Type: Package
Title: Useful Wrappers Around Commonly Used Functions
Description: The main functionalities of 'wrappedtools' are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well.
Version: 0.9.8
Date: 2025-07-07
Maintainer: Andreas Busjahn <andreas@busjahn.net>
License: GPL-3
Encoding: UTF-8
Imports: stats, boot, knitr, coin, utils, dplyr, tidyr, forcats, purrr, glue, rlang, stringr, ggplot2, tibble, tidyr, kableExtra, lifecycle, broom, rlist, DescTools, flextable, nortest
Depends: R (≥ 4.2)
RoxygenNote: 7.3.2
LazyData: true
VignetteBuilder: knitr
Suggests: rmarkdown, testthat, ggrepel
URL: https://github.com/abusjahn/wrappedtools
BugReports: https://github.com/abusjahn/wrappedtools/issues
NeedsCompilation: no
Packaged: 2025-07-10 15:50:35 UTC; abusj
Author: Andreas Busjahn ORCID iD [cre, aut], Franziska Eidloth [aut], Bilal Asser [aut]
Repository: CRAN
Date/Publication: 2025-07-10 16:10:02 UTC

wrappedtools: Useful Wrappers Around Commonly Used Functions

Description

logo

The main functionalities of 'wrappedtools' are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well.

Author(s)

Maintainer: Andreas Busjahn andreas@busjahn.net (ORCID)

Authors:

See Also

Useful links:

Find numeric index and names of columns based on class(es) and patterns

Description

ColSeeker looks up colnames (by default for tibble rawdata) based on type and parts of names, using regular expressions. Be warned that special characters as e.g. [ ( need to be escaped or replaced by . Exclusion rules may be specified as well.

Usage

ColSeeker(
  data = rawdata,
  namepattern = ".",
  varclass = NULL,
  exclude = NULL,
  excludeclass = NULL,
  casesensitive = TRUE,
  returnclass = FALSE
)

Arguments

data

tibble or data.frame, where columns are to be found; by default rawdata

namepattern

Vector of pattern to look for.

varclass

Vector, only columns of defined class(es) are returned

exclude

Vector of pattern to exclude from found names.

excludeclass

Vector, exclude columns of specified class(es)

casesensitive

Logical if case is respected in matching (default FALSE: a<>A)

returnclass

Logical if classes should be included in output

Value

A list with index, names, backticked names, and count; optionally the classes as well

Examples

ColSeeker(data = mtcars, namepattern = c("^c", "g"))
ColSeeker(data = mtcars, namepattern = c("^c", "g"), exclude = "r")
assign("rawdata", mtcars)
ColSeeker(namepattern = c("^c", "g"), varclass="numeric")
num_int_data <- data.frame(num1=rnorm(10), num2=runif(10), int1=1:10, int2=11:20)
ColSeeker(num_int_data, varclass="numeric")  # integers are not found
ColSeeker(num_int_data, varclass=c("numeric","integer"))

Find numeric index and names of columns based on patterns

Description

[Superseded]

Function ColSeeker extends this by adding class-checks.

FindVars looks up colnames (by default for data-frame rawdata) based on parts of names, using regular expressions. Be warned that special characters as e.g. [ ( need to be escaped or replaced by . Exclusion rules may be specified as well. New function ColSeeker() extends this by adding class-checks.

Usage

FindVars(
  varnames,
  allnames = colnames(rawdata),
  exact = FALSE,
  exclude = NA,
  casesensitive = TRUE,
  fixed = FALSE,
  return_symbols = FALSE
)

Arguments

varnames

Vector of pattern to look for.

allnames

Vector of values to detect pattern in; by default: colnames(rawdata).

exact

Partial matching or exact only (adding ^ and $)?

exclude

Vector of pattern to exclude from found names.

casesensitive

Logical if case is respected in matching (default FALSE: a<>A)

fixed

Logical, match as is, argument is passed to grep().

return_symbols

Should names be reported as symbols additionally? (Default FALSE)

Value

A list with index, names, backticked names, and symbols

Examples

FindVars(varnames = c("^c", "g"), allnames = colnames(mtcars))
FindVars(varnames = c("^c", "g"), allnames = colnames(mtcars), exclude = "r")

Standard Error of Mean.

Description

SEM computes standard error of mean.

Usage

SEM(x)

Arguments

x

Data for computation.

Value

numeric vector with SEM.

Examples

SEM(x = mtcars$wt)

Comparison for groups in clinical trials based on all possible combinations of subjects

Description

[Experimental]

WINratio computes the ratio of wins and losses for any number of comparison rules.

Usage

WINratio(data, groupvar, testvars, rules, idvar = NULL, p_digits = 3)

Arguments

data

name of data set (tibble/data.frame) to analyze.

groupvar

name of grouping variable, has to translate to 2 groups.

testvars

names of variables for sequential rules.

rules

list of rules (minimal cut-offs) for sequential comparison, negative if reduction is success, positive if increase is beneficial, must not be 0.

idvar

name of identifier variable. If NULL, rownumber is used.

p_digits

level for rounding p-value.

Value

A list with elements:

WINratio=vector with WINratio and CIs,

WINodds=odds ratio of wins and losses, taking ties into account,

p.value=p.value from prop.test,

WINratioCI=character with merged WINratio, CI, and p

testdata= tibble with testdata from cross-join.

Add backticks to names or remove them

Description

bt adds leading and trailing backticks to make illegal variable names usable. Optionally removes them.

