ge03

Exercise 1

library(tidyverse) #loads tidyverse package

#import data into R
monthly_temps <- read_csv("monthly_global_land_and_ocean_temperature_anomalies.csv")

## Parsed with column specification:
## cols(
##   YearMonth = col_integer(),
##   Value = col_double()
## )

library(lubridate) #loads lubridate

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:base':
## 
##     date

#convert class from integer to date, day is missing
monthly_temps <- mutate(monthly_temps, date = ymd(YearMonth, truncated=1))

# year and month stored as separate variables, keep name of month
monthly_temps <- mutate(monthly_temps,
                        year=year(date),
                        month=month(date,label=TRUE)
)

#rename value --> temperature_anomaly
monthly_temps <- rename(monthly_temps, temperature_anomaly = Value)

#drop the YearMonth variable (replaced by date variable)
monthly_temps <- select(monthly_temps, -YearMonth)

Exercise 2

You can also embed plots, for example:

library(tidyverse)
#import data into R
gridded_data <- read_csv("gridded_data.csv",col_names=F)

## Parsed with column specification:
## cols(
##   .default = col_integer()
## )

## See spec(...) for full column specifications.

#create variables for number of months and for monthly data
number_of_months <- nrow(gridded_data)/37
monthly_data <- vector("list",number_of_months)

library(lubridate)

#for loop
for(i in 1:number_of_months){
  #extract and store temperature anomalies
  monthly_data[[i]] <- gridded_data[((i-1)*37+2):(i*37),]
  #get date for current month in yyyymm format
  integer_date <- gridded_data[((i-1)*37+1),2]*100+gridded_data[((i-1)*37+1),1]
  #convert to Date class
  current_date <- ymd(integer_date ,truncated=1)
  #extract named month
  current_month <- month(current_date,label=T)
  #extract year
  current_year <- year(current_date)
  #paste together named month and year to name data
  names(monthly_data)[i] <- paste(current_month, current_year)
}

#for loop
for(i in 1:number_of_months){
  #save selected month into dataset
  current_data <- monthly_data[[i]]
  #create latitude vector
  latitudes <- rev(seq(from=-87.5, to=87.5, by=5))
  #create latitude variable
  current_data$latitude <- latitudes
  #create longitude vector
  longitudes <- seq(from=-177.5, to=177.5, by=5)
  #make these values the column names
  colnames(current_data)[1:72] <- longitudes
  #gather all our temperature anomaly values into a single variable 
  current_data <- gather(current_data, key="longitude", value="TempAnom", -latitude)
  #change class to numeric
  current_data$longitude <- as.numeric(current_data$longitude)
  #convert the -9999 to NA and convert to Celsius
  current_data <- mutate(current_data,
                         TempAnom = ifelse(TempAnom==-9999, NA, TempAnom/100))
  monthly_data[[i]] <- current_data
}

# To create graph for August 1916

library(maps)

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
## 
##     map

#upload map into R
world <- map_data("world")

#set current month
current_month <- "Aug 1916"
#create variable
current_data <- monthly_data[[current_month]]
#create variable
current_name <- names(monthly_data[current_month])

#graph temperature anomoly in tile plot
g<-ggplot()+
  geom_tile(data=current_data,
            aes(x=longitude, y=latitude, fill=TempAnom),
            width=5, height=5,alpha=.5)+
  scale_fill_gradient2(low="blue",mid="white",high="red",midpoint=0)+
  theme_void()+labs(title=current_name)

# project map onto tile plot
g <- g+geom_path(data = world, aes(x = long, y = lat, group = group))

g + coord_map("albers",parameters=c(0,0))

Exercise 3

#save manipulated data as new R file
save(monthly_temps, monthly_data, file="climate_data.RData")