Diabetes Dataset
The dataset is originally from the National Institute of Diabetes and Kidney Diseases. All the patients are females who are at least 21 years old and of Pima Indian heritage (they are Native Americans). It has a dependent variable, which is the outcome, and this outcome depends on several variables, including pregnancies, blood pressure, age, skin thickness, insulin, BMI, and the diabetes pedigree function. The dataset includes several independent variables that are believed to influence the outcome (whether a person has diabetes). A brief explanation of each variable is as follows:
Pregnancies: The number of times a patient has been pregnant. Higher pregnancy counts may increase the risk of diabetes due to hormonal changes and metabolic stress. The `plancenta produces hormone which makes it difficult for the insulin to regulate the glucose in the body.
Glucose: Higher glucose levels are directly associated with a greater likelihood of having diabetes.
Blood Pressure: Higher blood pressure may increase the chance of having diabetes because they share similar health risks.
Skin Thickness: Measures the thickness of the skin on the arm to estimate body fat. More body fat can raise the risk of diabetes.
Insulin: Shows how well the body handles blood sugar. Unusual insulin levels may mean the body is not using sugar properly, which can lead to diabetes.
BMI (Body Mass Index): Estimates body fat based on height and weight. A higher BMI often means a higher risk of diabetes.
Diabetes Pedigree Function: Shows the family history of diabetes. A higher value means a stronger inherited risk.
Age: Older people are more likely to develop diabetes.
AIM
To gain a clear understanding of the factors associated with diabetes among Pima Indian women by cleaning, analyzing, and visualizing the dataset to reveal patterns and insights.
QUESTIONS
- Are there any missing, incorrect, or inconsistent values in the diabetes dataset?
- How are the variables distributed across the population?
- Which variables show differences between diabetic and non-diabetic individuals when visualized?
OBJECTIVES
The objectives of this project is to:
- ensure the dataset is clean, accurate, and well-prepared for
analysis.
- explore the dataset to summarize and understand the key
characteristics of the variables.
- visualize important variables that may influence diabetes outcomes.
Loading the libraries
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.3## Warning: package 'tidyr' was built under R version 4.4.3## Warning: package 'purrr' was built under R version 4.4.3## Warning: package 'forcats' was built under R version 4.4.3## Warning: package 'lubridate' was built under R version 4.4.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
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## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ purrr::%||%() masks base::%||%()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(corrplot)## Warning: package 'corrplot' was built under R version 4.4.3## corrplot 0.95 loadedImporting dataset
diabetes <- read.csv("C:\\Users\\user\\Desktop\\Saheed\\Diabetes\\diabetes.csv")
View(diabetes)
head(diabetes) # to check the first few head of my data## Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
## 1 6 148 72 35 0 33.6
## 2 1 85 66 29 0 26.6
## 3 8 183 64 0 0 23.3
## 4 1 89 66 23 94 28.1
## 5 0 137 40 35 168 43.1
## 6 5 116 74 0 0 25.6
## DiabetesPedigreeFunction Age Outcome
## 1 0.627 50 1
## 2 0.351 31 0
## 3 0.672 32 1
## 4 0.167 21 0
## 5 2.288 33 1
## 6 0.201 30 0Checking for missing values
sapply(diabetes, function(x) sum(is.na(diabetes)))## Pregnancies Glucose BloodPressure
## 0 0 0
## SkinThickness Insulin BMI
## 0 0 0
## DiabetesPedigreeFunction Age Outcome
## 0 0 0dim(diabetes) # to know how many row and observation## [1] 768 9summary(diabetes) #to describe my data## Pregnancies Glucose BloodPressure SkinThickness
## Min. : 0.000 Min. : 0.0 Min. : 0.00 Min. : 0.00
## 1st Qu.: 1.000 1st Qu.: 99.0 1st Qu.: 62.00 1st Qu.: 0.00
## Median : 3.000 Median :117.0 Median : 72.00 Median :23.00
## Mean : 3.845 Mean :120.9 Mean : 69.11 Mean :20.54
## 3rd Qu.: 6.000 3rd Qu.:140.2 3rd Qu.: 80.00 3rd Qu.:32.00
## Max. :17.000 Max. :199.0 Max. :122.00 Max. :99.00
## Insulin BMI DiabetesPedigreeFunction Age
## Min. : 0.0 Min. : 0.00 Min. :0.0780 Min. :21.00
## 1st Qu.: 0.0 1st Qu.:27.30 1st Qu.:0.2437 1st Qu.:24.00
## Median : 30.5 Median :32.00 Median :0.3725 Median :29.00
## Mean : 79.8 Mean :31.99 Mean :0.4719 Mean :33.24
## 3rd Qu.:127.2 3rd Qu.:36.60 3rd Qu.:0.6262 3rd Qu.:41.00
## Max. :846.0 Max. :67.10 Max. :2.4200 Max. :81.00
## Outcome
## Min. :0.000
## 1st Qu.:0.000
## Median :0.000
## Mean :0.349
## 3rd Qu.:1.000
## Max. :1.000str(diabetes) #Used to check the structure of the dataset## 'data.frame': 768 obs. of 9 variables:
## $ Pregnancies : int 6 1 8 1 0 5 3 10 2 8 ...
## $ Glucose : int 148 85 183 89 137 116 78 115 197 125 ...
## $ BloodPressure : int 72 66 64 66 40 74 50 0 70 96 ...
## $ SkinThickness : int 35 29 0 23 35 0 32 0 45 0 ...
## $ Insulin : int 0 0 0 94 168 0 88 0 543 0 ...
## $ BMI : num 33.6 26.6 23.3 28.1 43.1 25.6 31 35.3 30.5 0 ...
## $ DiabetesPedigreeFunction: num 0.627 0.351 0.672 0.167 2.288 ...
## $ Age : int 50 31 32 21 33 30 26 29 53 54 ...
## $ Outcome : int 1 0 1 0 1 0 1 0 1 1 ...Converting integers to numeric
#Converting integers to numeric
diabetes$Pregnancies <- as.numeric(diabetes$Pregnancies)
diabetes$Glucose <- as.numeric(diabetes$Glucose)
diabetes$BloodPressure <- as.numeric(diabetes$BloodPressure)
diabetes$SkinThickness <- as.numeric(diabetes$SkinThickness)
diabetes$Insulin <- as.numeric(diabetes$Insulin)
diabetes$Age <- as.numeric(diabetes$Age)
diabetes$Outcome <- as.numeric(diabetes$Outcome)
diabetes$BMI <- as.numeric(diabetes$BMI)
str(diabetes)## 'data.frame': 768 obs. of 9 variables:
## $ Pregnancies : num 6 1 8 1 0 5 3 10 2 8 ...
## $ Glucose : num 148 85 183 89 137 116 78 115 197 125 ...
## $ BloodPressure : num 72 66 64 66 40 74 50 0 70 96 ...
## $ SkinThickness : num 35 29 0 23 35 0 32 0 45 0 ...
## $ Insulin : num 0 0 0 94 168 0 88 0 543 0 ...
## $ BMI : num 33.6 26.6 23.3 28.1 43.1 25.6 31 35.3 30.5 0 ...
## $ DiabetesPedigreeFunction: num 0.627 0.351 0.672 0.167 2.288 ...
## $ Age : num 50 31 32 21 33 30 26 29 53 54 ...
## $ Outcome : num 1 0 1 0 1 0 1 0 1 1 ...Checking for the count of each observation
#Checking for the count of each variables in my dataset
table(diabetes$Outcome)##
## 0 1
## 500 268table(diabetes$Pregnancies)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17
## 111 135 103 75 68 57 50 45 38 28 24 11 9 10 2 1 1table(diabetes$Glucose)##
## 0 44 56 57 61 62 65 67 68 71 72 73 74 75 76 77 78 79 80 81
## 5 1 1 2 1 1 1 1 3 4 1 3 4 2 2 2 4 3 6 6
## 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101
## 3 6 10 7 3 7 9 6 11 9 9 7 7 13 8 9 3 17 17 9
## 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121
## 13 9 6 13 14 11 13 12 6 14 13 5 11 10 7 11 6 11 11 6
## 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141
## 12 9 11 14 9 5 11 14 7 5 5 5 6 4 8 8 5 8 5 5
## 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
## 5 6 7 5 9 7 4 1 3 6 4 2 6 5 3 2 8 2 1 3
## 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181
## 6 3 3 4 3 3 4 1 2 3 1 6 2 2 2 1 1 5 5 5
## 182 183 184 186 187 188 189 190 191 193 194 195 196 197 198 199
## 1 3 3 1 4 2 4 1 1 2 3 2 3 4 1 1table(diabetes$BloodPressure)##
## 0 24 30 38 40 44 46 48 50 52 54 55 56 58 60 61 62 64 65 66
## 35 1 2 1 1 4 2 5 13 11 11 2 12 21 37 1 34 43 7 30
## 68 70 72 74 75 76 78 80 82 84 85 86 88 90 92 94 95 96 98 100
## 45 57 44 52 8 39 45 40 30 23 6 21 25 22 8 6 1 4 3 3
## 102 104 106 108 110 114 122
## 1 2 3 2 3 1 1table(diabetes$SkinThickness)##
## 0 7 8 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
## 227 2 2 5 6 7 11 6 14 6 14 20 18 13 10 16 22 12 16 16
## 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
## 23 20 17 27 19 31 20 8 15 14 16 7 18 16 15 11 6 5 6 8
## 47 48 49 50 51 52 54 56 60 63 99
## 4 4 3 3 1 2 2 1 1 1 1table(diabetes$Insulin)##
## 0 14 15 16 18 22 23 25 29 32 36 37 38 40 41 42 43 44 45 46
## 374 1 1 1 2 1 2 1 1 1 3 2 1 2 1 1 1 3 3 1
## 48 49 50 51 52 53 54 55 56 57 58 59 60 61 63 64 65 66 67 68
## 3 5 3 1 1 2 4 2 5 2 2 1 2 1 3 4 1 5 2 1
## 70 71 72 73 74 75 76 77 78 79 81 82 83 84 85 86 87 88 89 90
## 3 4 1 1 3 3 5 2 2 2 1 3 3 1 2 1 2 4 1 4
## 91 92 94 95 96 99 100 105 106 108 110 112 114 115 116 119 120 122 125 126
## 1 3 7 2 2 2 7 11 3 1 6 1 2 6 2 1 8 2 4 3
## 127 128 129 130 132 135 140 142 144 145 146 148 150 152 155 156 158 159 160 165
## 1 1 1 9 2 6 9 1 2 3 1 2 2 2 4 3 2 1 4 4
## 166 167 168 170 171 175 176 178 180 182 183 184 185 188 190 191 192 193 194 196
## 1 2 4 2 1 3 3 1 7 3 1 1 2 1 4 1 2 1 3 1
## 200 204 205 207 210 215 220 225 228 230 231 235 237 240 245 249 250 255 258 265
## 4 1 2 2 5 3 2 2 1 2 2 1 1 2 1 1 1 1 1 2
## 270 271 272 274 275 277 278 280 284 285 291 293 300 304 310 318 321 325 326 328
## 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 3 1 1
## 330 335 342 360 370 375 387 392 402 415 440 465 474 478 480 485 495 510 540 543
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1
## 545 579 600 680 744 846
## 1 1 1 1 1 1table(diabetes$Age)##
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
## 63 72 38 46 48 33 32 35 29 21 24 16 17 14 10 16 19 16 12 13 22 18 13 8 15 13
## 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 72 81
## 6 5 5 8 8 8 5 6 4 3 5 7 3 5 2 4 4 1 3 4 3 1 2 1 1 1table(diabetes$DiabetesPedigreeFunction)##
## 0.078 0.084 0.085 0.088 0.089 0.092 0.096 0.1 0.101 0.102 0.107 0.108 0.115
## 1 1 2 2 1 1 1 1 1 1 1 1 1
## 0.118 0.121 0.122 0.123 0.126 0.127 0.128 0.129 0.13 0.133 0.134 0.135 0.136
## 1 2 1 1 2 2 2 2 1 1 2 1 1
## 0.137 0.138 0.14 0.141 0.142 0.143 0.144 0.145 0.147 0.148 0.149 0.15 0.151
## 2 1 2 3 3 2 1 1 1 3 1 2 3
## 0.153 0.154 0.155 0.156 0.157 0.158 0.159 0.16 0.161 0.162 0.163 0.164 0.165
## 2 1 1 1 1 2 2 1 2 1 1 2 3
## 0.166 0.167 0.17 0.171 0.173 0.174 0.175 0.176 0.177 0.178 0.179 0.18 0.181
## 1 4 1 1 1 1 1 1 1 3 1 2 1
## 0.182 0.183 0.186 0.187 0.188 0.189 0.19 0.191 0.192 0.194 0.196 0.197 0.198
## 1 2 1 3 1 2 4 2 2 1 1 4 2
## 0.199 0.2 0.201 0.203 0.204 0.205 0.206 0.207 0.209 0.21 0.212 0.215 0.217
## 1 3 1 2 2 3 2 5 2 1 2 1 1
## 0.218 0.219 0.22 0.221 0.222 0.223 0.225 0.226 0.227 0.229 0.23 0.231 0.232
## 2 2 1 1 1 2 1 1 1 1 2 2 1
## 0.233 0.234 0.235 0.236 0.237 0.238 0.239 0.24 0.241 0.243 0.244 0.245 0.246
## 2 2 3 3 4 5 1 2 1 1 2 4 1
## 0.247 0.248 0.249 0.251 0.252 0.253 0.254 0.255 0.256 0.257 0.258 0.259 0.26
## 2 2 2 2 2 1 6 1 3 3 6 5 4
## 0.261 0.262 0.263 0.264 0.265 0.267 0.268 0.269 0.27 0.271 0.272 0.277 0.278
## 5 2 4 1 1 2 5 2 4 1 1 1 2
## 0.279 0.28 0.282 0.283 0.284 0.285 0.286 0.287 0.289 0.29 0.292 0.293 0.294
## 1 3 2 1 4 2 2 1 2 2 3 2 3
## 0.295 0.296 0.297 0.299 0.3 0.302 0.303 0.304 0.305 0.306 0.307 0.313 0.314
## 1 1 1 4 1 2 1 4 3 2 1 2 2
## 0.315 0.317 0.318 0.319 0.323 0.324 0.325 0.326 0.328 0.329 0.33 0.331 0.332
## 2 1 1 1 2 2 1 2 2 1 1 1 1
## 0.334 0.335 0.336 0.337 0.338 0.34 0.341 0.342 0.343 0.344 0.345 0.346 0.347
## 2 1 2 3 1 3 1 2 2 2 1 1 1
## 0.349 0.351 0.352 0.355 0.356 0.358 0.361 0.362 0.364 0.365 0.366 0.368 0.37
## 3 1 1 1 2 1 2 1 2 2 1 2 2
## 0.371 0.374 0.375 0.376 0.378 0.38 0.381 0.382 0.383 0.385 0.388 0.389 0.391
## 1 1 1 1 2 2 1 1 1 1 1 3 3
## 0.393 0.394 0.395 0.396 0.398 0.399 0.4 0.401 0.402 0.403 0.404 0.407 0.408
## 1 1 1 1 1 1 2 1 2 2 1 2 1
## 0.409 0.411 0.412 0.415 0.416 0.417 0.419 0.42 0.421 0.422 0.423 0.426 0.427
## 1 1 2 2 1 1 1 1 1 3 1 1 1
## 0.43 0.431 0.432 0.433 0.434 0.435 0.439 0.441 0.443 0.444 0.446 