~Get Your Files Here !

  Ex_Files_Data_Wrangling_in_R

   Exercise Files

    Ch03

     03_02

      csv_start.r

     03_04

      tsv_start.r

     03_05

      delimited_start.r

     03_06

      fixedwidth_start.r

     03_07

      excel_start.r

    Ch04

     04_02

      pew_start.r

     04_03

      mexicanweather_start.r

     04_04

      datatypes_start.r

     04_05

      dates_start.r

    Ch05

     05_01

      outliers_start.r

     05_02

      missing2_start.r

      missing_start.r

     05_03

      separate_start.r

     05_04

      unite_start.r

     05_05

      stringr2_start.r

      stringr_start.r

    Ch06

     06_02

      coal_2_start.r

     06_03

      coal_3_start.r

     06_04

      coal_4_start.r

     06_05

      coal_5_start.r

    Ch07

     07_02

      water_2_start.r

     07_03

      water_3_start.r

     07_04

      water_4_start.r

     07_05

      water_5_start.r

     07_06

      water_6_start.r

     07_07

      water_7_start.r

     07_08

      water_8_start.r

    Ch08

     08_02

      ssa_2_start.r

     08_03

      ssa_3_start.r

     08_04

      ssa_4_start.r

     08_05

      ssa_5_start.r

     08_06

      ssa_6_start.r

     08_07

      ssa_7_start.r

    breakfast.xlsx

    coal.csv

    inpatient.txt

    mexicanweather.csv

    ssadisability.csv

  [10] Conclusion

   [1] Next steps.srt

  [1] Introduction

   [1] Preparing for data wrangling .srt

   [2] What you need to know.srt

   [3] Exercise files.srt

  [2] 1. Tidy Data

   [1] What is tidy data.srt

   [2] Variables, observations, and values.srt

   [3] Common data problems.srt

   [4] Using the tidyverse.srt

  [3] 2. Working with Tibbles

   [2] Subsetting tibbles.mp4

   [3] Filtering tibbles.mp4

  [4] 3. Importing Data into R

   [1] What are CSV files.srt

   [2] Importing CSV files into R.srt

   [3] What are TSV files.srt

   [4] Importing TSV files into R.srt

   [5] Importing delimited files into R.srt

   [6] Importing fixed-width files into R.srt

   [7] Importing Excel files into R.srt

   [8] Reading data from databases and the web.srt

  [5] 4. Data Transformation

   [1] Wide vs. long datasets.srt

   [2] Making wide datasets long with pivot_longer().srt

   [3] Making long datasets wide with pivot_wider().srt

   [4] Converting data types in R.srt

   [5] Working with dates and times in R.srt

  [6] 5. Data Cleaning

   [1] Detecting outliers.srt

   [2] Missing and special values in R.srt

   [3] Breaking apart columns with separate().srt

   [4] Combining columns with unite().srt

   [5] Manipulating strings in R with stringr.srt

  [7] 6. Data Wrangling Case Study Coal Consumption

   [1] Understanding the coal dataset.srt

   [2] Reading in the coal dataset.srt

   [3] Converting the coal dataset from long to wide.srt

   [4] Segmenting the coal dataset.srt

   [5] Visualizing the coal dataset.srt

  [8] 7. Data Wrangling Case Study Water Quality

   [1] Understanding the water quality dataset.srt

   [2] Reading in the water quality dataset.srt

   [3] Filtering the water quality dataset.srt

   [4] Water quality data types.srt

   [5] Correcting data entry errors.srt

   [6] Identifying and removing outliers.srt

   [7] Converting temperature from Fahrenheit to Celsius.srt

  [9] 8. Data Wrangling Case Study Social Security Disability

   [1] Understanding the social security disability dataset.srt

   [2] Importing the social security disability dataset.srt

   [4] Formatting dates in the social security disability dataset.mp4

   [5] Fiscal years in the social security disability dataset.mp4

   [6] Widening the social security disability dataset.mp4

   [7] Visualizing the social security disability dataset.mp4