Function “mytable”” produce table for descriptive analysis easily. It is most useful to make table to describe baseline characteristics common in medical research papers.
require(moonBook)Loading required package: moonBookdata(acs)
mytable(Dx~.,data=acs)
Descriptive Statistics by 'Dx'
—————————————————————————————————————————————————————————————————
NSTEMI STEMI Unstable Angina p
(N=153) (N=304) (N=400)
—————————————————————————————————————————————————————————————————
age 64.3 ± 12.3 62.1 ± 12.1 63.8 ± 11.0 0.073
sex 0.012
- Female 50 (32.7%) 84 (27.6%) 153 (38.2%)
- Male 103 (67.3%) 220 (72.4%) 247 (61.8%)
cardiogenicShock 0.000
- No 149 (97.4%) 256 (84.2%) 400 (100.0%)
- Yes 4 ( 2.6%) 48 (15.8%) 0 ( 0.0%)
entry 0.001
- Femoral 58 (37.9%) 133 (43.8%) 121 (30.2%)
- Radial 95 (62.1%) 171 (56.2%) 279 (69.8%)
EF 55.0 ± 9.3 52.4 ± 9.5 59.2 ± 8.7 0.000
height 163.3 ± 8.2 165.1 ± 8.2 161.7 ± 9.7 0.000
weight 64.3 ± 10.2 65.7 ± 11.6 64.5 ± 11.6 0.361
BMI 24.1 ± 3.2 24.0 ± 3.3 24.6 ± 3.4 0.064
obesity 0.186
- No 106 (69.3%) 209 (68.8%) 252 (63.0%)
- Yes 47 (30.7%) 95 (31.2%) 148 (37.0%)
TC 193.7 ± 53.6 183.2 ± 43.4 183.5 ± 48.3 0.057
LDLC 126.1 ± 44.7 116.7 ± 39.5 112.9 ± 40.4 0.004
HDLC 38.9 ± 11.9 38.5 ± 11.0 37.8 ± 10.9 0.501
TG 130.1 ± 88.5 106.5 ± 72.0 137.4 ± 101.6 0.000
DM 0.209
- No 96 (62.7%) 208 (68.4%) 249 (62.2%)
- Yes 57 (37.3%) 96 (31.6%) 151 (37.8%)
HBP 0.002
- No 62 (40.5%) 150 (49.3%) 144 (36.0%)
- Yes 91 (59.5%) 154 (50.7%) 256 (64.0%)
smoking 0.000
- Ex-smoker 42 (27.5%) 66 (21.7%) 96 (24.0%)
- Never 50 (32.7%) 97 (31.9%) 185 (46.2%)
- Smoker 61 (39.9%) 141 (46.4%) 119 (29.8%)
————————————————————————————————————————————————————————————————— The first argument of function mytable is an object of class formula. Left side of ~ must contain the name of one grouping variable or two grouping variables in an additive way(e.g. sex+group~), and the right side of ~ must have variables in an additive way. . is allowed on the right side of formula which means all variables in the data.frame specified by the 2nd argument data. The sample data ‘acs’ containing demographic data and laboratory data of 857 patients with acute coronary syndrome(ACS). For more information about the data acs, type ?acs in your R console.
str(acs)'data.frame': 857 obs. of 17 variables:
$ age : int 62 78 76 89 56 73 58 62 59 71 ...
$ sex : chr "Male" "Female" "Female" "Female" ...
$ cardiogenicShock: chr "No" "No" "Yes" "No" ...
$ entry : chr "Femoral" "Femoral" "Femoral" "Femoral" ...
$ Dx : chr "STEMI" "STEMI" "STEMI" "STEMI" ...
$ EF : num 18 18.4 20 21.8 21.8 22 24.7 26.6 28.5 31.1 ...
$ height : num 168 148 NA 165 162 153 167 160 152 168 ...
$ weight : num 72 48 NA 50 64 59 78 50 67 60 ...
$ BMI : num 25.5 21.9 NA 18.4 24.4 ...
$ obesity : chr "Yes" "No" "No" "No" ...
