Easy Table

Description

The package comes without any warranty.

Details

Author(s)

Andreas Schulz
Maintainer: <ades-s@web.de>

Dichotomous and continuous variable combination cell function

Description

Calculates different statistics depending on the type of variable.

Usage

combi_cell(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min,
              digits=3, style=1)

Arguments

Author(s)

Andreas Schulz <ades-s@web.de>

Examples

sex     <- factor(rbinom(1000, 1, 0.4),  labels=c('Men', 'Women'))
height  <- rnorm(1000, mean=1.7, sd=0.1)
weight  <- rnorm(1000, mean=70, sd=5)
bmi     <- weight/height^2
event   <- factor(rbinom(1000, 1, 0.1), labels=c('no',  'yes'))
d<-data.frame(sex, height, weight, bmi, event)

tabular.ade(x_vars=names(d), cols=c('sex','ALL'), rnames=c('Gender'),
            data=d, FUN=combi_cell)

Correlation cell function

Description

Calculating Pearson product-moment correlation coefficient.

Usage

       corr_p_cell(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min,
       digits = 3)

Arguments

Author(s)

Andreas Schulz <ades-s@web.de>

Examples

sex     <- factor(rbinom(1000, 1, 0.4),  labels=c('Men', 'Women'))
height  <- rnorm(1000, mean=1.70, sd=0.1)
weight  <- rnorm(1000, mean=70, sd=5)
bmi     <- weight/height^2
d<-data.frame(sex, bmi, height, weight)

tabular.ade(x_vars=c('bmi','height','weight'), xname=c('BMI','Height','Weight'),
            y_vars=c('bmi','height','weight'), yname=c('BMI','Height','Weight'),
            rows=c('sex','ALL'), rnames=c('Gender'), data=d, FUN=corr_p_cell)

Factor level frequencies cell function

Description

Calculates frequencies or proportions of a certain level of factor x.

Usage

      eventpct_cell(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min,
      digits=1, digits2=0, event=2, type=1)

Arguments

Author(s)

Andreas Schulz <ades-s@web.de>

Examples

sex     <- factor(rbinom(1000, 1, 0.4), labels=c('Men', 'Women'))
event   <- factor(rbinom(1000, 1, 0.1), labels=c('no',  'yes'))
decades <- rbinom(1000, 3, 0.5)
decades <- factor(decades, labels=c('[35,45)','[45,55)','[55,65)','[65,75)'))
d<-data.frame(sex, decades, event)

tabular.ade(x_vars=c('event'), xname=c('Event'),
   rows=c('sex','ALL'), rnames=c('Gender'),
   cols=c('decades', 'ALL'),   cnames=c('Age decades'),
   data=d, FUN=eventpct_cell)

Median IQR cell function.

Description

For calculate median and interquartile range. (weighting is possible)

Usage

          iqr_cell(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min,
          digits = 3, add_n=FALSE)

Arguments

Author(s)

Andreas Schulz <ades-s@web.de>

Examples

sex     <- factor(rbinom(1000, 1, 0.4),  labels=c('Men', 'Women'))
height  <- rnorm(1000, mean=1.66, sd=0.1)
height[which(sex=='Men')]<-height[which(sex=='Men')]+0.1
weight  <- rnorm(1000, mean=70, sd=5)
decades <- rbinom(1000, 3, 0.5)
decades <- factor(decades, labels=c('[35,45)','[45,55)','[55,65)','[65,75)'))
d<-data.frame(sex, decades, height, weight)

tabular.ade(x_vars=c('height', 'weight'), xname=c('Height [m]','Weight [kg]'),
   rows=c('sex','ALL'), rnames=c('Gender'),
   cols=c('decades'),   cnames=c('Age decades'),
   data=d, FUN=iqr_cell, add_n=TRUE)

Mean and SD cell function

Description

Calculates mean and SD or weighted mead and SD.

