Package 'DescrTab2'

Load LaTeX packages

Description

Load LaTeX packages

Usage

.onLoad(libname = find.package("kableExtra"), pkgname = "kableExtra")

Arguments

libname	library name
pkgname	package name

Details

Thanks to Hao Zhu and his package kableExtra.

Author(s)

Hao Zhu

---

Check whether a vector (usually a factor) can be cleanly converted to a numeric

Description

From https://stackoverflow.com/a/47677916

Usage

can.be.numeric(x)

Arguments

x

a vector

---

Create code to load all SAS datasets in a folder.

Description

This is useful if you work with lots of separate SAS datasets spread out in the same folder.

Usage

codegen_load_all_sas_data(dir, format = NULL)

Arguments

dir	path to dataset folder
format	path to format file

Value

NULL. Relevant code is printed to the console.

Examples

codegen_load_all_sas_data(system.file("examples", package = "DescrTab2"))

---

Function to create (a part of a) nicely formatted table

Description

Function to create (a part of a) nicely formatted table

Usage

create_character_subtable(
  DescrVarObj,
  var_name,
  format_options,
  format_summary_stats,
  format_p,
  reshape_rows
)

Arguments

DescrVarObj	Variable object to be formatted
var_name	(character) Name of the variable
format_options	named list of options for formatting
format_summary_stats	named list of summary statistcs
format_p	formatting function for p-values
reshape_rows	named list of row reshaping functions

---

Function to create (a part of a) nicely formatted table

Description

Function to create (a part of a) nicely formatted table

Usage

create_numeric_subtable(
  DescrVarObj,
  var_name,
  format_options,
  format_summary_stats,
  format_p,
  reshape_rows
)

Arguments

DescrVarObj	Variable object to be formatted
var_name	(character) Name of the variable
format_options	named list of options for formatting
format_summary_stats	named list of summary statistcs
format_p	formatting function for p-values
reshape_rows	named list of row reshaping functions

---

Calculate descriptive statistics

Description

Generate a list of descriptive statistics. By default, the function calculates summary statistics such as mean, standard deviation, quantiles, minimum and maximum for continuous variables and relative and absolute frequencies for categorical variables. Also calculates p-values for an appropriately chosen statistical test. For two-group comparisons, confidence intervals for appropriate summary measures of group differences are calculated aswell. In particular, Wald confidence intervals from prop.test are used for categorical variables with 2 levels, confidence intervals from t.test are used for continuous variables and confidence intervals for the Hodges-Lehman estimator [1] from wilcox.test are used for ordinal variables.

Usage

descr(
  dat,
  group = NULL,
  group_labels = list(),
  var_labels = list(),
  var_options = list(),
  summary_stats_cont = list(N = DescrTab2:::.N, Nmiss = DescrTab2:::.Nmiss, mean =
    DescrTab2:::.mean, sd = DescrTab2:::.sd, median = DescrTab2:::.median, Q1 =
    DescrTab2:::.Q1, Q3 = DescrTab2:::.Q3, min = DescrTab2:::.min, max =
    DescrTab2:::.max),
  summary_stats_numeric_ord = list(N = DescrTab2:::.factorN, Nmiss =
    DescrTab2:::.factorNmiss, mean = DescrTab2:::.factormean, sd = DescrTab2:::.factorsd,
    median = DescrTab2:::.factormedian, Q1 = DescrTab2:::.factorQ1, Q3 =
    DescrTab2:::.factorQ3, min = DescrTab2:::.factormin, max = DescrTab2:::.factormax),
  summary_stats_cat = list(),
  format_summary_stats = list(N = function(x) {
     format(x, digits = 3, scientific =
    4)
 }, mean = function(x) {
     format(x, digits = 3, scientific = 4)
 }, sd =
    function(x) {
     format(x, digits = 3, scientific = 4)
 }, median = function(x) {
 
       format(x, digits = 3, scientific = 4)
 }, Q1 = function(x) {
     format(x, digits
    = 3, scientific = 4)
 }, Q3 = function(x) {
     format(x, digits = 3, scientific =
    4)
 }, min = function(x) {
     format(x, digits = 3, scientific = 4)
 }, max =
    function(x) {
     format(x, digits = 3, scientific = 4)
 }, CI = function(x) {
    
