Package 'scrutiny'

Description

audit() summarizes the results of scrutiny functions like grim_map() that perform tests on data frames.

See below for a record of such functions. Go to the documentation of any of them to learn about its audit() method.

Usage

audit(data)

Arguments

Details

audit() is an S3 generic. It looks up the (invisible) scrutiny class of a tibble returned by any function named below. You don't need to deal with the classes directly. Behind the scenes, they mediate between these functions and their associated summary statistics.

Value

A tibble (data frame) with test summary statistics.

Run before audit()

Examples

# For basic GRIM-testing:
pigs1 %>%
  grim_map() %>%
  audit()

# For duplicate detection:
pigs4 %>%
  duplicate_count() %>%
  audit()

Description

Call audit_cols_minimal() within your audit() methods for the output of consistency test mapper functions such as grim_map(). It will create a tibble with the three minimal, required columns:

1. incons_cases counts the inconsistent cases, i.e., the number of rows in the mapper's output where "consistency" is FALSE.

2. all_cases is the total number of rows in the mapper's output.

3. incons_rate is the ratio of incons_cases to all_cases.

You can still add other columns to this tibble. Either way, make sure to name your method correctly. See examples.

Usage

audit_cols_minimal(data, name_test)

Arguments

Value

A tibble (data frame) with the columns listed above.

See Also

For context, see vignette("consistency-tests-in-depth"). In case you don't call audit_cols_minimal(), you should call check_audit_special().

Examples

# For a mapper function called `schlim_map()`
# that applies a test called SCHLIM and returns
# a data frame with the `"scr_schlim_map"` class:
audit.scr_schlim_map <- function(data) {
  audit_cols_minimal(data, name_test = "SCHLIM")
}

# If you like, add other summary columns
# with `dplyr::mutate()` or similar.

Description

audit_seq() and audit_total_n() summarize the results of functions that end on ⁠_seq⁠ and ⁠_total_n⁠, respectively.

See below for a record of such functions. Go to the documentation of any of them to learn about the way its output is processed by audit_seq() or audit_total_n().

Usage

audit_seq(data)

audit_total_n(data)

Arguments

Details

All functions named below that end on ⁠_seq⁠ were made by function_map_seq(). All that end on ⁠_total_n⁠ were made by function_map_total_n().

Value

A tibble (data frame) with test summary statistics.

Before audit_seq()

Before audit_total_n()

Examples

# For GRIM-testing with dispersed inputs:
out <- pigs1 %>%
  grim_map_seq() %>%
  audit_seq()
out

# Follow up on `audit_seq()` or
# `audit_total_n()` with `audit()`:
audit(out)

Description

(Note: Ignore this function if your audit() method calls audit_cols_minimal().)

Call check_audit_special() within an audit() method for a consistency test mapper function, such as audit.scr_grim_map(). It checks if the input data frame was the product of a function produced by function_map_seq() or function_map_total_n().

If so, the function issues a gentle alert to the user that points to audit_seq() or audit_total_n(), respectively.

Usage

check_audit_special(data, name_test)

Arguments

Value

No return value. Might print an alert.

See Also

vignette("consistency-tests-in-depth"), for context.

Description

When called within a consistency test mapper function, check_mapper_input_colnames() makes sure that the input data frame has correct column names:

• They include all the key columns corresponding to the test applied by the mapper.

• They don't already include "consistency".

If either check fails, the function throws an informative error.

Usage

check_mapper_input_colnames(data, reported, name_test)

Arguments

Value

No return value. Might throw an error.

See Also

vignette("consistency-tests-in-depth"), for context and the "key columns" terminology.

Description

• is_map_df() tests whether an object is the output of a scrutiny-style mapper function for consistency tests, like grim_map(). These mapper functions also include those produced by function_map(), function_map_seq(), and function_map_total_n().

• is_map_basic_df() is a variant of is_map_df() that tests whether an object is the output of a "basic" mapper function. This includes functions like grim_map() and those produced by function_map(), but not those produced by function_map_seq() or function_map_total_n().

• is_map_seq_df() tests whether an object is the output of a function that was produced by function_map_seq().

• is_map_total_n_df() tests whether an object is the output of a function that was produced by function_map_total_n().

Usage

is_map_df(x)

is_map_basic_df(x)

is_map_seq_df(x)

is_map_total_n_df(x)

Arguments

Details

Sections 3, 6, and 7 of vignette("consistency-tests-in-depth") discuss which function factories produce which functions, and which of these new, factory-made functions return which kinds of tibbles.

These tibbles are what the ⁠is_map_*()⁠ functions test for. As an example, function_map_seq() produces grim_map_seq(), and this new function returns a tibble. is_map_df() and is_map_seq_df() return TRUE for this tibble, but is_map_basic_df() and is_map_total_n_df() return FALSE.

For an overview, see the table at the end of vignette("consistency-tests-in-depth").

Value

Logical (length 1).

Examples

# Example test output:
df1 <- grim_map(pigs1)
df2 <- grim_map_seq(pigs1)
df3 <- grim_map_total_n(tibble::tribble(
  ~x1,    ~x2,   ~n,
  "3.43", "5.28", 90,
  "2.97", "4.42", 103
))

# All three tibbles are mapper output:
is_map_df(df1)
is_map_df(df2)
is_map_df(df3)

# However, only `df1` is the output of a
# basic mapper...
is_map_basic_df(df1)
is_map_basic_df(df2)
is_map_basic_df(df3)

# ...only `df2` is the output of a
# sequence mapper...
is_map_seq_df(df1)
is_map_seq_df(df2)
is_map_seq_df(df3)

# ...and only `df3` is the output of a
# total-n mapper:
is_map_total_n_df(df1)
is_map_total_n_df(df2)
is_map_total_n_df(df3)

Description

debit() tests summaries of binary data for consistency: If the mean and the sample standard deviation of binary data are given, are they consistent with the reported sample size?

The function is vectorized, but it is recommended to use debit_map() for testing multiple cases.

Usage

debit(
  x,
  sd,
  n,
  formula = "mean_n",
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE
)

Arguments

Value

Logical. TRUE if x, sd, and n are mutually consistent, FALSE if not.

References

Heathers, James A. J., and Brown, Nicholas J. L. 2019. DEBIT: A Simple Consistency Test For Binary Data. https://osf.io/5vb3u/.

See Also

debit_map() applies debit() to any number of cases at once.

Examples

# Check single cases of binary
# summary data:
debit(x = "0.36", sd = "0.11", n = 20)

Description

Call debit_map() to use DEBIT on multiple combinations of mean, sample standard deviation, and sample size of binary distributions. Mapping function for debit().

For summary statistics, call audit() on the results.

Usage

debit_map(
  data,
  x = NULL,
  sd = NULL,
  n = NULL,
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE,
  show_rec = TRUE,
  extra = Inf
)

Arguments

Value

A tibble with (at least) these columns –

• x, sd, n: the inputs.

• consistency: DEBIT consistency of x, sd, and n.

  By default, the tibble also includes the rounding method, boundary values, and information about the boundary values being inclusive or not. The tibble has the scr_debit_map class, which is recognized by the audit() generic.

Summaries with audit()

There is an S3 method for the audit() generic, so you can call audit() following debit_map(). It returns a tibble with these columns —

1. incons_cases: the number of DEBIT-inconsistent cases.

2. all_cases: the total number of cases.

3. incons_rate: the rate of inconsistent cases.

4. mean_x: the mean x (mean) value.

5. mean_sd: the mean sd value.

6. distinct_n: the number of distinct n values.

References

Heathers, James A. J., and Brown, Nicholas J. L. 2019. DEBIT: A Simple Consistency Test For Binary Data. https://osf.io/5vb3u/.

Examples

# Call `debit_map()` on binary summary
# data such as these:
pigs3

# The `consistency` column shows
# whether the values to its left
# are DEBIT-consistent:
pigs3 %>%
  debit_map()

# Get test summaries with `audit()`:
pigs3 %>%
  debit_map() %>%
  audit()

Description

debit_map_seq() applies DEBIT with values surrounding the input values. This provides an easy and powerful way to assess whether small errors in computing or reporting may be responsible for DEBIT inconsistencies in published statistics.

Usage

debit_map_seq(
  data,
  x = NULL,
  sd = NULL,
  n = NULL,
  var = Inf,
  dispersion = 1:5,
  out_min = "auto",
  out_max = NULL,
  include_reported = FALSE,
  include_consistent = FALSE,
  ...
)

Arguments

Value

A tibble (data frame) with detailed test results.

Summaries with audit_seq()

You can call audit_seq() following debit_map_seq(). It will return a data frame with these columns:

• x, sd, and n are the original inputs, tested for consistency here.

• hits_total is the total number of DEBIT-consistent value sets found within the specified dispersion range.

• hits_x is the number of DEBIT-consistent value sets found by varying x.

• Accordingly with sd and hits_sd as well as n and hits_n.

• (Note that any consistent reported cases will be counted by the ⁠hits_*⁠ columns if both include_reported and include_consistent are set to TRUE.)

• diff_x reports the absolute difference between x and the next consistent dispersed value (in dispersion steps, not the actual numeric difference). diff_x_up and diff_x_down report the difference to the next higher or lower consistent value, respectively.

• diff_sd, diff_sd_up, and diff_sd_down do the same for sd.

• Likewise with diff_n, diff_n_up, and diff_n_down.

Call audit() following audit_seq() to summarize results even further. It's mostly self-explaining, but na_count and na_rate are the number and rate of times that a difference could not be computed because of a lack of corresponding hits within the dispersion range.

Examples

# `debit_map_seq()` can take any input
# that `debit_map()` can take:
pigs3

# Results from testing some few rows:
out <- pigs3 %>%
  dplyr::slice(3:4) %>%
  debit_map_seq()

out

# Case-wise summaries with `audit_seq()`
# can be more important than the raw results:
out %>%
  audit_seq()

Description

debit_map_total_n() extends DEBIT to cases where only group means and standard deviations (SDs) were reported, not group sizes.

The function is analogous to grim_map_total_n() and grimmer_map_total_n(), relying on the same infrastructure.

Usage

debit_map_total_n(
  data,
  x1 = NULL,
  x2 = NULL,
  sd1 = NULL,
  sd2 = NULL,
  dispersion = 0:5,
  n_min = 1L,
  n_max = NULL,
  constant = NULL,
  constant_index = NULL,
  ...
)

Arguments

Value

A tibble with these columns:

• x and sd, the group-wise reported input statistics, are repeated in row pairs.

• n is dispersed from half the input n, with n_change tracking the differences.

• both_consistent flags scenarios where both reported x and sd values are consistent with the hypothetical n values.

• case corresponds to the row numbers of the input data frame.

• dir is "forth" in the first half of rows and "back" in the second half. "forth" means that x2 and sd2 from the input are paired with the larger dispersed n, whereas "back" means that x1 and sd1 are paired with the larger dispersed n.

• Other columns from debit_map() are preserved.

