Ngày 1: giới thiệu Rmarkdown

Việc 1: Tải R

Việc 2: Cài đặt packages

install.packages(c(“lessR”, “table1”, “simpleboot”, “boot”, “gapminder”, “ggfortify”, “DescTools”, “epiDisplay”, “BMA”, “ggplot2”, “gridExtra”, “metafor”, “MatchIt”, “cobalt”), dependencies = TRUE)

Việc 3: Đọc dữ liệu

df=read.csv("C:\\Users\\NGAN\\Downloads\\DỮ LIỆU THỰC HÀNH (Gửi học viên)-20260105T085751Z-1-001\\DỮ LIỆU THỰC HÀNH (Gửi học viên)\\Stroke Data.csv")
dim(df)
## [1] 5110   12

tiêu đề

head(df,10)
##       id gender age hypertension heart_disease ever_married     work_type
## 1   9046   Male  67            0             1          Yes       Private
## 2  51676 Female  61            0             0          Yes Self-employed
## 3  31112   Male  80            0             1          Yes       Private
## 4  60182 Female  49            0             0          Yes       Private
## 5   1665 Female  79            1             0          Yes Self-employed
## 6  56669   Male  81            0             0          Yes       Private
## 7  53882   Male  74            1             1          Yes       Private
## 8  10434 Female  69            0             0           No       Private
## 9  27419 Female  59            0             0          Yes       Private
## 10 60491 Female  78            0             0          Yes       Private
##    Residence_type avg_glucose_level  bmi  smoking_status stroke
## 1           Urban            228.69 36.6 formerly smoked      1
## 2           Rural            202.21   NA    never smoked      1
## 3           Rural            105.92 32.5    never smoked      1
## 4           Urban            171.23 34.4          smokes      1
## 5           Rural            174.12 24.0    never smoked      1
## 6           Urban            186.21 29.0 formerly smoked      1
## 7           Rural             70.09 27.4    never smoked      1
## 8           Urban             94.39 22.8    never smoked      1
## 9           Rural             76.15   NA         Unknown      1
## 10          Urban             58.57 24.2         Unknown      1

Việc 5: Biên tập dữ liệu

5.1. Mã hóa biến Sex

5.1. Mã hóa biến gender (Female/Male/Other) thành biến sex với giá trị 0/1/2 (0= Male; 1= Female; 2= Other)

df$sex[df$gender == "Female"]= 0
df$sex[df$gender == "Male"]= 1
df$sex[df$gender == "Other"]= 2

head(df)
##      id gender age hypertension heart_disease ever_married     work_type
## 1  9046   Male  67            0             1          Yes       Private
## 2 51676 Female  61            0             0          Yes Self-employed
## 3 31112   Male  80            0             1          Yes       Private
## 4 60182 Female  49            0             0          Yes       Private
## 5  1665 Female  79            1             0          Yes Self-employed
## 6 56669   Male  81            0             0          Yes       Private
##   Residence_type avg_glucose_level  bmi  smoking_status stroke sex
## 1          Urban            228.69 36.6 formerly smoked      1   1
## 2          Rural            202.21   NA    never smoked      1   0
## 3          Rural            105.92 32.5    never smoked      1   1
## 4          Urban            171.23 34.4          smokes      1   0
## 5          Rural            174.12 24.0    never smoked      1   0
## 6          Urban            186.21 29.0 formerly smoked      1   1
table(df$sex, df$gender)
##    
##     Female Male Other
##   0   2994    0     0
##   1      0 2115     0
##   2      0    0     1

5.2. Mã hoá biến bmi thành biến bmi_cat với 4 nhóm như sau:

Nếu bmi < 18.5 thì bmi_cat = “Underweight” 
Nếu 18.5  bmi < 25.0 thì bmi_cat = “Normal”  
Nếu 25.0  bmi < 30 thì bmi_cat = “Overweight” 
Nếu bmi ≥ 30.0 thì bmi = “Obese” 
df$bmi_cat[df$bmi<18.5]="Underweight"
df$bmi_cat[df$bmi>=18.5 & df$bmi<25.0]="Normal"
df$bmi_cat[df$bmi>=25 & df$bmi<30]="Overweight"
df$bmi_cat[df$bmi>=30]="Obese"

table(df$bmi_cat)
## 
##      Normal       Obese  Overweight Underweight 
##        1243        1920        1409         337

5.3. Bien Stroke

df$stroke1=as.factor(df$stroke)
stroke2=as.factor(df$stroke)

table(df$stroke1, df$stroke)
##    
##        0    1
##   0 4861    0
##   1    0  249
head(df)
##      id gender age hypertension heart_disease ever_married     work_type
## 1  9046   Male  67            0             1          Yes       Private
## 2 51676 Female  61            0             0          Yes Self-employed
## 3 31112   Male  80            0             1          Yes       Private
## 4 60182 Female  49            0             0          Yes       Private
## 5  1665 Female  79            1             0          Yes Self-employed
## 6 56669   Male  81            0             0          Yes       Private
##   Residence_type avg_glucose_level  bmi  smoking_status stroke sex    bmi_cat
## 1          Urban            228.69 36.6 formerly smoked      1   1      Obese
## 2          Rural            202.21   NA    never smoked      1   0       <NA>
## 3          Rural            105.92 32.5    never smoked      1   1      Obese
## 4          Urban            171.23 34.4          smokes      1   0      Obese
## 5          Rural            174.12 24.0    never smoked      1   0     Normal
## 6          Urban            186.21 29.0 formerly smoked      1   1 Overweight
##   stroke1
## 1       1
## 2       1
## 3       1
## 4       1
## 5       1
## 6       1

