Creating a four way dygraph of ireland weather staions through R language
1. Introduction
1.1 R language and environment for statistical computing and graphics
R language is widely used across a variety of disciplines including environmental detection, such as, flooding, rainfall, and other extreme events. R comes with many useful packages that are equipped with analysis, visualization, detection, and include model forecasting. One of the most important elements of the R statistical programme is the well-designed publication, Quarto, which provides an aesthetic presentation of journals and is reproducible. Moreover, researchers can embed their produced R code and visualization effects such as graphs through dygraph, plots through GGplot, and maps through the TMap package, enhancing in an impactful way the appearance of the journal. In this blog, rainfall datasets of Dublin, Belfast, Cork airport and University College Galway are used as an example of how to create rainfall dygrapghs and publish them through Rpub via R programme.
2. Data and method
2.1 Data
RainfallR.data is used and consists of 2 variables, weather stations and rain data. Four weather stations in Dublin, Belfast, Cork airport and University College Galway are selected for analysis along with rainfall data from the period of 1850-2014. Total rainfall data to be used for analysis and plotting dygraph is from a total of 600 months from all four weather stations. R packages, dplyr, tidyverse,sp, and rcolourbrewer; Dplyr is part of tidyverse package and aided with data manipulation and transformation through filtering, selecting and grouping along with pipe operator which acts as a conveyor belt to pass one function to another, making exhaustive data easy to manage and organise in a concise format.
2.2 Method
2.2.1. step one; time series graph with interactive window and rolling mean through dyRange selector.
The timeseries created start with the year 1850 and break down into monthly rainfall totals of twelve months through pull function. Timeframe was rechecked again to ensure data is corrected through pipe operator between rain_ts and window, with results beginning in 1850 for rainfall data and ending in 2014. A total of 1980 months, or 165 years, for all four stations. To create a time series graph, the dygraph package is downloaded. Instruct command rain time series pipe operator with dygraph, add window command with start year 1850 and end year 2014 with 12 months frequency, add value of height and width for a better visualization. Subsequently, use pipe operator command to chain rain timeseries with dygraph and with dyranegselector, the result is a pop-up window timescale at the bottom of the graph that the user can slide a cursor over and enlarge the time series in the graph. Lastly, add pipe operator to dyroller with a rollperiod of the total 1980 months to create roller mean, and connect to previous three functions.
2.2.2 step two; create four-way digraph with rolling mean
To create four-way dygraph, first, assign variables to values, and in this case the variable is Dublin weather station, and the value is rainfall data. Add pipe operator command to filter command to select the weather station which is Dublin airport and chain with group_by (year, month). Connect previous function through pipe operator to get a summary of rainfall sum by month, and discard NA values with na.rm. equal true, remove grouping after summarization to get the single result. Use pull command to extract rainfall column and turn it into a vector act as an input. Then connect previous functions with pipe operator again to time series, start year is 1850 with 12-month frequency. TS function is used to convert rainfall vector into time series object. Repeat the same steps for the remaining three weather stations.
However, as R programme is regularly updated and some packages may be out of date, also new packages emerge which have similar functions of chosen package, conflicts can arise. To avoid and reduce any conflict that may occur, filter and summarise command are used to ensure chosen package is a primary choice. For time efficiency and to minimize unforeseen problems, use function code to instruct R what we would like R to do, and in this case, repeat the previous queries and apply to each selected weather station. The final step is a combined time series for all four weather stations, using cbind command. As previously stated, to avoid unforeseen problems that may arise, ls() function to ensure all listed objects that we created are in the R environment. After checking, combine all timeseries of four stations by using dygraph which creates the graph consisting of all four weather stations monthly rainfall of period given above, including dyrangeselector.
3. Results
From the results, Cork and Galway Counties received more rainfall than Dublin and Belfast. It could be interpreted as Cork and Galway are geographically located along the coast line which is dominated by ocean along with numerous mountainous areas that enhance cloud formation to produce latent heat such as rain , along with wind direction where the dominant wind is a south-westerly wind. Once warm moist air from south-westerly direction encounter moist warm westerly wind from the Atlantic ocean, this could develop the formation of rain. While the east coast , such as Belfast and Dublin, are located along the Irish sea and the UK mainland, have a comparatively low potential for rain to develop due to the land-sea-air interaction.
4. Conclusion
R programme is very useful and time efficient method of analysis and visualization through the production of graphs, in a meaningful format. However, it may be complicated at times for inexperienced users to understand computer code and its functions along with understanding the varieties of packages to choose from. Nevertheless, the benefits in the form of data analysis and representation outweigh any negatives and of course use of the program becomes easier with experience. Overall R is a useful and powerful tool, providing valuable data in an easily understandable format.
# Example code to create the beldubcorkgalway_ts objectbeldubcorkgalway_ts <-data.frame(Year =1850:2014,Rainfall =runif(1980, min =0, max =1980) )# Convert to time seriesbeldubcorkgalway_ts <-ts(beldubcorkgalway_ts$Rainfall, start =c(1850, 1), frequency =12)
# Install conflicted package if not already installedif (!requireNamespace("conflicted", quietly =TRUE)) {install.packages("conflicted")}# Load conflicted packagelibrary(conflicted)
Warning: package 'conflicted' was built under R version 4.4.2
conflicts_prefer(dplyr::filter)
[conflicted] Will prefer dplyr::filter over any other package.
conflicts_prefer(dplyr::summarise)
[conflicted] Will prefer dplyr::summarise over any other package.
library(dplyr)# Ensure you're using dplyr's functions and avoid conflictsconflicts_prefer(dplyr::filter)
[conflicted] Removing existing preference.
[conflicted] Will prefer dplyr::filter over any other package.
conflicts_prefer(dplyr::summarise)
[conflicted] Removing existing preference.
[conflicted] Will prefer dplyr::summarise over any other package.
