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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.  

Exploring the data

• Get object summary statistics by using the summary function on subset.male and subset.female
• Use the boxplot function to plot BMI at age 7 against gender. HINT: You will only need to use the arguments formula= and data=
• Output your boxplot as a .png file using the png function. 
  

• Use the hist function to plot histograms of BMI age 7 for females and males.  HINT: You can layer graphs over one another by using the argument add=T in the second histogram.  Line colour of the histogram can be set using the argument e.g. border="red"
• Make the plot more readable by using the legend to add an appropriate key.
• Output your histogram as a .png file using the png function. 
  
  
• Use the plot function to create a scatter plot of height and weight age 7 for males. 
• Use lm function to generate a linear model called lm1 for the two variables.  HINT: R uses formula notation in formula argument e.g. formula=y~x
• Use the summary function on lm1 to get the coefficients.
• You can add your regression line to the scatterplot by running the abline function  on lm1 after your plot function

Modelling

• Apply a generalised linear model (glm) using the glm function to find the linear relationship between BMI age 7 and gender

ds.glm("D$LAB_GLUC_FASTING~1+D$GENDER",family="gaussian")