# Package etable.

## Introduction

The main purpose of this package is to create descriptive tables for various subgroups in a quick and easy way. Most of the statistics can also be calculated using weights. Below are some examples of packet functionality using artificial data.

## Simple tables

With the predifined cell-function iqr_cell one can generate a simple table with interquartile range of a variable. It calculates the median, Q1 and Q3 for bmi variable in data.frame d. The variable to be analysed is selected by setting x_vars='bmi'. With rows='sex' the factor to separate the table by rows is selected. Parameter rnames='Sex' set the label for the row groups.

tab <- tabular.ade(x_vars='bmi', rows='sex', rnames='Sex', data=d, FUN=iqr_cell)
knitr::kable(tab, caption='Median (Q1/Q3) of BMI')
Median (Q1/Q3) of BMI
1 2
Sex
Men 31.2 (27.6/35.2)
Women 31.4 (27.8/35.2)

### A simple 2 x 2 table

For a simple 2 x 2 table, the second separation factor for columns needs to be specified, what is done with cols='ethnic' and cnames='Ethnicity'.

tab<-tabular.ade(x_vars='bmi', rows='sex', rnames='Sex', cols='ethnic', cnames='Ethnicity', data=d, FUN=iqr_cell)
knitr::kable(tab, caption='Median (Q1/Q3) of BMI')
Median (Q1/Q3) of BMI
1 2 3 4
Sex
Ethnicity Other Caucasian
Men 31.2 (27.6/35.2) 31.3 (27.6/35.2)
Women 31.5 (27.8/35.2) 31.3 (27.9/35.2)

### A n(nested) x 2 table

More than one factor at once can be used for rows or columns to create nested tables using rows=c('sex', 'dec'), rnames=c('Sex', 'Decades').

knitr::kable(tab, caption='Median (Q1/Q3) of BMI')
Median (Q1/Q3) of BMI
1 2 3 4 5
Ethnicity Other Caucasian
Men (20,30] 31.5 (28.1/35.5) 30.9 (27.5/35.1)
(30,40] 31.4 (27.4/35.3) 31.3 (27.5/34.9)
(40,50] 31.1 (27.7/35.4) 31.1 (27.9/35.1)
(50,60] 31.9 (27.7/35.2) 31.3 (27.7/34.9)
(60,70] 31.2 (27.6/35.1) 31.4 (27.5/35.7)
(70,80] 30.1 (26.7/34.4) 31.4 (27.7/35.2)
Women (20,30] 30.9 (26.8/35.8) 31.1 (27.6/35.8)
(30,40] 31.6 (27.5/34.9) 31.1 (27.7/34.8)
(40,50] 31.9 (27.8/36.0) 31.6 (28.0/35.5)
(50,60] 31.0 (27.9/34.7) 31.3 (28.2/35.5)
(60,70] 31.8 (28.2/35.0) 31.4 (27.8/34.9)
(70,80] 31.7 (28.8/35.6) 31.2 (27.9/34.6)

### A n x n nested table

The cell function n_cell returns the number of non-missing observations in each cell. Missing values of x_vars variable will be excluded.

knitr::kable(tab, caption='N of Obs.')
N of Obs.
1 2 3 4 5 6 7
Sex Men Women
Ethnicity Other Caucasian Other Caucasian
(20,30] (14.8,27.7] 47 173 64 155
(27.7,31.3] 64 167 45 150
(31.3,35.2] 57 150 42 125
(35.2,65.2] 58 162 53 164
(30,40] (14.8,27.7] 50 172 57 157
(27.7,31.3] 37 144 48 169
(31.3,35.2] 48 164 67 161
(35.2,65.2] 46 149 50 146
(40,50] (14.8,27.7] 63 156 53 145
(27.7,31.3] 65 180 42 158
(31.3,35.2] 55 153 56 157
(35.2,65.2] 64 161 63 173
(50,60] (14.8,27.7] 55 137 47 144
(27.7,31.3] 48 139 55 171
(31.3,35.2] 63 142 57 152
(35.2,65.2] 56 131 44 162
(60,70] (14.8,27.7] 49 167 47 151
(27.7,31.3] 52 135 55 160
(31.3,35.2] 46 145 58 177
(35.2,65.2] 48 168 50 148
(70,80] (14.8,27.7] 60 150 39 141
(27.7,31.3] 50 140 54 160
(31.3,35.2] 33 154 57 163
(35.2,65.2] 44 154 56 132

