as_ascii_table

Description

This function takes a data.frame and attempts to convert it into a simple ascii format suitable for printing to the screen It is assumed all variable values have a as.character() method in order to cast them to character.

Usage

as_ascii_table(dat, line_prefix = "  ")

Arguments

as_character

Description

Stub function to enable mocking in unit tests

Usage

as_character()

Format vector to printable string

Description

Coerces a vector of any type into a printable string. The most significant transformation is performed on existing character vectors which will be truncated, have newlines converted to explicit symbols and will be wrapped in quotes if they contain white space.

Usage

as_fmt_char(x, ...)

## S3 method for class 'numeric'
as_fmt_char(x, ...)

## S3 method for class ''NULL''
as_fmt_char(x, ...)

## S3 method for class 'list'
as_fmt_char(x, ...)

## S3 method for class 'factor'
as_fmt_char(x, ...)

## S3 method for class 'character'
as_fmt_char(x, add_quotes = TRUE, crop_at = 30, ...)

## Default S3 method:
as_fmt_char(x, ...)

## S3 method for class 'POSIXt'
as_fmt_char(x, ...)

Arguments

Assert that keys are valid

Description

Utility function to check that user provided "keys" aren't listed as a problem variable of the current list of issues.

Usage

assert_valid_keys(COMPARE, KEYS, component, msg)

Arguments

cast_variables

Description

Function to cast datasets columns if they have differing types Restricted to specific cases, currently integer and double, and character and factor

Usage

cast_variables(
  BASE,
  COMPARE,
  ignore_vars = NULL,
  cast_integers = FALSE,
  cast_factors = FALSE
)

Arguments

class_merge

Description

Convenience function to put all classes an object has into one string

Usage

class_merge(x)

Arguments

compare_vectors

Description

Compare two vectors looking for differences

Usage

compare_vectors(target, current, ...)

Arguments

compare_vectors.default

Description

Default method, if the vector is not numeric or factor. Basic comparison

Usage

## Default S3 method:
compare_vectors(target, current, ...)

Arguments

compare_vectors.factor

Description

Compares factors. Sets them as character and then compares

Usage

## S3 method for class 'factor'
compare_vectors(target, current, ...)

Arguments

compare_vectors.int64

Description

Handle int64 vectors. Uses numeric comparison

Usage

## S3 method for class 'integer64'
compare_vectors(
  target,
  current,
  tolerance = sqrt(.Machine$double.eps),
  scale = NULL,
  ...
)

Arguments

compare_vectors.numeric

Description

This is a modified version of the all.equal function which returns a vector rather than a message

Usage

## S3 method for class 'numeric'
compare_vectors(
  target,
  current,
  tolerance = sqrt(.Machine$double.eps),
  scale = NULL,
  ...
)

Arguments

construct_issue

Description

Make an s3 object with class issue and possible additional class, and assign other arguments to attributes

Usage

construct_issue(value, message, add_class = NULL)

Arguments

convert_to_issue

Description

converts the count value into the correct issue format

Usage

convert_to_issue(datin)

Arguments

Describe the datasets being compared

Description

This function is used to produce a basic summary table of the core features of the two data.frame's being compared.

Usage

describe_dataframe(base, comp, base_name, comp_name)

Arguments

diffdf

Description

Compares 2 dataframes and outputs any differences.

Usage

diffdf(
  base,
  compare,
  keys = NULL,
  suppress_warnings = FALSE,
  strict_numeric = TRUE,
  strict_factor = TRUE,
  file = NULL,
  tolerance = sqrt(.Machine$double.eps),
  scale = NULL,
  check_column_order = FALSE,
  check_df_class = FALSE
)

Arguments

Examples

x <- subset(iris, -Species)
x[1, 2] <- 5
COMPARE <- diffdf(iris, x)
print(COMPARE)

#### Sample data frames

DF1 <- data.frame(
    id = c(1, 2, 3, 4, 5, 6),
    v1 = letters[1:6],
    v2 = c(NA, NA, 1, 2, 3, NA)
)

