Example 2 - Exploring Storm Ophelia

The data set can be explored for specific events. For example, on October 16th 2017, Storm Ophelia landed in Ireland. We can analyse this data using dplyr and ggplot2. First, we load the libraries.

library(aimsir17)
library(dplyr)
library(ggplot2)

Next, we filter the observations for this date.

o <- observations %>%
  filter(month==10, day==16)
o

## # A tibble: 600 x 12
##    station  year month   day  hour date                 rain  temp  rhum
##    <chr>   <dbl> <dbl> <int> <int> <dttm>              <dbl> <dbl> <dbl>
##  1 ATHENRY  2017    10    16     0 2017-10-16 00:00:00   0.4   9.9    95
##  2 ATHENRY  2017    10    16     1 2017-10-16 01:00:00   0.3   9.9    95
##  3 ATHENRY  2017    10    16     2 2017-10-16 02:00:00   0.4   9.9    95
##  4 ATHENRY  2017    10    16     3 2017-10-16 03:00:00   0     9.8    95
##  5 ATHENRY  2017    10    16     4 2017-10-16 04:00:00   0.5   9.9    95
##  6 ATHENRY  2017    10    16     5 2017-10-16 05:00:00   0.1  10.5    96
##  7 ATHENRY  2017    10    16     6 2017-10-16 06:00:00   0    11.8    96
##  8 ATHENRY  2017    10    16     7 2017-10-16 07:00:00   0    12.4    94
##  9 ATHENRY  2017    10    16     8 2017-10-16 08:00:00   0    14.2    90
## 10 ATHENRY  2017    10    16     9 2017-10-16 09:00:00   0    16.6    72
## # … with 590 more rows, and 3 more variables: msl <dbl>, wdsp <dbl>,
## #   wddir <dbl>

Next, we can take a selection of stations that had the lowest atmospheric pressure.

lowest <- o %>% arrange(msl) %>% 
  slice(1:6) %>% 
  pull(station) %>%
  unique()
lowest

## [1] "VALENTIA OBSERVATORY" "MACE HEAD"            "CLAREMORRIS"         
## [4] "KNOCK AIRPORT"        "NEWPORT"

This is then visualised using ggplot2

ggplot(filter(o,station %in% lowest),aes(x=date,y=msl,colour=station))+
  geom_point()+geom_line()

The stations with the highest mean hourly windspeed can be found

highest<- o %>% arrange(desc(wdsp)) %>% 
  slice(1:6) %>% 
  pull(station) %>%
  unique()
highest

## [1] "ROCHES POINT"  "SherkinIsland"

ggplot(filter(o,station %in% highest),aes(x=date,y=wdsp,colour=station))+
  geom_point()+geom_line()