12.2 Grouping and summarizing data

Grouping and summarizing data are key skills in data manipulation. They let you organize info into meaningful chunks and crunch numbers to get useful insights. These techniques are super handy for spotting patterns and trends in your data.

Using dplyr functions like group_by() and summarize(), you can slice and dice data in countless ways. You'll learn to calculate stats for different groups, sort data, and pull out the most important bits. It's like giving your data superpowers!

Grouping Data

Creating and Manipulating Groups

Top images from around the web for Creating and Manipulating Groups

• 

  R Data Structures View original

  Is this image relevant?

• 

  Data Analysis with R View original

  Is this image relevant?

• 

  (Class 6) Part 5 contd: Data summarizing, reshaping, and wrangling with multiple tables (contd ... View original

  Is this image relevant?

• 

  R Data Structures View original

  Is this image relevant?

• 

  Data Analysis with R View original

  Is this image relevant?

1 of 3

Top images from around the web for Creating and Manipulating Groups

• 

  R Data Structures View original

  Is this image relevant?

• 

  Data Analysis with R View original

  Is this image relevant?

• 

  (Class 6) Part 5 contd: Data summarizing, reshaping, and wrangling with multiple tables (contd ... View original

  Is this image relevant?

• 

  R Data Structures View original

  Is this image relevant?

• 

  Data Analysis with R View original

  Is this image relevant?

1 of 3

• group_by()

  function organizes data into groups based on specified variables
• Groups remain intact for subsequent operations until explicitly ungrouped

• ungroup()

  removes grouping structure from a data frame
• Grouping affects how other functions operate on the data
• Multiple variables can be used for grouping, creating nested group structures

Sorting and Selecting Data

• [arrange()](https://www.fiveableKeyTerm:arrange())

  orders rows of a data frame based on values in specified columns

• [desc()](https://www.fiveableKeyTerm:desc())

  function used within

  arrange()

  to sort in descending order
• Sorting can be applied to grouped data, maintaining group structure

• slice()

  selects rows from a data frame by their integer positions
• When used with grouped data,

  slice()

  operates within each group independently

Summarizing Data

Basic Summary Functions

• summarize()

  (or

  summarise()

  ) computes summary statistics for a data frame
• Creates a new data frame with one row per group when used with grouped data

• [n()](https://www.fiveableKeyTerm:n())

  counts the number of rows in each group or the entire dataset

• [mean()](https://www.fiveableKeyTerm:mean())

  calculates the arithmetic average of a numeric vector

• [median()](https://www.fiveableKeyTerm:median())

  finds the middle value in a sorted set of numbers

Advanced Summary Functions

• max()

  returns the highest value in a vector or column

• min()

  identifies the lowest value in a vector or column

• sum()

  computes the total of all values in a numeric vector
• Aggregate functions perform calculations across multiple rows or an entire column
  • Include functions like

    var()

    for variance and

    sd()

    for standard deviation
  • Can be used within

    summarize()

    to compute group-wise statistics
• Custom summary functions can be defined and used within

  summarize()

Applying Summary Functions

• Summary functions can be used on both grouped and ungrouped data
• Multiple summary statistics can be computed in a single

  summarize()

  call
• Results of summary functions often combined with

  group_by()

  for group-wise analysis
• Summary functions handle missing values (NA) differently, some have

  [na.rm](https://www.fiveableKeyTerm:na.rm)

  argument
• Summarized results can be further manipulated or visualized for data analysis