$ TC : num 215 NA NA 121 195 184 161 136 239 169 ...
$ LDLC : int 154 NA NA 73 151 112 91 88 161 88 ...
$ HDLC : int 35 NA NA 20 36 38 34 33 34 54 ...
$ TG : int 155 166 NA 89 63 137 196 30 118 141 ...
$ DM : chr "Yes" "No" "No" "No" ...
$ HBP : chr "No" "Yes" "Yes" "No" ...
$ smoking : chr "Smoker" "Never" "Never" "Never" ...You can use mytable function to explore a data.frame.
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%)
————————————————————————————————————————————— You can use labels of the data. For example, you can add column labels and value labels of mtcars data using set_label() and set_labels() function of the sjlabelled package.
if(!require(sjlabelled)) {
install.packages("sjlabelled")
require(sjlabelled)
}Loading required package: sjlabelleddf=mtcars
df$am<-set_label(df$am,label="Transmission")
df$am<-set_labels(df$am,labels=c("automatic","manual"))
df$vs<-set_label(df$vs,label="Engine")
df$vs<-set_labels(df$vs,labels=c("V-shaped","straight"))You can see the labels of this data.
str(df)'data.frame': 32 obs. of 11 variables:
$ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
$ disp: num 160 160 108 258 360 ...
$ hp : num 110 110 93 110 175 105 245 62 95 123 ...
$ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt : num 2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num 16.5 17 18.6 19.4 17 ...
$ vs : num 0 0 1 1 0 1 0 1 1 1 ...
..- attr(*, "label")= chr "Engine"
..- attr(*, "labels")= Named num [1:2] 0 1
.. ..- attr(*, "names")= chr [1:2] "V-shaped" "straight"
$ am : num 1 1 1 0 0 0 0 0 0 0 ...
..- attr(*, "label")= chr "Transmission"
..- attr(*, "labels")= Named num [1:2] 0 1
.. ..- attr(*, "names")= chr [1:2] "automatic" "manual"
$ gear: num 4 4 4 3 3 3 3 4 4 4 ...
$ carb: num 4 4 1 1 2 1 4 2 2 4 ...You can use the column label and value labels with mytable() function. The default values of use.labels and use.column.labels arguments of mytable() function are TRUE. You can change this arguments if you want.
mytable(df)
Descriptive Statistics
————————————————————————————————————————————————
Mean ± SD or % N Missing (%)
————————————————————————————————————————————————
mpg 20.1 ± 6.0 32 0 ( 0.0%)
cyl 32 0 ( 0.0%)
- 4 11 (34.4%)
- 6 7 (21.9%)
- 8 14 (43.8%)
disp 230.7 ± 123.9 32 0 ( 0.0%)
hp 146.7 ± 68.6 32 0 ( 0.0%)
drat 3.6 ± 0.5 32 0 ( 0.0%)
wt 3.2 ± 1.0 32 0 ( 0.0%)
qsec 17.8 ± 1.8 32 0 ( 0.0%)
Engine 32 0 ( 0.0%)
- V-shaped 18 (56.2%)
- straight 14 (43.8%)
Transmission 32 0 ( 0.0%)
- automatic 19 (59.4%)
- manual 13 (40.6%)
gear 32 0 ( 0.0%)
- 3 15 (46.9%)
- 4 12 (37.5%)
- 5 5 (15.6%)
carb 2.8 ± 1.6 32 0 ( 0.0%)
———————————————————————————————————————————————— mytable(df,use.labels=FALSE, use.column.label=FALSE)
Descriptive Statistics
————————————————————————————————————————
Mean ± SD or % N Missing (%)
————————————————————————————————————————
mpg 20.1 ± 6.0 32 0 ( 0.0%)
cyl 32 0 ( 0.0%)
- 4 11 (34.4%)
- 6 7 (21.9%)
- 8 14 (43.8%)
disp 230.7 ± 123.9 32 0 ( 0.0%)
hp 146.7 ± 68.6 32 0 ( 0.0%)
