Analyze Variables — analyze

We use the S3 generic function s_summary() to implement summaries for different x objects. This is used as a statistics function in combination with the analyze function analyze_vars(). Deprecation cycle started for summarize_vars as it is going to renamed into analyze_vars. Intention is to reflect better the core underlying rtables functions; in this case rtables::analyze().

Usage

s_summary(x, na.rm = TRUE, denom, .N_row, .N_col, .var, ...)

# S3 method for numeric
s_summary(
  x,
  na.rm = TRUE,
  denom,
  .N_row,
  .N_col,
  .var,
  control = control_analyze_vars(),
  ...
)

# S3 method for factor
s_summary(
  x,
  na.rm = TRUE,
  denom = c("n", "N_row", "N_col"),
  .N_row,
  .N_col,
  ...
)

# S3 method for character
s_summary(
  x,
  na.rm = TRUE,
  denom = c("n", "N_row", "N_col"),
  .N_row,
  .N_col,
  .var,
  verbose = TRUE,
  ...
)

# S3 method for logical
s_summary(
  x,
  na.rm = TRUE,
  denom = c("n", "N_row", "N_col"),
  .N_row,
  .N_col,
  ...
)

a_summary(
  x,
  .N_col,
  .N_row,
  .var = NULL,
  .df_row = NULL,
  .ref_group = NULL,
  .in_ref_col = FALSE,
  compare = FALSE,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  na.rm = TRUE,
  na_level = lifecycle::deprecated(),
  na_str = NA_character_,
  ...
)

analyze_vars(
  lyt,
  vars,
  var_labels = vars,
  na_level = lifecycle::deprecated(),
  na_str = NA_character_,
  nested = TRUE,
  ...,
  na.rm = TRUE,
  show_labels = "default",
  table_names = vars,
  section_div = NA_character_,
  .stats = c("n", "mean_sd", "median", "range", "count_fraction"),
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

summarize_vars(...)

Arguments

x

(numeric)
vector of numbers we want to analyze.

na.rm

(flag)
whether NA values should be removed from x prior to analysis.

denom

(string)
choice of denominator for proportion. Options are:

n: number of values in this row and column intersection.
N_row: total number of values in this row across columns.
N_col: total number of values in this column across rows.

.N_row

(integer)
row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting) that is typically passed by rtables.

.N_col

(integer)
column-wise N (column count) for the full column being analyzed that is typically passed by rtables.

.var

(string)
single variable name that is passed by rtables when requested by a statistics function.

...

arguments passed to s_summary().

control

(list)
parameters for descriptive statistics details, specified by using the helper function control_analyze_vars(). Some possible parameter options are:

conf_level (proportion)
confidence level of the interval for mean and median.
quantiles (numeric)
vector of length two to specify the quantiles.
quantile_type (numeric)
between 1 and 9 selecting quantile algorithms to be used. See more about type in stats::quantile().
test_mean (numeric)
value to test against the mean under the null hypothesis when calculating p-value.

verbose

(logical)
Defaults to TRUE, which prints out warnings and messages. It is mainly used to print out information about factor casting.

.df_row

(data.frame)
data frame across all of the columns for the given row split.

.ref_group

(data.frame or vector)
the data corresponding to the reference group.

.in_ref_col

(logical)
TRUE when working with the reference level, FALSE otherwise.

compare

(logical)
Whether comparison statistics should be analyzed instead of summary statistics (compare = TRUE adds pval statistic comparing against reference group).

.stats

(character)
statistics to select for the table.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named vector of integer)
indent modifiers for the labels. Each element of the vector should be a name-value pair with name corresponding to a statistic specified in .stats and value the indentation for that statistic's row label.

na_level

Please use the na_str argument instead.

na_str

(string)
string used to replace all NA or empty values in the output.

lyt

(layout)
input layout where analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

var_labels

(character)
character for label.

nested

(flag)
whether this layout instruction should be applied within the existing layout structure if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split. underneath analyses, which is not allowed.

show_labels

(string)
label visibility: one of "default", "visible" and "hidden".

table_names

(character)
this can be customized in case that the same vars are analyzed multiple times, to avoid warnings from rtables.

section_div

(string)
string which should be repeated as a section divider after each group defined by this split instruction, or NA_character_ (the default) for no section divider.

Value

s_summary() returns different statistics depending on the class of x.

If x is of class numeric, returns a list with the following named numeric items:
- n: The length() of x.
- sum: The sum() of x.
- mean: The mean() of x.
- sd: The stats::sd() of x.
- se: The standard error of x mean, i.e.: (sd(x) / sqrt(length(x))).
- mean_sd: The mean() and stats::sd() of x.
- mean_se: The mean() of x and its standard error (see above).
- mean_ci: The CI for the mean of x (from stat_mean_ci()).
- mean_sei: The SE interval for the mean of x, i.e.: (mean() -/+ stats::sd() / sqrt()).
- mean_sdi: The SD interval for the mean of x, i.e.: (mean() -/+ stats::sd()).
- mean_pval: The two-sided p-value of the mean of x (from stat_mean_pval()).
- median: The stats::median() of x.
- mad: The median absolute deviation of x, i.e.: (stats::median() of xc, where xc = x - stats::median()).
- median_ci: The CI for the median of x (from stat_median_ci()).
- quantiles: Two sample quantiles of x (from stats::quantile()).
- iqr: The stats::IQR() of x.
- range: The range_noinf() of x.
- min: The max() of x.
- max: The min() of x.
- median_range: The median() and range_noinf() of x.
- cv: The coefficient of variation of x, i.e.: (stats::sd() / mean() * 100).
- geom_mean: The geometric mean of x, i.e.: (exp(mean(log(x)))).
- geom_cv: The geometric coefficient of variation of x, i.e.: (sqrt(exp(sd(log(x)) ^ 2) - 1) * 100).

