# R Programming

## Stationarity in Time Series Data Using R

Stationarity is a primary principle in time series analysis. A time series is considered to be stationary if its statistical aspects, such as mean, variance, and autocorrelation, remain constant through time. To elucidate, the time series does not reflect trends, seasonality, or other regular patterns that alternate with time. Stationarity is essential in time series […]

## Filling gaps in Time series and Panel data using R

Time series and panel are import data sets in certain fields of study like economics, finance, engineering etc. However, these data sets contain missing values, which can lead to biased or inaccurate results. Handling missing data is a crucial aspect of data analysis, particularly in time series and panel data settings where observations may be

## Creating dummy variables in R

In statistical modeling and data analysis, dummy variables are often used to represent categorical data. The dummy variables are binary variables represented as either 0 or 1. This article deals with creating dummy variables in R. Let’s create the following data set for students containing their score of different subjects, by using the following commands

## Count Number of Observations by Group (Category) in R

While we are dealing with data analysis in R, it is essential to understand data structure and count number of observations by each variable or based on certain categories etc. This article deals with counting number of observations in diamonds data set. Before starting the article, first load the data set by using the following

## Identify, Remove and Tag Duplicate Observations in R

Data cleaning is the most fundamental aspect of data analysis, ensuring the reliable and accurate results. Duplicate observations can, however, pose a challenge in this accuracy of data analysis, leading to skewed results. The handling of duplicate observations in R is a straightforward task, where the accuracy and reliability of data analysis can be ensured.