Handling Missing Data and Missing Values in R Programming | NA Values, Imputation, naniar Package

Handling missing data and missing values in R programming is easy! In this video, we'll cover everything you need to know to manage NA values effectively, ensuring your data analysis is accurate and reliable. Whether you're a beginner in R programming or an experienced data scientist, this guide will provide valuable insights and techniques for your data science projects.

🔍 What You'll Learn:

Understanding NA values in R
Using the drop_na() function to remove missing values
Various imputation techniques to handle missing data
Exploring the powerful naniar package for visualizing and managing missing data
Practical examples and hands-on coding in R
📊 Key Topics:

Data analysis in R
Statistical analysis using R
Data science best practices
R programming for beginners
Effective handling of missing values
Imputation methods in R
💡 Why This Video?
Handling missing data is crucial for accurate data analysis and statistical analysis. This video provides a step-by-step approach, making it easy to follow along and apply these techniques in your own projects. Whether you're dealing with large datasets or just getting started with R programming, this tutorial is designed to enhance your skills and improve your data analysis workflow.

[francorodriguezravello]

I learned this in a course last semester, it was tough at the beginning but I managed to pass. But this video would’ve been extremely useful. Nice explanation 👍

[John-vz]

Hello Dr. Greg Martin, The videos on R programming are very interesting. I have two questions. 1. How can I impute the missing values precipitation of different stations in the basin using Multivariate Imputation by Chained Equations? 2). How can I check the quality of the precipitation and temperature data using RClimDex 1.1?

[AgneKif]

Hi, thaks for the video! When I make the ggplot checking for interations between NA and varibels, I get a plot with circles in rows, both in the false and the true grahps. Is that because my data NA varibels are really not random ?
Thanks in advance

[M.Nagah]

I think dlookr package is easier in handlig missing values and outliers

[aminajumagidudu]

Hello Dr. Greg Martin when i execute that same code for the relationship between Ozone and wind my histogram never shows. I am getting this error
"Error in `mutate()`:

ℹ In argument: `... <- NULL`.
Caused by error in `fortify()`:
! `data` must be a <data.frame>, or an object
coercible by `fortify()`, or a valid
<data.frame>-like object coercible by
`as.data.frame()`, not a <uneval> object.
ℹ Did you accidentally pass `aes()` to the `data`
argument?
Run `rlang::last_trace()` to see where the error occurred.
"

any help please

[truth]

I need to be an expert in data analysis i need help