Alternative-Assessment
HangGu-s2124920
2022/1/9
#Cardiac analysis report
#Getting Data:
#The main purpose is to predict whether a given person has heart disease, with the help of several factors such as age,
#cholesterol level, type of chest pain, etc.
#Description of the dataset:
#The data has 303 observations and 14 variables. Each observation contains the following information about the individual.
#age:- the age of the individual in years
#sex:- gender (1=male; 0=female)
#cp – Type of chest pain (1=typical angina; 2=atypical angina; 3=non-angina; 4=asymptomatic).
#trestbps – Resting Blood Pressure
#chol – Serum cholesterol, unit: mg/dl
#fbs – Fasting blood glucose level >120 mg/dl (1=true; 0=false)
#restecg – Resting ECG results (0=normal; 1=with ST-T; 2=hypertrophic)
#thalach - maximum heart rate reached
#exang – exercise-induced angina (1=yes; 0=no)
#oldpeak – exercise-induced ST depression relative to resting state
#slope – the slope of the ST segment peak during exercise (1=up-slope; 2=flat; 3=down-slope)
#ca – the number of major vessels (0-4), colored by Flourosopy
#thalassemia – Thalassemia is an inherited blood disorder that affects the body’s ability to produce #hemoglobin and red blood cells. 1=normal; 2=fixed defect; 3=reversible defect
#target – predictive attribute – diagnosis of cardiac disease (angiographic disease state) (value 0=<50% diameter stenosis; value 1=>50% diameter stenosis)
#Here we introduce the datasets and dependencies we need
setwd("F:\\RData")
library(ggplot2)
library(rpart)
library(rpart.plot)
library(ROCR)
library(randomForest)## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## 载入程辑包：'randomForest'## The following object is masked from 'package:ggplot2':
##
## marginlibrary(e1071)
library(lattice)
library(caret)
library(ROCR)
#Read the incoming dataset，header = T means that the given data has its own header, or in other words, the first observation is also considered for prediction.
heart<-read.csv("Heart.csv",header = T)
#Understanding Data
#When we use the head function to examine the first six observations of the data.
head(heart)## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 1 63 1 3 145 233 1 0 150 0 2.3 0 0 1
## 2 37 1 2 130 250 0 1 187 0 3.5 0 0 2
## 3 41 0 1 130 204 0 0 172 0 1.4 2 0 2
## 4 56 1 1 120 236 0 1 178 0 0.8 2 0 2
## 5 57 0 0 120 354 0 1 163 1 0.6 2 0 2
## 6 57 1 0 140 192 0 1 148 0 0.4 1 0 1
## target
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1#When we use the tail function to examine the last six observations of the data.
tail(heart)## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 298 59 1 0 164 176 1 0 90 0 1.0 1 2 1
## 299 57 0 0 140 241 0 1 123 1 0.2 1 0 3
## 300 45 1 3 110 264 0 1 132 0 1.2 1 0 3
## 301 68 1 0 144 193 1 1 141 0 3.4 1 2 3
## 302 57 1 0 130 131 0 1 115 1 1.2 1 1 3
## 303 57 0 1 130 236 0 0 174 0 0.0 1 1 2
## target
## 298 0
## 299 0
## 300 0
## 301 0
## 302 0
## 303 0#Check our data structure
str(heart)## 'data.frame': 303 obs. of 14 variables:
## $ age : int 63 37 41 56 57 57 56 44 52 57 ...
## $ sex : int 1 1 0 1 0 1 0 1 1 1 ...
## $ cp : int 3 2 1 1 0 0 1 1 2 2 ...
## $ trestbps: int 145 130 130 120 120 140 140 120 172 150 ...
## $ chol : int 233 250 204 236 354 192 294 263 199 168 ...
## $ fbs : int 1 0 0 0 0 0 0 0 1 0 ...
## $ restecg : int 0 1 0 1 1 1 0 1 1 1 ...
## $ thalach : int 150 187 172 178 163 148 153 173 162 174 ...
## $ exang : int 0 0 0 0 1 0 0 0 0 0 ...
## $ oldpeak : num 2.3 3.5 1.4 0.8 0.6 0.4 1.3 0 0.5 1.6 ...
## $ slope : int 0 0 2 2 2 1 1 2 2 2 ...
## $ ca : int 0 0 0 0 0 0 0 0 0 0 ...
## $ thal : int 1 2 2 2 2 1 2 3 3 2 ...
## $ target : int 1 1 1 1 1 1 1 1 1 1 ...#View our data digest
summary(heart)## age sex cp trestbps
## Min. :29.00 Min. :0.0000 Min. :0.000 Min. : 94.0
## 1st Qu.:47.50 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:120.0
## Median :55.00 Median :1.0000 Median :1.000 Median :130.0
## Mean :54.37 Mean :0.6832 Mean :0.967 Mean :131.7
## 3rd Qu.:61.00 3rd Qu.:1.0000 3rd Qu.:2.000 3rd Qu.:140.0
## Max. :77.00 Max. :1.0000 Max. :3.000 Max. :200.0
## NA's :1
## chol fbs restecg thalach
## Min. :126.0 Min. :0.0000 Min. :0.0000 Min. : 71.0
## 1st Qu.:211.0 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:133.5
## Median :240.0 Median :0.0000 Median :1.0000 Median :153.0
## Mean :246.3 Mean :0.1485 Mean :0.5281 Mean :149.6
## 3rd Qu.:274.5 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:166.0
## Max. :564.0 Max. :1.0000 Max. :2.0000 Max. :202.0
##
## exang oldpeak slope ca
## Min. :0.0000 Min. :0.00 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.00 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :0.80 Median :1.000 Median :0.0000
## Mean :0.3267 Mean :1.04 Mean :1.399 Mean :0.7294
## 3rd Qu.:1.0000 3rd Qu.:1.60 3rd Qu.:2.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :6.20 Max. :2.000 Max. :4.0000
##
## thal target
## Min. :0.000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:0.0000
## Median :2.000 Median :1.0000
## Mean :2.314 Mean :0.5446
## 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :3.000 Max. :1.0000
## #Four ways of subsetting / choosing row or columns
##1.Extracting one column from a data frame
head(heart[1])## age
## 1 63
## 2 37
## 3 41
## 4 56
## 5 57
## 6 57##2.Extracting a column as a single character vector
head(heart[[1]])## [1] 63 37 41 56 57 57##3.Extracting multiple columns of a data frame
head(heart[1:3])## age sex cp
## 1 63 1 3
## 2 37 1 2
## 3 41 0 1
## 4 56 1 1
## 5 57 0 0
## 6 57 1 0##4.Selecting the 2st & 2nd row then select the 1st & 3rd row
heart[c(2,2),c(2,3)]## sex cp
## 2 1 2
## 2.1 1 2#Four ways to Preprocess data (Cleaning, etc)
##Check for missing values
colSums(is.na(heart))## age sex cp trestbps chol fbs restecg thalach
## 0 0 0 1 0 0 0 0
## exang oldpeak slope ca thal target
## 0 0 0 0 0 0## handle missing values
###(1) Delete missing values
dat1 <- na.omit(heart)
dat1## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 1 63 1 3 145 233 1 0 150 0 2.3 0 0 1
## 2 37 1 2 130 250 0 1 187 0 3.5 0 0 2
## 3 41 0 1 130 204 0 0 172 0 1.4 2 0 2
## 4 56 1 1 120 236 0 1 178 0 0.8 2 0 2
## 5 57 0 0 120 354 0 1 163 1 0.6 2 0 2
## 6 57 1 0 140 192 0 1 148 0 0.4 1 0 1
## 7 56 0 1 140 294 0 0 153 0 1.3 1 0 2
## 8 44 1 1 120 263 0 1 173 0 0.0 2 0 3
## 9 52 1 2 172 199 1 1 162 0 0.5 2 0 3
## 10 57 1 2 150 168 0 1 174 0 1.6 2 0 2
## 11 54 1 0 140 239 0 1 160 0 1.2 2 0 2
## 12 48 0 2 130 275 0 1 139 0 0.2 2 0 2
## 13 49 1 1 130 266 0 1 171 0 0.6 2 0 2
## 14 64 1 3 110 211 0 0 144 1 1.8 1 0 2
## 15 58 0 3 150 283 1 0 162 0 1.0 2 0 2
## 16 50 0 2 120 219 0 1 158 0 1.6 1 0 2
## 17 58 0 2 120 340 0 1 172 0 0.0 2 0 2
## 18 66 0 3 150 226 0 1 114 0 2.6 0 0 2
## 19 43 1 0 150 247 0 1 171 0 1.5 2 0 2
## 20 69 0 3 140 239 0 1 151 0 1.8 2 2 2
## 21 59 1 0 135 234 0 1 161 0 0.5 1 0 3
## 22 44 1 2 130 233 0 1 179 1 0.4 2 0 2
## 23 42 1 0 140 226 0 1 178 0 0.0 2 0 2
## 24 61 1 2 150 243 1 1 137 1 1.0 1 0 2
## 25 40 1 3 140 199 0 1 178 1 1.4 2 0 3
## 26 71 0 1 160 302 0 1 162 0 0.4 2 2 2
## 27 59 1 2 150 212 1 1 157 0 1.6 2 0 2
## 28 51 1 2 110 175 0 1 123 0 0.6 2 0 2
## 29 65 0 2 140 417 1 0 157 0 0.8 2 1 2
## 30 53 1 2 130 197 1 0 152 0 1.2 0 0 2
## 31 41 0 1 105 198 0 1 168 0 0.0 2 1 2
## 32 65 1 0 120 177 0 1 140 0 0.4 2 0 3
## 33 44 1 1 130 219 0 0 188 0 0.0 2 0 2
## 34 54 1 2 125 273 0 0 152 0 0.5 0 1 2
## 35 51 1 3 125 213 0 0 125 1 1.4 2 1 2
## 36 46 0 2 142 177 0 0 160 1 1.4 0 0 2
## 37 54 0 2 135 304 1 1 170 0 0.0 2 0 2
## 38 54 1 2 150 232 0 0 165 0 1.6 2 0 3
## 39 65 0 2 155 269 0 1 148 0 0.8 2 0 2
## 40 65 0 2 160 360 0 0 151 0 0.8 2 0 2
## 41 51 0 2 140 308 0 0 142 0 1.5 2 1 2
## 42 48 1 1 130 245 0 0 180 0 0.2 1 0 2
## 43 45 1 0 104 208 0 0 148 1 3.0 1 0 2
## 44 53 0 0 130 264 0 0 143 0 0.4 1 0 2
## 45 39 1 2 140 321 0 0 182 0 0.0 2 0 2
## 46 52 1 1 120 325 0 1 172 0 0.2 2 0 2
## 47 44 1 2 140 235 0 0 180 0 0.0 2 0 2
## 48 47 1 2 138 257 0 0 156 0 0.0 2 0 2
## 49 53 0 2 128 216 0 0 115 0 0.0 2 0 0
## 50 53 0 0 138 234 0 0 160 0 0.0 2 0 2
## 51 51 0 2 130 256 0 0 149 0 0.5 2 0 2
## 52 66 1 0 120 302 0 0 151 0 0.4 1 0 2
## 53 62 1 2 130 231 0 1 146 0 1.8 1 3 3
## 54 44 0 2 108 141 0 1 175 0 0.6 1 0 2
## 55 63 0 2 135 252 0 0 172 0 0.0 2 0 2
## 56 52 1 1 134 201 0 1 158 0 0.8 2 1 2
## 57 48 1 0 122 222 0 0 186 0 0.0 2 0 2
## 58 45 1 0 115 260 0 0 185 0 0.0 2 0 2
## 59 34 1 3 118 182 0 0 174 0 0.0 2 0 2
## 60 57 0 0 128 303 0 0 159 0 0.0 2 1 2
## 61 71 0 2 110 265 1 0 130 0 0.0 2 1 2
## 62 54 1 1 108 309 0 1 156 0 0.0 2 0 3
## 63 52 1 3 118 186 0 0 190 0 0.0 1 0 1
## 64 41 1 1 135 203 0 1 132 0 0.0 1 0 1
## 65 58 1 2 140 211 1 0 165 0 0.0 2 0 2
## 66 35 0 0 138 183 0 1 182 0 1.4 2 0 2
## 67 51 1 2 100 222 0 1 143 1 1.2 1 0 2
## 68 45 0 1 130 234 0 0 175 0 0.6 1 0 2
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## 75 43 0 2 122 213 0 1 165 0 0.2 1 0 2
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## 105 50 1 2 129 196 0 1 163 0 0.0 2 0 2
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## 143 42 0 2 120 209 0 1 173 0 0.0 1 0 2
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## 150 42 1 2 130 180 0 1 150 0 0.0 2 0 2
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## 152 71 0 0 112 149 0 1 125 0 1.6 1 0 2
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## 163 41 1 1 120 157 0 1 182 0 0.0 2 0 2
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## 165 38 1 2 138 175 0 1 173 0 0.0 2 4 2
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## 175 60 1 0 130 206 0 0 132 1 2.4 1 2 3
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## 192 58 1 0 128 216 0 0 131 1 2.2 1 3 3
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## 195 60 1 2 140 185 0 0 155 0 3.0 1 0 2
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## 208 60 0 0 150 258 0 0 157 0 2.6 1 2 3
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## 210 59 1 0 140 177 0 1 162 1 0.0 2 1 3
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## 215 56 1 0 125 249 1 0 144 1 1.2 1 1 2
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## 217 62 0 2 130 263 0 1 97 0 1.2 1 1 3
## 218 63 1 0 130 330 1 0 132 1 1.8 2 3 3
## 219 65 1 0 135 254 0 0 127 0 2.8 1 1 3
## 220 48 1 0 130 256 1 0 150 1 0.0 2 2 3
## 221 63 0 0 150 407 0 0 154 0 4.0 1 3 3
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## 223 65 1 3 138 282 1 0 174 0 1.4 1 1 2
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## 225 54 1 0 110 239 0 1 126 1 2.8 1 1 3
## 226 70 1 0 145 174 0 1 125 1 2.6 0 0 3
## 227 62 1 1 120 281 0 0 103 0 1.4 1 1 3
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## 236 51 1 0 140 299 0 1 173 1 1.6 2 0 3
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## 264 63 0 0 108 269 0 1 169 1 1.8 1 2 2
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## 269 54 1 0 122 286 0 0 116 1 3.2 1 2 2
## 270 56 1 0 130 283 1 0 103 1 1.6 0 0 3
## 271 46 1 0 120 249 0 0 144 0 0.8 2 0 3
## 272 61 1 3 134 234 0 1 145 0 2.6 1 2 2
## 273 67 1 0 120 237 0 1 71 0 1.0 1 0 2
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## 275 47 1 0 110 275 0 0 118 1 1.0 1 1 2
## 276 52 1 0 125 212 0 1 168 0 1.0 2 2 3
## 277 58 1 0 146 218 0 1 105 0 2.0 1 1 3
## 278 57 1 1 124 261 0 1 141 0 0.3 2 0 3
## 279 58 0 1 136 319 1 0 152 0 0.0 2 2 2
## 280 61 1 0 138 166 0 0 125 1 3.6 1 1 2
## 281 42 1 0 136 315 0 1 125 1 1.8 1 0 1
## 282 52 1 0 128 204 1 1 156 1 1.0 1 0 0
## 283 59 1 2 126 218 1 1 134 0 2.2 1 1 1
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## 292 58 1 0 114 318 0 2 140 0 4.4 0 3 1
## 293 58 0 0 170 225 1 0 146 1 2.8 1 2 1
## 294 67 1 2 152 212 0 0 150 0 0.8 1 0 3
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## 301 68 1 0 144 193 1 1 141 0 3.4 1 2 3
## 302 57 1 0 130 131 0 1 115 1 1.2 1 1 3
## 303 57 0 1 130 236 0 0 174 0 0.0 1 1 2
## target
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 1
## 11 1
## 12 1
## 13 1
## 14 1
## 15 1
## 16 1
## 17 1
## 18 1
## 19 1
## 20 1
## 21 1
## 22 1
## 23 1
## 24 1
## 25 1
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## 303 0colSums(is.na(dat1))## age sex cp trestbps chol fbs restecg thalach
## 0 0 0 0 0 0 0 0
## exang oldpeak slope ca thal target
## 0 0 0 0 0 0###(2) Mean and median padding
#mean fill
heart$trestbps[is.na(heart$trestbps)] <- mean(heart$trestbps,na.rm=T)
#median padding
heart$trestbps[is.na(heart$trestbps)] <- median(heart$trestbps,na.rm = T)
#Calculate the number of missing values, if equal to 0, there is no missing value
sum(is.na(heart))## [1] 0# data sorting
#In R, you can use the Order() function to sort a data frame. The default sort order is ascending.
