Ireland’s Median Rainfall in January 1850-2014

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###set working directory where the data will be taken from and saved from

setwd('C:/Users/27736/Desktop/R data/Class Materials-20241230')

###load library (sf) to read simple feauture ### load and read the data

load ('Rainfall.Rdata') 
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

station <- read_sf ('weather_stations.geojson')

###check the structure of the data

str (station)

## sf [49,500 × 12] (S3: sf/tbl_df/tbl/data.frame)
##  $ Year        : num [1:49500] 1850 1851 1852 1853 1854 ...
##  $ Month       : chr [1:49500] "Jan" "Jan" "Jan" "Jan" ...
##  $ Rainfall    : num [1:49500] 169 236 250 209 188 ...
##  $ Station     : chr [1:49500] "Ardara" "Ardara" "Ardara" "Ardara" ...
##  $ Elevation   : int [1:49500] 15 15 15 15 15 15 15 15 15 15 ...
##  $ Easting     : num [1:49500] 180788 180788 180788 180788 180788 ...
##  $ Northing    : num [1:49500] 394679 394679 394679 394679 394679 ...
##  $ County      : chr [1:49500] "Donegal" "Donegal" "Donegal" "Donegal" ...
##  $ Abbreviation: chr [1:49500] "AR" "AR" "AR" "AR" ...
##  $ Source      : chr [1:49500] "Briffa" "Briffa" "Briffa" "Briffa" ...
##  $ coast_dist  : num [1:49500] 17.4 17.4 17.4 17.4 17.4 ...
##  $ geometry    :sfc_POINT of length 49500; first list element:  'XY' num [1:2] 181347 393613
##  - attr(*, "sf_column")= chr "geometry"
##  - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
##   ..- attr(*, "names")= chr [1:11] "Year" "Month" "Rainfall" "Station" ...

library (dplyr) #####this is done in order to compute the median by grouping

## 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 (tidyverse)

## Warning: package 'tidyverse' was built under R version 4.4.2

## Warning: package 'ggplot2' was built under R version 4.4.2

## Warning: package 'readr' was built under R version 4.4.2

## Warning: package 'lubridate' was built under R version 4.4.2

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ readr     2.1.5
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## ✔ lubridate 1.9.4     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

###Calculating median for the 25 station. library (dplyr) library (tidyverse)

Rain_med <- rain %>% filter(Month == "Jan" & Year >= 1850 & Year <= 2014) %>% group_by(Station) %>% summarise(medrainfall = median(Rainfall, na.rm = TRUE))

###visual representation to se how the median distributions differs from station to station from january 1850 to 2014.

barplot(Rain_med$medrainfall,names= Rain_med$Station, las= 3, col = "blue")

###the next step is to join median rainfall obtained to station in order to get coordinates for mapping purpose.

station %>% left_join(Rain_med)-> station_median

## Joining with `by = join_by(Station)`

###the next step will be to represent the different by first exporting the map of ireland from and inserting its coordinate for the map to be drawn on R

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(sf)


### Convert to sf object
stations_sf <- station_median %>% 
  st_as_sf(coords = c('Long', 'Lat'), crs = 4326)

### Set tmap to view mode
tmap_mode('view')

## tmap mode set to interactive viewing

### Create the map
tm_shape(stations_sf) + 
  tm_bubbles(size = "medrainfall", 
             col = "medrainfall", 
             palette = "viridis", 
             title.size = "Median Rainfall", 
             title.col = "Median Rainfall") + 
  tm_layout(title = "Median Rainfall by Station", 
            legend.outside = TRUE)

## Legend for symbol sizes not available in view mode.

###Notes on observable pattern

### It observable that stations on the west coast have higher median than those in the interior and the eastern side of the Island of Ireland on average. another discerning pattern is that Station that are close together having median within the same range of rainfall or within range below or above mostly therefore as Waldo Tobler has stated places that are close together have spatial similarity to place that are far apart .

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