Skip to content

LinkedIn Learning - Data Wrangling in R [CoursesGhar]

Sign up to create lists
Report LinkedIn Learning - Data Wrangling in R [CoursesGhar]
Unverified source. This magnet is from an unverified source. The content may be unsafe or mislabeled. Proceed with caution.
Title: LinkedIn LearningGroup: NOGRPSource: LinkedIn.Learning
Info Hash
8B52AFCFC258903FAE20A42F3D73CF39A78244F9
Source
Unverified
Total Size
611.33 MB
Total Files
100
Seeders
0
Leechers
3
Health
Score
3
Type
Bookware

File List

FileSize
!! IMPORTANT Note !!.txt287 B
!!! Please Support !!! [CoursesGhar.Com].txt197 B
00. Websites You May Like/CoursesGhar.Com.url114 B
00. Websites You May Like/New Internet Shortcut.url114 B
Uploaded by [Coursesghar.com].txt1.1 KB
Visit coursesghar.com for more awesome tutorials.url114 B
[10] Conclusion/[1] Next steps.mp42.43 MB
[10] Conclusion/[1] Next steps.srt1.67 KB
[1] Introduction/[1] Welcome.mp47.5 MB
[1] Introduction/[1] Welcome.srt1.74 KB
[1] Introduction/[2] What you need to know.mp41.33 MB
[1] Introduction/[2] What you need to know.srt1.42 KB
[1] Introduction/[3] Using the exercise files.mp43 MB
[1] Introduction/[3] Using the exercise files.srt2.57 KB
[2] 1. Tidy Data/[1] What is tidy data.mp410.65 MB
[2] 1. Tidy Data/[1] What is tidy data.srt6.47 KB
[2] 1. Tidy Data/[2] Variables, observations, and values.mp48.17 MB
[2] 1. Tidy Data/[2] Variables, observations, and values.srt8.45 KB
[2] 1. Tidy Data/[3] Common data problems.mp414.65 MB
[2] 1. Tidy Data/[3] Common data problems.srt14.49 KB
[2] 1. Tidy Data/[4] Using the tidyverse.mp415.14 MB
[2] 1. Tidy Data/[4] Using the tidyverse.srt8.9 KB
[3] 2. Working with Tibbles/[1] Building and printing tibbles.mp415.46 MB
[3] 2. Working with Tibbles/[1] Building and printing tibbles.srt12.04 KB
[3] 2. Working with Tibbles/[2] Subsetting tibbles.mp46.81 MB
[3] 2. Working with Tibbles/[2] Subsetting tibbles.srt5.55 KB
[3] 2. Working with Tibbles/[3] Filtering tibbles.mp46.96 MB
[3] 2. Working with Tibbles/[3] Filtering tibbles.srt6.6 KB
[4] 3. Importing Data into R/[1] What are CSV files.mp46.25 MB
[4] 3. Importing Data into R/[1] What are CSV files.srt5.08 KB
[4] 3. Importing Data into R/[2] Importing CSV files into R.mp423.6 MB
[4] 3. Importing Data into R/[2] Importing CSV files into R.srt13.71 KB
[4] 3. Importing Data into R/[3] What are TSV files.mp43.52 MB
[4] 3. Importing Data into R/[3] What are TSV files.srt3.15 KB
[4] 3. Importing Data into R/[4] Importing TSV files into R.mp425.06 MB
[4] 3. Importing Data into R/[4] Importing TSV files into R.srt18.28 KB
[4] 3. Importing Data into R/[5] Importing delimited files into R.mp411.9 MB
[4] 3. Importing Data into R/[5] Importing delimited files into R.srt7.84 KB
[4] 3. Importing Data into R/[6] Importing fixed-width files into R.mp410.29 MB
[4] 3. Importing Data into R/[6] Importing fixed-width files into R.srt9.44 KB
[4] 3. Importing Data into R/[7] Importing Excel files into R.mp420.4 MB
[4] 3. Importing Data into R/[7] Importing Excel files into R.srt15.31 KB
[4] 3. Importing Data into R/[8] Reading data from databases and the web.mp44.03 MB
[4] 3. Importing Data into R/[8] Reading data from databases and the web.srt4.51 KB
[5] 4. Data Transformation/[1] Wide vs. long datasets.mp45.59 MB
[5] 4. Data Transformation/[1] Wide vs. long datasets.srt6.22 KB
[5] 4. Data Transformation/[2] Making wide datasets long with gather().mp411.56 MB
[5] 4. Data Transformation/[2] Making wide datasets long with gather().srt10.45 KB
[5] 4. Data Transformation/[3] Making long datasets wide with spread().mp48.4 MB
[5] 4. Data Transformation/[3] Making long datasets wide with spread().srt8.09 KB
[5] 4. Data Transformation/[4] Converting data types in R.mp414.78 MB
[5] 4. Data Transformation/[4] Converting data types in R.srt14.02 KB
[5] 4. Data Transformation/[5] Working with dates and times in R.mp412.89 MB
[5] 4. Data Transformation/[5] Working with dates and times in R.srt13.83 KB
[6] 5. Data Cleaning/[1] Detecting outliers.mp424.87 MB
[6] 5. Data Cleaning/[1] Detecting outliers.srt22.02 KB
[6] 5. Data Cleaning/[2] Missing and special values in R.mp419.03 MB
[6] 5. Data Cleaning/[2] Missing and special values in R.srt16.24 KB
