I am having some problems with my assignment that is located in RStudio Cloud for my Computer Systems class, and I would appreciate it if when giving the codes for each question that you give a explanation for how the code is structured, because I keep on getting error messages. Thank you

Exploring Differences between Irish Weather Stations in 2017

First, load the following libraries:

library(dplyr) library(ggploty2) library(aimsir17) 

1. Gather the following summary annual information for each weather station, and store in the tibble annual.

> print(annual,n=25) # A tibble: 25 x 4

station

1. 1 ATHENRY

2. 2 BALLYHAISE

3. 3 BELMULLET

4. 4 CASEMENT

5.  5 CLAREMORRIS 

6.  6 CORK AIRPORT 

7.  7 DUBLIN AIRPORT 

8. 8 DUNSANY

9. 9 FINNER

10. 10 GURTEEN

11. 11 JOHNSTOWNII 

12. 12 KNOCK AIRPORT 

13. 13 MACE HEAD

14. 14 MALIN HEAD

15. 15 MARKREE

16. 16 MOORE PARK

17. 17 MT DILLON

18. 18 MULLINGAR

19. 19 NEWPORT

20. 20 OAK PARK

21. 21 PHOENIX PARK 

22. 22 ROCHES POINT 

23. 23 SHANNON AIRPORT 

24. 24 SherkinIsland 

25. 25 VALENTIA OBSERVATORY

TotalRain AvrWind AvrTemp 1199. 7.20 10.0

952. 6.32 9.75 1243. 12.0 10.6 705. 9.90 10.2

1204 8.02 9.76 1162. 9.48 10.2 662. 11.0 10.1

810. 7.98 9.90 1222. 10.6 10.3 983. 8.27 10.0 963 8.78 10.5

1343. 9.34 8.97 1114. 14.7 10.9 1147. 15.1 10.3 1182. NaN 9.87 1016. 6.08 10.6

992. 6.81 9.83

952. 6.08 9.66 1752. 9.29 10.5 759. 7.41 10.5 732 NaN 10.6 1013. 12.0 10.9 1069. 8.89 10.9 1072. 12.3 11.3 1598. 9.39 11.3

2. For each observation, add the ranking (highest to lowest), using the R function rank().

> print(annual,n=25) # A tibble: 25 x 8

station

1. 1 ATHENRY

2. 2 BALLYHAISE

3 BELMULLET 4 CASEMENT 5 CLAREMORRIS 6 CORK AIRPORT 7 DUBLIN AIRPORT 8 DUNSANY 9 FINNER

10 GURTEEN 11 JOHNSTOWNII

TotalRain AvrWind AvrTemp 1199. 7.20 10.0

952. 6.32 9.75 1243. 12.0 10.6 705. 9.90 10.2

1204 8.02 9.76 1162. 9.48 10.2 662. 11.0 10.1

810. 7.98 9.90 1222. 10.6 10.3 983. 8.27 10.0 963 8.78 10.5

Rank_Rain Rank_Wind 7 19 20 21 4 5 24 8 6 16 9 9 25 6 21 17 5 7 17 15 18 14

Rank_Temp Avr_Rank

18 14.7 23 21.3

6 5 15 15.7 22 14.7 14 10.7 16 15.7 19 19 12 8 17 16.3

9 13.7

1

12. 12 KNOCK AIRPORT

13. 13 MACE HEAD

14. 14 MALIN HEAD

15. 15 MARKREE

16. 16 MOORE PARK

17. 17 MT DILLON

18. 18 MULLINGAR

19. 19 NEWPORT

20. 20 OAK PARK

21. 21 PHOENIX PARK

22. 22 ROCHES POINT

23. 23 SHANNON AIRPORT

24. 24 SherkinIsland

25. 25 VALENTIA OBSERVATORY

>

1343. 9.34 8.97 3 11 1114. 14.7 10.9 11 2 1147. 15.1 10.3 10 1 1182. NaN 9.87 8 24 1016. 6.08 10.6 14 23

992. 6.81 9.83 16 20

952. 6.08 9.66 19 22 1752. 9.29 10.5 1 12 759. 7.41 10.5 22 18 732 NaN 10.6 23 25 1013. 12.0 10.9 15 4 1069. 8.89 10.9 13 13 1072. 12.3 11.3 12 3 1598. 9.39 11.3 2 10