### A n x 1 table

With the cell function quantile_cell, quantiles can be calculated. The parameter probs defines which quantile should be calculated.

tab<-tabular.ade(x_vars='bmi', xname='BMI', rows=c('sex','ethnic','disease','treat'), rnames=c('Sex', 'Ethnicity', 'Disease', 'Treatment'), data=d, FUN=quantile_cell, probs=0.95)
knitr::kable(tab, caption='95th quantile of BMI')
95th quantile of BMI
1 2 3 4 5
Sex Ethnicity Disease Treatment
Men Other no no 42.0
yes 41.9
yes no 43.0
yes 44.8
Caucasian no no 41.4
yes 42.8
yes no 43.2
yes 42.0
Women Other no no 41.3
yes 41.7
yes no 42.4
yes 39.0
Caucasian no no 41.8
yes 41.2
yes no 40.4
yes 43.7

## Predefined cell functions

There are several predefined cell functions in this package. See the help pages for more information. The stat_cell function includes a wide range of statistics and is the most usefull cell function of all.

• stat_cell basic parameters, digits=3, digits2=1
• combi_cell basic parameters, digits=3, style=1
• mean_sd_cell basic parameters, digits=3, style=1, nsd=1
• iqr_cell basic parameters, digits=3, add_n=FALSE
• quantile_cell basic parameters, digits=3, probs=0.5, plabels=FALSE
• eventpct_cell basic parameters, digits=1, digits2=0, event=2, type=1
• miss_cell basic parameters, digits=0, pct=FALSE, prefix="“, suffix=”"
• corr_p_cell basic parameters, digits=3
• mode_cell basic parameters, digits=3

Basic parameters are x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min. Each cell function must take these parameters. They will be automatically passed from tabular.ade function. Most of the functions use only the x variable for calculations and w for weighted calculations. Only corr_p_cell uses y variable. Additional parameters like digits = 3 can be used in tabular.ade( , ...) instead of the points.

## Writing custom cell function

There is a possibility to write custom cell function. It allows all possible designs of the cell and much more.

### An example of a custom cell function

my_cell<- function(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min)
{
out<- format(mean(x[cell_ids], na.rm=TRUE), digits = 3)
return(out)
}

knitr::kable(tab, caption='Mean Age')
Mean Age
1 2 3 4 5 6 7 8
Sex
Decades (20,30] (30,40] (40,50] (50,60] (60,70] (70,80]
Men 25.4 35.6 45.5 55.6 65.7 75.5
Women 25.4 35.4 45.4 55.2 65.5 75.2

### Another simple example of custom cell function

my_cell<- function(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min)
{
out<- NULL
tab<-table(x[cell_ids])
for(i in 1:length(tab)){
out<- paste(out, levels(x)[i],': ' ,tab[i], sep='')
if(i<length(tab)) out<- paste(out, ', ', sep='')
}
return(out)
}

knitr::kable(tab, caption='Frequencies')
Frequencies
1 2 3 4 5
Stage 1 2 3
(20,30] Men: 444, Women: 408 Men: 341, Women: 307 Men: 93, Women: 83
(30,40] Men: 404, Women: 418 Men: 341, Women: 357 Men: 65, Women: 81
(40,50] Men: 441, Women: 432 Men: 373, Women: 331 Men: 83, Women: 84
(50,60] Men: 369, Women: 427 Men: 330, Women: 330 Men: 72, Women: 75
(60,70] Men: 400, Women: 426 Men: 323, Women: 339 Men: 87, Women: 81
(70,80] Men: 391, Women: 365 Men: 321, Women: 347 Men: 73, Women: 90