# Process data for Dublin Airportdub_ts <- rain %>%filter(Station =="Dublin Airport") %>%# Filter data for Dublin Airportgroup_by(Year, Month) %>%# Group by Year and Monthsummarise(Rainfall =sum(Rainfall, na.rm =TRUE), .groups ="drop") %>%# Sum Rainfallpull(Rainfall) %>%# Extract the Rainfall values as a vectorts(start =c(1850, 1), freq =12) # Convert to time series# View the resultdub_ts
library(dplyr)# Define a function to process the data for each stationprocess_station_data <-function(station_name) { rain %>%filter(Station == station_name) %>%# Filter data for the stationgroup_by(Year, Month) %>%# Group by Year and Monthsummarise(Rainfall =sum(Rainfall, na.rm =TRUE), .groups ="drop") %>%# Sum Rainfallpull(Rainfall) %>%# Extract the Rainfall values as a vectorts(start =c(1850, 1), freq =12) # Convert to time series}# Apply the function to each stationdub_ts <-process_station_data("Dublin Airport")bel_ts <-process_station_data("Belfast")cork_ts <-process_station_data("Cork Airport")galway_ts <-process_station_data("University College Galway")# Create time series for each station using the previously defined functiondub_ts <-process_station_data("Dublin Airport")bel_ts <-process_station_data("Belfast")cork_ts <-process_station_data("Cork Airport")galway_ts <-process_station_data("University College Galway")# Combine the time series for all stationscombined_ts <-cbind(dub_ts, bel_ts, cork_ts, galway_ts)# Now you can refer to the combined time series correctlycombined_ts
dub_ts bel_ts cork_ts galway_ts
Jan 1850 75.8 115.700000 155.3 108.90000
Feb 1850 47.8 120.500000 92.6 131.50000
Mar 1850 18.5 56.800000 56.0 56.60000
Apr 1850 97.5 142.600000 207.2 120.50000
May 1850 58.6 57.900000 35.3 69.80000
Jun 1850 43.6 62.000000 11.4 74.70000
Jul 1850 66.0 96.300000 179.0 89.10000
Aug 1850 41.2 110.400000 46.5 136.80000
Sep 1850 54.2 65.800000 40.7 85.20000
Oct 1850 40.4 87.600000 53.8 90.70000
Nov 1850 60.0 104.400000 153.2 131.30000
Dec 1850 81.1 57.600000 169.4 90.60000
Jan 1851 112.0 156.400000 359.5 163.80000
Feb 1851 26.2 75.500000 47.1 92.80000
Mar 1851 63.1 43.500000 78.8 86.10000
Apr 1851 44.4 62.500000 39.0 69.30000
May 1851 29.3 34.600000 56.3 42.10000
Jun 1851 78.5 94.700000 104.7 114.20000
Jul 1851 70.5 133.700000 64.5 107.40000
Aug 1851 55.2 133.700000 57.5 155.90000
Sep 1851 24.6 61.000000 37.2 66.70000
Oct 1851 88.0 144.100000 88.7 141.30000
Nov 1851 27.5 40.400000 20.5 87.10000
Dec 1851 23.5 37.400000 98.6 71.40000
Jan 1852 80.3 157.200000 216.2 174.90000
Feb 1852 70.5 143.100000 43.5 136.50000
Mar 1852 18.2 33.300000 84.2 44.40000
Apr 1852 30.4 80.600000 45.7 55.70000
May 1852 93.0 53.500000 68.4 53.60000
Jun 1852 162.2 151.300000 145.6 167.70000
Jul 1852 56.0 68.100000 53.0 91.30000
Aug 1852 97.2 107.500000 94.7 130.20000
Sep 1852 78.2 34.600000 53.5 52.80000
Oct 1852 78.7 48.900000 121.9 75.80000
Nov 1852 156.1 142.600000 241.2 195.30000
Dec 1852 97.2 156.100000 209.6 216.70000
Jan 1853 74.7 107.200000 191.3 152.80000
Feb 1853 52.3 44.600000 15.0 52.50000
Mar 1853 71.7 75.500000 51.5 104.70000
Apr 1853 44.8 65.500000 93.6 84.20000
May 1853 22.7 17.600000 63.2 29.40000
Jun 1853 58.9 68.100000 68.8 78.20000
Jul 1853 49.9 119.000000 90.0 114.40000
Aug 1853 81.3 74.800000 85.3 93.80000
Sep 1853 35.5 29.200000 83.8 64.30000
Oct 1853 123.7 127.500000 252.1 149.10000
Nov 1853 40.3 64.000000 171.9 104.10000
Dec 1853 52.3 32.600000 56.4 52.40000
Jan 1854 101.1 116.200000 157.0 133.10000
Feb 1854 15.7 44.000000 16.4 53.80000
Mar 1854 18.4 44.200000 35.9 57.70000
Apr 1854 15.7 13.300000 3.7 24.20000
May 1854 69.3 58.900000 97.7 117.20000
Jun 1854 93.6 92.100000 108.7 104.00000
Jul 1854 58.1 86.300000 118.5 91.70000
Aug 1854 29.1 99.600000 47.1 87.70000
Sep 1854 33.1 49.700000 42.8 67.30000
Oct 1854 45.9 78.600000 48.3 84.00000
Nov 1854 76.8 94.700000 23.7 109.70000
Dec 1854 51.2 142.800000 40.3 153.00000
Jan 1855 11.3 16.100000 9.9 33.90000
Feb 1855 101.2 50.400000 88.6 55.50000
Mar 1855 38.6 71.500000 118.5 100.00000
Apr 1855 12.6 49.400000 43.2 48.90000
May 1855 42.1 75.500000 104.7 74.20000
Jun 1855 52.1 47.900000 64.4 71.60000
Jul 1855 81.2 59.400000 33.3 89.00000
Aug 1855 65.5 93.200000 91.5 115.30000
Sep 1855 41.9 44.200000 56.7 56.70000
Oct 1855 113.8 103.200000 36.9 111.00000
Nov 1855 43.1 26.400000 75.7 50.60000
Dec 1855 32.6 59.900000 137.5 91.30000
Jan 1856 58.4 72.500000 174.9 111.90000
Feb 1856 36.5 62.700000 138.6 77.10000
Mar 1856 9.2 30.200000 53.5 44.60000
Apr 1856 41.8 62.700000 104.7 81.40000
