Update in R package moonBook

Explore a data.frame

You can use mytable function to explore a data.frame.

require(moonBook)
require(ztable)
require(magrittr)
options(ztable.type="html")

mytable(acs)

                 Descriptive Statistics                
———————————————————————————————————————————————————————— 
                    Mean ± SD or %   N   Missing (%)
———————————————————————————————————————————————————————— 
   age                  63.3 ± 11.7  857    0  ( 0.0%)
  sex                                857    0  ( 0.0%)
    - Female           287  (33.5%)                   
    - Male             570  (66.5%)                   
  cardiogenicShock                   857    0  ( 0.0%)
    - No               805  (93.9%)                   
    - Yes                52  (6.1%)                   
  entry                              857    0  ( 0.0%)
    - Femoral          312  (36.4%)                   
    - Radial           545  (63.6%)                   
  Dx                                 857    0  ( 0.0%)
    - NSTEMI           153  (17.9%)                   
    - STEMI            304  (35.5%)                   
    - Unstable Angina  400  (46.7%)                   
   EF                    55.8 ± 9.6  723  134  (15.6%)
   height               163.2 ± 9.1  764   93  (10.9%)
   weight               64.8 ± 11.4  766   91  (10.6%)
   BMI                   24.3 ± 3.3  764   93  (10.9%)
  obesity                            857    0  ( 0.0%)
    - No               567  (66.2%)                   
    - Yes              290  (33.8%)                   
   TC                  185.2 ± 47.8  834   23  ( 2.7%)
   LDLC                116.6 ± 41.1  833   24  ( 2.8%)
   HDLC                 38.2 ± 11.1  834   23  ( 2.7%)
   TG                  125.2 ± 90.9  842   15  ( 1.8%)
  DM                                 857    0  ( 0.0%)
    - No               553  (64.5%)                   
    - Yes              304  (35.5%)                   
  HBP                                857    0  ( 0.0%)
    - No               356  (41.5%)                   
    - Yes              501  (58.5%)                   
  smoking                            857    0  ( 0.0%)
    - Ex-smoker        204  (23.8%)                   
    - Never            332  (38.7%)                   
    - Smoker           321  (37.5%)                   
————————————————————————————————————————————————————————

You can use formula without grouping variable(s).

mytable(~age+sex,data=acs)

           Descriptive Statistics           
————————————————————————————————————————————— 
           Mean ± SD or %   N  Missing (%)
————————————————————————————————————————————— 
   age         63.3 ± 11.7  857  0  ( 0.0%)
  sex                       857  0  ( 0.0%)
    - Female  287  (33.5%)                 
    - Male    570  (66.5%)                 
—————————————————————————————————————————————

Compress an object of class mytable

You can compress mytable. If rows dealing with categorical variables have two unique values, it can be printed in a single row rather than three rows.

mytable(Dx~sex,data=acs)

             Descriptive Statistics by 'Dx'             
————————————————————————————————————————————————————————— 
              NSTEMI       STEMI    Unstable Angina   p  
              (N=153)     (N=304)       (N=400)    
————————————————————————————————————————————————————————— 
 sex                                                0.012
   - Female 50 (32.7%)  84 (27.6%)    153 (38.2%)        
   - Male   103 (67.3%) 220 (72.4%)   247 (61.8%)        
—————————————————————————————————————————————————————————

mytable(Dx~sex,data=acs) %>% compress

            Descriptive Statistics by 'Dx'            
——————————————————————————————————————————————————————— 
            NSTEMI       STEMI    Unstable Angina   p  
            (N=153)     (N=304)       (N=400)    
——————————————————————————————————————————————————————— 
 sex:Male 103 (67.3%) 220 (72.4%)   247 (61.8%)   0.012
———————————————————————————————————————————————————————

The default representative group is the second group. If you want the first group to being representative group, please use the no argument.

mytable(Dx~sex,data=acs) %>% compress(no=1)

            Descriptive Statistics by 'Dx'            
——————————————————————————————————————————————————————— 
              NSTEMI     STEMI    Unstable Angina   p  
             (N=153)    (N=304)       (N=400)    
——————————————————————————————————————————————————————— 
 sex:Female 50 (32.7%) 84 (27.6%)   153 (38.2%)   0.012
———————————————————————————————————————————————————————

Sometimes it is more simple to omit the representative group name. You can do this by set the add.label argument FALSE.

mytable(Dx~cardiogenicShock+DM+obesity+HBP,data=acs) %>% compress

                  Descriptive Statistics by 'Dx'                 
—————————————————————————————————————————————————————————————————— 
                        NSTEMI      STEMI    Unstable Angina   p  
                       (N=153)     (N=304)       (N=400)    
—————————————————————————————————————————————————————————————————— 
 cardiogenicShock:Yes 4 ( 2.6%)  48 (15.8%)     0 ( 0.0%)    0.000
 DM:Yes               57 (37.3%) 96 (31.6%)    151 (37.8%)   0.209
 obesity:Yes          47 (30.7%) 95 (31.2%)    148 (37.0%)   0.186
 HBP:Yes              91 (59.5%) 154 (50.7%)   256 (64.0%)   0.002
——————————————————————————————————————————————————————————————————

mytable(Dx~cardiogenicShock+DM+obesity+HBP,data=acs) %>% compress(add.label=FALSE)

