Nicely done!
Each data frame contains an additional column named distance_covered_km that, as the name suggests, tells us how many kilometers the company's shipping truck traveled between the previous destination and the current one. For example, Lyon was company_a's eighth destination. Before Lyon, the truck was in Nantes. The company's truck traveled 685 km between these two cities.
| city | country | quantity | destination_number | distance_covered_km | production_center | |
|---|---|---|---|---|---|---|
| 1 | Prague | Czech Republic | 200000 | 0 | 0 | Y |
| 2 | Antwerp | Belgium | 200000 | 6 | 55 | Y |
| 3 | Madrid | Spain | 200000 | -1 | 0 | Y |
| 4 | Nantes | France | 30000 | 7 | 722 | N |
| 5 | Lyon | France | 25000 | 8 | 685 | N |
| 6 | Milan | Italy | 55000 | 9 | 447 | N |
| 7 | Salzburg | Austria | 40000 | 2 | 430 | N |
| 8 | Brno | Czech Republic | 20000 | 1 | 217 | N |
| 9 | Dresden | Germany | 10000 | 3 | 612 | N |
| 10 | Frankfurt | Germany | 50000 | 4 | 460 | N |
| 11 | Brussels | Belgium | 70000 | 5 | 399 | N |
Display the largest distance that was covered between two cities among companies a, b, and c. Return the result as a vector (that is, use sapply()).
You should use an anonymous function here that takes one argument, x, and returns the max element of the distance_covered_km column. Type:
sapply(companies,
function(x){
return(max(x$distance_covered_km))
}
)