0.447 0.451
## 2 1 1 2 2 1 2 1 3 2 1 1 1
## 0.452 0.453 0.454 0.455 0.457 0.46 0.463 0.464 0.465 0.466 0.467 0.471 0.472
## 3 1 1 2 1 1 1 1 1 2 1 2 1
## 0.479 0.482 0.483 0.484 0.485 0.487 0.488 0.491 0.493 0.495 0.496 0.497 0.498
## 1 1 1 1 1 1 1 1 1 1 3 2 1
## 0.499 0.501 0.502 0.503 0.507 0.509 0.51 0.512 0.514 0.515 0.516 0.52 0.525
## 1 1 1 1 1 1 1 1 2 1 1 3 1
## 0.526 0.527 0.528 0.529 0.532 0.534 0.536 0.537 0.539 0.542 0.543 0.545 0.546
## 1 2 2 1 1 1 2 1 1 2 2 1 1
## 0.547 0.549 0.551 0.554 0.557 0.559 0.56 0.561 0.564 0.565 0.569 0.571 0.572
## 1 1 4 1 2 2 1 1 1 1 1 1 1
## 0.575 0.578 0.58 0.582 0.583 0.586 0.587 0.588 0.591 0.593 0.595 0.597 0.598
## 1 1 1 2 3 2 3 1 2 1 1 1 1
## 0.6 0.601 0.605 0.607 0.61 0.612 0.613 0.614 0.615 0.619 0.624 0.626 0.627
## 2 1 2 1 1 1 1 1 1 1 1 1 1
## 0.629 0.63 0.631 0.637 0.64 0.645 0.646 0.647 0.649 0.652 0.654 0.655 0.658
## 1 1 1 2 2 1 1 2 1 2 2 1 1
## 0.66 0.661 0.665 0.666 0.672 0.673 0.674 0.677 0.678 0.68 0.682 0.686 0.687
## 2 1 1 1 1 1 2 1 2 1 1 2 4
## 0.692 0.693 0.695 0.696 0.698 0.699 0.702 0.703 0.704 0.705 0.709 0.711 0.717
## 4 1 1 1 1 1 1 1 1 1 1 1 1
## 0.718 0.719 0.721 0.722 0.725 0.727 0.73 0.731 0.732 0.733 0.734 0.735 0.738
## 1 1 1 1 1 2 1 1 1 2 1 1 1
## 0.741 0.742 0.743 0.744 0.745 0.748 0.757 0.759 0.761 0.766 0.767 0.771 0.773
## 1 1 1 1 1 1 1 1 2 1 1 1 1
## 0.785 0.787 0.801 0.803 0.804 0.805 0.808 0.813 0.816 0.817 0.821 0.825 0.826
## 1 2 1 1 1 1 1 1 1 1 1 1 1
## 0.828 0.831 0.832 0.833 0.839 0.84 0.845 0.851 0.855 0.856 0.867 0.871 0.874
## 1 1 1 1 2 1 1 1 1 1 1 1 1
## 0.875 0.878 0.88 0.881 0.886 0.892 0.893 0.904 0.905 0.917 0.925 0.926 0.93
## 2 1 1 1 1 1 1 1 2 1 1 1 1
## 0.932 0.933 0.944 0.947 0.949 0.955 0.956 0.962 0.966 0.968 0.97 0.997 1.001
## 1 1 1 1 1 1 1 2 1 2 1 1 1
## 1.021 1.022 1.034 1.057 1.072 1.076 1.095 1.096 1.101 1.114 1.127 1.136 1.138
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1.144 1.154 1.159 1.162 1.174 1.182 1.189 1.191 1.213 1.222 1.224 1.251 1.258
## 1 1 1 1 1 1 1 1 1 1 2 1 1
## 1.268 1.282 1.292 1.318 1.321 1.353 1.39 1.391 1.394 1.4 1.441 1.461 1.476
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1.6 1.698 1.699 1.731 1.781 1.893 2.137 2.288 2.329 2.42
## 1 1 1 1 1 1 1 1 1 1table(diabetes$BMI)##
## 0 18.2 18.4 19.1 19.3 19.4 19.5 19.6 19.9 20 20.1 20.4 20.8 21 21.1 21.2
## 11 3 1 1 1 1 2 3 1 1 1 2 2 2 4 1
## 21.7 21.8 21.9 22.1 22.2 22.3 22.4 22.5 22.6 22.7 22.9 23 23.1 23.2 23.3 23.4
## 1 5 3 2 2 1 2 3 2 1 2 2 4 3 2 1
## 23.5 23.6 23.7 23.8 23.9 24 24.1 24.2 24.3 24.4 24.5 24.6 24.7 24.8 24.9 25
## 3 3 2 2 2 4 1 6 4 3 1 4 5 3 1 6
## 25.1 25.2 25.3 25.4 25.5 25.6 25.8 25.9 26 26.1 26.2 26.3 26.4 26.5 26.6 26.7
## 3 6 2 4 2 6 2 7 4 3 4 1 3 3 4 1
## 26.8 26.9 27 27.1 27.2 27.3 27.4 27.5 27.6 27.7 27.8 27.9 28 28.1 28.2 28.3
## 4 1 2 3 2 4 5 5 7 4 7 2 5 1 2 2
## 28.4 28.5 28.6 28.7 28.8 28.9 29 29.2 29.3 29.5 29.6 29.7 29.8 29.9 30 30.1
## 6 3 2 7 2 6 5 1 5 5 4 8 3 5 7 9
## 30.2 30.3 30.4 30.5 30.7 30.8 30.9 31 31.1 31.2 31.3 31.6 31.9 32 32.1 32.2
## 1 1 7 7 1 9 5 2 1 12 1 12 2 13 1 1
## 32.3 32.4 32.5 32.6 32.7 32.8 32.9 33.1 33.2 33.3 33.5 33.6 33.7 33.8 33.9 34
## 3 10 6 1 3 9 9 3 7 10 1 8 5 5 2 6
## 34.1 34.2 34.3 34.4 34.5 34.6 34.7 34.8 34.9 35 35.1 35.2 35.3 35.4 35.5 35.6
## 4 8 6 4 5 5 4 2 6 4 3 2 4 4 7 2
## 35.7 35.8 35.9 36 36.1 36.2 36.3 36.4 36.5 36.6 36.7 36.8 36.9 37 37.1 37.2
## 4 5 5 2 3 1 3 2 4 5 1 6 3 1 2 4
## 37.3 37.4 37.5 37.6 37.7 37.8 37.9 38 38.1 38.2 38.3 38.4 38.5 38.6 38.7 38.8
## 1 3 2 5 5 3 2 2 3 4 1 2 6 1 3 1
## 38.9 39 39.1 39.2 39.3 39.4 39.5 39.6 39.7 39.8 39.9 40 40.1 40.2 40.5 40.6
## 1 4 4 2 1 7 3 1 1 2 3 2 1 1 3 4
## 40.7 40.8 40.9 41 41.2 41.3 41.5 41.8 42 42.1 42.2 42.3 42.4 42.6 42.7 42.8
## 1 1 2 1 1 3 2 1 1 2 1 3 3 1 2 1
## 42.9 43.1 43.2 43.3 43.4 43.5 43.6 44 44.1 44.2 44.5 44.6 45 45.2 45.3 45.4
## 4 1 1 5 2 2 2 2 1 2 2 1 1 1 3 1
## 45.5 45.6 45.7 45.8 46.1 46.2 46.3 46.5 46.7 46.8 47.9 48.3 48.8 49.3 49.6 49.7
## 1 2 1 1 2 2 1 1 1 2 2 1 1 1 1 1
## 50 52.3 52.9 53.2 55 57.3 59.4 67.1
## 1 2 1 1 1 1 1 1Univariate Analysis
# Checking the pregnancy count for the women.
preg <- diabetes %>%
count(Pregnancies)
preg## Pregnancies n
## 1 0 111
## 2 1 135
## 3 2 103
## 4 3 75
## 5 4 68
## 6 5 57
## 7 6 50
## 8 7 45
## 9 8 38
## 10 9 28
## 11 10 24
## 12 11 11
## 13 12 9
## 14 13 10
## 15 14 2
## 16 15 1
## 17 17 1ggplot(preg,aes(x=Pregnancies,y=n))+
geom_col(fill="blue")+
geom_text(aes(label = n),vjust=-0.1)+
labs(title="Count of Pregnancies",
x="Pregnancies",
y="Count"
)+
theme_minimal()
This displays the number of females and how many times they have been
pregnant.
out<-diabetes%>%
count(Outcome)
out## Outcome n
## 1 0 500
## 2 1 268ggplot(out,aes(x=Outcome,y=n))+
geom_col(fill="red3")+
geom_text(aes(label=n),vjust=-0.3)+
labs(title = "Count of Outcome",
x="Outcome",
y="Count"
)+
theme_minimal()
Bivariate Analysis
Categorizing the pregnancies into groups and visualizing the pregnancy group to see how they affect the diabetes outcome.
Pregnancy_group <- cut(
diabetes$Pregnancies,
breaks = c(0, 6, 12, 17),
labels = c("Normal Pregnancy", "Moderate Pregnancy", "High Pregnancy"),
right = TRUE, include.lowest = TRUE
)
ggplot(diabetes, aes(Pregnancy_group, fill = factor(Outcome))) +
geom_bar(position = "dodge") +
labs(title="Pregnancy Group vs Diabetes Outcome",
x="Pregnancy Group", y="Count") +
theme_minimal()
In the Pima dataset, most women fall into normal pregnancy counts. Since
this group is bigger, it will contain more diabetes cases, even if the
percentage is not higher.
Categorizing age into groups and checking their count and visualizing to see how age affects the diabetes outcome.
Age_group <- cut(diabetes$Age,
breaks = c(0, 30, 45, 60, 81),
labels = c("Young", "Middle-aged", "Older Adult", "Elderly"),
right = TRUE)
table(diabetes$Age_group)## < table of extent 0 >ggplot(diabetes, aes(Age_group, fill = factor(Outcome))) +
geom_bar(position = "dodge") +
labs(title="Age Group vs Diabetes Outcome",
x="Age Group", y="Count") +
theme_minimal()
The young age group has the highest population of non diabetes patient
while the people in the middle age has the highest number of diabetes
patient.
Categorizing BMI (Body Mass Index) into groups and checking their count and visualizing to see how BMI affects the diabetes outcome.
diabetes$BMI <- as.numeric(diabetes$BMI)
diabetes$BMI## [1] 33.6 26.6 23.3 28.1 43.1 25.6 31.0 35.3 30.5 0.0 37.6 38.0 27.1 30.1 25.8
## [16] 30.0 45.8 29.6 43.3 34.6 39.3 35.4 39.8 29.0 36.6 31.1 39.4 23.2 22.2 34.1
## [31] 36.0 31.6 24.8 19.9 27.6 24.0 33.2 32.9 38.2 37.1 34.0 40.2 22.7 45.4 27.4
## [46] 42.0 29.7 28.0 39.1 0.0 19.4 24.2 24.4 33.7 34.7 23.0 37.7 46.8 40.5 41.5
## [61] 0.0 32.9 25.0 25.4 32.8 29.0 32.5 42.7 19.6 28.9 32.9 28.6 43.4 35.1 32.0
## [76] 24.7 32.6 37.7 43.2 25.0 22.4 0.0 29.3 24.6 48.8 32.4 36.6 38.5 37.1 26.5
## [91] 19.1 32.0 46.7 23.8 24.7 33.9 31.6 20.4 28.7 49.7 39.0 26.1 22.5 26.6 39.6
## [106] 28.7 22.4 29.5 34.3 37.4 33.3 34.0 31.2 34.0 30.5 31.2 34.0 33.7 28.2 23.2
## [121] 53.2 34.2 33.6 26.8 33.3 55.0 42.9 33.3 34.5 27.9 29.7 33.3 34.5 38.3 21.1
## [136] 33.8 30.8 28.7 31.2 36.9 21.1 39.5 32.5 32.4 32.8 0.0 32.8 30.5 33.7 27.3
## [151] 37.4 21.9 34.3 40.6 47.9 50.0 24.6 25.2 29.0 40.9 29.7 37.2 44.2 29.7 31.6
## [166] 29.9 32.5 29.6 31.9 28.4 30.8 35.4 28.9 43.5 29.7 32.7 31.2 67.1 45.0 39.1
## [181] 23.2 34.9 27.7 26.8 27.6 35.9 30.1 32.0 27.9 31.6 22.6 33.1 30.4 52.3 24.4
## [196] 39.4 24.3 22.9 34.8 30.9 31.0 40.1 27.3 20.4 37.7 23.9 37.5 37.7 33.2 35.5
## [211] 27.7 42.8 34.2 42.6 34.2 41.8 35.8 30.0 29.0 37.8 34.6 31.6 25.2 28.8 23.6
## [226] 34.6 35.7 37.2 36.7 45.2 44.0 46.2 25.4 35.0 29.7 43.6 35.9 44.1 30.8 18.4
## [241] 29.2 33.1 25.6 27.1 38.2 30.0 31.2 52.3 35.4 30.1 31.2 28.0 24.4 35.8 27.6
## [256] 33.6 30.1 28.7 25.9 33.3 30.9 30.0 32.1 32.4 32.0 33.6 36.3 40.0 25.1 27.5
## [271] 45.6 25.2 23.0 33.2 34.2 40.5 26.5 27.8 24.9 25.3 37.9 35.9 32.4 30.4 27.0
## [286] 26.0 38.7 45.6 20.8 36.1 36.9 36.6 43.3 40.5 21.9 35.5 28.0 30.7 36.6 23.6
## [301] 32.3 31.6 35.8 52.9 21.0 39.7 25.5 24.8 30.5 32.9 26.2 39.4 26.6 29.5 35.9
## [316] 34.1 19.3 30.5 38.1 23.5 27.5 31.6 27.4 26.8 35.7 25.6 35.1 35.1 45.5 30.8
## [331] 23.1 32.7 43.3 23.6 23.9 47.9 33.8 31.2 34.2 39.9 25.9 25.9 32.0 34.7 36.8
## [346] 38.5 28.7 23.5 21.8 41.0 42.2 31.2 34.4 27.2 42.7 30.4 33.3 39.9 35.3 36.5
## [361] 31.2 29.8 39.2 38.5 34.9 34.0 27.6 21.0 27.5 32.8 38.4 0.0 35.8 34.9 36.2
## [376] 39.2 25.2 37.2 48.3 43.4 30.8 20.0 25.4 25.1 24.3 22.3 32.3 43.3 32.0 31.6
## [391] 32.0 45.7 23.7 22.1 32.9 27.7 24.7 34.3 21.1 34.9 32.0 24.2 35.0 31.6 32.9
## [406] 42.1 28.9 21.9 25.9 42.4 35.7 34.4 42.4 26.2 34.6 35.7 27.2 38.5 18.2 26.4
## [421] 45.3 26.0 40.6 30.8 42.9 37.0 0.0 34.1 40.6 35.0 22.2 30.4 30.0 25.6 24.5
## [436] 42.4 37.4 29.9 18.2 36.8 34.3 32.2 33.2 30.5 29.7 59.4 25.3 36.5 33.6 30.5
## [451] 21.2 28.9 39.9 19.6 37.8 33.6 26.7 30.2 37.6 25.9 20.8 21.8 35.3 27.6 24.0
## [466] 21.8 27.8 36.8 30.0 46.1 41.3 33.2 38.8 29.9 28.9 27.3 33.7 23.8 25.9 28.0
## [481] 35.5 35.2 27.8 38.2 44.2 42.3 40.7 46.5 25.6 26.1 36.8 33.5 32.8 28.9 0.0
## [496] 26.6 26.0 30.1 25.1 29.3 25.2 37.2 39.0 33.3 37.3 33.3 36.5 28.6 30.4 25.0
## [511] 29.7 22.1 24.2 27.3 25.6 31.6 30.3 37.6 32.8 19.6 25.0 33.2 0.0 34.2 31.6
## [526] 21.8 18.2 26.3 30.8 24.6 29.8 45.3 41.3 29.8 33.3 32.9 29.6 21.7 36.3 36.4
## [541] 39.4 32.4 34.9 39.5 32.0 34.5 43.6 33.1 32.8 28.5 27.4 31.9 27.8 29.9 36.9
## [556] 25.5 38.1 27.8 46.2 30.1 33.8 41.3 37.6 26.9 32.4 26.1 38.6 32.0 31.3 34.3
## [571] 32.5 22.6 29.5 34.7 30.1 35.5 24.0 42.9 27.0 34.7 42.1 25.0 26.5 38.7 28.7
## [586] 22.5 34.9 24.3 33.3 21.1 46.8 39.4 34.4 28.5 33.6 32.0 45.3 27.8 36.8 23.1
## [601] 27.1 23.7 27.8 35.2 28.4 35.8 40.0 19.5 41.5 24.0 30.9 32.9 38.2 32.5 36.1
## [616] 25.8 28.7 20.1 28.2 32.4 38.4 24.2 40.8 43.5 30.8 37.7 24.7 32.4 34.6 24.7
## [631] 27.4 34.5 26.2 27.5 25.9 31.2 28.8 31.6 40.9 19.5 29.3 34.3 29.5 28.0 27.6
## [646] 39.4 23.4 37.8 28.3 26.4 25.2 33.8 34.1 26.8 34.2 38.7 21.8 38.9 39.0 34.2
## [661] 27.7 42.9 37.6 37.9 33.7 34.8 32.5 27.5 34.0 30.9 33.6 25.4 35.5 57.3 35.6
## [676] 30.9 24.8 35.3 36.0 24.2 24.2 49.6 44.6 32.3 0.0 33.2 23.1 28.3 24.1 46.1
## [691] 24.6 42.3 39.1 38.5 23.5 30.4 29.9 25.0 34.5 44.5 35.9 27.6 35.0 38.5 28.4
## [706] 39.8 0.0 34.4 32.8 38.0 31.2 29.6 41.2 26.4 29.5 33.9 33.8 23.1 35.5 35.6
## [721] 29.3 38.1 29.3 39.1 32.8 39.4 36.1 32.4 22.9 30.1 28.4 28.4 44.5 29.0 23.3
## [736] 35.4 27.4 32.0 36.6 39.5 42.3 30.8 28.5 32.7 40.6 30.0 49.3 46.3 36.4 24.3
## [751] 31.2 39.0 26.0 43.3 32.4 36.5 32.0 36.3 37.5 35.5 28.4 44.0 22.5 32.9 36.8
## [766] 26.2 30.1 30.4is.numeric(diabetes$BMI)## [1] TRUEdiabetes$BMI_cat <- cut(
diabetes$BMI,
breaks = c(0,25,35, 40, 70),
labels = c("Underweight", "Normal", "Overweight", "Obese"),
right = FALSE
)
ggplot(diabetes, aes(BMI_cat, fill = factor(Outcome))) +
geom_bar(position = "dodge") +
labs(title="BMI vs Diabetes Outcome",
x="BMI category", y="Count") +
theme_minimal()
The normal BMI group has the highest number of diabetic patients simply
because they are the most common group in the dataset.
Categorizing Skin thickness into groups and visualizing against diabetes outcome.
Skin_group <- cut(
diabetes$SkinThickness,
breaks = c(-Inf, 10, 20, Inf),
labels = c("Normal", "High", "Very High"),
include.lowest = TRUE
)
ggplot(diabetes,aes(Skin_group,fill=factor(Outcome))) +
geom_bar(position = "dodge") +
labs(title = "Skin thickness vs Diabetes Outcome",
x="Skin thickness", y="Count") +
theme_minimal()
The patient with high skin thickness has the highest number of diabetes
patients followed by the patient with normal skin thickness.
