data-raw/Examples/03 Rainfall Summary/README.md
In JimDuggan/aimsir17: Irish Weather Observing Stations Hourly Records for 2017
Example 3 - Summarising Rainfall Data
R tools such as dplyr and tidyr can be used to summarise data
(e.g. add rain observations to obtain monthly and annual cumulative
amounts). The three libraries are first loaded.
library(aimsir17)
library(dplyr)
library(tidyr)
Next, we show the overall data set (219,000 observations)
observations
## # A tibble: 219,000 x 12
## station year month day hour date rain temp rhum
## <chr> <dbl> <dbl> <int> <int> <dttm> <dbl> <dbl> <dbl>
## 1 ATHENRY 2017 1 1 0 2017-01-01 00:00:00 0 5.2 89
## 2 ATHENRY 2017 1 1 1 2017-01-01 01:00:00 0 4.7 89
## 3 ATHENRY 2017 1 1 2 2017-01-01 02:00:00 0 4.2 90
## 4 ATHENRY 2017 1 1 3 2017-01-01 03:00:00 0.1 3.5 87
## 5 ATHENRY 2017 1 1 4 2017-01-01 04:00:00 0.1 3.2 89
## 6 ATHENRY 2017 1 1 5 2017-01-01 05:00:00 0 2.1 91
## 7 ATHENRY 2017 1 1 6 2017-01-01 06:00:00 0 2 89
## 8 ATHENRY 2017 1 1 7 2017-01-01 07:00:00 0 1.7 89
## 9 ATHENRY 2017 1 1 8 2017-01-01 08:00:00 0 1 91
## 10 ATHENRY 2017 1 1 9 2017-01-01 09:00:00 0 1.1 91
## # … with 218,990 more rows, and 3 more variables: msl <dbl>, wdsp <dbl>,
## # wddir <dbl>
We can confirm the number of records gathered for each month, and show
these
observations %>%
group_by(station, month) %>%
summarise(TotalObservations=n()) %>%
pivot_wider(names_from = month,values_from = TotalObservations) %>%
print(n=25)
## # A tibble: 25 x 13
## # Groups: station [25]
## station `1` `2` `3` `4` `5` `6` `7` `8` `9` `10`
## <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 ATHENRY 744 672 744 720 744 720 744 744 720 744
## 2 BALLYH… 744 672 744 720 744 720 744 744 720 744
## 3 BELMUL… 744 672 744 720 744 720 744 744 720 744
## 4 CASEME… 744 672 744 720 744 720 744 744 720 744
## 5 CLAREM… 744 672 744 720 744 720 744 744 720 744
## 6 CORK A… 744 672 744 720 744 720 744 744 720 744
## 7 DUBLIN… 744 672 744 720 744 720 744 744 720 744
## 8 DUNSANY 744 672 744 720 744 720 744 744 720 744
## 9 FINNER 744 672 744 720 744 720 744 744 720 744
## 10 GURTEEN 744 672 744 720 744 720 744 744 720 744
## 11 JOHNST… 744 672 744 720 744 720 744 744 720 744
## 12 KNOCK … 744 672 744 720 744 720 744 744 720 744
## 13 MACE H… 744 672 744 720 744 720 744 744 720 744
## 14 MALIN … 744 672 744 720 744 720 744 744 720 744
## 15 MARKREE 744 672 744 720 744 720 744 744 720 744
## 16 MOORE … 744 672 744 720 744 720 744 744 720 744
## 17 MT DIL… 744 672 744 720 744 720 744 744 720 744
## 18 MULLIN… 744 672 744 720 744 720 744 744 720 744
## 19 NEWPORT 744 672 744 720 744 720 744 744 720 744
## 20 OAK PA… 744 672 744 720 744 720 744 744 720 744
## 21 PHOENI… 744 672 744 720 744 720 744 744 720 744
## 22 ROCHES… 744 672 744 720 744 720 744 744 720 744
## 23 SHANNO… 744 672 744 720 744 720 744 744 720 744
## 24 Sherki… 744 672 744 720 744 720 744 744 720 744
## 25 VALENT… 744 672 744 720 744 720 744 744 720 744
## # … with 2 more variables: `11` <int>, `12` <int>
We can also confirm the number of missing rainfall values for each
month.
