TGx:LinkedIn Learning - Data Wrangling in R [CoursesGhar]

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LinkedIn Learning - Data Wrangling in R [CoursesGhar]

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English

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611.33 MB

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8b52afcfc258903fae20a42f3d73cf39a78244f9

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25-06-2021 20:28

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File	Size
!! IMPORTANT Note !!.txt	287.00 B
!!! Please Support !!! [CoursesGhar.Com].txt	197.00 B
00. Websites You May Like/CoursesGhar.Com.url	114.00 B
00. Websites You May Like/New Internet Shortcut.url	114.00 B
telegram @coursesghargate.url	128.00 B
Uploaded by [Coursesghar.com].txt	1.10 kB
Visit coursesghar.com for more awesome tutorials.url	114.00 B
[10] Conclusion/[1] Next steps.mp4	2.43 MB
[10] Conclusion/[1] Next steps.srt	1.67 kB
[1] Introduction/[1] Welcome.mp4	7.50 MB
[1] Introduction/[1] Welcome.srt	1.74 kB
[1] Introduction/[2] What you need to know.mp4	1.33 MB
[1] Introduction/[2] What you need to know.srt	1.42 kB
[1] Introduction/[3] Using the exercise files.mp4	3.00 MB
[1] Introduction/[3] Using the exercise files.srt	2.57 kB
[2] 1. Tidy Data/[1] What is tidy data.mp4	10.65 MB
[2] 1. Tidy Data/[1] What is tidy data.srt	6.47 kB
[2] 1. Tidy Data/[2] Variables, observations, and values.mp4	8.17 MB
[2] 1. Tidy Data/[2] Variables, observations, and values.srt	8.45 kB
[2] 1. Tidy Data/[3] Common data problems.mp4	14.65 MB
[2] 1. Tidy Data/[3] Common data problems.srt	14.49 kB
[2] 1. Tidy Data/[4] Using the tidyverse.mp4	15.14 MB
[2] 1. Tidy Data/[4] Using the tidyverse.srt	8.90 kB
[3] 2. Working with Tibbles/[1] Building and printing tibbles.mp4	15.46 MB
[3] 2. Working with Tibbles/[1] Building and printing tibbles.srt	12.04 kB
[3] 2. Working with Tibbles/[2] Subsetting tibbles.mp4	6.81 MB
[3] 2. Working with Tibbles/[2] Subsetting tibbles.srt	5.55 kB
[3] 2. Working with Tibbles/[3] Filtering tibbles.mp4	6.96 MB
[3] 2. Working with Tibbles/[3] Filtering tibbles.srt	6.60 kB
[4] 3. Importing Data into R/[1] What are CSV files.mp4	6.25 MB
[4] 3. Importing Data into R/[1] What are CSV files.srt	5.08 kB
[4] 3. Importing Data into R/[2] Importing CSV files into R.mp4	23.60 MB
[4] 3. Importing Data into R/[2] Importing CSV files into R.srt	13.71 kB
[4] 3. Importing Data into R/[3] What are TSV files.mp4	3.52 MB
[4] 3. Importing Data into R/[3] What are TSV files.srt	3.15 kB
[4] 3. Importing Data into R/[4] Importing TSV files into R.mp4	25.06 MB
[4] 3. Importing Data into R/[4] Importing TSV files into R.srt	18.28 kB
[4] 3. Importing Data into R/[5] Importing delimited files into R.mp4	11.90 MB
[4] 3. Importing Data into R/[5] Importing delimited files into R.srt	7.84 kB
[4] 3. Importing Data into R/[6] Importing fixed-width files into R.mp4	10.29 MB
[4] 3. Importing Data into R/[6] Importing fixed-width files into R.srt	9.44 kB
[4] 3. Importing Data into R/[7] Importing Excel files into R.mp4	20.40 MB
[4] 3. Importing Data into R/[7] Importing Excel files into R.srt	15.31 kB
[4] 3. Importing Data into R/[8] Reading data from databases and the web.mp4	4.03 MB
[4] 3. Importing Data into R/[8] Reading data from databases and the web.srt	4.51 kB
[5] 4. Data Transformation/[1] Wide vs. long datasets.mp4	5.59 MB
[5] 4. Data Transformation/[1] Wide vs. long datasets.srt	6.22 kB
[5] 4. Data Transformation/[2] Making wide datasets long with gather().mp4	11.56 MB
[5] 4. Data Transformation/[2] Making wide datasets long with gather().srt	10.45 kB
[5] 4. Data Transformation/[3] Making long datasets wide with spread().mp4	8.40 MB
[5] 4. Data Transformation/[3] Making long datasets wide with spread().srt	8.09 kB
[5] 4. Data Transformation/[4] Converting data types in R.mp4	14.78 MB
[5] 4. Data Transformation/[4] Converting data types in R.srt	14.02 kB
[5] 4. Data Transformation/[5] Working with dates and times in R.mp4	12.89 MB
[5] 4. Data Transformation/[5] Working with dates and times in R.srt	13.83 kB
[6] 5. Data Cleaning/[1] Detecting outliers.mp4	24.87 MB
[6] 5. Data Cleaning/[1] Detecting outliers.srt	22.02 kB
[6] 5. Data Cleaning/[2] Missing and special values in R.mp4	19.03 MB
[6] 5. Data Cleaning/[2] Missing and special values in R.srt	16.24 kB
[6] 5. Data Cleaning/[3] Breaking apart columns with separate().mp4	13.32 MB
[6] 5. Data Cleaning/[3] Breaking apart columns with separate().srt	10.36 kB
[6] 5. Data Cleaning/[4] Combining columns with unite().mp4	9.31 MB
[6] 5. Data Cleaning/[4] Combining columns with unite().srt	6.51 kB
[6] 5. Data Cleaning/[5] Manipulating strings in R with stringr.mp4	36.63 MB
[6] 5. Data Cleaning/[5] Manipulating strings in R with stringr.srt	23.92 kB
[7] 6. Data Wrangling Case Study Coal Consumption/[1] Understanding the coal dataset.mp4	1.07 MB
[7] 6. Data Wrangling Case Study Coal Consumption/[1] Understanding the coal dataset.srt	1.19 kB