                Descriptive Statistics by 'Dx'               
—————————————————————————————————————————————————————————————— 
                    NSTEMI      STEMI    Unstable Angina   p  
                   (N=153)     (N=304)       (N=400)    
—————————————————————————————————————————————————————————————— 
 cardiogenicShock 4 ( 2.6%)  48 (15.8%)     0 ( 0.0%)    0.000
 DM               57 (37.3%) 96 (31.6%)    151 (37.8%)   0.209
 obesity          47 (30.7%) 95 (31.2%)    148 (37.0%)   0.186
 HBP              91 (59.5%) 154 (50.7%)   256 (64.0%)   0.002
——————————————————————————————————————————————————————————————

You can print mytable object in ‘html5’ or ‘LaTex’ format with ztable.

mytable(Dx~cardiogenicShock+DM+obesity+HBP,data=acs) %>% compress(add.label=FALSE) %>% ztable


	NSTEMI	STEMI	Unstable Angina	p
	(N=153)	(N=304)	(N=400)	p
cardiogenicShock	4 ( 2.6%)	48 (15.8%)	0 ( 0.0%)	< 0.001
DM	57 (37.3%)	96 (31.6%)	151 (37.8%)	0.209
obesity	47 (30.7%)	95 (31.2%)	148 (37.0%)	0.186
HBP	91 (59.5%)	154 (50.7%)	256 (64.0%)	0.002

Delete Rows of an object of class mytable

You can delete rows of an object of class mytable.

mytable(sex~Dx,data=acs)

         Descriptive Statistics by 'sex'         
—————————————————————————————————————————————————— 
                       Female       Male       p  
                       (N=287)     (N=570)  
—————————————————————————————————————————————————— 
 Dx                                          0.012
   - NSTEMI          50 (17.4%)  103 (18.1%)      
   - STEMI           84 (29.3%)  220 (38.6%)      
   - Unstable Angina 153 (53.3%) 247 (43.3%)      
——————————————————————————————————————————————————

If you want to delete the second row, use the deleteRows() function.

mytable(sex~Dx,data=acs) %>% deleteRows(2)

         Descriptive Statistics by 'sex'         
—————————————————————————————————————————————————— 
                       Female       Male       p  
                       (N=287)     (N=570)  
—————————————————————————————————————————————————— 
 Dx                                          0.012
   - STEMI           84 (29.3%)  220 (38.6%)      
   - Unstable Angina 153 (53.3%) 247 (43.3%)      
——————————————————————————————————————————————————

You can delete rows of an object of class cbind.mytable.

mytable(sex+HBP~age+Dx,data=acs) %>% deleteRows(3)

              Descriptive Statistics stratified by 'sex' and 'HBP'             
———————————————————————————————————————————————————————————————————————————————— 
                                   Male                          Female            
                     ————————————————————————————— ————————————————————————————— 
                         No          Yes       p       No          Yes       p  
                       (N=273)     (N=297)          (N=83)      (N=204)       
———————————————————————————————————————————————————————————————————————————————— 
 age                 57.5 ± 11.3 63.5 ± 10.4 0.000 68.0 ± 12.3 69.0 ± 10.0 0.526
 Dx                                          0.016                         0.084
   - STEMI           122 (44.7%) 98 (33.0%)        28 (33.7%)  56 (27.5%)       
   - Unstable Angina 108 (39.6%) 139 (46.8%)       36 (43.4%)  117 (57.4%)      
————————————————————————————————————————————————————————————————————————————————

Methods for categorical variables

You can select method for categorical variables with catMethod argument. Possible values are :

0 : Perform chisq.test first. If warning present, perform fisher test
1 : Perform chisq.test without continuity correction
2 : Perform chisq.test with continuity correction
3 : Perform fisher.test
4 : perform prop.trend test

You can see which tests are used if you set show.all argument of mytable TRUE.

mytable(obesity~HBP,data=acs,catMethod=1,show.all=TRUE)

                                Descriptive Statistics by 'obesity'                                
———————————————————————————————————————————————————————————————————————————————————————————————————— 
             No          Yes       p   sig  p1   p2  p3      class              ptest             N 
           (N=567)     (N=290)  
———————————————————————————————————————————————————————————————————————————————————————————————————— 
 HBP                             0.034 **  0.034    2.000 categorical Pearson's Chi-squared test 857
   - No  250 (44.1%) 106 (36.6%)                                                                    
   - Yes 317 (55.9%) 184 (63.4%)                                                                    
————————————————————————————————————————————————————————————————————————————————————————————————————

For formatted numbers: addComma()

