An R package is a collection of functions, code, data, and documentation, representing an extension of the R programming language and designed for solving specific kinds of tasks. R comes with a bunch of preinstalled packages, and other packages can be installed by users from repositories. The most popular centralized repository storing thousands of various R packages is called Comprehensive R Archive Network (CRAN). To install an R package directly from CRAN, we need to pass the package name enclosed in quotation marks to the install.packages() function, as follows: install.packages(“package_name”). To install more than one package from CRAN in one go, we need to use a character vector containing the package names enclosed in quotation marks, as follows: install.packages(c(“package_name_1”, “package_name_2”). To install an R package manually, we need first to download the package as a zip file on our computer and then run the install.packages() function: To load an installed R package in the working R environment, we can use either library() or require() functions. Each of them takes in the package name without quotation marks and loads the package, e.g., library(caret). However, the behavior of these functions is different when they can’t find the necessary package: library() throws an error and stops the program execution, while require() outputs a warning and continues the program execution.
How to import data in R?
The base R provides essential functions for importing data: In practice, any of these functions can be used to import tabular data with any kind of field and decimal separators: using them for the specified formats of files is only the question of convention and default settings. For example, here is the syntax of the first function: read.table(file, header = FALSE, sep = “”, dec = “.”). The other functions have the same parameters with different default settings that can always be explicitly overwritten. The tidyverse packages readr and readxl provide some other functions for importing specific file formats. Each of those functions can be further fine-tuned by setting various optional parameters. readr readxl To dive deeper into data loading in R, you can go through the tutorial on How to Import Data Into R.
List and define some basic data structures in R.
List and define some basic data types in R.
There are a few data types in R, including:
What are some disadvantages of using R?
What is R, and what are its main characteristics?
R is a programming language and environment widely used for solving data science problems and particularly designed for statistical computing and data visualization. Its main characteristics include:
Chi Square Test
Chi-Square test is a statistical method to determine if two categorical variables have a significant correlation between them. Both those variables should be from same population and they should be categorical like − Yes/No, Male/Female, Red/Green etc. For example, we can build a data set with observations on people’s ice-cream buying pattern and try to correlate the gender of a person with the flavor of the ice-cream they prefer. If a correlation is found we can plan for appropriate stock of flavors by knowing the number of gender of people visiting. Syntax The function used for performing chi-Square test is chisq.test(). The basic syntax for creating a chi-square test in R is − chisq.test(data) Following is the description of the parameters used − Example We will take the Cars93 data in the “MASS” library which represents the sales of different models of car in the year 1993. When we execute the above code, it produces the following result − The above result shows the dataset has many Factor variables which can be considered as categorical variables. For our model we will consider the variables “AirBags” and “Type”. Here we aim to find out any significant correlation between the types of car sold and the type of Air bags it has. If correlation is observed we can estimate which types of cars can sell better with what types of air bags. When we execute the above code, it produces the following result − Explore our latest online courses and learn new skills at your own pace. Enroll and become a certified expert to boost your career. Conclusion The result shows the p-value of less than 0.05 which indicates a string correlation.