### More complicated cell function example

b_cell<- function(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min)
{
out<- NULL
if(length(unique(x))==2){
lv<-levels(x)
n <-sum(x[cell_ids]==lv[2])
N <-sum(table(x[cell_ids]))
out<-paste(levels(x)[2], ': ',format((n/N)*100, digits=3),'% (N:',n , ')',sep='')
}
if(!is.factor(x) & length(unique(x))> 2){
quant <- format(quantile(x[cell_ids], c(0.25, 0.5, 0.75), na.rm=TRUE), digits=3)
out<- paste(quant[1], ' (',quant[2],'/',quant[3],')', sep='')
}
if(is.factor(x) & length(unique(x))> 2){
lv<-levels(x)
n <-table(x[cell_ids])
N <-sum(table(x[cell_ids]))
out<- paste(lv, ': ', format((n/N)*100,  digits=3), '%', collapse=' | ', sep='')
}
return(out)
}

knitr::kable(tab, caption='Diverse variables')
Diverse variables
1 2 3 4
Sex Men Women
BMI 27.6 (31.2/35.2) 27.8 (31.4/35.2)
Ethnicity Caucasian: 74.5% (N:3713) Caucasian: 74.8% (N:3754)
Stages 1: 49.41% | 2: 41.04% | 3: 9.56% 1: 49.67% | 2: 40.45% | 3: 9.88%

### A T-test function, usage of x and y variables

t_test_cell<- function(x, y, z, w, cell_ids, row_ids, col_ids, vnames, vars, n_min)
{
v <- x[cell_ids]
group <- y[cell_ids]
test<-t.test(v[which(group==levels(group)[1])], v[which(group==levels(group)[2])])
mdiff<- format(diff(test$estimate), digits=3) p<- base:::format.pval(test$p.value, digits=2, eps=0.0001)
out<- paste('Diff: ', mdiff, ', p-value: ', p, sep='')
return(out)
}

knitr::kable(tab, caption='T-test for BMI between Ethnicity groups')
T-test for BMI between Ethnicity groups
1 2 3 4
Sex Men Women
(20,30] Diff: -0.483, p-value: 0.24 Diff: 0.302, p-value: 0.53
(30,40] Diff: -0.194, p-value: 0.71 Diff: -0.305, p-value: 0.47
(40,50] Diff: 0.171, p-value: 0.7 Diff: -0.0821, p-value: 0.86
(50,60] Diff: -0.442, p-value: 0.33 Diff: 0.185, p-value: 0.69
(60,70] Diff: -0.15, p-value: 0.76 Diff: -0.0931, p-value: 0.83
(70,80] Diff: 0.769, p-value: 0.13 Diff: -0.594, p-value: 0.19

### Multiple x or y variables

There is a possibility to pass more than one variable to x_vars or x_vars parameters. In this way a correlation matrix can be created.

vars    <-c('age', 'weight', 'height', 'bmi')
vlabels <-c('Age', 'Weight', 'Height', 'BMI')

tab<-tabular.ade(x_vars=vars, xname=vlabels, y_vars=vars, yname=vlabels,data=d, FUN=corr_p_cell, digits=2)
knitr::kable(tab, caption='Pearson correlation')
Pearson correlation
1 2 3 4 5
Age Weight Height BMI
Age 1.00 0.01 0.00 0.01
Weight 0.01 1.00 0.01 0.69
Height 0.00 0.01 1.00 -0.71
BMI 0.01 0.69 -0.71 1.00

### Multiple x with nested columns

If there are multiple x variables, then they are listed line by line.