observations %>%
group_by(station, month) %>%
summarise(TotalMissing=sum(is.na(rain))) %>%
pivot_wider(names_from = month,values_from = TotalMissing) %>%
print(n=25)
## # A tibble: 25 x 13
## # Groups: station [25]
## station `1` `2` `3` `4` `5` `6` `7` `8` `9` `10`
## <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 ATHENRY 0 0 0 0 0 0 0 0 0 0
## 2 BALLYH… 0 0 0 0 0 0 0 0 0 1
## 3 BELMUL… 0 0 0 0 0 0 0 0 0 0
## 4 CASEME… 0 0 0 0 0 0 0 0 0 0
## 5 CLAREM… 0 0 0 0 0 0 23 0 0 0
## 6 CORK A… 0 0 0 0 0 0 0 0 0 0
## 7 DUBLIN… 0 0 0 0 0 0 0 0 0 0
## 8 DUNSANY 0 0 0 0 0 0 0 0 0 0
## 9 FINNER 0 0 0 0 0 0 0 0 0 0
## 10 GURTEEN 0 0 0 0 0 0 0 1 0 0
## 11 JOHNST… 0 2 0 0 0 0 1 0 0 0
## 12 KNOCK … 0 0 0 0 0 0 0 0 0 0
## 13 MACE H… 0 0 0 0 0 0 0 0 0 0
## 14 MALIN … 0 0 0 0 0 0 0 0 0 0
## 15 MARKREE 0 0 0 0 2 0 9 0 1 0
## 16 MOORE … 0 0 0 0 0 0 0 0 0 0
## 17 MT DIL… 2 0 0 0 0 0 0 0 0 0
## 18 MULLIN… 0 0 0 0 0 0 0 3 0 0
## 19 NEWPORT 0 0 0 0 0 0 0 15 0 0
## 20 OAK PA… 0 0 0 0 0 0 0 0 2 0
## 21 PHOENI… 0 0 0 2 0 0 0 0 0 0
## 22 ROCHES… 0 0 0 0 0 0 0 0 0 0
## 23 SHANNO… 0 0 0 0 0 0 0 0 0 0
## 24 Sherki… 0 0 0 0 0 4 0 0 0 2
## 25 VALENT… 0 0 0 0 0 0 0 0 0 0
## # … with 2 more variables: `11` <int>, `12` <int>
We can sum all the rainfall values for each station by each month
observations %>%
group_by(station, month) %>%
summarise(TotalRainfall=sum(rain,na.rm = T)) %>%
pivot_wider(names_from = month,values_from = TotalRainfall) %>%
print(n=25)
## # A tibble: 25 x 13
## # Groups: station [25]
## station `1` `2` `3` `4` `5` `6` `7` `8` `9` `10`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ATHENRY 47.4 87.5 143. 13.5 61.4 120. 137. 103. 118. 123.
