Mimic tabulate command from Stata in R

Question

I'm trying to get a 2 way table in R similar to this one from Stata. I was trying to use CrossTable from gmodels package, but the table is not the same. Do you known how can this be done in R?

I hope at least to get the frequencies from

when cursmoke1 == "Yes" & cursmoke2 == "No" and reversed

In R I'm only getting totals from yes, no and NA.

Here is the output:

Stata

. tabulate cursmoke1 cursmoke2, cell column miss row


+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
|  cell percentage  |
+-------------------+

   Current |
   smoker, |      Current smoker, exam 2
    exam 1 |        No        Yes          . |     Total
-----------+---------------------------------+----------
        No |     1,898        131        224 |     2,253 
           |     84.24       5.81       9.94 |    100.00 
           |     86.16       7.59      44.44 |     50.81 
           |     42.81       2.95       5.05 |     50.81 
-----------+---------------------------------+----------
       Yes |       305      1,596        280 |     2,181 
           |     13.98      73.18      12.84 |    100.00 
           |     13.84      92.41      55.56 |     49.19 
           |      6.88      35.99       6.31 |     49.19 
-----------+---------------------------------+----------
     Total |     2,203      1,727        504 |     4,434 
           |     49.68      38.95      11.37 |    100.00 
           |    100.00     100.00     100.00 |    100.00 
           |     49.68      38.95      11.37 |    100.00

R

> CrossTable(cursmoke2, cursmoke1, missing.include = T, format="SAS")


   Cell Contents
|-------------------------|
|                       N |
| Chi-square contribution |
|           N / Row Total |
|           N / Col Total |
|         N / Table Total |
|-------------------------|


Total Observations in Table:  4434 


             | cursmoke1 
   cursmoke2 |        No |       Yes |        NA | Row Total | 
-------------|-----------|-----------|-----------|-----------|
          No |      2203 |         0 |         0 |      2203 | 
             |  1122.544 |   858.047 |   250.409 |           | 
             |     1.000 |     0.000 |     0.000 |     0.497 | 
             |     1.000 |     0.000 |     0.000 |           | 
             |     0.497 |     0.000 |     0.000 |           | 
-------------|-----------|-----------|-----------|-----------|
         Yes |         0 |      1727 |         0 |      1727 | 
             |   858.047 |  1652.650 |   196.303 |           | 
             |     0.000 |     1.000 |     0.000 |     0.389 | 
             |     0.000 |     1.000 |     0.000 |           | 
             |     0.000 |     0.389 |     0.000 |           | 
-------------|-----------|-----------|-----------|-----------|
          NA |         0 |         0 |       504 |       504 | 
             |   250.409 |   196.303 |  3483.288 |           | 
             |     0.000 |     0.000 |     1.000 |     0.114 | 
             |     0.000 |     0.000 |     1.000 |           | 
             |     0.000 |     0.000 |     0.114 |           | 
-------------|-----------|-----------|-----------|-----------|
Column Total |      2203 |      1727 |       504 |      4434 | 
             |     0.497 |     0.389 |     0.114 |           | 
-------------|-----------|-----------|-----------|-----------|

You will need to present more information about the structure and content of cursmoke1 and cursmoke2. What does table(cursmoke1, cursmoke2) produce? What is str(cusmoke1)? Did you use attach? — IRTFM

A5C1D2H2I1M1N2O1R2T1 A5C1D2H2I1M1N2O1R2T1 · Accepted Answer · 2012-10-24T09:32:24

Maybe I'm missing something here. The default settings for CrossTable seem to provide essentially what you are looking for.