drat 3.6 ± 0.5 32 0 ( 0.0%)
wt 3.2 ± 1.0 32 0 ( 0.0%)
qsec 17.8 ± 1.8 32 0 ( 0.0%)
vs 32 0 ( 0.0%)
- 0 18 (56.2%)
- 1 14 (43.8%)
am 32 0 ( 0.0%)
- 0 19 (59.4%)
- 1 13 (40.6%)
gear 32 0 ( 0.0%)
- 3 15 (46.9%)
- 4 12 (37.5%)
- 5 5 (15.6%)
carb 2.8 ± 1.6 32 0 ( 0.0%)
———————————————————————————————————————— mytable(am~.,data=df)
Descriptive Statistics by 'Transmission'
——————————————————————————————————————————————
automatic manual p
(N=19) (N=13)
——————————————————————————————————————————————
mpg 17.1 ± 3.8 24.4 ± 6.2 0.000
cyl 0.013
- 4 3 (15.8%) 8 (61.5%)
- 6 4 (21.1%) 3 (23.1%)
- 8 12 (63.2%) 2 (15.4%)
disp 290.4 ± 110.2 143.5 ± 87.2 0.000
hp 160.3 ± 53.9 126.8 ± 84.1 0.180
drat 3.3 ± 0.4 4.0 ± 0.4 0.000
wt 3.8 ± 0.8 2.4 ± 0.6 0.000
qsec 18.2 ± 1.8 17.4 ± 1.8 0.206
Engine 0.556
- V-shaped 12 (63.2%) 6 (46.2%)
- straight 7 (36.8%) 7 (53.8%)
gear 0.000
- 3 15 (78.9%) 0 ( 0.0%)
- 4 4 (21.1%) 8 (61.5%)
- 5 0 ( 0.0%) 5 (38.5%)
carb 2.7 ± 1.1 2.9 ± 2.2 0.781
—————————————————————————————————————————————— # mytable(vs+am~.,data=df)You can choose the grouping variable(s) and row-variable(s) with the formula.
mytable(sex~age+Dx,data=acs)
Descriptive Statistics by 'sex'
——————————————————————————————————————————————————
Female Male p
(N=287) (N=570)
——————————————————————————————————————————————————
age 68.7 ± 10.7 60.6 ± 11.2 0.000
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%)
—————————————————————————————————————————————————— You can choose row-variable(s) with . and + and - and variable name in an additive way.
mytable(am~.-hp-disp-cyl-carb-gear,data=mtcars)
Descriptive Statistics by 'am'
————————————————————————————————————
0 1 p
(N=19) (N=13)
————————————————————————————————————
mpg 17.1 ± 3.8 24.4 ± 6.2 0.000
drat 3.3 ± 0.4 4.0 ± 0.4 0.000
wt 3.8 ± 0.8 2.4 ± 0.6 0.000
qsec 18.2 ± 1.8 17.4 ± 1.8 0.206
vs 0.556
- 0 12 (63.2%) 6 (46.2%)
- 1 7 (36.8%) 7 (53.8%)
———————————————————————————————————— By default continuous variables are analyzed as normal-distributed and are described with mean and standard deviation. To change default options, you can use the method argument. Possible values of method argument are:
When continuous variables are analyzed as non-normal, they are described with median and interquartile range.
mytable(sex~height+weight+BMI,data=acs,method=3)
Descriptive Statistics by 'sex'
—————————————————————————————————————————————————————
Female Male p
(N=287) (N=570)
—————————————————————————————————————————————————————
height 155.0 [150.0;158.0] 168.0 [164.0;172.0] 0.000
weight 58.0 [50.0;63.0] 68.0 [62.0;75.0] 0.000
BMI 24.0 [22.1;26.2] 24.2 [22.2;26.2] 0.471
————————————————————————————————————————————————————— Because the method argument is selected as 3, a Shapiro-Wilk test normality test is used to decide if the variable is normal or non-normal distributed. Note that height and BMI was described as mean \(\pm\) sd, whereas the weight was described as median and interquartile range.