    format(x, digits = 3, scientific = 4)
 }),
  format_p = scales::pvalue_format(),
  format_options = list(print_Total = NULL, print_p = TRUE, print_CI = FALSE,
    combine_mean_sd = FALSE, combine_median_Q1_Q3 = FALSE, omit_factor_level = "none",
    omit_Nmiss_if_0 = TRUE, omit_missings_in_group = TRUE, percent_accuracy = 0.1,
    percent_suffix = "%", row_percent = FALSE, Nmiss_row_percent = FALSE,
    absolute_relative_frequency_mode = c("both", "only_absolute", "only_relative"),
    omit_missings_in_categorical_var = FALSE, categorical_missing_percent_mode =
    c("no_missing_percent", "missing_as_regular_category", 
    
    "missing_as_separate_category"), caption = NULL, replace_empty_string_with_NA = TRUE,
    categories_first_summary_stats_second = FALSE, max_first_col_width = 7.5),
  test_options = list(paired = FALSE, nonparametric = FALSE, exact = FALSE, var_equal =
    FALSE, indices = c(), guess_id = FALSE, include_group_missings_in_test = FALSE,
    include_categorical_missings_in_test = FALSE, test_override = NULL,
    additional_test_args = list(), boschloo_max_n = 200),
  reshape_rows = list(`Q1 - Q3` = list(args = c("Q1", "Q3"), fun = function(Q1, Q3) {
   
     paste0(Q1, " -- ", Q3)
 }), `min - max` = list(args = c("min", "max"), fun =
    function(min, max) {
     paste0(min, " -- ", max)
 })),
  ...
)

Arguments

dat	Data frame or tibble. The data set to be analyzed. Can contain continuous or factor (also ordered) variables.
group	name (as character) of the group variable in dat.
group_labels	named list of labels for the levels of the group variable in dat.
var_labels	named list of variable labels.
var_options	named list of lists. For each variable, you can have special options that apply only to that variable. These options are specified in this argument. See the details and examples for more explanation.
summary_stats_cont	named list of summary statistic functions to be used for numeric variables.
summary_stats_numeric_ord	named list of summary statistic function to be used for ordered factor variables which can be converted to numeric.
summary_stats_cat	named list of summary statistic function to be used for categorical variables.
format_summary_stats	named list of formatting functions for summary statistics.
format_p	formatting function for p-values.
format_options	named list of formatting options.
test_options	named list of test options.
reshape_rows	named list of lists. Describes how to combine different summary statistics into the same row.
...	further argument to be passed along

Value

Returns a A DescrList object, which is a named list of descriptive statistics which can be passed along to the print function to create pretty summary tables.

Labels

group_labels and var_labels need to be named lists of character elements. The names of the list elements have to match the variable names in your dataset. The values of the list elements are the labels that will be assigned to these variables when printing.

Custom summary statistics

summary_stats_cont and summary_stats_cat are both named lists of functions. The names of the list elements are what will be displayed in the leftmost column of the descriptive table. These functions should take a vector and return a value.
Each summary statistic has to have an associated formatting function in the format_summary_stats list. The functions in format_summary_stats take a numeric value and convert it to a character string, e.g. 0.2531235 -> "0.2".
The format_p function converts p-values to character strings, e.g. 0.05 -> "0.05" or 0.000001 -> "<0.001".

Formatting options

Further formatting options can be specified in the format_options list. It contains the following members:

• print_Total (logical) controls whether to print the "Total" column. If print_Total = NULL, print_Total will be set to TRUE if test_options$paired == FALSE, else it will be set to FALSE.

• print_p (logical) controls whether to print the p-value column.

• print_CI (logical) controls whether to print the confidence intervals for group-differences.

• combine_mean_sd (logical) controls whether to combine the mean and sd row into one mean ± sd row. This is a shortcut argument for the specification of an appropriate entry in the reshape_rows argument.

• combine_median_Q1_Q3 (logical) controls whether to combine the median, Q1 and Q3 row into one median (Q1, Q3) row. This is a shortcut argument for the specification of an appropriate entry in the reshape_rows argument.

• omit_Nmiss_if_0 (logical) controls whether to omit the Nmiss row in continuous variables there are no missings in the variable.