Usage

bt(x, remove = FALSE)

Arguments

x

Names to add backtick to.

remove

Option to remove existing backticks, default=FALSE.

Value

Character vector with backticks added.

Examples

bt('name 1')

Compute absolute and relative frequencies.

Description

cat_desc_stats computes absolute and relative frequencies for categorical data with a number of formatting options.

Usage

cat_desc_stats(
  source = NULL,
  separator = " ",
  return_level = TRUE,
  ndigit = 0,
  groupvar = NULL,
  singleline = FALSE,
  percent = TRUE,
  prettynum = FALSE,
  .german = FALSE,
  quelle = NULL
)

Arguments

source

Data for computation. Previously "quelle".

separator

delimiter between results per level, preset as ' '.

return_level

Should levels be reported?

ndigit

Digits for rounding of relative frequencies.

groupvar

Optional grouping factor.

singleline

Put all group levels in a single line?

percent

Logical, add percent-symbol after relative frequencies?

prettynum

logical, apply prettyNum to results?

.german

logical, should "." and "," be used as bigmark and decimal? Sets prettynum to TRUE.

quelle

deprecated, retained for compatibility, use 'source' instead.

Value

Structure depends on parameter return_level: if FALSE than a tibble with descriptives, otherwise a list with two tibbles with levels of factor and descriptives. If parameter singleline is FALSE (default), results for each factor level is reported in a separate line, otherwise they are pasted. Number of columns for result tibbles is one or number of levels of the additional grouping variable.

Examples

cat_desc_stats(mtcars$gear)
cat_desc_stats(mtcars$gear, return_level = FALSE)
cat_desc_stats(mtcars$gear, groupvar = mtcars$am)
cat_desc_stats(mtcars$gear, groupvar = mtcars$am, singleline = TRUE)

Compute absolute and relative frequencies for a table.

Description

cat_desc_table computes absolute and relative frequencies for categorical data with a number of formatting options.

Usage

cat_desc_table(
  data,
  desc_vars,
  round_desc = 2,
  singleline = FALSE,
  spacer = " ",
  indentor = ""
)

Arguments

data

name of data set (tibble/data.frame) to analyze.

desc_vars

vector of column names for dependent variables.

round_desc

number of significant digits for rounding of descriptive stats.

singleline

Put all group levels in a single line?

spacer

Text element to indent levels and fill empty cells, defaults to " ".

indentor

Optional text to indent factor levels

Value

A tibble with variable names and descriptive statistics.

Examples

cat_desc_table(
  data = mtcars, desc_vars = c("gear", "cyl", "carb")
)

cat_desc_table(
  data = mtcars, desc_vars = c("gear", "cyl", "carb"), singleline = TRUE
)

Shortcut for colnames()

Description

cn lists column names, by default for variable rawdata.

Usage

cn(data = rawdata)

Arguments

data

Data structure to read column names from.

Value

Character vector with column names.

Examples

cn(mtcars)

Comparison for columns of numbers for 2 groups

Description

compare2numvars computes either t_var_test or wilcox.test, depending on parameter gaussian. Descriptive statistics, depending on distribution, are reported as well.

Usage

compare2numvars(
  data,
  dep_vars,
  indep_var,
  gaussian,
  round_p = 3,
  round_desc = 2,
  range = FALSE,
  rangesep = " ",
  pretext = FALSE,
  mark = FALSE,
  n = FALSE,
  add_n = FALSE,
  singleline = TRUE,
  indentor = "   ",
  ci = FALSE,
  n_boot = 10^3
)

Arguments

data

name of dataset (tibble/data.frame) to analyze.

dep_vars

vector of column names for independent variables.

indep_var

name of grouping variable, has to translate to 2 groups. If more levels are encountered, an error is produced.

gaussian

logical specifying normal or ordinal values.

round_p

level for rounding p-value.

round_desc

number of significant digits for rounding of descriptive stats.

range

include min/max?

rangesep

text between statistics and range or other elements.

pretext

for function formatP.

mark

for function formatP.

n

create columns for n per group?

add_n

add n to descriptive statistics. Will automatically be set to TRUE, if singleline = FALSE and n = TRUE to keep it for the long table format.

singleline

Put all computed stats in a single line (default) or below each other.

indentor

Optional text element to indent descriptivestats when using singleline = FALSE. Defaults to " ".

ci

Computes lower and upper confidence limits for the estimated mean/median, based on bootstrapping.

n_boot

Number of bootstrap samples for confidence limits.

Value

A tibble with variable names, descriptive statistics, and p-value, number of rows is number of dep_vars.

Examples

# Assuming Normal distribution:
compare2numvars(
  data = mtcars, dep_vars = c("wt", "mpg", "qsec"), indep_var = "am",
  gaussian = TRUE
)
compare2numvars(
  data = mtcars, dep_vars = c("wt", "mpg", "qsec"), indep_var = "am",
  gaussian = TRUE, singleline = FALSE
)
# Ordinal scale:
compare2numvars(
  data = mtcars, dep_vars = c("wt", "mpg", "qsec"), indep_var = "am",
  gaussian = FALSE, add_n = TRUE, range = TRUE
)
# If dependent variable has more than 2 levels, consider fct_lump:
mtcars |>
  dplyr::mutate(gear = factor(gear) |> forcats::fct_lump_n(n = 1)) |>
  compare2numvars(dep_vars = "wt", indep_var = "gear", gaussian = TRUE)

Comparison for columns of factors for 2 groups

Description

compare2qualvars computes fisher.test with simulated p-value and descriptive statistics for a group of categorical dependent variables.

