size, transparency, and linetype.

Create sf objects in R by converting a dataframe into point, line, and polygon geometries using coordinates. Learn to assign x and y, build line strings, and plot the results.

create a spatial point object in R by building a dataframe, assign x and y coordinates with the coordinates function, and plot sp object, noting no coordinate reference system yet.

Load a zip data file, read its district layers with sf, and plot them in the plot tab; then filter and subset by state seven to visualize the resulting geometry.

Load and display geospatial data in R with the rgdal library, including installing and loading the package, setting the working directory, and plotting and subsetting spatial polygons data frame.

Learn to read a CSV, create spatial points from latitude and longitude, assign a coordinate reference system, and write the spatial object to a file.

Learn to reproject an sf object in R by transforming from azimuthal equidistant to 84, compare results visually and by coordinates, and save the reprojected dataset.

Learn how to perform projection transformation of sp objects in R by reprojecting district data, including two districts and a district-wide set, and saving the reprojected results.

Learn to create point data in R by building simple feature geometry, forming a simple feature column, assigning a coordinate reference system, and enriching points with attributes for attribute-driven plotting.

Learn to create line data in r by building line strings from coordinate matrices, forming a simple feature geometry object with road name and speed limit attributes, then plot.

Create polygons in R with the polygon function to build a two-polygon example, plot the simple feature geometry, and explain sfg, svg, and esf data structures.

Learn how to load external shapefiles with metadata in R, inspect geometry, coordinate reference system, and fields, convert to a data frame, summarize data, and plot provincial maps.

Learn to join attribute data to a shapefile in R by performing a left join with dplyr, using common fields like palika and napa to enrich geospatial polygons.

Learn to perform attribute queries in R by loading data, filtering by total family and district type using the pipe operator, and visualizing results.

Explore geoprocessing in R by performing buffer, union, intersection, and cropping on spatial data, transforming projections, and visualizing results.

Create and customize raster layers in R with the raster package, defining extent and resolution, setting CRS (UTM zone 48, WGS 84), and populating cells with random values.

Learn how to read the raster layer from an external source using the raster function and the file choose dialog, and plot the loaded raster.

Stack Landsat 9 raster bands 2, 3, and 4 in R with the raster package, create three-layer stack, plot a true color image, and save it as a stack file.

Explore raster algebra by performing operations on multiple rasters, including addition, subtraction, rounding, and conditional mapping, then stack rasters and calculate cell statistics.

Modify raster objects by cropping, masking, and merging rasters, then reproject to a common coordinate reference system. Apply calc operations and overlay, and reclassify values for a consolidated raster analysis.

Learn to plot a raster file with custom axes and title, then plot a raster stack of layers as true color imagery using red, green, blue channels and histogram stretch.

Explore cell level functions in the raster package to access cell numbers, row and column indices, and coordinates. Learn to extract values and blocks from raster data.

learn to resample a raster to a 30 meter resolution using the raster package, create a new raster with updated values and defined extent, choosing near neighborhood method.

Learn to reproject a raster with differing coordinate reference systems using the raster package’s project raster function, then verify the new crs matches the target.

Explore creating maps in R with tmap, ggplot, and leaflet, building static, animated, and interactive maps by adding shapes, color fills with alpha, borders, and type-based polygons from data.

Learn to plot multiple shapes and layers in R using shapefiles and raster layers, including boundaries, roads, rivers, and points, with color palettes, lines, polygons, and an adjustable legend.

Learn to add a compass to a map with tmap. Adjust orientation, type (arrow or four star), size, labels for cardinal directions, color, and position; hide the legend if needed.

Learn to add grid lines and graticules to maps with tmap in R, including customizing labels, densities, and colors using a Nepal example.

Add and customize a scale bar in tmap for maps and plots, using breaks and position. Adjust break values and move the bar to left bottom to refine distance display.

Learn to add a legend in maps with the tmap package in R, sizing bubbles by normalized population and coloring by total population, using palettes, breaks, and legend placement.

Adjust the bounding box by expanding margins on the right and bottom to create space for map elements, then plot with the updated bounds.

Create a purple map layout in R using established code to plot lines, polygons, and population bubbles, then add graticules, a legend, compass, and scale bar.

Learn to create a facet map with tmap by plotting Nepal's provinces as polygons and visualizing total area across seven maps.

Learn to create an animated map in R for geospatial analysis, using data for Nepal's seven provinces, with faceting, palette, and GIF rendering.

Learn to build an interactive map with the tmap package in R, including basemaps, zoom and pan, data filtering, and exporting maps as image or standalone html file.

Learn to build interactive maps in R with the leaflet package, adding markers, tiles and popups to OpenStreetMap basemaps and configuring popup options.

Learn to add shapefiles from zip archives to a leaflet map in R, import data, render polygons and roads, and customize styling with colors and opacity.

Learn how to add a raster image to a Leaflet map, customize colors, and handle large rasters by resampling and near-neighborhood sampling to display a clear raster visualization.

Learn to add legends to a Leaflet map in R by visualizing a shapefile of Nepal data, using total area values, palettes, and both continuous and color-quantile legends.

Learn to show and hide layers on a Leaflet map by adding base maps and overlay layers, grouping them, and using layer controls to toggle airports, roads, and Nepal polygons.

Create an interactive choropleth map in R using Leaflet to visualize Nepal urea data with color bins, a palette, hover highlights, and a legend.

Learn to extract image properties, information, and statistics from remote sensing data in R by loading Landsat 9 bands, examining CRS, resolution, dimensions, and band comparisons.

Learn to create a raster stack, combine red, green, and blue bands into a true color image, and explore false color composites using Landsat bands.

Learn to build a 3D raster stack from Landsat bands by exploiting a common file pattern and programmatically concatenating names in R.

Learn how to plot multiple single-band images in one frame using a grid, label each band with titles, and adjust gray scale to visualize blue, green, and near-infrared bands.

Learn to subset a raster stack by selecting specific bands (including non-contiguous bands) and rename bands to blue, green, and red to streamline analysis in geospatial workflows with R.

Subset geospatial data by extent and crop rasters in R, create and adjust extents, align coordinate reference systems with sf, and compare not cropped, cropped, and file-based crops.

Visualize band relationships in R with a scatter plot matrix from the raster package, comparing blue vs green and red vs near-infrared to reveal high correlations.

Learn to extract raster pixel values at given points using the extract function on a raster stack (Landsat 9), including coordinate reference system alignment and retrieving values from three bands.

Explore Landsat 8 spectral profiles and top-of-atmosphere reflectance, comparing bands 1–7 across agriculture, buildings, roads, and water, using R to load, convert, stack, and plot data.

Explore creating vegetation indices from Landsat data, including the naturalized difference vegetation index (ndvi), by stacking bands, computing index values (nir and red), and visualizing results.

Learn to create a histogram in R to visualize the distribution of raster values, customize the x axis, frequency axis, color, and breaks, and identify outliers and bad data.

Apply thresholding to Landsat 8 ndvi rasters to filter values, isolate vegetation, and reclassify into vegetation classes, then visualize with false color and overlays.

Explore unsupervised classification in R by applying mean, Clara, and random forest using k-means to classify raster data based on spectral similarity and statistical grouping.

Apply supervised classification in geospatial analysis by selecting training samples to train a CART classifier, then classify rasters into water, built-up, agriculture, and barren areas.

Input a digital elevation model in R, compute derivatives such as slope, aspect, and hillshade with the terrain function, and visualize them on a map using tmap.