6.2 Subsetting data frames

Subsetting data frames is a crucial skill in R programming, allowing you to extract specific parts of your data. This topic covers various methods, from basic square bracket notation to advanced dplyr functions, giving you the tools to manipulate your data effectively.

Understanding these techniques is essential for data analysis and manipulation. By mastering subsetting, you'll be able to efficiently filter, select, and transform your data, setting the foundation for more complex data operations in R.

Indexing and Subsetting

Square Bracket and Dollar Sign Notation

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• Square bracket notation

  []

  accesses specific elements, rows, or columns in a data frame
• Single square brackets

  []

  return a data frame, while double square brackets

  [[]]

  return a vector
• Use comma inside brackets to specify rows and columns

  dataframe[row, column]

• Dollar sign notation

  $

  extracts a single column from a data frame as a vector
• Combine dollar sign with square brackets to subset specific elements

  dataframe$column[1:5]

• Square bracket notation allows for more complex subsetting operations (multiple rows or columns)
• Dollar sign notation provides a quick way to access individual columns by name

Subset() Function and Logical Indexing

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

  function creates a subset of a data frame based on specified conditions
• Syntax:

  subset(dataframe, condition, select = columns)

• Logical indexing uses boolean expressions to filter data
• Create logical vectors with comparison operators (

  ==

  ,

  !=

  ,

  >

  ,

  <

  ,

  >=

  ,

  <=

  )
• Combine multiple conditions using logical operators (

  &

  ,

  |

  ,

  !

  )
• Use

  [which](https://www.fiveableKeyTerm:which)()

  function to find indices of TRUE values in a logical vector
• Logical indexing allows for flexible and powerful data filtering

Numeric and Character Indexing

• Numeric indexing uses integer values to select specific rows or columns
• Positive integers select elements at those positions
• Negative integers exclude elements at those positions
• Character indexing uses column names or row names to select data
• Combine numeric and character indexing for more precise subsetting
• Use

  c()

  function to create vectors of indices or names for multiple selections
• Negative indexing removes specified elements while keeping the rest

Selecting Rows and Columns

Row and Column Selection Techniques

• Use single square brackets to select entire rows or columns

  dataframe[1:5, ]

  or

  dataframe[, c("col1", "col2")]

• Combine row and column selection in a single operation

  dataframe[1:5, c("col1", "col2")]

• Utilize logical vectors for conditional row selection

  dataframe[dataframe$age > 30, ]

• Employ the

  which()

  function to find row indices based on conditions

  dataframe[which(dataframe$status == "active"), ]

• Create custom functions for complex selection criteria

Conditional Subsetting and Column Manipulation

• Apply conditional subsetting to filter data based on specific criteria
• Use logical operators to combine multiple conditions

  dataframe[dataframe$age > 30 & dataframe$income < 50000, ]

• Drop columns by assigning NULL

  dataframe$column_to_drop <- NULL

• Select multiple columns using a character vector of column names

  dataframe[, c("col1", "col2", "col3")]

• Implement slicing to extract continuous blocks of data

  dataframe[10:20, 3:5]

• Reorder columns by specifying a new order in the column selection

  dataframe[, c("col3", "col1", "col2")]

• Create new columns based on existing data during subsetting

  dataframe$new_column <- dataframe$column1 + dataframe$column2

dplyr Functions for Subsetting

Powerful dplyr Selection Tools

• [dplyr::select()](https://www.fiveableKeyTerm:dplyr::select())

  function chooses specific columns from a data frame
• Use

  select()

  with column names, indices, or helper functions (starts_with(), ends_with(), contains())
• Rename columns within

  select()

  using the new_name = old_name syntax
• Negate column selection with

  -

  to exclude specific columns
• Reorder columns easily by specifying the desired order in

  select()

• Combine

  select()

  with other dplyr functions using the pipe operator

  %>%

Efficient Filtering with dplyr

• dplyr::[filter()](https://www.fiveableKeyTerm:filter())

  function subsets rows based on specified conditions
• Use comparison operators and logical operators to create filtering conditions
• Chain multiple conditions within a single

  filter()

  call
• Utilize

  filter()

  with

  between()

  ,

  %in%

  , and other dplyr helper functions for complex filtering
• Combine

  filter()

  with

  select()

  to subset both rows and columns in a single pipeline
• Employ

  filter()

  with

  group_by()

  to apply filtering conditions within groups
• Leverage

  filter()

  for efficient data cleaning and preparation tasks