Reading the data
- Read the data dictionary for this simulated dataset to familiarise yourself with the variables.
Information: the data dictionary
The names of all other variables end in either .7 or .11 (depending whether they were measured at the age 7 clinic or the age 11 clinic)
male codes sex: 1=male, 0=female
age.yrs and age.yrs are the age (in decimal years) on the day of the clinic at age 7 or 11
ht is height in cm
ht.sit is sitting height in cm
ws is waist circumference in cm
hp is waist circumference in cm
wt is weight in Kg
sbp is systolic blood pressure (the top of the blood pressure fluctuation) measured (as is conventional) in mm of Hg (mercury)
dbp is diastolic blood pressure (the bottom of the blood pressure fluctuation) measured (as is conventional) in mm of Hg (mercury)
pulse is pulse rate measured in beats per minute
BMI is body mass index derived as wt/(ht/100)2 The height variable is divided by 100 to express it in metres rather than centimeters
- start a new script and save it as a .R file in an appropriate location
- comment in some header information: what is the script for? who is it written by? what data set is being used? etc
- set the working directory using
setwd
and read the dataset into R and assign it the variablesim.alspac
using theread.csv
function - look up the
colnames
function in the help file and apply it tosim.alspac
to list all the column headings in the data. - look up the
dim
function in the help file and apply it to tosim.alspac
to get the dimensions of the dataset. Number of columns is the number of variables, number of rows is the number of participants.
Selecting and subsetting
Selecting variables can be done a number of ways including selection by column number or column name. It is best practice to use the column name as the column number may vary between datasets.
select.1<-dataframe[,x] #assign the variable select1 column number x in dataframe select.2<-dataframe[,"x"] #assign the variable select2 column named x in dataframe select.3<-dataframe$x #assign the variable select3 dataframe column x
It is also possible to use operators to subset between a range of values. See the help file for the subset
function for further explanation
subset.4<-subset(dataframe, x < 5) #subset of the whole dataframe where x < 5 subset.4<-subset(dataframe, x == 5) #subset of the whole dataframe where x = 5
- create a subset of
sim.alspac
for males calledsubset.male
and for females calledsubset.female
- How many participants are female and how many are male? HINT: Use
dim
to check the dimensions ofsubset.male
andsubset.female
.
Changing class
- The data dictionary tells us that the variable
male
is categorical. Check the class ofmale
using theclass
function.
Rounding numbers
signif(x, digits = 6)
# set how many significant figures using digits =
or use
format(round(x, 2), nsmall = 2)
# for two d.p
Adding text to graphs
text(70,12, labels=paste("y=", RegM11$coefficients[2], "+", RegM11$coefficients[1]), col="orange")
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