May 1856 121.2 97.600000 123.7 125.70000
Jun 1856 62.8 63.000000 34.0 86.00000
Jul 1856 47.3 44.200000 24.9 62.10000
Aug 1856 72.3 86.300000 97.3 101.50000
Sep 1856 83.9 111.900000 78.7 92.20000
Oct 1856 32.6 52.000000 130.2 72.40000
Nov 1856 17.1 63.500000 27.5 70.30000
Dec 1856 51.5 106.000000 199.7 124.90000
Jan 1857 51.5 119.300000 116.0 123.40000
Feb 1857 29.2 63.700000 44.7 70.10000
Mar 1857 50.5 126.500000 115.6 105.50000
Apr 1857 86.8 99.100000 149.7 129.30000
May 1857 34.7 35.600000 92.6 46.70000
Jun 1857 87.8 35.600000 83.4 88.30000
Jul 1857 51.8 53.000000 61.4 55.10000
Aug 1857 40.2 61.200000 82.4 72.80000
Sep 1857 33.1 71.500000 134.6 109.80000
Oct 1857 58.1 46.600000 108.3 72.10000
Nov 1857 51.3 57.600000 107.6 92.40000
Dec 1857 13.7 48.900000 83.8 79.00000
Jan 1858 31.8 79.300000 82.4 86.80000
Feb 1858 15.0 44.600000 217.0 59.10000
Mar 1858 33.1 67.800000 12.5 76.30000
Apr 1858 112.8 99.100000 191.0 113.40000
May 1858 35.5 132.100000 87.1 102.60000
Jun 1858 40.2 46.400000 56.4 53.30000
Jul 1858 87.8 107.200000 68.8 93.20000
Aug 1858 33.9 76.300000 79.4 87.60000
Sep 1858 66.8 92.400000 116.0 96.90000
Oct 1858 79.9 86.300000 71.0 100.00000
Nov 1858 24.7 37.400000 116.7 58.50000
Dec 1858 50.5 134.200000 226.4 168.10000
Jan 1859 38.6 92.100000 75.7 110.80000
Feb 1859 13.9 77.600000 72.8 73.60000
Mar 1859 55.5 86.600000 77.5 114.60000
Apr 1859 99.4 82.700000 143.0 103.40000
May 1859 15.8 5.800000 27.8 17.70000
Jun 1859 36.5 24.300000 41.0 52.40000
Jul 1859 29.7 51.500000 74.6 76.90000
Aug 1859 36.8 86.300000 109.4 97.80000
Sep 1859 66.0 117.200000 134.6 153.70000
Oct 1859 34.2 78.600000 146.0 73.40000
Nov 1859 73.9 111.400000 173.4 127.80000
Dec 1859 67.6 78.800000 217.3 126.60000
Jan 1860 91.5 199.700000 197.2 184.00000
Feb 1860 15.8 77.800000 49.7 80.10000
Mar 1860 54.9 97.100000 92.9 118.00000
Apr 1860 54.4 64.000000 38.4 71.60000
May 1860 79.4 82.100000 121.4 113.30000
Jun 1860 109.6 178.900000 162.8 163.20000
Jul 1860 65.2 71.500000 38.8 80.30000
Aug 1860 107.5 142.600000 105.0 180.10000
Sep 1860 59.7 37.400000 60.8 54.90000
Oct 1860 51.8 86.000000 47.1 94.80000
Nov 1860 70.5 53.000000 103.5 90.00000
Dec 1860 58.1 91.900000 149.7 92.80000
Jan 1861 44.7 75.800000 133.6 78.50000
Feb 1861 68.4 58.200000 187.7 110.90000
Mar 1861 81.8 131.800000 72.8 166.90000
Apr 1861 36.5 25.300000 65.2 29.60000
May 1861 15.2 8.700000 0.8 26.40000
Jun 1861 71.8 53.700000 128.4 67.60000
Jul 1861 129.3 141.100000 138.0 155.20000
Aug 1861 83.1 160.600000 139.8 205.60000
Sep 1861 77.8 135.500000 162.8 251.40000
Oct 1861 45.7 83.800000 180.7 94.70000
Nov 1861 41.5 113.200000 103.9 141.70000
Dec 1861 26.8 64.500000 87.1 91.30000
Jan 1862 76.5 148.300000 186.9 177.10000
Feb 1862 9.7 43.200000 78.0 57.10000
Mar 1862 79.9 94.700000 134.6 95.40000
Apr 1862 110.2 112.400000 74.9 97.40000
May 1862 72.6 114.400000 99.1 80.90000
Jun 1862 68.1 86.000000 100.9 65.90000
Jul 1862 94.4 114.700000 118.9 101.70000
Aug 1862 35.0 76.300000 91.8 85.70000
Sep 1862 46.8 39.700000 94.1 64.90000
Oct 1862 82.3 136.700000 143.4 161.80000
Nov 1862 64.4 111.100000 109.7 53.30000
Dec 1862 53.6 134.400000 161.0 140.70000
Jan 1863 56.8 134.400000 95.9 125.60000
Feb 1863 15.5 32.800000 23.7 121.60000
Mar 1863 29.9 71.000000 155.5 107.60000
Apr 1863 31.2 72.500000 42.1 102.20000
May 1863 38.9 84.300000 46.5 127.20000
Jun 1863 62.7 101.100000 115.6 125.50000
Jul 1863 23.2 15.300000 42.4 9.90000
Aug 1863 106.4 122.200000 165.4 129.80000
Sep 1863 72.8 102.400000 58.5 170.30000
Oct 1863 153.4 206.600000 241.2 118.70000
Nov 1863 59.2 119.300000 115.6 195.70000
Dec 1863 54.4 80.100000 110.5 101.30000
Jan 1864 33.3 52.500000 131.7 111.80000
Feb 1864 25.6 59.700000 39.2 100.80000
Mar 1864 75.3 127.500000 89.2 129.10000
Apr 1864 20.9 65.300000 54.1 72.40000
May 1864 45.7 57.900000 72.8 69.80000
Jun 1864 41.2 81.400000 52.3 131.00000
Jul 1864 25.6 50.900000 39.2 72.90000
Aug 1864 34.3 74.000000 19.4 99.30000
Sep 1864 49.4 78.300000 75.7 177.30000
Oct 1864 72.6 104.200000 165.8 118.20000
Nov 1864 87.4 111.600000 165.8 223.50000
Dec 1864 41.5 48.900000 102.9 100.30000
Jan 1865 45.9 128.300000 138.0 144.60000
Feb 1865 74.4 92.600000 77.5 122.00000
Mar 1865 40.3 89.100000 106.1 61.50000
Apr 1865 34.7 14.300000 44.7 17.90000
May 1865 94.7 120.900000 131.0 101.50000
Jun 1865 24.3 29.700000 17.2 30.50000
Jul 1865 52.5 47.900000 110.5 111.40000
Aug 1865 110.9 102.700000 140.4 118.90000
Sep 1865 2.7 11.800000 20.5 53.50000
Oct 1865 88.8 151.800000 126.3 143.10000
