A Collection of Tools and Helpers Extending the Tidyverse
Format numeric columns for display
Ordering function: identity order
A notion of valid and invalid
Inverting name and value
"Variable generating" functions
Add results of prop.test to data frame
All() giving NA only if all values are NA
Any() giving NA only if all values are NA
Appending in a pipe, never unlisting
Vectorised conversion to logical, treating NA as False
Format numeric value for output
Formatting p values
Format as percentage for output
Categorical test in a pipe
Convert contingency table to classical R matrix
Create data frame formed like a contingency-table
Count by multiple variables
Count according to grouping
The DIN A paper formats
Compare vectors, treating NA like a value
Execute code after tidy evaluation
Extract symbols from an expression of symbols and operators
Row-wise first value which is not NA
Row-wise first value that is not NA
First argument that does not equal a given value
First argument that is not NA
Row-wise first index of column that is not NA
First which() is not na
Format numeric columns for display
Lookup in a dictionary
Creating a lookup function from dictionary
Generic lumping
Lump rows of a tibble
Named color palette
Reorder a factor
Orderer function for complex sorting
Pluck with simplified return value
Directory creation
Directory creation and file path concatenation
Prepending in a pipe, never unlisting
Print deparsed language
Rename a factor.
Rename and reorder a factor.
Replace sequential duplicates
Save plot as PDF
Save plot as PNG
Detect sequential duplicates
Combine str_match and str_locate
Make quosure from symbol
Syntactically safe names
Test for logical true or NA
A python / javascript-like "truthy" notion
Infix operator for python-style tuple assignment
Get indices of non-NA values
Slice by name
Slice by value
A selection of various tools to extend a data analysis workflow based on the 'tidyverse' packages. This includes high-level data frame editing methods (in the style of 'mutate'/'mutate_at'), some methods in the style of 'purrr' and 'forcats', 'lookup' methods for dict-like lists, a generic method for lumping a data frame by a given count, various low-level methods for special treatment of 'NA' values, 'python'-style tuple-assignment and 'truthy'/'falsy' checks, saving to PDF and PNG from a pipe and various small utilities.