format_output.Rmd
# Create a data set
dt <- mtcars[1:5, 1:6] %>%
tibble::rownames_to_column(var = "Model") %>%
mutate_if(is.numeric, scales::number)
format_flextable(dt)
Model |
mpg |
cyl |
disp |
hp |
drat |
wt |
Mazda RX4 |
21.00 |
6.0 |
160 |
110 |
3.900 |
2.62 |
Mazda RX4 Wag |
21.00 |
6.0 |
160 |
110 |
3.900 |
2.88 |
Datsun 710 |
22.80 |
4.0 |
108 |
93 |
3.850 |
2.32 |
Hornet 4 Drive |
21.40 |
6.0 |
258 |
110 |
3.080 |
3.22 |
Hornet Sportabout |
18.70 |
8.0 |
360 |
175 |
3.150 |
3.44 |
tableby(arm ~ age + sex + race + bmi + fu.stat, data = mockstudy) %>%
format_tableby() %>%
add_footer("P values arise from linear models for continuous variables and Pearson's chi-squared test for categorical")
A: IFL (N=428) |
F: FOLFOX (N=691) |
G: IROX (N=380) |
Total (N=1499) |
P value |
|
Age in Years |
0.61 |
||||
Mean (SD) |
59.673 (11.365) |
60.301 (11.632) |
59.763 (11.499) |
59.985 (11.519) |
|
Range |
27.000 - 88.000 |
19.000 - 88.000 |
26.000 - 85.000 |
19.000 - 88.000 |
|
sex |
0.19 |
||||
Male |
277 (64.7%) |
411 (59.5%) |
228 (60.0%) |
916 (61.1%) |
|
Female |
151 (35.3%) |
280 (40.5%) |
152 (40.0%) |
583 (38.9%) |
|
Race |
0.37 |
||||
N-Miss |
0 |
6 |
1 |
7 |
|
African-Am |
39 (9.1%) |
49 (7.2%) |
27 (7.1%) |
115 (7.7%) |
|
Asian |
1 (0.2%) |
14 (2.0%) |
3 (0.8%) |
18 (1.2%) |
|
Caucasian |
371 (86.7%) |
586 (85.5%) |
331 (87.3%) |
1288 (86.3%) |
|
Hawaii/Pacific |
1 (0.2%) |
3 (0.4%) |
1 (0.3%) |
5 (0.3%) |
|
Hispanic |
12 (2.8%) |
28 (4.1%) |
14 (3.7%) |
54 (3.6%) |
|
Native-Am/Alaska |
2 (0.5%) |
1 (0.1%) |
2 (0.5%) |
5 (0.3%) |
|
Other |
2 (0.5%) |
4 (0.6%) |
1 (0.3%) |
7 (0.5%) |
|
Body Mass Index (kg/m^2) |
0.89 |
||||
N-Miss |
9 |
20 |
4 |
33 |
|
Mean (SD) |
27.290 (5.552) |
27.210 (5.173) |
27.106 (5.751) |
27.206 (5.432) |
|
Range |
14.053 - 53.008 |
16.649 - 49.130 |
15.430 - 60.243 |
14.053 - 60.243 |
|
fu.stat |
<0.001 |
||||
Mean (SD) |
1.958 (0.201) |
1.857 (0.351) |
1.932 (0.253) |
1.905 (0.294) |
|
Range |
1.000 - 2.000 |
1.000 - 2.000 |
1.000 - 2.000 |
1.000 - 2.000 |
|
P values arise from linear models for continuous variables and Pearson's chi-squared test for categorical |
modelsum(mdquality.s ~ age + bmi, data=mockstudy, adjust=~sex, family=binomial, show.adjust = F, show.intercept = F) %>%
format_modelsum() %>%
add_footer("P values from logistic regression adjusting for sex.")
OR (95% CI) |
P value |
|
Age in Years |
1.00 (0.98, 1.01) |
0.78 |
Body Mass Index (kg/m^2) |
1.02 (0.99, 1.06) |
0.22 |
P values from logistic regression adjusting for sex. |
second_header <- list(values = c("", "Group 1", "Group 2"), colwidths = c(1,3,3))
dt %>%
format_flextable(header2 = second_header, bold_header = F)
Group 1 |
Group 2 |
|||||
Model |
mpg |
cyl |
disp |
hp |
drat |
wt |
Mazda RX4 |
21.00 |
6.0 |
160 |
110 |
3.900 |
2.62 |
Mazda RX4 Wag |
21.00 |
6.0 |
160 |
110 |
3.900 |
2.88 |
Datsun 710 |
22.80 |
4.0 |
108 |
93 |
3.850 |
2.32 |
Hornet 4 Drive |
21.40 |
6.0 |
258 |
110 |
3.080 |
3.22 |
Hornet Sportabout |
18.70 |
8.0 |
360 |
175 |
3.150 |
3.44 |