If x is of class factor or converted from character, returns a list with named numeric items:
- n: The length() of x.
- count: A list with the number of cases for each level of the factor x.
- count_fraction: Similar to count but also includes the proportion of cases for each level of the factor x relative to the denominator, or NA if the denominator is zero.

If x is of class logical, returns a list with named numeric items:
- n: The length() of x (possibly after removing NAs).
- count: Count of TRUE in x.
- count_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here.

a_summary() returns the corresponding list with formatted rtables::CellValue().

analyze_vars() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout.

Details

It is possible to use "auto" for analyze_vars on a subset of methods. This uses format_auto() to determine automatically the number of digits from the analyzed variable (.vars), but only for the current row data (.df_row[[.var]], see ?rtables::additional_fun_params), and not for the whole data. Also no column split is considered.

Functions

s_summary(): S3 generic function to produces a variable summary.
s_summary(numeric): Method for numeric class.
s_summary(factor): Method for factor class.
s_summary(character): Method for character class. This makes an automatic conversion to factor (with a warning) and then forwards to the method for factors.
s_summary(logical): Method for logical class.
a_summary(): Formatted analysis function which is used as afun in analyze_vars() and compare_vars() and as cfun in summarize_colvars().
analyze_vars(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().
summarize_vars(): Use analyze_vars instead.

Note

If x is an empty vector, NA is returned. This is the expected feature so as to return rcell content in rtables when the intersection of a column and a row delimits an empty data selection.
When the mean function is applied to an empty vector, NA will be returned instead of NaN, the latter being standard behavior in R.

If x is an empty factor, a list is still returned for counts with one element per factor level. If there are no levels in x, the function fails.
If factor variables contain NA, these NA values are excluded by default. To include NA values set na.rm = FALSE and missing values will be displayed as an NA level. Alternatively, an explicit factor level can be defined for NA values during pre-processing via df_explicit_na() - the default na_level ("<Missing>") will also be excluded when na.rm is set to TRUE.

Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually created from character variables before passing the dataset to rtables::build_table().

To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE.
Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is.

Examples

# `s_summary.numeric`

## Basic usage: empty numeric returns NA-filled items.
s_summary(numeric())
#> $n
#> n 
#> 0 
#> 
#> $sum
#> sum 
#>  NA 
#> 
#> $mean
#> mean 
#>   NA 
#> 
#> $sd
#> sd 
#> NA 
#> 
#> $se
#> se 
#> NA 
#> 
#> $mean_sd
#> mean   sd 
#>   NA   NA 
#> 
#> $mean_se
#> mean   se 
#>   NA   NA 
#> 
#> $mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $mean_sei
#> mean_sei_lwr mean_sei_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $mean_pval
#> p_value 
#>      NA 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $median
#> median 
#>     NA 
#> 
#> $mad
#> mad 
#>  NA 
#> 
#> $median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $quantiles
#> quantile_0.25 quantile_0.75 
#>            NA            NA 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $iqr
#> iqr 
#>  NA 
#> 
#> $range
#> min max 
#>  NA  NA 
#> 
#> $min
#> min 
#>  NA 
#> 
#> $max
#> max 
#>  NA 
#> 
#> $median_range
#> median    min    max 
#>     NA     NA     NA 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $cv
#> cv 
#> NA 
#> 
#> $geom_mean
#> geom_mean 
#>       NaN 
#> 
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $geom_cv
#> geom_cv 
#>      NA 
#> 

## Management of NA values.
x <- c(NA_real_, 1)
s_summary(x, na.rm = TRUE)
#> $n
#> n 
#> 1 
#> 
#> $sum
#> sum 
#>   1 
#> 
#> $mean
#> mean 
#>    1 
#> 
#> $sd
#> sd 
#> NA 
#> 
#> $se
#> se 
#> NA 
#> 
#> $mean_sd
#> mean   sd 
#>    1   NA 
#> 
#> $mean_se
#> mean   se 
#>    1   NA 
#> 
#> $mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $mean_sei
#> mean_sei_lwr mean_sei_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $mean_pval
#> p_value 
#>      NA 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $median
#> median 
#>      1 
#> 
#> $mad
#> mad 
#>   0 
#> 
#> $median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $quantiles
#> quantile_0.25 quantile_0.75 
#>             1             1 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $iqr
#> iqr 
#>   0 
#> 
#> $range
#> min max 
#>   1   1 
#> 
#> $min
#> min 
#>   1 
#> 
#> $max
#> max 
#>   1 
#> 
#> $median_range
#> median    min    max 
#>      1      1      1 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $cv
#> cv 
#> NA 
#> 
#> $geom_mean
#> geom_mean 
#>         1 
#> 
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $geom_cv
#> geom_cv 
#>      NA 
#> 
s_summary(x, na.rm = FALSE)
#> $n
#> n 
#> 2 
#> 
#> $sum
#> sum 
#>  NA 
#> 
#> $mean
#> mean 
#>   NA 
#> 
#> $sd
#> sd 
#> NA 
#> 
#> $se
#> se 
#> NA 
#> 
#> $mean_sd
#> mean   sd 
#>   NA   NA 
#> 
#> $mean_se
#> mean   se 
#>   NA   NA 
#> 
#> $mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $mean_sei
#> mean_sei_lwr mean_sei_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $mean_pval
#> p_value 
#>      NA 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $median
#> median 
#>     NA 
#> 
#> $mad
#> mad 
#>  NA 
#> 
#> $median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $quantiles
#> quantile_0.25 quantile_0.75 
#>            NA            NA 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $iqr
#> iqr 
#>  NA 
#> 
#> $range
#> min max 
#>  NA  NA 
#> 
#> $min
#> min 
#>  NA 
#> 
#> $max
#> max 
#>  NA 
#> 
#> $median_range
#> median    min    max 
#>     NA     NA     NA 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $cv
#> cv 
#> NA 
#> 
#> $geom_mean
#> geom_mean 
#>        NA 
#> 
#> $geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $geom_cv
#> geom_cv 
#>      NA 
#> 