25 13 4 5.67

13 8 20 17.3 7 14.7

21 19 24 21.7 10 7.67 11 17

8 18.7 3 7.33 5 10.3 2 5.67 1 4.33

3. Find the values observations with the highest and lowest rank.

> target # A tibble: 2 x 8

station

1. 1 MULLINGAR

2. 2 VALENTIA OBSERVATORY

TotalRain AvrWind AvrTemp Rank_Rain 952. 6.08 9.66 19 1598. 9.39 11.3 2

Rank_Wind Rank_Temp Avr_Rank

22 24 21.7 10 1 4.33

4. Extract the station names from the tibble, make use of the function pull() > target

[1] "MULLINGAR" "VALENTIA OBSERVATORY"

5. Based on the variable target filter the observations tibble so that it contains only those records for the highest and lowest ranked stations.

> my_obs # A tibble: 17,520 x 12

station year month

1. 1 MULLINGAR 2017 1

2. 2 MULLINGAR 2017 1

3. 3 MULLINGAR 2017 1

4. 4 MULLINGAR 2017 1

5. 5 MULLINGAR 2017 1

6. 6 MULLINGAR 2017 1

7. 7 MULLINGAR 2017 1

8. 8 MULLINGAR 2017 1

9. 9 MULLINGAR 2017 1

day hour date

 rain temp   

rhum msl 93 1021.

90 1021. 92 1021. 90 1021. 90 1022. 92 1022. 92 1022 92 1022. 91 1023. 89 1025.

 wdsp wddir   3 320 5 320 5 320 5 330 5 320 6 340 6 340 7 340 8 340 6 360 

10 MULLINGAR 2017 1 # ... with 17,510 more rows

1 0 2017-01-01 00:00:00 0.2 1 1 2017-01-01 01:00:00 0.1 1 2 2017-01-01 02:00:00 0 1 3 2017-01-01 03:00:00 0 1 4 2017-01-01 04:00:00 0 1 5 2017-01-01 05:00:00 0 1 6 2017-01-01 06:00:00 0 1 7 2017-01-01 07:00:00 0 1 8 2017-01-01 08:00:00 0 1 9 2017-01-01 09:00:00 0

5.2 4.3 3.5 3.1 2.4 1.9 1.8 1.9 1.9 1.8

2

6. Plot the following graph showing the distribution of temperature.

7. Plot the following graph showing the distribution of windspeed

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8. Plot the following graph showing the distribution of rainfall

9. Generate a summary tibble of monthly statistics for each station.

> print(monthly,n=24) # A tibble: 24 x 5

# Groups: station 

station [2]

1. 1 MULLINGAR

2. 2 MULLINGAR

3. 3 MULLINGAR

4. 4 MULLINGAR

5. 5 MULLINGAR

6. 6 MULLINGAR

7. 7 MULLINGAR

8. 8 MULLINGAR

9. 9 MULLINGAR

10. 10 MULLINGAR

11. 11 MULLINGAR

12. 12 MULLINGAR

13. 13 VALENTIA OBSERVATORY

14. 14 VALENTIA OBSERVATORY

15. 15 VALENTIA OBSERVATORY

16. 16 VALENTIA OBSERVATORY

17. 17 VALENTIA OBSERVATORY

18. 18 VALENTIA OBSERVATORY

19. 19 VALENTIA OBSERVATORY

20. 20 VALENTIA OBSERVATORY

21. 21 VALENTIA OBSERVATORY

22. 22 VALENTIA OBSERVATORY

23. 23 VALENTIA OBSERVATORY

24. 24 VALENTIA OBSERVATORY

month Rain 1 41.6

2 75 3 83.8 4 14.2 5 70.7 6 98.1 7 90.4 8 77 9 113.

10 104. 11 78.2 12 106

1 169. 2 139. 3 135. 4 51.4 5 76.3 6 143 7 108. 8 103. 9 204.

10 162. 11 107. 12 199.

AvrWind AvrTemp 6.08 5.58 7.83 5.67 7.01 7.52 5.46 8.39

6.12 12.1 6.19 13.8 5.23 14.6 5.24 13.9 5.85 12 6.76 11.0 5.26 6.22 6.04 4.94 8.78 8.03 11.6 8.26 10.9 9.36 7.11 9.60 7.83 12.6

10.0 14.1 7.90 14.8 8.57 14.8 9.54 13.4

10.3 12.7 9.25 9.66 11.0 8.26

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10. Generate the following plot of rainfall for each month.

11. Generate the following plot of temperature for each month.

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12. Generate the following plot of windspeed for each month.

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