Categorizing Glucose level into groups and visualizing against diabetes outcome.
diabetes$Glucose_cat <- cut(diabetes$Glucose, breaks = c(0,100,125,200),
labels = c("Normal","Prediabetic","Diabetic"),right = FALSE)
diabetes$Glucose_cat## [1] Diabetic Normal Diabetic Normal Diabetic Prediabetic
## [7] Normal Prediabetic Diabetic Diabetic Prediabetic Diabetic
## [13] Diabetic Diabetic Diabetic Prediabetic Prediabetic Prediabetic
## [19] Prediabetic Prediabetic Diabetic Normal Diabetic Prediabetic
## [25] Diabetic Diabetic Diabetic Normal Diabetic Prediabetic
## [31] Prediabetic Diabetic Normal Normal Prediabetic Prediabetic
## [37] Diabetic Prediabetic Normal Prediabetic Diabetic Diabetic
## [43] Prediabetic Diabetic Diabetic Diabetic Diabetic Normal
## [49] Prediabetic Prediabetic Prediabetic Prediabetic Normal Diabetic
## [55] Diabetic Normal Diabetic Prediabetic Diabetic Prediabetic
## [61] Normal Diabetic Normal Diabetic Prediabetic Normal
## [67] Prediabetic Prediabetic Normal Diabetic Prediabetic Diabetic
## [73] Diabetic Diabetic Normal Normal Normal Normal
## [79] Diabetic Prediabetic Prediabetic Normal Normal Prediabetic
## [85] Diabetic Prediabetic Prediabetic Prediabetic Diabetic Prediabetic
## [91] Normal Prediabetic Normal Diabetic Diabetic Diabetic
## [97] Normal Normal Normal Prediabetic Diabetic Diabetic
## [103] Diabetic Normal Normal Diabetic Normal Diabetic
## [109] Normal Normal Diabetic Diabetic Normal Normal
## [115] Diabetic Diabetic Prediabetic Normal Normal Normal
## [121] Diabetic Prediabetic Prediabetic Diabetic Prediabetic Normal
## [127] Prediabetic Prediabetic Prediabetic Prediabetic Diabetic Prediabetic
## [133] Diabetic Normal Normal Diabetic Prediabetic Normal
## [139] Diabetic Prediabetic Diabetic Prediabetic Prediabetic Prediabetic
## [145] Diabetic Prediabetic Normal Prediabetic Diabetic Normal
## [151] Diabetic Prediabetic Diabetic Diabetic Diabetic Diabetic
## [157] Normal Prediabetic Normal Diabetic Diabetic Prediabetic
## [163] Prediabetic Prediabetic Diabetic Prediabetic Diabetic Prediabetic
## [169] Prediabetic Prediabetic Prediabetic Diabetic Normal Normal
## [175] Normal Diabetic Normal Diabetic Diabetic Diabetic
## [181] Normal Prediabetic Normal Normal Diabetic Diabetic
## [187] Diabetic Diabetic Prediabetic Diabetic Prediabetic Prediabetic
## [193] Diabetic Diabetic Normal Diabetic Prediabetic Prediabetic
## [199] Prediabetic Diabetic Prediabetic Diabetic Prediabetic Normal
## [205] Prediabetic Prediabetic Diabetic Diabetic Normal Diabetic
## [211] Normal Diabetic Diabetic Diabetic Prediabetic Diabetic
## [217] Prediabetic Diabetic Normal Prediabetic Diabetic Diabetic
## [223] Prediabetic Diabetic Prediabetic Normal Prediabetic Diabetic
## [229] Diabetic Prediabetic Diabetic Diabetic Normal Prediabetic
## [235] Normal Diabetic Diabetic Diabetic Diabetic Prediabetic
## [241] Normal Normal Diabetic Prediabetic Diabetic Diabetic
## [247] Prediabetic Diabetic Prediabetic Prediabetic Prediabetic Diabetic
## [253] Normal Normal Normal Prediabetic Prediabetic Prediabetic
## [259] Diabetic Diabetic Diabetic Diabetic Normal Diabetic
## [265] Prediabetic Normal Diabetic Diabetic Prediabetic Diabetic
## [271] Prediabetic Prediabetic Prediabetic Normal Prediabetic Prediabetic
## [277] Prediabetic Prediabetic Prediabetic Prediabetic Diabetic Diabetic
## [283] Diabetic Diabetic Prediabetic Diabetic Diabetic Prediabetic
## [289] Normal Prediabetic Normal Prediabetic Diabetic Diabetic
## [295] Diabetic Diabetic Diabetic Diabetic Prediabetic Prediabetic
## [301] Diabetic Diabetic Normal Prediabetic Diabetic Prediabetic
## [307] Diabetic Diabetic Diabetic Prediabetic Normal Prediabetic
## [313] Diabetic Prediabetic Prediabetic Prediabetic Normal Diabetic
## [319] Prediabetic Diabetic Diabetic Prediabetic Prediabetic Diabetic
## [325] Prediabetic Diabetic Prediabetic Diabetic Prediabetic Prediabetic
## [331] Prediabetic Normal Diabetic Prediabetic Normal Diabetic
## [337] Prediabetic Prediabetic Diabetic Diabetic Diabetic Normal
## [343] Normal Prediabetic Normal Diabetic Diabetic Prediabetic
## [349] Normal Normal Normal Diabetic Normal Normal
## [355] Normal Diabetic Diabetic Diabetic Normal Diabetic
## [361] Diabetic Diabetic Prediabetic Diabetic Diabetic Normal
## [367] Prediabetic Prediabetic Normal Diabetic Diabetic Prediabetic
## [373] Normal Prediabetic Prediabetic Diabetic Normal Normal
## [379] Diabetic Normal Prediabetic Prediabetic Prediabetic Normal
## [385] Diabetic Prediabetic Prediabetic Prediabetic Diabetic Prediabetic
## [391] Prediabetic Diabetic Diabetic Prediabetic Diabetic Diabetic
## [397] Normal Diabetic Normal Diabetic Normal Diabetic
## [403] Diabetic Normal Diabetic Prediabetic Prediabetic Prediabetic
## [409] Diabetic Diabetic Prediabetic Prediabetic Diabetic Diabetic
## [415] Diabetic Diabetic Normal Diabetic Normal Diabetic
## [421] Prediabetic Normal Prediabetic Prediabetic Diabetic Diabetic
## [427] Normal Diabetic Diabetic Normal Normal Normal
## [433] Normal Diabetic Normal Diabetic Diabetic Diabetic
## [439] Normal Prediabetic Diabetic Normal Prediabetic Prediabetic
## [445] Prediabetic Diabetic Prediabetic Normal Prediabetic Prediabetic
## [451] Normal Diabetic Normal Prediabetic Prediabetic Diabetic
## [457] Diabetic Normal Diabetic Diabetic Prediabetic Normal
## [463] Normal Normal Prediabetic Prediabetic Normal Normal
## [469] Prediabetic Diabetic Diabetic Diabetic Prediabetic Diabetic
## [475] Prediabetic Diabetic Prediabetic Prediabetic Diabetic Diabetic
## [481] Diabetic Prediabetic Normal Normal Diabetic Diabetic
## [487] Diabetic Diabetic Normal Diabetic Normal Normal
## [493] Normal Diabetic Normal Diabetic Prediabetic Normal
## [499] Diabetic Diabetic Prediabetic Normal Normal Normal
## [505] Normal Normal Diabetic Diabetic Normal Prediabetic
## [511] Normal Diabetic Normal Normal Normal Diabetic
## [517] Diabetic Diabetic Normal Diabetic Normal Prediabetic
## [523] Prediabetic Diabetic Diabetic Normal Normal Prediabetic
## [529] Prediabetic Prediabetic Prediabetic Prediabetic Normal Normal
## [535] Normal Diabetic Prediabetic Normal Diabetic Diabetic
## [541] Prediabetic Diabetic Normal Normal Normal Diabetic
## [547] Diabetic Diabetic Diabetic Diabetic Prediabetic Normal
## [553] Prediabetic Normal Normal Prediabetic Normal Prediabetic
## [559] Prediabetic Normal Diabetic Diabetic Normal Normal
## [565] Normal Normal Normal Normal Diabetic Prediabetic
## [571] Normal Diabetic Prediabetic Normal Diabetic Prediabetic
## [577] Prediabetic Prediabetic Diabetic Diabetic Diabetic Prediabetic
## [583] Prediabetic Prediabetic Prediabetic Normal Diabetic Prediabetic
## [589] Diabetic Normal Prediabetic Prediabetic Diabetic Normal
## [595] Prediabetic Diabetic Normal Normal Diabetic Prediabetic
## [601] Prediabetic Normal Prediabetic Diabetic Diabetic Prediabetic
## [607] Diabetic Normal Diabetic Prediabetic Prediabetic Diabetic
## [613] Diabetic Prediabetic Diabetic Prediabetic Prediabetic Normal
## [619] Prediabetic Prediabetic Prediabetic Normal Diabetic Normal
## [625] Prediabetic Normal Diabetic Diabetic Diabetic Normal
## [631] Prediabetic Prediabetic Prediabetic Diabetic Normal Prediabetic
## [637] Prediabetic Normal Normal Prediabetic Prediabetic Diabetic
## [643] Diabetic Normal Prediabetic Diabetic Diabetic Diabetic
## [649] Diabetic Prediabetic Normal Prediabetic Prediabetic Prediabetic
## [655] Prediabetic Diabetic Prediabetic Prediabetic Diabetic Normal
## [661] Diabetic Diabetic Diabetic Diabetic Prediabetic Prediabetic
## [667] Diabetic Prediabetic Normal Diabetic Diabetic Normal
## [673] Normal Prediabetic Normal Diabetic Diabetic Normal
## [679] Prediabetic Prediabetic Normal Diabetic Normal Diabetic
## [685] Diabetic Diabetic Diabetic Prediabetic Diabetic Diabetic
## [691] Prediabetic Diabetic Prediabetic Diabetic Normal Diabetic
## [697] Diabetic Normal Diabetic Prediabetic Prediabetic Diabetic
## [703] Diabetic Diabetic Prediabetic Normal Prediabetic Diabetic
## [709] Diabetic Normal Diabetic Diabetic Diabetic Diabetic
## [715] Prediabetic Diabetic Diabetic Normal Prediabetic Normal
## [721] Normal Prediabetic Diabetic Prediabetic Prediabetic Prediabetic
## [727] Prediabetic Diabetic Diabetic Normal Diabetic Prediabetic
## [733] Diabetic Prediabetic Prediabetic Normal Diabetic Normal
## [739] Normal Prediabetic Prediabetic Prediabetic Prediabetic Diabetic
## [745] Diabetic Prediabetic Diabetic Normal Diabetic Diabetic
## [751] Diabetic Prediabetic Prediabetic Diabetic Diabetic Diabetic
## [757] Diabetic Prediabetic Prediabetic Diabetic Normal Diabetic
## [763] Normal Prediabetic Prediabetic Prediabetic Diabetic Normal
## Levels: Normal Prediabetic Diabeticggplot(diabetes,aes(Glucose_cat,fill=factor(Outcome))) +
geom_bar(position="dodge") +
labs(title="Glucose level vs Diabetes Outcome",
x="Glucose category", y="Count") +
theme_minimal()
As glucose levels rise, there is likelihood for the patient to be
diabetic.
Categorizing Blood Pressure into Groups and visualizing against diabetes outcome.
diabetes$BloodPressure_cat <- cut(diabetes$BloodPressure, breaks = c(-Inf, 79, 89, Inf),
labels = c("Normal", "Prehypertension", "Hypertension"),
right = TRUE
)
table(diabetes$BloodPressure)##
## 0 24 30 38 40 44 46 48 50 52 54 55 56 58 60 61 62 64 65 66
## 35 1 2 1 1 4 2 5 13 11 11 2 12 21 37 1 34 43 7 30
## 68 70 72 74 75 76 78 80 82 84 85 86 88 90 92 94 95 96 98 100
## 45 57 44 52 8 39 45 40 30 23 6 21 25 22 8 6 1 4 3 3
## 102 104 106 108 110 114 122
## 1 2 3 2 3 1 1ggplot(diabetes,aes(BloodPressure_cat,fill=factor(Outcome))) +
geom_bar(position="dodge") +
labs(title="Blood Pressure vs Diabetes Outcome",
x="Blood Pressure", y="Count") +
theme_minimal()
Even though high blood pressure can increase the risk of diabetes, most
people in the dataset have normal blood pressure. That’s why the largest
number of diabetes cases is seen in the normal BP group.
Categorizing and visualizing for insulin levels
diabetes$Insulin[diabetes$Insulin > 300] <- 300
diabetes[is.na(diabetes)] <- 0
diabetes$Insulin_cat <- cut(
diabetes$Insulin,
breaks = c(-Inf, 25, 100, 300, Inf),
labels = c("Low", "Normal", "High", "Extremely High"),
right = TRUE,
include.lowest = TRUE
)
table(diabetes$Diabetes, diabetes$Insulin_cat)##
