Cleaning Data in R

RStudio    |    Intermediate
  • 13 videos | 1h 2m 7s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 197 users Rating 4.5 of 197 users (197)
R is a programming language that is essential for data science, used for statistical computing and graphics. In this 13-video course, learners explore essential methods for wrangling and cleaning data with R. Begin by recognizing types of unclean data and criteria for ensuring data quality. First, learners see how to fetch a JSON (JavaScript Object Notation) document over HTTP and load data into a dplyr table. Learn how to load multiple sheets from an Excel document and how to handle common errors encountered when reading CSV (comma-separated values) data. Read data from a relational database with a SQL (structured query language) query. Explore joining tabular data by combining two related data sets by using a join operation, and spreading data-reshaping tabular data by spreading values from rows to columns. Look at summarizing data, applying a summary function using dplyr; imputing data, using mean imputation to replace missing values; and extracting matches, using a regular expression and data wrangling tools from the tidyverse package. The closing exercise practices data wrangling functions using R.

WHAT YOU WILL LEARN

  • Recognize types of unclean data
    Recognize criteria for ensuring data quality
    Fetch a json document over http and load it using dplyr
    Load multiple sheets from an excel document
    Handle common errors encountered when reading csv data
    Read data from a relational database using a sql query
  • Combine two related datasets using a join operation
    Reshape tabular data by spreading values from rows to columns
    Apply a summary function using dplyr
    Use mean imputation to replace missing values
    Use a regular expression to extract data into a new column
    Practice applying data wrangling functions using r

IN THIS COURSE

  • 1m 13s
  • 4m 22s
    Upon completion of this video, you will be able to recognize types of data that are not clean. FREE ACCESS
  • Locked
    3.  Data Quality
    3m 44s
    After completing this video, you will be able to recognize criteria for ensuring data quality. FREE ACCESS
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    4.  Downloading JSON Data
    8m 11s
    In this video, learn how to fetch a JSON document over HTTP and load it using dplyr. FREE ACCESS
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    5.  Excel Sheets
    4m 8s
    During this video, you will learn how to load multiple sheets from an Excel document. FREE ACCESS
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    6.  Reading Dirty CSVs
    8m 40s
    Find out how to handle common errors encountered when reading data from a CSV file. FREE ACCESS
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    7.  Querying Relational Databases
    5m 13s
    Find out how to read data from a relational database using SQL queries. FREE ACCESS
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    8.  Joining Tabular Data
    2m 59s
    In this video, find out how to combine two related datasets using a join operation. FREE ACCESS
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    9.  Spreading Data
    3m 38s
    In this video, you will reshape tabular data by spreading values from columns to rows. FREE ACCESS
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    10.  Summarizing Data
    4m 23s
    During this video, you will learn how to apply a summary function using dplyr. FREE ACCESS
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    11.  Imputing Data
    5m 40s
    To replace missing values, find out how to use mean imputation. FREE ACCESS
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    12.  Extracting Matches
    4m 42s
    In this video, you will learn how to use a regular expression to extract data into a new column. FREE ACCESS
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    13.  Exercise: Wrangling Data
    5m 14s
    During this video, you will learn how to apply data wrangling functions using R. FREE ACCESS

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