4 Frequency Distribution Tables

Using furniture::tableF() function

library(furniture)       # nice tables of descriptives

4.1 For a Single Categorical Variable

cancer_clean %>% 
  furniture::tableF(stage)

------------------------------------
 stage Freq CumFreq Percent CumPerc
 0     1    1       4.00%   4.00%  
 1     12   13      48.00%  52.00% 
 2     6    19      24.00%  76.00% 
 3     1    20      4.00%   80.00% 
 4     5    25      20.00%  100.00%
------------------------------------

data_ihno %>% 
  furniture::tableF(majorF)

-----------------------------------------
 majorF     Freq CumFreq Percent CumPerc
 Psychology 29   29      29.00%  29.00% 
 Premed     25   54      25.00%  54.00% 
 Biology    21   75      21.00%  75.00% 
 Sociology  15   90      15.00%  90.00% 
 Economics  10   100     10.00%  100.00%
-----------------------------------------

4.2 For a Single Continuous Variable

The table can get very long if there are many different values for a continuous variable.

data_ihno %>% 
  furniture::tableF(statquiz)

---------------------------------------
 statquiz Freq CumFreq Percent CumPerc
 1        1    1       1.00%   1.00%  
 3        5    6       5.00%   6.00%  
 4        5    11      5.00%   11.00% 
 5        8    19      8.00%   19.00% 
 6        12   31      12.00%  31.00% 
 7        26   57      26.00%  57.00% 
 8        33   90      33.00%  90.00% 
 9        8    98      8.00%   98.00% 
 10       2    100     2.00%   100.00%
---------------------------------------

You may specify how many values you would like displayed with the n = # option.

data_ihno %>% 
  furniture::tableF(statquiz, n = 6)

---------------------------------------
 statquiz Freq CumFreq Percent CumPerc
 1        1    1       1.00%   1.00%  
 3        5    6       5.00%   6.00%  
 4        5    11      5.00%   11.00% 
 ...      ...  ...     ...     ...    
 7        26   57      26.00%  57.00% 
 8        33   90      33.00%  90.00% 
 9        8    98      8.00%   98.00% 
 10       2    100     2.00%   100.00%
---------------------------------------

4.3 Stratify -by- a categorical factor

data_ihno %>% 
  dplyr::group_by(genderF) %>% 
  furniture::tableF(majorF)

-----------------------------------------
 Female     Freq CumFreq Percent CumPerc
 Psychology 19   19      33.33%  33.33% 
 Premed     11   30      19.30%  52.63% 
 Biology    11   41      19.30%  71.93% 
 Sociology  12   53      21.05%  92.98% 
 Economics  4    57      7.02%   100.00%
-----------------------------------------

-----------------------------------------
 Male       Freq CumFreq Percent CumPerc
 Psychology 10   10      23.26%  23.26% 
 Premed     14   24      32.56%  55.81% 
 Biology    10   34      23.26%  79.07% 
 Sociology  3    37      6.98%   86.05% 
 Economics  6    43      13.95%  100.00%
-----------------------------------------