vars    <-c('age', 'weight', 'height', 'bmi')
vlabels <-c('Age', 'Weight', 'Height', 'BMI')

tab<-tabular.ade(x_vars=vars, xname=vlabels, cols=c('sex','stage'), cnames=c('Sex','Stage'), data=d, FUN=quantile_cell)
knitr::kable(tab, caption='Medians')
Medians
1 2 3 4 5 6 7 8
Sex Men Women
Stage 1 2 3 1 2 3
Age 49.0 49.0 50.0 50.0 51.0 50.0
Weight 80.1 79.7 80.4 80.2 80.5 80.5
Height 1.60 1.60 1.59 1.60 1.60 1.60
BMI 31.2 31.3 31.4 31.3 31.4 31.5

## Complex tables

### The ALL keyword

The keyword ALL, after a factor in rows or cols statement, adds a row for overall sample.

tab<-tabular.ade(x_vars='sex', rows=c('treat', 'ALL'), rnames=c('Treatment'), cols=c('disease', 'ALL'), cnames=c('Disease'), data=d, FUN=n_cell, alllabel='both')
knitr::kable(tab, caption='Contingency table')
Contingency table
1 2 3 4 5
Treatment
Disease no yes both
no 7159 833 7992
yes 1798 210 2008
both 8957 1043 10000

### Weighted tables

Most of the predefined cell functions support weighting with w=weights parameter. This way weighted statistics can be calculated.

tab<-tabular.ade(x_vars='sex', rows=c('sex', 'ALL', 'ethnic', 'stage'), rnames=c('Sex','Ethnicity', 'Stage'), w='ws', data=d, FUN=n_cell, digits=1)
knitr::kable(tab, caption='weighted N')
weighted N
1 2 3 4
Sex Ethnicity Stage
Men Other 1 486.1
2 408.0
3 95.8
Caucasian 1 1459.6
2 1224.7
3 283.6
Women Other 1 504.5
2 413.3
3 107.0
Caucasian 1 1504.3
2 1212.1
3 295.2
Total Other 1 990.5
2 821.2
3 202.7
Caucasian 1 2963.9
2 2436.7
3 578.8

### Various statistics in a table

The predefined cell functions stat_cell can calculate several statistics at once. The statistics are set using keywords in x_vars or y_vars parameters.

vars    <-c('age', 'weight', 'height', 'bmi')
vlabels <-c('Age', 'Weight', 'Height', 'BMI')

keywords  <-c('MIN', 'MAX', 'MEAN', 'SD', 'CV', 'SKEW',     'KURT')
keylabels <-c('Min', 'Max', 'Mean', 'SD', 'CV', 'Skewness', 'Kurtosis')

tab<-tabular.ade(x_vars=vars, xname=vlabels, y_vars=keywords, yname=keylabels, data=d, FUN=stat_cell)
knitr::kable(tab, caption='Various statistics')
Various statistics
1 2 3 4 5 6 7 8
Min Max Mean SD CV Skewness Kurtosis
Age 20.0 80.0 49.9 17.4 0.348 0.0150 -1.20
Weight 35.2 115 80.0 10.0 0.125 -0.0475 0.0314
Height 1.19 2.02 1.60 0.101 0.0630 0.0107 0.0500
BMI 14.8 65.2 31.7 5.69 0.180 0.484 0.671

### Various statistics combined with rows parameter

keywords  <-c('N', 'MIN', 'MAX', 'MEAN', 'SD')
keylabels <-c('N', 'Min', 'Max', 'Mean', 'SD')