Summaries with audit_total_n()

You can call audit_total_n() following up on debit_map_total_n() to get a tibble with summary statistics. It will have these columns:

• x1, x2, sd1, sd2, and n are the original inputs.

• hits_total is the number of scenarios in which all of x1, x2, sd1, and sd2 are DEBIT-consistent. It is the sum of hits_forth and hits_back below.

• hits_forth is the number of both-consistent cases that result from pairing x2 and sd2 with the larger dispersed n value.

• hits_back is the same, except x1 and sd1 are paired with the larger dispersed n value.

• scenarios_total is the total number of test scenarios, whether or not both x1 and sd1 as well as x2 and sd2 are DEBIT-consistent.

• hit_rate is the ratio of hits_total to scenarios_total.

Call audit() following audit_total_n() to summarize results even further.

References

Bauer, P. J., & Francis, G. (2021). Expression of Concern: Is It Light or Dark? Recalling Moral Behavior Changes Perception of Brightness. Psychological Science, 32(12), 2042–2043. https://journals.sagepub.com/doi/10.1177/09567976211058727

Heathers, J. A. J., & Brown, N. J. L. (2019). DEBIT: A Simple Consistency Test For Binary Data. https://osf.io/5vb3u/.

See Also

function_map_total_n(), which created the present function using debit_map().

Examples

# Run `debit_map_total_n()` on data like these:
df <- tibble::tribble(
  ~x1,  ~x2,  ~sd1,  ~sd2,  ~n,
  "0.30", "0.28", "0.17", "0.10", 70,
  "0.41", "0.39", "0.09", "0.15", 65
)
df

debit_map_total_n(df)

Description

Plot a distribution of binary data and their mutual DEBIT consistency. Call this function only on a data frame that resulted from a call to debit_map().

Various parameters of the individual geoms can be controlled via arguments.

Usage

debit_plot(
  data,
  show_outer_boxes = TRUE,
  show_labels = TRUE,
  show_full_scale = TRUE,
  show_theme_other = TRUE,
  color_cons = "royalblue1",
  color_incons = "red",
  line_alpha = 1,
  line_color = "black",
  line_linetype = 1,
  line_width = 0.5,
  line_size = 0.5,
  rect_alpha = 1,
  tile_alpha = 0.15,
  tile_height_offset = 0.025,
  tile_width_offset = 0.025,
  tile_height_min = 0.0375,
  tile_width_min = 0.0385,
  label_alpha = 0.5,
  label_linetype = 3,
  label_size = 3.5,
  label_linesize = 0.75,
  label_force = 175,
  label_force_pull = 0.75,
  label_padding = 0.5
)

Arguments

Details

The labels are created via ggrepel::geom_text_repel(), so the algorithm is designed to minimize overlap with the tiles and other labels. Yet, they don't take the DEBIT line into account, and their locations are ultimately random. You might therefore have to resize the plot or run the function a few times until the labels are localized in a satisfactory way.

An alternative to the present function would be an S3 method for ggplot2::autoplot(). However, a standalone function such as this allows for customizing geom parameters and might perhaps provide better accessibility overall.

Value

A ggplot object.

References

Heathers, James A. J., and Brown, Nicholas J. L. 2019. DEBIT: A Simple Consistency Test For Binary Data. https://osf.io/5vb3u/.

Examples

# Run `debit_plot()` on the output
# of `debit_map()`:
pigs3 %>%
  debit_map() %>%
  debit_plot()

Description

decimal_places() counts the decimal places in a numeric vector, or in a string vector that can be coerced to numeric.

decimal_places_scalar() is much faster but only takes a single input. It is useful as a helper within other single-case functions.

Usage

decimal_places(x, sep = "\\.")

decimal_places_scalar(x, sep = "\\.")

Arguments

Details

Decimal places in numeric values can't be counted accurately if the number has 15 or more characters in total, including the integer part and the decimal point. A possible solutions is to enter the number as a string to count all digits. (Converting to string is not sufficient – those numbers need to be entered in quotes.)

The functions ignore any whitespace at the end of a string, so they won't mistake spaces for decimal places.

Value

Integer. Number of decimal places in x.

Trailing zeros

If trailing zeros matter, don't convert numeric values to strings: In numeric values, any trailing zeros have already been dropped, and any information about them was lost (e.g., 3.70 returns 3.7). Enter those values as strings instead, such as "3.70" instead of 3.70. However, you can restore lost trailing zeros with restore_zeros() if the original number of decimal places is known.

If you need to enter many such values as strings, consider using tibble::tribble() and drawing quotation marks around all values in a tribble() column at once via RStudio's multiple cursors.

See Also

decimal_places_df(), which applies decimal_places() to all numeric-like columns in a data frame.

Examples

# `decimal_places()` works on both numeric values
# and strings...
decimal_places(x = 2.851)
decimal_places(x = "2.851")

# ... but trailing zeros are only counted within
# strings:
decimal_places(x = c(7.3900, "7.3900"))

# This doesn't apply to non-trailing zeros; these
# behave just like any other digit would:
decimal_places(x = c(4.08, "4.08"))

# Whitespace at the end of a string is not counted:
decimal_places(x = "6.0     ")

# `decimal_places_scalar()` is much faster,
# but only works with a single number or string:
decimal_places_scalar(x = 8.13)
decimal_places_scalar(x = "5.024")

Description

For every value in a column, decimal_places_df() counts its decimal places. By default, it operates on all columns that are coercible to numeric.

Usage

decimal_places_df(
  data,
  cols = everything(),
  check_numeric_like = TRUE,
  sep = "\\."
)

Arguments

Value

Data frame. The values of the selected columns are replaced by the numbers of their decimal places.

See Also

Wrapped functions: decimal_places(), dplyr::across().

Examples

# Coerce all columns to string:
iris <- iris %>%
  tibble::as_tibble() %>%
  dplyr::mutate(across(everything(), as.character))

# The function will operate on all
# numeric-like columns but not on `"Species"`:
iris %>%
  decimal_places_df()

# Operate on some select columns only
# (from among the numeric-like columns):
iris %>%
  decimal_places_df(cols = starts_with("Sepal"))

Description

Some published studies only report a total sample size but no group sizes. However, group sizes are crucial for consistency tests such as GRIM. Call disperse() to generate possible group sizes that all add up to the total sample size, if that total is even.

disperse2() is a variant for odd totals. It takes two consecutive numbers and generates decreasing values from the lower as well as increasing values from the upper. In this way, all combinations still add up to the total.

disperse_total() directly takes the total sample size, checks if it's even or odd, splits it up accordingly, and applies disperse() or disperse2(), respectively.

These functions are primarily intended as helpers. They form the backbone of grim_map_total_n() and all other functions created with function_map_total_n().

Usage

disperse(
  n,
  dispersion = 0:5,
  n_min = 1L,
  n_max = NULL,
  constant = NULL,
  constant_index = NULL
)

disperse2(
  n,
  dispersion = 0:5,
  n_min = 1L,
  n_max = NULL,
  constant = NULL,
  constant_index = NULL
)

disperse_total(
  n,
  dispersion = 0:5,
  n_min = 1L,
  n_max = NULL,
  constant = NULL,
  constant_index = NULL
)

Arguments

Details

If any group size is less than n_min or greater than n_max, it is removed. The complementary size of the other group is also removed.

constant values are pairwise repeated. That is why constant must be a length-2 atomic vector or a list of such vectors. If constant is a data frame or some other named list, the resulting columns will have the same names as the list-element names. If the list is not named, the new column names will be "constant1", "constant2", etc; or just "constant", for a single pair.

Value

A tibble (data frame) with these columns:

• n includes the dispersed n values. Every pair of consecutive rows has n values that each add up to the total.

• n_change records how the input n was transformed to the output n. In disperse2(), the n_change strings label the lower of the input n values n1 and the higher one n2.

References

Bauer, P. J., & Francis, G. (2021). Expression of Concern: Is It Light or Dark? Recalling Moral Behavior Changes Perception of Brightness. Psychological Science, 32(12), 2042–2043. https://journals.sagepub.com/doi/10.1177/09567976211058727

See Also

function_map_total_n(), grim_map_total_n(), and seq_distance_df().

Examples

# For a total sample size of 40,
# set `n` to `20`:
disperse(n = 20)

# Specify `dispersion` to control
# the steps up and down from `n`:
disperse(n = 20, dispersion = c(3, 6, 10))

# In `disperse2()`, specify `n` as two
# consecutive numbers -- i.e., group sizes:
disperse2(n = c(25, 26))

# Use the total sample size directly
# with `disperse_total()`. An even total
# internally triggers `disperse()`...
disperse_total(n = 40)

# ...whereas an odd total triggers `disperse2()`:
disperse_total(n = 51)

# You may add values that repeat along with the
# dispersed ones but remain constant themselves.
# Such values can be stored in a length-2 vector
# for a single column...
disperse_total(37, constant = c("5.24", "3.80"))

# ... or a list of length-2 vectors for multiple
# columns. This includes data frames with 2 rows:
df_constant <- tibble::tibble(
  name = c("Paul", "Mathilda"), age = 27:28,
  registered = c(TRUE, FALSE)
)
disperse_total(37, constant = df_constant)

Description

duplicate_count() returns a frequency table. When searching a data frame, it includes values from all columns for each frequency count.

This function is a blunt tool designed for initial data checking. It is not too informative if many values have few characters each.

For summary statistics, call audit() on the results.

Usage

duplicate_count(x, ignore = NULL, locations_type = c("character", "list"))

Arguments

Value

If x is a data frame or another named vector, a tibble with four columns. If x isn't named, only the first two columns appear:

• value: All the values from x.

• frequency: Absolute frequency of each value in x, in descending order.

• locations: Names of all columns from x in which value appears.

• locations_n: Number of columns named in locations.

The tibble has the scr_dup_count class, which is recognized by the audit() generic.

Summaries with audit()

There is an S3 method for the audit() generic, so you can call audit() following duplicate_count(). It returns a tibble with summary statistics for the two numeric columns, frequency and locations_n (or, if x isn't named, only for frequency).

See Also

• duplicate_count_colpair() to check each combination of columns for duplicates.

• duplicate_tally() to show instances of a value next to each instance.

• janitor::get_dupes() to search for duplicate rows.

Examples

# Count duplicate values...
iris %>%
  duplicate_count()

# ...and compute summaries:
iris %>%
  duplicate_count() %>%
  audit()

# Any values can be ignored:
iris %>%
  duplicate_count(ignore = c("setosa", "versicolor", "virginica"))

Description

duplicate_count_colpair() takes a data frame and checks each combination of columns for duplicates. Results are presented in a tibble, ordered by the number of duplicates.

Usage

duplicate_count_colpair(data, ignore = NULL, show_rates = TRUE)

Arguments

Value

A tibble (data frame) with these columns –

• x and y: Each line contains a unique combination of data's columns, stored in the x and y output columns.

• count: Number of "duplicates", i.e., values that are present in both x and y.

• total_x, total_y, rate_x, and rate_y (added by default): total_x is the number of non-missing values in the column named under x. Also, rate_x is the proportion of x values that are duplicated in y, i.e., count / total_x. Likewise with total_y and rate_y. The two ⁠rate_*⁠ columns will be equal unless NA values are present.

