Ireland’s Median Rainfall in January 2024

library(leaflet)
library(dplyr)

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(readxl)

file_path <- "C:/Users/SANGETHA/Desktop/weather_stations_map/rainfall.RData.xlsx"
data <- read_excel(file_path)


#median rainfall in Jan
median_rainfall <- data %>%
  filter(Month == "Jan") %>%
  group_by(Stations, latitude, longitude) %>%
  summarize(median_rainfall = median(Total_rainfall, na.rm = TRUE) 
            ,.groups="drop")

print(median_rainfall)

# A tibble: 25 × 4
   Stations           latitude longitude median_rainfall
   <chr>                 <dbl>     <dbl>           <dbl>
 1 Athenry                53.3      -8.8            92.4
 2 Ballyhaise             54.1      -7.3            62  
 3 Belmullet              54.2     -10             117. 
 4 Casement Aerodrome     53.3      -6.4            57.7
 5 Claremorris            53.7      -9             116. 
 6 Cork Airport           51.8      -8.5            99.5
 7 Dublin Airport         53.4      -6.2            61.3
 8 Dunsany (Grange)       53.5      -6.7            60.9
 9 Finner Camp            54.5      -8.2           106. 
10 Gurteen                53        -8              68.8
# ℹ 15 more rows

# Create the map
leaflet(median_rainfall) %>%
  addTiles() %>%
  addCircleMarkers(
    ~longitude, ~latitude,
    color = ~colorNumeric("viridis", median_rainfall)(median_rainfall),
    popup = ~paste0("Stations: ", Stations, "<br>Rainfall: ", median_rainfall)
  )

Explanation

Mapping rainfall in Ireland- An analysis of january data

1.Data description The dataset used in this analysis contains rainfall data from 25 weather stations across Ireland. The data includes information on monthly rainfall (in millimeters) along with the geographical coordinates (latitude and longitude) for each station. for this study, we focused on rainfall data for the month of January, aggregating it by station to calculate the median monthly rainfall. This allowed us to identify variations in rainfall across different locations.The median is used as a robust measure to account for outliers or variability in rainfall, providing a central tendency that is less affected by extreme values.

2.Code explanation

Load required libraries: leaflet: For creating the interactive map; dplyr: For data manipulation; readxl: For reading the dataset from an Excel file.

-read data: The rainfall data was imported using the read_excel function, specifying the file path to the dataset.

-Filter and Aggregate Data: The data is filtered to include only January (filter(Month == “Jan”)). Grouped by station and geographical coordinates. The median() function computes the median rainfall for each station.

-Generate the Map: Using the leaflet library, a map is created where: Each station is represented by a circle marker. The color of the marker corresponds to the station’s median rainfall level, using a gradient from the “viridis” color palette. Popups display the station name and its median rainfall.

3.Discussion of Patterns

The map highlights clear spatial patterns in median rainfall levels for January across Ireland:

High Rainfall Areas: Regions in the west and along the Atlantic coast generally show higher median rainfall levels. This pattern aligns with Ireland’s prevailing westerly winds and the influence of moist Atlantic air masses, which bring more precipitation to western areas.

Lower Rainfall Areas: Eastern and southeastern parts of Ireland, including areas around Dublin, typically show lower median rainfall. These areas are in the rain shadow of Ireland’s central mountains, leading to reduced precipitation.

Geographical Variation: There is a noticeable gradient from west to east, with rainfall levels decreasing as one moves inland and towards the east.

Conclusion

This analysis provides a visual representation of rainfall patterns in January across Ireland. By leveraging spatial data and interactive mapping techniques, we can effectively communicate complex patterns and draw meaningful insights from meteorological data.

Ps:As for the previous assignment, i assume that since the data was not normally distributed, I used a non-parametric test. I performed it as an additional step to ensure the data met assumptions commonly required for statistical analyses like linear regression or correlation analysis. while now that i understand that the normality of the raw data is not strictly necessary for these methods, my intention was to demonstrate a comprehensive understanding of the dataset and account for any potential deviations that could affect the analysis. I recognize that including the normality test without directly linking it to the methods used may have caused some confusion, and I will ensure that future analyses focus on methods directly relevant to the objectives of the assignment.