Usage

compare2qualvars(
  data,
  dep_vars,
  indep_var,
  round_p = 3,
  round_desc = 2,
  pretext = FALSE,
  mark = FALSE,
  singleline = FALSE,
  spacer = " ",
  linebreak = "\n",
  p_subgroups = FALSE
)

Arguments

data

name of data set (tibble/data.frame) to analyze.

dep_vars

vector of column names for dependent variables.

indep_var

name of grouping variable, has to translate to 2 groups.

round_p

level for rounding p-value.

round_desc

number of significant digits for rounding of descriptive stats.

pretext

for function formatP.

mark

for function formatP.

singleline

Put all group levels in a single line?

spacer

Text element to indent levels and fill empty cells, defaults to " ".

linebreak

place holder for newline.

p_subgroups

test subgroups by recoding other levels into other, default is not to do this.

Value

A tibble with variable names, descriptive statistics, and p-value, number of rows is number of dep_vars.

Examples

compare2qualvars(
  data = mtcars, dep_vars = c("gear", "cyl", "carb"), indep_var = "am",
  spacer = " "
)
compare2qualvars(
  data = mtcars, dep_vars = c("gear", "cyl", "carb"), indep_var = "am",
  spacer = " ", singleline = TRUE
)
compare2qualvars(
  data = mtcars, dep_vars = c("gear", "cyl", "carb"), indep_var = "am",
  spacer = " ", p_subgroups = TRUE
)

Comparison for columns of Gaussian or ordinal measures for n groups

Description

Some names were changed in August 2022, to reflect the update of the function to handle ordinal data using non-parametric equivalents.

Usage

compare_n_numvars(
  .data = rawdata,
  dep_vars,
  indep_var,
  gaussian,
  round_desc = 2,
  range = FALSE,
  rangesep = " ",
  pretext = FALSE,
  mark = FALSE,
  round_p = 3,
  add_n = FALSE
)

Arguments

.data

name of dataset (tibble/data.frame) to analyze, defaults to rawdata.

dep_vars

vector of column names.

indep_var

name of grouping variable.

gaussian

Logical specifying normal or ordinal indep_var (and chooses comparison tests accordingly)

round_desc

number of significant digits for rounding of descriptive stats.

range

include min/max?

rangesep

text between statistics and range or other elements.

pretext, mark

for function formatP.

round_p

level for rounding p-value.

add_n

add n to descriptive statistics?

Value

A list with elements "results": tibble with descriptive statistics, p-value from ANOVA/Kruskal-Wallis test, p-values for pairwise comparisons, significance indicators, and descriptives pasted with significance. "raw": nested list with output from all underlying analyses.

Examples

# Usually,only the result table is relevant:
compare_n_numvars(
  .data = mtcars, dep_vars = c("wt", "mpg", "hp"),
  indep_var = "cyl",
  gaussian = TRUE
)$results
# For a report, result columns may be filtered as needed:
compare_n_numvars(
  .data = mtcars, dep_vars = c("wt", "mpg", "hp"),
  indep_var = "cyl",
  gaussian = FALSE
)$results |>
  dplyr::select(Variable, `cyl 4 fn`:`cyl 8 fn`, multivar_p)

Comparison for columns of factors for more than 2 groups with post-hoc

Description

Comparison for columns of factors for more than 2 groups with post-hoc

Usage

compare_n_qualvars(
  data,
  dep_vars,
  indep_var,
  round_p = 3,
  round_desc = 2,
  pretext = FALSE,
  mark = FALSE,
  singleline = FALSE,
  spacer = " ",
  linebreak = "\n",
  prettynum = FALSE
)

Arguments

data

name of data set (tibble/data.frame) to analyze.

dep_vars

vector of column names.

indep_var

name of grouping variable.

round_p

level for rounding p-value.

round_desc

number of significant digits for rounding of descriptive stats

pretext

for function formatP

mark

for function formatP

singleline

Put all group levels in a single line?

spacer

Text element to indent levels, defaults to " ".

linebreak

place holder for newline.

prettynum

Apply prettyNum to results?

Value

A tibble with variable names, descriptive statistics, and p-value of fisher.test and pairwise_fisher_test, number of rows is number of dep_vars.

Examples

# Separate lines for each factor level:
compare_n_qualvars(
  data = mtcars, dep_vars = c("am", "cyl", "carb"), indep_var = "gear",
  spacer = " "
)
# All levels in one row but with linebreaks:
compare_n_qualvars(
  data = mtcars, dep_vars = c("am", "cyl", "carb"), indep_var = "gear",
  singleline = TRUE
)
# All levels in one row, separateted by ";":
compare_n_qualvars(
  data = mtcars, dep_vars = c("am", "cyl", "carb"), indep_var = "gear",
  singleline = TRUE, linebreak = "; "
)

Correlations with significance

Description

cortestR computes correlations and their significance level based on cor.test. Coefficients and p-values may be combined or reported separately.

Usage

cortestR(
  cordata,
  method = "pearson",
  digits = 3,
  digits_p = 3,
  sign_symbol = TRUE,
  split = FALSE,
  space = ""
)

Arguments

cordata

data frame or matrix with rawdata.

method

as in cor.test.

digits

rounding level for estimate.

digits_p

rounding level for p value.

sign_symbol

If true, use significance indicator instead of p-value.

split

logical, report correlation and p combined (default) or split in list.

space

character to fill empty upper triangle.

