
Install and configure R and RStudio, set the working directory, and run descriptive statistics—mean, median, mode, skewness, and kurtosis—through practical demonstrations.
Learn how to create and customize bar plots in R using a prepared data frame, and explore color fills, patterns, and intercept lines to compare regional values.
Learn to visualize data in GIS modeling with R by creating a histogram plot from a dataset variable and interpreting the mean and green color cues in the distribution.
Explore regression analysis, fit a linear model, compare correlation and regression, and visualize with scatter plots, qq plots, residuals, r-squared, and observed versus estimated values.
Explore clipping and cropping in GIS modeling with R: set up tidyverse and ggplot2, read and project spatial data, perform point-in-polygon clipping, and crop raster to the Jharkhand study area.
Stack multiple TIFF rasters in R for GIS analysis using raster stack, set directory, define paths, and visualize the result with color palettes.
Analysing data requires a lot of research. But do you know anything that can make it easier? R is used to create projects and models. These projects are dependent on data. Researchers use this programming language to create business models and perform data analysis functions. This course benefits research scholars and those who want to be data scientists. R has been around since 1995, becoming the most popular programming language among data scientists worldwide. This course includes several data packages and functions, making it an attractive programming language for data scientists. R gives an excellent platform for data analysis, data wrangling, data visualisation, machine learning and open source. This course covers traditional statistics to advance statistics and GIS applications, such as models, graph descriptive statistics, mathematical trend modelling and spatial plots. R is designed to be a tool that helps scientists analyse data, and It has many excellent functions that make plots and fit models to data. Because of this, many statisticians learn to use R as if it were a piece of software; they discover which functions accomplish what they need and ignore the rest. This course has been divided into two measures part: the first part is the data visualisation, and the second part is the GIS in R. The course has been done on the actual data set.