Usage

mean_sd_cell(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min,
             digits = 3, style=1, nsd=1)

Arguments

Author(s)

Andreas Schulz <ades-s@web.de>

Examples

sex     <- factor(rbinom(1000, 1, 0.4),  labels=c('Men', 'Women'))
height  <- rnorm(1000, mean=1.66, sd=0.1)
height[which(sex=='Men')]<-height[which(sex=='Men')]+0.1
weight  <- rnorm(1000, mean=70, sd=5)
decades <- rbinom(1000, 3, 0.5)
decades <- factor(decades, labels=c('[35,45)','[45,55)','[55,65)','[65,75)'))
d<-data.frame(sex, decades, height, weight)

tabular.ade(x_vars=c('height', 'weight'), xname=c('Height [m]','Weight [kg]'),
   rows=c('sex','ALL'), rnames=c('Gender'),
   cols=c('decades'),   cnames=c('Age decades'),
   data=d, FUN=mean_sd_cell, style=2, nsd=1.96)

Missing values cell function

Description

Counting the number of missing values in each cell.

Usage

miss_cell(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min,
          pct = FALSE, digits = 0, prefix='', suffix='')

Arguments

Author(s)

Andreas Schulz <ades-s@web.de>

Examples

sex     <- factor(rbinom(1000, 1, 0.4),  labels=c('Men', 'Women'))
height  <- rnorm(1000, mean=1.66, sd=0.1)
height[which(sex=='Men')]<-height[which(sex=='Men')]+0.1
weight  <- rnorm(1000, mean=70, sd=5)
decades <- rbinom(1000, 3, 0.5)
decades <- factor(decades, labels=c('[35,45)','[45,55)','[55,65)','[65,75)'))
d<-data.frame(sex, decades, height, weight)
d$height[round(runif(250,1,1000))]<- NA
d$weight[round(runif(25 ,1,1000))]<- NA

tabular.ade(x_vars=c('height', 'weight'), xname=c('Height [m]','Weight [kg]'),
        cols=c('sex','decades','ALL'), cnames=c('Gender', 'Age decades'),
        data=d, FUN=miss_cell, prefix='Miss:')

Mode cell function

Description

Shows the most frequent value (mode)

Usage

   mode_cell(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min,
          digits=3)

Arguments

Author(s)

Andreas Schulz <ades-s@web.de>

Examples

sex     <- factor(rbinom(1000, 1, 0.4),  labels=c('Men', 'Women'))
note    <- as.factor(rbinom(1000, 4, 0.5)+1)
decades <- rbinom(1000, 3, 0.5)
decades <- factor(decades, labels=c('[35,45)','[45,55)','[55,65)','[65,75)'))
d<-data.frame(sex, decades, note)

tabular.ade(x_vars=c('note'), xname=c('Noten'),
       rows=c('sex','ALL','decades'), rnames=c('Gender', 'Age decades'),
       data=d, FUN=mode_cell)

Frequency Cell FUN

Description

For calculation of relative or absolute frequencies.

Usage

   n_cell(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min,
          digits=0, digits2=1, type="n")

Arguments

Details

The function calculate frequencies for cell. If x has no missing values the frequencies are independent of x. Missing values in x will be removed from calculation.

Author(s)

Andreas schulz <ades-s@web.de>

Examples

sex     <- factor(rbinom(1000, 1, 0.4),  labels=c('Men', 'Women'))
decades <- rbinom(1000, 3, 0.5)
decades <- factor(decades, labels=c('[35,45)','[45,55)','[55,65)','[65,75)'))
d<-data.frame(sex, decades)

tabular.ade(x_var='sex',  rows=c('sex',     'ALL'), rnames=c('Gender'),
                          cols=c('decades', 'ALL'), cnames=c('Age decades'),
            data=d, FUN=n_cell, , type="all")

Quantile cell function

Description

Calculating simple or weighted quantiles

Usage

quantile_cell(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min,
              digits = 3, probs = 0.5, plabels=FALSE)

Arguments

Author(s)

