We will use the New York flights dataset from last weeks homework. Let us enhance the analysis from last week with visual exploration. While learning how to code visual exploration is important, equally important is the ability to interpret the observations accurately and precisely. So, please focus on both these aspects as you work on the questions. Each question carries 20 pts. 1. Generate a plot that shows the average flight delay on the Y-axis and the distance groups (that you created for HW2 Q3 part 1) along the X-axis. Summarize your observations. As a data analytics professional you can provide more value by associating your knowledge of the context with your analysis. Try to explain the possible reason behind the trend you see in the scatterplot. 2. Create box plots for air time from each New York airport to Chicago OHAre airport. Summarize your observations. 3. Explore how arrival delay varies with different times of the day. First, split each day into 2 groups according to the departure time, 6am to 6pm and 6pm to 6am. Plot histograms for arrival delay and describe the distribution. Now, try making the group size smaller, make 4 groups in terms of departure time (split each prior group midway). Make histograms of arrival delay and report your observations. What did you learn extra after increasing the number of groups? 4. Make a scatterplot of avg_speed vs. distance(miles). What inferences would you draw from the plot?. Note that for each flight you will need to compute average speed as distance/air-time. 5. Suppose you define a flight to be on time if it gets to the destination on time or earlier than expected, regardless of any departure delays. Mutate the data frame to create a new variable called arr_type with levels on time and delayed based on this definition. Within the on time arrivals, plot the distribution of the departure delays. Within the on time arrivals, what percent of flights that were delayed during departure arrived on time?

please answer all of the above quesions in r programming