Package 'etable' reference manual

Title:	Easy Table
Description:	Creates simple to highly customized tables for a wide selection of descriptive statistics, with or without weighting the data.
Authors:	Andreas Schulz [aut, cre]
Maintainer:	Andreas Schulz <[email protected]>
License:	GPL (>= 3)
Version:	1.3.1
Built:	2024-12-16 06:52:59 UTC
Source:	CRAN

Easy Table

Description

The package comes without any warranty.

Details

Package:	etable
Title:	Easy Table
Type:	Package
Version:	1.3.0
Date:	2021-05-23
Depends:	R (>= 3.0.0)
Imports:	xtable, Hmisc
License:	GPL version 3 or newer
LazyLoad:	yes

Author(s)

Andreas Schulz
Maintainer: <[email protected]>

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

Arguments

`x`	The x variable for calculations, if not using y
`y`	The y variable for calculations, if not using x
`z`	NOT USED
`w`	Weights for x or y variable.
`cell_ids`	Index vector for selecting values in cell.
`row_ids`	NOT USED
`col_ids`	NOT USED
`vnames`	NOT USED
`vars`	NOT USED
`n_min`	Minimum n in the cell for useful calculation. Cells with n<n_min deliver no output.
`digits`	Integer indicating the number of significant digits.
`style`	Type of representation. 1 N, Proportion, Median, Q1, Q3 2 N, Proportion, Mean, SD

Author(s)

Andreas Schulz <[email protected]>

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)


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

Arguments

`x`	The x variable
`y`	The y variable
`z`	NOT USED
`w`	Weights for x and y variable.
`cell_ids`	Index vector for selecting values in cell.
`row_ids`	NOT USED
`col_ids`	NOT USED
`vnames`	NOT USED
`vars`	NOT USED
`n_min`	Minimum n in the cell for useful calculation. Cells with n<n_min deliver no output.
`digits`	Integer indicating the number of decimal places.

Author(s)

Andreas Schulz <[email protected]>

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)


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)
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

`x`	The factor x for calculations
`y`	NOT USED
`z`	NOT USED
`w`	Weights for x factor, only if calculating weighted frequencies.
`cell_ids`	Index vector for selecting values in cell.
`row_ids`	NOT USED
`col_ids`	NOT USED
`vnames`	NOT USED
`vars`	NOT USED
`n_min`	Minimum n in the cell for useful calculation. Cells with n<n_min deliver no output.
`digits`	Integer indicating the number of decimal places (for percentages)
`digits2`	Integer indicating the number of decimal places (N, needed if N is not integer because of weighting)
`event`	The Number of factor level to calculate frequencies. from 1 to nlevels(x)
`type`	Type of representation, one of following. 1, pct (n) 2, n (pct) 3, pct 4, n 5, pct (n/N)

Author(s)

Andreas Schulz <[email protected]>

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)


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

Arguments

`x`	The x variable for calculations
`y`	NOT USED
`z`	NOT USED
`w`	Weights for x variable.
`cell_ids`	Index vector for selecting values in cell.
`row_ids`	NOT USED
`col_ids`	NOT USED
`vnames`	NOT USED
`vars`	NOT USED
`n_min`	Minimum n in the cell for useful calculation. Cells with n<n_min deliver no output.
`digits`	Integer indicating the number of significant digits.
`add_n`	Logical asking whether to draw N for each cell.

Author(s)

Andreas Schulz <[email protected]>

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)


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

Arguments

`x`	The x variable for calculations
`y`	NOT USED
`z`	NOT USED
`w`	Weights for x variable.
`cell_ids`	Index vector for selecting values in cell.
`row_ids`	NOT USED
`col_ids`	NOT USED
`vnames`	NOT USED
`vars`	NOT USED
`n_min`	Minimum n in the cell for useful calculation. Cells with n<n_min deliver no output.
`digits`	Integer indicating the number of significant digits.
`style`	Type of representation. 1. mean (sd) 2. mean (mean-sdnsd, mean+sdnsd) 3. mean plus-minus sd
`nsd`	Multiplier for sd in stlyle 2. (for normal distribution) nsd=1 –> 68.27 % values nsd=1.645 –> 90 % values nsd=1.96 –> 95 % values nsd=2 –> 95.45 % values nsd=2.575 –> 99 % values nsd=3 –> 99.73 % values

Author(s)

Andreas Schulz <[email protected]>

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)


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

Arguments

`x`	The x variable
`y`	NOT USED
`z`	NOT USED
`w`	NOT USED (The number of missing will not be weighted!).
`cell_ids`	Index vector for selecting values in cell.
`row_ids`	NOT USED
`col_ids`	NOT USED
`vnames`	NOT USED
`vars`	NOT USED
`n_min`	NOT USED
`pct`	Logical asking whatever to draw absolute or relative frequency of missing values.
`digits`	Integer indicating the number of decimal places.
`prefix`	Free text added in each cell bevor results.
`suffix`	Free text added in each cell after results.