Nov 1865 94.7 115.700000 234.8 162.00000
Dec 1865 89.1 85.300000 223.1 109.50000
Jan 1866 61.1 109.100000 206.7 153.90000
Feb 1866 51.7 88.800000 71.7 112.40000
Mar 1866 88.0 106.000000 112.3 79.70000
Apr 1866 51.2 48.900000 103.5 53.80000
May 1866 58.7 51.700000 57.8 47.70000
Jun 1866 96.3 78.300000 106.8 103.60000
Jul 1866 38.1 57.900000 17.6 47.00000
Aug 1866 57.6 100.100000 140.1 123.80000
Sep 1866 78.7 153.600000 125.5 168.60000
Oct 1866 30.9 58.900000 93.3 62.90000
Nov 1866 48.5 132.100000 81.2 97.40000
Dec 1866 49.1 114.400000 175.7 135.60000
Jan 1867 71.2 107.200000 134.6 115.60000
Feb 1867 48.5 82.700000 65.8 120.60000
Mar 1867 95.2 66.500000 99.0 81.60000
Apr 1867 58.9 109.900000 76.1 100.00000
May 1867 65.6 128.300000 165.8 113.10000
Jun 1867 13.3 29.900000 10.2 42.40000
Jul 1867 71.5 134.200000 78.7 76.30000
Aug 1867 58.9 68.800000 64.0 108.70000
Sep 1867 30.4 53.200000 114.1 85.20000
Oct 1867 83.7 141.300000 110.8 128.10000
Nov 1867 21.6 23.600000 35.4 17.90000
Dec 1867 21.3 65.000000 54.9 54.00000
Jan 1868 78.7 88.300000 191.8 128.10000
Feb 1868 36.5 91.600000 49.1 127.20000
Mar 1868 50.4 119.000000 89.2 130.30000
Apr 1868 37.9 79.100000 80.1 55.20000
May 1868 29.3 65.800000 55.6 90.60000
Jun 1868 17.6 10.800000 26.3 42.10000
Jul 1868 16.8 17.600000 35.9 60.80000
Aug 1868 110.7 141.600000 210.3 95.20000
Sep 1868 83.5 57.600000 208.2 58.40000
Oct 1868 29.6 71.500000 81.6 149.20000
Nov 1868 41.6 83.200000 181.1 63.70000
Dec 1868 120.3 150.300000 307.7 157.10000
Jan 1869 96.8 119.300000 267.5 103.50000
Feb 1869 45.9 137.800000 75.4 148.20000
Mar 1869 70.1 64.300000 75.7 83.30000
Apr 1869 42.9 59.700000 68.8 82.60000
May 1869 139.0 57.000000 180.7 116.20000
Jun 1869 18.7 39.900000 2.3 38.70000
Jul 1869 19.2 62.500000 55.9 93.70000
Aug 1869 33.3 56.000000 21.6 35.60000
Sep 1869 111.5 153.300000 149.7 204.90000
Oct 1869 36.5 41.700000 11.4 40.40000
Nov 1869 71.5 132.800000 71.3 121.40000
Dec 1869 73.6 82.700000 210.8 195.50000
Jan 1870 38.4 75.500000 146.3 99.50000
Feb 1870 45.9 81.900000 125.5 65.90000
Mar 1870 55.5 43.200000 95.9 35.10000
Apr 1870 20.3 41.700000 26.0 63.20000
May 1870 31.2 81.600000 74.6 105.60000
Jun 1870 26.1 37.400000 24.5 35.10000
Jul 1870 34.9 49.700000 30.0 83.00000
Aug 1870 44.5 64.500000 33.0 79.70000
Sep 1870 70.1 44.200000 91.5 85.10000
Oct 1870 159.2 306.200000 180.7 234.70000
Nov 1870 31.2 31.800000 94.4 110.70000
Dec 1870 63.7 74.000000 118.9 88.40000
Jan 1871 100.5 135.500000 128.7 156.00000
Feb 1871 39.5 100.100000 126.3 115.80000
Mar 1871 47.7 55.000000 82.7 53.50000
Apr 1871 92.0 99.600000 113.4 83.30000
May 1871 7.7 17.900000 14.3 31.00000
Jun 1871 65.6 61.000000 117.0 84.80000
Jul 1871 132.5 119.300000 119.6 150.20000
Aug 1871 38.1 87.600000 57.1 88.20000
Sep 1871 37.3 104.900000 144.5 52.80000
Oct 1871 94.7 95.300000 150.3 53.30000
Nov 1871 31.7 53.700000 128.7 33.50000
Dec 1871 30.9 56.800000 121.9 59.10000
Jan 1872 77.1 131.800000 189.2 160.60000
Feb 1872 50.9 109.900000 148.5 95.20000
Mar 1872 89.9 55.300000 90.7 65.90000
Apr 1872 74.9 80.100000 20.5 41.20000
May 1872 48.0 80.400000 16.1 55.70000
Jun 1872 82.7 129.000000 23.1 126.60000
Jul 1872 29.9 94.200000 241.2 67.80000
Aug 1872 88.5 100.600000 144.2 127.20000
Sep 1872 81.3 132.300000 66.2 167.10000
Oct 1872 119.2 157.700000 92.6 156.20000
Nov 1872 90.7 140.000000 168.7 139.80000
Dec 1872 121.9 163.600000 309.6 141.20000
Jan 1873 76.0 110.600000 264.1 172.70000
Feb 1873 14.4 23.600000 109.4 36.60000
Mar 1873 72.0 68.800000 133.6 75.80000
Apr 1873 10.9 10.300000 70.6 78.00000
May 1873 30.1 65.500000 42.1 58.20000
Jun 1873 32.0 56.000000 70.6 85.70000
Jul 1873 97.6 157.700000 98.0 156.70000
Aug 1873 115.7 174.100000 105.8 152.10000
Sep 1873 65.1 75.500000 96.2 100.30000
Oct 1873 85.3 122.900000 86.3 100.30000
Nov 1873 61.1 78.800000 102.9 133.20000
Dec 1873 20.5 18.900000 16.9 70.70000
Jan 1874 60.5 58.200000 87.1 125.00000
Feb 1874 49.6 77.300000 167.9 64.90000
Mar 1874 30.1 52.200000 55.6 105.10000
Apr 1874 28.8 43.000000 53.0 74.40000
May 1874 53.1 31.400000 5.5 74.80000
Jun 1874 17.9 34.100000 28.1 89.20000
Jul 1874 60.8 90.600000 50.5 120.60000
Aug 1874 135.8 155.900000 48.3 88.40000
Sep 1874 65.3 120.500000 122.9 96.90000
Oct 1874 79.2 154.900000 158.1 162.90000
Nov 1874 86.4 152.100000 163.5 112.10000
Dec 1874 74.7 105.200000 133.9 141.20000
Jan 1875 54.1 137.200000 271.4 163.70000
Feb 1875 94.9 32.800000 64.0 30.80000
Mar 1875 32.8 30.900000 53.0 35.60000
Apr 1875 25.3 9.000000 43.9 64.90000