Value

Depending on parameters split and sign_symbol, either a single data frame with coefficient and p-values or significance symbols or a list with two data frames.

Examples

# with defaults
cortestR(mtcars[, c("wt", "mpg", "qsec")], split = FALSE, sign_symbol = TRUE)
# separate coefficients and p-values
cortestR(mtcars[, c("wt", "mpg", "qsec")], split = TRUE, sign_symbol = FALSE)

Find outliers based on IQR

Description

[Experimental]

detect_outliers computes IQR and finds outliers. It gives the same results as geom_boxplot and thus differs slightly from boxplot.stats.

Usage

detect_outliers(x, coef = 1.5)

Arguments

x

numeric vector.

coef

coefficient for boxplot.stats, defaults to 1.5.

Value

A list with elements positions and outliers as numeric vectors.

Examples

detect_outliers(rnorm(100))

Estimation of glomerular filtration rate (eGFR) based on sex, age, and either serum creatinine and/or cystatin C

Description

[Experimental]

eGFR computes eGFR according to different rules (see references).

Usage

eGFR(data, age_var = "age", sex_var = "sex", crea_var = NULL, cys_var = NULL)

Arguments

data

name of data set (tibble/data.frame) to analyze.

age_var

name of column with patient age in years, default=age.

sex_var

name of column with sex, assumed as female and male.

crea_var

name of column with creatinine in mg/dl. If not available, leave as NULL.

cys_var

name of column with cystatin C in mg/l. If not available, leave as NULL.

Value

A list with 3 elements:

eGFR_crea

eGFR_cystatin

eGFR_creatinine_cystatin

References

https://www.kidney.org/content/ckd-epi-creatinine-cystatin-equation-2021

https://www.kidney.org/content/ckd-epi-creatinine-equation-2021

https://www.kidney.org/content/ckd-epi-cystatin-c-equation-2012

Results from a simulated clinical trial with interaction effects.

Description

A dataset containing physiological data, biomarkers, and categorical data.

Usage

faketrial

Format

A tibble with 300 rows and 24 variables:

Sex

Sex of animal, factor with levels 'female', 'male'

Agegroup

Factor with levels 'young','middle','old'

Treatment

Factor with levels 'sham', 'OP'

HR

Heart rate

sysRR,diaRR

Systolic and diastolic blood pressure

Med xxx

Pseudo-medications, factors with levels 'y','n'

Biomarker x units

Biomarkers with log-normal distribution

Responder

factor yes/no, systolic plood pressure >= 120?

Transform flextable to rmd if non-interactive

Description

flex2rmd takes a flextable and returns a markdown table if not in an interactive session

Usage

flex2rmd(ft)

Arguments

ft

a flextable

Value

either a markdown table or the flextable

Re-format p-values, avoiding rounding to 0 and adding surprisal if requested

Description

formatP simplifies p-values by rounding to the maximum of p or a predefined level. Optionally < or = can be added, as well as symbols according to significance level.

Usage

formatP(
  pIn,
  ndigits = 3,
  textout = TRUE,
  pretext = FALSE,
  mark = FALSE,
  german_num = FALSE,
  add.surprisal = FALSE,
  sprecision = 1
)

Arguments

pIn

A numeric vector or matrix with p-values.

ndigits

Number of digits (default=3).

textout

Cast output to character (default=TRUE)?

pretext

Should = or < be added before p (default=FALSE)?

mark

Should significance level be added after p (default=FALSE)?

german_num

change dot (default) to comma?

add.surprisal

Add surprisal aka Shannon information to p-value (default=FALSE)?

sprecision

Rounding level for surprisal (default=1).

Value

vector or matrix (depending on type of pIn) with type character (default) or numeric, depending on parameter textout

Examples

formatP(0.012345)
formatP(0.012345, add.surprisal = TRUE)
formatP(0.012345, ndigits = 4)
formatP(0.000122345, ndigits = 3, pretext = TRUE)

Print graphical representation of a correlation matrix.

Description

ggcormat makes the same correlation matrix as cortestR and graphically represents it in a plot

Usage

ggcormat(
  cor_mat,
  p_mat = NULL,
  method = "Correlation",
  title = "",
  maxpoint = 2.1,
  textsize = 5,
  axistextsize = 2,
  titlesize = 3,
  breaklabels = NULL,
  lower_only = TRUE,
  .low = "blue3",
  .high = "red2",
  .legendtitle = NULL
)

Arguments

cor_mat

correlation matrix as produced by cor.

p_mat

Optional matrix of p-values; if provided, this is used to define size of dots rather than absolute correlation.

method

text specifying type of correlation.

title

plot title.

maxpoint

maximum for scale_size_manual, may need adjustment depending on plotsize.

textsize

for theme text.

axistextsize

relative text size for axes.

titlesize

as you already guessed, relative text size for title.

breaklabels

currently not used, intended for str_wrap.

lower_only

should only lower triangle be plotted?

.low

Color for heatmap.

.high

Color for heatmap.

.legendtitle

Optional name for color legend.

Value

A ggplot object, allowing further styling.

Examples

coeff_pvalues <- cortestR(mtcars[, c("wt", "mpg", "qsec", "hp")],
  split = TRUE, sign_symbol = FALSE
)
# focus on coefficients:
ggcormat(cor_mat = coeff_pvalues$corout, maxpoint = 5)
# size taken from p-value:
ggcormat(
  cor_mat = coeff_pvalues$corout,
  p_mat = coeff_pvalues$pout, maxpoint = 5)

Confidence interval for generalized linear models

Description

glm_CI computes and formats CIs for glm.