Summaries with audit()

There is an S3 method for audit(), so you can call audit() following duplicate_count_colpair(). It returns a tibble with summary statistics.

See Also

• duplicate_count() for a frequency table.

• duplicate_tally() to show instances of a value next to each instance.

• janitor::get_dupes() to search for duplicate rows.

• corrr::colpair_map(), a versatile tool for pairwise column analysis which the present function wraps.

Examples

# Basic usage:
mtcars %>%
  duplicate_count_colpair()

# Summaries with `audit()`:
mtcars %>%
  duplicate_count_colpair() %>%
  audit()

Description

duplicate_detect() is superseded because it's less informative than duplicate_tally() and duplicate_count(). Use these functions instead.

For every value in a vector or data frame, duplicate_detect() tests whether there is at least one identical value. Test results are presented next to every value.

This function is a blunt tool designed for initial data checking. Don't put too much weight on its results.

For summary statistics, call audit() on the results.

Usage

duplicate_detect(x, ignore = NULL, colname_end = "dup")

Arguments

Details

This function is not very informative with many input values that only have a few characters each. Many of them may have duplicates just by chance. For example, in R's built-in iris data set, 99% of values have duplicates.

In general, the fewer values and the more characters per value, the more significant the results.

Value

A tibble (data frame). It has all the columns from x, and to each of these columns' right, the corresponding test result column.

The tibble has the scr_dup_detect class, which is recognized by the audit() generic.

Summaries with audit()

There is an S3 method for the audit() generic, so you can call audit() following duplicate_detect(). It returns a tibble with these columns —

• term: The original data frame's variables.

• dup_count: Number of "duplicated" values of that term variable: those which have at least one duplicate anywhere in the data frame.

• total: Number of all non-NA values of that term variable.

• dup_rate: Rate of "duplicated" values of that term variable.

The final row, .total, summarizes across all other rows: It adds up the dup_count and total_count columns, and calculates the mean of the dup_rate column.

See Also

• duplicate_tally() to count instances of a value instead of just stating whether it is duplicated.

• duplicate_count() for a frequency table.

• duplicate_count_colpair() to check each combination of columns for duplicates.

• janitor::get_dupes() to search for duplicate rows.

Examples

# Find duplicate values in a data frame...
duplicate_detect(x = pigs4)

# ...or in a single vector:
duplicate_detect(x = pigs4$snout)

# Summary statistics with `audit()`:
pigs4 %>%
  duplicate_detect() %>%
  audit()

# Any values can be ignored:
pigs4 %>%
  duplicate_detect(ignore = c(8.131, 7.574))

Description

For every value in a vector or data frame, duplicate_tally() counts how often it appears in total. Tallies are presented next to each value.

For summary statistics, call audit() on the results.

Usage

duplicate_tally(x, ignore = NULL, colname_end = "n")

Arguments

Details

This function is not very informative with many input values that only have a few characters each. Many of them may have duplicates just by chance. For example, in R's built-in iris data set, 99% of values have duplicates.

In general, the fewer values and the more characters per value, the more significant the results.

Value

A tibble (data frame). It has all the columns from x, and to each of these columns' right, the corresponding tally column.

The tibble has the scr_dup_detect class, which is recognized by the audit() generic.

Summaries with audit()

There is an S3 method for the audit() generic, so you can call audit() following duplicate_tally(). It returns a tibble with summary statistics.

See Also

• duplicate_count() for a frequency table.

• duplicate_count_colpair() to check each combination of columns for duplicates.

• janitor::get_dupes() to search for duplicate rows.

Examples

# Tally duplicate values in a data frame...
duplicate_tally(x = pigs4)

# ...or in a single vector:
duplicate_tally(x = pigs4$snout)

# Summary statistics with `audit()`:
pigs4 %>%
  duplicate_tally() %>%
  audit()

# Any values can be ignored:
pigs4 %>%
  duplicate_tally(ignore = c(8.131, 7.574))

Description

Two functions that round numbers to specific fractions, not just to the next higher decimal level. They are inspired by janitor::round_to_fraction() but feature all the options of reround():

• reround_to_fraction() closely follows janitor::round_to_fraction() by first rounding to fractions of a whole number, then optionally rounding the result to a specific number of digits in the usual way.

• reround_to_fraction_level() rounds to the nearest fraction of a number at the specific decimal level (i.e., number of digits), without subsequent rounding. This is closer to conventional rounding functions.

Usage

reround_to_fraction(
  x = NULL,
  denominator = 1,
  digits = Inf,
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE
)

reround_to_fraction_level(
  x = NULL,
  denominator = 1,
  digits = 0L,
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE
)

Arguments

Value

Numeric vector of the same length as x unless rounding is either of "up_or_down", "up_from_or_down_from", and "ceiling_or_floor". In these cases, it will always have length 2.

See Also

reround(), which the functions wrap, and janitor::round_to_fraction(), part of which they copy.

Examples

#`reround_to_fraction()` rounds `0.4`
# to `0` if `denominator` is `1`, which
# is the usual integer rounding...
reround_to_fraction(0.4, denominator = 1, rounding = "even")

# ...but if `denominator` is `2`, it rounds to the nearest
# fraction of 2, which is `0.5`:
reround_to_fraction(0.4, denominator = 2, rounding = "even")

# Likewise with fractions of 3:
reround_to_fraction(0.25, denominator = 3, rounding = "even")

# The default for `rounding` is to round
# both up and down, as in `reround()`:
reround_to_fraction(0.4, denominator = 2)

# These two rounding procedures differ
# at the tie points:
reround_to_fraction(0.25, denominator = 2)

# `reround_to_fraction_level()`, in contrast,
# uses `digits` to determine some decimal level,
# and then rounds to the closest fraction at
# that level:
reround_to_fraction_level(0.12345, denominator = 2, digits = 0)
reround_to_fraction_level(0.12345, denominator = 2, digits = 1)
reround_to_fraction_level(0.12345, denominator = 2, digits = 2)

Description

function_map() creates new basic mapper functions for consistency tests, such as grim_map() or debit_map().

For context, see Creating basic mappers with function_map().

Usage

function_map(
  .fun,
  .reported,
  .name_test,
  .name_key_result = "consistency",
  .name_class = NULL,
  .args_disabled = NULL,
  .col_names = NULL,
  .col_control = NULL,
  .col_filler = NULL,
  ...
)

Arguments

Details

The output tibble returned by the factory-made function will inherit one or two classes independently of the .name_class argument:

• It will inherit a class named "scr_{tolower(.name_test)}_map"; for example, the class is "scr_grim_map" if .name_test is "GRIM".

• If a rounding argument is specified via ..., or else if .fun has a rounding argument with a default, the output tibble will inherit a class named "scr_rounding_{rounding}"; for example, "scr_rounding_up_or_down".

Value

A factory-made function with these arguments:

• data: Data frame with all the columns named in .reported. It must have columns named after the key arguments in .fun. Other columns are permitted.

• Arguments named after the .reported values. They can be specified as the names of data columns so that the function will rename that column using the .reported name.

• reported, fun, name_class: Same as when calling function_map() but spelled without dots. You can override these defaults when calling the factory-made function.

• ...: Arguments passed down to .fun. This does not include the column-identifying arguments derived from .reported.

Value returned by the factory-made function

A tibble that includes "consistency": a logical column showing whether the values to its left are mutually consistent (TRUE) or not (FALSE).

Examples

# Basic test implementation for "SCHLIM",
# a mock test with no real significance:
schlim_scalar <- function(y, n) {
  (y / 3) > n
}

# Let the function factory produce
# a mapper function for SCHLIM:
schlim_map <- function_map(
  .fun = schlim_scalar,
  .reported = c("y", "n"),
  .name_test = "SCHLIM"
)

# Example data:
df1 <- tibble::tibble(y = 16:25, n = 3:12)

# Call the "factory-made" function:
schlim_map(df1)

Description

function_map_seq() is the engine that powers functions such as grim_map_seq(). It creates new, "factory-made" functions that apply consistency tests such as GRIM or GRIMMER to sequences of specified variables. The sequences are centered around the reported values of those variables.

By default, only inconsistent values are dispersed from and tested. This provides an easy and powerful way to assess whether small errors in computing or reporting may be responsible for inconsistencies in published statistics.

For background and more examples, see the sequence mapper section of Consistency tests in depth.

Usage

function_map_seq(
  .fun,
  .var = Inf,
  .reported,
  .name_test,
  .name_key_result = "consistency",
  .name_class = NULL,
  .args_disabled = NULL,
  .dispersion = 1:5,
  .out_min = "auto",
  .out_max = NULL,
  .include_reported = FALSE,
  .include_consistent = FALSE,
  ...
)

Arguments

Details

All arguments of function_map_seq() set the defaults for the arguments in the manufactured function. They can still be specified differently when calling the latter.

If functions created this way are exported from other packages, they should be written as if they were created with purrr adverbs; see explanations there, and examples in the export section of Consistency tests in depth.

This function is a so-called function factory: It produces other functions, such as grim_map_seq(). More specifically, it is a function operator because it also takes functions as inputs, such as grim_map(). See Wickham (2019, ch. 10-11).

Value

A function such as those below. ("Testable statistics" are variables that can be selected via var, and are then varied. All variables except for those in parentheses are selected by default.)

The factory-made function will also have dots, ..., to pass arguments down to .fun, i.e., the basic mapper function such as grim_map().

Conventions

The name of a function returned by function_map_seq() should mechanically follow from that of the input function. For example, grim_map_seq() derives from grim_map(). This pattern fits best if the input function itself is named after the test it performs on a data frame, followed by ⁠_map⁠: grim_map() applies GRIM, grimmer_map() applies GRIMMER, etc.

Much the same is true for the classes of data frames returned by the manufactured function via the .name_class argument of function_map_seq(). It should be the function's own name preceded by the name of the package that contains it, or by an acronym of that package's name. Therefore, some existing classes are scr_grim_map_seq and scr_grimmer_map_seq.

References

Wickham, H. (2019). Advanced R (Second Edition). CRC Press/Taylor and Francis Group. https://adv-r.hadley.nz/index.html

Examples

# Function definition of `grim_map_seq()`:
grim_map_seq <- function_map_seq(
  .fun = grim_map,
  .reported = c("x", "n"),
  .name_test = "GRIM",
)

Description

function_map_total_n() is the engine that powers functions such as grim_map_total_n(). It creates new, "factory-made" functions for consistency tests such as GRIM or GRIMMER. The new functions take reported summary statistics (e.g., means) and apply those tests in cases where only a total sample size is known, not group sizes.

This works by making disperse_total() create multiple pairs of hypothetical group sizes, all of which add up to the reported total. There need to be exactly two groups.

For background and more examples, see the total-n mapper section of Consistency tests in depth.

