Cleaning Data in R

RStudio 1.1.4
  • 13 Videos | 1h 7m 37s
  • Includes Assessment
  • Earns a Badge
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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

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