DF2 <- data.frame(
    id = c(1, 2, 3, 4, 5, 7),
    v1 = letters[1:6],
    v2 = c(NA, NA, 1, 2, NA, NA),
    v3 = c(NA, NA, 1, 2, NA, 4)
)

diffdf(DF1, DF1, keys = "id")

# We can control matching with scale/location for example:

DF1 <- data.frame(
    id = c(1, 2, 3, 4, 5, 6),
    v1 = letters[1:6],
    v2 = c(1, 2, 3, 4, 5, 6)
)
DF2 <- data.frame(
    id = c(1, 2, 3, 4, 5, 6),
    v1 = letters[1:6],
    v2 = c(1.1, 2, 3, 4, 5, 6)
)

diffdf(DF1, DF2, keys = "id")
diffdf(DF1, DF2, keys = "id", tolerance = 0.2)
diffdf(DF1, DF2, keys = "id", scale = 10, tolerance = 0.2)

# We can use strict_factor to compare factors with characters for example:

DF1 <- data.frame(
    id = c(1, 2, 3, 4, 5, 6),
    v1 = letters[1:6],
    v2 = c(NA, NA, 1, 2, 3, NA),
    stringsAsFactors = FALSE
)

DF2 <- data.frame(
    id = c(1, 2, 3, 4, 5, 6),
    v1 = letters[1:6],
    v2 = c(NA, NA, 1, 2, 3, NA)
)

diffdf(DF1, DF2, keys = "id", strict_factor = TRUE)
diffdf(DF1, DF2, keys = "id", strict_factor = FALSE)

diffdf_has_issues

Description

Utility function which returns TRUE if an diffdf object has issues or FALSE if an diffdf object does not have issues

Usage

diffdf_has_issues(x)

Arguments

Examples

# Example with no issues
x <- diffdf(iris, iris)
diffdf_has_issues(x)

# Example with issues
iris2 <- iris
iris2[2, 2] <- NA
x <- diffdf(iris, iris2, suppress_warnings = TRUE)
diffdf_has_issues(x)

Identify Issue Rows

Description

This function takes a diffdf object and a dataframe and subsets the data.frame for problem rows as identified in the comparison object. If vars has been specified only issue rows associated with those variable(s) will be returned.

Usage

diffdf_issuerows(df, diff, vars = NULL)

Arguments

Details

Note that diffdf_issuerows can be used to subset against any dataframe. The only requirement is that the original variables specified in the keys argument to diffdf are present on the dataframe you are subsetting against. However please note that if no keys were specified in diffdf then the row number is used. This means using diffdf_issuerows without a keys against an arbitrary dataset can easily result in nonsense rows being returned. It is always recommended to supply keys to diffdf.

Examples

iris2 <- iris
for (i in 1:3) iris2[i, i] <- 99
x <- diffdf(iris, iris2, suppress_warnings = TRUE)
diffdf_issuerows(iris, x)
diffdf_issuerows(iris2, x)
diffdf_issuerows(iris2, x, vars = "Sepal.Length")
diffdf_issuerows(iris2, x, vars = c("Sepal.Length", "Sepal.Width"))

factor_to_character

Description

Takes a dataframe and converts any factor variables to character

Usage

factor_to_character(dsin, vars = NULL)

Arguments

find_difference

Description

This determines if two vectors are different. It expects vectors of the same length and type, and is intended to be used after checks have already been done Initially picks out any NA's (matching NA's count as a match) Then compares remaining vector

Usage

find_difference(target, current, ...)

Arguments

Generate unique key name

Description

Function to generate a name for the keys if not provided

Usage

generate_keyname(
  BASE,
  COMP,
  replace_names = c("..ROWNUMBER..", "..RN..", "..ROWN..", "..N..")
)

Arguments

get_casted_dataset

Description

Internal utility function to loop across a dataset casting all target variables

Usage

get_casted_dataset(df, columns, whichdat)

Arguments

get_casted_vector

Description

casts a vector depending on its type and input

Usage

get_casted_vector(colin, colname, whichdat)

Arguments

get_issue_dataset

Description

Internal function used by diffdf_issuerows to extract the dataframe from each a target issue. In particular it also strips off any non-key variables

Usage

get_issue_dataset(issue, diff)

Arguments

get_issue_message

Description

Simple function to grab the issue message

Usage

get_issue_message(object, ...)