Usage

function_map_total_n(
  .fun,
  .reported,
  .name_test,
  .name_key_result = "consistency",
  .name_class = NULL,
  .dispersion = 0:5,
  .n_min = 1L,
  .n_max = NULL,
  .constant = NULL,
  .constant_index = NULL,
  ...
)

Arguments

Details

If functions created by function_map_total_n() are exported from other packages, they should be written as if they were created with purrr adverbs; see explanations there, and examples in the export section of Consistency tests in depth.

This function is a so-called function factory: It produces other functions, such as grim_map_total_n(). More specifically, it is a function operator because it also takes functions as inputs, such as grim_map(). See Wickham (2019), ch. 10-11.

Value

A function such as these:

The factory-made function will also have dots, ..., to pass arguments down to .fun, i.e., the basic mapper function.

Conventions

The name of a function returned by function_map_total_n() should mechanically follow from that of the input function. For example, grim_map_total_n() derives from grim_map(). This pattern fits best if the input function itself is named after the test it performs on a data frame, followed by ⁠_map⁠: grim_map() applies GRIM, grimmer_map() applies GRIMMER, etc.

Much the same is true for the classes of data frames returned by the manufactured function via the .name_class argument of function_map_total_n(). It should be the function's own name preceded by the name of the package that contains it, or by an acronym of that package's name. Therefore, some existing classes are scr_grim_map_total_n and scr_grimmer_map_total_n.

References

Bauer, P. J., & Francis, G. (2021). Expression of Concern: Is It Light or Dark? Recalling Moral Behavior Changes Perception of Brightness. Psychological Science, 32(12), 2042–2043. https://journals.sagepub.com/doi/10.1177/09567976211058727

Wickham, H. (2019). Advanced R (Second Edition). CRC Press/Taylor and Francis Group. https://adv-r.hadley.nz/index.html

See Also

function_map_seq()

Examples

# Function definition of `grim_map_total_n()`:
grim_map_total_n <- function_map_total_n(
  .fun = grim_map,
  .reported = "x",
  .name_test = "GRIM"
)

Description

grim() checks if a reported mean value of integer data is mathematically consistent with the reported sample size and the number of items that compose the mean value.

Set percent to TRUE if x is a percentage. This will convert x to a decimal number and adjust the decimal count accordingly.

The function is vectorized, but it is recommended to use grim_map() for testing multiple cases.

Usage

grim(
  x,
  n,
  items = 1,
  percent = FALSE,
  show_rec = FALSE,
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE,
  tolerance = .Machine$double.eps^0.5
)

Arguments

Details

The x values need to be strings because only strings retain trailing zeros, which are as important for the GRIM test as any other decimal digits.

Use restore_zeros() on numeric values (or values that were numeric values at some point) to easily supply the trailing zeros they might once have had. See documentation there.

Browse the source code in the grim.R file. grim() is a vectorized version of the internal grim_scalar() function found there.

Value

Logical. TRUE if x, n, and items are mutually consistent, FALSE if not.

References

Brown, N. J. L., & Heathers, J. A. J. (2017). The GRIM Test: A Simple Technique Detects Numerous Anomalies in the Reporting of Results in Psychology. Social Psychological and Personality Science, 8(4), 363–369. https://journals.sagepub.com/doi/10.1177/1948550616673876

See Also

grim_map() applies grim() to any number of cases at once.

Examples

# A mean of 5.19 is not consistent with a sample size of 28:
grim(x = "5.19", n = 28)    # `x` in quotes!

# However, it is consistent with a sample size of 32:
grim(x = "5.19", n = 32)

# For a scale composed of two items:
grim(x = "2.84", n = 16, items = 2)

# With percentages instead of means -- here, 71%:
grim(x = "71", n = 43, percent = TRUE)

Description

grim_granularity() computes the minimal difference between two means or proportions of ordinal or interval data.

grim_items() is the reverse: It converts granularity values to the number of scale items, which might then be used for consistency testing functions such as grim().

Usage

grim_granularity(n, items = 1)

grim_items(n, gran, tolerance = .Machine$double.eps^0.5)

Arguments

Details

These two functions differ only in the names of their arguments — the underlying formula is the same (and it's very simple). However, for clarity, they are presented as distinct.

The output of grim_items() should be whole numbers, because scale items have a granularity of 1.

It would be wrong to determine a scale's granularity from the minimal distance between two values in a given distribution. This would only signify how those values actually do differ, not how they can differ a priori based on scale design. Also, keep in mind that continuous scales have no granularity at all.

Value

Numeric. Granularity or number of items.

References

Brown, N. J. L., & Heathers, J. A. J. (2017). The GRIM Test: A Simple Technique Detects Numerous Anomalies in the Reporting of Results in Psychology. Social Psychological and Personality Science, 8(4), 363–369. https://journals.sagepub.com/doi/10.1177/1948550616673876

Examples

# If a non-Likert scale ranges from 0 to 3
# and measures 16 cases:
grim_granularity(n = 16)   # `items = 1` by default

# Same but Likert scale with 2 items:
grim_granularity(n = 16, items = 2)

# If a scale is applied to a single case
# and has a granularity of 0.5:
grim_items(n = 1, gran = 0.5)

# With more cases, a warning appears
# because items can only be whole numbers:
grim_items(n = c(10, 15, 20), gran = 0.5)

Description

Call grim_map() to GRIM-test any number of combinations of mean/proportion, sample size, and number of items. Mapping function for GRIM-testing.

Set percent to TRUE if the x values are percentages. This will convert x values to decimals and adjust the decimal count accordingly.

Display intermediary numbers from GRIM-testing in columns by setting show_rec to TRUE.

For summary statistics, call audit() on the results.

Usage

grim_map(
  data,
  items = 1,
  merge_items = TRUE,
  percent = FALSE,
  x = NULL,
  n = NULL,
  show_rec = FALSE,
  show_prob = deprecated(),
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE,
  tolerance = .Machine$double.eps^0.5,
  testables_only = FALSE,
  extra = Inf
)

Arguments

Value

A tibble with these columns –

• x, n: the inputs.

• consistency: GRIM consistency of x, n, and items.

• probability: the probability of GRIM inconsistency; see grim_probability().

• ⁠<extra>⁠: any columns from data other than x, n, and items.

  The tibble has the scr_grim_map class, which is recognized by the audit() generic.

Reconstructed numbers

If show_rec is set to TRUE, the output includes the following additional columns:

• rec_sum: the sum total from which the mean or proportion was ostensibly derived.

• rec_x_upper: the upper reconstructed x value.

• rec_x_lower: the lower reconstructed x value.

• rec_x_upper_rounded: the rounded rec_x_upper value.

• rec_x_lower_rounded: the rounded rec_x_lower value.

With the default for rounding, "up_or_down", each of the last two columns is replaced by two columns that specify the rounding procedures (i.e., "_up" and "_down").

Summaries with audit()

There is an S3 method for audit(), so you can call audit() following grim_map() to get a summary of grim_map()'s results. It is a tibble with one row and these columns –

1. incons_cases: number of GRIM-inconsistent value sets.

2. all_cases: total number of value sets.

3. incons_rate: proportion of GRIM-inconsistent value sets.

4. mean_grim_prob: average probability of GRIM inconsistency.

5. incons_to_prob: ratio of incons_rate to mean_grim_prob.

6. testable_cases: number of GRIM-testable value sets (i.e., those with a positive probability).

7. testable_rate: proportion of GRIM-testable value sets.

References

Brown, N. J. L., & Heathers, J. A. J. (2017). The GRIM Test: A Simple Technique Detects Numerous Anomalies in the Reporting of Results in Psychology. Social Psychological and Personality Science, 8(4), 363–369. https://journals.sagepub.com/doi/10.1177/1948550616673876

Examples

# Use `grim_map()` on data like these:
pigs1

# The `consistency` column shows
# whether the values to its left
# are GRIM-consistent:
pigs1 %>%
  grim_map()

# Display intermediary numbers from
# GRIM-testing with `show_rec = TRUE`:
pigs1 %>%
  grim_map(show_rec = TRUE)

# Get summaries with `audit()`:
pigs1 %>%
  grim_map() %>%
  audit()

Description

grim_map_seq() performs GRIM-testing with values surrounding the input values. This provides an easy and powerful way to assess whether small errors in computing or reporting may be responsible for GRIM inconsistencies in published statistics.

Call audit_seq() on the results for summary statistics.

Usage

grim_map_seq(
  data,
  x = NULL,
  n = NULL,
  var = Inf,
  dispersion = 1:5,
  out_min = "auto",
  out_max = NULL,
  include_reported = FALSE,
  include_consistent = FALSE,
  ...
)

Arguments

Value

A tibble (data frame) with detailed test results.

Summaries with audit_seq()

You can call audit_seq() following grim_map_seq(). It will return a data frame with these columns:

• x and n are the original inputs, tested for consistency here.

• hits_total is the total number of GRIM-consistent value sets found within the specified dispersion range.

• hits_x is the number of GRIM-consistent value sets found by varying x.

• Accordingly with n and hits_n.

• (Note that any consistent reported cases will be counted by the ⁠hits_*⁠ columns if both include_reported and include_consistent are set to TRUE.)

• diff_x reports the absolute difference between x and the next consistent dispersed value (in dispersion steps, not the actual numeric difference). diff_x_up and diff_x_down report the difference to the next higher or lower consistent value, respectively.

• diff_n, diff_n_up, and diff_n_down do the same for n.

Call audit() following audit_seq() to summarize results even further. It's mostly self-explaining, but na_count and na_rate are the number and rate of times that a difference could not be computed because of a lack of corresponding hits within the dispersion range.

Examples

# `grim_map_seq()` can take any input
# that `grim_map()` can take:
pigs1

# All the results:
out <- grim_map_seq(pigs1, include_consistent = TRUE)
out

# Case-wise summaries with `audit_seq()`
# can be more important than the raw results:
out %>%
  audit_seq()

Description

When reporting group means, some published studies only report the total sample size but no group sizes corresponding to each mean. However, group sizes are crucial for GRIM-testing.

In the two-groups case, grim_map_total_n() helps in these ways:

• It creates hypothetical group sizes. With an even total sample size, it incrementally moves up and down from half the total sample size. For example, with a total sample size of 40, it starts at 20, goes on to 19 and 21, then to 18 and 22, etc. With odd sample sizes, it starts from the two integers around half.

• It GRIM-tests all of these values together with the group means.

• It reports all the scenarios in which both "dispersed" hypothetical group sizes are GRIM-consistent with the group means.

All of this works with one or more total sample sizes at a time. Call audit_total_n() for summary statistics.

Usage

grim_map_total_n(
  data,
  x1 = NULL,
  x2 = NULL,
  dispersion = 0:5,
  n_min = 1L,
  n_max = NULL,
  constant = NULL,
  constant_index = NULL,
  ...
)

Arguments

Value

A tibble with these columns:

• x, the group-wise reported input statistic, is repeated in row pairs.

• n is dispersed from half the input n, with n_change tracking the differences.

• both_consistent flags scenarios where both reported x values are consistent with the hypothetical n values.

• case corresponds to the row numbers of the input data frame.