• omit_missings_in_group (logical) controls whether to omit all observations where the group variable is missing.

• percent_accuracy (numeric) A number to round to. Use (e.g.) 0.01 to show 2 decimal places of precision. If NULL, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values. See documentation of scales::label_percent

• percent_suffix (character) the symbol to be used where "%" is appropriate, sensible choices are usually "%" (default) or "" (i.e., empty string)

• row_percent (logical) controls wheter percentages of regular categorical variables should be calculated column-wise (default) or row-wise

• Nmiss_row_percent (logical) controls whether percentages of the "Nmiss"-statistic (number of missing values) should be calculated column-wise (default) or row-wise

• absolute_relative_frequency_mode (character) controls how to display frequencies. It may be set to one of the following options:

  • "both" will display absolute and relative frequencies.

  • "only_absolute" will only display absolute frequencies.

  • "only_relative" will only display relative frequencies.

• omit_missings_in_categorical_var (logical) controls whether to omit missing values in categorical variables completely.

• categorical_missing_percent_mode (character) controls how to display percentages in categorical variables with a (Missing) category. It may be set to one of the following options:

  • "no_missing_percent" omits a percentage in the missing category entirely.

  • "missing_as_regular_category" treats (Missing) as a regular category for %-calculation This means that if You have three categories: "A" with 10 counts, "B" with 10 counts and "(Missing)" with 10 counts, they will become "A": 10 (33%), "B": 10 (33%), "(Missing)": 10 (33% purposes.)

  • "missing_as_separat_category" calculates (Missing) percentages with respect to all observations (i.e. #(Missing) / N), but calculates all other catetgory percentages with respect to the non-missing observations (e.g. #A / N_nonmissing). This means that if You have three categories: "A" with 10 counts, "B" with 10 counts and "(Missing)" with 10 counts, they will become "A": 10 (50%), "B": 10 (50%), "(Missing)": 10 (33%)

• "caption" adds a table caption to the LaTeX, Word or PDf document

• replace_empty_string_with_NA (logical) controls whether empty strings ("") should be replaced with missing value (NA_character_).

• categories_first_summary_stats_second (logical) controls whether the categories should be printed first in the summary statistics table.

• max_first_col_width (numeric) controls the maximum width of the first column in LaTeX tables.

Test options

test_options is a named list with test options. It's members paired, nonparametric, and exact (logicals) control which test in the corresponding situation. For details, check out the vignette: https://imbi-heidelberg.github.io/DescrTab2/articles/b_test_choice_tree_pdf.pdf. The test_options = list(test_override="<some test name>") option can be specified to force usage of a specific test. This will produce errors if the data does not allow calculation of that specific test, so be wary. Use print_test_names() to see a list of all available test names. If paired = TRUE is specified, you need to supply an index variable indices that specifies which datapoints in your dataset are paired. indices may either be a length one character vector that describes the name of the index variable in your dataset, or a vector containing the respective indices. If you have guess_id set to TRUE (the default), DescrTab2 will try to guess the ID variable from your dataset and report a warning if it succeedes. See https://imbi-heidelberg.github.io/DescrTab2/articles/a_usage_guide.html#Paired-observations-1 for a bit more explanation. The optional list additional_test_args can be used to pass arguments along to test functions, e.g. additional_test_args=list(correct=TRUE) will request continuity correction if available.

Customization for single variables

The var_options list can be used to conduct customizations that should only apply to a single variable and leave the rest of the table unchanged.
var_options is a list of named lists. This means that each member of var_options is itself a list again. The names of the list elements of var_options determine the variables to which the options will apply. Let's say you have an age variable in your dataset. To change 'descr' options only for age, you will need to pass a list of the form var_options = list(age = list(<Your options here>)).
You can replace <Your options here> with the following options:

• label a character string containing the label for the variable

• summary_stats a list of summary statistics. See section "Custom summary statistics"

• format_summary_stats a list of formatting functions for summary statistics. See section "Custom summary statistics"

• format_p a function to format p-values. See section "Custom summary statistics"

• format_options a list of formatting options. See section "Formatting options"

• test_options a list of test options. See section "Test options"

• test_override manually specify the name of the test you want to apply. You can see a list of choices by typing print_test_names(). Possible choices are:

  • "Cochran's Q test"

  • "McNemar's test"

  • "Chi-squared goodness-of-fit test"

  • "Pearson's chi-squared test"

  • "Exact McNemar's test"

  • "Boschloo's test"

  • "Wilcoxon's one-sample signed-rank test"

  • "Mann-Whitney's U test"

  • "Kruskal-Wallis's one-way ANOVA"

  • "Student's paired t-test"

  • "Mixed model ANOVA"

  • "Student's one-sample t-test"

  • "Student's two-sample t-test"

  • "Welch's two-sample t-test"

  • "F-test (ANOVA)"

Combining rows

The reshape_rows argument offers a framework for combining multiple rows of the output table into a single one. reshape_rows is a named list of lists. The names of it's member-lists determine the name that will be displayed as the name of the combined summary stats in the table (e.g. "mean ± sd "). The member lists need to contain two elements: args, contains the names of the summary statistics to be combined as characters, and fun which contains a function to combine these summary stats. The argument names of this function need to match the character strings specified in args. Check out the default options for an exemplary definition.

References

[1] Hodges, J. L.; Lehmann, E. L. (1963). "Estimation of location based on ranks". Annals of Mathematical Statistics. 34 (2): 598-611. doi:10.1214/aoms/1177704172. JSTOR 2238406. MR 0152070. Zbl 0203.21105. PE euclid.aoms/1177704172

Examples

descr(iris)
DescrList <- descr(iris)
DescrList$variables$results$Sepal.Length$Total$mean
print(DescrList)
descr(iris, "Species")

---

DescrTab2

Description

Publication quality descriptive statistics tables with R

Details

Provides functions to create descriptive statistics tables for continuous and categorical variables. By default, summary statistics such as mean, standard deviation, quantiles, minimum and maximum for continuous variables and relative and absolute frequencies for categorical variables are calculated. 'DescrTab2' features a sophisticated algorithm to choose appropriate test statistics for your data and provides p-values. On top of this, confidence intervals for group differences of appropriated summary measures are automatically produces for two-group comparison. Tables generated by 'DescrTab2' can be integrated in a variety of document formats, including .html, .tex and .docx documents. 'DescrTab2' also allows printing tables to console and saving table objects for later use.

Check out our documentation online: https://imbi-heidelberg.github.io/DescrTab2/ or browse the help files in the Rstudio viewer. You can access the vignettes by typing: browseVignettes("DescrTab2")

The most important function you probably want to check out is called descr.

---

Escape LaTeX Symbols

Description

Escape LaTeX Symbols

Usage

escape_latex_symbols(tibl, numEscapes = 1)

Arguments

tibl	A tibble filled with characters
numEscapes	(logical) chooses between "\" and "\\"#

Value

a tibble with appropriately escape LaTeX code

---

Extract the label attribute from data

Description

Extract the label attribute from data

Usage

extract_labels(dat)

Arguments

dat	data in the form of a list, data.frame or tibble, or a vector

Value

list of labels

Examples

a <- c(1, 2)
attr(a, "label") <- "b"
identical(extract_labels(a), list(a = attr(a, "label")))

---

Farrington-Manning test for rate difference

Description

The Farrington-Manning test for rate differences can be used to compare the rate difference of successes between two groups to a preset value. It uses an explicit formula for the standard deviation of the test statistic under the null hypothesis [1].

Usage

farrington.manning(
  group1,
  group2,
  delta = 0,
  alternative = "greater",
  alpha = 0.025
)

Arguments

group1	a logical vector of data from group 1, where TRUE indicates a success
group2	a logical vector of data from group 2, where TRUE indicates a success
delta	the rate difference under the null hypothesis
alternative	character string indicating the alternative to use, either of "two.sided", "less", "greater"
alpha	the significance level (acceptable error of the first kind), a two-sided confidence intnerval is returned with confidence level 1 - 2*alpha, such that the lower bound is a valid one sided confidence interval at the confidence level 1 - alpha.