## Low Normal High Extremely High
## 0.078 1 0 0 0
## 0.084 1 0 0 0
## 0.085 1 0 1 0
## 0.088 1 0 1 0
## 0.089 0 1 0 0
## 0.092 1 0 0 0
## 0.096 1 0 0 0
## 0.1 1 0 0 0
## 0.101 0 1 0 0
## 0.102 1 0 0 0
## 0.107 0 0 1 0
## 0.108 1 0 0 0
## 0.115 0 0 1 0
## 0.118 0 1 0 0
## 0.121 2 0 0 0
## 0.122 0 1 0 0
## 0.123 0 0 1 0
## 0.126 2 0 0 0
## 0.127 1 0 1 0
## 0.128 0 1 1 0
## 0.129 2 0 0 0
## 0.13 1 0 0 0
## 0.133 1 0 0 0
## 0.134 1 0 1 0
## 0.135 1 0 0 0
## 0.136 1 0 0 0
## 0.137 2 0 0 0
## 0.138 0 0 1 0
## 0.14 2 0 0 0
## 0.141 3 0 0 0
## 0.142 2 0 1 0
## 0.143 1 0 1 0
## 0.144 1 0 0 0
## 0.145 1 0 0 0
## 0.147 1 0 0 0
## 0.148 3 0 0 0
## 0.149 0 1 0 0
## 0.15 0 0 2 0
## 0.151 3 0 0 0
## 0.153 1 0 1 0
## 0.154 0 1 0 0
## 0.155 0 1 0 0
## 0.156 0 1 0 0
## 0.157 1 0 0 0
## 0.158 1 0 1 0
## 0.159 0 1 1 0
## 0.16 0 0 1 0
## 0.161 1 0 1 0
## 0.162 0 1 0 0
## 0.163 0 0 1 0
## 0.164 1 1 0 0
## 0.165 2 0 1 0
## 0.166 0 1 0 0
## 0.167 2 1 1 0
## 0.17 1 0 0 0
## 0.171 0 0 1 0
## 0.173 0 0 1 0
## 0.174 1 0 0 0
## 0.175 0 0 1 0
## 0.176 0 0 1 0
## 0.177 1 0 0 0
## 0.178 3 0 0 0
## 0.179 1 0 0 0
## 0.18 2 0 0 0
## 0.181 1 0 0 0
## 0.182 1 0 0 0
## 0.183 1 1 0 0
## 0.186 1 0 0 0
## 0.187 2 1 0 0
## 0.188 1 0 0 0
## 0.189 1 1 0 0
## 0.19 4 0 0 0
## 0.191 2 0 0 0
## 0.192 1 1 0 0
## 0.194 1 0 0 0
## 0.196 1 0 0 0
## 0.197 4 0 0 0
## 0.198 1 0 1 0
## 0.199 0 0 1 0
## 0.2 3 0 0 0
## 0.201 1 0 0 0
## 0.203 1 0 1 0
## 0.204 1 0 1 0
## 0.205 1 1 1 0
## 0.206 2 0 0 0
## 0.207 4 0 1 0
## 0.209 2 0 0 0
## 0.21 1 0 0 0
## 0.212 2 0 0 0
## 0.215 0 0 1 0
## 0.217 0 0 1 0
## 0.218 2 0 0 0
## 0.219 0 0 2 0
## 0.22 1 0 0 0
## 0.221 0 0 1 0
## 0.222 0 0 1 0
## 0.223 1 1 0 0
## 0.225 0 1 0 0
## 0.226 1 0 0 0
## 0.227 1 0 0 0
## 0.229 0 1 0 0
## 0.23 1 0 1 0
## 0.231 1 0 1 0
## 0.232 1 0 0 0
## 0.233 0 0 2 0
## 0.234 0 1 1 0
## 0.235 2 1 0 0
## 0.236 3 0 0 0
## 0.237 3 0 1 0
## 0.238 3 1 1 0
## 0.239 1 0 0 0
## 0.24 1 0 1 0
## 0.241 1 0 0 0
## 0.243 1 0 0 0
## 0.244 2 0 0 0
## 0.245 2 0 2 0
## 0.246 0 0 1 0
## 0.247 1 1 0 0
## 0.248 1 1 0 0
## 0.249 1 1 0 0
## 0.251 1 0 1 0
## 0.252 2 0 0 0
## 0.253 1 0 0 0
## 0.254 4 0 2 0
## 0.255 0 0 1 0
## 0.256 2 1 0 0
## 0.257 3 0 0 0
## 0.258 5 0 1 0
## 0.259 4 0 1 0
## 0.26 1 1 2 0
## 0.261 2 3 0 0
## 0.262 1 0 1 0
## 0.263 3 1 0 0
## 0.264 0 0 1 0
## 0.265 1 0 0 0
## 0.267 1 1 0 0
## 0.268 3 0 2 0
## 0.269 0 2 0 0
## 0.27 3 0 1 0
## 0.271 0 1 0 0
## 0.272 1 0 0 0
## 0.277 1 0 0 0
## 0.278 2 0 0 0
## 0.279 0 1 0 0
## 0.28 1 1 1 0
## 0.282 1 0 1 0
## 0.283 0 1 0 0
## 0.284 2 1 1 0
## 0.285 0 2 0 0
## 0.286 1 1 0 0
## 0.287 0 0 1 0
## 0.289 0 1 1 0
## 0.29 2 0 0 0
## 0.292 2 0 1 0
## 0.293 1 1 0 0
## 0.294 3 0 0 0
## 0.295 0 0 1 0
## 0.296 1 0 0 0
## 0.297 1 0 0 0
## 0.299 1 2 1 0
## 0.3 1 0 0 0
## 0.302 2 0 0 0
## 0.303 1 0 0 0
## 0.304 4 0 0 0
## 0.305 2 0 1 0
## 0.306 0 2 0 0
## 0.307 1 0 0 0
## 0.313 1 1 0 0
## 0.314 1 1 0 0
## 0.315 1 1 0 0
## 0.317 1 0 0 0
## 0.318 0 1 0 0
## 0.319 0 0 1 0
## 0.323 0 2 0 0
## 0.324 0 1 1 0
## 0.325 0 0 1 0
## 0.326 1 0 1 0
## 0.328 1 0 1 0
## 0.329 0 0 1 0
## 0.33 0 1 0 0
## 0.331 1 0 0 0
## 0.332 0 1 0 0
## 0.334 1 1 0 0
## 0.335 0 0 1 0
## 0.336 2 0 0 0
## 0.337 2 0 1 0
## 0.338 0 0 1 0
## 0.34 2 1 0 0
## 0.341 0 1 0 0
## 0.342 2 0 0 0
## 0.343 2 0 0 0
## 0.344 1 0 1 0
## 0.345 1 0 0 0
## 0.346 1 0 0 0
## 0.347 0 0 1 0
## 0.349 1 0 2 0
## 0.351 1 0 0 0
## 0.352 0 0 1 0
## 0.355 1 0 0 0
## 0.356 0 1 1 0
## 0.358 1 0 0 0
## 0.361 0 0 2 0
## 0.362 0 1 0 0
## 0.364 1 1 0 0
## 0.365 0 1 1 0
## 0.366 0 0 1 0
## 0.368 2 0 0 0
## 0.37 1 1 0 0
## 0.371 1 0 0 0
## 0.374 0 1 0 0
## 0.375 1 0 0 0
## 0.376 1 0 0 0
## 0.378 1 1 0 0
## 0.38 2 0 0 0
## 0.381 0 0 1 0
## 0.382 1 0 0 0
## 0.383 1 0 0 0
## 0.385 0 0 1 0
## 0.388 1 0 0 0
## 0.389 2 0 1 0
## 0.391 3 0 0 0
## 0.393 1 0 0 0
## 0.394 1 0 0 0
## 0.395 0 0 1 0
## 0.396 1 0 0 0
## 0.398 0 0 1 0
## 0.399 0 0 1 0
## 0.4 1 1 0 0
## 0.401 0 1 0 0
## 0.402 2 0 0 0
## 0.403 1 0 1 0
## 0.404 0 1 0 0
## 0.407 1 0 1 0
## 0.408 0 0 1 0
## 0.409 1 0 0 0
## 0.411 0 0 1 0
## 0.412 1 0 1 0
## 0.415 0 1 1 0
## 0.416 1 0 0 0
## 0.417 1 0 0 0
## 0.419 1 0 0 0
## 0.42 1 0 0 0
## 0.421 1 0 0 0
## 0.422 0 2 1 0
## 0.423 0 0 1 0
## 0.426 0 0 1 0
## 0.427 0 0 1 0
## 0.43 0 1 1 0
## 0.431 0 0 1 0
## 0.432 1 0 0 0
## 0.433 1 1 0 0
## 0.434 1 1 0 0
## 0.435 1 0 0 0
## 0.439 1 0 1 0
## 0.441 1 0 0 0
## 0.443 2 0 1 0
## 0.444 1 0 1 0
## 0.446 0 1 0 0
## 0.447 0 0 1 0
## 0.451 1 0 0 0
## 0.452 0 0 3 0
## 0.453 0 0 1 0
## 0.454 0 0 1 0
## 0.455 1 0 1 0
## 0.457 1 0 0 0
## 0.46 0 1 0 0
## 0.463 0 1 0 0
## 0.464 0 0 1 0
## 0.465 1 0 0 0
## 0.466 0 0 2 0
## 0.467 0 0 1 0
## 0.471 1 0 1 0
## 0.472 0 0 1 0
## 0.479 1 0 0 0
## 0.482 0 1 0 0
## 0.483 0 0 1 0
## 0.484 1 0 0 0
## 0.485 1 0 0 0
## 0.487 0 0 1 0
## 0.488 0 0 1 0
## 0.491 0 1 0 0
## 0.493 0 0 1 0
## 0.495 0 1 0 0
## 0.496 0 2 1 0
## 0.497 0 2 0 0
## 0.498 0 0 1 0
## 0.499 0 1 0 0
## 0.501 1 0 0 0
## 0.502 0 0 1 0
## 0.503 1 0 0 0
## 0.507 0 0 1 0
## 0.509 0 1 0 0
## 0.51 0 1 0 0
## 0.512 1 0 0 0
## 0.514 1 0 1 0
## 0.515 0 0 1 0
## 0.516 0 0 1 0
## 0.52 1 0 2 0
## 0.525 1 0 0 0
## 0.526 0 1 0 0
## 0.527 0 1 1 0
## 0.528 0 0 2 0
## 0.529 0 1 0 0
## 0.532 0 1 0 0
## 0.534 0 0 1 0
## 0.536 1 0 1 0
## 0.537 1 0 0 0
## 0.539 1 0 0 0
## 0.542 1 0 1 0
## 0.543 1 0 1 0
## 0.545 0 1 0 0
## 0.546 1 0 0 0
## 0.547 0 1 0 0
## 0.549 0 0 1 0
## 0.551 2 1 1 0
## 0.554 1 0 0 0
## 0.557 1 0 1 0
## 0.559 2 0 0 0
## 0.56 1 0 0 0
## 0.561 0 1 0 0
## 0.564 1 0 0 0
## 0.565 1 0 0 0
## 0.569 1 0 0 0
## 0.571 0 0 1 0
## 0.572 1 0 0 0
## 0.575 1 0 0 0
## 0.578 1 0 0 0
## 0.58 0 1 0 0
## 0.582 1 0 1 0
## 0.583 1 1 1 0
## 0.586 1 0 1 0
## 0.587 2 0 1 0
## 0.588 0 0 1 0
## 0.591 0 0 2 0
## 0.593 0 0 1 0
## 0.595 1 0 0 0
## 0.597 0 1 0 0
## 0.598 0 0 1 0
## 0.6 1 1 0 0
## 0.601 1 0 0 0
## 0.605 0 0 2 0
## 0.607 1 0 0 0
## 0.61 1 0 0 0
## 0.612 1 0 0 0
## 0.613 0 0 1 0
## 0.614 0 0 1 0
## 0.615 0 0 1 0
## 0.619 0 0 1 0
## 0.624 1 0 0 0
## 0.626 0 1 0 0
## 0.627 1 0 0 0
## 0.629 0 1 0 0
## 0.63 1 0 0 0
## 0.631 0 0 1 0
## 0.637 0 1 1 0
## 0.64 1 0 1 0
## 0.645 1 0 0 0
## 0.646 0 0 1 0
## 0.647 0 1 1 0
## 0.649 0 1 0 0
## 0.652 2 0 0 0
## 0.654 1 1 0 0
## 0.655 0 0 1 0
## 0.658 0 1 0 0
## 0.66 2 0 0 0
## 0.661 0 0 1 0
## 0.665 1 0 0 0
## 0.666 0 1 0 0
## 0.672 1 0 0 0
## 0.673 0 1 0 0
## 0.674 1 0 1 0
## 0.677 0 1 0 0
## 0.678 0 2 0 0
## 0.68 1 0 0 0
## 0.682 0 0 1 0
## 0.686 2 0 0 0
## 0.687 1 0 3 0
## 0.692 0 0 4 0
## 0.693 1 0 0 0
## 0.695 0 0 1 0
## 0.696 1 0 0 0
## 0.698 0 0 1 0
## 0.699 0 0 1 0
## 0.702 0 0 1 0
## 0.703 1 0 0 0
## 0.704 0 0 1 0
## 0.705 0 1 0 0
## 0.709 1 0 0 0
## 0.711 0 0 1 0
## 0.717 0 0 1 0
## 0.718 0 0 1 0
## 0.719 0 0 1 0
## 0.721 0 0 1 0
## 0.722 0 0 1 0
## 0.725 0 1 0 0
## 0.727 2 0 0 0
## 0.73 0 0 1 0
## 0.731 0 1 0 0
## 0.732 1 0 0 0
## 0.733 0 1 1 0
## 0.734 1 0 0 0
## 0.735 1 0 0 0
## 0.738 0 1 0 0
## 0.741 1 0 0 0
## 0.742 0 0 1 0
## 0.743 1 0 0 0
## 0.744 1 0 0 0
## 0.745 1 0 0 0
## 0.748 0 1 0 0
## 0.757 0 1 0 0
## 0.759 0 1 0 0
## 0.761 1 1 0 0
## 0.766 1 0 0 0
## 0.767 0 1 0 0
## 0.771 0 0 1 0
## 0.773 1 0 0 0
## 0.785 0 0 1 0
## 0.787 1 0 1 0
## 0.801 0 0 1 0
## 0.803 1 0 0 0
## 0.804 0 0 1 0
## 0.805 1 0 0 0
## 0.808 0 0 1 0
## 0.813 0 0 1 0
## 0.816 0 0 1 0
## 0.817 0 0 1 0
## 0.821 0 1 0 0
## 0.825 1 0 0 0
## 0.826 0 0 1 0
## 0.828 0 0 1 0
## 0.831 1 0 0 0
## 0.832 1 0 0 0
## 0.833 0 0 1 0
## 0.839 1 0 1 0
## 0.84 1 0 0 0
## 0.845 1 0 0 0
## 0.851 0 0 1 0
## 0.855 1 0 0 0
## 0.856 1 0 0 0
## 0.867 0 1 0 0
## 0.871 0 1 0 0
## 0.874 1 0 0 0
## 0.875 0 0 2 0
## 0.878 1 0 0 0
## 0.88 0 0 1 0
## 0.881 0 0 1 0
## 0.886 0 1 0 0
## 0.892 0 0 1 0
## 0.893 0 0 1 0
## 0.904 1 0 0 0
## 0.905 1 1 0 0
## 0.917 0 1 0 0
## 0.925 1 0 0 0
## 0.926 0 0 1 0
## 0.93 1 0 0 0
## 0.932 1 0 0 0
## 0.933 1 0 0 0
## 0.944 0 0 1 0
## 0.947 0 0 1 0
## 0.949 0 1 0 0
## 0.955 1 0 0 0
## 0.956 1 0 0 0
## 0.962 0 0 2 0
## 0.966 0 0 1 0
## 0.968 0 1 1 0
## 0.97 0 0 1 0
## 0.997 0 1 0 0
## 1.001 0 0 1 0
## 1.021 0 1 0 0
## 1.022 1 0 0 0
## 1.034 0 0 1 0
## 1.057 0 0 1 0
## 1.072 0 0 1 0
## 1.076 0 0 1 0
## 1.095 1 0 0 0
## 1.096 0 1 0 0
## 1.101 1 0 0 0
## 1.114 1 0 0 0
## 1.127 1 0 0 0
## 1.136 1 0 0 0
## 1.138 1 0 0 0
## 1.144 0 0 1 0
## 1.154 0 0 1 0
## 1.159 0 0 1 0
## 1.162 0 0 1 0
## 1.174 0 0 1 0
## 1.182 1 0 0 0
## 1.189 0 0 1 0
## 1.191 1 0 0 0
## 1.213 1 0 0 0
## 1.222 1 0 0 0
## 1.224 1 0 1 0
## 1.251 0 1 0 0
## 1.258 0 0 1 0
## 1.268 0 1 0 0
## 1.282 1 0 0 0
## 1.292 0 1 0 0
## 1.318 0 0 1 0
## 1.321 0 1 0 0
## 1.353 0 0 1 0
## 1.39 0 0 1 0
## 1.391 0 0 1 0
## 1.394 1 0 0 0
## 1.4 0 0 1 0
## 1.441 1 0 0 0
## 1.461 1 0 0 0
## 1.476 1 0 0 0
## 1.6 0 0 1 0
## 1.698 1 0 0 0
## 1.699 0 0 1 0
## 1.731 0 1 0 0
## 1.781 1 0 0 0
## 1.893 1 0 0 0
## 2.137 0 0 1 0
## 2.288 0 0 1 0
## 2.329 0 0 1 0
## 2.42 1 0 0 0prop.table(table(diabetes$Diabetes, diabetes$Insulin_cat), margin=2)##
## Low Normal High Extremely High
## 0.078 0.002610966 0.000000000 0.000000000
## 0.084 0.002610966 0.000000000 0.000000000
## 0.085 0.002610966 0.000000000 0.004115226
## 0.088 0.002610966 0.000000000 0.004115226
## 0.089 0.000000000 0.007042254 0.000000000
## 0.092 0.002610966 0.000000000 0.000000000
## 0.096 0.002610966 0.000000000 0.000000000
## 0.1 0.002610966 0.000000000 0.000000000
## 0.101 0.000000000 0.007042254 0.000000000
## 0.102 0.002610966 0.000000000 0.000000000
## 0.107 0.000000000 0.000000000 0.004115226
## 0.108 0.002610966 0.000000000 0.000000000
## 0.115 0.000000000 0.000000000 0.004115226
## 0.118 0.000000000 0.007042254 0.000000000
## 0.121 0.005221932 0.000000000 0.000000000
## 0.122 0.000000000 0.007042254 0.000000000
## 0.123 0.000000000 0.000000000 0.004115226
## 0.126 0.005221932 0.000000000 0.000000000
## 0.127 0.002610966 0.000000000 0.004115226
## 0.128 0.000000000 0.007042254 0.004115226
## 0.129 0.005221932 0.000000000 0.000000000
## 0.13 0.002610966 0.000000000 0.000000000
## 0.133 0.002610966 0.000000000 0.000000000
## 0.134 0.002610966 0.000000000 0.004115226
## 0.135 0.002610966 0.000000000 0.000000000
## 0.136 0.002610966 0.000000000 0.000000000
## 0.137 0.005221932 0.000000000 0.000000000
## 0.138 0.000000000 0.000000000 0.004115226
## 0.14 0.005221932 0.000000000 0.000000000
## 0.141 0.007832898 0.000000000 0.000000000
## 0.142 0.005221932 0.000000000 0.004115226
## 0.143 0.002610966 0.000000000 0.004115226
## 0.144 0.002610966 0.000000000 0.000000000
## 0.145 0.002610966 0.000000000 0.000000000
## 0.147 0.002610966 0.000000000 0.000000000
## 0.148 0.007832898 0.000000000 0.000000000
## 0.149 0.000000000 0.007042254 0.000000000
## 0.15 0.000000000 0.000000000 0.008230453
## 0.151 0.007832898 0.000000000 0.000000000
## 0.153 0.002610966 0.000000000 0.004115226
## 0.154 0.000000000 0.007042254 0.000000000
## 0.155 0.000000000 0.007042254 0.000000000
## 0.156 0.000000000 0.007042254 0.000000000
## 0.157 0.002610966 0.000000000 0.000000000
## 0.158 0.002610966 0.000000000 0.004115226
## 0.159 0.000000000 0.007042254 0.004115226
## 0.16 0.000000000 0.000000000 0.004115226
## 0.161 0.002610966 0.000000000 0.004115226
## 0.162 0.000000000 0.007042254 0.000000000
## 0.163 0.000000000 0.000000000 0.004115226
## 0.164 0.002610966 0.007042254 0.000000000
## 0.165 0.005221932 0.000000000 0.004115226
## 0.166 0.000000000 0.007042254 0.000000000
## 0.167 0.005221932 0.007042254 0.004115226
## 0.17 0.002610966 0.000000000 0.000000000
## 0.171 0.000000000 0.000000000 0.004115226
## 0.173 0.000000000 0.000000000 0.004115226
## 0.174 0.002610966 0.000000000 0.000000000
## 0.175 0.000000000 0.000000000 0.004115226
## 0.176 0.000000000 0.000000000 0.004115226
## 0.177 0.002610966 0.000000000 0.000000000
## 0.178 0.007832898 0.000000000 0.000000000
## 0.179 0.002610966 0.000000000 0.000000000
## 0.18 0.005221932 0.000000000 0.000000000
## 0.181 0.002610966 0.000000000 0.000000000
## 0.182 0.002610966 0.000000000 0.000000000
## 0.183 0.002610966 0.007042254 0.000000000
## 0.186 0.002610966 0.000000000 0.000000000
## 0.187 0.005221932 0.007042254 0.000000000
## 0.188 0.002610966 0.000000000 0.000000000
## 0.189 0.002610966 0.007042254 0.000000000
## 0.19 0.010443864 0.000000000 0.000000000
## 0.191 0.005221932 0.000000000 0.000000000
## 0.192 0.002610966 0.007042254 0.000000000
## 0.194 0.002610966 0.000000000 0.000000000
## 0.196 0.002610966 0.000000000 0.000000000
## 0.197 0.010443864 0.000000000 0.000000000
## 0.198 0.002610966 0.000000000 0.004115226
## 0.199 0.000000000 0.000000000 0.004115226
## 0.2 0.007832898 0.000000000 0.000000000
## 0.201 0.002610966 0.000000000 0.000000000
## 0.203 0.002610966 0.000000000 0.004115226
## 0.204 0.002610966 0.000000000 0.004115226
## 0.205 0.002610966 0.007042254 0.004115226
## 0.206 0.005221932 0.000000000 0.000000000
## 0.207 0.010443864 0.000000000 0.004115226
## 0.209 0.005221932 0.000000000 0.000000000
## 0.21 0.002610966 0.000000000 0.000000000
## 0.212 0.005221932 0.000000000 0.000000000
## 0.215 0.000000000 0.000000000 0.004115226
## 0.217 0.000000000 0.000000000 0.004115226
## 0.218 0.005221932 0.000000000 0.000000000
## 0.219 0.000000000 0.000000000 0.008230453
## 0.22 0.002610966 0.000000000 0.000000000
## 0.221 0.000000000 0.000000000 0.004115226
## 0.222 0.000000000 0.000000000 0.004115226
## 0.223 0.002610966 0.007042254 0.000000000
## 0.225 0.000000000 0.007042254 0.000000000
## 0.226 0.002610966 0.000000000 0.000000000
## 0.227 0.002610966 0.000000000 0.000000000
## 0.229 0.000000000 0.007042254 0.000000000
