getDescriptionStatsBy: Creating of description statistics

In raredd/Gmisc0: Collection of functions for plotting relations, generating tables, and more.

Description

A function that returns a description statistic that can be used for creating a publication "table 1" when you want it by groups. The function identifies if the variable is a continuous, binary or a factored variable. The format is inspired by NEJM, Lancet & BMJ.

Usage

getDescriptionStatsBy(x, by, digits = 1, html = FALSE, NEJMstyle = FALSE,
  numbers_first = TRUE, statistics = FALSE, sig.limit = 10^-4,
  two_dec.limit = 10^-2, show_missing = FALSE,
  show_missing_digits = digits, continuous_fn = describeMean,
  prop_fn = describeProp, factor_fn = describeFactors,
  show_all_values = FALSE, hrzl_prop = FALSE, add_total_col,
  total_col_show_perc = TRUE, use_units = FALSE, default_ref = "First",
  percentage_sign = TRUE)

Arguments

x	The variable that you want the statistics for
by	The variable that you want to split into different columns
digits	The number of decimals used
html	If HTML compatible output shoudl be used instead of default LaTeX
NEJMstyle	Adds - no (%) at the end to proportions
numbers_first	If the number should be given or if the percentage should be presented first. The second is encapsulated in parentheses ().
statistics	Add statistics, fisher test for proportions and Wilcoxon for continuous variables
sig.limit	The significance limit for < sign, i.e. p-value 0.0000312 should be < 0.0001 with the default setting.
two_dec.limit	The limit for showing two decimals .
show_missing	Show the missing values. This adds another row if there are any missing values.
show_missing_digits	The number of digits to use for the missing percentage, defaults to the overall digits.
continuous_fn	The method to describe continuous variables. The default is describeMean.
prop_fn	The method used to describe proportions, see describeProp.
factor_fn	The method used to describe factors, see describeFactors.
show_all_values	This is by default false as for instance if there is no missing and there is only one variable then it is most sane to only show one option as the other one will just be a complement to the first. For instance sex - if you know gender then automatically you know the distribution of the other sex as it's 100 % - other %. To choose which one you want to show then set the default_ref parameter.
hrzl_prop	This is default FALSE and indicates that the proportions are to be interpreted in a vertical manner. If we want the data to be horizontal, i.e. the total should be shown and then how these differ in the different groups then set this to TRUE.
add_total_col	This adds a total column to the resulting table. You can also specify if you want the total column "first" or "last" in the column order.
total_col_show_perc	This is by default true but if requested the percentages are surpressed as this sometimes may be confusing.
use_units	If the Hmisc package's units() function has been employed it may be interesting to have a column at the far right that indicates the unit measurement. If this column is specified then the total column will appear before the units (if specified as last).
default_ref	If you use proportions with only one variable, i.e. not show_all_values, then it can be useful to set the reference level that is of interest to show. This can wither be "First", level name or level number.
percentage_sign	If you want to surpress the percentage sign you can set this variable to FALSE. You can also choose something else that the default % if you so wish by setting this variable.

Value

Returns a vector if vars wasn't specified and it's a continuous or binary statistic. If vars was a matrix then it appends the result to the end of that matrix. If the x variable is a factor then it does not append and you get a warning.

See Also

describeMean, describeProp, describeFactors, htmlTable

Examples

data(mtcars)

label(mtcars$mpg) <- "Gas"
units(mtcars$mpg) <- "Miles/(US) gallon"

label(mtcars$wt) <- "Weight"
units(mtcars$wt) <- "10<sup>3</sup> kg" # not sure the unit is correct

mtcars$am <- factor(mtcars$am, levels=0:1, labels=c("Automatic", "Manual"))
label(mtcars$am) <- "Transmission"

mtcars$gear <- factor(mtcars$gear)
label(mtcars$gear) <- "Gears"

# Make up some data for making it slightly more interesting
mtcars$col <- factor(sample(c("red", "black", "silver"),
                     size=NROW(mtcars), replace=TRUE))
label(mtcars$col) <- "Car color"

mpg_data <- getDescriptionStatsBy(mtcars$mpg, mtcars$am,
                                  use_units = TRUE,
                                  html = TRUE)
wt_data <- getDescriptionStatsBy(mtcars$wt, mtcars$am,
                                 use_units = TRUE,
                                 html = TRUE)

htmlTable(
  rbind(mpg_data, wt_data),
  caption  = "Continuous & binary variables",
  headings = c(sprintf("%s (SD)", levels(mtcars$am)), "Units"),
  rowlabel = "Variable",
  ctable   = TRUE)

gear_data <- getDescriptionStatsBy(mtcars$gear, mtcars$am)
col_data <- getDescriptionStatsBy(mtcars$col, mtcars$am)

htmlTable(rbind(gear_data, col_data),
  caption  = "Factored variables",
  colheads = sprintf("%s (%%)", levels(mtcars$am)),
  rowlabel = "Variable",
  rgroup   = c(label(gear_data),
               label(col_data)),
  n.rgroup = c(NROW(gear_data),
               NROW(col_data)),
  ctable   = TRUE)

# A little more advanced
mtcars$mpg[sample(1:NROW(mtcars), size=4)] <- NA
getDescriptionStatsBy(mtcars$mpg, mtcars$am, statistics=TRUE,
                      show_missing=TRUE)

# Do the horizontal version
getDescriptionStatsBy(mtcars$col, mtcars$am, statistics=TRUE,
                      show_missing=TRUE, hrzl_prop = TRUE)

mtcars$wt_with_missing <- mtcars$wt
mtcars$wt_with_missing[sample(1:NROW(mtcars), size=8)] <- NA
getDescriptionStatsBy(mtcars$wt_with_missing, mtcars$am, statistics=TRUE,
                      show_missing=TRUE, hrzl_prop = TRUE, total_col_show_perc = FALSE)


mtcars$col_with_missing <- mtcars$col
mtcars$col_with_missing[sample(1:NROW(mtcars), size=5)] <- NA
getDescriptionStatsBy(mtcars$col_with_missing, mtcars$am, statistics=TRUE,
                      show_missing=TRUE, hrzl_prop = TRUE, total_col_show_perc = FALSE)

raredd/Gmisc0 documentation built on May 27, 2019, 2:02 a.m.