Details

The Farrington-Maning test for rate differences test the null hypothesis of

$H_{0}: p_{1} - p_{2} = \delta$

for the "two.sided" alternative (or $\geq$ for the "greater" respectively $\leq$ for the "less" alternative). This formulation allows to specify non-inferiority and superiority test in a consistent manner:

non-inferiority

for delta < 0 and alternative == "greater" the null hypothesis reads $H_{0}: p_{1} - p_{2} \geq \delta$ and consequently rejection allows concluding that $p_1 \geq p_2 + \delta$ i.e. that the rate of success in group one is at least the success rate in group two plus delta - as delta is negagtive this is equivalent to the success rate of group 1 being at worst |delta| smaller than that of group 2.

superiority

for delta >= 0 and alternative == "greater" the null hypothesis reads $H_{0}: p_{1} - p_{2} \geq \delta$ and consequently rejection allows concluding that $p_1 \geq p_2 + \delta$ i.e. that the rate of success in group one is at least delta greater than the success rate in group two.

The confidence interval is always computed as two-sided, but with 1-2$\alpha$ confidence level in case of a one-sided hypthesis. This means that the lower or upper vound are valid one-sided confidence bounds at level $\alpha$ in this case. The confidence interval is constructed by inverting the two-sided test directly.

Value

A list of class "htest" containing the following components:

statistic:	the value of the Z-statistic
parameter:	delta, rate difference (group 1 - group 2) under the null hypothesis
p.value:	the p-value for the Farrington-Manning test
null.value:	rate difference (group 1 - group 2) under the null
alternative:	a character string indicating the alternative hypothesis
method:	a character string indicating the exact method employed
data.name:	a character string giving the names of the data used
estimate:	the estimated rate difference (maximum likelihood)
conf.int:	a confidence interval for the rate difference
sample.size:	the total sample size used for the test

Author(s)

Kevin Kunzmann

References

[1] Farrington, Conor P., and Godfrey Manning. "Test statistics and sample size formulae for comparative binomial trials with null hypothesis of non-zero risk difference or non-unity relative risk." Statistics in medicine 9.12 (1990): 1447-1454.

Examples

x <- c(rep(TRUE, 20), rep(FALSE, 15))
y <- c(rep(TRUE, 30), rep(FALSE, 25))

farrington.manning(x, y, -.3)

---

Formatting function for absolute and relative frequencies

Description

Formatting function for absolute and relative frequencies

Usage

format_freqs(
  numerator,
  denominator = 1,
  absolute_relative_frequency_mode = c("both", "only_absolute", "only_relative"),
  percent_accuracy = NULL,
  percent_suffix = "%"
)

Arguments

numerator	(numeric) numerator for % calculations
denominator	(numeric) denominator for % calculations
absolute_relative_frequency_mode	one of c("both", "only_absolute", "only_relative"). "both" will print "Absolute Freq. (Relative Freq. %)", the other options work accordingly.
percent_accuracy	NULL or numeric. Refer to the accuracy argument in percent.
percent_suffix	usually "%" or "". Refer to the suffix argument in percent.

Value

string of formatted frequencies

---

Make an educated guess about the name of the ID variable from a dataset

Description

Make an educated guess about the name of the ID variable from a dataset

Usage

guess_ID_variable(dat, suppressWarnings = FALSE)

Arguments

dat	a dataset with names (list, data.frame, tibble)
suppressWarnings	(logical) suppress warning messages if you know what you are dooing

Value

if exactly one possible

Examples

dat <- data.frame(ID = c(1,2,3,4,5),
                 other = c(1,2,3,4,5))
guess_ID_variable(dat)

---

do.call but without an error for unused arguments

Description

do.call but without an error for unused arguments

Usage

ignore_unused_args(what, args)

Arguments

what	either a function or a non-empty character string naming the function to be called.
args	a list of arguments to the function call. The names attribute of args gives the argument names.

Value

The result of the (evaluated) function call.

Examples

# works:
DescrTab2:::ignore_unused_args(
  chisq.test,
  list(x = factor(c(1, 0, 1, 1, 1, 0)), y = factor(c(0, 1, 1, 0, 1, 0)), abc = 3)
)

# would produce error:
# do.call(chisq.test, list(x=factor(c(1,0,1,1,1,0)), y=factor(c(0,1,1,0,1,0)), abc=3 ) )

---

Wrap cell text in minipage LaTeX environment with stretchy space

Description

Wrap cell text in minipage LaTeX environment with stretchy space

Usage

in_minipage(text, width, numEscapes = 1, strechSpace = FALSE)

Arguments

text	text to be placed in minipage
width	width adjustment
numEscapes	(logical) chooses between "\" and "\\"#
strechSpace	(logical) will add strethcy space

Value

appropriate LaTeX code

References

https://stackoverflow.com/a/50892682

---

S3 override for knit_print function for DescrList objects.