#Add a minus sign in front of the sorting variable to get the sorting result in descending order
#example
#The order() function returns the order of the sorted columns from small to large
order(heart$trestbps)## [1] 72 125 67 254 274 88 83 85 43 31 80 119 144 54 62 98 124 231
## [19] 264 14 28 61 89 110 114 134 139 172 176 190 200 201 207 225 265 275
## [37] 289 300 81 95 123 127 152 184 186 262 266 292 58 86 142 177 59 63
## [55] 87 126 147 213 268 4 5 8 16 17 32 46 52 69 71 104 106 109
## [73] 116 118 130 133 137 141 143 149 161 163 167 173 179 193 199 209 212 227
## [91] 228 234 260 271 273 295 57 75 158 269 263 70 91 188 246 278 297 34
## [109] 35 77 159 198 202 215 230 237 239 276 135 240 283 49 60 79 82 138
## [127] 140 192 206 211 257 282 290 105 2 3 12 13 22 30 33 42 44 51
## [145] 53 68 73 99 100 115 117 121 136 150 156 157 160 169 171 175 183 187
## [163] 191 217 218 220 235 270 302 303 90 92 94 162 174 181 216 245 252 56
## [181] 132 247 272 287 21 37 55 64 76 219 129 279 281 48 50 66 93 108
## [199] 120 122 155 164 165 223 253 280 6 7 11 20 23 25 29 41 45 47
## [217] 65 74 78 97 103 113 145 168 170 178 189 195 210 222 236 238 250 251
## [235] 285 286 296 299 36 96 256 258 301 1 194 214 226 243 154 277 101 291
## [253] 10 15 18 19 24 27 38 112 148 180 182 185 197 203 208 221 259 84
## [271] 128 244 284 294 288 39 146 26 40 107 131 151 166 205 233 241 248 255
## [289] 298 232 153 196 229 293 9 242 102 261 111 204 267 249 224newdata <- heart[order(heart$trestbps),]
newdata## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 72 51 1 2 94.0000 227 0 1 154 1 0.0 2 1 3
## 125 39 0 2 94.0000 199 0 1 179 0 0.0 2 0 2
## 67 51 1 2 100.0000 222 0 1 143 1 1.2 1 0 2
## 254 67 1 0 100.0000 299 0 0 125 1 0.9 1 2 2
## 274 58 1 0 100.0000 234 0 1 156 0 0.1 2 1 3
## 88 46 1 1 101.0000 197 1 1 156 0 0.0 2 0 3
## 83 60 0 2 102.0000 318 0 1 160 0 0.0 2 1 2
## 85 42 0 0 102.0000 265 0 0 122 0 0.6 1 0 2
## 43 45 1 0 104.0000 208 0 0 148 1 3.0 1 0 2
## 31 41 0 1 105.0000 198 0 1 168 0 0.0 2 1 2
## 80 58 1 2 105.0000 240 0 0 154 1 0.6 1 0 3
## 119 46 0 1 105.0000 204 0 1 172 0 0.0 2 0 2
## 144 67 0 0 106.0000 223 0 1 142 0 0.3 2 2 2
## 54 44 0 2 108.0000 141 0 1 175 0 0.6 1 0 2
## 62 54 1 1 108.0000 309 0 1 156 0 0.0 2 0 3
## 98 52 1 0 108.0000 233 1 1 147 0 0.1 2 3 3
## 124 54 0 2 108.0000 267 0 0 167 0 0.0 2 0 2
## 231 47 1 2 108.0000 243 0 1 152 0 0.0 2 0 2
## 264 63 0 0 108.0000 269 0 1 169 1 1.8 1 2 2
## 14 64 1 3 110.0000 211 0 0 144 1 1.8 1 0 2
## 28 51 1 2 110.0000 175 0 1 123 0 0.6 2 0 2
## 61 71 0 2 110.0000 265 1 0 130 0 0.0 2 1 2
## 89 54 0 2 110.0000 214 0 1 158 0 1.6 1 0 2
## 110 50 0 0 110.0000 254 0 0 159 0 0.0 2 0 2
## 114 43 1 0 110.0000 211 0 1 161 0 0.0 2 0 3
## 134 41 1 1 110.0000 235 0 1 153 0 0.0 2 0 2
## 139 57 1 0 110.0000 201 0 1 126 1 1.5 1 0 1
## 172 48 1 1 110.0000 229 0 1 168 0 1.0 0 0 3
## 176 40 1 0 110.0000 167 0 0 114 1 2.0 1 0 3
## 190 41 1 0 110.0000 172 0 0 158 0 0.0 2 0 3
## 200 65 1 0 110.0000 248 0 0 158 0 0.6 2 2 1
## 201 44 1 0 110.0000 197 0 0 177 0 0.0 2 1 2
## 207 59 1 0 110.0000 239 0 0 142 1 1.2 1 1 3
## 225 54 1 0 110.0000 239 0 1 126 1 2.8 1 1 3
## 265 54 1 0 110.0000 206 0 0 108 1 0.0 1 1 2
## 275 47 1 0 110.0000 275 0 0 118 1 1.0 1 1 2
## 289 57 1 0 110.0000 335 0 1 143 1 3.0 1 1 3
## 300 45 1 3 110.0000 264 0 1 132 0 1.2 1 0 3
## 81 41 1 2 112.0000 250 0 1 179 0 0.0 2 0 2
## 95 45 0 1 112.0000 160 0 1 138 0 0.0 1 0 2
## 123 41 0 2 112.0000 268 0 0 172 1 0.0 2 0 2
## 127 47 1 0 112.0000 204 0 1 143 0 0.1 2 0 2
## 152 71 0 0 112.0000 149 0 1 125 0 1.6 1 0 2
## 184 58 1 2 112.0000 230 0 0 165 0 2.5 1 1 3
## 186 44 1 0 112.0000 290 0 0 153 0 0.0 2 1 2
## 262 52 1 0 112.0000 230 0 1 160 0 0.0 2 1 2
## 266 66 1 0 112.0000 212 0 0 132 1 0.1 2 1 2
## 292 58 1 0 114.0000 318 0 2 140 0 4.4 0 3 1
## 58 45 1 0 115.0000 260 0 0 185 0 0.0 2 0 2
## 86 67 0 2 115.0000 564 0 0 160 0 1.6 1 0 3
## 142 43 1 0 115.0000 303 0 1 181 0 1.2 1 0 2
## 177 60 1 0 117.0000 230 1 1 160 1 1.4 2 2 3
## 59 34 1 3 118.0000 182 0 0 174 0 0.0 2 0 2
## 63 52 1 3 118.0000 186 0 0 190 0 0.0 1 0 1
## 87 68 1 2 118.0000 277 0 1 151 0 1.0 2 1 3
## 126 34 0 1 118.0000 210 0 1 192 0 0.7 2 0 2
## 147 44 0 2 118.0000 242 0 1 149 0 0.3 1 1 2
## 213 39 1 0 118.0000 219 0 1 140 0 1.2 1 0 3
## 268 49 1 2 118.0000 149 0 0 126 0 0.8 2 3 2
## 4 56 1 1 120.0000 236 0 1 178 0 0.8 2 0 2
## 5 57 0 0 120.0000 354 0 1 163 1 0.6 2 0 2
## 8 44 1 1 120.0000 263 0 1 173 0 0.0 2 0 3
## 16 50 0 2 120.0000 219 0 1 158 0 1.6 1 0 2
## 17 58 0 2 120.0000 340 0 1 172 0 0.0 2 0 2
## 32 65 1 0 120.0000 177 0 1 140 0 0.4 2 0 3
## 46 52 1 1 120.0000 325 0 1 172 0 0.2 2 0 2
## 52 66 1 0 120.0000 302 0 0 151 0 0.4 1 0 2
## 69 44 1 1 120.0000 220 0 1 170 0 0.0 2 0 2
## 71 54 1 2 120.0000 258 0 0 147 0 0.4 1 0 3
## 104 42 1 2 120.0000 240 1 1 194 0 0.8 0 0 3
## 106 68 0 2 120.0000 211 0 0 115 0 1.5 1 0 2
## 109 50 0 1 120.0000 244 0 1 162 0 1.1 2 0 2
## 116 37 0 2 120.0000 215 0 1 170 0 0.0 2 0 2
## 118 56 1 3 120.0000 193 0 0 162 0 1.9 1 0 3
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## 133 42 1 1 120.0000 295 0 1 162 0 0.0 2 0 2
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## 161 56 1 1 120.0000 240 0 1 169 0 0.0 0 0 2
## 163 41 1 1 120.0000 157 0 1 182 0 0.0 2 0 2
## 167 67 1 0 120.0000 229 0 0 129 1 2.6 1 2 3
## 173 58 1 1 120.0000 284 0 0 160 0 1.8 1 0 2
## 179 43 1 0 120.0000 177 0 0 120 1 2.5 1 0 3
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## 199 62 1 0 120.0000 267 0 1 99 1 1.8 1 2 3
## 209 49 1 2 120.0000 188 0 1 139 0 2.0 1 3 3
## 212 61 1 0 120.0000 260 0 1 140 1 3.6 1 1 3
## 227 62 1 1 120.0000 281 0 0 103 0 1.4 1 1 3
## 228 35 1 0 120.0000 198 0 1 130 1 1.6 1 0 3
## 234 64 1 0 120.0000 246 0 0 96 1 2.2 0 1 2
## 260 38 1 3 120.0000 231 0 1 182 1 3.8 1 0 3
## 271 46 1 0 120.0000 249 0 0 144 0 0.8 2 0 3
## 273 67 1 0 120.0000 237 0 1 71 0 1.0 1 0 2
## 295 44 1 0 120.0000 169 0 1 144 1 2.8 0 0 1
## 57 48 1 0 122.0000 222 0 0 186 0 0.0 2 0 2
## 75 43 0 2 122.0000 213 0 1 165 0 0.2 1 0 2
## 158 35 1 1 122.0000 192 0 1 174 0 0.0 2 0 2
## 269 54 1 0 122.0000 286 0 0 116 1 3.2 1 2 2
## 263 53 1 0 123.0000 282 0 1 95 1 2.0 1 2 3
## 70 62 0 0 124.0000 209 0 1 163 0 0.0 2 0 2
## 91 48 1 2 124.0000 255 1 1 175 0 0.0 2 2 2
## 188 54 1 0 124.0000 266 0 0 109 1 2.2 1 1 3
## 246 48 1 0 124.0000 274 0 0 166 0 0.5 1 0 3
## 278 57 1 1 124.0000 261 0 1 141 0 0.3 2 0 3
## 297 63 0 0 124.0000 197 0 1 136 1 0.0 1 0 2
## 34 54 1 2 125.0000 273 0 0 152 0 0.5 0 1 2
## 35 51 1 3 125.0000 213 0 0 125 1 1.4 2 1 2
## 77 51 1 2 125.0000 245 1 0 166 0 2.4 1 0 2
## 159 58 1 1 125.0000 220 0 1 144 0 0.4 1 4 3
## 198 67 1 0 125.0000 254 1 1 163 0 0.2 1 2 3
## 202 60 1 0 125.0000 258 0 0 141 1 2.8 1 1 3
## 215 56 1 0 125.0000 249 1 0 144 1 1.2 1 1 2
## 230 64 1 2 125.0000 309 0 1 131 1 1.8 1 0 3
## 237 58 1 0 125.0000 300 0 0 171 0 0.0 2 2 3
## 239 77 1 0 125.0000 304 0 0 162 1 0.0 2 3 2
## 276 52 1 0 125.0000 212 0 1 168 0 1.0 2 2 3
## 135 41 0 1 126.0000 306 0 1 163 0 0.0 2 0 2
## 240 35 1 0 126.0000 282 0 0 156 1 0.0 2 0 3
## 283 59 1 2 126.0000 218 1 1 134 0 2.2 1 1 1
## 49 53 0 2 128.0000 216 0 0 115 0 0.0 2 0 0
## 60 57 0 0 128.0000 303 0 0 159 0 0.0 2 1 2
## 79 52 1 1 128.0000 205 1 1 184 0 0.0 2 0 2
## 82 45 1 1 128.0000 308 0 0 170 0 0.0 2 0 2
## 138 62 1 1 128.0000 208 1 0 140 0 0.0 2 0 2
## 140 64 1 0 128.0000 263 0 1 105 1 0.2 1 1 3
## 192 58 1 0 128.0000 216 0 0 131 1 2.2 1 3 3
## 206 52 1 0 128.0000 255 0 1 161 1 0.0 2 1 3
## 211 57 1 2 128.0000 229 0 0 150 0 0.4 1 1 3
## 257 58 1 0 128.0000 259 0 0 130 1 3.0 1 2 3
## 282 52 1 0 128.0000 204 1 1 156 1 1.0 1 0 0
## 290 55 0 0 128.0000 205 0 2 130 1 2.0 1 1 3
## 105 50 1 2 129.0000 196 0 1 163 0 0.0 2 0 2
## 2 37 1 2 130.0000 250 0 1 187 0 3.5 0 0 2
## 3 41 0 1 130.0000 204 0 0 172 0 1.4 2 0 2
## 12 48 0 2 130.0000 275 0 1 139 0 0.2 2 0 2
## 13 49 1 1 130.0000 266 0 1 171 0 0.6 2 0 2
## 22 44 1 2 130.0000 233 0 1 179 1 0.4 2 0 2
## 30 53 1 2 130.0000 197 1 0 152 0 1.2 0 0 2
## 33 44 1 1 130.0000 219 0 0 188 0 0.0 2 0 2
## 42 48 1 1 130.0000 245 0 0 180 0 0.2 1 0 2
## 44 53 0 0 130.0000 264 0 0 143 0 0.4 1 0 2
## 51 51 0 2 130.0000 256 0 0 149 0 0.5 2 0 2
## 53 62 1 2 130.0000 231 0 1 146 0 1.8 1 3 3
## 68 45 0 1 130.0000 234 0 0 175 0 0.6 1 0 2
## 73 29 1 1 130.0000 204 0 0 202 0 0.0 2 0 2
## 99 43 1 2 130.0000 315 0 1 162 0 1.9 2 1 2
## 100 53 1 2 130.0000 246 1 0 173 0 0.0 2 3 2
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## 245 56 1 0 132.0000 184 0 0 105 1 2.1 1 1 1