• dir is "forth" in the first half of rows and "back" in the second half. "forth" means that x2 from the input is paired with the larger dispersed n, whereas "back" means that x1 is paired with the larger dispersed n.

• Other columns from grim_map() are preserved.

Summaries with audit_total_n()

You can call audit_total_n() following up on grim_map_total_n() to get a tibble with summary statistics. It will have these columns:

• x1, x2, and n are the original inputs.

• hits_total is the number of scenarios in which both x1 and x2 are GRIM-consistent. It is the sum of hits_forth and hits_back below.

• hits_forth is the number of both-consistent cases that result from pairing x2 with the larger dispersed n value.

• hits_back is the same, except x1 is paired with the larger dispersed n value.

• scenarios_total is the total number of test scenarios, whether or not both x1 and x2 are GRIM-consistent.

• hit_rate is the ratio of hits_total to scenarios_total.

Call audit() following audit_total_n() to summarize results even further.

References

Bauer, P. J., & Francis, G. (2021). Expression of Concern: Is It Light or Dark? Recalling Moral Behavior Changes Perception of Brightness. Psychological Science, 32(12), 2042–2043. https://journals.sagepub.com/doi/10.1177/09567976211058727

Brown, N. J. L., & Heathers, J. A. J. (2017). The GRIM Test: A Simple Technique Detects Numerous Anomalies in the Reporting of Results in Psychology. Social Psychological and Personality Science, 8(4), 363–369. https://journals.sagepub.com/doi/10.1177/1948550616673876

See Also

function_map_total_n(), which created the present function using grim_map().

Examples

# Run `grim_map_total_n()` on data like these:
df <- tibble::tribble(
  ~x1,    ~x2,   ~n,
  "3.43", "5.28", 90,
  "2.97", "4.42", 103
)
df

grim_map_total_n(df)

# `audit_total_n()` summaries can be more important than
# the detailed results themselves.
# The `hits_total` column shows all scenarios in
# which both divergent `n` values are GRIM-consistent
# with the `x*` values when paired with them both ways:
df %>%
  grim_map_total_n() %>%
  audit_total_n()

# By default (`dispersion = 0:5`), the function goes
# five steps up and down from `n`. If this sequence
# gets longer, the number of hits tends to increase:
df %>%
  grim_map_total_n(dispersion = 0:10) %>%
  audit_total_n()

Description

grim_plot() visualizes summary data and their mutual GRIM consistency. Call this function only on a data frame that resulted from a call to grim_map().

Consistent and inconsistent value pairs from the input data frame are shown in distinctive colors. By default, consistent value pairs are blue and inconsistent ones are red. These and other parameters of the underlying geoms can be controlled via arguments.

The background raster follows the rounding argument from the grim_map() call (unless any of the plotted mean or proportion values has more than 2 decimal places, in which case a gradient is shown, not a raster).

Usage

grim_plot(
  data = NULL,
  show_data = TRUE,
  show_raster = TRUE,
  show_gradient = TRUE,
  n = NULL,
  digits = NULL,
  rounding = "up_or_down",
  color_cons = "royalblue1",
  color_incons = "red",
  tile_alpha = 1,
  tile_size = 1.5,
  raster_alpha = 1,
  raster_color = "grey75"
)

Arguments

Value

A ggplot object.

Background raster

The background raster shows the probability of GRIM-inconsistency for random means or proportions, from 0 (all inconsistent) to the greatest number on the x-axis (all consistent). If the number of decimal places in the inputs – means or percentages – is 3 or greater, individual points would be too small to display. In these cases, there will not be a raster but a gradient, showing the overall trend.

As any raster only makes sense with respect to one specific number of decimal places, the function will throw an error if these numbers differ among input x values (and show_raster is TRUE). You can avoid the error and force plotting by specifying digits as the number of decimal places for which the raster or gradient should be displayed.

For 1 or 2 decimal places, the raster will be specific to the rounding procedure. As the raster varies by rounding procedure, it will automatically correspond to the rounding argument specified in the preceding grim_map() call. This works fast because the raster is based on data saved in the package itself, so these data don't need to be generated anew every time the function is called. Inconsistent value sets are marked with dark boxes. All other places in the raster denote consistent value sets. The raster is independent of the data – it only follows the rounding specification in the grim_map() call and the digits argument in grim_plot().

Display an "empty" plot, one without empirical test results, by setting show_data to FALSE. You can then control key parameters of the plot with digits and rounding.

With grim_map()'s default for rounding, "up_or_down", strikingly few values are flagged as inconsistent for sample sizes 40 and 80 (or 4 and 8). This effect disappears if rounding is set to any other value (see vignette("rounding-options")).

The 4/8 leniency effect arises because accepting values rounded either up or down is more careful and conservative than any other rounding procedure. In any case, grim_plot() doesn't cause this effect — it only reveals it.

References

Brown, N. J. L., & Heathers, J. A. J. (2017). The GRIM Test: A Simple Technique Detects Numerous Anomalies in the Reporting of Results in Psychology. Social Psychological and Personality Science, 8(4), 363–369. https://journals.sagepub.com/doi/10.1177/1948550616673876

Examples

# Call `grim_plot()` following `grim_map()`:
pigs1 %>%
  grim_map() %>%
  grim_plot()

# If you change the rounding procedure
# in `grim_map()`, the plot will
# follow automatically if there is
# a difference:
pigs1 %>%
  grim_map(rounding = "ceiling") %>%
  grim_plot()

# For percentages, the y-axis
# label also changes automatically:
pigs2 %>%
  grim_map(percent = TRUE) %>%
  grim_plot()

Description

These functions compute statistics related to GRIM-testing. In general, grim_probability() is the most useful of them, and it is responsible for the probability column in a data frame returned by grim_map().

• grim_probability() returns the probability that a reported mean or percentage of integer data that is random except for the number of its decimal places is inconsistent with the reported sample size. For example, the mean 1.23 is treated like any other mean with two decimal places.

• grim_ratio() is equal to grim_probability() unless grim_ratio() is negative, which can occur if the sample size is very large. Strictly speaking, this is more informative than grim_probability(), but it is harder to interpret.

• grim_total() returns the absolute number of GRIM-inconsistencies that are possible given the mean or percentage's number of decimal places and the corresponding sample size.

For discussion, see vignette("grim"), section GRIM statistics.

Usage

grim_probability(x, n, items = 1, percent = FALSE)

grim_ratio(x, n, items = 1, percent = FALSE)

grim_total(x, n, items = 1, percent = FALSE)

Arguments

Value

Integer or double. The number of possible GRIM inconsistencies, or their probability for a random mean or percentage with a given number of decimal places.

References

Brown, N. J. L., & Heathers, J. A. J. (2017). The GRIM Test: A Simple Technique Detects Numerous Anomalies in the Reporting of Results in Psychology. Social Psychological and Personality Science, 8(4), 363–369. https://journals.sagepub.com/doi/10.1177/1948550616673876

See Also

grim() for the GRIM test itself; as well as grim_map() for applying it to many cases at once.

Examples

# Many value sets are inconsistent here:
grim_probability(x = "83.29", n = 21)
grim_total(x = "83.29", n = 21)

# No sets are inconsistent in this case...
grim_probability(x = "5.14", n = 83)
grim_total(x = "5.14", n = 83)

# ... but most would be if `x` was a percentage:
grim_probability(x = "5.14", n = 83, percent = TRUE)
grim_total(x = "5.14", n = 83, percent = TRUE)

Description

grimmer() checks if reported mean and SD values of integer data are mathematically consistent with the reported sample size and the number of items that compose the mean value. It works much like grim().

The function is vectorized, but it is recommended to use grimmer_map() for testing multiple cases.

Usage

grimmer(
  x,
  sd,
  n,
  items = 1,
  show_reason = FALSE,
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE,
  tolerance = .Machine$double.eps^0.5
)

Arguments

Details

GRIMMER was originally devised by Anaya (2016). The present implementation follows Allard's (2018) refined Analytic-GRIMMER algorithm. It uses a variant of Analytic-GRIMMER first implemented in rsprite2::GRIMMER_test() that can be applied to multi-item scales.

The scrutiny version embeds GRIMMER in the broader system of consistency testing, as laid out in Consistency tests in depth. The grimmer() function is a vectorized (multiple-case) version of this basic implementation. For more context and variable name translations, see the top of the R/grimmer.R source file.

Value

Logical. TRUE if x, sd, n, and items are mutually consistent, FALSE if not.

References

Allard, A. (2018). Analytic-GRIMMER: a new way of testing the possibility of standard deviations. https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/

Anaya, J. (2016). The GRIMMER test: A method for testing the validity of reported measures of variability. PeerJ Preprints. https://peerj.com/preprints/2400v1/

Examples

# A mean of 5.23 is not consistent with an SD of 2.55
# and a sample size of 35:
grimmer(x = "5.23", sd = "2.55", n = 35)

# However, mean and SD are consistent with a
# sample size of 31:
grimmer(x = "5.23", sd = "2.55", n = 31)

# For a scale composed of two items:
grimmer(x = "2.74", sd = "0.96", n = 63, items = 2)

Description

Call grimmer_map() to GRIMMER-test any number of combinations of mean, standard deviation, sample size, and number of items. Mapping function for GRIMMER testing.

For summary statistics, call audit() on the results. Visualize results using grim_plot(), as with GRIM results.

Usage

grimmer_map(
  data,
  items = 1,
  merge_items = TRUE,
  x = NULL,
  sd = NULL,
  n = NULL,
  show_reason = TRUE,
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE,
  tolerance = .Machine$double.eps^0.5
)

Arguments

Value

A tibble with these columns –

• x, sd, n: the inputs.

• consistency: GRIMMER consistency of x, n, and items.

• reason: If consistent, "Passed all". If inconsistent, it says which test was failed (see below).

• ⁠<extra>⁠: any columns from data other than x, n, and items.

The reason columns refers to GRIM and the three GRIMMER tests (Allard 2018). Briefly, these are:

1. The reconstructed sum of squared observations must be a whole number.

2. The reconstructed SD must match the reported one.

3. The parity of the reconstructed sum of squared observations must match the parity of the reconstructed sum of integers of which the reported means are fractions; i.e., either both are even or both are odd.

The tibble has the scr_grimmer_map class, which is recognized by the audit() generic. It also has the scr_grim_map class, so it can be visualized by grim_plot().

Summaries with audit()

There is an S3 method for audit(), so you can call audit() following grimmer_map() to get a summary of grimmer_map()'s results. It is a tibble with a single row and these columns –

1. incons_cases: number of GRIMMER-inconsistent value sets.

2. all_cases: total number of value sets.

3. incons_rate: proportion of GRIMMER-inconsistent value sets.

4. fail_grim: number of value sets that fail the GRIM test.

5. fail_test1: number of value sets that fail the first GRIMMER test (see below).

6. fail_test2: number of value sets that fail the second GRIMMER test.

7. fail_test3: number of value sets that fail the third GRIMMER test.