Usage

glmCI(model, min = .01, max = 100, cisep = '\U000022ef', ndigit=2)

Arguments

model

Output from glm.

min, max

Lower and upper limits for CIs, useful for extremely wide CIs.

cisep

Separator between CI values.

ndigit

rounding level.

Value

A list with coefficient, CIs, and pasted coef([CIs]).

Examples

glm_out <- glm(am ~ mpg, family = binomial, data = mtcars)
glmCI(glm_out)

Find and optionally remove identical columns in a data frame.

Description

This function identifies columns with identical values in a data frame and provides options to remove them, clean column names, and print the duplicated groups. It also includes an interactive mode where the user can choose to remove all, some, or none of the duplicated columns.

Usage

identical_cols(
  df,
  interactive = TRUE,
  remove_duplicates = TRUE,
  clean_names = TRUE,
  print_duplicates = TRUE
)

Arguments

df

A data frame or tibble.

interactive

Logical. If TRUE, the function prompts the user to choose how to handle duplicated columns. Defaults to TRUE.

remove_duplicates

Logical. If TRUE, removes duplicated columns. Defaults to TRUE.

clean_names

Logical. If TRUE, cleans column names by removing trailing "..." followed by digits. Defaults to TRUE.

print_duplicates

Logical. If TRUE, prints the groups of duplicated columns. Defaults to TRUE.

Value

A data frame with optionally removed and renamed columns.

Examples

library(tibble)

dummy <- tibble(
  A...1 = rnorm(10),
  A...2 = A...1,
  C = sample(letters, 10),
  A...4 = A...1,
  E = sample(1:10, 10),
  `F` = C
)

# Example usage:
identical_cols(dummy) # Interactive removal
identical_cols(dummy, remove_duplicates = FALSE) # Find identical columns only
identical_cols(dummy, print_duplicates = FALSE) # Interactive removal, no print
identical_cols(dummy, clean_names = FALSE) # Interactive removal, no clean names
identical_cols(dummy, interactive = FALSE) #Non interactive removal of all duplicates.

Kolmogorov-Smirnov-Test against Normal distribution

Description

ksnormal is a convenience function around ks.test, testing against Normal distribution. If less than 2 values are provided, NA is returned.

Usage

ksnormal(x, lillie = TRUE)

Arguments

x

Vector of data to test.

lillie

Logical, should the Lilliefors test be used? Defaults to TRUE

Value

p.value from ks.test.

Examples

# original ks.test:
ks.test(
  x = mtcars$wt, pnorm, mean = mean(mtcars$wt, na.rm = TRUE),
  sd = sd(mtcars$wt, na.rm = TRUE)
)
# wrapped version:
ksnormal(x = mtcars$wt, lillie = FALSE)

Add labels to outliers in boxplot/beeswarm.

Description

[Experimental]

label_outliers adds a text_repel layer to an existing ggplot object. It is intended to be used with boxplots or beeswarm plots. Faceting will result in separate computations for outliers. It requires the ggrepel package.

Usage

label_outliers(
  plotbase,
  labelvar = NULL,
  coef = 1.5,
  nudge_x = 0,
  nudge_y = 0,
  color = "darkred",
  size = 3,
  hjust = 0,
  face = "bold"
)

Arguments

plotbase

ggplot object to add labels to.

labelvar

variable to use as label. If NULL, rownames or rownumbers are used.

coef

coefficient for boxplot.stats, defaults to 1.5.

nudge_x

nudge in x direction, defaults to 0.

nudge_y

nudge in y direction, defaults to 0.

color

color of labels, defaults to darkred.

size

size of labels, defaults to 3.

hjust

horizontal justification of labels, defaults to 0.

face

font face of labels, defaults to bold.

Value

A ggplot object, allowing further styling.

Predefined sets of labels for plots with log-scaled axes

Description

logrange_1 returns a vector for log-labels at .1, 1, 100, 1000 ...

Usage

logrange_1

logrange_5

logrange_123456789

logrange_12357

logrange_15

Format

An object of class numeric of length 41.

An object of class numeric of length 738.

An object of class numeric of length 369.

An object of class numeric of length 205.

An object of class numeric of length 82.

Value

numeric vector

numeric vector

Functions

Examples

ggplot2::ggplot(mtcars) + 
ggplot2::aes(wt, mpg) +
  ggplot2::geom_point() +
  ggplot2::scale_y_log10(breaks = logrange_5)
ggplot2::ggplot(mtcars) +
  ggplot2::aes(wt, mpg) +
  ggplot2::geom_point() +
  ggplot2::scale_y_log10(breaks = logrange_123456789)

Convert significance levels to symbols

Description

markSign returns the symbol associated with a significance level.

Usage

markSign(SignIn, plabel = c("n.s.", "+", "*", "**", "***"))

Arguments

SignIn

A single p-value.

plabel

A translation table, predefined with the usual symbols.

Value

factor with label as defined in plabel.

Examples

markSign(0.012)

Compute confidence interval of mean by bootstrapping.

Description

mean_cl_boot computes lower and upper confidence limits for the estimated mean, based on bootstrapping.

Usage

mean_cl_boot(
  x,
  conf = 0.95,
  type = "basic",
  nrepl = 10^3,
  round = FALSE,
  roundDig = 2
)

Arguments

x

Data for computation.

conf

confidence interval with default 95%.

type

type for function boot.ci.

nrepl

number of bootstrap replications, defaults to 1000.

round

logical, applies roundR function to results. Output is character.

roundDig

Number of relevant digits for function roundR.

