library(readxl)
library(table1)
## 
## Attaching package: 'table1'
## The following objects are masked from 'package:base':
## 
##     units, units<-
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyverse)
## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ stringr 1.4.1
## ✔ tidyr   1.2.1     ✔ forcats 0.5.2
## ✔ readr   2.1.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
data01 <- read_excel("~/Desktop/Clean data.xlsx")

Drop id column

data01_dropid <- data01 %>% select(-id)

Rename of multiple variables

dataname <- data01_dropid %>% rename(SBP = sbp_mean, DBP = dbp_mean, 
                                     pulse = pulse_mean, stroke = target_st,
                                     eGFR = egfr_ckd_epi, hypertension = hyper,
                                     diabetes = diabetes_over_boundary, 
                                     smokingsts = tob, BMI = bmi)

Table1

dat = dataname

dat$hypertension <- as.factor(dat$hypertension)
dat$diabetes <- as.factor(dat$diabetes)
dat$stress <- as.factor(dat$stress)
dat$stroke <- as.factor(dat$stroke)
dat$smokingsts <- as.factor(dat$smokingsts)
dat$exer <- as.factor(dat$exer)
dat$sex <- as.factor(dat$sex)

table1(~ age + sex + smokingsts + BMI + SBP + DBP + eGFR + hypertension + diabetes  + stress +
         exer |stroke, dat)
0
(N=6951)		 1
(N=438)		 Overall
(N=7389)
age
Mean (SD) 54.6 (13.1) 64.4 (9.78) 55.1 (13.1)
Median [Min, Max] 55.0 [30.0, 84.0] 66.0 [32.0, 80.0] 56.0 [30.0, 84.0]
sex
0 3143 (45.2%) 234 (53.4%) 3377 (45.7%)
1 3808 (54.8%) 204 (46.6%) 4012 (54.3%)
smokingsts
1 1999 (28.8%) 141 (32.2%) 2140 (29.0%)
2 1075 (15.5%) 87 (19.9%) 1162 (15.7%)
3 3877 (55.8%) 210 (47.9%) 4087 (55.3%)
BMI
Mean (SD) 22.5 (3.00) 23.1 (3.21) 22.5 (3.01)
Median [Min, Max] 22.3 [14.2, 30.5] 23.0 [14.2, 30.5] 22.3 [14.2, 30.5]
SBP
Mean (SD) 126 (20.5) 138 (21.9) 126 (20.8)
Median [Min, Max] 123 [78.0, 180] 137 [86.0, 180] 124 [78.0, 180]
DBP
Mean (SD) 77.4 (11.7) 81.0 (12.5) 77.6 (11.8)
Median [Min, Max] 77.0 [47.5, 108] 81.0 [47.5, 108] 77.0 [47.5, 108]
eGFR
Mean (SD) 78.3 (14.3) 70.4 (13.5) 77.9 (14.3)
Median [Min, Max] 79.5 [40.7, 116] 72.6 [40.7, 105] 79.0 [40.7, 116]
hypertension
0 4897 (70.5%) 197 (45.0%) 5094 (68.9%)
1 2054 (29.5%) 241 (55.0%) 2295 (31.1%)
diabetes
0 6153 (88.5%) 338 (77.2%) 6491 (87.8%)
1 798 (11.5%) 100 (22.8%) 898 (12.2%)
stress
1 2306 (33.2%) 157 (35.8%) 2463 (33.3%)
2 3580 (51.5%) 208 (47.5%) 3788 (51.3%)
9 1065 (15.3%) 73 (16.7%) 1138 (15.4%)
exer
1 2711 (39.0%) 176 (40.2%) 2887 (39.1%)
2 4240 (61.0%) 262 (59.8%) 4502 (60.9%)