. In a Washington Post article from October 6, 2015, titled "Zero correlation between state homicide rate and state gun laws", Eugene Volokh examined the relationship between the total number of gun deaths (per 100,000 peOple) and gun laws. To do that, Volokh used the Brady score, which represents how difficult it is to obtain a gun in a state. A low Brady score means a low level of gun restrictions (so it is easier to obtain a gun), and a high score means that it is harder to get a gun (i.e. there are stricter gun laws). Load the data \"guns.csv\" into R . Create a new logical variable Homicide_index which divides the Homicide.rate into two categories: TRUE if the rate is greater than 4.3, and FALSE is it is lower or equal to 4.3. Use the table() function to show the number of TRUE's and the number of FALSE's. . Use the logical variable you created in part a. to create two numerical variables: HRate which contains all the Homiciderate values that are greater than 4.3, and LRate which contains all remaining Homiciderate values. Produce a side-by-side boxplot comparing HRate and LRate. Use at least two more options in the boxplotO function to improve the plot. Name the 2 states which have the highest numbers of gun deaths and provide their Brady score and their number of gun deaths. Produce a scatterplot of variables Sum versus Brady.score. Make the title as Number of gun deaths per 100,000 pe0p1e by Brady Score, x axis name as Brady Score, y axis name as Gun Deaths and change the point symbol to solid rhombus. Find all the states/ jurisdictions which have weak restrictions for buying guns (use Brady score less than 0), and also have a low number of gun deaths per 100,000 people (use less than 4). Use the functions which() and intersect(). DiSplay it as a table with the state/jurisdiction names, the Brady score, and the number of gun deaths per 100,000 people