Reading the data 

• Download the D2K simulated ALSPAC dataset. 

• Read the data dictionary for this simulated dataset to familiarise yourself with the variables. 

• 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 variable sim.alspac using the read.csv function
• look up the colnames function in the help file and apply it to sim.alspac to list all the column headings in the data.
• look up the dim function in the help file and apply it to to sim.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.

subset.1<-dataframe[,x] #assign the variable subset1 column number x in dataframe
subset.2<-dataframe[,"x"] #assign the variable subset2 column named x in dataframe
subset.3<-dataframe$x #assign the variable subset3 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 called subset.male and for females called subset.female
• How many participants are female and how many are male? HINT: Use dim to check the dimensions of subset.male and subset.female.  

Changing class

• The data dictionary tells us that the variable "male" is categorical.  Check the class of sim.alspac$male using the class function.

Descriptive / summary stats in R

• contingency table (a summary table of 3+ variables) gender, age, BMI

table( )

ftable( )

• summary stats  mean, min, max and quantiles

summary()

• histogram - to identify  types of distributions of a variable

hist()

• box and whisker plot summarizes graphically the min, max, 25-75 percentiles

boxplot()

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")