In this post, I explore visualizing continuous variables by combining a histogram (showing the frequency of values within specific intervals) and a density plot (illustrating probability distribution)

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

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.

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

The purpose of developing this app is to compare two versions of the same dataset.

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.

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

In this post, I explore different ways to combine multiple ggplot plots using patchwork package to produce publication-ready plots

The geom_histogram() function from ggplot2 package is used to create a histogram plot. For example, let’s plot the distribution of Sepal.Length from iris data.
library(ggplot2) theme_set(theme_bw()) ggplot(iris, aes(Sepal.Length)) + geom_histogram(fill = "orange") To add a vertical line to show the mean value of Sepal.

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.