Description

S3 override for knit_print function for DescrList objects.

Usage

## S3 method for class 'DescrList'
knit_print(x, print_format = options("print_format")[[1]], silent = FALSE, ...)

Arguments

x	a
print_format	b
silent	c
...	abc

Value

outputs formatted table depending on the environment (.RMD) which it is called from

---

S3 override for knit_print function for DescrPrint objects.

Description

S3 override for knit_print function for DescrPrint objects.

Usage

## S3 method for class 'DescrPrint'
knit_print(x, print_format = print_format, silent = silent, ...)

Arguments

x	a
print_format	b
silent	c
...	abc

Value

outputs formatted table depending on the environment (.RMD) which it is called from

---

Convenience function to apply descr to a list of datasets and print the results

Description

Convenience function to apply descr to a list of datasets and print the results

Usage

lapply_descr(list, ...)

Arguments

list	a list of datasets (tibbles or data.frames)
...	arguments to be passed to the descr call

Value

something printable.

Examples

l <- list()
for (i in 1:2){
  l <- append(l, list(iris))
}
lapply_descr(l, group="Species")

---

Create a markdown listing from a character dataset

Description

Create a markdown listing from a character dataset

Usage

list_freetext_markdown(dat)

Arguments

dat	a character data.frame or tibble.

Value

string containing markdown code listing all nonempty free text in the dataset

Examples

dat  <- data.frame(Freetext = c("Some text", "More text"))
list_freetext_markdown(dat)
# use inside a .Rmd document like this:
# `r list_freetext_markdown(dat)`

---

Digits before decimal -1

Description

Digits before decimal -1

Usage

n_int_digits(x)

Arguments

x

a

Details

https://stackoverflow.com/questions/47190693/count-the-number-of-integer-digits

Value

a

---

Parse a text file containing format information

Description

Useful to extract factor formatting information contained in a proc format SAS statement.

Usage

parse_formats(
  path_to_format_definition,
  ignore_keywords = c("value"),
  encoding = "ISO-8859-1"
)

Arguments

path_to_format_definition	(string) Path to the text file to be parsed
ignore_keywords	A vector of keywords to be ignored when searching for the name of the variable to be formatted
encoding	Encoding for the text file

Value

A named list with format definitions

Examples

tmpfile <- tempfile()
write(     "proc format;
             value yn  1=\"yes\"
                       0=\"no\";
             value sex 1=\"female\"
                       0=\"male\";
              run;",tmpfile)
parse_formats(tmpfile)

---

Prints all possible tests names

Description

Prints all possible tests names

Usage

print_test_names()

Value

Returns the names of all possible test names you can specify.

Examples

print_test_names()

---

S3 override for print function for DescrList objects.

Description

This function takes a DescrList object and converts it to either a DescrPrintCharacter or DescrPrintNumeric object, depending on the print_format option. This object is then printed in an appropriate format.

Usage

## S3 method for class 'DescrList'
print(x, print_format = options("print_format")[[1]], silent = FALSE, ...)

Arguments

x	A DescrList object returned from descr.
print_format	Possible values: "console" (default), "tex", "html", "word", "numeric"
silent	I TRUE, suppresses output to stdout.
...	further arguments to be passed along to print method

Details

There is no way to convert between DescrPrintCharacter and DescrPrintNumeric objects. The first type is for what you would usually want, the second type is mostly for debugging purposes. A DescrPrintCharacter object can be printed as html, tex code, as a flextable object or simply to the console.

Value

A DescrPrint object which can be printed in various formats.

You can use the print_format option to control the output type. If you use 'DescrTab2' inside an .Rmd document, you can set the clobal option option(print_format="tex") or option(print_format="html") or option(print_format="word") depending on your document type. This way, all your tables will be printed in the right format by default inside this document.

