EnglandTemp.r

setwd("/Users/davidcurran/Documents/monthlyEngland")
readLines("cetml1659on.dat", 
          n=10)

#Start of borrowed bit
## from https://gist.github.com/gavinsimpson/b52f6d375f57d539818b
CET <- url("http://www.metoffice.gov.uk/hadobs/hadcet/cetml1659on.dat")

cet <- read.table("cetml1659on.dat", sep = "", skip = 6, header = TRUE,
                  fill = TRUE, na.string = c(-99.99, -99.9))
names(cet) <- c(month.abb, "Annual") ## fix up df names

## remove last row of incomplete data
cet <- cet[-nrow(cet), ]
## get rid of the annual too - store for plotting
rn <- as.numeric(rownames(cet))
Years <- rn[1]:rn[length(rn)]
annCET <- data.frame(Temperature = cet[, ncol(cet)],
                     Year = Years)
cet <- cet[, -ncol(cet)]

## stack the data
cet <- stack(cet)[,2:1]
names(cet) <- c("Month","Temperature")
## add in Year and nMonth for numeric month and a proper Date class
cet <- transform(cet, Year = (Year <- rep(Years, times = 12)),
                 nMonth = rep(1:12, each = length(Years)),
                 Date = as.Date(paste(Year, Month, "15", sep = "-"),
                 format = "%Y-%b-%d"))

## sort into temporal order
cet <- cet[with(cet, order(Date)), ]

## Add in a Time variable
cet <- transform(cet, Time = as.numeric(Date) / 1000)
head(cet)
#end of borrowed bit
library(dplyr)
jan<- cet %>%
     group_by(Month)%>%
        summarise(avg_temp = mean(Temperature), 
              min_temp = min(Temperature), 
              max_temp = max(Temperature),
              total = n())  

jan

    Month  avg_temp min_temp max_temp total
   <fctr>     <dbl>    <dbl>    <dbl> <int>
1     Apr  7.935196      4.7     11.8   358
2     Aug 15.638827     12.9     19.2   358
3     Dec  4.103352     -0.8      9.7   358
4     Feb  3.871788     -1.9      7.9   358
5     Jan  3.258380     -3.1      7.5   358
6     Jul 15.973743     13.4     19.7   358
7     Jun 14.325698     11.5     18.2   358
8     Mar  5.334078      1.0      9.2   358
9     May 11.222067      8.5     15.1   358
10    Nov  6.072905      2.3     10.1   358
11    Oct  9.718436      5.3     13.3   358
12    Sep 13.345251     10.5     16.8   358

#now 2017
cet17 <- read.table("cet_mean_2017.dat", sep = "", skip = 4, header = FALSE,fill = TRUE, na.string = c(-32768,-32768))
names(cet17)[1] <- "day"
names(cet17)[2] <- "Jan"
names(cet17)[3] <- "Feb"
names(cet17)[4] <- "Mar"
names(cet17)[5] <- "Apr"
names(cet17)[6] <- "May"
names(cet17)[7] <- "Jun"
names(cet17)[8] <- "Jul"


cet17[9] <- NULL
cet17[10] <- NULL
cet17[11] <- NULL
cet17[12] <- NULL
cet17[13] <- NULL
summary(cet17)

#now graph
p<-ggplot(data=jan, aes(x=Month, y=avg_temp))+
  scale_x_discrete(limits = month.abb)+
  geom_line() +
  theme_tufte()+
  geom_point()
#jan

p<-p + annotate("text", x = 1, y = -3.1, label = "1795",color='blue')
p<-p + annotate("text", x = 1, y = 7.5, label = "1916",color='red')
p<-p + annotate("text", x = 1, y = 3.95, label = "*",color='dark green',size=5)
#feb
p<-p + annotate("text", x = 2, y = -1.9, label = "1947",color='blue')
p<-p + annotate("text", x = 2, y = 7.9, label = "1779",color='red')
p<-p + annotate("text", x = 2, y = 6.09, label = "*",color='dark green',size=5)
#mar
p<-p + annotate("text", x = 3, y = 1, label = "1674",color='blue')
p<-p + annotate("text", x = 3, y = 9.2, label = "1957",color='red')
p<-p + annotate("text", x = 3, y = 8.6, label = "*",color='dark green',size=5)
#apr
p<-p + annotate("text", x = 4, y = 4.7, label = "1837",color='blue')
p<-p + annotate("text", x = 4, y = 11.8, label = "2011",color='red')
p<-p + annotate("text", x = 4, y = 8.6, label = "*",color='dark green',size=5)

#May
p<-p + annotate("text", x = 5, y = 8.5, label = "1698",color='blue')
p<-p + annotate("text", x = 5, y = 15.1, label = "1833",color='red')
p<-p + annotate("text", x = 5, y = 13.2, label = "*",color='dark green',size=5)

#June
p<-p + annotate("text", x = 6, y = 11.5, label = "1675",color='blue')
p<-p + annotate("text", x = 6, y = 18.2, label = "1846",color='red')
p<-p + annotate("text", x = 6, y = 16.04, label = "*",color='dark green',size=5)

#Jul
p<-p + annotate("text", x = 7, y = 13.4, label = "1816",color='blue')
p<-p + annotate("text", x = 7, y = 19.7, label = "2006",color='red')
p<-p + annotate("text", x = 7, y = 16.77, label = "*",color='dark green',size=5)