May 1875 27.7 46.600000 83.4 109.20000
Jun 1875 80.5 94.200000 64.0 115.90000
Jul 1875 80.8 96.800000 41.7 33.50000
Aug 1875 62.4 89.600000 58.2 106.50000
Sep 1875 85.3 116.500000 190.6 32.60000
Oct 1875 177.9 152.100000 182.2 105.90000
Nov 1875 102.7 116.500000 146.0 84.20000
Dec 1875 50.9 66.300000 61.4 85.70000
Jan 1876 8.2 43.500000 78.7 87.50000
Feb 1876 78.0 128.800000 126.6 157.50000
Mar 1876 58.2 85.800000 101.7 141.80000
Apr 1876 69.5 41.400000 101.4 110.90000
May 1876 27.7 33.300000 3.7 25.20000
Jun 1876 39.3 90.400000 29.3 67.40000
Jul 1876 40.0 42.700000 27.1 100.50000
Aug 1876 59.5 99.600000 84.2 91.10000
Sep 1876 81.8 111.100000 139.8 166.40000
Oct 1876 94.4 161.800000 219.1 137.30000
Nov 1876 86.7 123.700000 178.1 135.80000
Dec 1876 163.4 271.800000 254.3 173.50000
Jan 1877 101.3 206.100000 198.6 204.70000
Feb 1877 41.3 82.100000 8.1 95.50000
Mar 1877 66.2 94.700000 97.0 74.10000
Apr 1877 99.0 131.300000 227.5 99.00000
May 1877 55.2 90.400000 98.5 104.70000
Jun 1877 28.0 61.000000 79.4 62.60000
Jul 1877 85.2 95.500000 46.5 94.90000
Aug 1877 95.2 101.400000 224.9 154.80000
Sep 1877 33.4 61.500000 129.9 71.80000
Oct 1877 45.2 134.200000 208.5 168.50000
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Dec 1955 108.2 179.800000 182.4 141.20000
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Feb 1956 30.7 41.700000 16.3 55.20000
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Jul 1956 101.8 79.800000 113.2 156.70000
Aug 1956 146.0 228.100000 92.9 126.90000
Sep 1956 90.3 85.900000 190.4 102.60000
Oct 1956 41.1 83.300000 52.9 107.00000
Nov 1956 31.5 45.200000 95.2 50.80000
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Feb 1957 60.0 65.300000 126.0 131.30000
Mar 1957 76.2 88.100000 172.6 124.70000
Apr 1957 20.5 51.800000 22.6 41.90000
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Jul 1957 85.7 124.200000 66.8 76.10000
Aug 1957 88.3 141.200000 77.8 61.80000
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Oct 1957 71.9 116.600000 94.9 174.30000
Nov 1957 39.1 56.100000 39.7 51.90000
Dec 1957 79.7 129.500000 114.7 111.40000
Jan 1958 35.2 102.100000 188.4 125.80000
Feb 1958 102.0 148.300000 137.5 140.10000
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Apr 1958 29.9 53.100000 18.9 34.20000
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Jul 1958 109.9 121.900000 134.6 131.30000
Aug 1958 140.2 128.800000 152.0 132.40000
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Dec 1958 90.4 120.600000 125.6 153.40000
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Mar 1959 40.2 73.700000 136.1 99.30000
Apr 1959 43.8 78.500000 86.6 93.80000
May 1959 73.4 31.500000 87.6 53.00000
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Jul 1959 44.8 111.800000 35.6 85.00000
Aug 1959 17.6 24.100000 30.7 46.30000
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Oct 1959 106.7 99.600000 141.0 176.50000
Nov 1959 90.8 95.800000 106.7 131.30000
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Jan 1960 87.0 124.000000 123.4 129.10000
Feb 1960 35.2 69.800000 115.6 108.10000
Mar 1960 97.1 57.900000 141.7 67.30000
Apr 1960 27.2 77.700000 72.4 99.30000
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Jul 1960 139.8 185.900000 114.3 149.00000
Aug 1960 86.4 152.400000 108.0 102.60000
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Oct 1960 178.2 160.800000 175.8 104.80000
Nov 1960 102.8 102.400000 201.4 209.60000
Dec 1960 93.7 82.800000 97.5 153.40000
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Oct 1961 59.0 104.100000 128.0 182.10000
Nov 1961 53.0 75.200000 79.0 123.60000
Dec 1961 57.0 74.900000 102.0 104.80000
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Feb 1962 26.0 60.700000 28.0 58.50000
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May 1962 57.0 60.700000 81.0 64.00000
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Aug 1962 73.0 148.800000 99.0 105.90000
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May 1963 42.0 87.600000 48.0 83.90000
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Jul 1963 24.0 76.700000 77.0 82.80000
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Nov 1963 97.0 157.500000 202.0 157.80000
Dec 1963 15.0 16.000000 112.0 39.70000
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Apr 1964 62.0 51.300000 83.0 77.20000
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Jun 1964 51.0 70.600000 90.0 80.50000
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Nov 1964 26.0 74.400000 74.0 114.70000