In many cases, categorical variables are usually coded as numeric. For example, many people usually code 0 and 1 instead of “No” and “Yes”. Similarly, factor variables with three or four levels are coded 0/1/2 or 0/1/2/3. In many cases, if we analyze these variables as continuous variables, we are not able to get the right result. In mytable, variables with less than five unique values are treated as a categorical variables.
mytable(am~.,data=mtcars)
Descriptive Statistics by 'am'
———————————————————————————————————————
0 1 p
(N=19) (N=13)
———————————————————————————————————————
mpg 17.1 ± 3.8 24.4 ± 6.2 0.000
cyl 0.013
- 4 3 (15.8%) 8 (61.5%)
- 6 4 (21.1%) 3 (23.1%)
- 8 12 (63.2%) 2 (15.4%)
disp 290.4 ± 110.2 143.5 ± 87.2 0.000
hp 160.3 ± 53.9 126.8 ± 84.1 0.180
drat 3.3 ± 0.4 4.0 ± 0.4 0.000
wt 3.8 ± 0.8 2.4 ± 0.6 0.000
qsec 18.2 ± 1.8 17.4 ± 1.8 0.206
vs 0.556
- 0 12 (63.2%) 6 (46.2%)
- 1 7 (36.8%) 7 (53.8%)
gear 0.000
- 3 15 (78.9%) 0 ( 0.0%)
- 4 4 (21.1%) 8 (61.5%)
- 5 0 ( 0.0%) 5 (38.5%)
carb 2.7 ± 1.1 2.9 ± 2.2 0.781
——————————————————————————————————————— In mtcars data, all variables are expressed as numeric. But as you can see, cyl, vs and gear is treated as categorical variables. The carb variables has six unique values and treated as continuous variables. If you wanted the carb variable to be treated as categorical variable, you can changed the max.ylev argument.
mytable(am~carb,data=mtcars,max.ylev=6)
Descriptive Statistics by 'am'
——————————————————————————————————
0 1 p
(N=19) (N=13)
——————————————————————————————————
carb 0.284
- 1 3 (15.8%) 4 (30.8%)
- 2 6 (31.6%) 4 (30.8%)
- 3 3 (15.8%) 0 ( 0.0%)
- 4 7 (36.8%) 3 (23.1%)
- 6 0 ( 0.0%) 1 ( 7.7%)
- 8 0 ( 0.0%) 1 ( 7.7%)
—————————————————————————————————— If you wanted to make two separate tables and combine into one table, mytable is the function of choice. For example, if you wanted to build separate table for female and male patients stratified by presence or absence of DM and combine it,
mytable(sex+DM~.,data=acs)
Descriptive Statistics stratified by 'sex' and 'DM'
—————————————————————————————————————————————————————————————————————————————————————
Male Female
———————————————————————————————— ———————————————————————————————
No Yes p No Yes p
(N=380) (N=190) (N=173) (N=114)
—————————————————————————————————————————————————————————————————————————————————————
age 60.9 ± 11.5 60.1 ± 10.6 0.472 69.3 ± 11.4 67.8 ± 9.7 0.257
cardiogenicShock 0.685 0.296
- No 355 (93.4%) 175 (92.1%) 168 (97.1%) 107 (93.9%)
- Yes 25 ( 6.6%) 15 ( 7.9%) 5 ( 2.9%) 7 ( 6.1%)