## 252 43 1 0 132.0000 247 1 0 143 1 0.1 1 4 3
## 56 52 1 1 134.0000 201 0 1 158 0 0.8 2 1 2
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## 64 41 1 1 135.0000 203 0 1 132 0 0.0 1 0 1
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## 48 47 1 2 138.0000 257 0 0 156 0 0.0 2 0 2
## 50 53 0 0 138.0000 234 0 0 160 0 0.0 2 0 2
## 66 35 0 0 138.0000 183 0 1 182 0 1.4 2 0 2
## 93 52 1 2 138.0000 223 0 1 169 0 0.0 2 4 2
## 108 45 0 0 138.0000 236 0 0 152 1 0.2 1 0 2
## 120 46 0 0 138.0000 243 0 0 152 1 0.0 1 0 2
## 122 59 1 0 138.0000 271 0 0 182 0 0.0 2 0 2
## 155 39 0 2 138.0000 220 0 1 152 0 0.0 1 0 2
## 164 38 1 2 138.0000 175 0 1 173 0 0.0 2 4 2
## 165 38 1 2 138.0000 175 0 1 173 0 0.0 2 4 2
## 223 65 1 3 138.0000 282 1 0 174 0 1.4 1 1 2
## 253 62 0 0 138.0000 294 1 1 106 0 1.9 1 3 2
## 280 61 1 0 138.0000 166 0 0 125 1 3.6 1 1 2
## 6 57 1 0 140.0000 192 0 1 148 0 0.4 1 0 1
## 7 56 0 1 140.0000 294 0 0 153 0 1.3 1 0 2
## 11 54 1 0 140.0000 239 0 1 160 0 1.2 2 0 2
## 20 69 0 3 140.0000 239 0 1 151 0 1.8 2 2 2
## 23 42 1 0 140.0000 226 0 1 178 0 0.0 2 0 2
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## 29 65 0 2 140.0000 417 1 0 157 0 0.8 2 1 2
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## 145 76 0 2 140.0000 197 0 2 116 0 1.1 1 0 2
## 168 62 0 0 140.0000 268 0 0 160 0 3.6 0 2 2
## 170 53 1 0 140.0000 203 1 0 155 1 3.1 0 0 3
## 178 64 1 2 140.0000 335 0 1 158 0 0.0 2 0 2
## 189 50 1 2 140.0000 233 0 1 163 0 0.6 1 1 3
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## 1 63 1 3 145.0000 233 1 0 150 0 2.3 0 0 1
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## 243 64 1 0 145.0000 212 0 0 132 0 2.0 1 2 1
## 154 66 0 2 146.0000 278 0 0 152 0 0.0 1 1 2
## 277 58 1 0 146.0000 218 0 1 105 0 2.0 1 1 3
## 101 42 1 3 148.0000 244 0 0 178 0 0.8 2 2 2
## 291 61 1 0 148.0000 203 0 1 161 0 0.0 2 1 3
## 10 57 1 2 150.0000 168 0 1 174 0 1.6 2 0 2
## 15 58 0 3 150.0000 283 1 0 162 0 1.0 2 0 2
## 18 66 0 3 150.0000 226 0 1 114 0 2.6 0 0 2
## 19 43 1 0 150.0000 247 0 1 171 0 1.5 2 0 2
## 24 61 1 2 150.0000 243 1 1 137 1 1.0 1 0 2
## 27 59 1 2 150.0000 212 1 1 157 0 1.6 2 0 2
## 38 54 1 2 150.0000 232 0 0 165 0 1.6 2 0 3
## 112 57 1 2 150.0000 126 1 1 173 0 0.2 2 1 3
## 148 60 0 3 150.0000 240 0 1 171 0 0.9 2 0 2
## 180 57 1 0 150.0000 276 0 0 112 1 0.6 1 1 1
## 182 65 0 0 150.0000 225 0 0 114 0 1.0 1 3 3
## 185 50 1 0 150.0000 243 0 0 128 0 2.6 1 0 3
## 197 46 1 2 150.0000 231 0 1 147 0 3.6 1 0 2
## 203 58 1 0 150.0000 270 0 0 111 1 0.8 2 0 3
## 208 60 0 0 150.0000 258 0 0 157 0 2.6 1 2 3
## 221 63 0 0 150.0000 407 0 0 154 0 4.0 1 3 3
## 259 62 0 0 150.0000 244 0 1 154 1 1.4 1 0 2
## 84 52 1 3 152.0000 298 1 1 178 0 1.2 1 0 3
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## 244 57 1 0 152.0000 274 0 1 88 1 1.2 1 1 3
## 284 40 1 0 152.0000 223 0 1 181 0 0.0 2 0 3
## 294 67 1 2 152.0000 212 0 0 150 0 0.8 1 0 3
## 288 57 1 1 154.0000 232 0 0 164 0 0.0 2 1 2
## 39 65 0 2 155.0000 269 0 1 148 0 0.8 2 0 2
## 146 70 1 1 156.0000 245 0 0 143 0 0.0 2 0 2
## 26 71 0 1 160.0000 302 0 1 162 0 0.4 2 2 2
## 40 65 0 2 160.0000 360 0 0 151 0 0.8 2 0 2
## 107 69 1 3 160.0000 234 1 0 131 0 0.1 1 1 2
## 131 54 0 2 160.0000 201 0 1 163 0 0.0 2 1 2
## 151 66 1 0 160.0000 228 0 0 138 0 2.3 2 0 1
## 166 67 1 0 160.0000 286 0 0 108 1 1.5 1 3 2
## 205 62 0 0 160.0000 164 0 0 145 0 6.2 0 3 3
## 233 55 1 0 160.0000 289 0 0 145 1 0.8 1 1 3
## 241 70 1 2 160.0000 269 0 1 112 1 2.9 1 1 3
## 248 66 1 1 160.0000 246 0 1 120 1 0.0 1 3 1
## 255 59 1 3 160.0000 273 0 0 125 0 0.0 2 0 2
## 298 59 1 0 164.0000 176 1 0 90 0 1.0 1 2 1
## 232 57 1 0 165.0000 289 1 0 124 0 1.0 1 3 3
## 153 64 1 3 170.0000 227 0 0 155 0 0.6 1 0 3
## 196 59 1 0 170.0000 326 0 0 140 1 3.4 0 0 3
## 229 59 1 3 170.0000 288 0 0 159 0 0.2 1 0 3
## 293 58 0 0 170.0000 225 1 0 146 1 2.8 1 2 1
## 9 52 1 2 172.0000 199 1 1 162 0 0.5 2 0 3
## 242 59 0 0 174.0000 249 0 1 143 1 0.0 1 0 2
## 102 59 1 3 178.0000 270 0 0 145 0 4.2 0 0 3
## 261 66 0 0 178.0000 228 1 1 165 1 1.0 1 2 3
## 111 64 0 0 180.0000 325 0 1 154 1 0.0 2 0 2
## 204 68 1 2 180.0000 274 1 0 150 1 1.6 1 0 3
## 267 55 0 0 180.0000 327 0 2 117 1 3.4 1 0 2
## 249 54 1 1 192.0000 283 0 0 195 0 0.0 2 1 3
## 224 56 0 0 200.0000 288 1 0 133 1 4.0 0 2 3
## target
## 72 1
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## 95 1
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## 34 1
## 35 1
## 77 1
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## 198 0
## 202 0
## 215 0
## 230 0
## 237 0
## 239 0
## 276 0
## 135 1
## 240 0
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## 49 1
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## 105 1
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## 44 1
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## 65 1
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## 103 1
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## 293 0
## 9 1
## 242 0
## 102 1
## 261 0
## 111 1
## 204 0
## 267 0
## 249 0
## 224 0#Descending
newdata <- heart[order(-heart$age),]
newdata## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 239 77 1 0 125.0000 304 0 0 162 1 0.0 2 3 2
## 145 76 0 2 140.0000 197 0 2 116 0 1.1 1 0 2
## 130 74 0 1 120.0000 269 0 0 121 1 0.2 2 1 2
## 26 71 0 1 160.0000 302 0 1 162 0 0.4 2 2 2
## 61 71 0 2 110.0000 265 1 0 130 0 0.0 2 1 2
## 152 71 0 0 112.0000 149 0 1 125 0 1.6 1 0 2
## 146 70 1 1 156.0000 245 0 0 143 0 0.0 2 0 2
## 226 70 1 0 145.0000 174 0 1 125 1 2.6 0 0 3
## 235 70 1 0 130.0000 322 0 0 109 0 2.4 1 3 2
## 241 70 1 2 160.0000 269 0 1 112 1 2.9 1 1 3
## 20 69 0 3 140.0000 239 0 1 151 0 1.8 2 2 2
## 107 69 1 3 160.0000 234 1 0 131 0 0.1 1 1 2
## 250 69 1 2 140.0000 254 0 0 146 0 2.0 1 3 3
## 87 68 1 2 118.0000 277 0 1 151 0 1.0 2 1 3
## 106 68 0 2 120.0000 211 0 0 115 0 1.5 1 0 2
## 204 68 1 2 180.0000 274 1 0 150 1 1.6 1 0 3
## 301 68 1 0 144.0000 193 1 1 141 0 3.4 1 2 3
## 86 67 0 2 115.0000 564 0 0 160 0 1.6 1 0 3
## 128 67 0 2 152.0000 277 0 1 172 0 0.0 2 1 2
## 144 67 0 0 106.0000 223 0 1 142 0 0.3 2 2 2
## 166 67 1 0 160.0000 286 0 0 108 1 1.5 1 3 2
## 167 67 1 0 120.0000 229 0 0 129 1 2.6 1 2 3
## 198 67 1 0 125.0000 254 1 1 163 0 0.2 1 2 3
## 254 67 1 0 100.0000 299 0 0 125 1 0.9 1 2 2
## 273 67 1 0 120.0000 237 0 1 71 0 1.0 1 0 2
## 294 67 1 2 152.0000 212 0 0 150 0 0.8 1 0 3
## 18 66 0 3 150.0000 226 0 1 114 0 2.6 0 0 2
## 52 66 1 0 120.0000 302 0 0 151 0 0.4 1 0 2
## 151 66 1 0 160.0000 228 0 0 138 0 2.3 2 0 1
## 154 66 0 2 146.0000 278 0 0 152 0 0.0 1 1 2
## 248 66 1 1 160.0000 246 0 1 120 1 0.0 1 3 1
## 261 66 0 0 178.0000 228 1 1 165 1 1.0 1 2 3
## 266 66 1 0 112.0000 212 0 0 132 1 0.1 2 1 2
## 29 65 0 2 140.0000 417 1 0 157 0 0.8 2 1 2
## 32 65 1 0 120.0000 177 0 1 140 0 0.4 2 0 3
## 39 65 0 2 155.0000 269 0 1 148 0 0.8 2 0 2