Sometimes, you want to display formatted numbers. For example, 1234.5 can be printed as 1,234.5. You can do this using addComma() function

data(diamonds,package="ggplot2")
mytable(diamonds) %>% addComma

                Descriptive Statistics               
—————————————————————————————————————————————————————— 
               Mean ± SD or %      N    Missing (%)
—————————————————————————————————————————————————————— 
   carat               0.8 ± 0.5  53,940  0  ( 0.0%)
  cut                             53,940  0  ( 0.0%)
    - Fair          1610  (3.0%)                    
    - Good          4906  (9.1%)                    
    - Very Good   12082  (22.4%)                    
    - Premium     13791  (25.6%)                    
    - Ideal       21551  (40.0%)                    
  color                           53,940  0  ( 0.0%)
    - D            6775  (12.6%)                    
    - E            9797  (18.2%)                    
    - F            9542  (17.7%)                    
    - G           11292  (20.9%)                    
    - H            8304  (15.4%)                    
    - I            5422  (10.1%)                    
    - J             2808  (5.2%)                    
  clarity                         53,940  0  ( 0.0%)
    - I1             741  (1.4%)                    
    - SI2          9194  (17.0%)                    
    - SI1         13065  (24.2%)                    
    - VS2         12258  (22.7%)                    
    - VS1          8171  (15.1%)                    
    - VVS2          5066  (9.4%)                    
    - VVS1          3655  (6.8%)                    
    - IF            1790  (3.3%)                    
   depth              61.7 ± 1.4  53,940  0  ( 0.0%)
   table              57.5 ± 2.2  53,940  0  ( 0.0%)
   price         3932.8 ± 3989.4  53,940  0  ( 0.0%)
   x                   5.7 ± 1.1  53,940  0  ( 0.0%)
   y                   5.7 ± 1.1  53,940  0  ( 0.0%)
   z                   3.5 ± 0.7  53,940  0  ( 0.0%)
——————————————————————————————————————————————————————

Also you can print this in ‘html5’ or ‘LaTex’ format with ztable.

mytable(cut~.,data=diamonds) %>% addComma %>% ztable


	Fair	Good	Very Good	Premium	Ideal	p
	(N= 1,610)	(N= 4,906)	(N=12,082)	(N=13,791)	(N=21,551)	p
carat	1.0 ± 0.5	0.8 ± 0.5	0.8 ± 0.5	0.9 ± 0.5	0.7 ± 0.4	< 0.001
color						< 0.001
D	163 (10.1%)	662 (13.5%)	1,513 (12.5%)	1,603 (11.6%)	2,834 (13.2%)
E	224 (13.9%)	933 (19%)	2,400 (19.9%)	2,337 (16.9%)	3,903 (18.1%)
F	312 (19.4%)	909 (18.5%)	2,164 (17.9%)	2,331 (16.9%)	3,826 (17.8%)
G	314 (19.5%)	871 (17.8%)	2,299 (19%)	2,924 (21.2%)	4,884 (22.7%)
H	303 (18.8%)	702 (14.3%)	1,824 (15.1%)	2,360 (17.1%)	3,115 (14.5%)
I	175 (10.9%)	522 (10.6%)	1,204 (10%)	1,428 (10.4%)	2,093 (9.7%)
J	119 (7.4%)	307 (6.3%)	678 (5.6%)	808 (5.9%)	896 (4.2%)
clarity						< 0.001
I1	210 (13%)	96 (2%)	84 (0.7%)	205 (1.5%)	146 (0.7%)
SI2	466 (28.9%)	1,081 (22%)	2,100 (17.4%)	2,949 (21.4%)	2,598 (12.1%)
SI1	408 (25.3%)	1,560 (31.8%)	3,240 (26.8%)	3,575 (25.9%)	4,282 (19.9%)
VS2	261 (16.2%)	978 (19.9%)	2,591 (21.4%)	3,357 (24.3%)	5,071 (23.5%)
VS1	170 (10.6%)	648 (13.2%)	1,775 (14.7%)	1,989 (14.4%)	3,589 (16.7%)
VVS2	69 (4.3%)	286 (5.8%)	1,235 (10.2%)	870 (6.3%)	2,606 (12.1%)
VVS1	17 (1.1%)	186 (3.8%)	789 (6.5%)	616 (4.5%)	2,047 (9.5%)
IF	9 (0.6%)	71 (1.4%)	268 (2.2%)	230 (1.7%)	1,212 (5.6%)
depth	64.0 ± 3.6	62.4 ± 2.2	61.8 ± 1.4	61.3 ± 1.2	61.7 ± 0.7	< 0.001
table	59.1 ± 3.9	58.7 ± 2.9	58.0 ± 2.1	58.7 ± 1.5	56.0 ± 1.2	< 0.001
price	4,358.8 ± 3,560.4	3,928.9 ± 3,681.6	3,981.8 ± 3,935.9	4,584.3 ± 4,349.2	3,457.5 ± 3,808.4	< 0.001
x	6.2 ± 1.0	5.8 ± 1.1	5.7 ± 1.1	6.0 ± 1.2	5.5 ± 1.1	< 0.001
z	4.0 ± 0.7	3.6 ± 0.7	3.6 ± 0.7	3.6 ± 0.7	3.4 ± 0.7	< 0.001

Update in R package moonBook

Keon-Woong Moon

2022-01-05

Function “mytable”

Basic Usage

Explore a data.frame

Compress an object of class mytable

Delete Rows of an object of class mytable

Methods for categorical variables

For formatted numbers: addComma()