Introduction

Weather patterns and rainfall distribution can reveal fascinating details about a country’s climate. In this post, I’ll walk you through how I created an interactive map of Ireland’s rainfall data for 25 weather stations, color-coded based on their median rainfall levels in January. For this project, I used R Studio, the leaflet library, and some data wrangling with dplyr. Here’s the journey!

Step 1: Loading the Data

First, I needed two datasets: one for rainfall measurements and another containing the geographical coordinates of weather stations in Ireland. After loading the data (rainfall.RData), I could move forward with the calculations.

The dataset provides rainfall measurements and metadata for various weather stations in Ireland. Key columns include:

# Load the required libraries
library(leaflet)
## Warning: package 'leaflet' was built under R version 4.4.2
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.2
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
# Load your data
load("rainfall.RData")

Step 2: Calculating Median Rainfall for January

To analyze the median rainfall in January for each weather station, I grouped the data by the station and calculated the median using dplyr. Here’s how I did it:

# Calculate median rainfall for January at each station
median_rainfall <- rain %>%
  filter(Month == "Jan") %>%
  group_by(Station) %>%
  summarize(MedianRainfall = median(Rainfall, na.rm = TRUE))

Step 3: Merging the Data

Next, I performed a merge operation to combine the geographical data of stations with the rainfall statistics. Using a left join ensured that all stations were included, even if there was no rainfall data available for some.

# Perform a normal merge without duplicates
stations_with_rainfall <- merge(
  stations,           # First dataset
  median_rainfall,    # Second dataset
  by = "Station",     # Column to match on
  all.x = TRUE        # Include all rows from 'stations' (left join)
)

Step 4: Creating the Interactive Map

Using the leaflet library, I built an interactive map where each station is marked with a circle, and the color represents its median rainfall. Here’s the code:

Rainfall_map <- leaflet(data = stations_with_rainfall) %>%
  addTiles() %>%
  addCircleMarkers(
    lng = ~Long,
    lat = ~Lat,
    radius = ~sqrt(MedianRainfall) * 1.0, # Adjust circle size
    color = ~colorNumeric("YlOrRd", MedianRainfall)(MedianRainfall), # Color scale
    stroke = FALSE,
    fillOpacity = 0.7,
    popup = ~paste(
      "<b>Station:</b>", Station, "<br>",
      "<b>Median Rainfall:</b>", round(MedianRainfall, 2), "mm", "<br>",
      "<b>County:</b>", County
    )
  ) %>%
  addLegend(
    "bottomright",
    pal = colorNumeric("YlOrRd", stations_with_rainfall$MedianRainfall),
    values = ~MedianRainfall,
    title = "Median Rainfall (mm)",
    opacity = 1
  )

Each marker shows the station name, median rainfall, and county, making the map not only visually appealing but also informative.

Step 5: The Final Output

Here’s the result of my work—a visually compelling map where the weather stations are color-coded by their January median rainfall. Brighter colors indicate higher rainfall levels, while lighter colors signify lower amounts. The circles’ sizes are proportional to the rainfall levels for added emphasis.

Rainfall_map

Key Observation from the Map

The interactive map reveals several key patterns:

  1. Regional Distribution:

    • Western Region: Rainfall levels are visibly higher in the western regions of Ireland, as represented by darker red points on the map. This is consistent with the influence of Atlantic weather systems, which bring more precipitation to the western coastline.

    • Eastern Region: Stations in the eastern regions, including near Dublin, generally show lower rainfall levels, as indicated by lighter shades on the map. This aligns with the rain shadow effect caused by the country’s topography.

    • Central Region: The central region of Ireland shows intermediate rainfall levels, with stations typically in the 100-120 mm or 120-140 mm range (mild yellow circles).This suggests a transition zone between the wetter western regions and the drier eastern regions

  2. Climatic Variability:
    • Stations near coastal areas exhibit more intense rainfall levels compared to those inland, reflecting the moderating and moisture-laden effects of the ocean.
  3. Spatial Clustering:
    • Specific clusters of stations in the central and eastern parts show similar rainfall levels, suggesting a more uniform climate in these areas.

Insights from the Map

  1. Western Dominance: Rainfall is predominantly concentrated in the west, where Atlantic weather systems exert their influence. This indicates the need for flood prevention infrastructure in these areas.

  2. Dry Eastern Zones: Lower rainfall levels in the eastern regions highlight the potential for irrigation projects to ensure water availability for agriculture.

  3. Localized Clusters: The clustered nature of rainfall in central Ireland may result from microclimatic conditions influenced by local geography.

Why This Map Matters

This map is a powerful tool for understanding the regional differences in Ireland’s rainfall patterns. It helps policymakers plan flood prevention in wetter areas and water conservation in drier regions. Farmers can use it to optimize irrigation and crop choices, while researchers can study patterns to improve climate models. It’s also useful for disaster preparedness and raising public awareness. Overall, this map bridges data analysis with actionable insights.

Conclusion

By combining R Studio, leaflet, and dplyr, I was able to create a visually compelling map of rainfall data. This method can easily be extended to other months or regions to analyze rainfall trends. With such insights, researchers, farmers, and weather enthusiasts can make informed decisions.

Happy coding and exploring weather data!