## 40 65 0 2 160.0000 360 0 0 151 0 0.8 2 0 2
## 182 65 0 0 150.0000 225 0 0 114 0 1.0 1 3 3
## 200 65 1 0 110.0000 248 0 0 158 0 0.6 2 2 1
## 219 65 1 0 135.0000 254 0 0 127 0 2.8 1 1 3
## 223 65 1 3 138.0000 282 1 0 174 0 1.4 1 1 2
## 14 64 1 3 110.0000 211 0 0 144 1 1.8 1 0 2
## 111 64 0 0 180.0000 325 0 1 154 1 0.0 2 0 2
## 113 64 0 2 140.0000 313 0 1 133 0 0.2 2 0 3
## 121 64 0 0 130.0000 303 0 1 122 0 2.0 1 2 2
## 140 64 1 0 128.0000 263 0 1 105 1 0.2 1 1 3
## 153 64 1 3 170.0000 227 0 0 155 0 0.6 1 0 3
## 178 64 1 2 140.0000 335 0 1 158 0 0.0 2 0 2
## 230 64 1 2 125.0000 309 0 1 131 1 1.8 1 0 3
## 234 64 1 0 120.0000 246 0 0 96 1 2.2 0 1 2
## 243 64 1 0 145.0000 212 0 0 132 0 2.0 1 2 1
## 1 63 1 3 145.0000 233 1 0 150 0 2.3 0 0 1
## 55 63 0 2 135.0000 252 0 0 172 0 0.0 2 0 2
## 103 63 0 1 140.0000 195 0 1 179 0 0.0 2 2 2
## 169 63 1 0 130.0000 254 0 0 147 0 1.4 1 1 3
## 218 63 1 0 130.0000 330 1 0 132 1 1.8 2 3 3
## 221 63 0 0 150.0000 407 0 0 154 0 4.0 1 3 3
## 264 63 0 0 108.0000 269 0 1 169 1 1.8 1 2 2
## 296 63 1 0 140.0000 187 0 0 144 1 4.0 2 2 3
## 297 63 0 0 124.0000 197 0 1 136 1 0.0 1 0 2
## 53 62 1 2 130.0000 231 0 1 146 0 1.8 1 3 3
## 70 62 0 0 124.0000 209 0 1 163 0 0.0 2 0 2
## 97 62 0 0 140.0000 394 0 0 157 0 1.2 1 0 2
## 138 62 1 1 128.0000 208 1 0 140 0 0.0 2 0 2
## 168 62 0 0 140.0000 268 0 0 160 0 3.6 0 2 2
## 199 62 1 0 120.0000 267 0 1 99 1 1.8 1 2 3
## 205 62 0 0 160.0000 164 0 0 145 0 6.2 0 3 3
## 217 62 0 2 130.0000 263 0 1 97 0 1.2 1 1 3
## 227 62 1 1 120.0000 281 0 0 103 0 1.4 1 1 3
## 253 62 0 0 138.0000 294 1 1 106 0 1.9 1 3 2
## 259 62 0 0 150.0000 244 0 1 154 1 1.4 1 0 2
## 24 61 1 2 150.0000 243 1 1 137 1 1.0 1 0 2
## 183 61 0 0 130.0000 330 0 0 169 0 0.0 2 0 2
## 212 61 1 0 120.0000 260 0 1 140 1 3.6 1 1 3
## 214 61 0 0 145.0000 307 0 0 146 1 1.0 1 0 3
## 272 61 1 3 134.0000 234 0 1 145 0 2.6 1 2 2
## 280 61 1 0 138.0000 166 0 0 125 1 3.6 1 1 2
## 285 61 1 0 140.0000 207 0 0 138 1 1.9 2 1 3
## 291 61 1 0 148.0000 203 0 1 161 0 0.0 2 1 3
## 83 60 0 2 102.0000 318 0 1 160 0 0.0 2 1 2
## 137 60 0 2 120.0000 178 1 1 96 0 0.0 2 0 2
## 148 60 0 3 150.0000 240 0 1 171 0 0.9 2 0 2
## 175 60 1 0 130.0000 206 0 0 132 1 2.4 1 2 3
## 177 60 1 0 117.0000 230 1 1 160 1 1.4 2 2 3
## 187 60 1 0 130.0000 253 0 1 144 1 1.4 2 1 3
## 194 60 1 0 145.0000 282 0 0 142 1 2.8 1 2 3
## 195 60 1 2 140.0000 185 0 0 155 0 3.0 1 0 2
## 202 60 1 0 125.0000 258 0 0 141 1 2.8 1 1 3
## 208 60 0 0 150.0000 258 0 0 157 0 2.6 1 2 3
## 238 60 1 0 140.0000 293 0 0 170 0 1.2 1 2 3
## 21 59 1 0 135.0000 234 0 1 161 0 0.5 1 0 3
## 27 59 1 2 150.0000 212 1 1 157 0 1.6 2 0 2
## 78 59 1 1 140.0000 221 0 1 164 1 0.0 2 0 2
## 102 59 1 3 178.0000 270 0 0 145 0 4.2 0 0 3
## 122 59 1 0 138.0000 271 0 0 182 0 0.0 2 0 2
## 196 59 1 0 170.0000 326 0 0 140 1 3.4 0 0 3
## 207 59 1 0 110.0000 239 0 0 142 1 1.2 1 1 3
## 210 59 1 0 140.0000 177 0 1 162 1 0.0 2 1 3
## 229 59 1 3 170.0000 288 0 0 159 0 0.2 1 0 3
## 242 59 0 0 174.0000 249 0 1 143 1 0.0 1 0 2
## 255 59 1 3 160.0000 273 0 0 125 0 0.0 2 0 2
## 283 59 1 2 126.0000 218 1 1 134 0 2.2 1 1 1
## 287 59 1 3 134.0000 204 0 1 162 0 0.8 2 2 2
## 298 59 1 0 164.0000 176 1 0 90 0 1.0 1 2 1
## 15 58 0 3 150.0000 283 1 0 162 0 1.0 2 0 2
## 17 58 0 2 120.0000 340 0 1 172 0 0.0 2 0 2
## 65 58 1 2 140.0000 211 1 0 165 0 0.0 2 0 2
## 80 58 1 2 105.0000 240 0 0 154 1 0.6 1 0 3
## 90 58 0 0 131.7285 248 0 0 122 0 1.0 1 0 2
## 156 58 0 0 130.0000 197 0 1 131 0 0.6 1 0 2
## 159 58 1 1 125.0000 220 0 1 144 0 0.4 1 4 3
## 173 58 1 1 120.0000 284 0 0 160 0 1.8 1 0 2
## 174 58 1 2 132.0000 224 0 0 173 0 3.2 2 2 3
## 184 58 1 2 112.0000 230 0 0 165 0 2.5 1 1 3
## 192 58 1 0 128.0000 216 0 0 131 1 2.2 1 3 3
## 203 58 1 0 150.0000 270 0 0 111 1 0.8 2 0 3
## 237 58 1 0 125.0000 300 0 0 171 0 0.0 2 2 3
## 257 58 1 0 128.0000 259 0 0 130 1 3.0 1 2 3
## 274 58 1 0 100.0000 234 0 1 156 0 0.1 2 1 3
## 277 58 1 0 146.0000 218 0 1 105 0 2.0 1 1 3
## 279 58 0 1 136.0000 319 1 0 152 0 0.0 2 2 2
## 292 58 1 0 114.0000 318 0 2 140 0 4.4 0 3 1
## 293 58 0 0 170.0000 225 1 0 146 1 2.8 1 2 1
## 5 57 0 0 120.0000 354 0 1 163 1 0.6 2 0 2
## 6 57 1 0 140.0000 192 0 1 148 0 0.4 1 0 1
## 10 57 1 2 150.0000 168 0 1 174 0 1.6 2 0 2
## 60 57 0 0 128.0000 303 0 0 159 0 0.0 2 1 2
## 92 57 1 0 132.0000 207 0 1 168 1 0.0 2 0 3
## 112 57 1 2 150.0000 126 1 1 173 0 0.2 2 1 3
## 139 57 1 0 110.0000 201 0 1 126 1 1.5 1 0 1
## 180 57 1 0 150.0000 276 0 0 112 1 0.6 1 1 1
## 211 57 1 2 128.0000 229 0 0 150 0 0.4 1 1 3
## 232 57 1 0 165.0000 289 1 0 124 0 1.0 1 3 3
## 244 57 1 0 152.0000 274 0 1 88 1 1.2 1 1 3
## 278 57 1 1 124.0000 261 0 1 141 0 0.3 2 0 3
## 288 57 1 1 154.0000 232 0 0 164 0 0.0 2 1 2
## 289 57 1 0 110.0000 335 0 1 143 1 3.0 1 1 3
## 299 57 0 0 140.0000 241 0 1 123 1 0.2 1 0 3
## 302 57 1 0 130.0000 131 0 1 115 1 1.2 1 1 3
## 303 57 0 1 130.0000 236 0 0 174 0 0.0 1 1 2
## 4 56 1 1 120.0000 236 0 1 178 0 0.8 2 0 2
## 7 56 0 1 140.0000 294 0 0 153 0 1.3 1 0 2
## 118 56 1 3 120.0000 193 0 0 162 0 1.9 1 0 3
## 160 56 1 1 130.0000 221 0 0 163 0 0.0 2 0 3
## 161 56 1 1 120.0000 240 0 1 169 0 0.0 0 0 2
## 171 56 1 2 130.0000 256 1 0 142 1 0.6 1 1 1
## 215 56 1 0 125.0000 249 1 0 144 1 1.2 1 1 2
## 224 56 0 0 200.0000 288 1 0 133 1 4.0 0 2 3
## 245 56 1 0 132.0000 184 0 0 105 1 2.1 1 1 1
## 247 56 0 0 134.0000 409 0 0 150 1 1.9 1 2 3
## 270 56 1 0 130.0000 283 1 0 103 1 1.6 0 0 3
## 76 55 0 1 135.0000 250 0 0 161 0 1.4 1 0 2
## 115 55 1 1 130.0000 262 0 1 155 0 0.0 2 0 2
## 162 55 0 1 132.0000 342 0 1 166 0 1.2 2 0 2
## 181 55 1 0 132.0000 353 0 1 132 1 1.2 1 1 3
## 222 55 1 0 140.0000 217 0 1 111 1 5.6 0 0 3
## 233 55 1 0 160.0000 289 0 0 145 1 0.8 1 1 3
## 267 55 0 0 180.0000 327 0 2 117 1 3.4 1 0 2
## 290 55 0 0 128.0000 205 0 2 130 1 2.0 1 1 3
## 11 54 1 0 140.0000 239 0 1 160 0 1.2 2 0 2
## 34 54 1 2 125.0000 273 0 0 152 0 0.5 0 1 2
## 37 54 0 2 135.0000 304 1 1 170 0 0.0 2 0 2
## 38 54 1 2 150.0000 232 0 0 165 0 1.6 2 0 3
## 62 54 1 1 108.0000 309 0 1 156 0 0.0 2 0 3
## 71 54 1 2 120.0000 258 0 0 147 0 0.4 1 0 3
## 89 54 0 2 110.0000 214 0 1 158 0 1.6 1 0 2
## 94 54 0 1 132.0000 288 1 0 159 1 0.0 2 1 2
## 124 54 0 2 108.0000 267 0 0 167 0 0.0 2 0 2
## 131 54 0 2 160.0000 201 0 1 163 0 0.0 2 1 2
## 188 54 1 0 124.0000 266 0 0 109 1 2.2 1 1 3
## 193 54 1 0 120.0000 188 0 1 113 0 1.4 1 1 3
## 225 54 1 0 110.0000 239 0 1 126 1 2.8 1 1 3
## 249 54 1 1 192.0000 283 0 0 195 0 0.0 2 1 3
## 265 54 1 0 110.0000 206 0 0 108 1 0.0 1 1 2
## 269 54 1 0 122.0000 286 0 0 116 1 3.2 1 2 2
## 30 53 1 2 130.0000 197 1 0 152 0 1.2 0 0 2
## 44 53 0 0 130.0000 264 0 0 143 0 0.4 1 0 2
## 49 53 0 2 128.0000 216 0 0 115 0 0.0 2 0 0
## 50 53 0 0 138.0000 234 0 0 160 0 0.0 2 0 2
## 96 53 1 0 142.0000 226 0 0 111 1 0.0 2 0 3
## 100 53 1 2 130.0000 246 1 0 173 0 0.0 2 3 2
## 170 53 1 0 140.0000 203 1 0 155 1 3.1 0 0 3
## 263 53 1 0 123.0000 282 0 1 95 1 2.0 1 2 3
## 9 52 1 2 172.0000 199 1 1 162 0 0.5 2 0 3
## 46 52 1 1 120.0000 325 0 1 172 0 0.2 2 0 2
## 56 52 1 1 134.0000 201 0 1 158 0 0.8 2 1 2
## 63 52 1 3 118.0000 186 0 0 190 0 0.0 1 0 1
## 79 52 1 1 128.0000 205 1 1 184 0 0.0 2 0 2
## 84 52 1 3 152.0000 298 1 1 178 0 1.2 1 0 3
## 93 52 1 2 138.0000 223 0 1 169 0 0.0 2 4 2
## 98 52 1 0 108.0000 233 1 1 147 0 0.1 2 3 3
## 129 52 0 2 136.0000 196 0 0 169 0 0.1 1 0 2
## 206 52 1 0 128.0000 255 0 1 161 1 0.0 2 1 3
## 262 52 1 0 112.0000 230 0 1 160 0 0.0 2 1 2
## 276 52 1 0 125.0000 212 0 1 168 0 1.0 2 2 3
## 282 52 1 0 128.0000 204 1 1 156 1 1.0 1 0 0
## 28 51 1 2 110.0000 175 0 1 123 0 0.6 2 0 2
## 35 51 1 3 125.0000 213 0 0 125 1 1.4 2 1 2
## 41 51 0 2 140.0000 308 0 0 142 0 1.5 2 1 2
## 51 51 0 2 130.0000 