Value

A tibble with one row and three columns: Mean, CIlow, CIhigh.

Examples

# basic usage of mean_cl_boot
mean_cl_boot(x = mtcars$wt)

Compute mean and sd and put together with the ± symbol.

Description

Compute mean and sd and put together with the ± symbol.

Usage

meansd(
  x,
  roundDig = 2,
  drop0 = FALSE,
  groupvar = NULL,
  range = FALSE,
  rangesep = " ",
  add_n = FALSE,
  .german = FALSE,
  ci = FALSE,
  nrepl = 10^3,
  singleline = TRUE
)

Arguments

x

Data for computation.

roundDig

Number of relevant digits for roundR.

drop0

Should trailing zeros be dropped?

groupvar

Optional grouping variable for subgroups.

range

Should min and max be included in output?

rangesep

How should min/max be separated from mean+-sd?

add_n

Should n be included in output?

.german

Logical, should "." and "," be used as bigmark and decimal?

ci

Should bootstrap based 95% confidence interval be computed?

nrepl

Number of bootstrap replications, defaults to 1000.

singleline

Put all descriptive stats in a single element (default) or below each other. singleline = FALSE sets ci and add_n as TRUE

Value

Either character vector with mean ± SD and additional results (singleline), or matrix with rows for n, mean with CI, and SD, and optionally range.

Examples

# basic usage of meansd
meansd(x = mtcars$wt)
# with additional options
meansd(x = mtcars$wt, groupvar = mtcars$am, add_n = TRUE)
meansd(x = mtcars$wt, groupvar = mtcars$am, add_n = TRUE, ci = TRUE, singleline = FALSE)

Compute mean and standard error of mean and put together with the ± symbol.

Description

meanse computes SEM based on Standard Deviation/square root(n)

Usage

meanse(x, mult = 1, roundDig = 2, drop0 = FALSE)

Arguments

x

Data for computation.

mult

multiplier for SEM, default 1, can be set to e.g. 2 or 1.96 to create confidence intervals

roundDig

Number of relevant digits for roundR.

drop0

Should trailing zeros be dropped?

Value

character vector with mean ± SEM, rounded to desired precision

Examples

# basic usage of meanse
meanse(x = mtcars$wt)

Compute confidence interval of median by bootstrapping.

Description

median_cl_boot computes lower and upper confidence limits for the estimated median, based on bootstrapping.

Usage

median_cl_boot(
  x,
  conf = 0.95,
  type = "basic",
  nrepl = 10^3,
  round = FALSE,
  roundDig = 2
)

Arguments

x

Data for computation.

conf

confidence interval with default 95%.

type

type for function boot.ci.

nrepl

number of bootstrap replications, defaults to 1000.

round

logical, applies roundR function to results. Output is character.

roundDig

number of relevant digits for function roundR.

Value

A tibble with one row and three columns: Median, CIlow, CIhigh.

Examples

# basic usage of median_cl_boot
median_cl_boot(x = mtcars$wt)

Rename output from median_cl_boot for use in ggplot.

Description

median_cl_boot_gg computes lower and upper confidence limits for the estimated median, based on bootstrapping, using default settings.

Usage

median_cl_boot_gg(x)

Arguments

x

Data for computation.

Value

A tibble with one row and three columns: y, ymin, ymax.

Examples

# basic usage of median_cl_boot
median_cl_boot_gg(x = mtcars$wt)

Compute median and quartiles and put together.

Description

Compute median and quartiles and put together.

Usage

median_quart(
  x,
  nround = NULL,
  qtype = 8,
  roundDig = 2,
  drop0 = FALSE,
  groupvar = NULL,
  range = FALSE,
  rangesep = " ",
  rangearrow = " -> ",
  prettynum = FALSE,
  .german = FALSE,
  add_n = FALSE,
  ci = FALSE,
  nrepl = 10^3,
  singleline = TRUE
)

Arguments

x

Data for computation.

nround

Number of digits for fixed round.

qtype

Type of quantiles.

roundDig

Number of relevant digits for roundR.

drop0

Should trailing zeros be dropped?

groupvar

Optional grouping variable for subgroups.

range

Should min and max be included in output?

rangesep

How should min/max be separated from mean+-sd?

rangearrow

What is put between min -> max?

prettynum

logical, apply prettyNum to results?

.german

logical, should "." and "," be used as bigmark and decimal?

add_n

Should n be included in output?

ci

Should bootstrap based 95% confidence interval be computed?

nrepl

Number of bootstrap replications, defaults to 1000.

singleline

Put all descriptive stats in a single element (default) or below each other. singleline = FALSE sets ci and add_n as TRUE

Value

Either character vector with median [1stQuartile/3rdQuartile] and additional results (singleline), or matrix with rows for n, median with CI, and quartiles, and optionally range.

Examples

# basic usage of median_quart
median_quart(x = mtcars$wt)
# with additional options
median_quart(x = mtcars$wt, groupvar = mtcars$am, add_n = TRUE)
data(faketrial)
median_quart(x = faketrial$`Biomarker 1 [units]`, groupvar = faketrial$Treatment)

Compute standard error of median.

Description

medianse is based on mad/square root(n)

Usage

medianse(x)

Arguments

x

Data for computation.

Value

numeric vector with SE Median.

