tabular.ade: Tabular representation of a wide selection of statistics
In etable: Easy Table

Description Usage Arguments Details Value Author(s) Examples

View source: R/tabular.ade.R

Creates simple to highly customized tables for a wide selection of descriptive statistics, with or without weighting the data.

tabular.ade(x_vars, xname=NULL, y_vars=NULL, yname=NULL,
            z_vars=NULL, zname=NULL,
            rows=NULL, rnames=NULL, cols=NULL, cnames=NULL, w=NULL,
            data=NULL, FUN, allnames=FALSE, nonames=TRUE, alllabel='Total',
            inset='?', remove='', n_min=0, ...)

`x_vars`	This variable will be used to calculate the statistics for it. a character string with the name of the variable in the data.frame a vector of character strings with names of variables in data.frame
`xname`	Labels for x. a character string with the label for x a vector of character strings with labels for x, with the same length as x.
`y_vars`	This variable can be used to calculate bivariable statistics. a character string with the name of the variable in the data.frame a vector of character strings with names of variables in data.frame
`yname`	Labels for y. a character string with the label for y a vector of character strings with labels for y, with same length as x.
`z_vars`	This variable can be used for additional calculations. a character string with the name of the variable in the data.frame
`zname`	Labels for z. a character string with the label for y
`rows`	These factors will be used to separate the rows of the table in subgroups. a character string with the name of the factor variable in the data.frame a vector of character strings with names of factor variables in data.frame (max 6) a vector with names of factors and/or Keyword 'ALL' adds extra overall group for leading factor.
`rnames`	Labels for rows. a character string with the label for rows a vector of character strings with labels for rows, with same length as rows. a vector with names of factors and/or keyword 'ALL' adds extra overall group for leading factor.
`cols`	These factors will be used to separate the columns of the table in subgroups. a character string with the name of the factor variable in the data.frame a vector of character strings with names of factor variables in data.frame (max 6)
`cnames`	Labels for cols. a character string with the label for cols a vector of character strings with labels for rows, with same length as cols.
`w`	This numeric variable will be used to weight the table. a character string with the name of the factor variable in the data.frame
`data`	A data frame with all used variables.
`FUN`	An abstract cell function to calculate statistics in every cell of the table. See details.
`allnames`	Logical asking whether to fill every cell with labels or only the first one.
`nonames`	Logical asking whether to use dimnames for variable labels or make all labeling in the table self.
`alllabel`	Label for overall group without splitting in this factor.
`inset`	Inset text in each cell, '?' will be replaced with the value of the cell.
`remove`	Remove a character string from each cell.
`n_min`	min N in each cell, it will be only passed in the cell function. But it is necessary to suppress calculation of statistics using only few values.
`...`	additional parameters passed to the FUN

FUN can be a cell function from this package or a custom cell function.

The custom cell function must take the following parameters, but it is not necessary to use them.

x, The whole x variable.
y, The whole y variable.
z, The whole z variable.
w, The whole w variable.
cell_ids, Index vector to select values that belong in this cell.
row_ids, Index vector to select values that belong in this row.
col_ids, Index vector to select values that belong in this col.
vnames, A vector of length 3, with labels of variables (x,y,z)
vars, A vector of length 3, with names of variables (x,y,z)
n_min , Min needed N for calculation.
... , additional custom parameters.

For an example with simple mean see below.

A character Matrix.(Table)

Andreas Schulz <ades-s@web.de>

# 1) simple own FUN cell function.
s_mean<- function(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min, ds=3){
out<- ''
if(length(cell_ids)>= n_min){
out<-  format(mean(x[cell_ids], na.rm=TRUE), digits=ds)
}
return(out)
}
##########################################
# 2) simple 2 x 2 table of means
sex   <- factor(rbinom(5000, 1, 0.5), labels=c('Men', 'Women'))
age   <- round(runif(5000, 18, 89))
treat <- factor(rbinom(5000, 1, 0.3), labels=c('control', 'treated'))
d<-data.frame(sex, age, treat)

tabular.ade(x_vars='age', xname='Age [y]', rows='sex', rnames='Sex', cols='treat',
cnames='Treatment', data=d, nonames=FALSE, FUN=s_mean)

##########################################
# 3) Relative frequency table
d$dosis <- round(runif(5000, 0.5, 6.49))
tabular.ade(x_vars='age', xname='Age [y]', rows=c('sex', 'treat'),
rnames=c('Sex', 'Treatment'), cols='dosis', cnames='Dosis', data=d, FUN=n_cell,
type='pct')

##########################################
# 4) Weighted median table
d$w <- runif(5000, 0.1, 5)
d$bmi <- rnorm(5000, 30, 3)
tabular.ade(x_vars=c('age', 'bmi'), xname=c('Age', 'BMI'),
cols=c('sex', 'ALL', 'treat'),
cnames=c('Sex', 'Treatment'), w='w', data=d, FUN=quantile_cell)

##########################################
# 5) Correlation table between age and bmi
tabular.ade(x_vars='age', xname='Age', y_vars='bmi', yname='BMI',
rows=c('dosis'), rnames=c('Dosis'), cols=c('sex', 'treat'),
cnames=c('Sex', 'Treatment'), data=d, FUN=corr_p_cell)

##########################################
# 6) Multiple statistics
tabular.ade(x_vars=c('N', 'MEAN', 'SD', 'SKEW', 'KURT', 'RANGE'),
y_vars=c('age', 'bmi'), yname=c('Age', 'BMI'),
cols=c('sex', 'ALL', 'treat'), cnames=c('Sex', 'Treatment'),
w='w', data=d, FUN=stat_cell)