# R

## Analyzing Multiple Response Questions

In this post you will learn how to analyze multiple response questions in R.

## Read Data from Multiple Excel Sheets and Convert them to Individual Data Frames

In this post I explore different ways of reading data from multiple Excel sheets and converting them into individual data frames in R using lapply() and purrr::map() funciton.

## WB Dashboard

This dashboard visualizes the economy & growth related indicators from the World Bank (WB)

## Making Predictions with Linear Regression

In this post you will learn how to build a linear regression, interpret the result, test its assumptions, and use the regression equation for predictions.

## Using Survey Weight

In R working with survey weight is made possible using the survey package.

## Exploring Relationship Between Variables | scatter-plot

When dealing with numerical data, the most common way to graphically explore the patterns and relationships between variables and draw conclusion about how varaibles relate to one another is by plotting the data points using a scatterplot. A scatterplot uses dots/markers to represent values for two numeric variables where the position of each dot indicates values for an individual data point in the (x,y) coordinates.

## Detecting Duplicates (base R vs. dplyr)

In this post, I provide an overview of duplicated( ) function from base R and the distinct( ) function from dplyr package to detect and remove duplicates.

## Methods for Transforming Data to Normal Distribution

In this post, I try to cover the most common methods of transforming a skewed distribution into a normal distribution, and the foundational step that you must consider prior deciding which method to apply.

## Skewness | Definition and its Importance in Data Science

In this post, you will learn the main concept of skewness, calculating the skewness in R and by hand, and its importance in the field of data analytics.

## Analysis of the Relationship Between Two Quantitative Variables | Correlation

While studying, for example, the relationship between GDP and life expectancy, you might be interested to know whether there exists any relationship between the two indicators? is it a positive relationship or a negative relationship? and how strong the association is? These questions can be answered by computing the correlation coefficient between the two indicators. Depending on the type of data, different methods of correlation exist. In this post, you will learn the Pearson correlation coefficient and the Spearman correlation coefficient.