Please add the solution and R codes using R Markdown.

Use group_by () and summarize () to create a data frame with the following columns . Count of the number of flights to each destination. . Mean arrival delay for each destination. . Standard deviation of arrival delays for each destination. Your R code should recreate the following table: ## N A tibble: 105 x 4 dest count arr_delay mean arr_delay_ad 1 ABQ 254 4.38 42.0 2 ACK 265 4. 85 30 .0 3 ALB 14.4 50.5 # # 4 ANC -2.5 26.4 5 ATL 17215 11.3 47.0 # # 6 AUS 2439 6.02 43.5 7 AVL 275 8.00 33.6 B BOL 443 7.05 42.1 9 BGR 375 8 . 03 46.4 ## 10 BHM 297 16.9 56. ## # . .. with 95 more rows b Use left_join () to combine the data frame of grouped summary statistics from part (a) with the airports data frame. The combined data frame should contain additional columns with information about the destination airport. Your code should should recreate the following table: ## # A tibble: 105 x 11 dest count arr_delay_mean arr_delay_ed name lat lon alt tz dat 1 ABU 254 4.38 42.0 Albuq- 35.0 -107. 6355 -7 A 2 ACK 265 4.85 30.0 Nantu- 41.3 -70.1 48 -5 A 3 ALB 439 14.4 50.5 Alban- 42.7 -73.8 285 -5 A 4 ANC -2.5 26.4 Tod S- 61.2 -150. 152 -9 A 5 ATL 17215 11.3 47 .0 Harts- 33.6 -84.4 1026 -5 A 6 AUS 243 6.02 43.5 Austi- 30.2 -97.7 542 -6 A # # 7 AVL 275 8.00 33.6 Ashev- 35.4 -82.5 2165 -5 A 8 BOL 443 7.06 42.1 Bradl- 41.9 -72.7 173 -5 A ## 9 BGR 375 8.03 46.4 Bango- 44.8 -68.8 192 -5 A ## 10 BHM 297 16.9 56.2 Birmi- 33.6 -86.8 644 -6 A of # ... with 95 more rows, and 1 more variable: tzone Which airports had the longest average arrival delays? Which airports had the greatest variability in their arrival delays? [Hint: use arrange ()] rows represent? In the combined data set from part (b), how many rows have NA values for the airport name? What do these