## ----Q5, echo=SOLUTIONS------------------------------------------------------- cardiac <- read.table('data/cardiacdata.txt', header = TRUE, sep = "\t", stringsAsFactors = TRUE) ## ----Q6, echo=SOLUTIONS------------------------------------------------------- head(cardiac) # display the first few rows names(cardiac) # display the variable names str(cardiac) # display the structure of the dataframe cardiac # 'data.frame': 163 obs. of 11 variables: # $ patno : Factor w/ 163 levels "0049B","0052H",..: 6 54 65 69 ... # $ age : num 71 68.2 62.9 69.9 65 ... # $ sex : int 2 2 2 2 2 2 2 1 1 2 ... # $ systolic : int 142 140 156 154 187 123 147 140 131 128 ... # $ diastolic : int 63 78 82 102 131 63 66 67 73 62 ... # $ tchol : num 7.03 5.32 9.34 7.19 8.84 6.17 6.2 6.96 7.02 7.21 ... # $ hdlchol : num 1.4 0.88 0.92 1.31 1.83 1.58 1.65 1.68 1.57 1.08 ... # $ triglyceride: num 0.81 3.4 4.67 2.53 1.76 0.73 1.11 0.69 1.29 1.16 ... # $ bmi : num 24.7 26 26.6 26.2 26.1 ... # $ alcohol : int 9 2 7 24 14 8 0 2 0 0 ... # $ smoking : int NA NA NA NA NA NA NA 1 1 1 ... # 163 observations and 11 variables. # R thinks sex and smoking are integers, because that is how they are stored # in the file: sex is coded 1 and 2, smoking is coded 1, 2 and 3. But they are # not really numbers, they are categories, and R has no way of knowing that. # Nothing stops you calculating mean(cardiac$sex) and it returns 1.52 - and that # number is mostly meaningless. You will fix both of them in the next question. # patno is a factor with 163 levels, one for every row. A factor with as many # levels as there are rows is a good sign that you are looking at an identifier # rather than a variable. ## ----Q7, echo=SOLUTIONS------------------------------------------------------- # Writing the labels out means you never have to remember the codes again, and # your output reads properly from here on. cardiac$Fsex <- factor(cardiac$sex, levels = c(1, 2), labels = c("Female", "Male")) cardiac$Fsmoking <- factor(cardiac$smoking, levels = c(1, 2, 3), labels = c("Current", "Ex", "Never")) str(cardiac) # $ sex : int 2 2 2 2 2 2 2 1 1 2 ... # $ smoking : int NA NA NA NA NA NA NA 1 1 1 ... # $ Fsex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 1 1 2 ... # $ Fsmoking : Factor w/ 3 levels "Current","Ex",..: NA NA NA NA NA NA NA 1 1 1 ... # the 7 patients with no smoking code stay missing in Fsmoking, which is exactly # right. factor() does not invent a category for them. # Why new variables rather than overwriting sex and smoking? # # 1. The original codes survive. You can always check your recoding against what was # actually in the file, and if you get the labels the wrong way round you can # simply redo it. Overwrite, and the codes are gone from your session and the # only way back is to import the file again. # # 2. The 'F' prefix says at a glance which variable is the factor version. You # will use the same convention every time you recode something. # and note: the originals are still integers, exactly as they were str(cardiac$sex) str(cardiac$smoking) ## ----Q8, echo=SOLUTIONS------------------------------------------------------- summary(cardiac) # NOTE: only some of the columns are shown here, and not in their original # order, to save space # Fsex sex hdlchol triglyceride bmi # Female:78 Min. :1.000 Min. :0.000 Min. :0.000 Min. : 17.57 # Male :85 1st Qu.:1.000 1st Qu.:1.080 1st Qu.:1.040 1st Qu.: 22.88 # Median :2.000 Median :1.360 Median :1.380 Median : 25.20 # Mean :1.521 Mean :1.404 Mean :1.547 Mean : 28.72 # 3rd Qu.:2.000 3rd Qu.:1.700 3rd Qu.:1.870 3rd Qu.: 28.24 # Max. :2.000 Max. :3.000 Max. :4.670 Max. :514.60 # NA's :2 NA's :2 # Fsex and sex are the same information side by side. Fsex is a factor, so # summary() counts patients and tells you there are 78 women and 85 men. sex is # still an integer, so summary() dutifully reports a median of 2 and a mean of # 1.521, neither of which means anything at all. # Four variables have missing values: tchol, hdlchol and triglyceride have 2 # each, and smoking has 7. Only hdlchol and triglyceride are visible above; # tchol and smoking are in the full output. # The maximum bmi is 514.60. A body mass index of 514 is not possible; the # heaviest person ever recorded had a BMI of around 200. Notice that nothing # went wrong when you imported the file, and R will happily calculate a mean # from it (28.72, when the median is 25.20). It is simply a wrong number sitting # quietly in a column of right ones. You will deal with it in Exercise 4. ## ----Q9, echo=SOLUTIONS------------------------------------------------------- # all rows and the columns patno, sex, smoking and tchol cardiac_risk <- cardiac[, c(1, 3, 11, 6)] # alternative way of indexing columns with named indexes - much easier to read, # and it still works if the column order changes cardiac_risk <- cardiac[, c("patno", "sex", "smoking", "tchol")] # excluding first 10 rows and last column using negative indexing. NOTE: the # last column is now Fsmoking, the second of the two you created in Q7. The # dataframe has 13 columns rather than the 11 you imported, which is exactly why # hard coded column numbers are fragile. cardiac_last <- cardiac[-c(1:10), -13] # more general way, and it stays correct however many columns you add cardiac_last <- cardiac[-c(1:10), -c(ncol(cardiac))] # NOTE: negative indexing does NOT work with column names. Uncomment the line # below and run it to see the error for yourself: # cardiac_last <- cardiac[-c(1:10), -c("Fsmoking")] ## ----Q10, echo=SOLUTIONS, tidy = TRUE----------------------------------------- cardiac_sys160 <- cardiac[cardiac$systolic > 160, ] cardiac_subset <- cardiac[cardiac$Fsex == "Male" & cardiac$Fsmoking == "Never" & cardiac$diastolic > 76, ] cardiac_notex <- cardiac[cardiac$Fsmoking != "Ex", ] ## ----Q11, echo=SOLUTIONS, tidy = TRUE----------------------------------------- # This results in a dataframe filled with NAs. Why? cardiac_new <- cardiac[cardiac$systolic > 160 & cardiac$tchol > mean(cardiac$tchol), ] # the variable tchol contains 2 NA values. By default the mean function will return an NA. # use the na.rm argument to ignore NAs cardiac_new <- cardiac[cardiac$systolic > 160 & cardiac$tchol > mean(cardiac$tchol, na.rm = TRUE), ]