Section 1 : Introduction
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Lecture 1 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 2 | What you need to know | 00:00:44 Duration |
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Lecture 3 | Exercise files |
Section 2 : Tidy Data
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Lecture 1 | What is tidy data | 00:03:36 Duration |
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Lecture 2 | Variables, observations, and values | 00:04:22 Duration |
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Lecture 3 | Common data problems | 00:07:39 Duration |
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Lecture 4 | Using the tidyverse | 00:04:37 Duration |
Section 3 : Working with Tibbles
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Lecture 1 | Building and printing tibbles | 00:05:04 Duration |
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Lecture 2 | Subsetting tibbles | 00:01:56 Duration |
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Lecture 3 | Filtering tibbles | 00:03:02 Duration |
Section 4 : Importing Data into R
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Lecture 1 | What are CSV files | 00:02:53 Duration |
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Lecture 2 | Importing CSV files into R | 00:06:39 Duration |
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Lecture 3 | What are TSV files | |
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Lecture 4 | Importing TSV files into R | 00:06:03 Duration |
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Lecture 5 | Importing delimited files into R | 00:03:33 Duration |
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Lecture 6 | Importing fixed-width files into R | 00:03:38 Duration |
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Lecture 7 | Importing Excel files into R | 00:05:44 Duration |
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Lecture 8 | Reading data from databases and the web | 00:02:20 Duration |
Section 5 : Data Transformation
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Lecture 1 | Wide vs | 00:03:11 Duration |
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Lecture 2 | Making wide datasets long with pivot_longer() | 00:03:54 Duration |
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Lecture 3 | Making long datasets wide with pivot_wider() | 00:03:29 Duration |
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Lecture 4 | Converting data types in R | 00:07:03 Duration |
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Lecture 5 | Working with dates and times in R | 00:04:51 Duration |
Section 6 : Data Cleaning
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Lecture 1 | Detecting outliers | 00:08:19 Duration |
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Lecture 2 | Missing and special values in R | 00:05:21 Duration |
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Lecture 3 | Breaking apart columns with separate() | 00:03:48 Duration |
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Lecture 4 | Combining columns with unite() | 00:02:29 Duration |
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Lecture 5 | Manipulating strings in R with stringr | 00:09:15 Duration |
Section 7 : Data Wrangling Case Study Coal Consumption
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Lecture 1 | Understanding the coal dataset | 00:01:59 Duration |
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Lecture 2 | Reading in the coal dataset | 00:02:39 Duration |
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Lecture 3 | Converting the coal dataset from wide to long | 00:02:53 Duration |
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Lecture 4 | Segmenting the coal dataset | 00:03:32 Duration |
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Lecture 5 | Visualizing the coal dataset | 00:02:29 Duration |
Section 8 : Data Wrangling Case Study Water Quality
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Lecture 1 | Understanding the water quality dataset | 00:01:32 Duration |
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Lecture 2 | Reading in the water quality dataset | 00:01:35 Duration |
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Lecture 3 | Filtering the water quality dataset | 00:05:17 Duration |
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Lecture 4 | Water quality data types | |
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Lecture 5 | Correcting data entry errors | 00:02:43 Duration |
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Lecture 6 | Identifying and removing outliers | |
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Lecture 7 | Converting temperature from Fahrenheit to Celsius | 00:02:20 Duration |
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Lecture 8 | Widening the water quality dataset | 00:04:33 Duration |
Section 9 : Data Wrangling Case Study Social Security Disability
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Lecture 1 | Understanding the social security disability dataset | 00:02:28 Duration |
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Lecture 2 | Importing the social security disability dataset | 00:01:22 Duration |
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Lecture 3 | Making the social security disability dataset long | 00:01:34 Duration |
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Lecture 4 | Formatting dates in the social security disability dataset | 00:04:06 Duration |
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Lecture 5 | Fiscal years in the social security disability dataset | 00:02:21 Duration |
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Lecture 6 | Widening the social security disability dataset | |
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Lecture 7 | Visualizing the social security disability dataset | 00:01:25 Duration |