Examples

# basic usage of medianse
medianse(x = mtcars$wt)

Pairwise Fisher's exact tests

Description

pairwise_fisher_test calculates pairwise comparisons between group levels with corrections for multiple testing.

Usage

pairwise_fisher_test(
  dep_var,
  indep_var,
  adjmethod = "fdr",
  plevel = 0.05,
  symbols = letters[-1],
  ref = FALSE
)

Arguments

dep_var

dependent variable, containing the data.

indep_var

independent variable, should be factor or coercible.

adjmethod

method for adjusting p values (see p.adjust).

plevel

threshold for significance.

symbols

predefined as b,c, d...; provides footnotes to mark group differences, e.g. b means different from group 2

ref

is the 1st subgroup the reference (like in Dunnett test)?

Value

A list with elements "methods" (character), "p.value" (matrix), "plevel" (numeric), and "sign_colwise" (vector of length number of levels - 1)

Examples

# All pairwise comparisons
pairwise_fisher_test(dep_var = mtcars$cyl, indep_var = mtcars$gear)
# Only comparison against reference gear=3
pairwise_fisher_test(dep_var = mtcars$cyl, indep_var = mtcars$gear, ref = TRUE)

Pairwise comparison for ordinal categories

Description

pairwise_ordcat_test calculates pairwise comparisons for ordinal categories between all group levels with corrections for multiple testing.

Usage

pairwise_ordcat_test(
  dep_var,
  indep_var,
  adjmethod = "fdr",
  plevel = 0.05,
  symbols = letters[-1],
  ref = FALSE,
  cmh = TRUE
)

Arguments

dep_var

dependent variable, containing the data

indep_var

independent variable, should be factor

adjmethod

method for adjusting p values (see p.adjust)

plevel

threshold for significance

symbols

predefined as b,c, d...; provides footnotes to mark group differences, e.g. b means different from group 2

ref

is the 1st subgroup the reference (like in Dunnett test)

cmh

Should Cochran-Mantel-Haenszel test (coin::cmh_test) be used for testing? If false, the linear-by-linear association test (coin::lbl_test) is applied.

Value

A list with elements "methods" (character), "p.value" (matrix), "plevel" (numeric), and "sign_colwise" (vector of length number of levels - 1)

Examples

# All pairwise comparisons
mtcars2 <- dplyr::mutate(mtcars, cyl = factor(cyl, ordered = TRUE))
pairwise_ordcat_test(dep_var = mtcars2$cyl, indep_var = mtcars2$gear)
# Only comparison against reference gear=3
pairwise_ordcat_test(dep_var = mtcars2$cyl, indep_var = mtcars2$gear, ref = TRUE)

Extended pairwise t-test

Description

pairwise_t_testcalculate pairwise comparisons between group levels with corrections for multiple testing based on pairwise.t.test

Usage

pairwise_t_test(
  dep_var,
  indep_var,
  adjmethod = "fdr",
  plevel = 0.05,
  symbols = letters[-1]
)

Arguments

dep_var

dependent variable, containing the data

indep_var

independent variable, should be factor

adjmethod

method for adjusting p values (see p.adjust)

plevel

threshold for significance

symbols

predefined as b,c, d...; provides footnotes to mark group differences, e.g. b means different from group 2

Value

A list with method output of pairwise.t.test, matrix of p-values, and character vector with significance indicators.

Examples

pairwise_t_test(dep_var = mtcars$wt, indep_var = mtcars$cyl)

Pairwise Wilcoxon tests

Description

pairwise_wilcox_test calculates pairwise comparisons on ordinal data between all group levels with corrections for multiple testing based on coin::wilcox_test from package 'coin'.

Usage

pairwise_wilcox_test(
  dep_var,
  indep_var,
  strat_var = NA,
  adjmethod = "fdr",
  distr = "exact",
  plevel = 0.05,
  symbols = letters[-1],
  sep = ""
)

Arguments

dep_var

dependent variable, containing the data.

indep_var

independent variable, should be factor.

strat_var

optional factor for stratification.

adjmethod

method for adjusting p values (see p.adjust)

distr

Computation of p-values, see coin::wilcox_test.

plevel

threshold for significance.

symbols

predefined as b,c, d...; provides footnotes to mark group differences, e.g. b means different from group 2.

sep

text between statistics and range or other elements.

Value

A list with matrix of adjusted p-values and character vector with significance indicators.

Examples

pairwise_wilcox_test(dep_var = mtcars$wt, indep_var = mtcars$cyl)

Enhanced knitr::kable with latex

Description

pdf_kable formats tibbles/df's for markdown

Usage

pdf_kable(
  .input,
  width1 = 6,
  twidth = 14,
  tposition = "left",
  innercaption = NULL,
  caption = "",
  foot = NULL,
  escape = TRUE
)

Arguments

.input

table to print

width1

Width of 1st column, default 6.

twidth

Default 14

tposition

Default left

innercaption

subheader

caption

header

foot

footnote

escape

see knitr::kable

Value

A character vector of the table source code.