## 0.23 0.002610966 0.000000000 0.004115226
## 0.231 0.002610966 0.000000000 0.004115226
## 0.232 0.002610966 0.000000000 0.000000000
## 0.233 0.000000000 0.000000000 0.008230453
## 0.234 0.000000000 0.007042254 0.004115226
## 0.235 0.005221932 0.007042254 0.000000000
## 0.236 0.007832898 0.000000000 0.000000000
## 0.237 0.007832898 0.000000000 0.004115226
## 0.238 0.007832898 0.007042254 0.004115226
## 0.239 0.002610966 0.000000000 0.000000000
## 0.24 0.002610966 0.000000000 0.004115226
## 0.241 0.002610966 0.000000000 0.000000000
## 0.243 0.002610966 0.000000000 0.000000000
## 0.244 0.005221932 0.000000000 0.000000000
## 0.245 0.005221932 0.000000000 0.008230453
## 0.246 0.000000000 0.000000000 0.004115226
## 0.247 0.002610966 0.007042254 0.000000000
## 0.248 0.002610966 0.007042254 0.000000000
## 0.249 0.002610966 0.007042254 0.000000000
## 0.251 0.002610966 0.000000000 0.004115226
## 0.252 0.005221932 0.000000000 0.000000000
## 0.253 0.002610966 0.000000000 0.000000000
## 0.254 0.010443864 0.000000000 0.008230453
## 0.255 0.000000000 0.000000000 0.004115226
## 0.256 0.005221932 0.007042254 0.000000000
## 0.257 0.007832898 0.000000000 0.000000000
## 0.258 0.013054830 0.000000000 0.004115226
## 0.259 0.010443864 0.000000000 0.004115226
## 0.26 0.002610966 0.007042254 0.008230453
## 0.261 0.005221932 0.021126761 0.000000000
## 0.262 0.002610966 0.000000000 0.004115226
## 0.263 0.007832898 0.007042254 0.000000000
## 0.264 0.000000000 0.000000000 0.004115226
## 0.265 0.002610966 0.000000000 0.000000000
## 0.267 0.002610966 0.007042254 0.000000000
## 0.268 0.007832898 0.000000000 0.008230453
## 0.269 0.000000000 0.014084507 0.000000000
## 0.27 0.007832898 0.000000000 0.004115226
## 0.271 0.000000000 0.007042254 0.000000000
## 0.272 0.002610966 0.000000000 0.000000000
## 0.277 0.002610966 0.000000000 0.000000000
## 0.278 0.005221932 0.000000000 0.000000000
## 0.279 0.000000000 0.007042254 0.000000000
## 0.28 0.002610966 0.007042254 0.004115226
## 0.282 0.002610966 0.000000000 0.004115226
## 0.283 0.000000000 0.007042254 0.000000000
## 0.284 0.005221932 0.007042254 0.004115226
## 0.285 0.000000000 0.014084507 0.000000000
## 0.286 0.002610966 0.007042254 0.000000000
## 0.287 0.000000000 0.000000000 0.004115226
## 0.289 0.000000000 0.007042254 0.004115226
## 0.29 0.005221932 0.000000000 0.000000000
## 0.292 0.005221932 0.000000000 0.004115226
## 0.293 0.002610966 0.007042254 0.000000000
## 0.294 0.007832898 0.000000000 0.000000000
## 0.295 0.000000000 0.000000000 0.004115226
## 0.296 0.002610966 0.000000000 0.000000000
## 0.297 0.002610966 0.000000000 0.000000000
## 0.299 0.002610966 0.014084507 0.004115226
## 0.3 0.002610966 0.000000000 0.000000000
## 0.302 0.005221932 0.000000000 0.000000000
## 0.303 0.002610966 0.000000000 0.000000000
## 0.304 0.010443864 0.000000000 0.000000000
## 0.305 0.005221932 0.000000000 0.004115226
## 0.306 0.000000000 0.014084507 0.000000000
## 0.307 0.002610966 0.000000000 0.000000000
## 0.313 0.002610966 0.007042254 0.000000000
## 0.314 0.002610966 0.007042254 0.000000000
## 0.315 0.002610966 0.007042254 0.000000000
## 0.317 0.002610966 0.000000000 0.000000000
## 0.318 0.000000000 0.007042254 0.000000000
## 0.319 0.000000000 0.000000000 0.004115226
## 0.323 0.000000000 0.014084507 0.000000000
## 0.324 0.000000000 0.007042254 0.004115226
## 0.325 0.000000000 0.000000000 0.004115226
## 0.326 0.002610966 0.000000000 0.004115226
## 0.328 0.002610966 0.000000000 0.004115226
## 0.329 0.000000000 0.000000000 0.004115226
## 0.33 0.000000000 0.007042254 0.000000000
## 0.331 0.002610966 0.000000000 0.000000000
## 0.332 0.000000000 0.007042254 0.000000000
## 0.334 0.002610966 0.007042254 0.000000000
## 0.335 0.000000000 0.000000000 0.004115226
## 0.336 0.005221932 0.000000000 0.000000000
## 0.337 0.005221932 0.000000000 0.004115226
## 0.338 0.000000000 0.000000000 0.004115226
## 0.34 0.005221932 0.007042254 0.000000000
## 0.341 0.000000000 0.007042254 0.000000000
## 0.342 0.005221932 0.000000000 0.000000000
## 0.343 0.005221932 0.000000000 0.000000000
## 0.344 0.002610966 0.000000000 0.004115226
## 0.345 0.002610966 0.000000000 0.000000000
## 0.346 0.002610966 0.000000000 0.000000000
## 0.347 0.000000000 0.000000000 0.004115226
## 0.349 0.002610966 0.000000000 0.008230453
## 0.351 0.002610966 0.000000000 0.000000000
## 0.352 0.000000000 0.000000000 0.004115226
## 0.355 0.002610966 0.000000000 0.000000000
## 0.356 0.000000000 0.007042254 0.004115226
## 0.358 0.002610966 0.000000000 0.000000000
## 0.361 0.000000000 0.000000000 0.008230453
## 0.362 0.000000000 0.007042254 0.000000000
## 0.364 0.002610966 0.007042254 0.000000000
## 0.365 0.000000000 0.007042254 0.004115226
## 0.366 0.000000000 0.000000000 0.004115226
## 0.368 0.005221932 0.000000000 0.000000000
## 0.37 0.002610966 0.007042254 0.000000000
## 0.371 0.002610966 0.000000000 0.000000000
## 0.374 0.000000000 0.007042254 0.000000000
## 0.375 0.002610966 0.000000000 0.000000000
## 0.376 0.002610966 0.000000000 0.000000000
## 0.378 0.002610966 0.007042254 0.000000000
## 0.38 0.005221932 0.000000000 0.000000000
## 0.381 0.000000000 0.000000000 0.004115226
## 0.382 0.002610966 0.000000000 0.000000000
## 0.383 0.002610966 0.000000000 0.000000000
## 0.385 0.000000000 0.000000000 0.004115226
## 0.388 0.002610966 0.000000000 0.000000000
## 0.389 0.005221932 0.000000000 0.004115226
## 0.391 0.007832898 0.000000000 0.000000000
## 0.393 0.002610966 0.000000000 0.000000000
## 0.394 0.002610966 0.000000000 0.000000000
## 0.395 0.000000000 0.000000000 0.004115226
## 0.396 0.002610966 0.000000000 0.000000000
## 0.398 0.000000000 0.000000000 0.004115226
## 0.399 0.000000000 0.000000000 0.004115226
## 0.4 0.002610966 0.007042254 0.000000000
## 0.401 0.000000000 0.007042254 0.000000000
## 0.402 0.005221932 0.000000000 0.000000000
## 0.403 0.002610966 0.000000000 0.004115226
## 0.404 0.000000000 0.007042254 0.000000000
## 0.407 0.002610966 0.000000000 0.004115226
## 0.408 0.000000000 0.000000000 0.004115226
## 0.409 0.002610966 0.000000000 0.000000000
## 0.411 0.000000000 0.000000000 0.004115226
## 0.412 0.002610966 0.000000000 0.004115226
## 0.415 0.000000000 0.007042254 0.004115226
## 0.416 0.002610966 0.000000000 0.000000000
## 0.417 0.002610966 0.000000000 0.000000000
## 0.419 0.002610966 0.000000000 0.000000000
## 0.42 0.002610966 0.000000000 0.000000000
## 0.421 0.002610966 0.000000000 0.000000000
## 0.422 0.000000000 0.014084507 0.004115226
## 0.423 0.000000000 0.000000000 0.004115226
## 0.426 0.000000000 0.000000000 0.004115226
## 0.427 0.000000000 0.000000000 0.004115226
## 0.43 0.000000000 0.007042254 0.004115226
## 0.431 0.000000000 0.000000000 0.004115226
## 0.432 0.002610966 0.000000000 0.000000000
## 0.433 0.002610966 0.007042254 0.000000000
## 0.434 0.002610966 0.007042254 0.000000000
## 0.435 0.002610966 0.000000000 0.000000000
## 0.439 0.002610966 0.000000000 0.004115226
## 0.441 0.002610966 0.000000000 0.000000000
## 0.443 0.005221932 0.000000000 0.004115226
## 0.444 0.002610966 0.000000000 0.004115226
## 0.446 0.000000000 0.007042254 0.000000000
## 0.447 0.000000000 0.000000000 0.004115226
## 0.451 0.002610966 0.000000000 0.000000000
## 0.452 0.000000000 0.000000000 0.012345679
## 0.453 0.000000000 0.000000000 0.004115226
## 0.454 0.000000000 0.000000000 0.004115226
## 0.455 0.002610966 0.000000000 0.004115226
## 0.457 0.002610966 0.000000000 0.000000000
## 0.46 0.000000000 0.007042254 0.000000000
## 0.463 0.000000000 0.007042254 0.000000000
## 0.464 0.000000000 0.000000000 0.004115226
## 0.465 0.002610966 0.000000000 0.000000000
## 0.466 0.000000000 0.000000000 0.008230453
## 0.467 0.000000000 0.000000000 0.004115226
## 0.471 0.002610966 0.000000000 0.004115226
## 0.472 0.000000000 0.000000000 0.004115226
## 0.479 0.002610966 0.000000000 0.000000000
## 0.482 0.000000000 0.007042254 0.000000000
## 0.483 0.000000000 0.000000000 0.004115226
## 0.484 0.002610966 0.000000000 0.000000000
## 0.485 0.002610966 0.000000000 0.000000000
## 0.487 0.000000000 0.000000000 0.004115226
## 0.488 0.000000000 0.000000000 0.004115226
## 0.491 0.000000000 0.007042254 0.000000000
## 0.493 0.000000000 0.000000000 0.004115226
## 0.495 0.000000000 0.007042254 0.000000000
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## 0.497 0.000000000 0.014084507 0.000000000
## 0.498 0.000000000 0.000000000 0.004115226
## 0.499 0.000000000 0.007042254 0.000000000
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## 0.502 0.000000000 0.000000000 0.004115226
## 0.503 0.002610966 0.000000000 0.000000000
## 0.507 0.000000000 0.000000000 0.004115226
## 0.509 0.000000000 0.007042254 0.000000000
## 0.51 0.000000000 0.007042254 0.000000000
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## 0.514 0.002610966 0.000000000 0.004115226
## 0.515 0.000000000 0.000000000 0.004115226
## 0.516 0.000000000 0.000000000 0.004115226
## 0.52 0.002610966 0.000000000 0.008230453
## 0.525 0.002610966 0.000000000 0.000000000
## 0.526 0.000000000 0.007042254 0.000000000
## 0.527 0.000000000 0.007042254 0.004115226
## 0.528 0.000000000 0.000000000 0.008230453
## 0.529 0.000000000 0.007042254 0.000000000
## 0.532 0.000000000 0.007042254 0.000000000
## 0.534 0.000000000 0.000000000 0.004115226
## 0.536 0.002610966 0.000000000 0.004115226
## 0.537 0.002610966 0.000000000 0.000000000
## 0.539 0.002610966 0.000000000 0.000000000
## 0.542 0.002610966 0.000000000 0.004115226
## 0.543 0.002610966 0.000000000 0.004115226
## 0.545 0.000000000 0.007042254 0.000000000
## 0.546 0.002610966 0.000000000 0.000000000
## 0.547 0.000000000 0.007042254 0.000000000
## 0.549 0.000000000 0.000000000 0.004115226
## 0.551 0.005221932 0.007042254 0.004115226
## 0.554 0.002610966 0.000000000 0.000000000
## 0.557 0.002610966 0.000000000 0.004115226
## 0.559 0.005221932 0.000000000 0.000000000
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## 0.564 0.002610966 0.000000000 0.000000000
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## 0.572 0.002610966 0.000000000 0.000000000
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## 0.578 0.002610966 0.000000000 0.000000000
## 0.58 0.000000000 0.007042254 0.000000000
## 0.582 0.002610966 0.000000000 0.004115226
## 0.583 0.002610966 0.007042254 0.004115226
## 0.586 0.002610966 0.000000000 0.004115226
## 0.587 0.005221932 0.000000000 0.004115226
## 0.588 0.000000000 0.000000000 0.004115226
## 0.591 0.000000000 0.000000000 0.008230453
## 0.593 0.000000000 0.000000000 0.004115226
## 0.595 0.002610966 0.000000000 0.000000000
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## 0.598 0.000000000 0.000000000 0.004115226
## 0.6 0.002610966 0.007042254 0.000000000
## 0.601 0.002610966 0.000000000 0.000000000
## 0.605 0.000000000 0.000000000 0.008230453
## 0.607 0.002610966 0.000000000 0.000000000
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## 0.613 0.000000000 0.000000000 0.004115226
## 0.614 0.000000000 0.000000000 0.004115226
## 0.615 0.000000000 0.000000000 0.004115226
## 0.619 0.000000000 0.000000000 0.004115226
## 0.624 0.002610966 0.000000000 0.000000000
## 0.626 0.000000000 0.007042254 0.000000000
## 0.627 0.002610966 0.000000000 0.000000000
## 0.629 0.000000000 0.007042254 0.000000000
## 0.63 0.002610966 0.000000000 0.000000000
## 0.631 0.000000000 0.000000000 0.004115226
## 0.637 0.000000000 0.007042254 0.004115226
## 0.64 0.002610966 0.000000000 0.004115226
## 0.645 0.002610966 0.000000000 0.000000000
## 0.646 0.000000000 0.000000000 0.004115226
## 0.647 0.000000000 0.007042254 0.004115226
## 0.649 0.000000000 0.007042254 0.000000000
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## 0.654 0.002610966 0.007042254 0.000000000