x <- c(NA_real_, 1, 2)
s_summary(x, stats = NULL)
#> $n
#> n 
#> 2 
#> 
#> $sum
#> sum 
#>   3 
#> 
#> $mean
#> mean 
#>  1.5 
#> 
#> $sd
#>        sd 
#> 0.7071068 
#> 
#> $se
#>  se 
#> 0.5 
#> 
#> $mean_sd
#>      mean        sd 
#> 1.5000000 0.7071068 
#> 
#> $mean_se
#> mean   se 
#>  1.5  0.5 
#> 
#> $mean_ci
#> mean_ci_lwr mean_ci_upr 
#>   -4.853102    7.853102 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $mean_sei
#> mean_sei_lwr mean_sei_upr 
#>            1            2 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>    0.7928932    2.2071068 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $mean_pval
#>   p_value 
#> 0.2048328 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $median
#> median 
#>    1.5 
#> 
#> $mad
#> mad 
#>   0 
#> 
#> $median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $quantiles
#> quantile_0.25 quantile_0.75 
#>             1             2 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $iqr
#> iqr 
#>   1 
#> 
#> $range
#> min max 
#>   1   2 
#> 
#> $min
#> min 
#>   1 
#> 
#> $max
#> max 
#>   2 
#> 
#> $median_range
#> median    min    max 
#>    1.5    1.0    2.0 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $cv
#>       cv 
#> 47.14045 
#> 
#> $geom_mean
#> geom_mean 
#>  1.414214 
#> 
#> $geom_mean_ci
#>  mean_ci_lwr  mean_ci_upr 
#>   0.01729978 115.60839614 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $geom_cv
#>  geom_cv 
#> 52.10922 
#> 

## Benefits in `rtables` contructions:
require(rtables)
dta_test <- data.frame(
  Group = rep(LETTERS[1:3], each = 2),
  sub_group = rep(letters[1:2], each = 3),
  x = 1:6
)

## The summary obtained in with `rtables`:
basic_table() %>%
  split_cols_by(var = "Group") %>%
  split_rows_by(var = "sub_group") %>%
  analyze(vars = "x", afun = s_summary) %>%
  build_table(df = dta_test)
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#> Warning: number of items to replace is not a multiple of replacement length
#>                                                  A                        B                       C                  
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> a                                                                                                                    
#>   n                                              2                        1                       0                  
#>   sum                                            3                        3                       NA                 
#>   mean                                          1.5                       3                       NA                 
#>   sd                                     0.707106781186548               NA                       NA                 
#>   se                                            0.5                      NA                       NA                 
#>   mean_sd                              1.5, 0.707106781186548           3, NA                     NA                 
#>   mean_se                                     1.5, 0.5                  3, NA                     NA                 
#>   Mean 95% CI                   -4.85310236808735, 7.85310236808735      NA                       NA                 
#>   Mean -/+ 1xSE                                 1, 2                     NA                       NA                 
#>   Mean -/+ 1xSD                 0.792893218813452, 2.20710678118655      NA                       NA                 
#>   Mean p-value (H0: mean = 0)            0.204832764699133               NA                       NA                 
#>   median                                        1.5                       3                       NA                 
#>   mad                                            0                        0                       NA                 
#>   Median 95% CI                                  NA                      NA                       NA                 
#>   25% and 75%-ile                               1, 2                    3, 3                      NA                 
#>   iqr                                            1                        0                       NA                 
#>   range                                         1, 2                    3, 3                      NA                 
#>   min                                            1                        3                       NA                 
#>   max                                            2                        3                       NA                 
#>   Median (Min - Max)                         1.5, 1, 2                 3, 3, 3                    NA                 
#>   cv                                      47.1404520791032               NA                       NA                 
#>   geom_mean                               1.41421356237309                3                       NA                 
#>   Geometric Mean 95% CI         0.0172997815631007, 115.608396135236     NA                       NA                 
#>   geom_cv                                 52.1092246837487               NA                       NA                 
#> b                                                                                                                    
#>   n                                              0                        1                       2                  
#>   sum                                            NA                       4                       11                 
#>   mean                                           NA                       4                      5.5                 
#>   sd                                             NA                      NA               0.707106781186548          
#>   se                                             NA                      NA                      0.5                 
#>   mean_sd                                        NA                     4, NA           5.5, 0.707106781186548       
#>   mean_se                                        NA                     4, NA                  5.5, 0.5              
#>   Mean 95% CI                                    NA                      NA      -0.853102368087347, 11.8531023680873
#>   Mean -/+ 1xSE                                  NA                      NA                      5, 6                
#>   Mean -/+ 1xSD                                  NA                      NA       4.79289321881345, 6.20710678118655 
#>   Mean p-value (H0: mean = 0)                    NA                      NA               0.0577158767526089         
#>   median                                         NA                       4                      5.5                 
#>   mad                                            NA                       0                       0                  
#>   Median 95% CI                                  NA                      NA                       NA                 
#>   25% and 75%-ile                                NA                     4, 4                     5, 6                
#>   iqr                                            NA                       0                       1                  
#>   range                                          NA                     4, 4                     5, 6                
#>   min                                            NA                       4                       5                  
#>   max                                            NA                       4                       6                  
#>   Median (Min - Max)                             NA                    4, 4, 4                5.5, 5, 6              
#>   cv                                             NA                      NA                12.8564869306645          
#>   geom_mean                                      NA                       4                5.47722557505166          
#>   Geometric Mean 95% CI                          NA                      NA       1.71994304449266, 17.4424380482025 
#>   geom_cv                                        NA                      NA                12.945835316564           