256 0 0 149 0 0.5 2 0 2
## 67 51 1 2 100.0000 222 0 1 143 1 1.2 1 0 2
## 72 51 1 2 94.0000 227 0 1 154 1 0.0 2 1 3
## 74 51 1 0 140.0000 261 0 0 186 1 0.0 2 0 2
## 77 51 1 2 125.0000 245 1 0 166 0 2.4 1 0 2
## 141 51 0 2 120.0000 295 0 0 157 0 0.6 2 0 2
## 191 51 0 0 130.0000 305 0 1 142 1 1.2 1 0 3
## 236 51 1 0 140.0000 299 0 1 173 1 1.6 2 0 3
## 251 51 1 0 140.0000 298 0 1 122 1 4.2 1 3 3
## 16 50 0 2 120.0000 219 0 1 158 0 1.6 1 0 2
## 105 50 1 2 129.0000 196 0 1 163 0 0.0 2 0 2
## 109 50 0 1 120.0000 244 0 1 162 0 1.1 2 0 2
## 110 50 0 0 110.0000 254 0 0 159 0 0.0 2 0 2
## 185 50 1 0 150.0000 243 0 0 128 0 2.6 1 0 3
## 189 50 1 2 140.0000 233 0 1 163 0 0.6 1 1 3
## 258 50 1 0 144.0000 200 0 0 126 1 0.9 1 0 3
## 13 49 1 1 130.0000 266 0 1 171 0 0.6 2 0 2
## 132 49 0 1 134.0000 271 0 1 162 0 0.0 1 0 2
## 136 49 0 0 130.0000 269 0 1 163 0 0.0 2 0 2
## 209 49 1 2 120.0000 188 0 1 139 0 2.0 1 3 3
## 268 49 1 2 118.0000 149 0 0 126 0 0.8 2 3 2
## 12 48 0 2 130.0000 275 0 1 139 0 0.2 2 0 2
## 42 48 1 1 130.0000 245 0 0 180 0 0.2 1 0 2
## 57 48 1 0 122.0000 222 0 0 186 0 0.0 2 0 2
## 91 48 1 2 124.0000 255 1 1 175 0 0.0 2 2 2
## 172 48 1 1 110.0000 229 0 1 168 0 1.0 0 0 3
## 220 48 1 0 130.0000 256 1 0 150 1 0.0 2 2 3
## 246 48 1 0 124.0000 274 0 0 166 0 0.5 1 0 3
## 48 47 1 2 138.0000 257 0 0 156 0 0.0 2 0 2
## 127 47 1 0 112.0000 204 0 1 143 0 0.1 2 0 2
## 157 47 1 2 130.0000 253 0 1 179 0 0.0 2 0 2
## 231 47 1 2 108.0000 243 0 1 152 0 0.0 2 0 2
## 275 47 1 0 110.0000 275 0 0 118 1 1.0 1 1 2
## 36 46 0 2 142.0000 177 0 0 160 1 1.4 0 0 2
## 88 46 1 1 101.0000 197 1 1 156 0 0.0 2 0 3
## 119 46 0 1 105.0000 204 0 1 172 0 0.0 2 0 2
## 120 46 0 0 138.0000 243 0 0 152 1 0.0 1 0 2
## 197 46 1 2 150.0000 231 0 1 147 0 3.6 1 0 2
## 271 46 1 0 120.0000 249 0 0 144 0 0.8 2 0 3
## 286 46 1 0 140.0000 311 0 1 120 1 1.8 1 2 3
## 43 45 1 0 104.0000 208 0 0 148 1 3.0 1 0 2
## 58 45 1 0 115.0000 260 0 0 185 0 0.0 2 0 2
## 68 45 0 1 130.0000 234 0 0 175 0 0.6 1 0 2
## 82 45 1 1 128.0000 308 0 0 170 0 0.0 2 0 2
## 95 45 0 1 112.0000 160 0 1 138 0 0.0 1 0 2
## 108 45 0 0 138.0000 236 0 0 152 1 0.2 1 0 2
## 256 45 1 0 142.0000 309 0 0 147 1 0.0 1 3 3
## 300 45 1 3 110.0000 264 0 1 132 0 1.2 1 0 3
## 8 44 1 1 120.0000 263 0 1 173 0 0.0 2 0 3
## 22 44 1 2 130.0000 233 0 1 179 1 0.4 2 0 2
## 33 44 1 1 130.0000 219 0 0 188 0 0.0 2 0 2
## 47 44 1 2 140.0000 235 0 0 180 0 0.0 2 0 2
## 54 44 0 2 108.0000 141 0 1 175 0 0.6 1 0 2
## 69 44 1 1 120.0000 220 0 1 170 0 0.0 2 0 2
## 147 44 0 2 118.0000 242 0 1 149 0 0.3 1 1 2
## 149 44 1 2 120.0000 226 0 1 169 0 0.0 2 0 2
## 186 44 1 0 112.0000 290 0 0 153 0 0.0 2 1 2
## 201 44 1 0 110.0000 197 0 0 177 0 0.0 2 1 2
## 295 44 1 0 120.0000 169 0 1 144 1 2.8 0 0 1
## 19 43 1 0 150.0000 247 0 1 171 0 1.5 2 0 2
## 75 43 0 2 122.0000 213 0 1 165 0 0.2 1 0 2
## 99 43 1 2 130.0000 315 0 1 162 0 1.9 2 1 2
## 114 43 1 0 110.0000 211 0 1 161 0 0.0 2 0 3
## 142 43 1 0 115.0000 303 0 1 181 0 1.2 1 0 2
## 179 43 1 0 120.0000 177 0 0 120 1 2.5 1 0 3
## 216 43 0 0 132.0000 341 1 0 136 1 3.0 1 0 3
## 252 43 1 0 132.0000 247 1 0 143 1 0.1 1 4 3
## 23 42 1 0 140.0000 226 0 1 178 0 0.0 2 0 2
## 85 42 0 0 102.0000 265 0 0 122 0 0.6 1 0 2
## 101 42 1 3 148.0000 244 0 0 178 0 0.8 2 2 2
## 104 42 1 2 120.0000 240 1 1 194 0 0.8 0 0 3
## 133 42 1 1 120.0000 295 0 1 162 0 0.0 2 0 2
## 143 42 0 2 120.0000 209 0 1 173 0 0.0 1 0 2
## 150 42 1 2 130.0000 180 0 1 150 0 0.0 2 0 2
## 281 42 1 0 136.0000 315 0 1 125 1 1.8 1 0 1
## 3 41 0 1 130.0000 204 0 0 172 0 1.4 2 0 2
## 31 41 0 1 105.0000 198 0 1 168 0 0.0 2 1 2
## 64 41 1 1 135.0000 203 0 1 132 0 0.0 1 0 1
## 81 41 1 2 112.0000 250 0 1 179 0 0.0 2 0 2
## 117 41 1 2 130.0000 214 0 0 168 0 2.0 1 0 2
## 123 41 0 2 112.0000 268 0 0 172 1 0.0 2 0 2
## 134 41 1 1 110.0000 235 0 1 153 0 0.0 2 0 2
## 135 41 0 1 126.0000 306 0 1 163 0 0.0 2 0 2
## 163 41 1 1 120.0000 157 0 1 182 0 0.0 2 0 2
## 190 41 1 0 110.0000 172 0 0 158 0 0.0 2 0 3
## 25 40 1 3 140.0000 199 0 1 178 1 1.4 2 0 3
## 176 40 1 0 110.0000 167 0 0 114 1 2.0 1 0 3
## 284 40 1 0 152.0000 223 0 1 181 0 0.0 2 0 3
## 45 39 1 2 140.0000 321 0 0 182 0 0.0 2 0 2
## 125 39 0 2 94.0000 199 0 1 179 0 0.0 2 0 2
## 155 39 0 2 138.0000 220 0 1 152 0 0.0 1 0 2
## 213 39 1 0 118.0000 219 0 1 140 0 1.2 1 0 3
## 164 38 1 2 138.0000 175 0 1 173 0 0.0 2 4 2
## 165 38 1 2 138.0000 175 0 1 173 0 0.0 2 4 2
## 260 38 1 3 120.0000 231 0 1 182 1 3.8 1 0 3
## 2 37 1 2 130.0000 250 0 1 187 0 3.5 0 0 2
## 116 37 0 2 120.0000 215 0 1 170 0 0.0 2 0 2
## 66 35 0 0 138.0000 183 0 1 182 0 1.4 2 0 2
## 158 35 1 1 122.0000 192 0 1 174 0 0.0 2 0 2
## 228 35 1 0 120.0000 198 0 1 130 1 1.6 1 0 3
## 240 35 1 0 126.0000 282 0 0 156 1 0.0 2 0 3
## 59 34 1 3 118.0000 182 0 0 174 0 0.0 2 0 2
## 126 34 0 1 118.0000 210 0 1 192 0 0.7 2 0 2
## 73 29 1 1 130.0000 204 0 0 202 0 0.0 2 0 2
## target
## 239 0
## 145 1
## 130 1
## 26 1
## 61 1
## 152 1
## 146 1
## 226 0
## 235 0
## 241 0
## 20 1
## 107 1
## 250 0
## 87 1
## 106 1
## 204 0
## 301 0
## 86 1
## 128 1
## 144 1
## 166 0
## 167 0
## 198 0
## 254 0
## 273 0
## 294 0
## 18 1
## 52 1
## 151 1
## 154 1
## 248 0
## 261 0
## 266 0
## 29 1
## 32 1
## 39 1
## 40 1
## 182 0
## 200 0
## 219 0
## 223 0
## 14 1
## 111 1
## 113 1
## 121 1
## 140 1
## 153 1
## 178 0
## 230 0
## 234 0
## 243 0
## 1 1
## 55 1
## 103 1
## 169 0
## 218 0
## 221 0
## 264 0
## 296 0
## 297 0
## 53 1
## 70 1
## 97 1
## 138 1
## 168 0
## 199 0
## 205 0
## 217 0
## 227 0
## 253 0
## 259 0
## 24 1
## 183 0
## 212 0
## 214 0
## 272 0
## 280 0
## 285 0
## 291 0
## 83 1
## 137 1
## 148 1
## 175 0
## 177 0
## 187 0
## 194 0
## 195 0
## 202 0
## 208 0
## 238 0
## 21 1
## 27 1
## 78 1
## 102 1
## 122 1
## 196 0
## 207 0
## 210 0
## 229 0
## 242 0
## 255 0
## 283 0
## 287 0
## 298 0
## 15 1
## 17 1
## 65 1
## 80 1
## 90 1
## 156 1
## 159 1
## 173 0
## 174 0
## 184 0
## 192 0
## 203 0
## 237 0
## 257 0
## 274 0
## 277 0
## 279 0
## 292 0
## 293 0
## 5 1
## 6 1
## 10 1
## 60 1
## 92 1
## 112 1
## 139 1
## 180 0
## 211 0
## 232 0
## 244 0
## 278 0
## 288 0
## 289 0
## 299 0
## 302 0
## 303 0
## 4 1
## 7 1
## 118 1
## 160 1
## 161 1
## 171 0
## 215 0
## 224 0
## 245 0
## 247 0
## 270 0
## 76 1
## 115 1
## 162 1
## 181 0
## 222 0
## 233 0
## 267 0
## 290 0
## 11 1
## 34 1
## 37 1
## 38 1
## 62 1
## 71 1
## 89 1
## 94 1
## 124 1
## 131 1
## 188 0
## 193 0
## 225 0
## 249 0
## 265 0
## 269 0
## 30 1
## 44 1
## 49 1
## 50 1
## 96 1
## 100 1
## 170 0
## 263 0
## 9 1
## 46 1
## 56 1
## 63 1
## 79 1
## 84 1
## 93 1
## 98 1
## 129 1
## 206 0
## 262 0
## 276 0
## 282 0
## 28 1
## 35 1
## 41 1
## 51 1
## 67 1
## 72 1
## 74 1
## 77 1
## 141 1
## 191 0
## 236 0
## 251 0
## 16 1
## 105 1
## 109 1
## 110 1
## 185 0
## 189 0
## 258 0
## 13 1
## 132 1
## 136 1
## 209 0
## 268 0
## 12 1
## 42 1
## 57 1
## 91 1
## 172 0
## 220 0
## 246 0
## 48 1
## 127 1
## 157 1
## 231 0
## 275 0
## 36 1
## 88 1
## 119 1
## 120 1
## 197 0
## 271 0
## 286 0
## 43 1
## 58 1
## 68 1
## 82 1
## 95 1
## 108 1
## 256 0
## 300 0
## 8 1
## 22 1
## 33 1
## 47 1
## 54 1
## 69 1
## 147 1
## 149 1
## 186 0
## 201 0
## 295 0
## 19 1
## 75 1
## 99 1
## 114 1
## 142 1
## 179 0
## 216 0
## 252 0
## 23 1
## 85 1
## 101 1
## 104 1
## 133 1
## 143 1
## 150 1
## 281 0
## 3 1
## 31 1
## 64 1
## 81 1
## 117 1
## 123 1
## 134 1
## 135 1
## 163 1
## 190 0
## 25 1
## 176 0
## 284 0
## 45 1
## 125 1
## 155 1
## 213 0
## 164 1
## 165 1
## 260 0
## 2 1
## 116 1
## 66 1
## 158 1
## 228 0
## 240 0
## 59 1
## 126 1
## 73 1#multi-column sorting
#Sort the rows in ascending order by gender, and in the same gender in descending order by age
newdata <-heart[order(heart$trestbps,-heart$age),]
