Ngay 1: Gioi thieu R Viec 1: Tai R Viec 1: Cai dat
#install.packages(c("lessR", "table1", "simpleboot", "boot", "gapminder", "ggfortify", "DescTools", "epiDisplay", "BMA", "ggplot2", "gridExtra", "metafor", "MatchIt", "cobalt"), dependencies = T)
df = read.csv("D:\\THU24\\LopAI-NCKH01.2026\\Stroke Data.csv")
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
## 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
summary(df)
## id gender age hypertension
## Min. : 67 Length:5110 Min. : 0.08 Min. :0.00000
## 1st Qu.:17741 Class :character 1st Qu.:25.00 1st Qu.:0.00000
## Median :36932 Mode :character Median :45.00 Median :0.00000
## Mean :36518 Mean :43.23 Mean :0.09746
## 3rd Qu.:54682 3rd Qu.:61.00 3rd Qu.:0.00000
## Max. :72940 Max. :82.00 Max. :1.00000
##
## heart_disease ever_married work_type Residence_type
## Min. :0.00000 Length:5110 Length:5110 Length:5110
## 1st Qu.:0.00000 Class :character Class :character Class :character
## Median :0.00000 Mode :character Mode :character Mode :character
## Mean :0.05401
## 3rd Qu.:0.00000
## Max. :1.00000
##
## avg_glucose_level bmi smoking_status stroke
## Min. : 55.12 Min. :10.30 Length:5110 Min. :0.00000
## 1st Qu.: 77.25 1st Qu.:23.50 Class :character 1st Qu.:0.00000
## Median : 91.89 Median :28.10 Mode :character Median :0.00000
## Mean :106.15 Mean :28.89 Mean :0.04873
## 3rd Qu.:114.09 3rd Qu.:33.10 3rd Qu.:0.00000
## Max. :271.74 Max. :97.60 Max. :1.00000
## NA's :201
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
df$bmi_cat[df$bmi<18.5] = "Underweight"
df$bmi_cat[18.5<=df$bmi & df$bmi<25.0] = "Normal"
df$bmi_cat[25.0<=df$bmi & df$bmi<30] = "Overweight"
table(df$bmi_cat)
##
## Normal Overweight Underweight
## 1243 1409 337
df$stroke = factor(df$stroke, levels = c(0, 1), labels = c("No", "Yes"))
summary(df)
## id gender age hypertension
## Min. : 67 Length:5110 Min. : 0.08 Min. :0.00000
## 1st Qu.:17741 Class :character 1st Qu.:25.00 1st Qu.:0.00000
## Median :36932 Mode :character Median :45.00 Median :0.00000
## Mean :36518 Mean :43.23 Mean :0.09746
## 3rd Qu.:54682 3rd Qu.:61.00 3rd Qu.:0.00000
## Max. :72940 Max. :82.00 Max. :1.00000
##
## heart_disease ever_married work_type Residence_type
## Min. :0.00000 Length:5110 Length:5110 Length:5110
## 1st Qu.:0.00000 Class :character Class :character Class :character
## Median :0.00000 Mode :character Mode :character Mode :character
## Mean :0.05401
## 3rd Qu.:0.00000
## Max. :1.00000
##
## avg_glucose_level bmi smoking_status stroke
## Min. : 55.12 Min. :10.30 Length:5110 No :4861
## 1st Qu.: 77.25 1st Qu.:23.50 Class :character Yes: 249
## Median : 91.89 Median :28.10 Mode :character
## Mean :106.15 Mean :28.89
## 3rd Qu.:114.09 3rd Qu.:33.10
## Max. :271.74 Max. :97.60
## NA's :201
## sex bmi_cat
## Min. :0.0000 Length:5110
## 1st Qu.:0.0000 Class :character
## Median :0.0000 Mode :character
## Mean :0.4143
## 3rd Qu.:1.0000
## Max. :2.0000
##
library(table1)
##
## Attaching package: 'table1'
## The following objects are masked from 'package:base':
##
## units, units<-
table1(~ age + gender + hypertension + heart_disease + ever_married + work_type + Residence_type + avg_glucose_level + bmi + smoking_status | stroke, data = df)
| No (N=4861) |
Yes (N=249) |
Overall (N=5110) |
|
|---|---|---|---|
| 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] |
| 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%) |
| 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] |
| ever_married | |||
| No | 1728 (35.5%) | 29 (11.6%) | 1757 (34.4%) |
| Yes | 3133 (64.5%) | 220 (88.4%) | 3353 (65.6%) |
| 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%) |
| 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%) |
| 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%) |
df$hypert.f = as.factor(df$hypertension)
table1(~hypertension + as.factor(hypertension) + hypert.f | stroke, data = df)
| No (N=4861) |
Yes (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%) |
| hypert.f | |||
| 0 | 4429 (91.1%) | 183 (73.5%) | 4612 (90.3%) |
| 1 | 432 (8.9%) | 66 (26.5%) | 498 (9.7%) |