## By comparison with `lapply`:
X <- split(dta_test, f = with(dta_test, interaction(Group, sub_group)))
lapply(X, function(x) s_summary(x$x))
#> $A.a
#> $A.a$n
#> n 
#> 2 
#> 
#> $A.a$sum
#> sum 
#>   3 
#> 
#> $A.a$mean
#> mean 
#>  1.5 
#> 
#> $A.a$sd
#>        sd 
#> 0.7071068 
#> 
#> $A.a$se
#>  se 
#> 0.5 
#> 
#> $A.a$mean_sd
#>      mean        sd 
#> 1.5000000 0.7071068 
#> 
#> $A.a$mean_se
#> mean   se 
#>  1.5  0.5 
#> 
#> $A.a$mean_ci
#> mean_ci_lwr mean_ci_upr 
#>   -4.853102    7.853102 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $A.a$mean_sei
#> mean_sei_lwr mean_sei_upr 
#>            1            2 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $A.a$mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>    0.7928932    2.2071068 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $A.a$mean_pval
#>   p_value 
#> 0.2048328 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $A.a$median
#> median 
#>    1.5 
#> 
#> $A.a$mad
#> mad 
#>   0 
#> 
#> $A.a$median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $A.a$quantiles
#> quantile_0.25 quantile_0.75 
#>             1             2 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $A.a$iqr
#> iqr 
#>   1 
#> 
#> $A.a$range
#> min max 
#>   1   2 
#> 
#> $A.a$min
#> min 
#>   1 
#> 
#> $A.a$max
#> max 
#>   2 
#> 
#> $A.a$median_range
#> median    min    max 
#>    1.5    1.0    2.0 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $A.a$cv
#>       cv 
#> 47.14045 
#> 
#> $A.a$geom_mean
#> geom_mean 
#>  1.414214 
#> 
#> $A.a$geom_mean_ci
#>  mean_ci_lwr  mean_ci_upr 
#>   0.01729978 115.60839614 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $A.a$geom_cv
#>  geom_cv 
#> 52.10922 
#> 
#> 
#> $B.a
#> $B.a$n
#> n 
#> 1 
#> 
#> $B.a$sum
#> sum 
#>   3 
#> 
#> $B.a$mean
#> mean 
#>    3 
#> 
#> $B.a$sd
#> sd 
#> NA 
#> 
#> $B.a$se
#> se 
#> NA 
#> 
#> $B.a$mean_sd
#> mean   sd 
#>    3   NA 
#> 
#> $B.a$mean_se
#> mean   se 
#>    3   NA 
#> 
#> $B.a$mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $B.a$mean_sei
#> mean_sei_lwr mean_sei_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $B.a$mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $B.a$mean_pval
#> p_value 
#>      NA 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $B.a$median
#> median 
#>      3 
#> 
#> $B.a$mad
#> mad 
#>   0 
#> 
#> $B.a$median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $B.a$quantiles
#> quantile_0.25 quantile_0.75 
#>             3             3 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $B.a$iqr
#> iqr 
#>   0 
#> 
#> $B.a$range
#> min max 
#>   3   3 
#> 
#> $B.a$min
#> min 
#>   3 
#> 
#> $B.a$max
#> max 
#>   3 
#> 
#> $B.a$median_range
#> median    min    max 
#>      3      3      3 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $B.a$cv
#> cv 
#> NA 
#> 
#> $B.a$geom_mean
#> geom_mean 
#>         3 
#> 
#> $B.a$geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $B.a$geom_cv
#> geom_cv 
#>      NA 
#> 
#> 
#> $C.a
#> $C.a$n
#> n 
#> 0 
#> 
#> $C.a$sum
#> sum 
#>  NA 
#> 
#> $C.a$mean
#> mean 
#>   NA 
#> 
#> $C.a$sd
#> sd 
#> NA 
#> 
#> $C.a$se
#> se 
#> NA 
#> 
#> $C.a$mean_sd
#> mean   sd 
#>   NA   NA 
#> 
#> $C.a$mean_se
#> mean   se 
#>   NA   NA 
#> 
#> $C.a$mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $C.a$mean_sei
#> mean_sei_lwr mean_sei_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $C.a$mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $C.a$mean_pval
#> p_value 
#>      NA 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $C.a$median
#> median 
#>     NA 
#> 
#> $C.a$mad
#> mad 
#>  NA 
#> 
#> $C.a$median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $C.a$quantiles
#> quantile_0.25 quantile_0.75 
#>            NA            NA 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $C.a$iqr
#> iqr 
#>  NA 
#> 
#> $C.a$range
#> min max 
#>  NA  NA 
#> 
#> $C.a$min
#> min 
#>  NA 
#> 
#> $C.a$max
#> max 
#>  NA 
#> 
#> $C.a$median_range
#> median    min    max 
#>     NA     NA     NA 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $C.a$cv
#> cv 
#> NA 
#> 
#> $C.a$geom_mean
#> geom_mean 
#>       NaN 
#> 
#> $C.a$geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $C.a$geom_cv
#> geom_cv 
#>      NA 
#> 
#> 
#> $A.b
#> $A.b$n
#> n 
#> 0 
#> 
#> $A.b$sum
