1. Create a script (not a function) task1.R with the following computations:
   1. Save dataframe Cars93 from the library MASS to the variable cars.
   2. Add the column kpl (kilometers per liter) to the dataframe, use a column MPG.city (miles per gallon)
   3. Select all cars with Horsepower greater than 200.
   4. Plot Horsepower vs kpl. Make informative labels for axes.
      • add a linear regression line, if you manage to find out, how to do this with the lm function.
   5. Make a copy of the cars dataframe, that has only columns Horsepower, kpl, and wt (weight in tonns, use the original Weight column that has weights in pounds). Pass this copy as the only argument for the plot function. What do you see on this plot?
2. Create a script (not a function) task2.R.
   1. Create a new dataframe about balls, you will use random values for it. It should have two columnssize and color. The first one is a factor with levels “big” and “small”, the second one is a factor with three levels “red”, “green”, “blue”. Use sample to create a create a vector of 1000 random values.
   2. Apply the following functions to this dataframe: table, summary, plot. How many big green balls are there?
   3. Add the column weight this should be normally distributed random numbers with different means for different balls sizes.
   4. Call the functions table, summary, plot again. What do they show?
3. Not finished. Process Google Play Apps dataset