atable: Create Tables for Reporting of Clinical Trials

Description Usage Arguments Value Methods (by class) Examples

View source: R/atable.R

Description

Applies descriptive statistics and hypothesis tests to data, and arranges the results for printing.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
atable(x, ...)

## S3 method for class 'data.frame'
atable(
  x,
  target_cols,
  group_col = NULL,
  split_cols = NULL,
  format_to = atable_options("format_to"),
  drop_levels = TRUE,
  add_levels_for_NA = FALSE,
  blocks = NULL,
  add_margins = atable_options("add_margins"),
  indent_character = NULL,
  indent = atable_options("indent"),
  ...
)

## S3 method for class 'formula'
atable(formula, data, ...)

Arguments

x

An object. If x is a data.frame, it must have unique and syntactically valid colnames, see is_syntactically_valid_name. If x is a formula, then its format must be target_cols ~ group_col | split_cols. See other arguments for more details.

...

Passed from and to other methods. You can use the ellipsis ... to modify atable: For example the default-statistics for numeric variables are mean and sd. To change these statistics pass a function to argument statistics.numeric, that calculates the statistics you prefer for your data.

See examples below how to modify atable by ... .

Actually statistics.numeric is passed to statistics and thus documented there, but for convenience it also documented here.

Here is a list of the statistics and hypothesis tests that can be modified by ... :

  • statistics.numeric: Either NULL or a function. Default is NULL. If a function, then it will replace atable:::statistics.numeric when atable is called. The function must mimic statistics: see the help there.

  • statistics.factor: Analog to argument statistics.numeric.

  • statistics.ordered: Analog to argument statistics.numeric.

  • two_sample_htest.numeric: Either NULL or a function. Default is NULL. If a function, then it will replace atable:::two_sample_htest.numeric when atable is called. The function must mimic two_sample_htest: see the help there.

  • two_sample_htest.factor: Analog to argument two_sample_htest.numeric

  • two_sample_htest.ordered: Analog to argument two_sample_htest.numeric

  • multi_sample_htest.numeric: Either NULL or a function. Default is NULL. If a function, then it will replace atable:::multi_sample_htest.numeric when atable is called. The function must mimic multi_sample_htest: see the help there.

  • multi_sample_htest.factor: Analog to argument multi_sample_htest.numeric

  • multi_sample_htest.ordered: Analog to argument multi_sample_htest.numeric

  • format_statistics.statistics_numeric: Either NULL or a function. Default is NULL. If a function, then it will replace atable:::format_statistics.statistics_numeric. The function must mimic format_statistics: see the help there.

  • format_statistics.statistics_factor: Analog to argument format_statistics.statistics_numeric

  • format_tests.htest: Either NULL or a function. Default is NULL. If a function, then it will replace format_tests.htest. The function must mimic format_tests: see the help there.

  • format_tests.htest_with_effect_size: Analog to argument format_tests.htest

target_cols

A character vector containing some column names of x.

Descriptive statistics and hypothesis test are applied to these columns depending on their class. The descriptive statistics are defined by statistics; their representation and format by format_statistics.

Hypothesis test are defined by two_sample_htest or multi_sample_htest (depending on the number of levels of group_col); their representation and format by format_tests. Note that atable always adds one name to target_cols to count the number of obsservations. This name is stored in atable_options('colname_for_observations').

group_col

A character of length 1 containing a column of x or NULL. This column defines the groups that are compared by the hypothesis tests. as.factor is applied to this column before further processing. Default is NULL, meaning that no hypothesis tests are applied.

split_cols

A character vector containing some of colnames(x) or NULL. x is splitted by these columns before descriptive statistics and hypothesis test are applied. as.factor is applied to this column before further processing. Default is NULL, meaning that no splitting is done.

format_to

A character vector of length 1. Specifies the format of the output of atable. Possible values are 'Latex', 'Word', 'Raw', 'HTML', 'Console', 'markdown', 'md'. Default is defined in atable_options.

drop_levels

A logical. If TRUE then droplevels is called on group_col and split_cols before further processing. Default is TRUE.

add_levels_for_NA

If TRUE then addNA is called on group_col and split_cols before further processing. Default is FALSE.