#> sum 
#>  NA 
#> 
#> $A.b$mean
#> mean 
#>   NA 
#> 
#> $A.b$sd
#> sd 
#> NA 
#> 
#> $A.b$se
#> se 
#> NA 
#> 
#> $A.b$mean_sd
#> mean   sd 
#>   NA   NA 
#> 
#> $A.b$mean_se
#> mean   se 
#>   NA   NA 
#> 
#> $A.b$mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $A.b$mean_sei
#> mean_sei_lwr mean_sei_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $A.b$mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $A.b$mean_pval
#> p_value 
#>      NA 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $A.b$median
#> median 
#>     NA 
#> 
#> $A.b$mad
#> mad 
#>  NA 
#> 
#> $A.b$median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $A.b$quantiles
#> quantile_0.25 quantile_0.75 
#>            NA            NA 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $A.b$iqr
#> iqr 
#>  NA 
#> 
#> $A.b$range
#> min max 
#>  NA  NA 
#> 
#> $A.b$min
#> min 
#>  NA 
#> 
#> $A.b$max
#> max 
#>  NA 
#> 
#> $A.b$median_range
#> median    min    max 
#>     NA     NA     NA 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $A.b$cv
#> cv 
#> NA 
#> 
#> $A.b$geom_mean
#> geom_mean 
#>       NaN 
#> 
#> $A.b$geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $A.b$geom_cv
#> geom_cv 
#>      NA 
#> 
#> 
#> $B.b
#> $B.b$n
#> n 
#> 1 
#> 
#> $B.b$sum
#> sum 
#>   4 
#> 
#> $B.b$mean
#> mean 
#>    4 
#> 
#> $B.b$sd
#> sd 
#> NA 
#> 
#> $B.b$se
#> se 
#> NA 
#> 
#> $B.b$mean_sd
#> mean   sd 
#>    4   NA 
#> 
#> $B.b$mean_se
#> mean   se 
#>    4   NA 
#> 
#> $B.b$mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $B.b$mean_sei
#> mean_sei_lwr mean_sei_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $B.b$mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>           NA           NA 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $B.b$mean_pval
#> p_value 
#>      NA 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $B.b$median
#> median 
#>      4 
#> 
#> $B.b$mad
#> mad 
#>   0 
#> 
#> $B.b$median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $B.b$quantiles
#> quantile_0.25 quantile_0.75 
#>             4             4 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $B.b$iqr
#> iqr 
#>   0 
#> 
#> $B.b$range
#> min max 
#>   4   4 
#> 
#> $B.b$min
#> min 
#>   4 
#> 
#> $B.b$max
#> max 
#>   4 
#> 
#> $B.b$median_range
#> median    min    max 
#>      4      4      4 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $B.b$cv
#> cv 
#> NA 
#> 
#> $B.b$geom_mean
#> geom_mean 
#>         4 
#> 
#> $B.b$geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>          NA          NA 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $B.b$geom_cv
#> geom_cv 
#>      NA 
#> 
#> 
#> $C.b
#> $C.b$n
#> n 
#> 2 
#> 
#> $C.b$sum
#> sum 
#>  11 
#> 
#> $C.b$mean
#> mean 
#>  5.5 
#> 
#> $C.b$sd
#>        sd 
#> 0.7071068 
#> 
#> $C.b$se
#>  se 
#> 0.5 
#> 
#> $C.b$mean_sd
#>      mean        sd 
#> 5.5000000 0.7071068 
#> 
#> $C.b$mean_se
#> mean   se 
#>  5.5  0.5 
#> 
#> $C.b$mean_ci
#> mean_ci_lwr mean_ci_upr 
#>  -0.8531024  11.8531024 
#> attr(,"label")
#> [1] "Mean 95% CI"
#> 
#> $C.b$mean_sei
#> mean_sei_lwr mean_sei_upr 
#>            5            6 
#> attr(,"label")
#> [1] "Mean -/+ 1xSE"
#> 
#> $C.b$mean_sdi
#> mean_sdi_lwr mean_sdi_upr 
#>     4.792893     6.207107 
#> attr(,"label")
#> [1] "Mean -/+ 1xSD"
#> 
#> $C.b$mean_pval
#>    p_value 
#> 0.05771588 
#> attr(,"label")
#> [1] "Mean p-value (H0: mean = 0)"
#> 
#> $C.b$median
#> median 
#>    5.5 
#> 
#> $C.b$mad
#> mad 
#>   0 
#> 
#> $C.b$median_ci
#> median_ci_lwr median_ci_upr 
#>            NA            NA 
#> attr(,"conf_level")
#> [1] NA
#> attr(,"label")
#> [1] "Median 95% CI"
#> 
#> $C.b$quantiles
#> quantile_0.25 quantile_0.75 
#>             5             6 
#> attr(,"label")
#> [1] "25% and 75%-ile"
#> 
#> $C.b$iqr
#> iqr 
#>   1 
#> 
#> $C.b$range
#> min max 
#>   5   6 
#> 
#> $C.b$min
#> min 
#>   5 
#> 
#> $C.b$max
#> max 
#>   6 
#> 
#> $C.b$median_range
#> median    min    max 
#>    5.5    5.0    6.0 
#> attr(,"label")
#> [1] "Median (Min - Max)"
#> 
#> $C.b$cv
#>       cv 
#> 12.85649 
#> 
#> $C.b$geom_mean
#> geom_mean 
#>  5.477226 
#> 
#> $C.b$geom_mean_ci
#> mean_ci_lwr mean_ci_upr 
#>    1.719943   17.442438 
#> attr(,"label")
#> [1] "Geometric Mean 95% CI"
#> 
#> $C.b$geom_cv
#>  geom_cv 
#> 12.94584 
#> 
#> 