The reason columns refers to the three GRIMMER tests (see Allard 2018). These are:

1. The reconstructed sum of squared observations must be a whole number.

2. The reconstructed SD must match the reported one.

3. The parity of the reconstructed sum of squared observations must match the parity of the reconstructed sum of integers of which the reported means are fractions; i.e., either both are even or both are odd.

References

Allard, A. (2018). Analytic-GRIMMER: a new way of testing the possibility of standard deviations. https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/

Anaya, J. (2016). The GRIMMER test: A method for testing the validity of reported measures of variability. PeerJ Preprints. https://peerj.com/preprints/2400v1/

Examples

# Use `grimmer_map()` on data like these:
pigs5

# The `consistency` column shows whether
# the values to its left are GRIMMER-consistent.
# If they aren't, the `reason` column says why:
pigs5 %>%
  grimmer_map()

# Get summaries with `audit()`:
pigs5 %>%
  grimmer_map() %>%
  audit()

Description

grimmer_map_seq() performs GRIMMER-testing with values surrounding the input values. This provides an easy and powerful way to assess whether small errors in computing or reporting may be responsible for GRIMMER inconsistencies in published statistics.

Call audit_seq() on the results for summary statistics.

Usage

grimmer_map_seq(
  data,
  x = NULL,
  sd = NULL,
  n = NULL,
  var = Inf,
  dispersion = 1:5,
  out_min = "auto",
  out_max = NULL,
  include_reported = FALSE,
  include_consistent = FALSE,
  ...
)

Arguments

Value

A tibble (data frame) with detailed test results. See grimmer_map() for an explanation of the reason column.

Summaries with audit_seq()

You can call audit_seq() following grimmer_map_seq(). It will return a data frame with these columns:

• x, sd, and n are the original inputs, tested for consistency here.

• hits_total is the total number of GRIMMER-consistent value sets found within the specified dispersion range.

• hits_x is the number of GRIMMER-consistent value sets found by varying x.

• Accordingly with sd and hits_sd as well as n and hits_n.

• (Note that any consistent reported cases will be counted by the ⁠hits_*⁠ columns if both include_reported and include_consistent are set to TRUE.)

• diff_x reports the absolute difference between x and the next consistent dispersed value (in dispersion steps, not the actual numeric difference). diff_x_up and diff_x_down report the difference to the next higher or lower consistent value, respectively.

• diff_sd, diff_sd_up, and diff_sd_down do the same for sd.

• Likewise with diff_n, diff_n_up, and diff_n_down.

Call audit() following audit_seq() to summarize results even further. It's mostly self-explaining, but na_count and na_rate are the number and rate of times that a difference could not be computed because of a lack of corresponding hits within the dispersion range.

Examples

# `grimmer_map_seq()` can take any input
# that `grimmer_map()` can take:
pigs5

# All the results:
out <- grimmer_map_seq(pigs5, include_consistent = TRUE)
out

# Case-wise summaries with `audit_seq()`
# can be more important than the raw results:
out %>%
  audit_seq()

Description

When reporting group means, some published studies only report the total sample size but no group sizes corresponding to each mean. However, group sizes are crucial for GRIMMER-testing.

In the two-groups case, grimmer_map_total_n() helps in these ways:

• It creates hypothetical group sizes. With an even total sample size, it incrementally moves up and down from half the total sample size. For example, with a total sample size of 40, it starts at 20, goes on to 19 and 21, then to 18 and 22, etc. With odd sample sizes, it starts from the two integers around half.

• It GRIMMER-tests all of these values together with the group means.

• It reports all the scenarios in which both "dispersed" hypothetical group sizes are GRIMMER-consistent with the group means.

All of this works with one or more total sample sizes at a time. Call audit_total_n() for summary statistics.

Usage

grimmer_map_total_n(
  data,
  x1 = NULL,
  x2 = NULL,
  sd1 = NULL,
  sd2 = NULL,
  dispersion = 0:5,
  n_min = 1L,
  n_max = NULL,
  constant = NULL,
  constant_index = NULL,
  ...
)

Arguments

Value

A tibble with these columns:

• x, the group-wise reported input statistic, is repeated in row pairs.

• n is dispersed from half the input n, with n_change tracking the differences.

• both_consistent flags scenarios where both reported x values are consistent with the hypothetical n values.

• case corresponds to the row numbers of the input data frame.

• dir is "forth" in the first half of rows and "back" in the second half. "forth" means that x2 from the input is paired with the larger dispersed n, whereas "back" means that x1 is paired with the larger dispersed n.

• Other columns from grimmer_map() are preserved. See there for an explanation of the reason column.

Summaries with audit_total_n()

You can call audit_total_n() following up on grimmer_map_total_n() to get a tibble with summary statistics. It will have these columns:

• x1, x2, sd1, sd2, and n are the original inputs.

• hits_total is the number of scenarios in which all of x1, x2, sd1, and sd2 are GRIMMER-consistent. It is the sum of hits_forth and hits_back below.

• hits_forth is the number of both-consistent cases that result from pairing x2 and sd2 with the larger dispersed n value.

• hits_back is the same, except x1 and sd1 are paired with the larger dispersed n value.

• scenarios_total is the total number of test scenarios, whether or not both x1 and sd1 as well as x2 and sd2 are GRIMMER-consistent.

• hit_rate is the ratio of hits_total to scenarios_total.

References

Bauer, P. J., & Francis, G. (2021). Expression of Concern: Is It Light or Dark? Recalling Moral Behavior Changes Perception of Brightness. Psychological Science, 32(12), 2042–2043. https://journals.sagepub.com/doi/10.1177/09567976211058727

Allard, A. (2018). Analytic-GRIMMER: a new way of testing the possibility of standard deviations. https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/

Bauer, P. J., & Francis, G. (2021). Expression of Concern: Is It Light or Dark? Recalling Moral Behavior Changes Perception of Brightness. Psychological Science, 32(12), 2042–2043. https://journals.sagepub.com/doi/10.1177/09567976211058727

See Also

function_map_total_n(), which created the present function using grimmer_map().

Examples

# Run `grimmer_map_total_n()` on data like these:
df <- tibble::tribble(
  ~x1,    ~x2,    ~sd1,   ~sd2,   ~n,
  "3.43", "5.28", "1.09", "2.12", 70,
  "2.97", "4.42", "0.43", "1.65", 65
)
df

grimmer_map_total_n(df)

# `audit_total_n()` summaries can be more important than
# the detailed results themselves.
# The `hits_total` column shows all scenarios in
# which both divergent `n` values are GRIMMER-consistent
# with the `x*` values when paired with them both ways:
df %>%
  grimmer_map_total_n() %>%
  audit_total_n()

# By default (`dispersion = 0:5`), the function goes
# five steps up and down from `n`. If this sequence
# gets longer, the number of hits tends to increase:
df %>%
  grimmer_map_total_n(dispersion = 0:10) %>%
  audit_total_n()

Description

is_numeric_like() tests whether an object is "coercible to numeric" by the particular standards of scrutiny. This means:

• Integer and double vectors are TRUE.

• Logical vectors are FALSE, as are non-vector objects.

• Other vectors (most likely strings) are TRUE if all their non-NA values can be coerced to non-NA numeric values, and FALSE otherwise.

• Factors are first coerced to string, then tested.

• Lists are tested like atomic vectors unless any of their elements have length greater 1, in which case they are always FALSE.

• If all values are non-numeric, non-logical NA, the output is also NA.

See details for discussion.

Usage

is_numeric_like(x)

Arguments

Details

The scrutiny package often deals with "number-strings", i.e., strings that can be coerced to numeric without introducing new NAs. This is a matter of displaying data in a certain way, as opposed to their storage mode.

is_numeric_like() returns FALSE for logical vectors simply because these are displayed as strings, not as numbers, and the usual coercion rules would be misleading in this context. Likewise, the function treats factors like strings because that is how they are displayed: the fact that factors are stored as integers is irrelevant.

Why store numbers as strings or factors? Only these data types can preserve trailing zeros, and only if the data were originally entered as strings. See vignette("wrangling"), section Trailing zeros.

Value

Logical (length 1).

See Also

The vctrs package, which provides a serious typing framework for R; in contrast to this rather ad-hoc function.

Examples

# Numeric vectors are `TRUE`:
is_numeric_like(x = 1:5)
is_numeric_like(x = 2.47)

# Logical vectors are always `FALSE`:
is_numeric_like(x = c(TRUE, FALSE))

# Strings are `TRUE` if all of their non-`NA`
# values can be coerced to non-`NA` numbers,
# and `FALSE` otherwise:
is_numeric_like(x = c("42", "0.7", NA))
is_numeric_like(x = c("42", "xyz", NA))

# Factors are treated like their
# string equivalents:
is_numeric_like(x = as.factor(c("42", "0.7", NA)))
is_numeric_like(x = as.factor(c("42", "xyz", NA)))

# Lists behave like atomic vectors if all of their
# elements have length 1...
is_numeric_like(x = list("42", "0.7", NA))
is_numeric_like(x = list("42", "xyz", NA))

# ...but if they don't, they are `FALSE`:
is_numeric_like(x = list("42", "0.7", NA, c(1, 2, 3)))

# If all values are `NA`, so is the output...
is_numeric_like(x = as.character(c(NA, NA, NA)))

# ...unless the `NA`s are numeric or logical:
is_numeric_like(x = as.numeric(c(NA, NA, NA)))
is_numeric_like(x = c(NA, NA, NA))

Description

If your consistency test mapper function supports helper columns, call manage_helper_col() internally; once for every such column. It will check whether a helper column is compatible with its eponymous argument, i.e., if the argument was not specified by the user but has its default value.

By default (affix = TRUE), the function will add the column to the mapper's input data frame. It returns the input data frame, so reassign its output to that variable.

All of this only works in mapper functions that were "handwritten" using ⁠function()⁠, as opposed to those produced by function_map(). See vignette("consistency-tests-in-depth"), section Writing mappers manually.

Usage

manage_helper_col(data, var_arg, default, affix = TRUE)

Arguments

Value

data, possibly modified (see affix argument).

Description

A handwritten mapper function for consistency tests, such as grim_map(), may include arguments named after the key columns in its input data frame. When such an argument is specified by the user as a column name of the input data frame, it identifies a differently-named column as that key column.

Create such functionality in three steps:

1. Add arguments to your mapper function named after the respective key columns. They should be NULL by default; e.g., ⁠x = NULL, n = NULL⁠.

2. Within the mapper, capture the user input by quoting it using rlang::enexpr(). Reassign these values to the argument variables; e.g., x <- rlang::enexpr(x) and n <- rlang::enexpr(n).

3. For every such argument, call manage_key_colnames() and reassign its value to the input data frame variable, adding a short description; e.g.,data <- manage_key_colnames(data, x, "mean/proportion") and data <- manage_key_colnames(data, n, "sample size").

Usage

manage_key_colnames(data, arg, description = NULL)

Arguments

Value

The input data frame, data, possibly modified.

See Also

vignette("consistency-tests-in-depth"), for context.

Description

before_parens() and inside_parens() extract substrings from before or inside parentheses, or similar separators like brackets or curly braces.

