In this set of exercises, you are going to apply stratified sampling to the above-ground biomass (agb) of the trees in the BCI dataset. The final sample should contain #1000# individuals.

Let's first take a look at the distribution of agb and divide the dataset into strata. Since the distribution is strongly positively skewed, it is recommended to visualise the log10 distribution.

hist(log10(BCI$ agb))

Let's divide the dataset into #5# classes. We take -3.5 -3.0 -2.5 -1.5 as class limits.

BCI$ biomasscl <- NA
BCI$ biomasscl[ log10(BCI$ agb)<  -3.5 ] <- 1
BCI$ biomasscl[ log10(BCI$ agb)>=  -3.5  & log10(BCI$ agb)<  -3 ] <- 2
BCI$ biomasscl[ log10(BCI$ agb)>=  -3  & log10(BCI$ agb)<  -2.5 ] <- 3
BCI$ biomasscl[ log10(BCI$ agb)>=  -2.5  & log10(BCI$ agb)<  -1.5 ] <- 4
BCI$ biomasscl[ log10(BCI$ agb)>=  -1.5  ] <- 5

Use the code above (i.e. the variable biomasscl) to divide the BCI dataframe into #5# separate dataframes. You can name these dataframes class1_trees, class2_trees etc.

How many observations does class4_trees contain?