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Feb 1965 5.0 16.300000 6.0 9.90000
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Nov 1966 47.0 89.700000 42.0 80.50000
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Oct 1968 60.0 143.500000 153.0 205.30000
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Dec 1968 109.0 61.700000 174.0 205.90000
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Feb 1969 42.0 89.900000 54.0 50.00000
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Dec 1969 62.0 110.200000 102.0 173.70000
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Mar 1970 33.0 102.900000 35.0 59.90000
Apr 1970 85.0 118.100000 72.0 142.30000
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Mar 1991 91.1 93.100000 139.0 117.80000
Apr 1991 92.3 110.700000 120.8 141.40000
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Dec 1992 41.5 96.500000 92.3 89.90000
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Apr 1993 48.7 107.200000 109.5 110.60000
May 1993 149.9 150.800000 151.8 116.70000
Jun 1993 148.1 71.900000 105.8 100.60000
Jul 1993 60.1 85.900000 90.5 91.40000
Aug 1993 36.8 78.000000 27.5 60.00000
Sep 1993 90.3 137.500000 198.9 87.90000
Oct 1993 86.2 65.200000 57.6 38.40000
Nov 1993 45.4 62.400000 121.2 83.50000
Dec 1993 105.7 156.000000 198.8 247.70000
Jan 1994 83.3 136.600000 139.6 195.40000
Feb 1994 99.1 148.700000 251.7 130.40000
Mar 1994 72.1 93.600000 100.3 176.20000
Apr 1994 60.3 66.600000 98.6 118.40000
May 1994 51.5 35.800000 124.6 66.30000
Jun 1994 20.9 51.200000 45.9 78.90000
Jul 1994 64.7 72.000000 113.7 133.70000
Aug 1994 57.8 114.600000 80.3 122.70000
Sep 1994 97.0 62.600000 69.5 95.90000
Oct 1994 41.2 98.800000 88.9 64.40000
Nov 1994 54.8 89.400000 145.9 114.10000
Dec 1994 88.9 117.200000 191.2 219.10000
Jan 1995 105.5 121.300000 178.7 232.60000
Feb 1995 92.0 95.100000 164.5 147.80000
Mar 1995 44.7 97.200000 97.8 111.20000
Apr 1995 27.3 30.700000 23.7 33.60000
May 1995 49.2 32.700000 76.7 72.30000
Jun 1995 13.0 77.500000 21.3 37.40000
Jul 1995 70.0 73.200000 81.2 102.70000
Aug 1995 7.0 11.700000 11.5 19.20000
Sep 1995 44.0 87.600000 68.2 59.90000
Oct 1995 38.0 147.200000 234.4 192.90000
Nov 1995 146.0 183.200000 121.5 91.50000
Dec 1995 73.0 66.300000 174.6 59.70000
Jan 1996 87.0 205.700000 218.2 67.10000
Feb 1996 62.0 73.400000 135.6 141.20000
Mar 1996 70.0 69.400000 201.5 89.80000
Apr 1996 58.0 92.300000 113.5 83.40000
May 1996 70.0 103.000000 70.7 75.10000
Jun 1996 21.0 34.300000 46.1 32.90000
Jul 1996 37.0 69.000000 67.1 63.40000
Aug 1996 103.0 97.200000 135.3 61.70000
Sep 1996 19.0 23.900000 36.9 45.80000
Oct 1996 102.0 110.200000 239.7 179.70000
Nov 1996 104.0 91.900000 118.0 122.90000
Dec 1996 54.0 67.900000 50.9 45.40000
Jan 1997 14.0 31.900000 57.4 26.20000
Feb 1997 62.0 122.600000 159.3 178.50000
Mar 1997 15.0 37.000000 31.4 39.70000
Apr 1997 48.0 38.600000 48.0 49.30000
May 1997 61.0 141.600000 66.1 77.20000
Jun 1997 125.0 116.000000 101.4 112.90000
Jul 1997 54.0 107.300000 47.8 104.40000
Aug 1997 81.0 54.300000 231.3 142.70000
Sep 1997 19.0 63.200000 73.1 92.80000
Oct 1997 68.0 65.800000 113.1 129.00000
Nov 1997 87.0 194.700000 213.5 100.30000
Dec 1997 91.0 124.800000 126.8 124.20000
Jan 1998 79.0 107.200000 181.5 155.10000
Feb 1998 14.0 12.900000 20.3 64.90000
Mar 1998 66.0 80.900000 105.3 115.80000
Apr 1998 116.0 108.900000 141.7 81.40000
May 1998 27.0 30.200000 48.9 39.90000
Jun 1998 113.0 112.500000 132.7 133.50000
Jul 1998 54.0 116.200000 74.1 70.30000
Aug 1998 45.0 85.100000 102.3 139.40000
Sep 1998 96.0 65.100000 92.4 83.50000
Oct 1998 75.0 147.200000 169.2 159.00000
Nov 1998 77.0 104.800000 137.7 172.80000
Dec 1998 70.0 95.200000 172.3 141.50000
Jan 1999 73.0 129.700000 145.6 170.30000
Feb 1999 32.0 44.000000 26.8 95.90000
Mar 1999 36.0 65.800000 52.7 74.90000
Apr 1999 75.0 75.800000 107.9 68.30000
May 1999 39.0 68.100000 56.5 62.20000
Jun 1999 55.0 67.400000 35.4 54.60000
Jul 1999 35.0 46.400000 20.5 75.60000
Aug 1999 107.0 137.300000 200.4 110.30000
Sep 1999 104.0 176.200000 180.0 171.70000
Oct 1999 31.0 54.500000 74.3 58.70000
Nov 1999 44.0 68.000000 81.3 160.10000
Dec 1999 83.0 169.300000 195.7 274.20000
Jan 2000 40.0 45.500000 50.2 88.90000
Feb 2000 46.0 107.200000 112.0 137.50000
Mar 2000 21.0 40.400000 20.2 58.50000
Apr 2000 90.0 97.200000 50.2 67.80000
May 2000 50.0 69.800000 70.8 54.00000
Jun 2000 31.0 75.400000 63.4 60.50000
Jul 2000 48.0 35.400000 68.1 67.50000
Aug 2000 63.0 88.000000 39.9 93.80000
