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Series
The series covers various topics such as data types, data manipulation, data import, and export. It also includes descriptive statistics, correlation analysis, MLR, and data visualization.
We recommend installing and using the latest versions of R and RStudio IDE. The common R data types for research are numeric, factor, and character. Numeric a <- c(-1, -3, 0, 5, 7, 8, 4, 6.3, 10) "<-" is the assignment operator. "c()" function com...
A function is a collection of statements combined to perform a specific task. The basic syntax of an R function is: function_name <- function(arg_1, arg_2, ...) { Function body } Built-in Functions: For example, max(x) returns the maximum value of th...
R packages are combinations of functions and datasets created as extensions to R. The library function in R is used to indicate the location where the package is stored. Installing Packages To install a package, use the install.packages function. For...
Mainly, researchers use .csv, .txt, .sav, and .xlsx format files. R has several base functions for importing data, including read.table(), read.delim(), read.csv(), and read.csv2(). To follow along with this tutorial, you can download the data from t...
Create data: data <- c(1977, 1979, 2013, 2014,'', 2015) Save data as comma-separated CSV format: write.csv(data, 'created_data.csv') Save as semicolon-separated CSV file: write.csv2(data, file='data_semicolon.csv') Save data as text file: write.ta...
Set a default working directory in RStudio Go to Tools > Global Options > General > Default working directory (when not in a project) > Browse > Choose your directory > Apply > OK. Get current directory getwd() # stands for "get working directory" ...