Cross Table of 5 Variables

[Ohoud]

Good day,
I will appreciate your help. I am a new R user.
Kindly, I have a data set as follow (example):
|Var1|Var2|Var3|Var4|Var5|
|0|0|0|0|0|
|0|0|0|0|1|
|0|0|0|0|2|
|0|0|0|1|0|
Variables values are as follow:
Var1 bear values (0,1,2,3)
Var2, Var3, Var4, Var5 bear values (0,1,2)

I would like to build cross table that contains all possible values with its count such as follow:
|Var1|Var2|Var3|Var4|Var5|count|
|0|0|0|0|0|0|
|0|0|0|0|1|0|
|0|0|0|0|2|0|
|0|0|0|1|0|1|
|0|0|0|1|1|1|
|0|0|0|1|2|0|
|0|0|0|2|0|6|
So |0|0|0|0|0| never appear , |0|0|0|2|0| appears 6 times (6 rows) and so on.

I tried both table, crosstab and xtab but couldn't figure it out.
Regards,
Ohoud

[jrkrideau]

Can you provide so usable sample data? A handy way to supply sample data is to use the dput() function. See ?dput. If you have a very large data set then something like head(dput(myfile), 100) will likely supply enough data for us to work with.

You may also find this useful:

How to produce a
reproducible example (reprex)

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[nirgrahamuk]

if your variables are factors with known levels, then dplyr::group_by* variants can summarise for all levels even non present ones.

set.seed(42)

(exdf <- data.frame(a=factor(sample.int(3,size=3,replace=TRUE)-1,levels=(0:2)),
           b=factor(sample.int(3,size=3,replace=TRUE)-1,levels=(0:2)),
          c=factor(sample.int(3,size=3,replace=TRUE)-1,levels=(0:2))))

library(tidyverse)

group_by_all(exdf,.drop = FALSE) %>% 
  summarise(n=n()) %>% ungroup()