Author(s)

Andreas Schulz <[email protected]>

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:')

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

Arguments

`x`	The x variable
`y`	NOT USED
`z`	NOT USED
`w`	Weights for x variable. Only if calculating weighted mode.
`cell_ids`	Index vector for selecting values in cell.
`row_ids`	Index vector for selecting values in row.
`col_ids`	Index vector for selecting values in col.
`vnames`	NOT USED
`vars`	NOT USED
`n_min`	NOT USED
`digits`	Integer indicating the number of significant digits.

Author(s)

Andreas Schulz <[email protected]>

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)


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

Arguments

`x`	The x variable (can be just 1:N if without missings values)
`y`	NOT USED
`z`	NOT USED
`w`	Weights for x variable. Only if calculating weigted frequences.
`cell_ids`	Index vector for selecting values in cell.
`row_ids`	Index vector for selecting values in row.
`col_ids`	Index vector for selecting values in col.
`vnames`	NOT USED
`vars`	NOT USED
`n_min`	NOT USED
`digits`	Integer indicating the number of decimal places (N)
`digits2`	Integer indicating the number of decimal places (percentages)
`type`	Type of frequencies, one of following. n, number in cell. pct, overall percentages. pctn, overall percentages and n. rowpct, percentages of rows. colpct, percentages of cols. rowpctn, percentages of rows and n. colpctn, percentages of cols and n. all, overall, row, col percentages.

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 <[email protected]>

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")


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)

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

Arguments

`x`	The x variable for calculations
`y`	NOT USED
`z`	NOT USED
`w`	Weights for x variable.
`cell_ids`	Index vector for selecting values in cell.
`row_ids`	NOT USED
`col_ids`	NOT USED
`vnames`	NOT USED
`vars`	NOT USED
`n_min`	Minimum n in the cell for useful calculation. Cells with n<n_min deliver no output.
`digits`	Integer indicating the number of significant digits.
`probs`	A single or a vector of numeric probabilities for sample quantile with values in [0,1].
`plabels`	Logical asking whether to label the quantile in the cell or only draw the value.

Author(s)

Andreas Schulz <[email protected]>

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)


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

Arguments

`x`	The x variable
`y`	NOT USED
`z`	NOT USED
`w`	Weights for x variable.
`cell_ids`	Index vector for selecting values in cell.
`row_ids`	NOT USED
`col_ids`	NOT USED
`vnames`	NOT USED
`vars`	A vector of character strings with names of variables in data.frame for x, y and z. Use names of x or y as keywords, to choose a certain statistic.
`n_min`	Minimum n in the cell for useful calculation. Cells with n<n_min deliver no output.
`digits`	Integer indicating the number of significant digits.
`digits2`	Integer indicating the number of decimal places for percentages.

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 <[email protected]>

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)



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, ...)


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

`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

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 <[email protected]>

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)


# 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)

Package 'etable'

Help Index

Easy Table

Description

Details

Author(s)

Dichotomous and continuous variable combination cell function

Description

Usage

Arguments

Author(s)

Examples

Correlation cell function

Description

Usage

Arguments

Author(s)

Examples

Factor level frequencies cell function

Description

Usage

Arguments

Author(s)

Examples

Median IQR cell function.

Description

Usage

Arguments

Author(s)

Examples

Mean and SD cell function

Description

Usage

Arguments

Author(s)

Examples

Missing values cell function

Description

Usage

Arguments

Author(s)

Examples

Mode cell function

Description

Usage

Arguments

Author(s)

Examples

Frequency Cell FUN

Description

Usage

Arguments

Details

Author(s)

Examples

Quantile cell function

Description

Usage

Arguments

Author(s)

Examples

Diverse statistics cell function

Description

Usage

Arguments

Details

Author(s)

Examples

Tabular representation of a wide selection of statistics

Description

Usage

Arguments

Details

Value

Author(s)

Examples