tab<-tabular.ade(x_vars=vars, xname=vlabels, y_vars=keywords, yname=keylabels, rows=c('sex','ALL','ethnic'), rnames=c('Sex','Ethnicity'), data=d, FUN=stat_cell)
knitr::kable(tab, caption='Various statistics')
Various statistics
1 2 3 4 5 6 7 8
Sex Ethnicity
N Min Max Mean SD
Age Men Other 1268 20.0 80.0 49.7 17.2
Caucasian 3713 20.0 80.0 49.8 17.6
Women Other 1265 20.0 80.0 50.1 17.3
Caucasian 3754 20.0 80.0 50.1 17.2
Total Other 2533 20.0 80.0 49.9 17.3
Caucasian 7467 20.0 80.0 50.0 17.4
Weight Men Other 1268 44.2 110 80.0 9.98
Caucasian 3713 41.1 114 79.8 10.1
Women Other 1265 43.1 111 80.5 10.0
Caucasian 3754 35.2 115 80.0 9.97
Total Other 2533 43.1 111 80.2 10.0
Caucasian 7467 35.2 115 79.9 10.0
Height Men Other 1268 1.19 1.98 1.60 0.103
Caucasian 3713 1.23 2.02 1.60 0.101
Women Other 1265 1.24 1.98 1.60 0.0989
Caucasian 3754 1.21 1.95 1.60 0.101
Total Other 2533 1.19 1.98 1.60 0.101
Caucasian 7467 1.21 2.02 1.60 0.101
BMI Men Other 1268 15.0 58.7 31.7 5.88
Caucasian 3713 15.8 65.2 31.6 5.68
Women Other 1265 15.5 64.9 31.8 5.64
Caucasian 3754 14.8 58.6 31.7 5.66
Total Other 2533 15.0 64.9 31.7 5.76
Caucasian 7467 14.8 65.2 31.7 5.67

### An example using the statistic keywords in x_vars parameter

keywords  <-c('N', 'MIN', 'MAX', 'MEAN', 'SD')
keylabels <-c('N', 'Min', 'Max', 'Mean', 'SD')

tab<-tabular.ade(x_vars=keywords, xname=keylabels, y_vars=vars, yname=vlabels, rows=c('sex', 'ALL'), rnames=c('Sex'),data=d, FUN=stat_cell)
knitr::kable(tab, caption='Various statistics')
Various statistics
1 2 3 4 5 6
Sex
Age Weight Height BMI
N Men 4981 4981 4981 4981
Women 5019 5019 5019 5019
Total 10000 10000 10000 10000
Min Men 20.0 41.1 1.19 15.0
Women 20.0 35.2 1.21 14.8
Total 20.0 35.2 1.19 14.8
Max Men 80.0 114 2.02 65.2
Women 80.0 115 1.98 64.9
Total 80.0 115 2.02 65.2
Mean Men 49.8 79.9 1.60 31.6
Women 50.1 80.1 1.60 31.7
Total 49.9 80.0 1.60 31.7
SD Men 17.5 10.0 0.101 5.73
Women 17.3 9.98 0.100 5.66
Total 17.4 10.0 0.101 5.69

### And finally, an example of a weighted, multivariable, nested table with several statistics

vars    <-c('age', 'weight', 'height', 'bmi')
vlabels <-c('Age', 'Weight', 'Height', 'BMI')

keywords  <-c('N', 'MEDIAN', 'IQR')
keylabels <-c('N', 'Median', 'IQR')

tab<-tabular.ade(x_vars=vars, xname=vlabels, y_vars=keywords, yname=keylabels, rows=c('sex', 'ALL'), rnames=c('Sex'),cols=c('ethnic'),cnames=c('Ethnicity'),w='ws',data=d,FUN=stat_cell)
knitr::kable(tab, caption='Various statistics')
Various statistics
1 2 3 4 5 6 7 8 9
Sex
N Median IQR
Ethnicity Other Caucasian Other Caucasian Other Caucasian
Age Men 990 2968 49.0 49.0 29.0 31.0
Women 1025 3012 50.0 50.0 30.0 29.0
Total 2015 5979 50.0 49.0 30.0 30.0
Weight Men 990 2968 80.3 80.1 13.2 13.3
Women 1025 3012 80.6 80.2 12.9 13.3
Total 2015 5979 80.5 80.2 13.2 13.4
Height Men 990 2968 1.60 1.60 0.140 0.137
Women 1025 3012 1.60 1.60 0.122 0.139
Total 2015 5979 1.60 1.60 0.132 0.138
BMI Men 990 2968 31.2 31.3 7.99 7.50
Women 1025 3012 31.4 31.3 7.53 7.18
Total 2015 5979 31.3 31.3 7.77 7.35