## 2 BALLYH… 32.8 72.6 80.3 16.2 54.5 84.2 109. 95.9 108. 95.8
## 3 BELMUL… 60 111. 109. 25.2 48.6 98.6 98.7 134. 139. 132.
## 4 CASEME… 27.6 63.6 65.9 8.8 67.1 91.8 42.9 65.4 70.5 57.2
## 5 CLAREM… 50.6 82.9 136. 16.8 76.7 95.3 97.2 107. 144. 115.
## 6 CORK A… 110. 113. 115. 24.7 60.6 109 60.2 72.2 169. 128
## 7 DUBLIN… 22.8 41.6 67.2 10 43.5 86.4 42.2 73.2 82.3 47.8
## 8 DUNSANY 35.8 52.9 92.2 14.5 66.9 97.9 72.3 61.1 102. 73.9
## 9 FINNER 54.4 63.4 141. 25.7 69.9 63 105. 164. 148. 141.
## 10 GURTEEN 41.5 62.9 98.7 16.9 81.5 92.5 58.7 96 104. 111
## 11 JOHNST… 63.9 69.5 78.8 17.5 67.6 125. 60.1 75.2 161. 64.8
## 12 KNOCK … 55.5 87.5 135 23.6 65.4 113 134. 144. 170. 122.
## 13 MACE H… 58.2 97.1 111. 19.4 44.3 100. 93 104. 111 122.
## 14 MALIN … 56.7 85 97 41.1 50.9 77.7 115. 172. 88.2 110.
## 15 MARKREE 51.4 87.7 102. 30.8 69.4 83.6 99.5 134. 174. 103.
## 16 MOORE … 87 108. 116. 19.3 72.4 93.3 54.1 72.3 116. 102.
## 17 MT DIL… 31.9 84.5 103. 15.9 63.8 72.9 94 103. 105. 108.
## 18 MULLIN… 41.6 75 83.8 14.2 70.7 98.1 90.4 77 113. 104.
## 19 NEWPORT 94.9 151 216. 31.2 64.2 108. 164 196 153. 191.
## 20 OAK PA… 38.6 57.8 66.6 15.8 81.8 91 52.7 62.3 91.4 62.9
## 21 PHOENI… 25.2 62 75.3 11.4 47.7 95.6 52.8 80.5 87.2 51.3
## 22 ROCHES… 87.3 73.6 121. 9.2 53.8 104. 50.9 78.6 142. 126.
## 23 SHANNO… 54.1 71.1 134. 17.9 45.2 86.5 134. 82.7 99.1 80.7
## 24 Sherki… 67.6 78.5 133. 14.6 39.2 109. 89.9 78.6 151. 112.
## 25 VALENT… 169. 139. 135. 51.4 76.3 143 108. 103. 204. 162.
## # … with 2 more variables: `11` <dbl>, `12` <dbl>
Order the months from driest to wettest
observations %>%
group_by(month) %>%
summarise(TotalRainfall=sum(rain,na.rm = T)) %>%
arrange(TotalRainfall) %>%
print(n=12)
## # A tibble: 12 x 2
## month TotalRainfall
## <dbl> <dbl>
## 1 4 506.
## 2 1 1466.
## 3 5 1543.
## 4 2 2079.
## 5 7 2216.
## 6 11 2409
## 7 6 2440.
## 8 8 2536
## 9 10 2648
## 10 3 2754.
## 11 12 3098.
## 12 9 3151.
Finally, order the weather stations from driest to wettest, with an
index value where 100 is the wettest for 2017 (Newport!)
observations %>%
group_by(station) %>%
summarise(TotalRainfall=sum(rain,na.rm = T)) %>%
arrange(TotalRainfall) %>%
mutate(Index=100*TotalRainfall/max(TotalRainfall)) %>%
print(n=25)
## # A tibble: 25 x 3
## station TotalRainfall Index
## <chr> <dbl> <dbl>
## 1 DUBLIN AIRPORT 662. 37.8
## 2 CASEMENT 705. 40.2
## 3 PHOENIX PARK 732 41.8
## 4 OAK PARK 759. 43.3
## 5 DUNSANY 810. 46.2
## 6 BALLYHAISE 952. 54.4
## 7 MULLINGAR 952. 54.4
## 8 JOHNSTOWNII 963 55.0
## 9 GURTEEN 983. 56.1
## 10 MT DILLON 992. 56.6
## 11 ROCHES POINT 1013. 57.8
## 12 MOORE PARK 1016. 58.0
## 13 SHANNON AIRPORT 1069. 61.0
## 14 SherkinIsland 1072. 61.2
## 15 MACE HEAD 1114. 63.6
## 16 MALIN HEAD 1147. 65.5
## 17 CORK AIRPORT 1162. 66.3
## 18 MARKREE 1182. 67.5
## 19 ATHENRY 1199. 68.5
## 20 CLAREMORRIS 1204 68.7
## 21 FINNER 1222. 69.7
## 22 BELMULLET 1243. 71.0
## 23 KNOCK AIRPORT 1343. 76.7
## 24 VALENTIA OBSERVATORY 1598. 91.2
## 25 NEWPORT 1752. 100
JimDuggan/aimsir17 documentation built on Aug. 22, 2020, 9:46 p.m.
