R

Introduction Geospatial analysis often involves understanding the spatial relationships between data points. One foundational concept in this domain is the variogram. In this article, we will explore what a variogram is, how it is used in Kriging, and how to compute experimental and semi-variograms for hypothetical borehole data. Finally, we will use these concepts to interpolate water levels across all data points using Kriging. A Note on Spatial Relationships The First Law of Geography, proposed by Waldo Tobler, states:...

Climatic Data in Hydrological Analysis Climatic data such as temperature and precipitation are essential for any hydrological analysis. Usually, several years of data are required to do any type of hydrological analysis. The climatic data is obtained from either satellite-based observation or in situ observation. For hydrological analysis historical precipitation and temperature data is essential. Usually these data can be obtained from Indian Meterolgocal Department (भारतीय मौसम विज्ञान विभाग:IMD) free of cost....

SBI Submits all the data to ECI and ECI publish it on Thursday 21 Mar 2024, 6:32 PM Links of 2 files are provided here: Disclosure of Electoral Bonds Details of Electoral Bonds submitted by SBI on 21st March 2024 (EB_Redemption_Details) Details of Electoral Bonds submitted by SBI on 21st March 2024 (EB_Purchase_Details) To know the background of the case read this: SBI submits all electoral bond details, including unique numbers, to ECI...

Getting started with gdal GDAL (Geospatial Data Abstraction Library) is a free and open source translator library for raster and vector geospatial data formats. It also comes with a variety of useful command line utilities for data translation and processing. It is used by many GIS software i.e. QGIS, ArcGIS, and GRASS GIS and R. As a core R use, working in climate change and hydrology, I need to process spatia-tempral data daily on daily basis....

How to make a better boxplot with custom fonts, let’s explore this in this post, it can be used for the standard template for boxplot with facet and user defined fonts. Required R libraries: if(!require(tidyverse)){install.packages("tidyverse");library(tidyverse)} # for ggplot2 function if(!require(gapminder)){install.packages("gapminder");library(gapminder)} # for sample data if(!require(showtext)){install.packages("showtext");library(showtext)} # to import fonts Add fonts in R session font_add_google("Karla", "Karla") # adding local font font_add(family = "Helvetica", regular = "C:/Windows/Fonts/Helvetica 400.ttf") # Adding from Google fonts font_add_google("Roboto Slab", "Roboto Slab") # adding font from the web/google font font_families() ## [1] "sans" "serif" "mono" "wqy-microhei" "Karla" ## [6] "Helvetica" "Roboto Slab" Define theme for Boxplot and fonts themeBox <- function(base_family = "sans", exFont, ....

R is a very versatile statistical tool and programming language. It’s my all-time good-to-go data analysis tool. It is fast, reliable and nifty. It provides great flexibility for my daily work and analysis tasks. If one uses R, they could consider RStudio as a more sophisticated GUI than the Base R once. 1. Installing R Step 01: Install R-Binaries from R-Project Step 02: Install RStudio IDE from RStudio 2. Customizing RStudio A lot of ways you can make RStudio more useful for your personal use....