Publications by Nina Jankovič
Homework 4
mydata <- read.csv("./HW4.csv", header = TRUE, sep = ';', dec = ',') colnames(mydata) <- c('Gender','Age', 'Residence_Type','Glucose_Level','BMI') head(mydata) ## Gender Age Residence_Type Glucose_Level BMI ## 1 0 67 1 229 37 ## 2 1 80 0 ...
5317 sym R (16583 sym/69 pcs) 7 img
Homework 4
mydata <- read.csv("./HW3.csv", header = TRUE, sep = ';', dec = ',') head(mydata) ## id age education sex is_smoking cigsPerDay totChol sysBP diaBP BMI heartRate ## 1 1 36 4 0 0 0 212 168.0 98 30 72 ## 2 2 46 1 1 1 ...
4272 sym R (20751 sym/69 pcs) 9 img
Homework 3
mydata <- read.csv("./HW3.csv", header = TRUE, sep = ';', dec = ',') head(mydata) ## id age education sex is_smoking cigsPerDay totChol sysBP diaBP BMI heartRate ## 1 1 36 4 0 0 0 212 168.0 98 30 72 ## 2 2 46 1 1 1 ...
5139 sym R (35536 sym/80 pcs) 7 img
Homework 2
mydata <- read.csv("./HW2.csv", header = TRUE, sep = ';', dec = ',') head(mydata) ## id Age education sex is_smoking totChol BMI heartRate glucose TenYearCHD ## 1 1 36 4 0 NO 212 29.77 72 75 0 ## 2 2 46 1 1 YES 250 ...
5560 sym R (11722 sym/59 pcs) 3 img
Exam - R Studio
#Task 1 I have found the data on Kaggle website in datasets. mydata1 <- read.csv('~/Desktop/bootcamp R/Exam.csv', header = TRUE, sep = ';', dec = ',') print(mydata1) ## gender age Residence_type avg_glucose_level bmi ## 1 0 67 Urban 229 37 ## 2 ...
9449 sym R (28159 sym/96 pcs) 6 img
Homework 1
mydata1 <- read.csv("./hw111.csv", header = TRUE, sep = ';', dec = ',') mydata1$glucose <- strtoi(mydata1$glucose) head(mydata1) ## id age sex is_smoking totChol BMI heartRate glucose TenYearCHD ## 1 1 36 0 NO 212 29.77 72 75 0 ## 2 2 46 1 ...
5517 sym R (6598 sym/32 pcs) 3 img