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Example 3 - Summarising Rainfall Data
R tools such as dplyr and tidyr can be used to summarise data (e.g. add rain observations to obtain monthly and annual cumulative amounts). The three libraries are first loaded.
library(aimsir17)
library(dplyr)
library(tidyr)
Next, we show the overall data set (219,000 observations)
observations
## # A tibble: 219,000 x 12
## station year month day hour date rain temp rhum
## <chr> <dbl> <dbl> <int> <int> <dttm> <dbl> <dbl> <dbl>
## 1 ATHENRY 2017 1 1 0 2017-01-01 00:00:00 0 5.2 89
## 2 ATHENRY 2017 1 1 1 2017-01-01 01:00:00 0 4.7 89
## 3 ATHENRY 2017 1 1 2 2017-01-01 02:00:00 0 4.2 90
## 4 ATHENRY 2017 1 1 3 2017-01-01 03:00:00 0.1 3.5 87
## 5 ATHENRY 2017 1 1 4 2017-01-01 04:00:00 0.1 3.2 89
## 6 ATHENRY 2017 1 1 5 2017-01-01 05:00:00 0 2.1 91
## 7 ATHENRY 2017 1 1 6 2017-01-01 06:00:00 0 2 89
## 8 ATHENRY 2017 1 1 7 2017-01-01 07:00:00 0 1.7 89
## 9 ATHENRY 2017 1 1 8 2017-01-01 08:00:00 0 1 91
## 10 ATHENRY 2017 1 1 9 2017-01-01 09:00:00 0 1.1 91
## # … with 218,990 more rows, and 3 more variables: msl <dbl>, wdsp <dbl>,
## # wddir <dbl>
We can confirm the number of records gathered for each month, and show these
observations %>%
group_by(station, month) %>%
summarise(TotalObservations=n()) %>%
pivot_wider(names_from = month,values_from = TotalObservations) %>%
print(n=25)
## # A tibble: 25 x 13
## # Groups: station [25]
## station `1` `2` `3` `4` `5` `6` `7` `8` `9` `10`
## <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 ATHENRY 744 672 744 720 744 720 744 744 720 744
## 2 BALLYH… 744 672 744 720 744 720 744 744 720 744
## 3 BELMUL… 744 672 744 720 744 720 744 744 720 744
## 4 CASEME… 744 672 744 720 744 720 744 744 720 744
## 5 CLAREM… 744 672 744 720 744 720 744 744 720 744
## 6 CORK A… 744 672 744 720 744 720 744 744 720 744
## 7 DUBLIN… 744 672 744 720 744 720 744 744 720 744
## 8 DUNSANY 744 672 744 720 744 720 744 744 720 744
## 9 FINNER 744 672 744 720 744 720 744 744 720 744
## 10 GURTEEN 744 672 744 720 744 720 744 744 720 744
## 11 JOHNST… 744 672 744 720 744 720 744 744 720 744
## 12 KNOCK … 744 672 744 720 744 720 744 744 720 744
## 13 MACE H… 744 672 744 720 744 720 744 744 720 744
## 14 MALIN … 744 672 744 720 744 720 744 744 720 744
## 15 MARKREE 744 672 744 720 744 720 744 744 720 744
## 16 MOORE … 744 672 744 720 744 720 744 744 720 744
## 17 MT DIL… 744 672 744 720 744 720 744 744 720 744
## 18 MULLIN… 744 672 744 720 744 720 744 744 720 744
## 19 NEWPORT 744 672 744 720 744 720 744 744 720 744
## 20 OAK PA… 744 672 744 720 744 720 744 744 720 744
## 21 PHOENI… 744 672 744 720 744 720 744 744 720 744
## 22 ROCHES… 744 672 744 720 744 720 744 744 720 744
## 23 SHANNO… 744 672 744 720 744 720 744 744 720 744
## 24 Sherki… 744 672 744 720 744 720 744 744 720 744
## 25 VALENT… 744 672 744 720 744 720 744 744 720 744
## # … with 2 more variables: `11` <int>, `12` <int>
We can also confirm the number of missing rainfall values for each month.