[7] 6. Data Wrangling Case Study Coal Consumption/[2] Reading in the coal dataset.mp4	16.85 MB
[7] 6. Data Wrangling Case Study Coal Consumption/[2] Reading in the coal dataset.srt	9.30 kB
[7] 6. Data Wrangling Case Study Coal Consumption/[3] Converting the coal dataset from long to wide.mp4	15.31 MB
[7] 6. Data Wrangling Case Study Coal Consumption/[3] Converting the coal dataset from long to wide.srt	9.72 kB
[7] 6. Data Wrangling Case Study Coal Consumption/[4] Segmenting the coal dataset.mp4	27.44 MB
[7] 6. Data Wrangling Case Study Coal Consumption/[4] Segmenting the coal dataset.srt	14.56 kB
[7] 6. Data Wrangling Case Study Coal Consumption/[5] Visualizing the coal dataset.mp4	8.00 MB
[7] 6. Data Wrangling Case Study Coal Consumption/[5] Visualizing the coal dataset.srt	5.75 kB
[8] 7. Data Wrangling Case Study Water Quality/[1] Understanding the water quality dataset.mp4	8.30 MB
[8] 7. Data Wrangling Case Study Water Quality/[1] Understanding the water quality dataset.srt	3.99 kB
[8] 7. Data Wrangling Case Study Water Quality/[2] Reading in the water quality dataset.mp4	7.08 MB
[8] 7. Data Wrangling Case Study Water Quality/[2] Reading in the water quality dataset.srt	4.37 kB
[8] 7. Data Wrangling Case Study Water Quality/[3] Filtering the water quality dataset.mp4	25.81 MB
[8] 7. Data Wrangling Case Study Water Quality/[3] Filtering the water quality dataset.srt	13.49 kB
[8] 7. Data Wrangling Case Study Water Quality/[4] Water quality data types.mp4	14.00 MB
[8] 7. Data Wrangling Case Study Water Quality/[4] Water quality data types.srt	7.65 kB
[8] 7. Data Wrangling Case Study Water Quality/[5] Correcting data entry errors.mp4	25.31 MB
[8] 7. Data Wrangling Case Study Water Quality/[5] Correcting data entry errors.srt	13.99 kB
[8] 7. Data Wrangling Case Study Water Quality/[6] Identifying and removing outliers.mp4	21.42 MB
[8] 7. Data Wrangling Case Study Water Quality/[6] Identifying and removing outliers.srt	11.98 kB
[8] 7. Data Wrangling Case Study Water Quality/[7] Converting temperature from Fahrenheit to Celsius.mp4	9.64 MB
[8] 7. Data Wrangling Case Study Water Quality/[7] Converting temperature from Fahrenheit to Celsius.srt	6.29 kB
[8] 7. Data Wrangling Case Study Water Quality/[8] Widening the water quality dataset.mp4	22.01 MB
[8] 7. Data Wrangling Case Study Water Quality/[8] Widening the water quality dataset.srt	11.56 kB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[1] Understanding the Social Security Disability dataset.mp4	5.07 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[1] Understanding the Social Security Disability dataset.srt	5.23 kB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[2] Importing the Social Security Disability dataset.mp4	6.86 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[2] Importing the Social Security Disability dataset.srt	3.84 kB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[3] Making the Social Security Disability dataset long.mp4	7.03 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[3] Making the Social Security Disability dataset long.srt	3.97 kB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[4] Formatting dates in the Social Security Disability dataset.mp4	18.12 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[4] Formatting dates in the Social Security Disability dataset.srt	12.14 kB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[5] Handling fiscal years in the Social Security Disability dataset.mp4	6.00 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[5] Handling fiscal years in the Social Security Disability dataset.srt	5.43 kB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[6] Widening the Social Security Disability dataset.mp4	6.54 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[6] Widening the Social Security Disability dataset.srt	3.98 kB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[7] Visualizing the Social Security Disability dataset.mp4	5.50 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[7] Visualizing the Social Security Disability dataset.srt	3.80 kB

Description

LinkedIn Learning - Data Wrangling in R [CoursesGhar]

Course details
Tidy data is a data format that provides a standardized way of organizing data values within a dataset. By leveraging tidy data principles, statisticians, analysts, and data scientists can spend less time cleaning data and more time tackling the more compelling aspects of data analysis. In this course, learn about the principles of tidy data, and discover how to create and manipulate data tibbles—transforming them from source data into tidy formats. Instructor Mike Chapple uses the R programming language and the tidyverse packages to teach the concept of data wrangling—the data cleaning and data transformation tasks that consume a substantial portion of analysts' time. He wraps up with three hands-on case studies that help to reinforce the data wrangling principles and tactics covered in this course.

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