## 0.655 0.000000000 0.000000000 0.004115226
## 0.658 0.000000000 0.007042254 0.000000000
## 0.66 0.005221932 0.000000000 0.000000000
## 0.661 0.000000000 0.000000000 0.004115226
## 0.665 0.002610966 0.000000000 0.000000000
## 0.666 0.000000000 0.007042254 0.000000000
## 0.672 0.002610966 0.000000000 0.000000000
## 0.673 0.000000000 0.007042254 0.000000000
## 0.674 0.002610966 0.000000000 0.004115226
## 0.677 0.000000000 0.007042254 0.000000000
## 0.678 0.000000000 0.014084507 0.000000000
## 0.68 0.002610966 0.000000000 0.000000000
## 0.682 0.000000000 0.000000000 0.004115226
## 0.686 0.005221932 0.000000000 0.000000000
## 0.687 0.002610966 0.000000000 0.012345679
## 0.692 0.000000000 0.000000000 0.016460905
## 0.693 0.002610966 0.000000000 0.000000000
## 0.695 0.000000000 0.000000000 0.004115226
## 0.696 0.002610966 0.000000000 0.000000000
## 0.698 0.000000000 0.000000000 0.004115226
## 0.699 0.000000000 0.000000000 0.004115226
## 0.702 0.000000000 0.000000000 0.004115226
## 0.703 0.002610966 0.000000000 0.000000000
## 0.704 0.000000000 0.000000000 0.004115226
## 0.705 0.000000000 0.007042254 0.000000000
## 0.709 0.002610966 0.000000000 0.000000000
## 0.711 0.000000000 0.000000000 0.004115226
## 0.717 0.000000000 0.000000000 0.004115226
## 0.718 0.000000000 0.000000000 0.004115226
## 0.719 0.000000000 0.000000000 0.004115226
## 0.721 0.000000000 0.000000000 0.004115226
## 0.722 0.000000000 0.000000000 0.004115226
## 0.725 0.000000000 0.007042254 0.000000000
## 0.727 0.005221932 0.000000000 0.000000000
## 0.73 0.000000000 0.000000000 0.004115226
## 0.731 0.000000000 0.007042254 0.000000000
## 0.732 0.002610966 0.000000000 0.000000000
## 0.733 0.000000000 0.007042254 0.004115226
## 0.734 0.002610966 0.000000000 0.000000000
## 0.735 0.002610966 0.000000000 0.000000000
## 0.738 0.000000000 0.007042254 0.000000000
## 0.741 0.002610966 0.000000000 0.000000000
## 0.742 0.000000000 0.000000000 0.004115226
## 0.743 0.002610966 0.000000000 0.000000000
## 0.744 0.002610966 0.000000000 0.000000000
## 0.745 0.002610966 0.000000000 0.000000000
## 0.748 0.000000000 0.007042254 0.000000000
## 0.757 0.000000000 0.007042254 0.000000000
## 0.759 0.000000000 0.007042254 0.000000000
## 0.761 0.002610966 0.007042254 0.000000000
## 0.766 0.002610966 0.000000000 0.000000000
## 0.767 0.000000000 0.007042254 0.000000000
## 0.771 0.000000000 0.000000000 0.004115226
## 0.773 0.002610966 0.000000000 0.000000000
## 0.785 0.000000000 0.000000000 0.004115226
## 0.787 0.002610966 0.000000000 0.004115226
## 0.801 0.000000000 0.000000000 0.004115226
## 0.803 0.002610966 0.000000000 0.000000000
## 0.804 0.000000000 0.000000000 0.004115226
## 0.805 0.002610966 0.000000000 0.000000000
## 0.808 0.000000000 0.000000000 0.004115226
## 0.813 0.000000000 0.000000000 0.004115226
## 0.816 0.000000000 0.000000000 0.004115226
## 0.817 0.000000000 0.000000000 0.004115226
## 0.821 0.000000000 0.007042254 0.000000000
## 0.825 0.002610966 0.000000000 0.000000000
## 0.826 0.000000000 0.000000000 0.004115226
## 0.828 0.000000000 0.000000000 0.004115226
## 0.831 0.002610966 0.000000000 0.000000000
## 0.832 0.002610966 0.000000000 0.000000000
## 0.833 0.000000000 0.000000000 0.004115226
## 0.839 0.002610966 0.000000000 0.004115226
## 0.84 0.002610966 0.000000000 0.000000000
## 0.845 0.002610966 0.000000000 0.000000000
## 0.851 0.000000000 0.000000000 0.004115226
## 0.855 0.002610966 0.000000000 0.000000000
## 0.856 0.002610966 0.000000000 0.000000000
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## 0.871 0.000000000 0.007042254 0.000000000
## 0.874 0.002610966 0.000000000 0.000000000
## 0.875 0.000000000 0.000000000 0.008230453
## 0.878 0.002610966 0.000000000 0.000000000
## 0.88 0.000000000 0.000000000 0.004115226
## 0.881 0.000000000 0.000000000 0.004115226
## 0.886 0.000000000 0.007042254 0.000000000
## 0.892 0.000000000 0.000000000 0.004115226
## 0.893 0.000000000 0.000000000 0.004115226
## 0.904 0.002610966 0.000000000 0.000000000
## 0.905 0.002610966 0.007042254 0.000000000
## 0.917 0.000000000 0.007042254 0.000000000
## 0.925 0.002610966 0.000000000 0.000000000
## 0.926 0.000000000 0.000000000 0.004115226
## 0.93 0.002610966 0.000000000 0.000000000
## 0.932 0.002610966 0.000000000 0.000000000
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## 0.944 0.000000000 0.000000000 0.004115226
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## 0.949 0.000000000 0.007042254 0.000000000
## 0.955 0.002610966 0.000000000 0.000000000
## 0.956 0.002610966 0.000000000 0.000000000
## 0.962 0.000000000 0.000000000 0.008230453
## 0.966 0.000000000 0.000000000 0.004115226
## 0.968 0.000000000 0.007042254 0.004115226
## 0.97 0.000000000 0.000000000 0.004115226
## 0.997 0.000000000 0.007042254 0.000000000
## 1.001 0.000000000 0.000000000 0.004115226
## 1.021 0.000000000 0.007042254 0.000000000
## 1.022 0.002610966 0.000000000 0.000000000
## 1.034 0.000000000 0.000000000 0.004115226
## 1.057 0.000000000 0.000000000 0.004115226
## 1.072 0.000000000 0.000000000 0.004115226
## 1.076 0.000000000 0.000000000 0.004115226
## 1.095 0.002610966 0.000000000 0.000000000
## 1.096 0.000000000 0.007042254 0.000000000
## 1.101 0.002610966 0.000000000 0.000000000
## 1.114 0.002610966 0.000000000 0.000000000
## 1.127 0.002610966 0.000000000 0.000000000
## 1.136 0.002610966 0.000000000 0.000000000
## 1.138 0.002610966 0.000000000 0.000000000
## 1.144 0.000000000 0.000000000 0.004115226
## 1.154 0.000000000 0.000000000 0.004115226
## 1.159 0.000000000 0.000000000 0.004115226
## 1.162 0.000000000 0.000000000 0.004115226
## 1.174 0.000000000 0.000000000 0.004115226
## 1.182 0.002610966 0.000000000 0.000000000
## 1.189 0.000000000 0.000000000 0.004115226
## 1.191 0.002610966 0.000000000 0.000000000
## 1.213 0.002610966 0.000000000 0.000000000
## 1.222 0.002610966 0.000000000 0.000000000
## 1.224 0.002610966 0.000000000 0.004115226
## 1.251 0.000000000 0.007042254 0.000000000
## 1.258 0.000000000 0.000000000 0.004115226
## 1.268 0.000000000 0.007042254 0.000000000
## 1.282 0.002610966 0.000000000 0.000000000
## 1.292 0.000000000 0.007042254 0.000000000
## 1.318 0.000000000 0.000000000 0.004115226
## 1.321 0.000000000 0.007042254 0.000000000
## 1.353 0.000000000 0.000000000 0.004115226
## 1.39 0.000000000 0.000000000 0.004115226
## 1.391 0.000000000 0.000000000 0.004115226
## 1.394 0.002610966 0.000000000 0.000000000
## 1.4 0.000000000 0.000000000 0.004115226
## 1.441 0.002610966 0.000000000 0.000000000
## 1.461 0.002610966 0.000000000 0.000000000
## 1.476 0.002610966 0.000000000 0.000000000
## 1.6 0.000000000 0.000000000 0.004115226
## 1.698 0.002610966 0.000000000 0.000000000
## 1.699 0.000000000 0.000000000 0.004115226
## 1.731 0.000000000 0.007042254 0.000000000
## 1.781 0.002610966 0.000000000 0.000000000
## 1.893 0.002610966 0.000000000 0.000000000
## 2.137 0.000000000 0.000000000 0.004115226
## 2.288 0.000000000 0.000000000 0.004115226
## 2.329 0.000000000 0.000000000 0.004115226
## 2.42 0.002610966 0.000000000 0.000000000ggplot(diabetes, aes(Insulin_cat, fill = factor(Outcome))) +
geom_bar(position = "dodge") +
labs(
title = "Insulin Categories vs Diabetes Outcome",
x = "Insulin Category",
y = "Count",
fill = "Outcome"
) +
theme_minimal()
In Type 1 diabetes, the body cannot make enough insulin, so insulin
levels are low. In early Type 2 diabetes, the body becomes resistant to
insulin, so it produces extra insulin to control blood sugar
#Visualizing and grouping for pedigree function
diabetes$PedigreeGroup <- cut(
diabetes$DiabetesPedigreeFunction,
breaks = c(0, 0.2, 0.5, 1.0, Inf),
labels = c("Very Low", "Low", "Moderate", "High"),
include.lowest = TRUE
)
ggplot(diabetes, aes(x = PedigreeGroup, fill = as.factor(Outcome))) +
geom_bar(position = "dodge") +
labs(
title = "Pedigree Category vs Diabetes Outcome",
x = "Pedigree Level",
fill = "Outcome"
) +
theme_minimal()
The Diabetes Pedigree Function shows how much family history may
increase your risk of diabetes, but it’s not the main cause. Things like
diet, exercise, weight, and lifestyle usually have a bigger impact. So
even people with low or moderate family risk can still get diabetes, and
not everyone with high family risk will develop it.
Visualizing Variables That Affect the Outcome
numeric_data <- diabetes[sapply(diabetes, is.numeric)]
numeric_data## Pregnancies Glucose BloodPressure SkinThickness Insulin BMI
## 1 6 148 72 35 0 33.6
## 2 1 85 66 29 0 26.6
## 3 8 183 64 0 0 23.3
## 4 1 89 66 23 94 28.1
## 5 0 137 40 35 168 43.1
## 6 5 116 74 0 0 25.6
## 7 3 78 50 32 88 31.0
## 8 10 115 0 0 0 35.3
## 9 2 197 70 45 300 30.5
## 10 8 125 96 0 0 0.0
## 11 4 110 92 0 0 37.6
## 12 10 168 74 0 0 38.0
## 13 10 139 80 0 0 27.1
## 14 1 189 60 23 300 30.1
## 15 5 166 72 19 175 25.8
## 16 7 100 0 0 0 30.0
## 17 0 118 84 47 230 45.8
## 18 7 107 74 0 0 29.6
## 19 1 103 30 38 83 43.3
## 20 1 115 70 30 96 34.6
## 21 3 126 88 41 235 39.3
## 22 8 99 84 0 0 35.4
## 23 7 196 90 0 0 39.8
## 24 9 119 80 35 0 29.0
## 25 11 143 94 33 146 36.6
## 26 10 125 70 26 115 31.1
## 27 7 147 76 0 0 39.4
## 28 1 97 66 15 140 23.2
## 29 13 145 82 19 110 22.2
## 30 5 117 92 0 0 34.1
## 31 5 109 75 26 0 36.0
## 32 3 158 76 36 245 31.6
## 33 3 88 58 11 54 24.8
## 34 6 92 92 0 0 19.9
## 35 10 122 78 31 0 27.6
## 36 4 103 60 33 192 24.0
## 37 11 138 76 0 0 33.2
## 38 9 102 76 37 0 32.9
## 39 2 90 68 42 0 38.2
## 40 4 111 72 47 207 37.1
## 41 3 180 64 25 70 34.0
## 42 7 133 84 0 0 40.2
## 43 7 106 92 18 0 22.7
## 44 9 171 110 24 240 45.4
## 45 7 159 64 0 0 27.4
## 46 0 180 66 39 0 42.0
## 47 1 146 56 0 0 29.7
## 48 2 71 70 27 0 28.0
## 49 7 103 66 32 0 39.1
## 50 7 105 0 0 0 0.0
## 51 1 103 80 11 82 19.4
## 52 1 101 50 15 36 24.2
## 53 5 88 66 21 23 24.4
## 54 8 176 90 34 300 33.7
## 55 7 150 66 42 300 34.7
## 56 1 73 50 10 0 23.0
## 57 7 187 68 39 300 37.7
## 58 0 100 88 60 110 46.8
## 59 0 146 82 0 0 40.5
## 60 0 105 64 41 142 41.5
## 61 2 84 0 0 0 0.0
## 62 8 133 72 0 0 32.9
## 63 5 44 62 0 0 25.0
## 64 2 141 58 34 128 25.4
## 65 7 114 66 0 0 32.8
## 66 5 99 74 27 0 29.0
## 67 0 109 88 30 0 32.5
## 68 2 109 92 0 0 42.7
## 69 1 95 66 13 38 19.6
## 70 4 146 85 27 100 28.9
## 71 2 100 66 20 90 32.9
## 72 5 139 64 35 140 28.6
## 73 13 126 90 0 0 43.4
## 74 4 129 86 20 270 35.1
## 75 1 79 75 30 0 32.0
## 76 1 0 48 20 0 24.7
## 77 7 62 78 0 0 32.6
## 78 5 95 72 33 0 37.7
## 79 0 131 0 0 0 43.2
## 80 2 112 66 22 0 25.0
## 81 3 113 44 13 0 22.4
## 82 2 74 0 0 0 0.0
## 83 7 83 78 26 71 29.3
## 84 0 101 65 28 0 24.6
## 85 5 137 108 0 0 48.8
## 86 2 110 74 29 125 32.4
## 87 13 106 72 54 0 36.6
## 88 2 100 68 25 71 38.5
## 89 15 136 70 32 110 37.1
## 90 1 107 68 19 0 26.5
## 91 1 80 55 0 0 19.1
## 92 4 123 80 15 176 32.0
## 93 7 81 78 40 48 46.7
## 94 4 134 72 0 0 23.8
## 95 2 142 82 18 64 24.7
## 96 6 144 72 27 228 33.9
## 97 2 92 62 28 0 31.6
## 98 1 71 48 18 76 20.4
## 99 6 93 50 30 64 28.7
## 100 1 122 90 51 220 49.7
## 101 1 163 72 0 0 39.0
## 102 1 151 60 0 0 26.1
## 103 0 125 96 0 0 22.5
## 104 1 81 72 18 40 26.6
## 105 2 85 65 0 0 39.6
## 106 1 126 56 29 152 28.7
## 107 1 96 122 0 0 22.4
## 108 4 144 58 28 140 29.5
## 109 3 83 58 31 18 34.3
## 110 0 95 85 25 36 37.4
## 111 3 171 72 33 135 33.3
## 112 8 155 62 26 300 34.0
## 113 1 89 76 34 37 31.2
## 114 4 76 62 0 0 34.0
## 115 7 160 54 32 175 30.5
## 116 4 146 92 0 0 31.2
## 117 5 124 74 0 0 34.0
## 118 5 78 48 0 0 33.7
## 119 4 97 60 23 0 28.2
## 120 4 99 76 15 51 23.2
## 121 0 162 76 56 100 53.2
## 122 6 111 64 39 0 34.2
## 123 2 107 74 30 100 33.6
## 124 5 132 80 0 0 26.8
## 125 0 113 76 0 0 33.3
## 126 1 88 30 42 99 55.0
## 127 3 120 70 30 135 42.9
## 128 1 118 58 36 94 33.3
## 129 1 117 88 24 145 34.5
## 130 0 105 84 0 0 27.9
## 131 4 173 70 14 168 29.7
## 132 9 122 56 0 0 33.3
## 133 3 170 64 37 225 34.5
## 134 8 84 74 31 0 38.3
## 135 2 96 68 13 49 21.1
## 136 2 125 60 20 140 33.8
## 137 0 100 70 26 50 30.8
## 138 0 93 60 25 92 28.7
## 139 0 129 80 0 0 31.2
## 140 5 105 72 29 300 36.9
## 141 3 128 78 0 0 21.1
## 142 5 106 82 30 0 39.5
## 143 2 108 52 26 63 32.5
## 144 10 108 66 0 0 32.4
## 145 4 154 62 31 284 32.8
## 