In statcourses/BristolVis: Data visualisation and web applications course

knitr::opts_chunk$set(echo = FALSE, eval = FALSE)

Exercise 1:

Data on heights, weights and gender were collected for 10 individuals in early-adulthood. The data were reported in the table below (heights measured in cm, weights in Kg and m refers to a male gender):

DT = data.frame(id = 1:10,
                ht=c(155, 152, 164, 175, 193, 203, 190, 183, 155, 169),
                wt=c(80, 85, 72, 69, 86, 110, 106, 96, 90, 89),
                gender=c("m", "m", "f", "m", "f", "f", "f", "m", "f", "m"))
knitr::kable(DT)

a) Create vectors for height, weight and gender and assigned them to the names: ht; wt; gender respectively.

ht = c(155, 152, 164, 175, 193, 203, 190, 183, 155, 169)
wt = c(80, 85, 72, 69, 86, 110, 106, 96, 90, 89)
gender = c("m", "m", "f", "m", "f", "f", "f", "m", "f", "m")

b) Using ht and wt vectors, creat a new variable for the BMI (Hint: BMI is calculated by dividing weight measured in Kg by the squared height measured in meters)

# convert 'ht' into meters
ht_meters = ht / 100
# BMI calculations
(BMI = wt/(ht_meters^2))

c) Show the length of the ht vector.

length(ht)

d) Show a frequency table for the gender variable (Hint: search the help for the table function by typing in ?table)

?table
table(gender)

e) Round the calculated BMI values to 2 decimel digits only.

(BMI = round(BMI, digits = 2))

f) Create a new data.frame with the name DT that includes height, in meters, weight, in Kg, BMI, and gender.

(DT = data.frame(ht_meters = ht/100, wt = wt, BMI = BMI, gender = gender))

g) Add a logical variable to the DT, with a name of obese whose values are TRUE for subjects with weights over 95 Kg.

(DT$obese = DT$wt > 95)

h) Find out how many subjects with weights over 95 Kg.

sum(DT$wt > 95)
# or alternatively
sum(DT$obese)

i) Extract the BMI for the 3rd and 5th individuals.

DT$BMI[c(3,5)]

statcourses/BristolVis documentation built on Jan. 31, 2021, 9:24 p.m.