The dataset, rainfall.RData, contains historical monthly rainfall data from weather stations across Ireland. The key variables include:
Station: The name of the weather station (e.g., Belfast, Dublin Airport, Cork Airport and University College Galway.).
Year and Month: Indicate the date of the measurements.
Rainfall: The amount of rainfall measured in millimeters.
For this blog, I focused on the following four stations: - Belfast: A station located in the North of Ireland, known for its consistently high rainfall levels. - Dublin Airport: A coastal station with relatively moderate rainfall. - University College Galway: Located in the west of Ireland, this station experiences high rainfall due to its proximity to the Atlantic Ocean. - Cork Airport: A southern station with lower but variable rainfall patterns. —
Methods
To visualise the data, I followed these steps: 1. Data Loading and Exploration: Load the dataset and inspect its structure. 2. Filtering and Aggregation: Extract data for the four selected stations and aggregate monthly rainfall totals. 3. Time Series Conversion: Convert the data into time series objects for each station. 4. Combining and Visualising: Combine the time series into a single graph and create an interactive dygraph with sooming and filtering features.
The interactive dygraph allows users to explore monthly rainfall patterns over time, compare stations, and identify anomalies.
Code Used to Create the Dygraph
library(dygraphs)
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library(dplyr)
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library(tidyverse)
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library(xts)
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load("C:/Users/Jgnwh/Desktop/HTML Blog Working DIrectory/rainfall.RData")ls()
# A tibble: 6 × 4
Year Month Rainfall Station
<dbl> <fct> <dbl> <chr>
1 1850 Jan 169 Ardara
2 1851 Jan 236. Ardara
3 1852 Jan 250. Ardara
4 1853 Jan 209. Ardara
5 1854 Jan 188. Ardara
6 1855 Jan 32.3 Ardara
stations <-c("Dublin Airport", "Belfast", "University College Galway", "Cork Airport")
ucg_ts %>%dygraph(width=700,height=130,group="dub_belf",main="University College Galway") %>%dyRangeSelector()
belfast_graph <-dygraph(bel_ts, main ="Monthly Rainfall - Belfast")belfast_graph <-dyRangeSelector(belfast_graph)belfast_graph <-dyAxis(belfast_graph, "y", label ="Rainfall (mm)")belfast_graph <-dyLegend(belfast_graph, show ="always")belfast_graph <-dyOptions(belfast_graph, drawGrid =TRUE)
Key Observations
From the interactive dygraph, Ican observe several interesting patterns:
Regional Variations:
Belfast and University College Galway consistently record higher rainfall compared to Dublin Airport and Cork Airport.
This aligns with their geographical locations, as coastal and western regions of Ireland typically receive more precipitation.
Seasonal Trends:
Across all stations, rainfall peaks in the winter months, while the summer months show lower rainfall.
These seasonal trends are expected, given Ireland’s climate.
Anomalies:
Sharp spikes in rainfall are occasionally observed, likely corresponding to extreme weather events such as storms or prolonged rainy periods.
These anomalies are very much noticeable in Belfast and Galway.
Insights
The variability in rainfall across regions suggests the need for localised water management strategies.
Seasonal trends may help plan agricultural activities, especially in rain-dependent sectors.
Further analysis could focus on extreme rainfall events and their impact.
Conclusion
This analysis provides an interactive exploration of rainfall data across four key weather stations in Ireland. By visualising the data with a dygraph, we can identify regional and seasonal trends, as well as anomalies that merit further investigation. Such insights are crucial for understanding Ireland’s weather patterns and their implications for infrastructure, agriculture, and daily life.