Arguments

get_print_message

Description

Get the required text depending on type of issue

Usage

get_print_message(object, ...)

Arguments

get_print_message.default

Description

Errors, as this should only ever be given an issue

Usage

## Default S3 method:
get_print_message(object, ...)

Arguments

get_print_message.issue

Description

Get text from a basic issue, based on the class of the value of the issue

Usage

## S3 method for class 'issue'
get_print_message(object, row_limit, ...)

Arguments

get_table

Description

Generate nice looking table from a data frame

Usage

get_table(dsin, row_limit = 10)

Arguments

has_unique_rows

Description

Check if a data sets rows are unique

Usage

has_unique_rows(DAT, KEYS)

Arguments

Identify differences in attributes

Description

Identifies any attribute differences between two data frames

Usage

identify_att_differences(BASE, COMP, exclude_cols = "")

Arguments

identify_class_differences

Description

Identifies any class differences between two data frames

Usage

identify_class_differences(BASE, COMP)

Arguments

Find column ordering differences

Description

Compares two datasets and outputs a table listing any differences in the column orders between the two datasets. Columns that are not contained within both are ignored however column ordering is derived prior to removing these columns.

Usage

identify_column_order_differences(BASE, COMP)

Arguments

identify_differences

Description

Compares each column within 2 datasets to identify any values which they mismatch on.

Usage

identify_differences(
  BASE,
  COMP,
  KEYS,
  exclude_cols,
  tolerance = sqrt(.Machine$double.eps),
  scale = NULL
)

Arguments

identify_extra_cols

Description

Identifies columns that are in a baseline dataset but not in a comparator dataset

Usage

identify_extra_cols(DS1, DS2)

Arguments

identify_extra_rows

Description

Identifies rows that are in a baseline dataset but not in a comparator dataset

Usage

identify_extra_rows(DS1, DS2, KEYS)

Arguments

identify_matching_cols

Description

Identifies columns with the same name in two data frames

Usage

identify_matching_cols(DS1, DS2, EXCLUDE = "")

Arguments

identify_mode_differences

Description

Identifies any mode differences between two data frames

Usage

identify_mode_differences(BASE, COMP)

Arguments

identify_properties

Description

Returns a dataframe of metadata for a given dataset. Returned values include variable names , class , mode , type & attributes

Usage

identify_properties(dsin)

Arguments

identify_unsupported_cols

Description

Identifies any columns for which the package is not setup to handle

Usage

identify_unsupported_cols(dsin)

Arguments

invert

Description

Utility function used to replicated purrr::transpose. Turns a list inside out.

Usage

invert(x)

Arguments

is_variable_different

Description

This subsets the data set on the variable name, picks out differences and returns a tibble of differences for the given variable

Usage

is_variable_different(variablename, keynames, datain, ...)

Arguments

Value

A boolean vector which is T if target and current are different

Print diffdf objects

Description

Print nicely formatted version of an diffdf object

Usage

## S3 method for class 'diffdf'
print(x, row_limit = 10, as_string = FALSE, ...)

Arguments

Examples

x <- subset(iris, -Species)
x[1, 2] <- 5
COMPARE <- diffdf(iris, x)
print(COMPARE)
print(COMPARE, row_limit = 5)

recursive_reduce

Description

Utility function used to replicated purrr::reduce. Recursively applies a function to a list of elements until only 1 element remains

Usage

recursive_reduce(.l, .f)

Arguments

Details

This function is essentially performing the following operation:

.l[[1]] <- .f( .l[[1]] , .l[[2]]) ; .l[[1]] <- .f( .l[[1]] , .l[[3]])

sort_then_join

Description

Convenience function to sort two strings and paste them together

Usage

sort_then_join(string1, string2)

Arguments

Pad String

Description

Utility function used to replicate str_pad. Adds white space to either end of a string to get it to equal the desired length

Usage

string_pad(x, width)

Arguments