Dealing With Missing Data in R

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5

(3)

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

Missing data is an issue in any data analysis. Data missing can occur in unexpected places, making it hard to understand analyses. This course will show you how to use tidyverse, the naniar package, and visualize missing values. You will clean up missing values and then use them to analyze data. Other patterns of missing data will also be revealed. You will also learn how to fill out missing values with imputation models. These imputed datasets will also help you visualise, evaluate and make decisions.

Course Overview

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Introduction to R

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Introduction to the Tidyverse

What You Will Learn

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Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data

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You will also learn how to "fill in the blanks" of missing values with imputation models, and how to visualize, assess, and make decisions based on these imputed datasets

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In this chapter, you will learn about filling in the missing values in your data, which is called imputation. You will learn how to impute and track missing values, and what the good and bad features of imputations are so that you can explore, visualise,

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You will learn how to use ggplot to explore and visualize how values changes as other variables go missing

Course Instructors

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Nicholas Tierney

Statistician

I recently completed my PhD in Statistics at QUT, and am now a Research Fellow in Statistics at Monash University working with Rob Hyndman and Di Cook in the NUMBAT group. My research aims to improve...

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