Việc 6 Phân tích mô tả

6.1. Mô tả đặc điểm tuổi (age), giới tính (gender), bệnh cao huyết áp (hypertension), bệnh tim (heart_disease), tình trạng gia đình (ever_married), việc làm (work_type), nơi ở (Residence_type), nồng độ đường huyết (avg_glucose_level), chỉ số khối cơ thể (bmi), và tình trạng hút thuốc (smoking_status) theo tình trạng đột quị (stroke)

library(table1)
## 
## Attaching package: 'table1'
## The following objects are masked from 'package:base':
## 
##     units, units<-
table1(~ gender + age + hypertension + heart_disease + work_type + smoking_status + ever_married + Residence_type + avg_glucose_level + bmi | stroke, data = df)
## Warning in table1.formula(~gender + age + hypertension + heart_disease + :
## Terms to the right of '|' in formula 'x' define table columns and are expected
## to be factors with meaningful labels.
0
(N=4861)		 1
(N=249)		 Overall
(N=5110)
gender
Female 2853 (58.7%) 141 (56.6%) 2994 (58.6%)
Male 2007 (41.3%) 108 (43.4%) 2115 (41.4%)
Other 1 (0.0%) 0 (0%) 1 (0.0%)
age
Mean (SD) 42.0 (22.3) 67.7 (12.7) 43.2 (22.6)
Median [Min, Max] 43.0 [0.0800, 82.0] 71.0 [1.32, 82.0] 45.0 [0.0800, 82.0]
hypertension
Mean (SD) 0.0889 (0.285) 0.265 (0.442) 0.0975 (0.297)
Median [Min, Max] 0 [0, 1.00] 0 [0, 1.00] 0 [0, 1.00]
heart_disease
Mean (SD) 0.0471 (0.212) 0.189 (0.392) 0.0540 (0.226)
Median [Min, Max] 0 [0, 1.00] 0 [0, 1.00] 0 [0, 1.00]
work_type
children 685 (14.1%) 2 (0.8%) 687 (13.4%)
Govt_job 624 (12.8%) 33 (13.3%) 657 (12.9%)
Never_worked 22 (0.5%) 0 (0%) 22 (0.4%)
Private 2776 (57.1%) 149 (59.8%) 2925 (57.2%)
Self-employed 754 (15.5%) 65 (26.1%) 819 (16.0%)
smoking_status
formerly smoked 815 (16.8%) 70 (28.1%) 885 (17.3%)
never smoked 1802 (37.1%) 90 (36.1%) 1892 (37.0%)
smokes 747 (15.4%) 42 (16.9%) 789 (15.4%)
Unknown 1497 (30.8%) 47 (18.9%) 1544 (30.2%)
ever_married
No 1728 (35.5%) 29 (11.6%) 1757 (34.4%)
Yes 3133 (64.5%) 220 (88.4%) 3353 (65.6%)
Residence_type
Rural 2400 (49.4%) 114 (45.8%) 2514 (49.2%)
Urban 2461 (50.6%) 135 (54.2%) 2596 (50.8%)
avg_glucose_level
Mean (SD) 105 (43.8) 133 (61.9) 106 (45.3)
Median [Min, Max] 91.5 [55.1, 268] 105 [56.1, 272] 91.9 [55.1, 272]
bmi
Mean (SD) 28.8 (7.91) 30.5 (6.33) 28.9 (7.85)
Median [Min, Max] 28.0 [10.3, 97.6] 29.7 [16.9, 56.6] 28.1 [10.3, 97.6]
Missing 161 (3.3%) 40 (16.1%) 201 (3.9%) 
table1(~ hypertension + as.factor(hypertension) + heart_disease + as.factor(heart_disease) | stroke, data = df)
## Warning in table1.formula(~hypertension + as.factor(hypertension) +
## heart_disease + : Terms to the right of '|' in formula 'x' define table columns
## and are expected to be factors with meaningful labels.
0
(N=4861)		 1
(N=249)		 Overall
(N=5110)
hypertension
Mean (SD) 0.0889 (0.285) 0.265 (0.442) 0.0975 (0.297)
Median [Min, Max] 0 [0, 1.00] 0 [0, 1.00] 0 [0, 1.00]
as.factor(hypertension)
0 4429 (91.1%) 183 (73.5%) 4612 (90.3%)
1 432 (8.9%) 66 (26.5%) 498 (9.7%)
heart_disease
Mean (SD) 0.0471 (0.212) 0.189 (0.392) 0.0540 (0.226)
Median [Min, Max] 0 [0, 1.00] 0 [0, 1.00] 0 [0, 1.00]
as.factor(heart_disease)
0 4632 (95.3%) 202 (81.1%) 4834 (94.6%)
1 229 (4.7%) 47 (18.9%) 276 (5.4%)

6.2. Bạn nhận xét như thế nào về kết quả của bệnh cao huyết áp và bệnh tim. Làm cách nào để trình bày kết quả tốt hơn?