R Programming Language Resources

1. Download the R language
2. Download the R interface
3. Download rtools42 bundle

Program in R Studio, it is easier.

install.packages(“readxl”)  -> Import .xlsx files in R

install.packages(“writexl”) -> Export .xlsx files in R

install.packages(“ggplot2”) -> Create graphics in R

install.packages(“prospectr”) -> Spectral preprocessing in R

install.packages(“hyperSpec”) -> Import .spc files in R

install.packages(“tidyverse”) -> Data science in R

install.packages(“plotly”) -> Create graphics in R

install.packages(“plsdepot”) -> Develop PCA model in R

install.packages(“PRROC”) -> Evaluate classifier models in R

Merging multiple *.csv files

Merging multiple *.txt files

Plotting multiple spectra in R

Correcting baseline for multiple spectra in R

Normalizing multiple spectra with SNV in R

Normalizing multiple spectra with MSC in R

Normalizing multiple spectra with MAX-MIN normalization in R

Developing PCA model using NIPALS algorithm

Determining Normality in R

Determining Homoscedasticity in R

Performing One-Way(Factor) ANOVA in R

Developing Variogram in R

R is a programming language and software environment for statistical computing and graphics. It is widely used among statisticians and data miners for developing statistical software and data analysis. R provides a wide variety of statistical and graphical techniques, and is highly extensible. Researchers can use R to develop statistical models, visualize data, and perform other data analysis tasks. In addition, R is open source, which means that anyone can use and modify the software freely. This makes it a popular choice among researchers who are looking for a powerful yet affordable tool for data analysis.

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