Sep 2000 100.0 128.200000 109.5 140.00000
Oct 2000 87.0 169.100000 183.9 213.60000
Nov 2000 141.0 177.600000 191.5 165.50000
Dec 2000 122.0 148.800000 183.2 136.10000
Jan 2001 32.0 68.700000 87.0 59.40000
Feb 2001 53.0 69.100000 134.0 63.90000
Mar 2001 56.0 83.700000 116.0 89.20000
Apr 2001 78.0 66.600000 71.0 90.10000
May 2001 49.0 60.400000 20.0 41.20000
Jun 2001 41.0 75.200000 58.0 80.80000
Jul 2001 30.0 74.200000 118.0 95.80000
Aug 2001 97.0 99.100000 154.0 101.70000
Sep 2001 42.0 47.400000 71.0 85.20000
Oct 2001 90.0 90.600000 142.0 122.80000
Nov 2001 41.0 46.600000 31.0 103.60000
Dec 2001 22.0 55.600000 73.0 64.90000
Jan 2002 48.0 90.800000 225.0 171.70000
Feb 2002 126.0 132.100000 153.0 233.50000
Mar 2002 30.0 92.800000 64.0 97.20000
Apr 2002 82.0 96.000000 118.0 89.30000
May 2002 121.0 141.100000 163.0 140.10000
Jun 2002 81.0 118.500000 105.0 119.20000
Jul 2002 69.0 142.200000 54.0 74.30000
Aug 2002 51.0 61.000000 48.0 99.20000
Sep 2002 23.0 33.900000 36.0 47.20000
Oct 2002 181.0 209.100000 214.0 186.40000
Nov 2002 187.0 202.700000 215.0 211.00000
Dec 2002 98.0 85.500000 145.0 117.40000
Jan 2003 63.0 107.500000 76.0 86.30000
Feb 2003 21.0 33.600000 79.0 50.20000
Mar 2003 27.0 63.800000 70.0 78.10000
Apr 2003 37.0 54.200000 127.0 62.90000
May 2003 97.0 117.800000 100.0 133.40000
Jun 2003 87.0 51.100000 115.0 94.60000
Jul 2003 48.0 91.200000 96.0 156.70000
Aug 2003 15.0 15.600000 23.0 24.60000
Sep 2003 37.0 55.900000 55.0 63.60000
Oct 2003 107.0 33.600000 28.0 53.10000
Nov 2003 55.0 98.200000 125.0 167.60000
Dec 2003 60.0 76.900000 73.0 102.30000
Jan 2004 83.0 131.700000 107.0 128.30000
Feb 2004 20.0 44.500000 79.0 45.20000
Mar 2004 44.0 68.300000 114.0 92.70000
Apr 2004 32.0 78.500000 65.0 82.90000
May 2004 50.0 32.800000 46.0 38.10000
Jun 2004 51.0 105.300000 100.0 102.30000
Jul 2004 38.0 56.300000 55.0 70.70000
Aug 2004 134.0 191.100000 178.0 69.00000
Sep 2004 47.0 105.500000 97.0 157.50000
Oct 2004 120.0 166.200000 170.0 182.50000
Nov 2004 35.4 59.500000 35.0 92.50000
Dec 2004 45.0 76.000000 70.0 132.60000
Jan 2005 64.0 105.600000 125.0 172.50000
Feb 2005 41.0 49.300000 29.0 75.30000
Mar 2005 22.0 50.900000 83.0 72.00000
Apr 2005 72.0 80.500000 133.0 102.70000
May 2005 67.0 94.100000 89.0 128.10000
Jun 2005 31.0 55.400000 59.0 92.30000
Jul 2005 83.0 64.700000 107.0 46.60000
Aug 2005 26.0 59.300000 52.0 110.10000
Sep 2005 56.0 83.100000 111.0 95.70000
Oct 2005 102.0 135.900000 220.0 144.00000
Nov 2005 47.0 57.100000 105.0 104.10000
Dec 2005 73.0 126.600000 78.0 101.50000
Jan 2006 16.0 39.800000 49.0 75.30000
Feb 2006 38.0 45.900000 29.0 52.20000
Mar 2006 70.0 154.300000 111.0 133.50000
Apr 2006 41.0 58.600000 31.0 58.10000
May 2006 117.0 108.800000 135.0 136.40000
Jun 2006 27.0 33.200000 15.0 42.20000
Jul 2006 18.0 82.000000 58.0 62.90000
Aug 2006 58.0 144.300000 67.0 62.70000
Sep 2006 85.0 128.000000 179.0 232.80000
Oct 2006 107.0 158.500000 188.0 148.20000
Nov 2006 69.0 120.800000 155.0 149.30000
Dec 2006 95.0 148.100000 182.0 219.20000
Jan 2007 40.0 94.000000 89.0 160.30000
Feb 2007 78.0 78.100000 142.0 111.50000
Mar 2007 39.0 78.400000 89.0 90.20000
Apr 2007 15.0 12.300000 27.0 24.80000
May 2007 35.0 59.700000 65.0 93.80000
Jun 2007 126.0 236.900000 156.0 128.30000
Jul 2007 127.0 211.500000 118.0 162.30000
Aug 2007 96.0 133.800000 80.0 101.00000
Sep 2007 39.0 29.200000 36.0 62.50000
Oct 2007 16.0 38.100000 64.0 83.50000
Nov 2007 54.0 103.900000 57.0 97.60000
Dec 2007 64.0 105.400000 135.0 198.60000
Jan 2008 97.0 229.400000 193.0 193.50000
Feb 2008 15.0 41.600000 52.0 123.30000
Mar 2008 102.0 105.600000 113.0 140.60000
Apr 2008 28.0 17.400000 54.0 53.00000
May 2008 33.0 17.500000 76.0 21.60000
Jun 2008 76.0 68.300000 129.0 98.30000
Jul 2008 111.0 154.200000 156.0 84.20000
Aug 2008 192.4 255.300000 165.0 240.20000
Sep 2008 114.0 127.300000 124.0 136.70000
Oct 2008 93.0 129.300000 140.0 175.90000
Nov 2008 45.0 63.200000 79.0 114.40000
Dec 2008 39.0 73.700000 60.0 102.80000
Jan 2009 62.0 98.800000 198.0 133.60000
Feb 2009 56.0 28.800000 21.0 24.30000
Mar 2009 26.0 49.200000 41.0 76.10000
Apr 2009 71.0 107.300000 155.0 106.30000
May 2009 76.0 95.800000 91.0 127.70000
Jun 2009 64.0 57.700000 85.0 49.20000
Jul 2009 165.0 157.500000 204.0 161.10000
Aug 2009 70.0 152.700000 157.0 215.70000
Sep 2009 24.0 48.500000 55.0 63.90000
Oct 2009 63.0 64.900000 167.0 113.40000
Nov 2009 171.0 195.600000 228.0 329.40000
Dec 2009 70.0 64.600000 160.0 114.90000
Jan 2010 45.0 93.500000 126.0 40.00000