See split_by_parens() to split some or all columns in a data frame into both parts.

Usage

before_parens(string, sep = "parens")

inside_parens(string, sep = "parens")

Arguments

Value

String vector of the same length as string. The part of string before or inside sep, respectively.

Examples

x <- c(
  "3.72 (0.95)",
  "5.86 (2.75)",
  "3.06 (6.48)"
)

before_parens(string = x)

inside_parens(string = x)

Description

A fictional dataset with means and sample sizes of flying pigs. It can be used to demonstrate the functionality of grim_map() and functions building up on it.

Usage

pigs1

Format

A tibble (data frame) with 12 rows and 2 columns. The columns are:

x

String. Means.

n

Numeric. Sample sizes.

Value

A tibble (data frame).

See Also

pigs2 for GRIM-testing percentages instead of means, pigs3 for DEBIT-testing, and pigs4 for detecting duplicates.

pigs2 for GRIM-testing percentages instead of means, pigs3 for DEBIT-testing, pigs4 for detecting duplicates, and pigs5 for GRIMMER-testing.

Description

A fictional dataset with percentages and sample sizes of flying pigs. It can be used to demonstrate the functionality of grim_map(), particularly its percent argument, and functions building up on it.

Usage

pigs2

Format

A tibble (data frame) with 6 rows and 2 columns. The columns are:

x

String. Percentages.

n

Numeric. Sample sizes.

Value

A tibble (data frame).

See Also

pigs1 for GRIM-testing means instead of percentages, pigs3 for DEBIT-testing, and pigs4 for detecting duplicates.

pigs1 for GRIM-testing means, pigs3 for DEBIT-testing, pigs4 for detecting duplicates, and pigs5 for GRIMMER-testing.

Description

A fictional dataset with means and standard deviations from a binary distribution related to flying pigs. It can be used to demonstrate the functionality of debit_map() and functions building up on it.

Usage

pigs3

Format

A tibble (data frame) with 7 rows and 3 columns. The columns are:

x

String. Means.

sd

String. Standard deviations.

n

Numeric. Sample sizes.

Value

A tibble (data frame).

See Also

pigs1 for GRIM-testing means, pigs2 for GRIM-testing percentages, and pigs4 for detecting duplicates.

pigs1 for GRIM-testing means, pigs2 for GRIM-testing percentages instead of means, pigs4 for detecting duplicates, and pigs5 for GRIMMER-testing.

Description

A fictional dataset with observations of flying pigs. It contains multiple duplicates. The dataset can be used to demonstrate the functionality of ⁠duplicate_*()⁠ functions such as duplicate_count().

Usage

pigs4

Format

A tibble (data frame) with 5 rows and 3 columns, describing various body measures of the fictional pigs. The columns are:

snout

String. Snout width.

tail

String. Tail length.

wings

String. Wingspan.

Value

A tibble (data frame).

See Also

pigs1 for GRIM-testing means, pigs2 for GRIM-testing percentages, pigs3 for using DEBIT, and pigs5 for GRIMMER-testing.

Description

A fictional dataset with means, standard deviations (SDs), and sample sizes of flying pigs. It can be used to demonstrate the functionality of grimmer_map() and functions building up on it.

Usage

pigs5

Format

A tibble (data frame) with 12 rows and 3 columns. The columns are:

x

String. Means.

sd

String. Standard deviations.

n

Numeric. Sample sizes.

Value

A tibble (data frame).

See Also

pigs1 for (only) GRIM-testing the same means as here, pigs2 for GRIM-testing percentages instead of means, pigs3 for DEBIT-testing, and pigs4 for detecting duplicates.

Description

reround() takes one or more intermediate reconstructed values and rounds them in some specific way – namely, the way they are supposed to have been rounded originally, in the process that generated the reported values.

This function provides an interface to all of scrutiny's rounding functions as well as base::round(). It is used as a helper within grim(), grimmer(), and debit(); and it might find use in other places for consistency testing or reconstruction of statistical analyses.

Usage

reround(
  x,
  digits = 0L,
  rounding = "up_or_down",
  threshold = 5,
  symmetric = FALSE
)

Arguments

Details

reround() internally calls the appropriate rounding function(s) determined by the rounding argument. See vignette("rounding-options") for a complete list of values that rounding can take.

For the specific rounding functions themselves, see documentation at round_up(), round_ceiling(), and base::round().

Value

Numeric vector of length 1 or 2. (It has length 1 unless rounding is "up_or_down", "up_from_or_down_from", or"ceiling_or_floor", in which case it has length 2.)

Description

restore_zeros() takes a vector with values that might have lost trailing zeros, most likely from being registered as numeric. It turns each value into a string and adds trailing zeros until the mantissa hits some limit.

The default for that limit is the number of digits in the longest mantissa of the vector's values. The length of the integer part plays no role.

Don't rely on the default limit without checking: The original width could have been larger because the longest extant mantissa might itself have lost trailing zeros.

restore_zeros_df() is a variant for data frames. It wraps restore_zeros() and, by default, applies it to all columns that are coercible to numeric.

Usage

restore_zeros(x, width = NULL, sep_in = "\\.", sep_out = sep_in)

restore_zeros_df(
  data,
  cols = everything(),
  check_numeric_like = TRUE,
  check_decimals = FALSE,
  width = NULL,
  sep_in = "\\.",
  sep_out = NULL,
  ...
)

Arguments

Details

These functions exploit the fact that groups of summary values such as means or percentages are often reported to the same number of decimal places. If such a number is known but values were not entered as strings, trailing zeros will be lost. In this case, restore_zeros() or restore_zeros_df() will be helpful to prepare data for consistency testing functions such as grim_map() or grimmer_map().

Value

• For restore_zeros(), a string vector. At least some of the strings will have newly restored zeros, unless (1) all input values had the same number of decimal places, and (2) width was not specified as a number greater than that single number of decimal places.

• For restore_zeros_df(), a data frame.

Displaying decimal places

You might not see all decimal places of numeric values in a vector, and consequently wonder if restore_zeros(), when applied to the vector, adds too many zeros. That is because displayed numbers, unlike stored numbers, are often rounded.

For a vector x, you can count the characters of the longest mantissa from among its values like this:

x %>% decimal_places() %>% max()

See Also

Wrapped functions: sprintf().

Examples

# By default, the target width is that of
# the longest mantissa:
vec <- c(212, 75.38, 4.9625)
vec %>%
  restore_zeros()

# Alternatively, supply a number via `width`:
vec %>%
  restore_zeros(width = 6)

# For better printing:
iris <- tibble::as_tibble(iris)

# Apply `restore_zeros()` to all numeric
# columns, but not to the factor column:
iris %>%
  restore_zeros_df()

# Select columns as in `dplyr::select()`:
iris %>%
  restore_zeros_df(starts_with("Sepal"), width = 3)

Description

reverse_map_seq() takes the output of a function created by function_map_seq() and reconstructs the original data frame.

See audit_seq(), which takes reverse_map_seq() as a basis.

Usage

reverse_map_seq(data)

Arguments

Value

The reconstructed tibble (data frame) which a factory-made ⁠*_map_seq()⁠ function took as its data argument.

Examples

# Originally reported summary data...
pigs1

# ...GRIM-tested with varying inputs...
out <- grim_map_seq(pigs1, include_consistent = TRUE)

# ...and faithfully reconstructed:
reverse_map_seq(out)

Description

reverse_map_total_n() takes the output of a function created by function_map_total_n() and reconstructs the original data frame.

See audit_total_n(), which takes reverse_map_total_n() as a basis.

Usage

reverse_map_total_n(data)

Arguments

Value

The reconstructed tibble (data frame) which a factory-made ⁠*_map_total_n()⁠ function took as its data argument.

Examples

# Originally reported summary data...
df <- tibble::tribble(
  ~x1,    ~x2,   ~n,
  "3.43", "5.28", 90,
  "2.97", "4.42", 103
)
df

# ...GRIM-tested with dispersed `n` values...
out <- grim_map_total_n(df)
out

# ...and faithfully reconstructed:
reverse_map_total_n(out)

Description

Rounding often leads to bias, such that the mean of a rounded distribution is different from the mean of the original distribution. Call rounding_bias() to compute the amount of this bias.

Usage

rounding_bias(
  x,
  digits,
  rounding = "up",
  threshold = 5,
  symmetric = FALSE,
  mean = TRUE
)

Arguments

Details

Bias is calculated by subtracting the original vector, x, from a vector rounded in the specified way.

The function passes all arguments except for mean down to reround(). Other than there, however, rounding is "up" by default, and it can't be set to "up_or_down", "up_from_or_down_from", or"ceiling_or_floor".

Value

Numeric. By default of mean, the length is 1; otherwise, it is the same length as x.

Examples

# Define example vector:
vec <- seq_distance(0.01, string_output = FALSE)
vec

# The default rounds `x` up from 5:
rounding_bias(x = vec, digits = 1)

# Other rounding procedures are supported,
# such as rounding down from 5...
rounding_bias(x = vec, digits = 1, rounding = "down")

# ...or rounding to even with `base::round()`:
rounding_bias(x = vec, digits = 1, rounding = "even")

Description

round_up() rounds up from 5, round_down() rounds down from 5. Otherwise, both functions work like base::round().

round_up() and round_down() are special cases of round_up_from() and round_down_from(), which allow users to choose custom thresholds for rounding up or down, respectively.

Usage

round_up_from(x, digits = 0L, threshold, symmetric = FALSE)

round_down_from(x, digits = 0L, threshold, symmetric = FALSE)

round_up(x, digits = 0L, symmetric = FALSE)

round_down(x, digits = 0L, symmetric = FALSE)

Arguments

Details

These functions differ from base::round() mainly insofar as the decision about rounding 5 up or down is not based on the integer portion of x (i.e., no "rounding to even"). Instead, in round_up_from(), that decision is determined by the threshold argument for rounding up, and likewise with round_down_from(). The threshold is constant at 5 for round_up() and round_down().

As a result, these functions are more predictable and less prone to floating-point number quirks than base::round(). Compare round_down() and base::round() in the data frame for rounding 5 created in the Examples section below: round_down() yields a continuous sequence of final digits from 0 to 9, whereas base::round() behaves in a way that can only be explained by floating point issues.

However, this surprising behavior on the part of base::round() is not necessarily a flaw (see its documentation, or this vignette: https://rpubs.com/maechler/Rounding). In the present version of R (4.0.0 or later), base::round() works fine, and the functions presented here are not meant to replace it. Their main purpose as helpers within scrutiny is to reconstruct the computations of researchers who might have used different software. See vignette("rounding-options").

Value

Numeric. x rounded to digits.

See Also

round_ceiling() always rounds up, round_floor() always rounds down, round_trunc() always rounds toward 0, and round_anti_trunc() always round away from 0.