# `s_summary.factor`

## Basic usage:
s_summary(factor(c("a", "a", "b", "c", "a")))
#> $n
#> [1] 5
#> 
#> $count
#> $count$a
#> [1] 3
#> 
#> $count$b
#> [1] 1
#> 
#> $count$c
#> [1] 1
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> [1] 3.0 0.6
#> 
#> $count_fraction$b
#> [1] 1.0 0.2
#> 
#> $count_fraction$c
#> [1] 1.0 0.2
#> 
#> 
#> $n_blq
#> [1] 0
#> 

# Empty factor returns zero-filled items.
s_summary(factor(levels = c("a", "b", "c")))
#> $n
#> [1] 0
#> 
#> $count
#> $count$a
#> [1] 0
#> 
#> $count$b
#> [1] 0
#> 
#> $count$c
#> [1] 0
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> [1] 0 0
#> 
#> $count_fraction$b
#> [1] 0 0
#> 
#> $count_fraction$c
#> [1] 0 0
#> 
#> 
#> $n_blq
#> [1] 0
#> 

## Management of NA values.
x <- factor(c(NA, "Female"))
x <- explicit_na(x)
s_summary(x, na.rm = TRUE)
#> $n
#> [1] 1
#> 
#> $count
#> $count$Female
#> [1] 1
#> 
#> 
#> $count_fraction
#> $count_fraction$Female
#> [1] 1 1
#> 
#> 
#> $n_blq
#> [1] 0
#> 
s_summary(x, na.rm = FALSE)
#> $n
#> [1] 2
#> 
#> $count
#> $count$Female
#> [1] 1
#> 
#> $count$`<Missing>`
#> [1] 1
#> 
#> 
#> $count_fraction
#> $count_fraction$Female
#> [1] 1.0 0.5
#> 
#> $count_fraction$`<Missing>`
#> [1] 1.0 0.5
#> 
#> 
#> $n_blq
#> [1] 0
#> 

## Different denominators.
x <- factor(c("a", "a", "b", "c", "a"))
s_summary(x, denom = "N_row", .N_row = 10L)
#> $n
#> [1] 5
#> 
#> $count
#> $count$a
#> [1] 3
#> 
#> $count$b
#> [1] 1
#> 
#> $count$c
#> [1] 1
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> [1] 3.0 0.3
#> 
#> $count_fraction$b
#> [1] 1.0 0.1
#> 
#> $count_fraction$c
#> [1] 1.0 0.1
#> 
#> 
#> $n_blq
#> [1] 0
#> 
s_summary(x, denom = "N_col", .N_col = 20L)
#> $n
#> [1] 5
#> 
#> $count
#> $count$a
#> [1] 3
#> 
#> $count$b
#> [1] 1
#> 
#> $count$c
#> [1] 1
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> [1] 3.00 0.15
#> 
#> $count_fraction$b
#> [1] 1.00 0.05
#> 
#> $count_fraction$c
#> [1] 1.00 0.05
#> 
#> 
#> $n_blq
#> [1] 0
#> 

# `s_summary.character`

## Basic usage:
s_summary(c("a", "a", "b", "c", "a"), .var = "x", verbose = FALSE)
#> $n
#> [1] 5
#> 
#> $count
#> $count$a
#> [1] 3
#> 
#> $count$b
#> [1] 1
#> 
#> $count$c
#> [1] 1
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> [1] 3.0 0.6
#> 
#> $count_fraction$b
#> [1] 1.0 0.2
#> 
#> $count_fraction$c
#> [1] 1.0 0.2
#> 
#> 
#> $n_blq
#> [1] 0
#> 
s_summary(c("a", "a", "b", "c", "a", ""), .var = "x", na.rm = FALSE, verbose = FALSE)
#> $n
#> [1] 6
#> 
#> $count
#> $count$a
#> [1] 3
#> 
#> $count$b
#> [1] 1
#> 
#> $count$c
#> [1] 1
#> 
#> $count$`NA`
#> [1] 1
#> 
#> 
#> $count_fraction
#> $count_fraction$a
#> [1] 3.0 0.5
#> 
#> $count_fraction$b
#> [1] 1.0000000 0.1666667
#> 
#> $count_fraction$c
#> [1] 1.0000000 0.1666667
#> 
#> $count_fraction$`NA`
#> [1] 1.0000000 0.1666667
#> 
#> 
#> $n_blq
#> [1] 0
#> 

# `s_summary.logical`

## Basic usage:
s_summary(c(TRUE, FALSE, TRUE, TRUE))
#> $n
#> [1] 4
#> 
#> $count
#> [1] 3
#> 
#> $count_fraction
#> [1] 3.00 0.75
#> 
#> $n_blq
#> [1] 0
#> 

# Empty factor returns zero-filled items.
s_summary(as.logical(c()))
#> $n
#> [1] 0
#> 
#> $count
#> [1] 0
#> 
#> $count_fraction
#> [1] 0 0
#> 
#> $n_blq
#> [1] 0
#> 

## Management of NA values.
x <- c(NA, TRUE, FALSE)
s_summary(x, na.rm = TRUE)
#> $n
#> [1] 2
#> 
#> $count
#> [1] 1
#> 
#> $count_fraction
#> [1] 1.0 0.5
#> 
#> $n_blq
#> [1] 0
#> 
s_summary(x, na.rm = FALSE)
#> $n
#> [1] 3
#> 
#> $count
#> [1] 1
#> 
#> $count_fraction
#> [1] 1.0000000 0.3333333
#> 
#> $n_blq
#> [1] 0
#> 

## Different denominators.
x <- c(TRUE, FALSE, TRUE, TRUE)
s_summary(x, denom = "N_row", .N_row = 10L)
#> $n
#> [1] 4
#> 
#> $count
#> [1] 3
#> 
#> $count_fraction
#> [1] 3.0 0.3
#> 
#> $n_blq
#> [1] 0
#> 
s_summary(x, denom = "N_col", .N_col = 20L)
#> $n
#> [1] 4
#> 
#> $count
#> [1] 3
#> 
#> $count_fraction
#> [1] 3.00 0.15
#> 
#> $n_blq
#> [1] 0
#> 

a_summary(factor(c("a", "a", "b", "c", "a")), .N_row = 10, .N_col = 10)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>   row_name formatted_cell indent_mod row_label
#> 1        n              5          0         n
#> 2        a              3          0         a
#> 3        b              1          0         b
#> 4        c              1          0         c
#> 5        a        3 (60%)          0         a
#> 6        b        1 (20%)          0         b
#> 7        c        1 (20%)          0         c
#> 8    n_blq              0          0     n_blq
a_summary(
  factor(c("a", "a", "b", "c", "a")),
  .ref_group = factor(c("a", "a", "b", "c")), compare = TRUE
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>                     row_name formatted_cell indent_mod
#> 1                          n              5          0
#> 2                          a              3          0
#> 3                          b              1          0
#> 4                          c              1          0
#> 5                          a        3 (60%)          0
#> 6                          b        1 (20%)          0
#> 7                          c        1 (20%)          0
#> 8                      n_blq              0          0
#> 9 p-value (chi-squared test)         0.9560          0
#>                    row_label
#> 1                          n
#> 2                          a
#> 3                          b
#> 4                          c
#> 5                          a
#> 6                          b
#> 7                          c
#> 8                      n_blq
#> 9 p-value (chi-squared test)

