R

Blogs related to R programming

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 …

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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. …

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Standardizing, normalization and Mean Centering of Variable in R

Standardization, normalization and mean centering of variable are common data processing techniques in Statistics and data analysis. It is important to standardize variables in statistics to compare and analyze different variables on the same scale. If you have two variables, one in inches and the other in centimeters, it’s not possible to compare these variables …

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