#Aug
p<-p + annotate("text", x = 8, y = 12.9, label = "1912",color='blue')
p<-p + annotate("text", x = 8, y = 19.2, label = "1995",color='red')
p<-p + annotate("text", x = 8, y = 17, label = "*",color='dark green',size=5)

#Sep
p<-p + annotate("text", x = 9, y = 10.5, label = "1807",color='blue')
p<-p + annotate("text", x = 9, y = 16.8, label = "2006",color='red')
p<-p + annotate("text", x = 9, y = 16, label = "*",color='dark green',size=5)

#Oct
p<-p + annotate("text", x = 10, y = 5.3, label = "1740",color='blue')
p<-p + annotate("text", x = 10, y = 13.3, label = "2001",color='red')
p<-p + annotate("text", x = 10, y = 10.9, label = "*",color='dark green',size=5)

#Nov
p<-p + annotate("text", x = 11, y = 2.3, label = "1782",color='blue')
p<-p + annotate("text", x = 11, y = 10.1, label = "1994",color='red')
p<-p + annotate("text", x = 11, y = 5.6, label = "*",color='dark green',size=5)

#Dec
p<-p + annotate("text", x = 12, y = -0.8, label = "1890",color='blue')
p<-p + annotate("text", x = 12, y = 9.7, label = "2015",color='red')
p<-p + annotate("text", x = 12, y = 6, label = "*",color='dark green',size=5)

p<-p + annotate("text", x = 2, y = 15.4, label = "* Temp in last year",color='dark green')
#p<-p + annotate("text", x = 2, y = 15.4, label = " Average Temp",color='black')
p<-p + labs(y = "Average Temperature")
p<-p + labs(title = "Temperature in England per month since 1659", subtitle = "Average, highest, lowest and last year")
#may
#p<-p + annotate("text", x = 10, y = -4, label = "HadCET data #graph by @iamreddave")


ggsave("temperatur2017.jpg")
p
#low
janm<-filter(cet, Temperature == -3.1 & Month=='Jan')
#high
janx<-filter(cet, Temperature == 7.5 & Month=='Jan') 
#2016
jan2<-filter(cet, Year=='2016'& Month=='Jan') 
print(jan2)
#low
febn<-filter(cet, Temperature == -1.9 & Month=='Feb')
#high
febx<-filter(cet, Temperature == 7.9 & Month=='Feb') 
#2016
feb2<-filter(cet, Year=='2016'& Month=='Feb') 
print(feb2)
#low
marn<-filter(cet, Temperature == 1 & Month=='Mar')
#high
marx<-filter(cet, Temperature == 9.2 & Month=='Mar') 
#2016
mar<-filter(cet, Year=='2016'& Month=='Mar') 
print(mar)
#low
aprn<-filter(cet, Temperature == 4.7 & Month=='Apr')
#high
aprx<-filter(cet, Temperature == 11.8 & Month=='Apr') 
#2016
apr<-filter(cet, Year=='2016'& Month=='Apr') 
print(aprn)

#low
mayn<-filter(cet, Temperature == 8.5 & Month=='May')
#high
mayx<-filter(cet, Temperature == 15.1 & Month=='May') 
#2016
may<-filter(cet, Year=='2016'& Month=='May') 
print(may)
#low
junn<-filter(cet, Temperature == 11.5 & Month=='Jun')
#high
junx<-filter(cet, Temperature == 18.2 & Month=='Jun') 
#2016
jun<-filter(cet, Year=='2016'& Month=='Jun') 
print(jun)
#low
juln<-filter(cet, Temperature == 13.4 & Month=='Jul')
#high
julx<-filter(cet, Temperature == 19.7 & Month=='Jul') 
#2016
jul<-filter(cet, Year=='2016'& Month=='Jul') 
print(jul)
#low
augn<-filter(cet, Temperature == 12.9 & Month=='Aug')
#high
augx<-filter(cet, Temperature == 19.2 & Month=='Aug') 
#2016
aug<-filter(cet, Year=='2016'& Month=='Aug') 
print(aug)
#low
Sepn<-filter(cet, Temperature == 10.5 & Month=='Sep')
#high
Sepx<-filter(cet, Temperature == 16.8 & Month=='Sep') 
#2016
Sep<-filter(cet, Year=='2016'& Month=='Sep') 
print(Sep)
#low
Octn<-filter(cet, Temperature == 5.3 & Month=='Oct')
#high
Octx<-filter(cet, Temperature == 13.3 & Month=='Oct') 
#2016
Oct<-filter(cet, Year=='2016'& Month=='Oct') 
print(Oct)
#low
Novn<-filter(cet, Temperature == 2.3 & Month=='Nov')
#high
Novx<-filter(cet, Temperature == 10.1 & Month=='Nov') 
#2016
Nov<-filter(cet, Year=='2016'& Month=='Nov') 
print(Nov)
#low
Decn<-filter(cet, Temperature == -0.8 & Month=='Dec')
#high
Decx<-filter(cet, Temperature == 9.7 & Month=='Dec') 
#2016
Dec<-filter(cet, Year=='2016'& Month=='Dec') 
print(Dec)