Basics of R Programming

The apply family functions belong to the R base package, they are especially useful when combining their use with functions to manipulate parts of matrices, arrays, lists and data frames. These functions allow you to process the data in multiple ways to avoid the use of for loops that are usually computationally heavier.

The apply functions form the basis of more complex combinations and helps to perform operations with very few lines of code. The family comprises: apply, lapply , sapply, vapply, mapply, rapply, and tapply. In this video, we will touch upon the use of apply, lapply, sapply, and tapply functions.

The markdown file can be found at Github repo:
https://github.com/siddharth-sahasrabudhe/Youtube-video-files/commit/82c2ddc030a42f71d8365c2aa2817d55865dd8b9

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As a programmer, you’re constantly faced with the need to repeat tasks efficiently. Repetition is a fundamental concept in programming, and R provides a powerful tool to accomplish this: the rep() function. In this video, we will explore the syntax of the rep() function and delve into several examples to showcase its versatility and practical applications.

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To use for loops, you must know, or be able to easily calculate, the number of times the loop should repeat. In situations where you don’t know how many times the desired operations need to be run, you can turn to the while loop.
A while loop runs and repeats while a specified condition returns TRUE.
The while loop uses loop-condition to control how many times it repeats. If the condition is TRUE, the braced area code is executed line by line, until loop-condition becomes FALSE. Once loop-condition becomes FALSE, the while loop will terminate.

If the while loop condition never becomes FALSE, the loop goes into infinite iterations – a situation to be avoided.

the R-code is available on Github page:
https://github.com/siddharth-sahasrabudhe/Youtube-video-files/blob/main/while%20loop.Rmd

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You can also nest for loops, just like if statements. When a for loop is nested in another for loop, the inner loop is executed in full before the outer loop loop-index is incremented, at which point the inner loop is executed all over again.
Note that nested loops require a unique loop-index for each use of for.

A word of caution – FOR loops typically requires lot of execution time and are not memory efficient. Thus, imagine running FOR loops on data set spanning across thousands of data points. This will be very memory intensive task, hence FOR loops should be avoided wherever possible.

R is vectorised language, thus use the apply() family functions instead of using FOR loops.

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Loops are core programming mechanism, which repeats a specified section of code often while incrementing an index or counter. There are two styles of looping: the for loop repeats code as it works its way through a vector, element by element; the while loop simply repeats code until a specific condition evaluates to FALSE.

This video explains how FOR loops are constructed in R. FOR loops has two parts – Loop-index and Loop-vector. Loop-index acts as counter, which is used to iterate the loop using values of Loop-vector inside the code.

The Rmarkdown file used in this video is available at:
https://github.com/siddharth-sahasrabudhe/Youtube-video-files/commit/37aa876f78385f4b605274a4c68bdd2ba564e1dd

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This is the second video of the if-else statement video series. In the first part, basic if-else structure was introduced. The link to the first video can be found at:
https://youtu.be/QUnQnwj27qc

An if statement can itself be placed within the outcome of another if statement. By nesting or stacking several statements, you can weave intricate paths of decision-making by checking a number of conditions at various stages during execution.

Switch statement is most commonly used when you want to choose a code to run based on the value of the single object.

This video illustrates the use of nested if-else structure and switch statement.

The R markdown file used in this video can be found at Git-hub repository under:
https://github.com/siddharth-sahasrabudhe/Youtube-video-files

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There are a lot of situations where you do not just want to execute one statement after another: in fact you have to control the flow of execution also. Usually this means that you merely want to execute some code if a condition is fulfilled. In that case control flow statements are implemented within R Program.

R programming provides three different types if statements that allows programmers to control their statements within source code. These are:

if statement
if….else statement
switch statement

In this video, we will discuss about if-else statement structure.

The R markdown file used in this video can be found at Git-hub repository under:
https://github.com/siddharth-sahasrabudhe/Youtube-video-files

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Two types of objects in R obey special system of notation – A dollar $ operator. You can extract values from data frames and list using dollar $ syntax.
When you use normal sub-setting for list (use of single square bracket), R will return another list with the contents in it. Since the object type is list, many of the pre-defined functions in R won't run on the list as an argument. We can circumvent this problem by using double square bracket notation system.
This video discusses on use of $ operator and double bracket notations to sub-st the list.

This video is second part of R notations video series. This link for the first part of video is as below:
https://youtu.be/mn3dSnBpj7k

The Markdown file used in this session is uploaded on GitHub repository under:
https://github.com/siddharth-sahasrabudhe/Youtube-video-files/blob/main/R%20Notation.Rmd

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In all the results you have seen printed to the R console screen, you may
have noticed a curious feature. Immediately to the left of the output there
is a square-bracketed [1]. When the output is a long vector that spans the
width of the console and wraps onto the following line, another square-bracketed number appears to the left of the new line. These numbers represent the index of the entry directly to the right. Quite simply, the index
corresponds to the position of a value within a vector, and that’s precisely why the first value always has a [1] next to it (even if it’s the only value and not part of a larger vector).
These indexes allow you to retrieve specific elements from a vector,
which is known as sub-setting. Suppose you have a vector called myvec in
your workspace. You can access individual elements by asking R to return
the values of myvec at specific locations, done by entering the name of the
vector followed by the position in square brackets. The same technique can be used in sub-setting the values in Data Frame, by indexing row and columns in square brackets.
This video illustrates various types of Indexing in R with the examples.

The Markdown file used in this session is uploaded on GitHub repository under:
https://github.com/siddharth-sahasrabudhe/Youtube-video-files/blob/main/R%20Notation.Rmd

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