a_summary(c("A", "B", "A", "C"), .var = "x", .N_col = 10, .N_row = 10, verbose = FALSE)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>   row_name formatted_cell indent_mod row_label
#> 1        n              4          0         n
#> 2        A              2          0         A
#> 3        B              1          0         B
#> 4        C              1          0         C
#> 5        A        2 (50%)          0         A
#> 6        B        1 (25%)          0         B
#> 7        C        1 (25%)          0         C
#> 8    n_blq              0          0     n_blq
a_summary(
  c("A", "B", "A", "C"),
  .ref_group = c("B", "A", "C"), .var = "x", compare = TRUE, verbose = FALSE
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>                     row_name formatted_cell indent_mod
#> 1                          n              4          0
#> 2                          A              2          0
#> 3                          B              1          0
#> 4                          C              1          0
#> 5                          A        2 (50%)          0
#> 6                          B        1 (25%)          0
#> 7                          C        1 (25%)          0
#> 8                      n_blq              0          0
#> 9 p-value (chi-squared test)         0.9074          0
#>                    row_label
#> 1                          n
#> 2                          A
#> 3                          B
#> 4                          C
#> 5                          A
#> 6                          B
#> 7                          C
#> 8                      n_blq
#> 9 p-value (chi-squared test)

a_summary(c(TRUE, FALSE, FALSE, TRUE, TRUE), .N_row = 10, .N_col = 10)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>         row_name formatted_cell indent_mod      row_label
#> 1              n              5          0              n
#> 2          count              3          0          count
#> 3 count_fraction        3 (60%)          0 count_fraction
#> 4          n_blq              0          0          n_blq
a_summary(
  c(TRUE, FALSE, FALSE, TRUE, TRUE),
  .ref_group = c(TRUE, FALSE), .in_ref_col = TRUE, compare = TRUE
)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>                     row_name formatted_cell indent_mod
#> 1                          n              5          0
#> 2                      count              3          0
#> 3             count_fraction        3 (60%)          0
#> 4                      n_blq              0          0
#> 5 p-value (chi-squared test)                         0
#>                    row_label
#> 1                          n
#> 2                      count
#> 3             count_fraction
#> 4                      n_blq
#> 5 p-value (chi-squared test)

a_summary(rnorm(10), .N_col = 10, .N_row = 20, .var = "bla")
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>                       row_name   formatted_cell indent_mod
#> 1                            n               10          0
#> 2                          Sum              3.4          0
#> 3                         Mean              0.3          0
#> 4                           SD              0.7          0
#> 5                           SE              0.2          0
#> 6                    Mean (SD)        0.3 (0.7)          0
#> 7                    Mean (SE)        0.3 (0.2)          0
#> 8                  Mean 95% CI    (-0.18, 0.85)          0
#> 9                Mean -/+ 1xSE     (0.11, 0.56)          0
#> 10               Mean -/+ 1xSD    (-0.38, 1.05)          0
#> 11 Mean p-value (H0: mean = 0)             0.17          0
#> 12                      Median              0.4          0
#> 13   Median Absolute Deviation              0.0          0
#> 14               Median 95% CI    (-0.48, 0.80)          0
#> 15             25% and 75%-ile       -0.3 - 0.8          0
#> 16                         IQR              1.0          0
#> 17                   Min - Max       -0.5 - 1.7          0
#> 18                     Minimum             -0.5          0
#> 19                     Maximum              1.7          0
#> 20          Median (Min - Max) 0.4 (-0.5 - 1.7)          0
#> 21                      CV (%)            214.2          0
#> 22              Geometric Mean               NA          0
#> 23       Geometric Mean 95% CI               NA          0
#> 24         CV % Geometric Mean               NA          0
#>                      row_label
#> 1                            n
#> 2                          Sum
#> 3                         Mean
#> 4                           SD
#> 5                           SE
#> 6                    Mean (SD)
#> 7                    Mean (SE)
#> 8                  Mean 95% CI
#> 9                Mean -/+ 1xSE
#> 10               Mean -/+ 1xSD
#> 11 Mean p-value (H0: mean = 0)
#> 12                      Median
#> 13   Median Absolute Deviation
#> 14               Median 95% CI
#> 15             25% and 75%-ile
#> 16                         IQR
#> 17                   Min - Max
#> 18                     Minimum
#> 19                     Maximum
#> 20          Median (Min - Max)
#> 21                      CV (%)
#> 22              Geometric Mean
#> 23       Geometric Mean 95% CI
#> 24         CV % Geometric Mean
a_summary(rnorm(10, 5, 1), .ref_group = rnorm(20, -5, 1), .var = "bla", compare = TRUE)
#> RowsVerticalSection (in_rows) object print method:
#> ----------------------------
#>                       row_name  formatted_cell indent_mod
#> 1                            n              10          0
#> 2                          Sum            52.0          0
#> 3                         Mean             5.2          0
#> 4                           SD             1.1          0
#> 5                           SE             0.3          0
#> 6                    Mean (SD)       5.2 (1.1)          0
#> 7                    Mean (SE)       5.2 (0.3)          0
#> 8                  Mean 95% CI    (4.42, 5.99)          0
#> 9                Mean -/+ 1xSE    (4.86, 5.55)          0
#> 10               Mean -/+ 1xSD    (4.11, 6.30)          0
#> 11 Mean p-value (H0: mean = 0)            0.00          0
#> 12                      Median             5.5          0
#> 13   Median Absolute Deviation             0.0          0
#> 14               Median 95% CI    (4.36, 6.04)          0
#> 15             25% and 75%-ile       4.7 - 5.9          0
#> 16                         IQR             1.2          0
#> 17                   Min - Max       2.6 - 6.4          0
#> 18                     Minimum             2.6          0
#> 19                     Maximum             6.4          0
#> 20          Median (Min - Max) 5.5 (2.6 - 6.4)          0
#> 21                      CV (%)            21.1          0
#> 22              Geometric Mean             5.1          0
#> 23       Geometric Mean 95% CI    (4.20, 6.11)          0
#> 24         CV % Geometric Mean            26.8          0
#> 25            p-value (t-test)         <0.0001          0
#>                      row_label
#> 1                            n
#> 2                          Sum
#> 3                         Mean
#> 4                           SD
#> 5                           SE
#> 6                    Mean (SD)
#> 7                    Mean (SE)
#> 8                  Mean 95% CI
#> 9                Mean -/+ 1xSE
#> 10               Mean -/+ 1xSD
#> 11 Mean p-value (H0: mean = 0)
#> 12                      Median
#> 13   Median Absolute Deviation
#> 14               Median 95% CI
#> 15             25% and 75%-ile
#> 16                         IQR
#> 17                   Min - Max
#> 18                     Minimum
#> 19                     Maximum
#> 20          Median (Min - Max)
#> 21                      CV (%)
#> 22              Geometric Mean
#> 23       Geometric Mean 95% CI
#> 24         CV % Geometric Mean
#> 25            p-value (t-test)