Here is CrossTable with minimal arguments. (I've loaded the dataset as "temp".) Note that the results are the same as what you posted from the Stata output (you just need to multiply by 100 if you want the result as a percentage).

library(gmodels)
with(temp, CrossTable(cursmoke1, cursmoke2, missing.include=TRUE))

   Cell Contents
|-------------------------|
|                       N |
| Chi-square contribution |
|           N / Row Total |
|           N / Col Total |
|         N / Table Total |
|-------------------------|

Total Observations in Table:  4434 

             | cursmoke2 
   cursmoke1 |        No |       Yes |        NA | Row Total | 
-------------|-----------|-----------|-----------|-----------|
          No |      1898 |       131 |       224 |      2253 | 
             |   541.582 |   635.078 |     4.022 |           | 
             |     0.842 |     0.058 |     0.099 |     0.508 | 
             |     0.862 |     0.076 |     0.444 |           | 
             |     0.428 |     0.030 |     0.051 |           | 
-------------|-----------|-----------|-----------|-----------|
         Yes |       305 |      1596 |       280 |      2181 | 
             |   559.461 |   656.043 |     4.154 |           | 
             |     0.140 |     0.732 |     0.128 |     0.492 | 
             |     0.138 |     0.924 |     0.556 |           | 
             |     0.069 |     0.360 |     0.063 |           | 
-------------|-----------|-----------|-----------|-----------|
Column Total |      2203 |      1727 |       504 |      4434 | 
             |     0.497 |     0.389 |     0.114 |           | 
-------------|-----------|-----------|-----------|-----------|

Alternatively, you can use format="SPSS" if you want the numbers displayed as percentages.

with(temp, CrossTable(cursmoke1, cursmoke2, missing.include=TRUE, format="SPSS"))

   Cell Contents
|-------------------------|
|                   Count |
| Chi-square contribution |
|             Row Percent |
|          Column Percent |
|           Total Percent |
|-------------------------|

Total Observations in Table:  4434 

             | cursmoke2 
   cursmoke1 |       No  |      Yes  |       NA  | Row Total | 
-------------|-----------|-----------|-----------|-----------|
          No |     1898  |      131  |      224  |     2253  | 
             |  541.582  |  635.078  |    4.022  |           | 
             |   84.243% |    5.814% |    9.942% |   50.812% | 
             |   86.155% |    7.585% |   44.444% |           | 
             |   42.806% |    2.954% |    5.052% |           | 
-------------|-----------|-----------|-----------|-----------|
         Yes |      305  |     1596  |      280  |     2181  | 
             |  559.461  |  656.043  |    4.154  |           | 
             |   13.984% |   73.177% |   12.838% |   49.188% | 
             |   13.845% |   92.415% |   55.556% |           | 
             |    6.879% |   35.995% |    6.315% |           | 
-------------|-----------|-----------|-----------|-----------|
Column Total |     2203  |     1727  |      504  |     4434  | 
             |   49.684% |   38.949% |   11.367% |           | 
-------------|-----------|-----------|-----------|-----------|

Update: `prop.table()`

Just FYI (to save you the tedious work you did in making your own data.frame as you did), you may also be interested in the prop.table() function.

Again, using the data you linked to and assuming it is named "temp", the following gives you the underlying data from which you can construct your data.frame. You may also be interested in looking into the functions margin.table() or addmargins():

## Your basic table
CurSmoke <- with(temp, table(cursmoke1, cursmoke2, useNA = "ifany"))
CurSmoke
#          cursmoke2
# cursmoke1   No  Yes <NA>
#       No  1898  131  224
#       Yes  305 1596  280

## Row proportions
prop.table(CurSmoke, 1) # * 100 # If you so desire
#          cursmoke2
# cursmoke1         No        Yes       <NA>
#       No  0.84243231 0.05814470 0.09942299
#       Yes 0.13984411 0.73177442 0.12838148

## Column proportions
prop.table(CurSmoke, 2) # * 100 # If you so desire
#          cursmoke2
# cursmoke1         No        Yes       <NA>
#       No  0.86155243 0.07585408 0.44444444
#       Yes 0.13844757 0.92414592 0.55555556

## Cell proportions
prop.table(CurSmoke)    # * 100 # If you so desire
#          cursmoke2
# cursmoke1         No        Yes       <NA>
#       No  0.42805593 0.02954443 0.05051872
#       Yes 0.06878665 0.35994587 0.06314840

Mimic tabulate command from Stata in R

1 Answers

Update: prop.table()

Update: `prop.table()`