entry 0.552 0.665
- Femoral 125 (32.9%) 68 (35.8%) 74 (42.8%) 45 (39.5%)
- Radial 255 (67.1%) 122 (64.2%) 99 (57.2%) 69 (60.5%)
Dx 0.219 0.240
- NSTEMI 71 (18.7%) 32 (16.8%) 25 (14.5%) 25 (21.9%)
- STEMI 154 (40.5%) 66 (34.7%) 54 (31.2%) 30 (26.3%)
- Unstable Angina 155 (40.8%) 92 (48.4%) 94 (54.3%) 59 (51.8%)
EF 56.5 ± 8.3 53.9 ± 11.0 0.007 56.0 ± 10.1 56.6 ± 10.0 0.655
height 168.1 ± 5.8 167.5 ± 6.7 0.386 153.9 ± 6.5 153.6 ± 5.8 0.707
weight 68.1 ± 10.4 69.8 ± 10.2 0.070 56.5 ± 8.7 58.4 ± 10.0 0.106
BMI 24.0 ± 3.1 24.9 ± 3.5 0.005 23.8 ± 3.2 24.8 ± 4.0 0.046
obesity 0.027 0.359
- No 261 (68.7%) 112 (58.9%) 121 (69.9%) 73 (64.0%)
- Yes 119 (31.3%) 78 (41.1%) 52 (30.1%) 41 (36.0%)
TC 184.1 ± 46.7 181.8 ± 44.5 0.572 186.0 ± 43.1 193.3 ± 60.8 0.274
LDLC 117.9 ± 41.8 112.1 ± 39.4 0.115 116.3 ± 35.2 119.8 ± 48.6 0.519
HDLC 38.4 ± 11.4 36.8 ± 9.6 0.083 39.2 ± 10.9 38.8 ± 12.2 0.821
TG 115.2 ± 72.2 153.4 ± 130.7 0.000 114.2 ± 82.4 128.4 ± 65.5 0.112
HBP 0.000 0.356
- No 205 (53.9%) 68 (35.8%) 54 (31.2%) 29 (25.4%)
- Yes 175 (46.1%) 122 (64.2%) 119 (68.8%) 85 (74.6%)
smoking 0.386 0.093
- Ex-smoker 101 (26.6%) 54 (28.4%) 34 (19.7%) 15 (13.2%)
- Never 77 (20.3%) 46 (24.2%) 118 (68.2%) 91 (79.8%)
- Smoker 202 (53.2%) 90 (47.4%) 21 (12.1%) 8 ( 7.0%)
————————————————————————————————————————————————————————————————————————————————————— If you want more beautiful table in your R markdown file, you can use myhtml function.
out=mytable(Dx~.,data=acs)
myhtml(out)| Dx |
NSTEMI (N=153) |
STEMI (N=304) |
Unstable Angina (N=400) |
p |
|---|---|---|---|---|
| age | 64.3 ± 12.3 | 62.1 ± 12.1 | 63.8 ± 11.0 | 0.073 |
| sex | 0.012 | |||
| Female | 50 (32.7%) | 84 (27.6%) | 153 (38.2%) | |
| Male | 103 (67.3%) | 220 (72.4%) | 247 (61.8%) | |
| cardiogenicShock | 0.000 | |||
| No | 149 (97.4%) | 256 (84.2%) | 400 (100.0%) | |
| Yes | 4 ( 2.6%) | 48 (15.8%) | 0 ( 0.0%) | |
| entry | 0.001 | |||
| Femoral | 58 (37.9%) | 133 (43.8%) | 121 (30.2%) | |
| Radial | 95 (62.1%) | 171 (56.2%) | 279 (69.8%) | |
| EF | 55.0 ± 9.3 | 52.4 ± 9.5 | 59.2 ± 8.7 | 0.000 |
| height | 163.3 ± 8.2 | 165.1 ± 8.2 | 161.7 ± 9.7 | 0.000 |
| weight | 64.3 ± 10.2 | 65.7 ± 11.6 | 64.5 ± 11.6 | 0.361 |
| BMI | 24.1 ± 3.2 | 24.0 ± 3.3 | 24.6 ± 3.4 | 0.064 |
| obesity | 0.186 | |||
| No | 106 (69.3%) | 209 (68.8%) | 252 (63.0%) | |
| Yes | 47 (30.7%) | 95 (31.2%) | 148 (37.0%) | |
| TC | 193.7 ± 53.6 | 183.2 ± 43.4 | 183.5 ± 48.3 | 0.057 |
| LDLC | 126.1 ± 44.7 | 116.7 ± 39.5 | 112.9 ± 40.4 | 0.004 |
| HDLC | 38.9 ± 11.9 | 38.5 ± 11.0 | 37.8 ± 10.9 | 0.501 |