146 0 102 75 23 0 0.0
## 147 9 57 80 37 0 32.8
## 148 2 106 64 35 119 30.5
## 149 5 147 78 0 0 33.7
## 150 2 90 70 17 0 27.3
## 151 1 136 74 50 204 37.4
## 152 4 114 65 0 0 21.9
## 153 9 156 86 28 155 34.3
## 154 1 153 82 42 300 40.6
## 155 8 188 78 0 0 47.9
## 156 7 152 88 44 0 50.0
## 157 2 99 52 15 94 24.6
## 158 1 109 56 21 135 25.2
## 159 2 88 74 19 53 29.0
## 160 17 163 72 41 114 40.9
## 161 4 151 90 38 0 29.7
## 162 7 102 74 40 105 37.2
## 163 0 114 80 34 285 44.2
## 164 2 100 64 23 0 29.7
## 165 0 131 88 0 0 31.6
## 166 6 104 74 18 156 29.9
## 167 3 148 66 25 0 32.5
## 168 4 120 68 0 0 29.6
## 169 4 110 66 0 0 31.9
## 170 3 111 90 12 78 28.4
## 171 6 102 82 0 0 30.8
## 172 6 134 70 23 130 35.4
## 173 2 87 0 23 0 28.9
## 174 1 79 60 42 48 43.5
## 175 2 75 64 24 55 29.7
## 176 8 179 72 42 130 32.7
## 177 6 85 78 0 0 31.2
## 178 0 129 110 46 130 67.1
## 179 5 143 78 0 0 45.0
## 180 5 130 82 0 0 39.1
## 181 6 87 80 0 0 23.2
## 182 0 119 64 18 92 34.9
## 183 1 0 74 20 23 27.7
## 184 5 73 60 0 0 26.8
## 185 4 141 74 0 0 27.6
## 186 7 194 68 28 0 35.9
## 187 8 181 68 36 300 30.1
## 188 1 128 98 41 58 32.0
## 189 8 109 76 39 114 27.9
## 190 5 139 80 35 160 31.6
## 191 3 111 62 0 0 22.6
## 192 9 123 70 44 94 33.1
## 193 7 159 66 0 0 30.4
## 194 11 135 0 0 0 52.3
## 195 8 85 55 20 0 24.4
## 196 5 158 84 41 210 39.4
## 197 1 105 58 0 0 24.3
## 198 3 107 62 13 48 22.9
## 199 4 109 64 44 99 34.8
## 200 4 148 60 27 300 30.9
## 201 0 113 80 16 0 31.0
## 202 1 138 82 0 0 40.1
## 203 0 108 68 20 0 27.3
## 204 2 99 70 16 44 20.4
## 205 6 103 72 32 190 37.7
## 206 5 111 72 28 0 23.9
## 207 8 196 76 29 280 37.5
## 208 5 162 104 0 0 37.7
## 209 1 96 64 27 87 33.2
## 210 7 184 84 33 0 35.5
## 211 2 81 60 22 0 27.7
## 212 0 147 85 54 0 42.8
## 213 7 179 95 31 0 34.2
## 214 0 140 65 26 130 42.6
## 215 9 112 82 32 175 34.2
## 216 12 151 70 40 271 41.8
## 217 5 109 62 41 129 35.8
## 218 6 125 68 30 120 30.0
## 219 5 85 74 22 0 29.0
## 220 5 112 66 0 0 37.8
## 221 0 177 60 29 300 34.6
## 222 2 158 90 0 0 31.6
## 223 7 119 0 0 0 25.2
## 224 7 142 60 33 190 28.8
## 225 1 100 66 15 56 23.6
## 226 1 87 78 27 32 34.6
## 227 0 101 76 0 0 35.7
## 228 3 162 52 38 0 37.2
## 229 4 197 70 39 300 36.7
## 230 0 117 80 31 53 45.2
## 231 4 142 86 0 0 44.0
## 232 6 134 80 37 300 46.2
## 233 1 79 80 25 37 25.4
## 234 4 122 68 0 0 35.0
## 235 3 74 68 28 45 29.7
## 236 4 171 72 0 0 43.6
## 237 7 181 84 21 192 35.9
## 238 0 179 90 27 0 44.1
## 239 9 164 84 21 0 30.8
## 240 0 104 76 0 0 18.4
## 241 1 91 64 24 0 29.2
## 242 4 91 70 32 88 33.1
## 243 3 139 54 0 0 25.6
## 244 6 119 50 22 176 27.1
## 245 2 146 76 35 194 38.2
## 246 9 184 85 15 0 30.0
## 247 10 122 68 0 0 31.2
## 248 0 165 90 33 300 52.3
## 249 9 124 70 33 300 35.4
## 250 1 111 86 19 0 30.1
## 251 9 106 52 0 0 31.2
## 252 2 129 84 0 0 28.0
## 253 2 90 80 14 55 24.4
## 254 0 86 68 32 0 35.8
## 255 12 92 62 7 258 27.6
## 256 1 113 64 35 0 33.6
## 257 3 111 56 39 0 30.1
## 258 2 114 68 22 0 28.7
## 259 1 193 50 16 300 25.9
## 260 11 155 76 28 150 33.3
## 261 3 191 68 15 130 30.9
## 262 3 141 0 0 0 30.0
## 263 4 95 70 32 0 32.1
## 264 3 142 80 15 0 32.4
## 265 4 123 62 0 0 32.0
## 266 5 96 74 18 67 33.6
## 267 0 138 0 0 0 36.3
## 268 2 128 64 42 0 40.0
## 269 0 102 52 0 0 25.1
## 270 2 146 0 0 0 27.5
## 271 10 101 86 37 0 45.6
## 272 2 108 62 32 56 25.2
## 273 3 122 78 0 0 23.0
## 274 1 71 78 50 45 33.2
## 275 13 106 70 0 0 34.2
## 276 2 100 70 52 57 40.5
## 277 7 106 60 24 0 26.5
## 278 0 104 64 23 116 27.8
## 279 5 114 74 0 0 24.9
## 280 2 108 62 10 278 25.3
## 281 0 146 70 0 0 37.9
## 282 10 129 76 28 122 35.9
## 283 7 133 88 15 155 32.4
## 284 7 161 86 0 0 30.4
## 285 2 108 80 0 0 27.0
## 286 7 136 74 26 135 26.0
## 287 5 155 84 44 300 38.7
## 288 1 119 86 39 220 45.6
## 289 4 96 56 17 49 20.8
## 290 5 108 72 43 75 36.1
## 291 0 78 88 29 40 36.9
## 292 0 107 62 30 74 36.6
## 293 2 128 78 37 182 43.3
## 294 1 128 48 45 194 40.5
## 295 0 161 50 0 0 21.9
## 296 6 151 62 31 120 35.5
## 297 2 146 70 38 300 28.0
## 298 0 126 84 29 215 30.7
## 299 14 100 78 25 184 36.6
## 300 8 112 72 0 0 23.6
## 301 0 167 0 0 0 32.3
## 302 2 144 58 33 135 31.6
## 303 5 77 82 41 42 35.8
## 304 5 115 98 0 0 52.9
## 305 3 150 76 0 0 21.0
## 306 2 120 76 37 105 39.7
## 307 10 161 68 23 132 25.5
## 308 0 137 68 14 148 24.8
## 309 0 128 68 19 180 30.5
## 310 2 124 68 28 205 32.9
## 311 6 80 66 30 0 26.2
## 312 0 106 70 37 148 39.4
## 313 2 155 74 17 96 26.6
## 314 3 113 50 10 85 29.5
## 315 7 109 80 31 0 35.9
## 316 2 112 68 22 94 34.1
## 317 3 99 80 11 64 19.3
## 318 3 182 74 0 0 30.5
## 319 3 115 66 39 140 38.1
## 320 6 194 78 0 0 23.5
## 321 4 129 60 12 231 27.5
## 322 3 112 74 30 0 31.6
## 323 0 124 70 20 0 27.4
## 324 13 152 90 33 29 26.8
## 325 2 112 75 32 0 35.7
## 326 1 157 72 21 168 25.6
## 327 1 122 64 32 156 35.1
## 328 10 179 70 0 0 35.1
## 329 2 102 86 36 120 45.5
## 330 6 105 70 32 68 30.8
## 331 8 118 72 19 0 23.1
## 332 2 87 58 16 52 32.7
## 333 1 180 0 0 0 43.3
## 334 12 106 80 0 0 23.6
## 335 1 95 60 18 58 23.9
## 336 0 165 76 43 255 47.9
## 337 0 117 0 0 0 33.8
## 338 5 115 76 0 0 31.2
## 339 9 152 78 34 171 34.2
## 340 7 178 84 0 0 39.9
## 341 1 130 70 13 105 25.9
## 342 1 95 74 21 73 25.9
## 343 1 0 68 35 0 32.0
## 344 5 122 86 0 0 34.7
## 345 8 95 72 0 0 36.8
## 346 8 126 88 36 108 38.5
## 347 1 139 46 19 83 28.7
## 348 3 116 0 0 0 23.5
## 349 3 99 62 19 74 21.8
## 350 5 0 80 32 0 41.0
## 351 4 92 80 0 0 42.2
## 352 4 137 84 0 0 31.2
## 353 3 61 82 28 0 34.4
## 354 1 90 62 12 43 27.2
## 355 3 90 78 0 0 42.7
## 356 9 165 88 0 0 30.4
## 357 1 125 50 40 167 33.3
## 358 13 129 0 30 0 39.9
## 359 12 88 74 40 54 35.3
## 360 1 196 76 36 249 36.5
## 361 5 189 64 33 300 31.2
## 362 5 158 70 0 0 29.8
## 363 5 103 108 37 0 39.2
## 364 4 146 78 0 0 38.5
## 365 4 147 74 25 293 34.9
## 366 5 99 54 28 83 34.0
## 367 6 124 72 0 0 27.6
## 368 0 101 64 17 0 21.0
## 369 3 81 86 16 66 27.5
## 370 1 133 102 28 140 32.8
## 371 3 173 82 48 300 38.4
## 372 0 118 64 23 89 0.0
## 373 0 84 64 22 66 35.8
## 374 2 105 58 40 94 34.9
## 375 2 122 52 43 158 36.2
## 376 12 140 82 43 300 39.2
## 377 0 98 82 15 84 25.2
## 378 1 87 60 37 75 37.2
## 379 4 156 75 0 0 48.3
## 380 0 93 100 39 72 43.4
## 381 1 107 72 30 82 30.8
## 382 0 105 68 22 0 20.0
## 383 1 109 60 8 182 25.4
## 384 1 90 62 18 59 25.1
## 385 1 125 70 24 110 24.3
## 386 1 119 54 13 50 22.3
## 387 5 116 74 29 0 32.3
## 388 8 105 100 36 0 43.3
## 389 5 144 82 26 285 32.0
## 390 3 100 68 23 81 31.6
## 391 1 100 66 29 196 32.0
## 392 5 166 76 0 0 45.7
## 393 1 131 64 14 300 23.7
## 394 4 116 72 12 87 22.1
## 395 4 158 78 0 0 32.9
## 396 2 127 58 24 275 27.7
## 397 3 96 56 34 115 24.7
## 398 0 131 66 40 0 34.3
## 399 3 82 70 0 0 21.1
## 400 3 193 70 31 0 34.9
## 401 4 95 64 0 0 32.0
## 402 6 137 61 0 0 24.2
## 403 5 136 84 41 88 35.0
## 404 9 72 78 25 0 31.6
## 405 5 168 64 0 0 32.9
## 406 2 123 48 32 165 42.1
## 407 4 115 72 0 0 28.9
## 408 0 101 62 0 0 21.9
## 409 8 197 74 0 0 25.9
## 410 1 172 68 49 300 42.4
## 411 6 102 90 39 0 35.7
## 412 1 112 72 30 176 34.4
## 413 1 143 84 23 300 42.4
## 414 1 143 74 22 61 26.2
## 415 0 138 60 35 167 34.6
## 416 3 173 84 33 300 35.7
## 417 1 97 68 21 0 27.2
## 418 4 144 82 32 0 38.5
## 419 1 83 68 0 0 18.2
## 420 3 129 64 29 115 26.4
## 421 1 119 88 41 170 45.3
## 422 2 94 68 18 76 26.0
## 423 0 102 64 46 78 40.6
## 424 2 115 64 22 0 30.8
## 425 8 151 78 32 210 42.9
## 426 4 184 78 39 277 37.0
## 427 0 94 0 0 0 0.0
## 428 1 181 64 30 180 34.1
## 429 0 135 94 46 145 40.6
## 430 1 95 82 25 180 35.0
## 431 2 99 0 0 0 22.2
## 432 3 89 74 16 85 30.4
## 433 1 80 74 11 60 30.0
## 434 2 139 75 0 0 25.6
## 435 1 90 68 8 0 24.5
## 436 0 141 0 0 0 42.4
## 437 12 140 85 33 0 37.4
## 438 5 147 75 0 0 29.9
## 439 1 97 70 15 0 18.2
## 440 6 107 88 0 0 36.8
## 441 0 189 104 25 0 34.3
## 442 2 83 66 23 50 32.2
## 443 4 117 64 27 120 33.2
## 444 8 108 70 0 0 30.5
## 445 4 117 62 12 0 29.7
## 446 0 180 78 63 14 59.4
## 447 1 100 72 12 70 25.3
## 448 0 95 80 45 92 36.5
## 449 0 104 64 37 64 33.6
## 450 0 120 74 18 63 30.5
## 451 1 82 64 13 95 21.2
## 452 2 134 70 0 0 28.9
## 453 0 91 68 32 210 39.9
## 454 2 119 0 0 0 19.6
## 455 2 100 54 28 105 37.8
## 456 14 175 62 30 0 33.6
## 457 1 135 54 0 0 26.7
## 458 5 86 68 28 71 30.2
## 459 10 148 84 48 237 37.6
## 460 9 134 74 33 60 25.9
## 461 9 120 72 22 56 20.8
## 462 1 71 62 0 0 21.8
## 463 8 74 70 40 49 35.3
## 464 5 88 78 30 0 27.6
## 465 10 115 98 0 0 24.0
## 466 0 124 56 13 105 21.8
## 467 0 74 52 10 36 27.8
## 468 0 97 64 36 100 36.8
## 469 8 120 0 0 0 30.0
## 470 6 154 78 41 140 46.1
## 471 1 144 82 40 0 41.3
## 472 0 137 70 38 0 33.2
## 473 0 119 66 27 0 38.8
## 474 7 136 90 0 0 29.9
## 475 4 114 64 0 0 28.9
## 476 0 137 84 27 0 27.3
## 477 2 105 80 45 191 33.7
## 478 7 114 76 17 110 23.8
## 479 8 126 74 38 75 25.9
## 480 4 132 86 31 0 28.0
## 481 3 158 70 30 300 35.5
## 482 0 123 88 37 0 35.2
## 483 4 85 58 22 49 27.8
## 484 0 84 82 31 125 38.2
## 485 0 145 0 0 0 44.2
## 486 0 135 68 42 250 42.3
## 487 1 139 62 41 300 40.7
## 488 0 173 78 32 265 46.5
## 489 4 99 72 17 0 25.6
## 490 8 194 80 0 0 26.1
## 491 2 83 65 28 66 36.8
## 492 2 89 90 30 0 33.5
## 493 4 99 68 38 0 32.8
## 494 4 125 70 18 122 28.9
## 495 3 80 0 0 0 0.0
## 496 6 166 74 0 0 26.6
## 497 5 110 68 0 0 26.0
## 498 2 81 72 15 76 30.1
## 499 7 195 70 33 145 25.1
## 500 6 154 74 32 193 29.3
## 501 2 117 90 19 71 25.2
## 502 3 84 72 32 0 37.2
## 503 6 0 68 41 0 39.0
## 504 7 94 64 25 79 33.3
## 505 3 96 78 39 0 37.3
## 506 10 75 82 0 0 33.3
## 507 0 180 90 26 90 36.5
## 508 1 130 60 23 170 28.6
## 509 2 84 50 23 76 30.4
## 510 8 120 78 0 0 25.0
## 511 12 84 72 31 0 29.7
## 512 0 139 62 17 210 22.1
## 513 9 91 68 0 0 24.2
## 514 2 91 62 0 0 27.3
## 515 3 99 54 19 86 25.6
## 516 3 163 70 18 105 31.6
## 517 9 145 88 34 165 30.3
## 518 7 125 86 0 0 37.6
## 519 13 76 60 0 0 32.8
## 520 6 129 90 7 300 19.6
## 521 2 68 70 32 66 25.0
## 522 3 124 80 33 130 33.2
## 523 6 114 0 0 0 0.0
## 524 9 130 70 0 0 34.2
## 525 3 125 58 0 0 31.6
## 526 3 87 60 18 0 21.8
## 527 1 97 64 19 82 18.2
## 528 3 116 74 15 105 26.3
## 529 0 117 66 31 188 30.8
## 530 0 111 65 0 0 24.6
## 531 2 122 60 18 106 29.8
## 532 0 107 76 0 0 45.3
## 533 1 86 66 52 65 41.3
## 534 6 91 0 0 0 29.8
## 535 1 77 56 30 56 33.3
## 536 4 132 0 0 0 32.9
## 537 0 105 90 0 0 29.6
## 538 0 57 60 0 0 21.7
## 539 0 127 80 37 210 36.3
## 540 3 129 92 49 155 36.4
## 541 8 100 74 40 215 39.4
## 542 3 128 72 25 190 32.4
## 543 10 90 85 32 0 34.9
## 544 4 84 90 23 56 39.5
## 545 1 88 78 29 76 32.0
## 546 8 186 90 35 225 34.5
## 547 5 187 76 27 207 43.6
## 548 4 131 68 21 166 33.1
## 549 1 164 82 43 67 32.8
## 550 4 189 110 31 0 28.5
## 551 1 116 70 28 0 27.4
## 552 3 84 68 30 106 31.9
## 553 6 114 88 0 0 27.8
## 554 1 88 62 24 44 29.9
## 555 1 84 64 23 115 36.9
## 556 7 124 70 33 215 25.5
## 557 1 97 70 40 0 38.1
## 558 8 110 76 0 0 27.8
## 559 11 103 68 40 0 46.2
## 560 11 85 74 0 0 30.1
## 561 6 125 76 0 0 33.8
## 562 0 198 66 32 274 41.3
## 563 1 87 68 34 77 37.6
## 564 6 99 60 19 54 26.9
## 565 0 91 80 0 0 32.4
## 566 2 95 54 14 88 26.1
## 567 1 99 72 30 18 38.6
## 568 6 92 62 32 126 32.0
## 569 4 154 72 29 126 31.3
## 570 0 121 66 30 165 34.3
## 571 3 78 70 0 0 32.5
## 572 2 130 96 0 0 22.6
## 573 3 111 58 31 44 29.5
## 574 2 98 60 17 120 34.7
## 575 1 143 86 30 300 30.1
## 576 1 119 44 47 63 35.5
## 577 6 108 44 20 130 24.0
## 578 2 118 80 0 0 42.9
## 579 10 133 68 0 0 27.0
## 580 2 197 70 99 0 34.7
## 581 0 151 90 46 0 42.1
## 582 6 109 60 27 0 25.0
## 583 12 121 78 17 0 26.5
## 584 8 100 76 0 0 38.7
## 585 8 124 76 24 300 28.7
## 586 1 93 56 11 0 22.5
## 587 8 143 66 0 0 34.9
## 588 6 103 66 0 0 24.3
## 589 3 176 86 27 156 33.3
## 590 0 73 0 0 0 21.1
## 591 11 111 84 40 0 46.8
## 592 2 112 78 50 140 39.4
## 593 3 132 80 0 0 34.4
## 594 2 82 52 22 115 28.5
## 595 6 123 72 45 230 33.6
