Reading the data 

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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.

subsetselect.1<-dataframe[,x] #assign the variable subset1select1 column number x in dataframe
subsetselect.2<-dataframe[,"x"] #assign the variable subset2select2 column named x in dataframe
subsetselect.3<-dataframe$x #assign the variable subset3select3 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" variable male is categorical.  Check the class of sim.alspac$male male using the class function.

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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)

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