In JimDuggan/aimsir17: Irish Weather Observing Stations Hourly Records for 2017

knitr::opts_chunk$set(echo = FALSE)

R tools such as dplyr and tidyr can be used to summarise data (e.g. add rain observations to obtain monthly and annual cumulative amounts). The three libraries are first loaded.

library(aimsir17)
library(dplyr)
library(tidyr)

Next, we show the overall data set (219,000 observations)

observations

We can confirm the number of records gathered for each month, and show these

observations %>% 
  group_by(station, month) %>%
  summarise(TotalObservations=n()) %>%
  pivot_wider(names_from = month,values_from = TotalObservations) %>%
  print(n=25)

We can also confirm the number of missing rainfall values for each month.

observations %>% 
  group_by(station, month) %>%
  summarise(TotalMissing=sum(is.na(rain))) %>%
  pivot_wider(names_from = month,values_from = TotalMissing) %>%
  print(n=25)

We can sum all the rainfall values for each station by each month

observations %>% 
  group_by(station, month) %>%
  summarise(TotalRainfall=sum(rain,na.rm = T)) %>%
  pivot_wider(names_from = month,values_from = TotalRainfall) %>%
  print(n=25)

Order the months from driest to wettest

observations %>% 
  group_by(month) %>%
  summarise(TotalRainfall=sum(rain,na.rm = T)) %>%
  arrange(TotalRainfall) %>%
  print(n=12)

Finally, order the weather stations from driest to wettest, with an index value where 100 is the wettest for 2017 (Newport!)

observations %>% 
  group_by(station) %>%
  summarise(TotalRainfall=sum(rain,na.rm = T)) %>%
  arrange(TotalRainfall) %>%
  mutate(Index=100*TotalRainfall/max(TotalRainfall)) %>%
  print(n=25)