Unravelling the Power of R Programming for Statistical Analysis

This article explores the key features and capabilities of R programming for statistical analysis, providing a comprehensive guide to getting started and advancing your skills.

Getting Started with R

Here is how a standard Data Analyst Course that covers R language will be organised. 

Installing R and RStudio

Install R: Download and install R from the Comprehensive R Archive Network (CRAN).

Install RStudio: RStudio is a popular integrated development environment (IDE) for R that provides a user-friendly interface. Download and install RStudio from the official website.

Understanding Basic Syntax

Variables and Data Types: R supports various data types, including vectors, lists, matrices, data frames, and factors.

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x <- 5            # Numeric

y <- "Hello"      # Character

z <- TRUE         # Logical

vec <- c(1, 2, 3) # Numeric vector

Basic Operations: Perform arithmetic operations, logical operations, and use functions.

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sum <- x + 10

is_true <- z & FALSE

mean_vec <- mean(vec)

Data Manipulation in R

Data Frames

Data frames are a primary data structure in R for storing tabular data.

Creating Data Frames: Use data.frame() to create a data frame.

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df <- data.frame(Name = c("Tom", "Jerry"), Age = c(20, 18))

Accessing Data: Access data using indexing or column names.

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df[1, ]       # First row

df$Name       # 'Name' column

df[df$Age > 18, ]  # Rows where Age > 18

Data Manipulation with dplyr

dplyr is a powerful package for data manipulation.

Installing and Loading dplyr:

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install.packages("dplyr")

library(dplyr)

Basic Operations: Use functions like filter(), select(), mutate(), summarize(), and arrange().

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df_filtered <- filter(df, Age > 18)

df_selected <- select(df, Name, Age)

df_mutated <- mutate(df, AgeNextYear = Age + 1)

df_summary <- summarize(df, AvgAge = mean(Age))

df_sorted <- arrange(df, desc(Age))

Statistical Analysis in R

Descriptive Statistics

Summary Statistics: Use functions like mean(), median(), sd(), summary().

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mean_age <- mean(df$Age)

summary(df)

Hypothesis Testing

t-test: Perform a t-test to compare means.

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t_test_result <- t.test(df$Age, mu = 18)

Chi-Squared Test: Perform a chi-squared test for independence.

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observed <- matrix(c(50, 30, 20, 10), nrow = 2)

chi_squared_result <- chisq.test(observed)

Data Visualisation in R

Base R Graphics

Basic Plots: Use functions like plot(), hist(), boxplot().

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plot(df$Age, main = "Age Plot", xlab = "Index", ylab = "Age")

hist(df$Age, main = "Age Distribution", xlab = "Age")

boxplot(df$Age, main = "Age Boxplot")

Advanced Visualisation with ggplot2

ggplot2 is a powerful package for creating complex and customisable plots.

Installing and Loading ggplot2:

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install.packages("ggplot2")

library(ggplot2)

Creating Plots: Use the ggplot() function and various geom functions.

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p <- ggplot(data = df, aes(x = Name, y = Age)) +

     geom_bar(stat = "identity") +

     theme_minimal() +

     labs(title = "Age by Name", x = "Name", y = "Age")

print(p)

Advanced Statistical Modelling in R

Linear Regression

Fitting a Model: Use lm() to fit a linear model.

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model <- lm(Age ~ Name, data = df)

summary(model)

Logistic Regression

Fitting a Model: Use glm() to fit a logistic regression model.

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df$Passed <- c(1, 0)  # Binary outcome variable

log_model <- glm(Passed ~ Age, data = df, family = binomial)

summary(log_model)

Automating Analysis with R Scripts

Writing Functions: Create reusable functions for repetitive tasks.

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clean_data <- function(data) {

    data <- na.omit(data)

    data <- unique(data)

    return(data)

}

df_clean <- clean_data(df)

Running Scripts: Write and run scripts for your entire workflow.

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source("analysis_script.R")

An inclusive Data Analyst Course will cover the above-mentioned applications of R in data analysis, that is, manipulation, analysing, testing, visualisation and modelling.

Conclusion

R is an indispensable tool for statistical analysis, offering a rich ecosystem of packages and functions for data manipulation, visualisation, and modelling. By mastering the essentials and advancing your skills in R, you can effectively analyse complex datasets and uncover valuable insights. Continuous practice and exploration of new techniques will enhance your proficiency and enable you to tackle a wide range of data analysis challenges with confidence. R represents one such advanced learning option now commonly conducted in learning centres across cities. Thus, you can gain extensive training on the applications of R language by enrolling for a Data Analyst Course in Pune, Mumbai, Bangalore and such cities.

Name: ExcelR - Data Science, Data Analytics Course Training in Pune

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