tapply

Now, suppose instead of prices for 2018 only, we have prices for these items for 2017, 2016, and 2015 as well. This new data frame is defined as follows:

all_prices = data.frame(items = rep(c("potato", "rice", "oil"), 4), 
            jan_price = c(10, 20, 30, 10, 18, 25, 9, 17, 24, 9, 19,27),
            mar_price = c(11, 22, 33, 13, 25, 32, 12, 21, 33, 15, 27,39),
            june_price = c(20, 25, 33, 21, 24, 40, 17, 22, 27, 13, 18,23)
                                                   )
all_prices

The output for the preceding lines of code can be seen as follows:

Now suppose we want to take the mean price of different items for very March in all years. We can do this by using tapply(numerical_variable, categorical_variable, function). So, we will need to convert the items column of the all_prices data frame to a categorical variable to take the mean price.

tapply(all_prices$mar_price, factor(all_prices$items), mean)

This gives us a mean March price for oil, potato, and rice in all years, as follows:

Note the use of factor() to convert the items column to a factor variable.

There are other apply functions, but that's it for now, folks. We will introduce new functions as and when it will be necessary as we proceed to new chapters for geospatial analysis.

To install a new package, we need to write install.packages("package_name"), and to use any package, we need to write load.packages("package_name").