Examples

# Both `round_up()` and `round_down()` work like
# `base::round()` unless the closest digit to be
# cut off by rounding is 5:

   round_up(x = 9.273, digits = 1)     # 7 cut off
 round_down(x = 9.273, digits = 1)     # 7 cut off
base::round(x = 9.273, digits = 1)     # 7 cut off

   round_up(x = 7.584, digits = 2)     # 4 cut off
 round_down(x = 7.584, digits = 2)     # 4 cut off
base::round(x = 7.584, digits = 2)     # 4 cut off


# Here is the borderline case of 5 rounded by
# `round_up()`, `round_down()`, and `base::round()`:

original <- c(    # Define example values
  0.05, 0.15, 0.25, 0.35, 0.45,
  0.55, 0.65, 0.75, 0.85, 0.95
)
tibble::tibble(   # Output table
  original,
  round_up = round_up(x = original, digits = 1),
  round_down = round_down(x = original, digits = 1),
  base_round = base::round(x = original, digits = 1)
)

# (Note: Defining `original` as `seq(0.05:0.95, by = 0.1)`
# would lead to wrong results unless `original` is rounded
# to 2 or so digits before it's rounded to 1.)

Description

Always round up, down, toward zero, or away from it:

• round_ceiling() always rounds up.

• round_floor() always rounds down.

• round_trunc() always rounds toward zero.

• round_anti_trunc() always rounds away from zero. (0 itself is rounded to 1.)

• anti_trunc() does not round but otherwise works like round_anti_trunc().

Despite not being widely used, they are featured here in case they are needed for reconstruction.

Usage

round_ceiling(x, digits = 0L)

round_floor(x, digits = 0L)

round_trunc(x, digits = 0L)

anti_trunc(x)

round_anti_trunc(x, digits = 0L)

Arguments

Details

round_ceiling(), round_floor(), and round_trunc() generalize the base R functions ceiling(), floor(), and trunc(), and include them as special cases: With the default value for digits, 0, these ⁠round_*⁠ functions are equivalent to their respective base counterparts.

The last ⁠round_*⁠ function, round_anti_trunc(), generalizes another function presented here: anti_trunc() works like trunc() except it moves away from 0, rather than towards it. That is, whereas trunc() minimizes the absolute value of x (as compared to the other rounding functions), anti_trunc() maximizes it. anti_trunc(x) is therefore equal to trunc(x) + 1 if x is positive, and to trunc(x) - 1 if x is negative.

round_anti_trunc(), then, generalizes anti_trunc() just as round_ceiling() generalizes ceiling(), etc.

Moreover, round_trunc() is equivalent to round_floor() for positive numbers and to round_ceiling() for negative numbers. The reverse is again true for round_anti_trunc(): It is equivalent to round_ceiling() for positive numbers and to round_floor() for negative numbers.

Value

Numeric. x rounded to digits (except for anti_trunc(), which has no digits argument).

See Also

round_up() and round_down() round up or down from 5, respectively. round_up_from() and round_down_from() allow users to specify custom thresholds for rounding up or down.

Examples

# Always round up:
round_ceiling(x = 4.52, digits = 1)        # 2 cut off

# Always round down:
round_floor(x = 4.67, digits = 1)          # 7 cut off

# Always round toward 0:
round_trunc(8.439, digits = 2)             # 9 cut off
round_trunc(-8.439, digits = 2)            # 9 cut off

# Always round away from 0:
round_anti_trunc(x = 8.421, digits = 2)    # 1 cut off
round_anti_trunc(x = -8.421, digits = 2)   # 1 cut off

Description

Data frames sometimes have wrong column names, while the correct column names are stored in one or more rows in the data frame itself. To remedy this issue, call row_to_colnames() on the data frame: It replaces the column names by the values of the specified rows (by default, only the first one). These rows are then dropped by default.

Usage

row_to_colnames(data, row = 1L, collapse = " ", drop = TRUE)

Arguments

Details

If multiple rows are specified, the row values for each individual column are pasted together. Some special characters might then be missing.

This function might be useful when importing tables from PDF, e.g. with tabulizer. In R, these data frames (converted from matrices) do sometimes have the issue described above.

Value

A tibble (data frame).

See Also

unheadr::mash_colnames(), a more sophisticated solution to the same problem.

Description

Compute the sample SD of binary data (i.e., only 0 and 1 values) in either of four ways, each based on different inputs:

• sd_binary_groups() takes the cell sizes of both groups, those coded as 0 and those coded as 1.

• sd_binary_0_n() takes the cell size of the group coded as 0 and the total sample size.

• sd_binary_1_n() takes the cell size of the group coded as 1 and the total sample size.

• sd_binary_mean_n() takes the mean and the total sample size.

These functions are used as helpers inside debit(), and consequently debit_map().

Usage

sd_binary_groups(group_0, group_1)

sd_binary_0_n(group_0, n)

sd_binary_1_n(group_1, n)

sd_binary_mean_n(mean, n)

Arguments

Value

Numeric. Sample standard deviation.

References

Heathers, James A. J., and Brown, Nicholas J. L. 2019. DEBIT: A Simple Consistency Test For Binary Data. https://osf.io/5vb3u/.

See Also

is_subset_of_vals(x, 0, 1) checks whether x (a list or atomic vector) contains nothing but binary numbers.

Examples

# If 127 values are coded as 0 and 153 as 1...
sd_binary_groups(group_0 = 127, group_1 = 153)

# ...so that n = 280:
sd_binary_0_n(group_0 = 127, n = 280)
sd_binary_1_n(group_1 = 153, n = 280)

# If only the mean and total sample size are
# given, or these are more convenient to use,
# they still lead to the same result as above
# if the mean is given with a sufficiently
# large number of decimal places:
sd_binary_mean_n(mean = 0.5464286, n = 280)

Description

seq_disperse() creates a sequence around a given number. It goes a specified number of steps up and down from it. Step size depends on the number's decimal places. For example, 7.93 will be surrounded by values like 7.91, 7.92, and 7.94, 7.95, etc.

seq_disperse_df() is a variant that creates a data frame. Further columns can be added as in tibble::tibble(). Regular arguments are the same as in seq_disperse(), but with a dot before each.

Usage

seq_disperse(
  from,
  by = NULL,
  dispersion = 1:5,
  offset_from = 0L,
  out_min = "auto",
  out_max = NULL,
  string_output = TRUE,
  include_reported = TRUE,
  track_diff_var = FALSE,
  track_var_change = deprecated()
)

seq_disperse_df(
  .from,
  .by = NULL,
  ...,
  .dispersion = 1:5,
  .offset_from = 0L,
  .out_min = "auto",
  .out_max = NULL,
  .string_output = TRUE,
  .include_reported = TRUE,
  .track_diff_var = FALSE,
  .track_var_change = FALSE
)

Arguments

Details

Unlike seq_endpoint() and friends, the present functions don't necessarily return continuous or even regular sequences. The greater flexibility is due to the dispersion (.dispersion) argument, which takes any numeric vector. By default, however, the output sequence is regular and continuous.

Underlying this difference is the fact that seq_disperse() and seq_disperse_df() do not wrap around base::seq(), although they are otherwise similar to seq_endpoint() and friends.

Value

• seq_disperse() returns a string vector by default (string_output = TRUE) and a numeric vector otherwise.

• seq_disperse_df() returns a tibble (data frame). The sequence is stored in the x column. x is string by default (.string_output = TRUE), numeric otherwise. Other columns might have been added via the dots (...).

See Also

Conceptually, seq_disperse() is a blend of two function families: those around seq_endpoint() and those around disperse(). The present functions were originally conceived for seq_disperse_df() to be a helper within the function_map_seq() implementation.

Examples

# Basic usage:
seq_disperse(from = 4.02)

# If trailing zeros don't matter,
# the output can be numeric:
seq_disperse(from = 4.02, string_output = FALSE)

# Control steps up and down with
# `dispersion` (default is `1:5`):
seq_disperse(from = 4.02, dispersion = 1:10)

# Sequences might be discontinuous...
disp1 <- seq(from = 2, to = 10, by = 2)
seq_disperse(from = 4.02, dispersion = disp1)

# ...or even irregular:
disp2 <- c(2, 3, 7)
seq_disperse(from = 4.02, dispersion = disp2)

# The data fame variant supports further
# columns added as in `tibble::tibble()`:
seq_disperse_df(.from = 4.02, n = 45)

Description

seq_length() seamlessly extends or shortens a linear sequence using the sequence's own step size.

Alternatively, you can directly set the length of a linear sequence in this way: seq_length(x) <- value.

Usage

seq_length(x, value)

seq_length(x) <- value

Arguments

Value

A vector of the same type as x, with length value.

• If value > length(x), all original element of x are preserved. A number of new elements equal to the difference is appended at the end.

• If value == length(x), nothing changes.

• If value < length(x), a number of elements of x equal to the difference is removed from the end.

Examples

x <- 3:7

# Increase the length of `x` from 5 to 10:
seq_length(x, 10)

# Modify `x` directly (but get
# the same results otherwise):
seq_length(x) <- 10
x

# Likewise, decrease the length:
x <- 3:7
seq_length(x, 2)

seq_length(x) <- 2
x

# The functions are sensitive to decimal levels.
# They also return a string vector if (and only if)
# `x` is a string vector:
x <- seq_endpoint(from = 0, to = 0.5)
x

seq_length(x, 10)

seq_length(x) <- 10
x

# Same with decreasing the length:
seq_length(x, 2)

seq_length(x) <- 2
x

Description

Run this function after generating a sequence with seq_endpoint_df() or seq_distance_df() and testing it with one of scrutiny's mapping functions, such as grim_map(). It will rank the test's consistent and inconsistent results by their positions in the sequence.

Usage

seq_test_ranking(x, explain = TRUE)

Arguments

Details

The function checks the provenance of the test results and throws a warning if it's not correct.

Value

A tibble (data frame). The function will also print an explanation of the results. See examples.

Examples

seq_distance_df(.from = "0.00", n = 50) %>%
  grim_map() %>%
  seq_test_ranking()

Description

Functions that provide a smooth interface to generating sequences based on the input values' decimal depth. Each function creates a sequence with a step size of one unit on the level of the input values' ultimate decimal digit (e.g., 2.45, 2.46, 2.47, ...):

• seq_endpoint() creates a sequence from one input value to another. For step size, it goes by the value with more decimal places.

• seq_distance() only takes the starting point and, instead of the endpoint, the desired output length. For step size, it goes by the starting point by default.

seq_endpoint_df() and seq_distance_df() are variants that create a data frame. Further columns can be added as in tibble::tibble(). Regular arguments are the same as in the respective non-df function, but with a dot before each.

Usage

seq_endpoint(from, to, offset_from = 0L, offset_to = 0L, string_output = TRUE)

seq_distance(
  from,
  by = NULL,
  length_out = 10L,
  dir = 1,
  offset_from = 0L,
  string_output = TRUE
)

seq_endpoint_df(
  .from,
  .to,
  ...,
  .offset_from = 0L,
  .offset_to = 0L,
  .string_output = TRUE
)

seq_distance_df(
  .from,
  .by = NULL,
  ...,
  .length_out = 10L,
  .dir = 1,
  .offset_from = 0L,
  .string_output = TRUE
)

Arguments