Introduction to R Programming
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Questions and Answers

Which statement best describes R's capability as a tool?

  • R can evaluate complicated mathematical expressions. (correct)
  • R is solely for data visualization.
  • R is primarily a word processor.
  • R can only handle statistical analysis.

    • R is used only for reading files and does not perform any calculations.

    False

    What are the two main roles R serves as mentioned in the introduction?

    Calculator and data analysis tool

    In R, the term ______ refers to the various types of data that can exist within an object.

    <p>classes</p> Signup and view all the answers

    Match the following features of R with their descriptions:

    <p>Functions = Reuse code and simplify tasks Reading files = Importing external data Examining an object = Understanding the structure of data Types of data = Different forms of data that can be processed</p> Signup and view all the answers

    How can R be utilized as a calculator?

    <p>R can evaluate complicated mathematical expressions and perform calculations just like a standard calculator.</p> Signup and view all the answers

    What is the significance of functions in R?

    <p>Functions in R enable users to execute specific tasks, manipulate data, and simplify repetitive operations.</p> Signup and view all the answers

    What types of objects can be examined in R?

    <p>In R, objects can be of various classes such as vectors, lists, data frames, and matrices.</p> Signup and view all the answers

    Describe the process of reading files in R.

    <p>Reading files in R typically involves using functions like <code>read.csv()</code> or <code>read.table()</code> to import data from various file formats.</p> Signup and view all the answers

    What is one simple and useful function in R and its purpose?

    <p>The <code>summary()</code> function provides a quick overview of the statistical properties of an object, such as mean and standard deviation.</p> Signup and view all the answers

    Study Notes

    Introduction to R

    • R is a calculator
    • R can evaluate complex mathematical expressions
    • Variables are assigned using the <- operator (e.g., x <- 10)
    • Basic arithmetic functions are available (e.g., 1+1, sqrt())
    • Functions for creating sequences (seq(), rep())
    • Functions for calculating absolute values (abs())
    • Functions for manipulating decimal places (e2, e-2)
    • Element-wise product can be performed with ab; uv

    Functions in R

    • R has built-in functions for performing various tasks, including mathematical calculations.
    • Functions can be used to perform simple arithmetic operations or complex analyses.

    Reading Files in R

    • read.delim() is used for tab separated files (.txt)
    • Default decimal separator is "."
    • read.table() reads files in tabular format to create a data frame
    • read.csv() reads comma separated values files (.csv) into a data frame.
    • read.csv2() is used when the decimal separator is "," and the field separator is ";".

    Exploring Data

    • View(x) displays the data frame's contents.
    • head(x) shows the top 6 rows, head(x, n=n) for the first few rows
    • tail(x) displays the last 6 rows, tail(x,n=n) for the last few rows
    • names(x) shows the names of the variables in the data frame.

    Types of data in R

    • R supports several data types including numeric, character and logical, vectors, matrices and dataframes

    Data Types

    • Scalars: Single value (e.g., a <- 5)
    • Vectors: Multiple values (e.g., v <- c(1,2,3))
    • Matrices: Two-dimensional array of values (e.g., m <- matrix(v, 3, 2))
    • Lists: Can contain varied data types (e.g., q <- list(a=v, b=x, c=u))
    • Data frames: Table-like structure, typically used for storing data with different variable types.

    Data Structures

    • Vectors store multiple values of the same type.
    • Matrices are two-dimensional structures.
    • Lists can contain elements of different data types.
    • Data frames are tabular structures, organized into rows and columns.

    Operations (Arithmetic)

    • 3 + exp(4) * 2^2
    • (3 + exp(4)) * 2^2
    • R follows operator precedence rules when evaluating expressions.

    Data Frames

    • A table-like structure with different variable types.

    Data Merging

    • cbind() merges by columns.
    • rbind() merges by rows.
    • merge() joins two datasets based on a common variable. (Commonly used for merging two datasets).

    Data Manipulation

    • na.omit() removes rows with NA (missing) values.
    • complete.cases() filters out rows with missing values.
    • apply, colMeans, rowMeans, mean, used to calculate and analyze data.
    • summary() provides summary statistics for data frames or vectors.

    Functions Creation

    • User-defined functions are created in R using the function() syntax.

    Data Types in R (continued)

    • Character data: Text data (e.g., name <- c("Ahmed", "Laila")).
    • Logical data: TRUE/FALSE values (e.g., smoker <- c(TRUE, FALSE, FALSE)).
    • Numeric data stores numbers.
    • Ordering variables: sort(c(4,2,6))
    • colnames(m)=paste("X",1:ncol(m),sep="") renames the column names.
    • rownames(m)=1:nrow(m) renames the row names.

    Data Preparation

    • Data import from different formats: read.csv, read.table.

    Missing Values Analysis

    • is.na(dataNA) identifies missing values.
    • sum(is.na(data), apply(data, 2, sum) gives counts or the total of missing data by column.
    • Common functions include na.omit(), complete.cases(), colSums(is.na(data)) to find the missing values in a column/data.
    • Handling missing values, including replacing them with imputation methods (mean, median etc).

    Outliers

    • Identifying: boxplot(data).
    • Removal: data[data > bench]
    • Filtering data using calculated quartiles (Q1, Q3, IQR).

    Descriptive Statistics

    • Calculating measures like mean, median, min, max, range, IQR, standard deviation, variance.
    • Using functions such as mean(), median(), min(), max(), range(), IQR(), sd(), var().
    • Determining the mode: Mode() calculated using the DescTools library.
    • summary() summarises numeric, logical and/or factor.

    Tables and Plots

    • Creating frequency tables, contingency tables (crosstabulations)
    • Creating histograms to plot data (including specific quantitative or qualitative variable(s)).
    • Constructing boxplots to identify data distribution.
    • Using table(), chisq.test(), fisher.test(), oddsratio(), assocstats(), and cor().

    Association and Correlation

    • Calculate and display correlations of variables.
    • Create contingency tables for association analysis - using measures like odds ratio, relative risk or chi-squared test vcd(), vcdExtra and nnet libraries.

    Data Manipulation & Visualisation

    • Data manipulation using data.frame(), cbind(), rbind(), dplyr.
    • Scatter plots: The plot() function can be used.

    Additional Considerations:

    • Load the necessary libraries at the start of your script: using library command.
    • Using attach() to make variables in a data frame directly accessible.
    • Appropriately handle data types when using specific functions (e.g., converting factors to numerical values with as.numeric()).

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    Introduction to R PDF

    Description

    This quiz covers the basics of R programming, including its functions as a calculator, variable assignment, and file reading techniques. Learn about various built-in functions for mathematical operations and data manipulation in R. Test your knowledge on sequences, absolute values, and reading different file formats in R.

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