observations %>%
group_by(station, month) %>%
summarise(TotalMissing=sum(is.na(rain))) %>%
pivot_wider(names_from = month,values_from = TotalMissing) %>%
print(n=25)
## # A tibble: 25 x 13
## # Groups: station [25]
## station `1` `2` `3` `4` `5` `6` `7` `8` `9` `10`
## <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 ATHENRY 0 0 0 0 0 0 0 0 0 0
## 2 BALLYH… 0 0 0 0 0 0 0 0 0 1
## 3 BELMUL… 0 0 0 0 0 0 0 0 0 0
## 4 CASEME… 0 0 0 0 0 0 0 0 0 0
## 5 CLAREM… 0 0 0 0 0 0 23 0 0 0
## 6 CORK A… 0 0 0 0 0 0 0 0 0 0
## 7 DUBLIN… 0 0 0 0 0 0 0 0 0 0
## 8 DUNSANY 0 0 0 0 0 0 0 0 0 0
## 9 FINNER 0 0 0 0 0 0 0 0 0 0
## 10 GURTEEN 0 0 0 0 0 0 0 1 0 0
## 11 JOHNST… 0 2 0 0 0 0 1 0 0 0
## 12 KNOCK … 0 0 0 0 0 0 0 0 0 0
## 13 MACE H… 0 0 0 0 0 0 0 0 0 0
## 14 MALIN … 0 0 0 0 0 0 0 0 0 0
## 15 MARKREE 0 0 0 0 2 0 9 0 1 0
## 16 MOORE … 0 0 0 0 0 0 0 0 0 0
## 17 MT DIL… 2 0 0 0 0 0 0 0 0 0
## 18 MULLIN… 0 0 0 0 0 0 0 3 0 0
## 19 NEWPORT 0 0 0 0 0 0 0 15 0 0
## 20 OAK PA… 0 0 0 0 0 0 0 0 2 0
## 21 PHOENI… 0 0 0 2 0 0 0 0 0 0
## 22 ROCHES… 0 0 0 0 0 0 0 0 0 0
## 23 SHANNO… 0 0 0 0 0 0 0 0 0 0
## 24 Sherki… 0 0 0 0 0 4 0 0 0 2
## 25 VALENT… 0 0 0 0 0 0 0 0 0 0
## # … with 2 more variables: `11` <int>, `12` <int>
We can sum all the rainfall values for each station by each month
observations %>%
group_by(station, month) %>%
summarise(TotalRainfall=sum(rain,na.rm = T)) %>%
pivot_wider(names_from = month,values_from = TotalRainfall) %>%
print(n=25)
## # A tibble: 25 x 13
## # Groups: station [25]
## station `1` `2` `3` `4` `5` `6` `7` `8` `9` `10`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ATHENRY 47.4 87.5 143. 13.5 61.4 120. 137. 103. 118. 123.
