multirow contingency table in R

Question

Let's consider this data set:

df <- data.frame(age=   sample(c(20:90), 20, rep=T), 
             sex =  sample(c('m', 'f'), 20, rep=T),
             smoker=sample(c("never", "former", "active"), 20, rep=T),
             size=  sample (c(8:40), 20, rep=T),
             fac =  as.factor(sample(c("neg","lo","med","hi"), 20, rep=T)),
             outcome = sample(c(0,1), 20, rep=T)
             )
# let's introduce some missing data         
for (i in (1:3)) {df[sample(c(1:20),1),  sample(c(1:6),1)]  <- NA}

In a medical manuscript the first table summarizes the population (or its subgroups as appropriate); here the rows would be age, sex, smoking status, etc and the two outcomes would be listed in separate columns. The continuous variables are reported as means; the categorical variables as counts.

1. I was wondering if there is a function that I am missing that creates such contingency tables. I can do that manually but would love to be able to automatically update if the data set changes. Ultimately I need to output in latex.
2. the function would need to ignore missing data, but not delete those rows.

Asking too much?!

Answer 1

In medical articles, 'Table 1' summarizes the demographics of the study population, usually broken down between subgroups

Generate data set

n <- 100
df <- data.frame(
age = sample(c(20:90), n, rep = T), 
sex = sample(c("m", "f"), 20, rep = T, prob = c(0.55, 0.45)), 
smoker = sample(c("never", "former", "active"), n, rep = T, prob = c(0.4, 0.45, 0.15)), 
size = abs(rnorm(n, 20, 8)), 
logitest = sample(c(TRUE, FALSE), n, rep = T, prob = c(0.1, 0.9)), 
labtest = as.factor(sample(c("neg", "lo", quot;med",quot;hi"), n, rep = T, prob = c(0.4, 0.3, 0.2, 0.1))), 
outcome = sample(c(0, 1), n, rep = T, prob = c(0.8, 0.2))
)

# let's introduce some missing data
for (i in (1:floor(n/6))) {
    df[sample(c(1:n), 1), sample(c(1:ncol(df)), 1)] <- NA
}
head(df)

##   age sex smoker  size logitest labtest outcome
## 1  70   m former 39.17       NA     med      NA
## 2  51   f former 33.64    FALSE      hi       1
## 3  58   f former 10.10    FALSE     neg       1
## 4  30   m former 43.24    FALSE     med       0
## 5  54   m former 22.78    FALSE      lo       0
## 6  86   f former  8.20    FALSE     neg       0

if working a real data set, use it instead

dx <- 7  #index of outcome/diagnosis
####################################
summary(df[, -dx])

Change this as needed: the column with the diagnosis has to be removed from the variables list!

attach(df)

 ## age sex smoker size logitest ## Min. :20.0 f :44 active:19 Min. : 0.91 Mode :logical ## 1st Qu.:42.5 m :54 former:49 1st Qu.:15.00 FALSE:85 ## Median :58.0 NA's: 2 never :30 Median :20.12 TRUE :12 ## Mean :57.3 NA's : 2 Mean :20.44 NA's :3 ## 3rd Qu.:74.0 3rd Qu.:27.10 ## Max. :88.0 Max. :43.24 ## NA's :1 NA's :2 ## labtest ## hi : 4 ## lo :29 ## med :20 ## neg :45 ## NA's: 2 ## ## 

vars <- colnames(df)
vars

Build list of vars

catvars <- NULL  #categorical variables
contvars <- NULL  #continuous variables
logivars <- NULL  #logic variables

vars <- vars[-dx]
vars

## [1] "age"      "sex"      "smoker"   "size"     "logitest" "labtest"

for (i in 1:length(vars)) {
    ifelse(is.factor(df[, i]), catvars <- c(catvars, vars[i]), ifelse(is.logical(df[, 
        i]), logivars <- c(logivars, vars[i]), contvars <- c(contvars, vars[i])))
}
contvars

## [1] "age"  "size"

catvars

## [1] "sex"     "smoker"  "labtest"

logivars

## [1] "logitest"

 logivars \n

bg <- df[df[, dx] == 0 & !is.na(df[, dx]), ]
nrow(bg)  #; bg

Create the subgroups

mg <- df[df[, dx] == 1 & !is.na(df[, dx]), ]
nrow(mg)  #; mg

## [1] 23

indet <- df[is.na(df[, dx]), ]
nrow(indet)

## [1] 4

indet

##    age sex smoker   size logitest labtest outcome
## 1   70   m former 39.173       NA     med      NA
## 9   87   m former 23.621    FALSE      lo      NA
## 18  65   m former  2.466    FALSE    <NA>      NA
## 67  88   f former 17.575    FALSE     med      NA

 indet \n

normality <- NULL
for (i in 1:length(contvars)) {
    j <- which(vars == contvars[i])  #find position of variable in the original data frame and its subsets
    st <- shapiro.test(df[, j])  #normality testing on all patients, bg and mg alike
    normality <- c(normality, st$p.value)  #normality testing on all patients, bg and mg alike
}
normality

For continuous variables

normality testing

contvarlist <- list(variables = contvars, normality = normality, ttest.by.subgroup = ttpvalue)

 ## [1] 0.00125 0.73602 

comparing the means of two samples; if normal, use t-test, otherwise wilcoxon

proppvalue <- NULL
for (i in 1:length(logivars)) {
    j <- which(vars == logivars[i])  #find position of variable in the original data frame and its subsets
    tbl <- table(df[, j], df[, dx])
    chisqtest <- summary(tbl)
    proppvalue <- c(proppvalue, chisqtest$p.value)
}
proppvalue

## [1] 0.5551

logivarlist = list(variables = logivars, chisq.by.subgroup = proppvalue)

For categorical variables

str(contvarlist)  #if shapiro p<.05 then pop likely NOT normally dist; if t-test p<.05 then pop likely have different means

## List of 3
##  $ variables        : chr [1:2] "age" "size"
##  $ normality        : num [1:2] 0.00125 0.73602
##  $ ttest.by.subgroup: num [1:2] 0.636 0.367

str(catvarlist)  #if chisq p<.05 then variables are likely NOT independent

For logic variables

str(logivarlist)  #if chisq p<.05 then variables are likely NOT independent

## List of 2
##  $ variables        : chr "logitest"
##  $ chisq.by.subgroup: num 0.555

 logivarlist = list(variables = logivars, chisq.by.subgroup = proppvalue) \n

And now, the results!

 str(contvarlist) #if shapiro p<.05 then pop likely NOT normally dist;  if t-test p<.05 then pop likely have different means \n

 ## List of 3 ## $ variables : chr [1:2] "age" "size" ## $ normality : num [1:2] 0.00125 0.73602 ## $ ttest.by.subgroup: num [1:2] 0.636 0.367 

 str(catvarlist) #if chisq p<.05 then variables are likely NOT independent \n

 ## List of 2 ## $ variables : chr [1:3] "sex" "smoker" "labtest" ## $ chisq.by.subgroup: num [1:3] 0.0158 0.7712 0.3948 

 str(logivarlist) #if chisq p<.05 then variables are likely NOT independent \n

 ## List of 2 ## $ variables : chr "logitest" ## $ chisq.by.subgroup: num 0.555