Statistics with R

In this section, a bunch of tutorials are given along with data set. Users may find them and start learning using the instruction given in the pdf doc

instruction mannual for R.pdf

Instruction for installing R in windows

https://drive.google.com/file/d/1hQghfnSEEp6P12GC-qPVC-wlKk4X0YiC/view?usp=sharing

script_presentation.pdf

Lets get started with R

https://drive.google.com/file/d/1-mvzHaILwmpYs1U3librCLdstR-YaHtn/view?usp=sharing

Spatial data in R

In this section, we're going to learn about the spatial data in R. This includes summary statistics, regression, spatial autocorrelation, etc. We'll also learn about climate data using spatial statistics. The R scripts with the data descriptions have been attached here.

Analysis of environmental data

We're going to deal with "meuse" data which comes with package 'sp'. The meuse data is environmental data which contains heavy metal concentration in soil. The data also contains info about the flood frequency, distance from the river, elevation, and land cover. The tutorial starts with a basic analysis and goes to spatial interpolation. Topics covered so far in it:

Understanding the spatial data
Summary statistics
Regression modeling
Graphics
Interpolation (IDW)

Script for environmental data analysis: https://drive.google.com/file/d/15yA5hu7foceh21aPorFs6V5OQXtoySJP/view?usp=sharing

Analysis of climate data

This part of the tutorial deals with climate data. Near-surface air temperature at 2 m height (t2m) is considered here to run the analytics. t2m is a reanalysis data obtained from the European Centre for Medium-Range Weather Forecast (ECMWF). The reanalysis data is bias-corrected weather data generated from an integrated forecast model that is a global scale large eddy simulation model. The data is obtained in the network common data format (.nc). The topic

covered so far: