A Systematic Data Wrangling Idiom
Identify Constant Features of a Data Frame
Identify Constant Features of an Object
Sort column subsets.
Show records with duplicate or duplicated values of grouping variables...
Show duplicate or duplicated elements.
Index records with with duplicate or duplicated values of grouping var...
Calculate dupGroups.
Count unique combinations of items in specified columns.
Drop columns in x that are present in y.
Drop columns in x that are entirely NA.
Drop columns in x that are entirely NA.
Show unique combinations of items in specified columns
Index records whose relative positions would change if sorted.
Show misplaced elements.
Show records with NA values of grouping variables.
Show na elements.
Index records with NA values of grouping variables.
Calculate naGroups.
Join Data Frames Safely
Join Data Safely
Arrange by groups.
Find unique records for subset of columns with one unique value.
Report status with respect to grouping variables.
Report status.
Extract records whose relative positions would change if sorted.
Show unsorted elements.
Show records with NA, duplicate or duplicated values of grouping varia...
Show na, duplicate, or duplicated elements.
Supports systematic scrutiny, modification, and integration of data. The function status() counts rows that have missing values in grouping columns (returned by na() ), have non-unique combinations of grouping columns (returned by dup() ), and that are not locally sorted (returned by unsorted() ). Functions enumerate() and itemize() give sorted unique combinations of columns, with or without occurrence counts, respectively. Function ignore() drops columns in x that are present in y, and informative() drops columns in x that are entirely NA; constant() returns values that are constant, given a key. Data that have defined unique combinations of grouping values behave more predictably during merge operations.