Examples

print(descr(iris), print_format = "console")
print(descr(iris), print_format = "tex")
print(descr(iris), print_format = "html")
print(descr(iris), print_format = "word")
print(descr(iris), print_format = "numeric")
options(print_format = "tex")
descr(iris)
options(print_format = "console")
descr(iris)
DescrPrint <- print(descr(iris))
DescrPrint$variables$results$Sepal.Length$Total$mean
print(DescrPrint)

---

Convencience function to load datasets downloaded from a Redcap database

Description

This function is specifically tailored to the way the default import script provided by a Redcap database functions. First, the Hmisc package is loaded. The .csv file containing the data is assumed to be located in the current working directory. Labels are assigned to all variables. Variables which are supposed to be factors are twice, once as a factor and once in an unformatted way.

Usage

read_redcap_formatted(path_to_redcap_script = NULL)

Arguments

path_to_redcap_script

(character) Path to the (automatically generated) redcap script for data import

Details

This script removes the "unformatted factor" variables and properly assignes labels.

Value

tibble with data

Examples

path_to_redcap_script <- system.file("examples", "testredcap.r", package = "DescrTab2")
read_redcap_formatted(path_to_redcap_script)

---

Convencience function to load SAS datasets

Description

Convencience function to load SAS datasets

Usage

read_sas_formatted(path_to_data = NULL, path_to_format = NULL)

Arguments

path_to_data	path to .sas7bdat file
path_to_format	path to .sas7bcat file

Value

tibble with data

Examples

path_to_data <- system.file("examples", "testsas.sas7bdat", package = "DescrTab2")
pat_to_format <- system.file("examples", "formats.sas7bcat", package = "DescrTab2")
read_sas_formatted(path_to_data, pat_to_format)

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Calculates a statistical significance test

Description

Calculates a statistical significance test

Usage

sig_test(
  var,
  group = NULL,
  test_options = list(),
  test = NULL,
  var_name = NULL
)

Arguments

var	A variable (a vector).
group	A variable containing the grouping information.
test_options	Named list containing test options.
test	Name of a statistical test.
var_name	Name of variable to be tested (only used in warning messages).

Value

A list of test test results.

Examples

cont_var <- c(1, 2, 3)
sig_test(cont_var)

---

Format number to a specified number of digits, considering threshold for usage of scientific notation

Description

Format number to a specified number of digits, considering threshold for usage of scientific notation

Usage

sigfig(
  x,
  digits = 3,
  scientific_high_threshold = 6,
  scientific_low_threshold = -6,
  force_0_behind_0 = FALSE
)

Arguments

x	a
digits	a
scientific_high_threshold	a
scientific_low_threshold	a
force_0_behind_0	a

Value

a

---

Generator function for nice formatting functions

Description

Generator function for nice formatting functions

Usage

sigfig_gen(
  digits = 3,
  scientific_high_threshold = 6,
  scientific_low_threshold = -6,
  force_0_behind_0 = FALSE
)

Arguments

digits	a
scientific_high_threshold	a
scientific_low_threshold	a
force_0_behind_0	a

---

Split a dataset imported from Redcap into convenient subsets

Description

This function seperates a datasets into three parts: "Singular" data, which is the data from non-repeating instruments. "missings_everywhere", which is data which is missing for each row. The last parts are all the repeating instruments, which are referred to by their name as recorded in dat$redcap_repeat_instrument.

Usage

split_redcap_dataset(dat, id_name = "patid")

Arguments

dat	a tibble produced by read_redcap_formatted.
id_name	(character) the name of the subject ID variable.

Value

a list of datasets separated into the categories as described

Examples

path_to_redcap_script <- system.file("examples", "testredcap.r", package = "DescrTab2")
dat <- read_redcap_formatted(path_to_redcap_script)
d <- split_redcap_dataset(dat, guess_ID_variable(dat, TRUE))

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Remove the label attribute from data

Description

Remove the label attribute from data

Usage

unlabel(dat)

Arguments

dat	data in the form of a list, data.frame or tibble, or a vector

Value

data with the labels removed

Examples

a <- c(1, 2)
attr(a, "label") <- "b"
identical(unlabel(a), c(1, 2))

---

Function that returns true in CRAN submission

Description

Function that returns true in CRAN submission

Usage

write_in_tmpfile_for_cran()

Value

TRUE for CRAN submission, FALSE otherwise

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