Analysing meteorological data 2

library(DT)
library(readr)
library(ggplot2)
library(dplyr)

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library(lubridate)

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library(aqm)

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library(plotly)

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library(aqm)
library(dygraphs)
library(xts)

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data("met_office")
d<-met_office

d %>% filter(!is.na(tmin)) %>% 
  group_by(station,Year) %>% summarise(n=n(),tmin=round(mean(tmin),2),tmax=round(mean(tmax),2),rain=sum(rain)) %>% 
  filter(n==12) -> yrly

dd <-filter(d, station=="oxford")
xts(x = dd$rain, order.by = dd$date) %>% dygraph(group = "Oxford") %>% dyRangeSelector() %>%  dyRoller(rollPeriod = 12*10)