newdata## age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
## 72 51 1 2 94.0000 227 0 1 154 1 0.0 2 1 3
## 125 39 0 2 94.0000 199 0 1 179 0 0.0 2 0 2
## 254 67 1 0 100.0000 299 0 0 125 1 0.9 1 2 2
## 274 58 1 0 100.0000 234 0 1 156 0 0.1 2 1 3
## 67 51 1 2 100.0000 222 0 1 143 1 1.2 1 0 2
## 88 46 1 1 101.0000 197 1 1 156 0 0.0 2 0 3
## 83 60 0 2 102.0000 318 0 1 160 0 0.0 2 1 2
## 85 42 0 0 102.0000 265 0 0 122 0 0.6 1 0 2
## 43 45 1 0 104.0000 208 0 0 148 1 3.0 1 0 2
## 80 58 1 2 105.0000 240 0 0 154 1 0.6 1 0 3
## 119 46 0 1 105.0000 204 0 1 172 0 0.0 2 0 2
## 31 41 0 1 105.0000 198 0 1 168 0 0.0 2 1 2
## 144 67 0 0 106.0000 223 0 1 142 0 0.3 2 2 2
## 264 63 0 0 108.0000 269 0 1 169 1 1.8 1 2 2
## 62 54 1 1 108.0000 309 0 1 156 0 0.0 2 0 3
## 124 54 0 2 108.0000 267 0 0 167 0 0.0 2 0 2
## 98 52 1 0 108.0000 233 1 1 147 0 0.1 2 3 3
## 231 47 1 2 108.0000 243 0 1 152 0 0.0 2 0 2
## 54 44 0 2 108.0000 141 0 1 175 0 0.6 1 0 2
## 61 71 0 2 110.0000 265 1 0 130 0 0.0 2 1 2
## 200 65 1 0 110.0000 248 0 0 158 0 0.6 2 2 1
## 14 64 1 3 110.0000 211 0 0 144 1 1.8 1 0 2
## 207 59 1 0 110.0000 239 0 0 142 1 1.2 1 1 3
## 139 57 1 0 110.0000 201 0 1 126 1 1.5 1 0 1
## 289 57 1 0 110.0000 335 0 1 143 1 3.0 1 1 3
## 89 54 0 2 110.0000 214 0 1 158 0 1.6 1 0 2
## 225 54 1 0 110.0000 239 0 1 126 1 2.8 1 1 3
## 265 54 1 0 110.0000 206 0 0 108 1 0.0 1 1 2
## 28 51 1 2 110.0000 175 0 1 123 0 0.6 2 0 2
## 110 50 0 0 110.0000 254 0 0 159 0 0.0 2 0 2
## 172 48 1 1 110.0000 229 0 1 168 0 1.0 0 0 3
## 275 47 1 0 110.0000 275 0 0 118 1 1.0 1 1 2
## 300 45 1 3 110.0000 264 0 1 132 0 1.2 1 0 3
## 201 44 1 0 110.0000 197 0 0 177 0 0.0 2 1 2
## 114 43 1 0 110.0000 211 0 1 161 0 0.0 2 0 3
## 134 41 1 1 110.0000 235 0 1 153 0 0.0 2 0 2
## 190 41 1 0 110.0000 172 0 0 158 0 0.0 2 0 3
## 176 40 1 0 110.0000 167 0 0 114 1 2.0 1 0 3
## 152 71 0 0 112.0000 149 0 1 125 0 1.6 1 0 2
## 266 66 1 0 112.0000 212 0 0 132 1 0.1 2 1 2
## 184 58 1 2 112.0000 230 0 0 165 0 2.5 1 1 3
## 262 52 1 0 112.0000 230 0 1 160 0 0.0 2 1 2
## 127 47 1 0 112.0000 204 0 1 143 0 0.1 2 0 2
## 95 45 0 1 112.0000 160 0 1 138 0 0.0 1 0 2
## 186 44 1 0 112.0000 290 0 0 153 0 0.0 2 1 2
## 81 41 1 2 112.0000 250 0 1 179 0 0.0 2 0 2
## 123 41 0 2 112.0000 268 0 0 172 1 0.0 2 0 2
## 292 58 1 0 114.0000 318 0 2 140 0 4.4 0 3 1
## 86 67 0 2 115.0000 564 0 0 160 0 1.6 1 0 3
## 58 45 1 0 115.0000 260 0 0 185 0 0.0 2 0 2
## 142 43 1 0 115.0000 303 0 1 181 0 1.2 1 0 2
## 177 60 1 0 117.0000 230 1 1 160 1 1.4 2 2 3
## 87 68 1 2 118.0000 277 0 1 151 0 1.0 2 1 3
## 63 52 1 3 118.0000 186 0 0 190 0 0.0 1 0 1
## 268 49 1 2 118.0000 149 0 0 126 0 0.8 2 3 2
## 147 44 0 2 118.0000 242 0 1 149 0 0.3 1 1 2
## 213 39 1 0 118.0000 219 0 1 140 0 1.2 1 0 3
## 59 34 1 3 118.0000 182 0 0 174 0 0.0 2 0 2
## 126 34 0 1 118.0000 210 0 1 192 0 0.7 2 0 2
## 130 74 0 1 120.0000 269 0 0 121 1 0.2 2 1 2
## 106 68 0 2 120.0000 211 0 0 115 0 1.5 1 0 2
## 167 67 1 0 120.0000 229 0 0 129 1 2.6 1 2 3
## 273 67 1 0 120.0000 237 0 1 71 0 1.0 1 0 2
## 52 66 1 0 120.0000 302 0 0 151 0 0.4 1 0 2
## 32 65 1 0 120.0000 177 0 1 140 0 0.4 2 0 3
## 234 64 1 0 120.0000 246 0 0 96 1 2.2 0 1 2
## 199 62 1 0 120.0000 267 0 1 99 1 1.8 1 2 3
## 227 62 1 1 120.0000 281 0 0 103 0 1.4 1 1 3
## 212 61 1 0 120.0000 260 0 1 140 1 3.6 1 1 3
## 137 60 0 2 120.0000 178 1 1 96 0 0.0 2 0 2
## 17 58 0 2 120.0000 340 0 1 172 0 0.0 2 0 2
## 173 58 1 1 120.0000 284 0 0 160 0 1.8 1 0 2
## 5 57 0 0 120.0000 354 0 1 163 1 0.6 2 0 2
## 4 56 1 1 120.0000 236 0 1 178 0 0.8 2 0 2
## 118 56 1 3 120.0000 193 0 0 162 0 1.9 1 0 3
## 161 56 1 1 120.0000 240 0 1 169 0 0.0 0 0 2
## 71 54 1 2 120.0000 258 0 0 147 0 0.4 1 0 3
## 193 54 1 0 120.0000 188 0 1 113 0 1.4 1 1 3
## 46 52 1 1 120.0000 325 0 1 172 0 0.2 2 0 2
## 141 51 0 2 120.0000 295 0 0 157 0 0.6 2 0 2
## 16 50 0 2 120.0000 219 0 1 158 0 1.6 1 0 2
## 109 50 0 1 120.0000 244 0 1 162 0 1.1 2 0 2
## 209 49 1 2 120.0000 188 0 1 139 0 2.0 1 3 3
## 271 46 1 0 120.0000 249 0 0 144 0 0.8 2 0 3
## 8 44 1 1 120.0000 263 0 1 173 0 0.0 2 0 3
## 69 44 1 1 120.0000 220 0 1 170 0 0.0 2 0 2
## 149 44 1 2 120.0000 226 0 1 169 0 0.0 2 0 2
## 295 44 1 0 120.0000 169 0 1 144 1 2.8 0 0 1
## 179 43 1 0 120.0000 177 0 0 120 1 2.5 1 0 3
## 104 42 1 2 120.0000 240 1 1 194 0 0.8 0 0 3
## 133 42 1 1 120.0000 295 0 1 162 0 0.0 2 0 2
## 143 42 0 2 120.0000 209 0 1 173 0 0.0 1 0 2
## 163 41 1 1 120.0000 157 0 1 182 0 0.0 2 0 2
## 260 38 1 3 120.0000 231 0 1 182 1 3.8 1 0 3
## 116 37 0 2 120.0000 215 0 1 170 0 0.0 2 0 2
## 228 35 1 0 120.0000 198 0 1 130 1 1.6 1 0 3
## 269 54 1 0 122.0000 286 0 0 116 1 3.2 1 2 2
## 57 48 1 0 122.0000 222 0 0 186 0 0.0 2 0 2
## 75 43 0 2 122.0000 213 0 1 165 0 0.2 1 0 2
## 158 35 1 1 122.0000 192 0 1 174 0 0.0 2 0 2
## 263 53 1 0 123.0000 282 0 1 95 1 2.0 1 2 3
## 297 63 0 0 124.0000 197 0 1 136 1 0.0 1 0 2
## 70 62 0 0 124.0000 209 0 1 163 0 0.0 2 0 2
## 278 57 1 1 124.0000 261 0 1 141 0 0.3 2 0 3
## 188 54 1 0 124.0000 266 0 0 109 1 2.2 1 1 3
## 91 48 1 2 124.0000 255 1 1 175 0 0.0 2 2 2
## 246 48 1 0 124.0000 274 0 0 166 0 0.5 1 0 3
## 239 77 1 0 125.0000 304 0 0 162 1 0.0 2 3 2
## 198 67 1 0 125.0000 254 1 1 163 0 0.2 1 2 3
## 230 64 1 2 125.0000 309 0 1 131 1 1.8 1 0 3
## 202 60 1 0 125.0000 258 0 0 141 1 2.8 1 1 3
## 159 58 1 1 125.0000 220 0 1 144 0 0.4 1 4 3
## 237 58 1 0 125.0000 300 0 0 171 0 0.0 2 2 3
## 215 56 1 0 125.0000 249 1 0 144 1 1.2 1 1 2
## 34 54 1 2 125.0000 273 0 0 152 0 0.5 0 1 2
## 276 52 1 0 125.0000 212 0 1 168 0 1.0 2 2 3
## 35 51 1 3 125.0000 213 0 0 125 1 1.4 2 1 2
## 77 51 1 2 125.0000 245 1 0 166 0 2.4 1 0 2
## 283 59 1 2 126.0000 218 1 1 134 0 2.2 1 1 1
## 135 41 0 1 126.0000 306 0 1 163 0 0.0 2 0 2
## 240 35 1 0 126.0000 282 0 0 156 1 0.0 2 0 3
## 140 64 1 0 128.0000 263 0 1 105 1 0.2 1 1 3
## 138 62 1 1 128.0000 208 1 0 140 0 0.0 2 0 2
## 192 58 1 0 128.0000 216 0 0 131 1 2.2 1 3 3
## 257 58 1 0 128.0000 259 0 0 130 1 3.0 1 2 3
## 60 57 0 0 128.0000 303 0 0 159 0 0.0 2 1 2
## 211 57 1 2 128.0000 229 0 0 150 0 0.4 1 1 3
## 290 55 0 0 128.0000 205 0 2 130 1 2.0 1 1 3
## 49 53 0 2 128.0000 216 0 0 115 0 0.0 2 0 0
## 79 52 1 1 128.0000 205 1 1 184 0 0.0 2 0 2
## 206 52 1 0 128.0000 255 0 1 161 1 0.0 2 1 3
## 282 52 1 0 128.0000 204 1 1 156 1 1.0 1 0 0
## 82 45 1 1 128.0000 308 0 0 170 0 0.0 2 0 2
## 105 50 1 2 129.0000 196 0 1 163 0 0.0 2 0 2
## 235 70 1 0 130.0000 322 0 0 109 0 2.4 1 3 2
## 121 64 0 0 130.0000 303 0 1 122 0 2.0 1 2 2
## 169 63 1 0 130.0000 254 0 0 147 0 1.4 1 1 3
## 218 63 1 0 130.0000 330 1 0 132 1 1.8 2 3 3
## 53 62 1 2 130.0000 231 0 1 146 0 1.8 1 3 3
## 217 62 0 2 130.0000 263 0 1 97 0 1.2 1 1 3
## 183 61 0 0 130.0000 330 0 0 169 0 0.0 2 0 2
## 175 60 1 0 130.0000 206 0 0 132 1 2.4 1 2 3
## 187 60 1 0 130.0000 253 0 1 144 1 1.4 2 1 3
## 156 58 0 0 130.0000 197 0 1 131 0 0.6 1 0 2
## 302 57 1 0 130.0000 131 0 1 115 1 1.2 1 1 3
## 303 57 0 1 130.0000 236 0 0 174 0 0.0 1 1 2
## 160 56 1 1 130.0000 221 0 0 163 0 0.0 2 0 3
## 171 56 1 2 130.0000 256 1 0 142 1 0.6 1 1 1
## 270 56 1 0 130.0000 283 1 0 103 1 1.6 0 0 3
## 115 55 1 1 130.0000 262 0 1 155 0 0.0 2 0 2
## 30 53 1 2 130.0000 197 1 0 152 0 1.2 0 0 2
## 44 53 0 0 130.0000 264 0 0 143 0 0.4 1 0 2
## 100 53 1 2 130.0000 246 1 0 173 0 0.0 2 3 2
## 51 51 0 2 130.0000 256 0 0 149 0 0.5 2 0 2
## 191 51 0 0 130.0000 305 0 1 142 1 1.2 1 0 3
## 13 49 1 1 130.0000 266 0 1 171 0 0.6 2 0 2
## 136 49 0 0 130.0000 269 0 1 163 0 0.0 2 0 2
## 12 48 0 2 130.0000 275 0 1 139 0 0.2 2 0 2
## 42 48 1 1 130.0000 245 0 0 180 0 0.2 1 0 2
## 220 48 1 0 130.0000 256 1 0 150 1 0.0 2 2 3
## 157 47 1 2 130.0000 253 0 1 179 0 0.0 2 0 2
## 68 45 0 1 130.0000 234 0 0 175 0 0.6 1 0 2
## 