blocks

NULL or a list. If blocks is a list, then the names of the list must be non-NA characters. The elements of the list must be some of target_cols, retaining the order of target_cols. Also in this case split_cols must be NULL as simultaneous blocking and splitting is not supported. Default is NULL, meaning that no blocking is done. Variables of a block are additionally indented. Blocking has no effect on the statistics, it only affects the indentation of the resulting table. See Examples.

add_margins

A logical with length one, TRUE or FALSE. Default is defined in atable_options as FALSE. When add_margins is TRUE and group_col is not NULL, a column containing the results of an ungrouped atable-call is added to the results. See Examples.

indent_character

A character with length 1 or NULL (default). This character is used for indentation in the resulting table. If NULL, then the value stored in atable_options is taken instead, depending on format_to. indent_data_frame does the indentation. See help there.

indent

A logical with length one, TRUE or FALSE. Default is defined in atable_options. Decides if indentation is done or not. The resulting table will have a different layout. If FALSE, then blocks is ignored.

formula

A formula of the form target_cols ~ group_col | split_cols. The | separates the group_col from the split_cols. Read the | as 'given' as in a conditional probability P(target_cols | split_cols). target_cols and split_cols may contain multiple names separated by +. group_col must be a single name if given. group_col and split_cols may be omitted and can be replaced by 1 in this case. The | may also be omitted if no split_cols are given.

data

Passed to atable(x = data, ...).

Value

Results depend on format_to:

Methods (by class)

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
# See vignette for more examples:
# utils::vignette('atable_usage', package = 'atable')

# Analyse datasets::ToothGrowth:
# Length of tooth for each dose level and delivery method:
atable::atable(datasets::ToothGrowth,
  target_cols = 'len',
  group_col = 'supp',
  split_cols = 'dose',
  format_to = 'Word')
# Print in .docx with e.g. flextable::regulartable and officer::body_add_table

# Analyse datasets::ChickWeight:
# Weight of chickens for each time point and diet:
atable(weight ~ Diet | Time, datasets::ChickWeight, format_to = 'Latex')
# Print as .pdf with e.g. Hmisc::latex

# Analyse atable::test_data:
atable(Numeric + Logical + Factor + Ordered ~ Group | Split1 + Split2,
  atable::test_data, format_to = 'HTML')
# Print as .html with e.g. knitr::kable and options(knitr.kable.NA = '')

# Modify atable: calculate median and MAD for numeric variables
new_stats  <- function(x, ...){list(Median = median(x, na.rm = TRUE),
                                    MAD = mad(x, na.rm = TRUE))}
atable(atable::test_data,
       target_cols = c('Numeric', 'Numeric2'),
       statistics.numeric = new_stats,
       format_to = 'Console')
# Print in Console with format_to = 'Console'.

# Analyse mtcars and add labels and units of via package Hmisc
mtcars <- within(datasets::mtcars, {gear <- factor(gear)})
# Add labels and units.
attr(mtcars$mpg, 'alias') = 'Consumption [Miles (US)/ gallon]'
Hmisc::label(mtcars$qsec) = 'Quarter Mile Time'
units(mtcars$qsec) = 's'

# apply atable
atable::atable(mpg + hp + gear + qsec ~ cyl | vs,
               mtcars,
               format_to = 'Console')

# Blocks
# In datasets::mtcars the variables cyl, disp and mpg are related to the engine and am and gear are
# related to the gearbox. So grouping them together is desireable.
atable::atable(datasets::mtcars,
               target_cols = c("cyl", "disp", "hp", "am", "gear", "qsec") ,
               blocks = list("Engine" = c("cyl", "disp", "hp"),
                             "Gearbox" = c("am", "gear")),
               format_to = "Console")
# Note that Variable qsec is not blocked and thus not indented.



# add_margins
atable::atable(atable::test_data,
               target_cols = "Numeric",
               group_col = "Group",
               split_cols = "Split1",
               add_margins = TRUE,
               format_to = "Console")
# The column 'Total' contains the results of the ungrouped atable-call:
# The number of observations is the sum of observations of the groups.
# The default of add_margins can be changed via atable_options.

atable documentation built on Dec. 16, 2020, 1:06 a.m.