How to use R

This article is mostly to explain what I've learn from the course Statistical Data Analysis (CDS6224) regarding R studio.

Commands

R (Data) Commands

Let's assume x, y, z as our variables.
x <- c(number1, number2, number3 ... numberN): stores values into variable, outputs as num[1:N] number1, number2 ... numberN.
func(x): using a function to do x, includes commands such as mean, sd & median.
y <- func(x): stores the output of func(x) & stores into y.
x <- read.csv(filenamehere.csv): Imports dataset of your choice.

R (Non-Data) Commands

getwd(): Shows current working directory
setwd("your/folder/path"): Sets working directory
list.files(): Lists files in the current directory

R (Data Manipulation) Commands

These functions are used to clean, filter, and reshape data using tidyverse syntax.
filter(data, condition): Keep rows that match a condition
select(data, column1, column2): Keep specific columns only
mutate(data, new_column = formula): Create or modify columns
arrange(data, column): Sort data by a column (use desc() for descending)

pivot_longer(cols = ..., names_to = ..., values_to = ...): Reshape data from wide to long
pivot_wider(names_from = ..., values_from = ...): Reshape data from long to wide

R (Statistical Test) Functions

t.test(x, mu = 100): One-sample t-test to compare mean of x to 100
t.test(x ~ group, data = data): Two-sample t-test comparing means between groups
aov(response ~ group, data = data): One-way ANOVA test to compare group means
shapiro.test(x): Test if data x is normally distributed
cor.test(x, y): Pearson correlation test between two variables

Installing Useful Packages

install.packages("tidyverse"): Includes ggplot2, dplyr, readr, and more
install.packages("knitr"): Useful for making neat reports and tables
install.packages("psych"): Extra tools for descriptive stats
install.packages("lubridate"): Simplifies working with dates
install.packages("car"): Companion to lm() and aov(), useful for model diagnostics

Built in R (Data) Functions

mean: Calculates the average of a numeric vector.
median: Returns the middle value in a sorted numeric vector.
names(sort(table(x), decreasing = TRUE)[1]): Finds the mode (most frequently occurring value).
sd: Computes the standard deviation, showing how spread out the values are.
var: Calculates the variance (the square of the standard deviation).
range: Returns the minimum and maximum values in the data.
quantile: Provides percentile values (e.g. 25th, 50th, 75th).
IQR: Computes the interquartile range (Q3 - Q1), used to detect outliers.

Built in R (Plotting) Functions

Used as either func(thing), func(thing, col=colour), func(thing, main="name", col="colour", [x/y]="thing2")

hist: Creates a histogram to visualize the distribution of a numeric variable.
boxplot: Draws a boxplot to show median, quartiles, and potential outliers.
qqnorm: Generates a QQ plot to visually assess if data follows a normal distribution.
qqline: Adds a reference line to a QQ plot for easier comparison.
plot: Creates a general-purpose scatter or line plot (often used with density, regression, or base plots).

Non R Commands

CTRL + L: Clears the screen

Other Tips

Remember to add another / should you wish to import files from other places.
Otherwise, it is preferable to put the .csv directly into the folder of your workplace to prevent potential errors (for the less technically savvy).