| TG | 130.1 ± 88.5 | 106.5 ± 72.0 | 137.4 ± 101.6 | 0.000 |
| DM | 0.209 | |||
| No | 96 (62.7%) | 208 (68.4%) | 249 (62.2%) | |
| Yes | 57 (37.3%) | 96 (31.6%) | 151 (37.8%) | |
| HBP | 0.002 | |||
| No | 62 (40.5%) | 150 (49.3%) | 144 (36.0%) | |
| Yes | 91 (59.5%) | 154 (50.7%) | 256 (64.0%) | |
| smoking | 0.000 | |||
| Ex-smoker | 42 (27.5%) | 66 (21.7%) | 96 (24.0%) | |
| Never | 50 (32.7%) | 97 (31.9%) | 185 (46.2%) | |
| Smoker | 61 (39.9%) | 141 (46.4%) | 119 (29.8%) |
out1=mytable(sex+DM~.,data=acs)
myhtml(out1)| sex | Male | Female | ||||
|---|---|---|---|---|---|---|
| DM |
No (N=380) |
Yes (N=190) |
p |
No (N=173) |
Yes (N=114) |
p |
| age | 60.9 ± 11.5 | 60.1 ± 10.6 | 0.472 | 69.3 ± 11.4 | 67.8 ± 9.7 | 0.257 |
| cardiogenicShock | 0.685 | 0.296 | ||||
| No | 355 (93.4%) | 175 (92.1%) | 168 (97.1%) | 107 (93.9%) | ||
| Yes | 25 ( 6.6%) | 15 ( 7.9%) | 5 ( 2.9%) | 7 ( 6.1%) | ||
| entry | 0.552 | 0.665 | ||||
| Femoral | 125 (32.9%) | 68 (35.8%) | 74 (42.8%) | 45 (39.5%) | ||
| Radial | 255 (67.1%) | 122 (64.2%) | 99 (57.2%) | 69 (60.5%) | ||
| Dx | 0.219 | 0.240 | ||||
| NSTEMI | 71 (18.7%) | 32 (16.8%) | 25 (14.5%) | 25 (21.9%) | ||
| STEMI | 154 (40.5%) | 66 (34.7%) | 54 (31.2%) | 30 (26.3%) | ||
| Unstable Angina | 155 (40.8%) | 92 (48.4%) | 94 (54.3%) | 59 (51.8%) | ||
| EF | 56.5 ± 8.3 | 53.9 ± 11.0 | 0.007 | 56.0 ± 10.1 | 56.6 ± 10.0 | 0.655 |
| height | 168.1 ± 5.8 | 167.5 ± 6.7 | 0.386 | 153.9 ± 6.5 | 153.6 ± 5.8 | 0.707 |
| weight | 68.1 ± 10.4 | 69.8 ± 10.2 | 0.070 | 56.5 ± 8.7 | 58.4 ± 10.0 | 0.106 |
| BMI | 24.0 ± 3.1 | 24.9 ± 3.5 | 0.005 | 23.8 ± 3.2 | 24.8 ± 4.0 | 0.046 |
| obesity | 0.027 | 0.359 | ||||
| No | 261 (68.7%) | 112 (58.9%) | 121 (69.9%) | 73 (64.0%) | ||
| Yes | 119 (31.3%) | 78 (41.1%) | 52 (30.1%) | 41 (36.0%) | ||
| TC | 184.1 ± 46.7 | 181.8 ± 44.5 | 0.572 | 186.0 ± 43.1 | 193.3 ± 60.8 | 0.274 |
| LDLC | 117.9 ± 41.8 | 112.1 ± 39.4 | 0.115 | 116.3 ± 35.2 | 119.8 ± 48.6 | 0.519 |
| HDLC | 38.4 ± 11.4 | 36.8 ± 9.6 | 0.083 | 39.2 ± 10.9 | 38.8 ± 12.2 | 0.821 |
| TG | 115.2 ± 72.2 | 153.4 ± 130.7 | 0.000 | 114.2 ± 82.4 | 128.4 ± 65.5 | 0.112 |
| HBP | 0.000 | 0.356 | ||||
| No | 205 (53.9%) | 68 (35.8%) | 54 (31.2%) | 29 (25.4%) | ||
| Yes | 175 (46.1%) | 122 (64.2%) | 119 (68.8%) | 85 (74.6%) | ||
| smoking | 0.386 | 0.093 | ||||
| Ex-smoker | 101 (26.6%) | 54 (28.4%) | 34 (19.7%) | 15 (13.2%) | ||
| Never | 77 (20.3%) | 46 (24.2%) | 118 (68.2%) | 91 (79.8%) | ||
| Smoker | 202 (53.2%) | 90 (47.4%) | 21 (12.1%) | 8 ( 7.0%) | ||
If you want more beautiful table, you can use mylatex function.