Andreas Schulz <ades-s@web.de>

Examples

sex     <- factor(rbinom(1000, 1, 0.4),  labels=c('Men', 'Women'))
height  <- rnorm(1000, mean=1.66, sd=0.1)
height[which(sex=='Men')]<-height[which(sex=='Men')]+0.1
weight  <- rnorm(1000, mean=70, sd=5)
decades <- rbinom(1000, 3, 0.5)
decades <- factor(decades, labels=c('[35,45)','[45,55)','[55,65)','[65,75)'))
d<-data.frame(sex, decades, height, weight)

tabular.ade(x_vars=c('height', 'weight'), xname=c('Height [m]','Weight [kg]'),
   rows=c('sex',     'ALL'), rnames=c('Gender'),
   cols=c('decades', 'ALL'), cnames=c('Age decades'),
   data=d, FUN=quantile_cell, probs = 0.99)

Diverse statistics cell function

Description

Calculating values of several descriptive statistics.

Usage

       stat_cell(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min,
       digits = 3, digits2=1)

Arguments

Details

Keywords are:

• N: number in this cell

• MIN: minimum

• MAX: maximum

• SUM: sum

• MEAN: mean

• SD: standard deviation

• MSD: mean, standard deviation

• MCI: mean, 95% CI

• VAR: variance

• MEDIAN: median

• MD: mean deviation from the mean (*1.253)

• MAD: median absolute deviation (*1.4826)

• IQR: interquartile range

• MQQ: median (Q1/Q3)

• PROP: proportion

• POP: proportion of level 2 (only binar)

• PCI: proportion of level 2, 95% CI

• RANGE: range

• CV: coefficient of variation

• MODE: mode

• MISS: number of missing values

• PNM: proportion of non missing values

• COMB: POP for binar and MQQ for continues

• SKEW: skewness

• KURT: excess kurtosis

• GEO: geometric mean

• HARM: harmonic mean

• TM1: truncated mean 1%

• TM5: truncated mean 5%

• TM10: truncated mean 10%

• TM25: truncated mean 25%

• WM1: winsorized mean 1%

• WM5: winsorized mean 5%

• WM10: winsorized mean 10%

• WM25: winsorized mean 25%

• M1SD: mean-SD, mean+SD

• M2SD: mean-2SD, mean+2SD

• M3SD: mean-3SD, mean+3SD

• MM1SD: mean, mean-SD, mean+SD

• MM2SD: mean, mean-2SD, mean+2SD

• MM3SD: mean, mean-3SD, mean+3SD

• NORM50: mean-0.675SD, mean+0.675SD

• NORM90: mean-1.645SD, mean+1.645SD

• NORM95: mean-1.96SD, mean+1.96SD

• NORM99: mean-2.576SD, mean+2.576SD

• P1: 1th quantile

• P2.5: 2.5th quantile

• P5: 5th quantile

• P10: 10th quantile

• P20: 20th quantile

• P25: 25th quantile

• P30: 30th quantile

• P40: 40th quantile

• P50: 50th quantile

• P60: 60th quantile

• P70: 70th quantile

• P75: 75th quantile

• P80: 80th quantile

• P90: 90th quantile

• P95: 95th quantile

• P97.5: 97.5th quantile

• P99: 99th quantile

Author(s)

Andreas Schulz <ades-s@web.de>

Examples

sex     <- factor(rbinom(1000, 1, 0.4),  labels=c('Men', 'Women'))
height  <- rnorm(1000, mean=1.66, sd=0.1)
height[which(sex=='Men')]<-height[which(sex=='Men')]+0.1
weight  <- rnorm(1000, mean=70, sd=5)
decades <- rbinom(1000, 3, 0.5)
decades <- factor(decades, labels=c('[35,45)','[45,55)','[55,65)','[65,75)'))
d<-data.frame(sex, decades, height, weight)


tabular.ade(x_vars=c('height', 'weight'), xname=c('Height [m]','Weight [kg]'),
   y_vars=c('N', 'MEAN', 'SD', 'SKEW', 'KURT'),
   rows=c('sex', 'ALL', 'decades', 'ALL'), rnames=c('Gender', 'Age decades'),
   data=d, FUN=stat_cell)

Tabular representation of a wide selection of statistics

Description

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

Usage

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, ...)

Arguments

Details

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.

Value

A character Matrix.(Table)

Author(s)

Andreas Schulz <ades-s@web.de>

Examples

# 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)