Chapter 9 Generating the visualization

We first check to see if the necessary packages are loaded.

# check if the "tidyverse" and "lubridate" packages are installed
if(!require("tidyverse")) install.packages("tidyverse")
if(!require("lubridate")) install.packages("lubridate")

# load libraries
library(tidyverse)
library(lubridate)
library(RColorBrewer)

Then we import the dataset into the project file.

# import tropics annual data
tropics_annual <- read_csv("data/HadCRUT.4.6.0.0.annual_30S_30N.csv")
# the above file was converted to csv from the original downloaded file "data/HadCRUT.4.6.0.0.annual_30S_30N.txt"

# add header to the imported data based on the documentation
names(tropics_annual) <- c("date", 
                           "temp_anomaly",
                           "lb_bias_uncertainty", "ub_bias_uncertainty", 
                           "lb_measure_sample", "ub_measure_sample",
                           "lb_coverage", "ub_coverage", 
                           "lb_measure_sample_bias", "ub_measure_sample_bias", 
                           "lb_comb_all", "ub_comb_all")

# select only the date and temperature anomaly values for creating our warming stripes visualization
tropics_annual_yr <- select(tropics_annual, date, temp_anomaly)

# convert date column to a date object
tropics_annual_yr <- mutate(tropics_annual_yr,date=str_c(date,"01-01",sep="-")%>%ymd())

We define the theme for the plot, selecting the color palette. We also set the angle for the x-axis labels to be displaying vertically, at a 90 degrees angle.

# Define the theme for the output plot
theme_strip <- theme_minimal()+
                 theme(axis.text.y = element_blank(),
                       axis.line.y = element_blank(),
                       axis.title = element_blank(),
                       panel.grid.major=element_blank(),
                       legend.title = element_blank(),
                       axis.text.x=element_text(angle=90, hjust = 1),
                       panel.grid.minor=element_blank(),
                        plot.title=element_text(size=14,face="bold", hjust = 0.5)
                       )
col_strip <- brewer.pal(11,"RdBu")
#brewer.pal.info

Generate the plot and apply the theme to it. We set the scale for x-axis to be 5 years and only to display the Year value of the date.

# create the plot
ggplot(tropics_annual_yr,
             aes(x=date,y=1,fill=temp_anomaly))+
        geom_tile()+
           scale_x_date(date_breaks = "5 years",
                     date_labels = "%Y",
                     expand=c(0,0))+
           scale_y_continuous(expand=c(0,0))+
           scale_fill_gradientn(colors=rev(col_strip))+
             guides(fill=guide_colorbar(barwidth = 1))+
            labs(title="Tropics Annual Temperature Anomaly 1850 - 2019",
                caption="Data source: https://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html")+
              theme_strip