## 2 BALLYH… 32.8 72.6 80.3 16.2 54.5 84.2 109. 95.9 108. 95.8
## 3 BELMUL… 60 111. 109. 25.2 48.6 98.6 98.7 134. 139. 132.
## 4 CASEME… 27.6 63.6 65.9 8.8 67.1 91.8 42.9 65.4 70.5 57.2
## 5 CLAREM… 50.6 82.9 136. 16.8 76.7 95.3 97.2 107. 144. 115.
## 6 CORK A… 110. 113. 115. 24.7 60.6 109 60.2 72.2 169. 128
## 7 DUBLIN… 22.8 41.6 67.2 10 43.5 86.4 42.2 73.2 82.3 47.8
## 8 DUNSANY 35.8 52.9 92.2 14.5 66.9 97.9 72.3 61.1 102. 73.9
## 9 FINNER 54.4 63.4 141. 25.7 69.9 63 105. 164. 148. 141.
## 10 GURTEEN 41.5 62.9 98.7 16.9 81.5 92.5 58.7 96 104. 111
## 11 JOHNST… 63.9 69.5 78.8 17.5 67.6 125. 60.1 75.2 161. 64.8
## 12 KNOCK … 55.5 87.5 135 23.6 65.4 113 134. 144. 170. 122.
## 13 MACE H… 58.2 97.1 111. 19.4 44.3 100. 93 104. 111 122.
## 14 MALIN … 56.7 85 97 41.1 50.9 77.7 115. 172. 88.2 110.
## 15 MARKREE 51.4 87.7 102. 30.8 69.4 83.6 99.5 134. 174. 103.
## 16 MOORE … 87 108. 116. 19.3 72.4 93.3 54.1 72.3 116. 102.
## 17 MT DIL… 31.9 84.5 103. 15.9 63.8 72.9 94 103. 105. 108.
## 18 MULLIN… 41.6 75 83.8 14.2 70.7 98.1 90.4 77 113. 104.
## 19 NEWPORT 94.9 151 216. 31.2 64.2 108. 164 196 153. 191.
## 20 OAK PA… 38.6 57.8 66.6 15.8 81.8 91 52.7 62.3 91.4 62.9
## 21 PHOENI… 25.2 62 75.3 11.4 47.7 95.6 52.8 80.5 87.2 51.3
## 22 ROCHES… 87.3 73.6 121. 9.2 53.8 104. 50.9 78.6 142. 126.
## 23 SHANNO… 54.1 71.1 134. 17.9 45.2 86.5 134. 82.7 99.1 80.7
## 24 Sherki… 67.6 78.5 133. 14.6 39.2 109. 89.9 78.6 151. 112.
## 25 VALENT… 169. 139. 135. 51.4 76.3 143 108. 103. 204. 162.
## # … with 2 more variables: `11` <dbl>, `12` <dbl>
Order the months from driest to wettest
observations %>%
group_by(month) %>%
summarise(TotalRainfall=sum(rain,na.rm = T)) %>%
arrange(TotalRainfall) %>%
print(n=12)
## # A tibble: 12 x 2
## month TotalRainfall
## <dbl> <dbl>
## 1 4 506.
## 2 1 1466.
## 3 5 1543.
## 4 2 2079.
## 5 7 2216.
## 6 11 2409
## 7 6 2440.
## 8 8 2536
## 9 10 2648
## 10 3 2754.
## 11 12 3098.
## 12 9 3151.
Finally, order the weather stations from driest to wettest, with an index value where 100 is the wettest for 2017 (Newport!)
observations %>%
group_by(station) %>%
summarise(TotalRainfall=sum(rain,na.rm = T)) %>%
arrange(TotalRainfall) %>%
mutate(Index=100*TotalRainfall/max(TotalRainfall)) %>%
print(n=25)
## # A tibble: 25 x 3
## station TotalRainfall Index
## <chr> <dbl> <dbl>
## 1 DUBLIN AIRPORT 662. 37.8
## 2 CASEMENT 705. 40.2
## 3 PHOENIX PARK 732 41.8
## 4 OAK PARK 759. 43.3
## 5 DUNSANY 810. 46.2
## 6 BALLYHAISE 952. 54.4
## 7 MULLINGAR 952. 54.4
## 8 JOHNSTOWNII 963 55.0
## 9 GURTEEN 983. 56.1
## 10 MT DILLON 992. 56.6
## 11 ROCHES POINT 1013. 57.8
## 12 MOORE PARK 1016. 58.0
## 13 SHANNON AIRPORT 1069. 61.0
## 14 SherkinIsland 1072. 61.2
## 15 MACE HEAD 1114. 63.6
## 16 MALIN HEAD 1147. 65.5
## 17 CORK AIRPORT 1162. 66.3
## 18 MARKREE 1182. 67.5
## 19 ATHENRY 1199. 68.5
## 20 CLAREMORRIS 1204 68.7
## 21 FINNER 1222. 69.7
## 22 BELMULLET 1243. 71.0
## 23 KNOCK AIRPORT 1343. 76.7
## 24 VALENTIA OBSERVATORY 1598. 91.2
## 25 NEWPORT 1752. 100
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