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R in Statistical Analysis

R is a powerful and flexible statistical software that can help you analyze and understand data. It provides a wide range of statistical techniques including linear and non-linear modelling, classical statistical tests, time-series analysis, classification, clustering, etc. The purpose of this tutorial is to provide a clear understanding of using R in statistical analysis.

What is R?​

R is a programming language and software environment for statistical computing and graphics. It is an implementation of the S programming language combined with lexical scoping semantics. R is highly extensible and is available as Free Software under the terms of the Free Software Foundation's GNU General Public License in source code form.

R can be considered as a different implementation of S, and much of the code written in S runs unaltered in R. It provides a wide variety of statistical and graphical techniques and is highly extensible. The S language, of which R is a dialect, won the ACM Software System Award in 1998.

Installing R​

Before starting, you need to install R on your computer. Visit R-project and download the appropriate version for your operating system.

Basic Statistics in R​

R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques.

Descriptive Statistics​

Descriptive statistics provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.

Here is an example of how to calculate basic descriptive statistics in R:

# Create a vector of numbers
data <- c(2,7,4,9,6)
# Get the mean of the numbers
mean(data)
# Get the median of the numbers
median(data)
# Get the standard deviation of the numbers
sd(data)

Inferential Statistics​

Inferential statistics makes inferences and predictions about a population based on a sample of data taken from the population in question.

Here is an example of how to perform a t-test in R:

# Create two vectors of numbers
group1 <- c(2,4,6,8,10)
group2 <- c(1,3,5,7,9)
# Perform a t-test
t.test(group1, group2)

Graphical Analysis in R​

R is also a powerful tool for creating professional-quality graphics. You can create bar plots, scatter plots, histograms, box plots, etc. Here's an example of how to create a histogram:

# Create a vector of numbers
data <- c(2,7,4,9,6)
# Create a histogram
hist(data)

Conclusion​

R is a powerful tool for statistical analysis and graphics. It provides a wide range of statistical techniques and is highly extensible. This tutorial provides a basic introduction to R and its applications in statistical analysis. The next step is to dive deeper and start using R for your own data analysis.