Feb 2010 37.0 72.400000 43.0 42.00000
Mar 2010 55.0 90.900000 96.0 84.30000
Apr 2010 27.0 47.100000 41.0 96.40000
May 2010 38.0 41.400000 48.0 44.20000
Jun 2010 50.0 55.900000 48.0 52.60000
Jul 2010 79.0 184.900000 135.0 125.60000
Aug 2010 48.0 55.300000 17.0 57.40000
Sep 2010 104.0 156.500000 79.0 165.00000
Oct 2010 31.0 74.600000 121.0 102.00000
Nov 2010 100.0 172.500000 86.0 179.20000
Dec 2010 58.0 15.300000 67.0 42.50000
Jan 2011 29.2 83.373494 74.5 55.64356
Feb 2011 76.1 130.000000 129.0 121.58416
Mar 2011 19.4 56.470588 32.3 32.09091
Apr 2011 28.4 23.411765 42.7 61.54545
May 2011 37.0 64.117647 122.7 122.54545
Jun 2011 64.3 85.432099 88.7 69.40594
Jul 2011 42.6 72.222222 39.2 112.57426
Aug 2011 39.7 70.123457 55.0 118.71287
Sep 2011 65.2 108.333333 115.6 139.27835
Oct 2011 169.5 244.642857 91.7 203.29897
Nov 2011 48.5 119.285714 148.7 186.39175
Dec 2011 51.9 100.722892 82.6 179.90099
Jan 2012 63.0 96.987952 94.1 137.02970
Feb 2012 20.5 76.265060 42.4 77.32673
Mar 2012 25.6 22.352941 23.7 30.36364
Apr 2012 90.2 82.352941 82.6 56.72727
May 2012 60.4 47.529412 72.9 63.72727
Jun 2012 147.7 218.024691 228.3 227.82178
Jul 2012 85.3 113.827160 112.9 119.30693
Aug 2012 78.1 91.481481 186.2 124.55446
Sep 2012 83.4 115.595238 23.1 88.76289
Oct 2012 71.0 133.571429 100.1 130.30928
Nov 2012 72.0 81.666667 116.8 147.42268
Dec 2012 52.3 121.686747 145.2 151.68317
Jan 2013 94.4 131.807229 159.0 152.27723
Feb 2013 47.3 84.457831 51.7 73.16832
Mar 2013 85.5 126.705882 134.9 43.00000
Apr 2013 40.1 70.823529 97.2 96.27273
May 2013 45.8 105.882353 64.4 122.18182
Jun 2013 60.8 104.938272 116.4 67.82178
Jul 2013 68.8 55.679012 67.5 80.99010
Aug 2013 48.5 97.160494 51.6 72.77228
Sep 2013 35.1 86.309524 42.9 73.81443
Oct 2013 127.8 167.261905 182.9 126.08247
Nov 2013 26.6 207.500000 64.2 64.94845
Dec 2013 83.2 108.433735 211.4 209.00990
Jan 2014 101.6 158.915663 200.4 206.03960
Feb 2014 88.5 154.216867 240.5 233.86139
Mar 2014 53.7 70.235294 107.8 106.36364
Apr 2014 34.2 42.823529 104.6 39.45455
May 2014 91.5 90.823529 70.6 120.72727
Jun 2014 36.2 48.271605 85.7 66.33663
Jul 2014 35.0 89.876543 43.6 70.29703
Aug 2014 173.0 231.358025 88.3 140.79208
Sep 2014 26.5 5.238095 19.7 19.27835
Oct 2014 90.2 159.523810 180.1 167.11340
Nov 2014 140.9 188.452381 192.0 154.32990
Dec 2014 55.9 16.626506 58.9 168.21782
# Create time series for each stationdub_ts <-process_station_data("Dublin Airport")bel_ts <-process_station_data("Belfast")cork_ts <-process_station_data("Cork Airport")galway_ts <-process_station_data("University College Galway")ls() # This will list all objects in your environment. Ensure the time series variables are listed.
# Combine the time series for all stationsbeldubcorkgalway_ts <-cbind(dub_ts, bel_ts, cork_ts, galway_ts)# Ensure the combined time series is created successfullyls() # Check again that 'beldubcorkgalway_ts' is listed in the environment.
library(dygraphs)# Plot the combined time series with dygraphbeldubcorkgalway_ts %>%dygraph(width =960, height =360) %>%dyRangeSelector() %>%dyRoller(rollPeriod =1980) # Apply the rolling mean with a period of 1979
# Example: Create time series for Dublin Airportdub_ts <- rain %>%filter(Station =="Dublin Airport") %>%group_by(Year, Month) %>%summarise(Rainfall =sum(Rainfall, na.rm =TRUE), .groups ="drop") %>%pull(Rainfall) %>%ts(start =c(1850, 1), freq =12)# Similarly, create time series for Belfast, Cork Airport, and University College Galwaybel_ts <- rain %>%filter(Station =="Belfast") %>%group_by(Year, Month) %>%summarise(Rainfall =sum(Rainfall, na.rm =TRUE), .groups ="drop") %>%pull(Rainfall) %>%ts(start =c(1850, 1), freq =12)cork_ts <- rain %>%filter(Station =="Cork Airport") %>%group_by(Year, Month) %>%summarise(Rainfall =sum(Rainfall, na.rm =TRUE), .groups ="drop") %>%pull(Rainfall) %>%ts(start =c(1850, 1), freq =12)galway_ts <- rain %>%filter(Station =="University College Galway") %>%group_by(Year, Month) %>%summarise(Rainfall =sum(Rainfall, na.rm =TRUE), .groups ="drop") %>%pull(Rainfall) %>%ts(start =c(1850, 1), freq =12)ls() # This should list dub_ts, bel_ts, cork_ts, and galway_ts if created successfully
# Combine the time series for all stations into one objectbeldubcorkgalway_ts <-cbind(dub_ts, bel_ts, cork_ts, galway_ts)ls() # Ensure 'beldubcorkgalway_ts' is in the environment
library(dygraphs)# Plot the combined time series with dygraphbeldubcorkgalway_ts %>%dygraph(width =960, height =360) %>%dyRangeSelector() %>%dyRoller(rollPeriod =12)