Lineweaver-Burk diagram

Description

plot_LB plots a Lineweaver-Burk diagram and computes the linear model

Usage

plot_LB(
  data,
  substrate,
  velocity,
  group = NULL,
  title = "Lineweaver-Burk-Plot",
  xlab = "1/substrate",
  ylab = "1/velocity"
)

Arguments

data

data structure with columns for model data

substrate

colname for substrate concentration

velocity

colname for reaction velocity

group

colname for optional grouping factor

title

title of the plot

xlab

label of the abscissa

ylab

label of the ordinate

Examples

MMdata <- data.frame(subst = c(2.00, 1.00, 0.50, 0.25),
                 velo = c(0.2253, 0.1795, 0.1380, 0.1000))
                 
plot_LB(data=MMdata,
        substrate = 'subst',velocity = 'velo')

MMdata <- data.frame(subst = rep(c(2.00, 1.00, 0.50, 0.25),2),
                 velo = c(0.2253, 0.1795, 0.1380, 0.1000,
                          0.4731333, 0.4089333, 0.3473000, 0.2546667),
                 condition = rep(c('C1','C2'),each=4))
                 
plot_LB(data=MMdata,substrate = 'subst',
        velocity = 'velo',group='condition')

Michaelis-Menten enzyme kinetics model and plot

Description

plot_MM creates a Michaelis-Menten type Enzyme kinetics plot and returns model as well

Usage

plot_MM(
  data,
  substrate,
  velocity,
  group = NULL,
  title = "Michaelis-Menten",
  xlab = "substrate",
  ylab = "velocity"
)

Arguments

data

data structure with columns for model data

substrate

colname for substrate concentration

velocity

colname for reaction velocity

group

colname for optional grouping factor

title

title of the plot

xlab

label for x-axis

ylab

label for y-axis

Value

a list with elements "MMfit" and "MMplot"

Examples

MMdata <- data.frame(subst = c(2.00, 1.00, 0.50, 0.25),
                 velo = c(0.2253, 0.1795, 0.1380, 0.1000))
                 
plot_MM(data=MMdata,
        substrate = 'subst',velocity = 'velo')

MMdata <- data.frame(subst = rep(c(2.00, 1.00, 0.50, 0.25),2),
                 velo = c(0.2253, 0.1795, 0.1380, 0.1000,
                          0.4731333, 0.4089333, 0.3473000, 0.2546667),
                 condition = rep(c('C1','C2'),each=4))
                 
plot_MM(data=MMdata,substrate = 'subst',
        velocity = 'velo',group='condition')

Description

[Superseded]

package flextable is a more powerful alternative

print_kable formats and prints tibbles/df's in markdown with splitting into sub-tables with repeated caption and header.

Usage

print_kable(t, nrows = 30, caption = "", ncols = 100, ...)

Arguments

t

table to print.

nrows

number of rows (30) before splitting.

caption

header.

ncols

number of columns (100) before splitting.

...

Further arguments passed to knitr::kable.

Value

No return value, called for side effects.

Examples

## Not run: 
print_kable(mtcars, caption = "test")

## End(Not run)

Automatic rounding to a reasonable length, based on largest number

Description

roundR takes a vector or matrix of numbers and returns rounded values with selected precision and various formatting options.

Usage

roundR(
  roundin,
  level = 2,
  smooth = FALSE,
  textout = TRUE,
  drop0 = FALSE,
  .german = FALSE,
  .bigmark = FALSE
)

Arguments

roundin

A vector or matrix of numbers.

level

A number specifying number of relevant digits to keep.

smooth

A logical specifying if you want rounding before the dot (e.g. 12345 to 12300).

textout

A logical if output is converted to text.

drop0

A logical if trailing zeros should be dropped.

.german

A logical if german numbers should be reported.

.bigmark

A logical if big.mark is to be shown, mark itself depends on parameter .german.

Value

vector of type character (default) or numeric, depending on parameter textout.

Examples

roundR(1.23456, level = 3)
roundR(1.23456, level = 3, .german = TRUE)
roundR(1234.56, level = 2, smooth = TRUE)

Compute standard error of median

Description

se_median is based on mad/square root(n) (Deprecated, please see medianse, which is the same but named more consistently)

Usage

se_median(x)

Arguments

x

Data for computation.

Value

numeric vector with SE Median.

Examples

# basic usage of se_median
## Not run: 
se_median(x = mtcars$wt)

## End(Not run)

Compute surprisal aka Shannon information from p-values

Description

surprisal takes p-values and returns s, a value representing the number of consecutive heads on a fair coin, that would be as surprising as the p-value

Usage

surprisal(p, precision = 1)

Arguments

p

a vector of p-values

precision

rounding level with default 1

Value

a character vector of s-values

Independent sample t-test with test for equal variance

Description

t_var_test tests for equal variance based on var.test and calls t.test, setting the option var.equal accordingly.

Usage

t_var_test(data, formula, cutoff = 0.05)

Arguments

data

Tibble or data_frame.

formula

Formula object with dependent and independent variable.

cutoff

is significance threshold for equal variances.

Value

A list from t.test

Examples

t_var_test(mtcars, wt ~ am)
# may be used in pipes:
mtcars |> t_var_test(wt ~ am)

Search within data.frame or tibble

Description

tab.search searches for pattern within a data-frame or tibble, returning column(s) and row(s)

Usage

tab.search(searchdata = rawdata, pattern, find.all = T, names.only = FALSE)

Arguments

searchdata

table to search in, predefined as rawdata

pattern

regex, for exact matches add ^findme$

find.all

return all row indices or only 1st per column,default=TRUE

names.only

return only vector of colnames rather than list with names and rows, default=FALSE

Value

A list with numeric vectors for each column giving row numbers of matched elements

Compute coefficient of variance.

Description

var_coeff computes relative variability as standard deviation/mean *100

Usage

var_coeff(x)

Arguments

x

Data for computation.

Value

numeric vector with coefficient of variance.

Examples

var_coeff(x = mtcars$wt)