## 596 0 188 82 14 185 32.0
## 597 0 67 76 0 0 45.3
## 598 1 89 24 19 25 27.8
## 599 1 173 74 0 0 36.8
## 600 1 109 38 18 120 23.1
## 601 1 108 88 19 0 27.1
## 602 6 96 0 0 0 23.7
## 603 1 124 74 36 0 27.8
## 604 7 150 78 29 126 35.2
## 605 4 183 0 0 0 28.4
## 606 1 124 60 32 0 35.8
## 607 1 181 78 42 293 40.0
## 608 1 92 62 25 41 19.5
## 609 0 152 82 39 272 41.5
## 610 1 111 62 13 182 24.0
## 611 3 106 54 21 158 30.9
## 612 3 174 58 22 194 32.9
## 613 7 168 88 42 300 38.2
## 614 6 105 80 28 0 32.5
## 615 11 138 74 26 144 36.1
## 616 3 106 72 0 0 25.8
## 617 6 117 96 0 0 28.7
## 618 2 68 62 13 15 20.1
## 619 9 112 82 24 0 28.2
## 620 0 119 0 0 0 32.4
## 621 2 112 86 42 160 38.4
## 622 2 92 76 20 0 24.2
## 623 6 183 94 0 0 40.8
## 624 0 94 70 27 115 43.5
## 625 2 108 64 0 0 30.8
## 626 4 90 88 47 54 37.7
## 627 0 125 68 0 0 24.7
## 628 0 132 78 0 0 32.4
## 629 5 128 80 0 0 34.6
## 630 4 94 65 22 0 24.7
## 631 7 114 64 0 0 27.4
## 632 0 102 78 40 90 34.5
## 633 2 111 60 0 0 26.2
## 634 1 128 82 17 183 27.5
## 635 10 92 62 0 0 25.9
## 636 13 104 72 0 0 31.2
## 637 5 104 74 0 0 28.8
## 638 2 94 76 18 66 31.6
## 639 7 97 76 32 91 40.9
## 640 1 100 74 12 46 19.5
## 641 0 102 86 17 105 29.3
## 642 4 128 70 0 0 34.3
## 643 6 147 80 0 0 29.5
## 644 4 90 0 0 0 28.0
## 645 3 103 72 30 152 27.6
## 646 2 157 74 35 300 39.4
## 647 1 167 74 17 144 23.4
## 648 0 179 50 36 159 37.8
## 649 11 136 84 35 130 28.3
## 650 0 107 60 25 0 26.4
## 651 1 91 54 25 100 25.2
## 652 1 117 60 23 106 33.8
## 653 5 123 74 40 77 34.1
## 654 2 120 54 0 0 26.8
## 655 1 106 70 28 135 34.2
## 656 2 155 52 27 300 38.7
## 657 2 101 58 35 90 21.8
## 658 1 120 80 48 200 38.9
## 659 11 127 106 0 0 39.0
## 660 3 80 82 31 70 34.2
## 661 10 162 84 0 0 27.7
## 662 1 199 76 43 0 42.9
## 663 8 167 106 46 231 37.6
## 664 9 145 80 46 130 37.9
## 665 6 115 60 39 0 33.7
## 666 1 112 80 45 132 34.8
## 667 4 145 82 18 0 32.5
## 668 10 111 70 27 0 27.5
## 669 6 98 58 33 190 34.0
## 670 9 154 78 30 100 30.9
## 671 6 165 68 26 168 33.6
## 672 1 99 58 10 0 25.4
## 673 10 68 106 23 49 35.5
## 674 3 123 100 35 240 57.3
## 675 8 91 82 0 0 35.6
## 676 6 195 70 0 0 30.9
## 677 9 156 86 0 0 24.8
## 678 0 93 60 0 0 35.3
## 679 3 121 52 0 0 36.0
## 680 2 101 58 17 265 24.2
## 681 2 56 56 28 45 24.2
## 682 0 162 76 36 0 49.6
## 683 0 95 64 39 105 44.6
## 684 4 125 80 0 0 32.3
## 685 5 136 82 0 0 0.0
## 686 2 129 74 26 205 33.2
## 687 3 130 64 0 0 23.1
## 688 1 107 50 19 0 28.3
## 689 1 140 74 26 180 24.1
## 690 1 144 82 46 180 46.1
## 691 8 107 80 0 0 24.6
## 692 13 158 114 0 0 42.3
## 693 2 121 70 32 95 39.1
## 694 7 129 68 49 125 38.5
## 695 2 90 60 0 0 23.5
## 696 7 142 90 24 300 30.4
## 697 3 169 74 19 125 29.9
## 698 0 99 0 0 0 25.0
## 699 4 127 88 11 155 34.5
## 700 4 118 70 0 0 44.5
## 701 2 122 76 27 200 35.9
## 702 6 125 78 31 0 27.6
## 703 1 168 88 29 0 35.0
## 704 2 129 0 0 0 38.5
## 705 4 110 76 20 100 28.4
## 706 6 80 80 36 0 39.8
## 707 10 115 0 0 0 0.0
## 708 2 127 46 21 300 34.4
## 709 9 164 78 0 0 32.8
## 710 2 93 64 32 160 38.0
## 711 3 158 64 13 300 31.2
## 712 5 126 78 27 22 29.6
## 713 10 129 62 36 0 41.2
## 714 0 134 58 20 291 26.4
## 715 3 102 74 0 0 29.5
## 716 7 187 50 33 300 33.9
## 717 3 173 78 39 185 33.8
## 718 10 94 72 18 0 23.1
## 719 1 108 60 46 178 35.5
## 720 5 97 76 27 0 35.6
## 721 4 83 86 19 0 29.3
## 722 1 114 66 36 200 38.1
## 723 1 149 68 29 127 29.3
## 724 5 117 86 30 105 39.1
## 725 1 111 94 0 0 32.8
## 726 4 112 78 40 0 39.4
## 727 1 116 78 29 180 36.1
## 728 0 141 84 26 0 32.4
## 729 2 175 88 0 0 22.9
## 730 2 92 52 0 0 30.1
## 731 3 130 78 23 79 28.4
## 732 8 120 86 0 0 28.4
## 733 2 174 88 37 120 44.5
## 734 2 106 56 27 165 29.0
## 735 2 105 75 0 0 23.3
## 736 4 95 60 32 0 35.4
## 737 0 126 86 27 120 27.4
## 738 8 65 72 23 0 32.0
## 739 2 99 60 17 160 36.6
## 740 1 102 74 0 0 39.5
## 741 11 120 80 37 150 42.3
## 742 3 102 44 20 94 30.8
## 743 1 109 58 18 116 28.5
## 744 9 140 94 0 0 32.7
## 745 13 153 88 37 140 40.6
## 746 12 100 84 33 105 30.0
## 747 1 147 94 41 0 49.3
## 748 1 81 74 41 57 46.3
## 749 3 187 70 22 200 36.4
## 750 6 162 62 0 0 24.3
## 751 4 136 70 0 0 31.2
## 752 1 121 78 39 74 39.0
## 753 3 108 62 24 0 26.0
## 754 0 181 88 44 300 43.3
## 755 8 154 78 32 0 32.4
## 756 1 128 88 39 110 36.5
## 757 7 137 90 41 0 32.0
## 758 0 123 72 0 0 36.3
## 759 1 106 76 0 0 37.5
## 760 6 190 92 0 0 35.5
## 761 2 88 58 26 16 28.4
## 762 9 170 74 31 0 44.0
## 763 9 89 62 0 0 22.5
## 764 10 101 76 48 180 32.9
## 765 2 122 70 27 0 36.8
## 766 5 121 72 23 112 26.2
## 767 1 126 60 0 0 30.1
## 768 1 93 70 31 0 30.4
## DiabetesPedigreeFunction Age Outcome
## 1 0.627 50 1
## 2 0.351 31 0
## 3 0.672 32 1
## 4 0.167 21 0
## 5 2.288 33 1
## 6 0.201 30 0
## 7 0.248 26 1
## 8 0.134 29 0
## 9 0.158 53 1
## 10 0.232 54 1
## 11 0.191 30 0
## 12 0.537 34 1
## 13 1.441 57 0
## 14 0.398 59 1
## 15 0.587 51 1
## 16 0.484 32 1
## 17 0.551 31 1
## 18 0.254 31 1
## 19 0.183 33 0
## 20 0.529 32 1
## 21 0.704 27 0
## 22 0.388 50 0
## 23 0.451 41 1
## 24 0.263 29 1
## 25 0.254 51 1
## 26 0.205 41 1
## 27 0.257 43 1
## 28 0.487 22 0
## 29 0.245 57 0
## 30 0.337 38 0
## 31 0.546 60 0
## 32 0.851 28 1
## 33 0.267 22 0
## 34 0.188 28 0
## 35 0.512 45 0
## 36 0.966 33 0
## 37 0.420 35 0
## 38 0.665 46 1
## 39 0.503 27 1
## 40 1.390 56 1
## 41 0.271 26 0
## 42 0.696 37 0
## 43 0.235 48 0
## 44 0.721 54 1
## 45 0.294 40 0
## 46 1.893 25 1
## 47 0.564 29 0
## 48 0.586 22 0
## 49 0.344 31 1
## 50 0.305 24 0
## 51 0.491 22 0
## 52 0.526 26 0
## 53 0.342 30 0
## 54 0.467 58 1
## 55 0.718 42 0
## 56 0.248 21 0
## 57 0.254 41 1
## 58 0.962 31 0
## 59 1.781 44 0
## 60 0.173 22 0
## 61 0.304 21 0
## 62 0.270 39 1
## 63 0.587 36 0
## 64 0.699 24 0
## 65 0.258 42 1
## 66 0.203 32 0
## 67 0.855 38 1
## 68 0.845 54 0
## 69 0.334 25 0
## 70 0.189 27 0
## 71 0.867 28 1
## 72 0.411 26 0
## 73 0.583 42 1
## 74 0.231 23 0
## 75 0.396 22 0
## 76 0.140 22 0
## 77 0.391 41 0
## 78 0.370 27 0
## 79 0.270 26 1
## 80 0.307 24 0
## 81 0.140 22 0
## 82 0.102 22 0
## 83 0.767 36 0
## 84 0.237 22 0
## 85 0.227 37 1
## 86 0.698 27 0
## 87 0.178 45 0
## 88 0.324 26 0
## 89 0.153 43 1
## 90 0.165 24 0
## 91 0.258 21 0
## 92 0.443 34 0
## 93 0.261 42 0
## 94 0.277 60 1
## 95 0.761 21 0
## 96 0.255 40 0
## 97 0.130 24 0
## 98 0.323 22 0
## 99 0.356 23 0
## 100 0.325 31 1
## 101 1.222 33 1
## 102 0.179 22 0
## 103 0.262 21 0
## 104 0.283 24 0
## 105 0.930 27 0
## 106 0.801 21 0
## 107 0.207 27 0
## 108 0.287 37 0
## 109 0.336 25 0
## 110 0.247 24 1
## 111 0.199 24 1
## 112 0.543 46 1
## 113 0.192 23 0
## 114 0.391 25 0
## 115 0.588 39 1
## 116 0.539 61 1
## 117 0.220 38 1
## 118 0.654 25 0
## 119 0.443 22 0
## 120 0.223 21 0
## 121 0.759 25 1
## 122 0.260 24 0
## 123 0.404 23 0
## 124 0.186 69 0
## 125 0.278 23 1
## 126 0.496 26 1
## 127 0.452 30 0
## 128 0.261 23 0
## 129 0.403 40 1
## 130 0.741 62 1
## 131 0.361 33 1
## 132 1.114 33 1
## 133 0.356 30 1
## 134 0.457 39 0
## 135 0.647 26 0
## 136 0.088 31 0
## 137 0.597 21 0
## 138 0.532 22 0
## 139 0.703 29 0
## 140 0.159 28 0
## 141 0.268 55 0
## 142 0.286 38 0
## 143 0.318 22 0
## 144 0.272 42 1
## 145 0.237 23 0
## 146 0.572 21 0
## 147 0.096 41 0
## 148 1.400 34 0
## 149 0.218 65 0
## 150 0.085 22 0
## 151 0.399 24 0
## 152 0.432 37 0
## 153 1.189 42 1
## 154 0.687 23 0
## 155 0.137 43 1
## 156 0.337 36 1
## 157 0.637 21 0
## 158 0.833 23 0
## 159 0.229 22 0
## 160 0.817 47 1
## 161 0.294 36 0
## 162 0.204 45 0
## 163 0.167 27 0
## 164 0.368 21 0
## 165 0.743 32 1
## 166 0.722 41 1
## 167 0.256 22 0
## 168 0.709 34 0
## 169 0.471 29 0
## 170 0.495 29 0
## 171 0.180 36 1
## 172 0.542 29 1
## 173 0.773 25 0
## 174 0.678 23 0
## 175 0.370 33 0
## 176 0.719 36 1
## 177 0.382 42 0
## 178 0.319 26 1
## 179 0.190 47 0
## 180 0.956 37 1
## 181 0.084 32 0
## 182 0.725 23 0
## 183 0.299 21 0
## 184 0.268 27 0
## 185 0.244 40 0
## 186 0.745 41 1
## 187 0.615 60 1
## 188 1.321 33 1
## 189 0.640 31 1
## 190 0.361 25 1
## 191 0.142 21 0
## 192 0.374 40 0
## 193 0.383 36 1
## 194 0.578 40 1
## 195 0.136 42 0
## 196 0.395 29 1
## 197 0.187 21 0
## 198 0.678 23 1
## 199 0.905 26 1
## 200 0.150 29 1
## 201 0.874 21 0
## 202 0.236 28 0
## 203 0.787 32 0
## 204 0.235 27 0
## 205 0.324 55 0
## 206 0.407 27 0
## 207 0.605 57 1
## 208 0.151 52 1
## 209 0.289 21 0
## 210 0.355 41 1
## 211 0.290 25 0
## 212 0.375 24 0
## 213 0.164 60 0
## 214 0.431 24 1
## 215 0.260 36 1
## 216 0.742 38 1
## 217 0.514 25 1
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## 710 0.674 23 1
## 711 0.295 24 0
## 712 0.439 40 0
## 713 0.441 38 1
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## 715 0.121 32 0
## 716 0.826 34 1
## 717 0.970 31 1
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## 719 0.415 24 0
## 720 0.378 52 1
## 721 0.317 34 0
## 722 0.289 21 0
## 723 0.349 42 1
## 724 0.251 42 0
## 725 0.265 45 0
## 726 0.236 38 0
## 727 0.496 25 0
## 728 0.433 22 0
## 729 0.326 22 0
## 730 0.141 22 0
## 731 0.323 34 1
## 732 0.259 22 1
## 733 0.646 24 1
## 734 0.426 22 0
## 735 0.560 53 0
## 736 0.284 28 0
## 737 0.515 21 0
## 738 0.600 42 0
## 739 0.453 21 0
## 740 0.293 42 1
## 741 0.785 48 1
## 742 0.400 26 0
## 743 0.219 22 0
## 744 0.734 45 1
## 745 1.174 39 0
## 746 0.488 46 0
## 747 0.358 27 1
## 748 1.096 32 0
## 749 0.408 36 1
## 750 0.178 50 1
## 751 1.182 22 1
## 752 0.261 28 0
## 753 0.223 25 0
## 754 0.222 26 1
## 755 0.443 45 1
## 756 1.057 37 1
## 757 0.391 39 0
## 758 0.258 52 1
## 759 0.197 26 0
## 760 0.278 66 1
## 761 0.766 22 0
## 762 0.403 43 1
## 763 0.142 33 0
## 764 0.171 63 0
## 765 0.340 27 0
## 766 0.245 30 0
## 767 0.349 47 1
## 768 0.315 23 0cor_matrix <- cor(numeric_data, use = "complete.obs")
cor_matrix## Pregnancies Glucose BloodPressure SkinThickness
## Pregnancies 1.00000000 0.12945867 0.14128198 -0.08167177
## Glucose 0.12945867 1.00000000 0.15258959 0.05732789
## BloodPressure 0.14128198 0.15258959 1.00000000 0.20737054
## SkinThickness -0.08167177 0.05732789 0.20737054 1.00000000
## Insulin -0.07823754 0.31048270 0.10225530 0.48823294
## BMI 0.01768309 0.22107107 0.28180529 0.39257320
## DiabetesPedigreeFunction -0.03352267 0.13733730 0.04126495 0.18392757
## Age 0.54434123 0.26351432 0.23952795 -0.11397026
## Outcome 0.22189815 0.46658140 0.06506836 0.07475223
## Insulin BMI DiabetesPedigreeFunction
## Pregnancies -0.07823754 0.01768309 -0.03352267
## Glucose 0.31048270 0.22107107 0.13733730
## BloodPressure 0.10225530 0.28180529 0.04126495
## SkinThickness 0.48823294 0.39257320 0.18392757
## Insulin 1.00000000 0.20909688 0.18734209
## BMI 0.20909688 1.00000000 0.14064695
## DiabetesPedigreeFunction 0.18734209 0.14064695 1.00000000
## Age -0.06880310 0.03624187 0.03356131
## Outcome 0.12346817 0.29269466 0.17384407
## Age Outcome
## Pregnancies 0.54434123 0.22189815
## Glucose 0.26351432 0.46658140
## BloodPressure 0.23952795 0.06506836
## SkinThickness -0.11397026 0.07475223
## Insulin -0.06880310 0.12346817
## BMI 0.03624187 0.29269466
## DiabetesPedigreeFunction 0.03356131 0.17384407
## Age 1.00000000 0.23835598
## Outcome 0.23835598 1.00000000corrplot(cor_matrix, method = "color",
type = "upper",
col=colorRampPalette(c("blue","white","red"))(200),
addCoef.col = "black",
tl.col = "black",
number.cex = 0.7,
tl.srt = 45
)
- Glucose:High glucose levels are most strongly linked to a positive Outcome (diabetes).
- BMI: Higher BMI is also linked to a positive Outcome.
- Age: Being older is linked to a positive Outcome.
- Pregnancies: More pregnancies have a weak link to a positive Outcome.
###Conclusion
The following steps should be taken to help reduce or monitor diabetes:
Monitor blood glucose levels regularly
Maintain a healthy body weight
Adopt a balanced diet and regular physical activity
Attend regular medical checkups
Increase education and awareness about diabetes risk factors like avoiding much intake of sugar and alcohol.