mylatex(mytable(sex+DM~age+Dx,data=acs))
latextest.png
You can adjust font size of latex table by using parameter size from 1 to 10.
out=mytable(sex~age+Dx,data=acs)
for(i in c(3,5))
mylatex(out,size=i,caption=paste("Table ",i,". Fontsize=",i,sep=""))latextest2.png
If you want to export your table into csv file format, you can use mycsv function.
mycsv(out,file="test.csv")
mycsv(out1,fil="test1.csv")Following figure is a screen-shot in which test.csv and test1.csv files are opened with Numbers.
csvtest.png
ztableYou can use ztable() function from the ztable package.
require(ztable)## Loading required package: ztable## Welcome to package ztable ver 0.2.3require(magrittr)## Loading required package: magrittrmytable(sex+DM~.,data=acs) %>%
ztable %>%
addSigColor %>%
print(type="html")| Male | Female | |||||||
| p | p | |||||||
|---|---|---|---|---|---|---|---|---|
| (N=380) | (N=190) | (N=173) | (N=114) | |||||
| age | 60.9 ± 11.5 | 60.1 ± 10.6 | 0.472 | 69.3 ± 11.4 | 67.8 ± 9.7 | 0.257 | ||
library(moonBook)
densityplot(age~sex,data=acs)
densityplot(age~Dx,data=acs)
require(survival)Loading required package: survivaldata(colon)Warning in data(colon): data set 'colon' not foundout1=glm(status~sex+age+rx+obstruct+node4,data=colon)
out2=glm(status~rx+node4,data=colon)
ORplot(out1,type=2,show.CI=TRUE,xlab="This is xlab",main="Odds Ratio")
ORplot(out2,type=1)
ORplot(out1,type=1,show.CI=TRUE,col=c("blue","red"))
ORplot(out1,type=4,show.CI=TRUE,sig.level=0.05)
ORplot(out1,type=1,show.CI=TRUE,main="Odds Ratio",sig.level=0.05,
pch=1,cex=2,lwd=4,col=c("red","blue"))
attach(colon)
colon$TS=Surv(time,status==1)
out=mycph(TS~.,data=colon)
mycph : perform coxph of individual expecting variables
Call: TS ~ ., data= colon
study was excluded : NA
status was excluded : infiniteout HR lcl ucl p
id 1.00 1.00 1.00 0.317
rxLev 0.98 0.84 1.14 0.786
rxLev+5FU 0.64 0.55 0.76 0.000
sex 0.97 0.85 1.10 0.610
age 1.00 0.99 1.00 0.382
obstruct 1.27 1.09 1.49 0.003
perfor 1.30 0.92 1.85 0.142
adhere 1.37 1.16 1.62 0.000
nodes 1.09 1.08 1.10 0.000
differ 1.36 1.19 1.55 0.000
extent 1.78 1.53 2.07 0.000
surg 1.28 1.11 1.47 0.001
node4 2.47 2.17 2.83 0.000
time 0.64 0.62 0.66 0.000
etype 0.81 0.71 0.92 0.001HRplot(out,type=2,show.CI=TRUE,cex=2,sig=0.05,
main="Hazard ratios of all individual variables")