22 44 1 2 130.0000 233 0 1 179 1 0.4 2 0 2
## 33 44 1 1 130.0000 219 0 0 188 0 0.0 2 0 2
## 99 43 1 2 130.0000 315 0 1 162 0 1.9 2 1 2
## 150 42 1 2 130.0000 180 0 1 150 0 0.0 2 0 2
## 3 41 0 1 130.0000 204 0 0 172 0 1.4 2 0 2
## 117 41 1 2 130.0000 214 0 0 168 0 2.0 1 0 2
## 2 37 1 2 130.0000 250 0 1 187 0 3.5 0 0 2
## 73 29 1 1 130.0000 204 0 0 202 0 0.0 2 0 2
## 90 58 0 0 131.7285 248 0 0 122 0 1.0 1 0 2
## 174 58 1 2 132.0000 224 0 0 173 0 3.2 2 2 3
## 92 57 1 0 132.0000 207 0 1 168 1 0.0 2 0 3
## 245 56 1 0 132.0000 184 0 0 105 1 2.1 1 1 1
## 162 55 0 1 132.0000 342 0 1 166 0 1.2 2 0 2
## 181 55 1 0 132.0000 353 0 1 132 1 1.2 1 1 3
## 94 54 0 1 132.0000 288 1 0 159 1 0.0 2 1 2
## 216 43 0 0 132.0000 341 1 0 136 1 3.0 1 0 3
## 252 43 1 0 132.0000 247 1 0 143 1 0.1 1 4 3
## 272 61 1 3 134.0000 234 0 1 145 0 2.6 1 2 2
## 287 59 1 3 134.0000 204 0 1 162 0 0.8 2 2 2
## 247 56 0 0 134.0000 409 0 0 150 1 1.9 1 2 3
## 56 52 1 1 134.0000 201 0 1 158 0 0.8 2 1 2
## 132 49 0 1 134.0000 271 0 1 162 0 0.0 1 0 2
## 219 65 1 0 135.0000 254 0 0 127 0 2.8 1 1 3
## 55 63 0 2 135.0000 252 0 0 172 0 0.0 2 0 2
## 21 59 1 0 135.0000 234 0 1 161 0 0.5 1 0 3
## 76 55 0 1 135.0000 250 0 0 161 0 1.4 1 0 2
## 37 54 0 2 135.0000 304 1 1 170 0 0.0 2 0 2
## 64 41 1 1 135.0000 203 0 1 132 0 0.0 1 0 1
## 279 58 0 1 136.0000 319 1 0 152 0 0.0 2 2 2
## 129 52 0 2 136.0000 196 0 0 169 0 0.1 1 0 2
## 281 42 1 0 136.0000 315 0 1 125 1 1.8 1 0 1
## 223 65 1 3 138.0000 282 1 0 174 0 1.4 1 1 2
## 253 62 0 0 138.0000 294 1 1 106 0 1.9 1 3 2
## 280 61 1 0 138.0000 166 0 0 125 1 3.6 1 1 2
## 122 59 1 0 138.0000 271 0 0 182 0 0.0 2 0 2
## 50 53 0 0 138.0000 234 0 0 160 0 0.0 2 0 2
## 93 52 1 2 138.0000 223 0 1 169 0 0.0 2 4 2
## 48 47 1 2 138.0000 257 0 0 156 0 0.0 2 0 2
## 120 46 0 0 138.0000 243 0 0 152 1 0.0 1 0 2
## 108 45 0 0 138.0000 236 0 0 152 1 0.2 1 0 2
## 155 39 0 2 138.0000 220 0 1 152 0 0.0 1 0 2
## 164 38 1 2 138.0000 175 0 1 173 0 0.0 2 4 2
## 165 38 1 2 138.0000 175 0 1 173 0 0.0 2 4 2
## 66 35 0 0 138.0000 183 0 1 182 0 1.4 2 0 2
## 145 76 0 2 140.0000 197 0 2 116 0 1.1 1 0 2
## 20 69 0 3 140.0000 239 0 1 151 0 1.8 2 2 2
## 250 69 1 2 140.0000 254 0 0 146 0 2.0 1 3 3
## 29 65 0 2 140.0000 417 1 0 157 0 0.8 2 1 2
## 113 64 0 2 140.0000 313 0 1 133 0 0.2 2 0 3
## 178 64 1 2 140.0000 335 0 1 158 0 0.0 2 0 2
## 103 63 0 1 140.0000 195 0 1 179 0 0.0 2 2 2
## 296 63 1 0 140.0000 187 0 0 144 1 4.0 2 2 3
## 97 62 0 0 140.0000 394 0 0 157 0 1.2 1 0 2
## 168 62 0 0 140.0000 268 0 0 160 0 3.6 0 2 2
## 285 61 1 0 140.0000 207 0 0 138 1 1.9 2 1 3
## 195 60 1 2 140.0000 185 0 0 155 0 3.0 1 0 2
## 238 60 1 0 140.0000 293 0 0 170 0 1.2 1 2 3
## 78 59 1 1 140.0000 221 0 1 164 1 0.0 2 0 2
## 210 59 1 0 140.0000 177 0 1 162 1 0.0 2 1 3
## 65 58 1 2 140.0000 211 1 0 165 0 0.0 2 0 2
## 6 57 1 0 140.0000 192 0 1 148 0 0.4 1 0 1
## 299 57 0 0 140.0000 241 0 1 123 1 0.2 1 0 3
## 7 56 0 1 140.0000 294 0 0 153 0 1.3 1 0 2
## 222 55 1 0 140.0000 217 0 1 111 1 5.6 0 0 3
## 11 54 1 0 140.0000 239 0 1 160 0 1.2 2 0 2
## 170 53 1 0 140.0000 203 1 0 155 1 3.1 0 0 3
## 41 51 0 2 140.0000 308 0 0 142 0 1.5 2 1 2
## 74 51 1 0 140.0000 261 0 0 186 1 0.0 2 0 2
## 236 51 1 0 140.0000 299 0 1 173 1 1.6 2 0 3
## 251 51 1 0 140.0000 298 0 1 122 1 4.2 1 3 3
## 189 50 1 2 140.0000 233 0 1 163 0 0.6 1 1 3
## 286 46 1 0 140.0000 311 0 1 120 1 1.8 1 2 3
## 47 44 1 2 140.0000 235 0 0 180 0 0.0 2 0 2
## 23 42 1 0 140.0000 226 0 1 178 0 0.0 2 0 2
## 25 40 1 3 140.0000 199 0 1 178 1 1.4 2 0 3
## 45 39 1 2 140.0000 321 0 0 182 0 0.0 2 0 2
## 96 53 1 0 142.0000 226 0 0 111 1 0.0 2 0 3
## 36 46 0 2 142.0000 177 0 0 160 1 1.4 0 0 2
## 256 45 1 0 142.0000 309 0 0 147 1 0.0 1 3 3
## 301 68 1 0 144.0000 193 1 1 141 0 3.4 1 2 3
## 258 50 1 0 144.0000 200 0 0 126 1 0.9 1 0 3
## 226 70 1 0 145.0000 174 0 1 125 1 2.6 0 0 3
## 243 64 1 0 145.0000 212 0 0 132 0 2.0 1 2 1
## 1 63 1 3 145.0000 233 1 0 150 0 2.3 0 0 1
## 214 61 0 0 145.0000 307 0 0 146 1 1.0 1 0 3
## 194 60 1 0 145.0000 282 0 0 142 1 2.8 1 2 3
## 154 66 0 2 146.0000 278 0 0 152 0 0.0 1 1 2
## 277 58 1 0 146.0000 218 0 1 105 0 2.0 1 1 3
## 291 61 1 0 148.0000 203 0 1 161 0 0.0 2 1 3
## 101 42 1 3 148.0000 244 0 0 178 0 0.8 2 2 2
## 18 66 0 3 150.0000 226 0 1 114 0 2.6 0 0 2
## 182 65 0 0 150.0000 225 0 0 114 0 1.0 1 3 3
## 221 63 0 0 150.0000 407 0 0 154 0 4.0 1 3 3
## 259 62 0 0 150.0000 244 0 1 154 1 1.4 1 0 2
## 24 61 1 2 150.0000 243 1 1 137 1 1.0 1 0 2
## 148 60 0 3 150.0000 240 0 1 171 0 0.9 2 0 2
## 208 60 0 0 150.0000 258 0 0 157 0 2.6 1 2 3
## 27 59 1 2 150.0000 212 1 1 157 0 1.6 2 0 2
## 15 58 0 3 150.0000 283 1 0 162 0 1.0 2 0 2
## 203 58 1 0 150.0000 270 0 0 111 1 0.8 2 0 3
## 10 57 1 2 150.0000 168 0 1 174 0 1.6 2 0 2
## 112 57 1 2 150.0000 126 1 1 173 0 0.2 2 1 3
## 180 57 1 0 150.0000 276 0 0 112 1 0.6 1 1 1
## 38 54 1 2 150.0000 232 0 0 165 0 1.6 2 0 3
## 185 50 1 0 150.0000 243 0 0 128 0 2.6 1 0 3
## 197 46 1 2 150.0000 231 0 1 147 0 3.6 1 0 2
## 19 43 1 0 150.0000 247 0 1 171 0 1.5 2 0 2
## 128 67 0 2 152.0000 277 0 1 172 0 0.0 2 1 2
## 294 67 1 2 152.0000 212 0 0 150 0 0.8 1 0 3
## 244 57 1 0 152.0000 274 0 1 88 1 1.2 1 1 3
## 84 52 1 3 152.0000 298 1 1 178 0 1.2 1 0 3
## 284 40 1 0 152.0000 223 0 1 181 0 0.0 2 0 3
## 288 57 1 1 154.0000 232 0 0 164 0 0.0 2 1 2
## 39 65 0 2 155.0000 269 0 1 148 0 0.8 2 0 2
## 146 70 1 1 156.0000 245 0 0 143 0 0.0 2 0 2
## 26 71 0 1 160.0000 302 0 1 162 0 0.4 2 2 2
## 241 70 1 2 160.0000 269 0 1 112 1 2.9 1 1 3
## 107 69 1 3 160.0000 234 1 0 131 0 0.1 1 1 2
## 166 67 1 0 160.0000 286 0 0 108 1 1.5 1 3 2
## 151 66 1 0 160.0000 228 0 0 138 0 2.3 2 0 1
## 248 66 1 1 160.0000 246 0 1 120 1 0.0 1 3 1
## 40 65 0 2 160.0000 360 0 0 151 0 0.8 2 0 2
## 205 62 0 0 160.0000 164 0 0 145 0 6.2 0 3 3
## 255 59 1 3 160.0000 273 0 0 125 0 0.0 2 0 2
## 233 55 1 0 160.0000 289 0 0 145 1 0.8 1 1 3
## 131 54 0 2 160.0000 201 0 1 163 0 0.0 2 1 2
## 298 59 1 0 164.0000 176 1 0 90 0 1.0 1 2 1
## 232 57 1 0 165.0000 289 1 0 124 0 1.0 1 3 3
## 153 64 1 3 170.0000 227 0 0 155 0 0.6 1 0 3
## 196 59 1 0 170.0000 326 0 0 140 1 3.4 0 0 3
## 229 59 1 3 170.0000 288 0 0 159 0 0.2 1 0 3
## 293 58 0 0 170.0000 225 1 0 146 1 2.8 1 2 1
## 9 52 1 2 172.0000 199 1 1 162 0 0.5 2 0 3
## 242 59 0 0 174.0000 249 0 1 143 1 0.0 1 0 2
## 261 66 0 0 178.0000 228 1 1 165 1 1.0 1 2 3
## 102 59 1 3 178.0000 270 0 0 145 0 4.2 0 0 3
## 204 68 1 2 180.0000 274 1 0 150 1 1.6 1 0 3
## 111 64 0 0 180.0000 325 0 1 154 1 0.0 2 0 2
## 267 55 0 0 180.0000 327 0 2 117 1 3.4 1 0 2
## 249 54 1 1 192.0000 283 0 0 195 0 0.0 2 1 3
## 224 56 0 0 200.0000 288 1 0 133 1 4.0 0 2 3
## target
## 72 1
## 125 1
## 254 0
## 274 0
## 67 1
## 88 1
## 83 1
## 85 1
## 43 1
## 80 1
## 119 1
## 31 1
## 144 1
## 264 0
## 62 1
## 124 1
## 98 1
## 231 0
## 54 1
## 61 1
## 200 0
## 14 1
## 207 0
## 139 1
## 289 0
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## 9 1
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## 224 0#Change the data for the convenience of processing
heart$target<-as.factor(heart$target)
levels(heart$target)<-c("No", "Yes")
heart$sex<-as.factor(heart$sex)
levels(heart$sex)<-c("Female","Male")
heart$cp<-as.factor(heart$cp)
levels(heart$cp)<-c("typical","atypical","non-anginal","asymptomatic")
heart$fbs<-as.factor(heart$fbs)
levels(heart$fbs)<-c("False","True")
heart$restecg<-as.factor(heart$restecg)
levels(heart$restecg)<-c("normal","stt","hypertrophy")
heart$exang<-as.factor(heart$exang)
levels(heart$exang)<-c("No","Yes")
heart$slope<-as.factor(heart$slope)
levels(heart$slope)<-c("upsloping","flat","downsloping")
heart$ca<-as.factor(heart$ca)
str(heart)## 'data.frame': 303 obs. of 14 variables:
## $ age : int 63 37 41 56 57 57 56 44 52 57 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 1 2 1 2 1 2 2 2 ...
## $ cp : Factor w/ 4 levels "typical","atypical",..: 4 3 2 2 1 1 2 2 3 3 ...
## $ trestbps: num 145 130 130 120 120 140 140 120 172 150 ...
## $ chol : int 233 250 204 236 354 192 294 263 199 168 ...
## $ fbs : Factor w/ 2 levels "False","True": 2 1 1 1 1 1 1 1 2 1 ...
## $ restecg : Factor w/ 3 levels "normal","stt",..: 1 2 1 2 2 2 1 2 2 2 ...
## $ thalach : int 150 187 172 178 163 148 153 173 162 174 ...
## $ exang : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 1 1 1 1 ...
## $ oldpeak : num 2.3 3.5 1.4 0.8 0.6 0.4 1.3 0 0.5 1.6 ...
## $ slope : Factor w/ 3 levels "upsloping","flat",..: 1 1 3 3 3 2 2 3 3 3 ...
## $ ca : Factor w/ 5 levels "0","1","2","3",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ thal : int 1 2 2 2 2 1 2 3 3 2 ...
## $ target : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...summary(heart)## age sex cp trestbps
## Min. :29.00 Female: 96 typical :143 Min. : 94.0
## 1st Qu.:47.50 Male :207 atypical : 50 1st Qu.:120.0
## Median :55.00 non-anginal : 87 Median :130.0
## Mean :54.37 asymptomatic: 23 Mean :131.7
## 3rd Qu.:61.00 3rd Qu.:140.0
## Max. :77.00 Max. :200.0
## chol fbs restecg thalach exang
## Min. :126.0 False:258 normal :147 Min. : 71.0 No :204
## 1st Qu.:211.0 True : 45 stt :152 1st Qu.:133.5 Yes: 99
## Median :240.0 hypertrophy: 4 Median :153.0
## Mean :246.3 Mean :149.6
## 3rd Qu.:274.5 3rd Qu.:166.0
## Max. :564.0 Max. :202.0
## oldpeak slope ca thal target
## Min. :0.00 upsloping : 21 0:175 Min. :0.000 No :138
## 1st Qu.:0.00 flat :140 1: 65 1st Qu.:2.000 Yes:165
## Median :0.80 downsloping:142 2: 38 Median :2.000
## Mean :1.04 3: 20 Mean :2.314
## 3rd Qu.:1.60 4: 5 3rd Qu.:3.000
## Max. :6.20 Max. :3.000#Splitting into training and test dataset : When the dataset is presented to us to do machine learning stuff we
#need some data as part of training and some to test the model after the learning stage is don. So we need to
#split the dataset into training and test, using below code we can do
#Set random seed
set.seed(20220109)
#Divide 70% of the data set as the training set and 30% of the test set
index <- createDataPartition(heart$age, p=0.7, list=FALSE)
#training data
data_train <- heart[index, ]
#test data
data_test <- heart[-index, ]
##Write one function and use the function for the dataset
function1 <- function(x,y){
plot(x,y)
return(x+y)
}
function1(heart$age,heart$trestbps)
## [1] 208.0000 167.0000 171.0000 176.0000 177.0000 197.0000 196.0000 164.0000
## [9] 224.0000 207.0000 194.0000 178.0000 179.0000 174.0000 208.0000 170.0000
## [17] 178.0000 216.0000 193.0000 209.0000 194.0000 174.0000 182.0000 211.0000
## [25] 180.0000 231.0000 209.0000 161.0000 205.0000 183.0000 146.0000 185.0000
## [33] 174.0000 179.0000 176.0000 188.0000 189.0000 204.0000 220.0000 225.0000
## [41] 191.0000 178.0000 149.0000 183.0000 179.0000 172.0000 184.0000 185.0000
## [49] 181.0000 191.0000 181.0000 186.0000 192.0000 152.0000 198.0000 186.0000
## [57] 170.0000 160.0000 152.0000 185.0000 181.0000 162.0000 170.0000 176.0000
## [65] 198.0000 173.0000 151.0000 175.0000 164.0000 186.0000 174.0000 145.0000
## [73] 159.0000 191.0000 165.0000 190.0000 176.0000 199.0000 180.0000 163.0000
## [81] 153.0000 173.0000 162.0000 204.0000 144.0000 182.0000 186.0000 147.0000
## [89] 164.0000 189.7285 172.0000 189.0000 190.0000 186.0000 157.0000 195.0000
## [97] 202.0000 160.0000 173.0000 183.0000 190.0000 237.0000 203.0000 162.0000
## [105] 179.0000 188.0000 229.0000 183.0000 170.0000 160.0000 244.0000 207.0000
## [113] 204.0000 153.0000 185.0000 157.0000 171.0000 176.0000 151.0000 184.0000
## [121] 194.0000 197.0000 153.0000 162.0000 133.0000 152.0000 159.0000 219.0000
## [129] 188.0000 194.0000 214.0000 183.0000 162.0000 151.0000 167.0000 179.0000
## [137] 180.0000 190.0000 167.0000 192.0000 171.0000 158.0000 162.0000 173.0000
## [145] 216.0000 226.0000 162.0000 210.0000 164.0000 172.0000 226.0000 183.0000
## [153] 234.0000 212.0000 177.0000 188.0000 177.0000 157.0000 183.0000 186.0000
## [161] 176.0000 187.0000 161.0000 176.0000 176.0000 227.0000 187.0000 202.0000
## [169] 193.0000 193.0000 186.0000 158.0000 178.0000 190.0000 190.0000 150.0000
## [177] 177.0000 204.0000 163.0000 207.0000 187.0000 215.0000 191.0000 170.0000
## [185] 200.0000 156.0000 190.0000 178.0000 190.0000 151.0000 181.0000 186.0000
## [193] 174.0000 205.0000 200.0000 229.0000 196.0000 192.0000 182.0000 175.0000
## [201] 154.0000 185.0000 208.0000 248.0000 222.0000 180.0000 169.0000 210.0000
## [209] 169.0000 199.0000 185.0000 181.0000 157.0000 206.0000 181.0000 175.0000
## [217] 192.0000 193.0000 200.0000 178.0000 213.0000 195.0000 203.0000 256.0000
## [225] 164.0000 215.0000 182.0000 155.0000 229.0000 189.0000 155.0000 222.0000
## [233] 215.0000 184.0000 200.0000 191.0000 183.0000 200.0000 202.0000 161.0000
## [241] 230.0000 233.0000 209.0000 209.0000 188.0000 172.0000 190.0000 226.0000
## [249] 246.0000 209.0000 191.0000 175.0000 200.0000 167.0000 219.0000 187.0000
## [257] 186.0000 194.0000 212.0000 158.0000 244.0000 164.0000 176.0000 171.0000
## [265] 164.0000 178.0000 235.0000 167.0000 176.0000 186.0000 166.0000 195.0000
## [273] 187.0000 158.0000 157.0000 177.0000 204.0000 181.0000 194.0000 199.0000
## [281] 178.0000 180.0000 185.0000 192.0000 201.0000 186.0000 193.0000 211.0000
## [289] 167.0000 183.0000 209.0000 172.0000 228.0000 219.0000 164.0000 203.0000
## [297] 187.0000 223.0000 197.0000 155.0000 212.0000 187.0000 187.0000#Exploratory Data Analysis
##Analysis of the relationship between heart disease and age
ggplot(heart,aes(x=age,fill=target,color=target)) + geom_histogram(binwidth = 1,color="black") + labs(x = "age",y = "Frequency", title = "heart disease and age")
## pie chart of chest pain
mytable <- table(heart$cp)
pct<-round(mytable/sum(mytable)*100)
lbls1<-paste(names(mytable),pct)
lbls<-paste(lbls1, "%", sep="")
pie(mytable, labels = lbls,col = 1:4,main="pie chart of chest pain",radius = 0.9)
#Execute machine learning algorithms
##Logistic regression
set.seed(100)
#100 is used to control the sampling permutation to 100.
index<-sample(nrow(heart),0.75*nrow(heart))
train<-heart[index,]
test<-heart[-index,]
modelblr<-glm(target~.,data = train,family = "binomial")
# family = " binomial" means it contains only two outcomes.
train$pred<-fitted(modelblr)
# fitted can be used only to get predicted score of the data on which model has been generated.
head(train)## age sex cp trestbps chol fbs restecg thalach exang oldpeak
## 202 60 Male typical 125 258 False normal 141 Yes 2.8
## 112 57 Male non-anginal 150 126 True stt 173 No 0.2
## 206 52 Male typical 128 255 False stt 161 Yes 0.0
## 4 56 Male atypical 120 236 False stt 178 No 0.8
## 98 52 Male typical 108 233 True stt 147 No 0.1
## 7 56 Female atypical 140 294 False normal 153 No 1.3
## slope ca thal target pred
## 202 flat 1 3 No 0.003339534
## 112 downsloping 1 3 Yes 0.669272473
## 206 downsloping 1 3 No 0.082730289
## 4 downsloping 0 2 Yes 0.975818787
## 98 downsloping 3 3 Yes 0.386335904
## 7 flat 0 2 Yes 0.914273903#We can see that the predicted score is the probability of having a heart attack. But we have to find an appropriate cut-off point from which it is easy to distinguish heart disease.
#For this, we need the ROC curve, which is a graph showing the performance of a classification model across all classification thresholds. It will allow us to take appropriate cutoffs.
pred<-prediction(train$pred,train$target)
perf<-performance(pred,"tpr","fpr")
plot(perf,colorize = T,print.cutoffs.at = seq(0.1,by = 0.1))
axis(side = 1 ,col=7)
axis(side = 2,col=7 )
grid()
#By using the ROC curve, we can observe that 0.6 has better sensitivity and specificity, so we choose 0.6 as the cut-off point for discrimination.
train$pred1<-ifelse(train$pred<0.6,"No","Yes")
confusionMatrix(factor(train$pred1),train$target)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 91 15
## Yes 13 108
##
## Accuracy : 0.8767
## 95% CI : (0.8267, 0.9164)
## No Information Rate : 0.5419
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.7519
##
## Mcnemar's Test P-Value : 0.8501
##
## Sensitivity : 0.8750
## Specificity : 0.8780
## Pos Pred Value : 0.8585
## Neg Pred Value : 0.8926
## Prevalence : 0.4581
## Detection Rate : 0.4009
## Detection Prevalence : 0.4670
## Balanced Accuracy : 0.8765
##
## 'Positive' Class : No
## # Accuracy of training data
acc_tr<-(109+92)/(227);acc_tr ## [1] 0.8854626test$pred<-predict(modelblr,test,type = "response")
## type = "response "is the result used to get the probability of having heart disease.
head(test)## age sex cp trestbps chol fbs restecg thalach exang oldpeak
## 6 57 Male typical 140 192 False stt 148 No 0.4
## 10 57 Male non-anginal 150 168 False stt 174 No 1.6
## 17 58 Female non-anginal 120 340 False stt 172 No 0.0
## 18 66 Female asymptomatic 150 226 False stt 114 No 2.6
## 21 59 Male typical 135 234 False stt 161 No 0.5
## 22 44 Male non-anginal 130 233 False stt 179 Yes 0.4
## slope ca thal target pred
## 6 flat 0 1 Yes 0.7140602
## 10 downsloping 0 2 Yes 0.9673484
## 17 downsloping 0 2 Yes 0.9983853
## 18 upsloping 0 2 Yes 0.9940466
## 21 flat 0 3 Yes 0.3130506
## 22 downsloping 0 2 Yes 0.9437295test$pred1<-ifelse(test$pred<0.6,"No","Yes")
confusionMatrix(factor(test$pred1),test$target)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 26 5
## Yes 8 37
##
## Accuracy : 0.8289
## 95% CI : (0.7253, 0.9057)
## No Information Rate : 0.5526
## P-Value [Acc > NIR] : 3.447e-07
##
## Kappa : 0.6511
##
## Mcnemar's Test P-Value : 0.5791
##
## Sensitivity : 0.7647
## Specificity : 0.8810
## Pos Pred Value : 0.8387
## Neg Pred Value : 0.8222
## Prevalence : 0.4474
## Detection Rate : 0.3421
## Detection Prevalence : 0.4079
## Balanced Accuracy : 0.8228
##
## 'Positive' Class : No
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