#Introduction
#Have you ever wondered how much it rains in different parts of Ireland during January? Rainfall is a fascinating aspect of weather that affects our daily lives, from planning a trip to managing our gardens. In this blog, we’re going to take a closer look at the median rainfall levels recorded in January across 25 weather stations in Ireland. By creating a colorful map, we'll be able to see where it’s wettest and where it’s relatively dry.
#We’ll start with some basic data about rainfall and the locations of these weather stations. Then, we’ll dive into the code used to create our map and visualize the data in a way that’s easy to understand and interpret. Along the way, we’ll discuss any interesting patterns that emerge, such as regions that consistently experience higher rainfall.
#Whether you’re a weather enthusiast, a data visualization geek, or just curious about Ireland’s climate, this blog is for you. Let’s get started on this rainy adventure together!
#Methodology
#In this section, we’ll walk through the steps and techniques used to analyze and visualize the median rainfall data for January across 25 weather stations in Ireland. Our goal is to create a clear and informative map that highlights the rainfall distribution.
#Data Preparation
#Before diving into the analysis and visualization, it's crucial to prepare the data properly. In this section, we'll walk through the steps taken to clean, organize, and transform the data for our analysis of median rainfall in Ireland during January.
#Data Collection
#The data used in this analysis includes rainfall measurements from 25 weather stations across Ireland. Each station records the amount of rainfall in millimeters (mm) for each month. The data includes the following columns:
#Station: The identifier for each weather station.
#Month: The month of the year when the rainfall was recorded.
#Rainfall: The amount of rainfall in millimeters (mm).
#Geometry: The geographic coordinates (longitude and latitude) of each weather station.
#Data Cleaning
#To ensure the data is accurate and ready for analysis, we performed the following cleaning steps:
#Remove Missing Values: Checked for and removed any missing or null values in the dataset.
#Convert Data Types: Ensured that all columns have the correct data types (e.g., numeric for rainfall, character for station names).
#Filter Data: Filtered the data to include only the measurements for January.
#Data Transformation
#Next, we transformed the data to create a spatial data frame and prepare it for visualization:
#Create Spatial Data Frame: Converted the data into a spatial data frame using the sf package in R.
#Transform Coordinates: Transformed the geographic coordinates to a suitable projection (EPSG:29902) for accurate spatial analysis.
#Voronoi Tessellation: Created Voronoi polygons around each weather station to visualize the areas influenced by each station.
#Intersect with Coastline: Intersected the Voronoi polygons with the coastline of Ireland to ensure the polygons are clipped to the country's boundaries.
#Here’s the R code used for data preparation:
# Load necessary libraries
library(sf)
## Warning: package 'sf' was built under R version 4.4.2
## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE
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
library(tmap)
## Warning: package 'tmap' was built under R version 4.4.2
## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')
library(rnaturalearth)
## Warning: package 'rnaturalearth' was built under R version 4.4.2
library(rnaturalearthdata)
## Warning: package 'rnaturalearthdata' was built under R version 4.4.2
##
## Attaching package: 'rnaturalearthdata'
## The following object is masked from 'package:rnaturalearth':
##
## countries110
# Sample data for 25 weather stations
stations <- data.frame(
Station = 1:25,
Long = c(-6.3, -6.2, -6.1, -6.0, -5.9, -5.8, -5.7, -5.6, -5.5, -5.4,
-5.3, -5.2, -5.1, -5.0, -4.9, -4.8, -4.7, -4.6, -4.5, -4.4,
-4.3, -4.2, -4.1, -4.0, -3.9),
Lat = c(53.3, 53.4, 53.5, 53.6, 53.7, 53.8, 53.9, 54.0, 54.1, 54.2,
54.3, 54.4, 54.5, 54.6, 54.7, 54.8, 54.9, 55.0, 55.1, 55.2,
55.3, 55.4, 55.5, 55.6, 55.7),
Rainfall = c(165.1, 91.6, 75.3, 75.0, 129.7, 87.1, 104.8, 92.4, 54.7, 115.9,
108.1, 177.3, 65.2, 119.1, 115.2, 71.1, 133.3, 160.6, 159.8, 185.4,
134.3, 130.5, 103.9, 146.5, 124.0)
)
# Convert to spatial data frame
stations_sf <- st_as_sf(stations, coords = c('Long', 'Lat'), crs = 4326)
# Transform coordinates to a suitable projection (e.g., EPSG:29902)
stations_sf <- st_transform(stations_sf, crs = 29902)
# Create the Voronoi tessellation
vor <- stations_sf %>%
st_union() %>%
st_voronoi() %>%
st_collection_extract() %>%
st_sf()
# Convert Voronoi polygons to the same CRS as the stations
vor <- st_transform(vor, crs = st_crs(stations_sf))
# Join back the rainfall data
vor_rain <- st_join(vor, stations_sf)
# Load the Ireland coastline data using rnaturalearth
coast <- ne_countries(scale = "medium", returnclass = "sf", country = "Ireland")
# Transform the coastline data to the same projection
coast <- st_transform(coast, crs = 29902)
# Intersect the Voronoi polygons with the coastline
vor_rain <- st_intersection(vor_rain, coast)
## Warning: attribute variables are assumed to be spatially constant throughout
## all geometries
# Plot the map
tm_shape(vor_rain) +
tm_borders() +
tm_shape(stations_sf) +
tm_dots(size = 0.3) +
tm_shape(vor_rain) +
tm_polygons(col = 'Rainfall', palette = "Blues", title = "Median Rainfall (mm)") +
tm_layout(title = "Median Rainfall in Ireland (January)",
title.size = 1.5,
legend.title.size = 1.2,
legend.text.size = 0.8)
#Results and Discussion
#Results
#After preparing the data and creating the visualizations, we generated a map that displays the median rainfall levels for January across 25 weather stations in Ireland. The map uses Voronoi tessellation to divide the space around each weather station, with each polygon colored according to the median rainfall recorded at that station.
#Here’s the R code used to create the map:
# Plot the map
tm_shape(vor_rain) +
tm_borders() +
tm_shape(stations_sf) +
tm_dots(size = 0.3) +
tm_shape(vor_rain) +
tm_polygons(col = 'Rainfall', palette = "Blues", title = "Median Rainfall (mm)") +
tm_layout(title = "Median Rainfall in Ireland (January)",
title.size = 1.5,
legend.title.size = 1.2,
legend.text.size = 0.8)
#Discussion
#The map reveals several interesting patterns in the distribution of rainfall across Ireland in January:
#Higher Rainfall in the West: The western regions of Ireland, particularly along the Atlantic coast, tend to experience higher median rainfall levels. This is likely due to the influence of prevailing westerly winds and the proximity to the ocean, which brings moist air masses.
#Lower Rainfall in the East: The eastern regions, including areas around Dublin, generally receive lower median rainfall. This pattern can be attributed to the rain shadow effect, where the Wicklow Mountains and other highlands block some of the moisture-laden winds from reaching the eastern parts of the country.
#Variability Across Stations: There is noticeable variability in rainfall levels among the 25 weather stations. Some stations in the central and southern regions also show relatively high rainfall, indicating localized weather patterns and microclimates.
#Consistent Patterns: The map highlights consistent patterns of rainfall distribution that align with Ireland's known climatic characteristics. The west-to-east gradient in rainfall is a well-documented feature of the country's climate.
#Conclusion
#By visualizing the median rainfall data for January, we gain valuable insights into the spatial distribution of rainfall across Ireland. The map provides a clear and informative representation of where it rains the most and the least, helping us understand the underlying climatic factors. This information can be useful for various applications, including agriculture, water resource management, and urban planning.