## Fabricated dataset.
dta_test <- data.frame(
  USUBJID = rep(1:6, each = 3),
  PARAMCD = rep("lab", 6 * 3),
  AVISIT  = rep(paste0("V", 1:3), 6),
  ARM     = rep(LETTERS[1:3], rep(6, 3)),
  AVAL    = c(9:1, rep(NA, 9))
)

# `analyze_vars()` in `rtables` pipelines
## Default output within a `rtables` pipeline.
l <- basic_table() %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by(var = "AVISIT") %>%
  analyze_vars(vars = "AVAL")

build_table(l, df = dta_test)
#>                   A           B       C 
#> ————————————————————————————————————————
#> V1                                      
#>   n               2           1       0 
#>   Mean (SD)   7.5 (2.1)   3.0 (NA)    NA
#>   Median         7.5         3.0      NA
#>   Min - Max   6.0 - 9.0   3.0 - 3.0   NA
#> V2                                      
#>   n               2           1       0 
#>   Mean (SD)   6.5 (2.1)   2.0 (NA)    NA
#>   Median         6.5         2.0      NA
#>   Min - Max   5.0 - 8.0   2.0 - 2.0   NA
#> V3                                      
#>   n               2           1       0 
#>   Mean (SD)   5.5 (2.1)   1.0 (NA)    NA
#>   Median         5.5         1.0      NA
#>   Min - Max   4.0 - 7.0   1.0 - 1.0   NA

## Select and format statistics output.
l <- basic_table() %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by(var = "AVISIT") %>%
  analyze_vars(
    vars = "AVAL",
    .stats = c("n", "mean_sd", "quantiles"),
    .formats = c("mean_sd" = "xx.x, xx.x"),
    .labels = c(n = "n", mean_sd = "Mean, SD", quantiles = c("Q1 - Q3"))
  )

build_table(l, df = dta_test)
#>                  A           B       C 
#> ———————————————————————————————————————
#> V1                                     
#>   n              2           1       0 
#>   Mean, SD   7.5, 2.1     3.0, NA    NA
#>   Q1 - Q3    6.0 - 9.0   3.0 - 3.0   NA
#> V2                                     
#>   n              2           1       0 
#>   Mean, SD   6.5, 2.1     2.0, NA    NA
#>   Q1 - Q3    5.0 - 8.0   2.0 - 2.0   NA
#> V3                                     
#>   n              2           1       0 
#>   Mean, SD   5.5, 2.1     1.0, NA    NA
#>   Q1 - Q3    4.0 - 7.0   1.0 - 1.0   NA

## Use arguments interpreted by `s_summary`.
l <- basic_table() %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by(var = "AVISIT") %>%
  analyze_vars(vars = "AVAL", na.rm = FALSE)

build_table(l, df = dta_test)
#>                   A       B    C 
#> —————————————————————————————————
#> V1                               
#>   n               2       2    2 
#>   Mean (SD)   7.5 (2.1)   NA   NA
#>   Median         7.5      NA   NA
#>   Min - Max   6.0 - 9.0   NA   NA
#> V2                               
#>   n               2       2    2 
#>   Mean (SD)   6.5 (2.1)   NA   NA
#>   Median         6.5      NA   NA
#>   Min - Max   5.0 - 8.0   NA   NA
#> V3                               
#>   n               2       2    2 
#>   Mean (SD)   5.5 (2.1)   NA   NA
#>   Median         5.5      NA   NA
#>   Min - Max   4.0 - 7.0   NA   NA

## Handle `NA` levels first when summarizing factors.
dta_test$AVISIT <- NA_character_
dta_test <- df_explicit_na(dta_test)
l <- basic_table() %>%
  split_cols_by(var = "ARM") %>%
  analyze_vars(vars = "AVISIT", na.rm = FALSE)

build_table(l, df = dta_test)
#>                A          B          C    
#> ——————————————————————————————————————————
#> n              6          6          6    
#> <Missing>   6 (100%)   6 (100%)   6 (100%)

# auto format
dt <- data.frame("VAR" = c(0.001, 0.2, 0.0011000, 3, 4))
basic_table() %>%
  analyze_vars(
    vars = "VAR",
    .stats = c("n", "mean", "mean_sd", "range"),
    .formats = c("mean_sd" = "auto", "range" = "auto")
  ) %>%
  build_table(dt)
#>                  all obs     
#> —————————————————————————————
#> n                   5        
#> Mean               1.4       
#> Mean (SD)   1.44042 (1.91481)
#> Min - Max    0.0010 - 4.0000