[6] 5. Data Cleaning/[3] Breaking apart columns with separate().mp413.32 MB
[6] 5. Data Cleaning/[3] Breaking apart columns with separate().srt10.36 KB
[6] 5. Data Cleaning/[4] Combining columns with unite().mp49.31 MB
[6] 5. Data Cleaning/[4] Combining columns with unite().srt6.51 KB
[6] 5. Data Cleaning/[5] Manipulating strings in R with stringr.mp436.63 MB
[6] 5. Data Cleaning/[5] Manipulating strings in R with stringr.srt23.92 KB
[7] 6. Data Wrangling Case Study Coal Consumption/[1] Understanding the coal dataset.mp41.07 MB
[7] 6. Data Wrangling Case Study Coal Consumption/[1] Understanding the coal dataset.srt1.19 KB
[7] 6. Data Wrangling Case Study Coal Consumption/[2] Reading in the coal dataset.mp416.85 MB
[7] 6. Data Wrangling Case Study Coal Consumption/[2] Reading in the coal dataset.srt9.3 KB
[7] 6. Data Wrangling Case Study Coal Consumption/[3] Converting the coal dataset from long to wide.mp415.31 MB
[7] 6. Data Wrangling Case Study Coal Consumption/[3] Converting the coal dataset from long to wide.srt9.72 KB
[7] 6. Data Wrangling Case Study Coal Consumption/[4] Segmenting the coal dataset.mp427.44 MB
[7] 6. Data Wrangling Case Study Coal Consumption/[4] Segmenting the coal dataset.srt14.56 KB
[7] 6. Data Wrangling Case Study Coal Consumption/[5] Visualizing the coal dataset.mp48 MB
[7] 6. Data Wrangling Case Study Coal Consumption/[5] Visualizing the coal dataset.srt5.75 KB
[8] 7. Data Wrangling Case Study Water Quality/[1] Understanding the water quality dataset.mp48.3 MB
[8] 7. Data Wrangling Case Study Water Quality/[1] Understanding the water quality dataset.srt3.99 KB
[8] 7. Data Wrangling Case Study Water Quality/[2] Reading in the water quality dataset.mp47.08 MB
[8] 7. Data Wrangling Case Study Water Quality/[2] Reading in the water quality dataset.srt4.37 KB
[8] 7. Data Wrangling Case Study Water Quality/[3] Filtering the water quality dataset.mp425.81 MB
[8] 7. Data Wrangling Case Study Water Quality/[3] Filtering the water quality dataset.srt13.49 KB
[8] 7. Data Wrangling Case Study Water Quality/[4] Water quality data types.mp414 MB
[8] 7. Data Wrangling Case Study Water Quality/[4] Water quality data types.srt7.65 KB
[8] 7. Data Wrangling Case Study Water Quality/[5] Correcting data entry errors.mp425.31 MB
[8] 7. Data Wrangling Case Study Water Quality/[5] Correcting data entry errors.srt13.99 KB
[8] 7. Data Wrangling Case Study Water Quality/[6] Identifying and removing outliers.mp421.42 MB
[8] 7. Data Wrangling Case Study Water Quality/[6] Identifying and removing outliers.srt11.98 KB
[8] 7. Data Wrangling Case Study Water Quality/[7] Converting temperature from Fahrenheit to Celsius.mp49.64 MB
[8] 7. Data Wrangling Case Study Water Quality/[7] Converting temperature from Fahrenheit to Celsius.srt6.29 KB
[8] 7. Data Wrangling Case Study Water Quality/[8] Widening the water quality dataset.mp422.01 MB
[8] 7. Data Wrangling Case Study Water Quality/[8] Widening the water quality dataset.srt11.56 KB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[1] Understanding the Social Security Disability dataset.mp45.07 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[1] Understanding the Social Security Disability dataset.srt5.23 KB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[2] Importing the Social Security Disability dataset.mp46.86 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[2] Importing the Social Security Disability dataset.srt3.84 KB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[3] Making the Social Security Disability dataset long.mp47.03 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[3] Making the Social Security Disability dataset long.srt3.97 KB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[4] Formatting dates in the Social Security Disability dataset.mp418.12 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[4] Formatting dates in the Social Security Disability dataset.srt12.14 KB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[5] Handling fiscal years in the Social Security Disability dataset.mp46 MB
[9] 8. Data Wrangling Case Study Social Security Disability Claims/[5] Handling fiscal years in the Social Security Disability dataset.srt5.43 KB

Trackers

No trackers found.