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This year, the so-called warming stripes, which were created by the scientist Ed Hawkins of the University of Reading, became very famous all over the world. These graphs represent and communicate climate change in a very illustrative and effective way.

From his idea, I created strips for examples of Spain, like the next one in Madrid.

In this post I will show how you can create these strips in R with the library ggplot2. Although I must say that there are many ways in R that can lead us to the same result or to a similar one, even within ggplot2.

## Data

In this case we will use the annual temperatures of Lisbon GISS Surface Temperature Analysis, homogenized time series, comprising the period from 1880 to 2018. Monthly temperatures or other time series could also be used. The file can be downloaded here. First, we should, as long as we have not done it, install the collection of tidyverse libraries that also include ggplot2. In addition, we will need the library lubridate for the treatment of dates. Then, we import the data of Lisbon in csv format.

#install the lubridate and tidyverse libraries
if(!require("lubridate")) install.packages("lubridate")
if(!require("tidyverse")) install.packages("tidyverse")

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

#import the annual temperatures

str(temp_lisboa)
## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 139 obs. of  18 variables:
##  $YEAR : num 1880 1881 1882 1883 1884 ... ##$ JAN   : num  9.17 11.37 10.07 10.86 11.16 ...
##  $FEB : num 12 11.8 11.9 11.5 10.6 ... ##$ MAR   : num  13.6 14.1 13.5 10.5 12.4 ...
##  $APR : num 13.1 14.4 14 13.8 12.2 ... ##$ MAY   : num  15.7 17.3 15.6 14.6 16.4 ...
##  $JUN : num 17 19.2 17.9 17.2 19.1 ... ##$ JUL   : num  19.1 21.8 20.3 19.5 21.4 ...
##  $AUG : num 20.6 23.5 21 21.6 22.4 ... ##$ SEP   : num  20.7 20 18 18.8 19.5 ...
##  $OCT : num 17.9 16.3 16.4 15.8 16.4 ... ##$ NOV   : num  12.5 14.7 13.7 13.5 12.5 ...
##  $DEC : num 11.07 9.97 10.66 9.46 10.25 ... ##$ D-J-F : num  10.7 11.4 10.6 11 10.4 ...
##  $M-A-M : num 14.1 15.2 14.3 12.9 13.6 ... ##$ J-J-A : num  18.9 21.5 19.7 19.4 20.9 ...
##  $S-O-N : num 17 17 16 16 16.1 ... ##$ metANN: num  15.2 16.3 15.2 14.8 15.3 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   YEAR = col_double(),
##   ..   JAN = col_double(),
##   ..   FEB = col_double(),
##   ..   MAR = col_double(),
##   ..   APR = col_double(),
##   ..   MAY = col_double(),
##   ..   JUN = col_double(),
##   ..   JUL = col_double(),
##   ..   AUG = col_double(),
##   ..   SEP = col_double(),
##   ..   OCT = col_double(),
##   ..   NOV = col_double(),
##   ..   DEC = col_double(),
##   ..   D-J-F = col_double(),
##   ..   M-A-M = col_double(),
##   ..   J-J-A = col_double(),
##   ..   S-O-N = col_double(),
##   ..   metANN = col_double()
##   .. )

We see in the columns that we have monthly and seasonal values, and the annual temperature value. But before proceeding to visualize the annual temperature, we must replace the missing values 999.9 with NA, using the ifelse( ) function that evaluates a condition and perform the given argument corresponding to true and false.

#select only the annual temperature and year column
temp_lisboa_yr <- select(temp_lisboa,YEAR,metANN)

#rename the temperature column
temp_lisboa_yr <- rename(temp_lisboa_yr,ta=metANN)

#missing values 999.9
summary(temp_lisboa_yr)
##       YEAR            ta
##  Min.   :1880   Min.   : 14.53
##  1st Qu.:1914   1st Qu.: 15.65
##  Median :1949   Median : 16.11
##  Mean   :1949   Mean   : 37.38
##  3rd Qu.:1984   3rd Qu.: 16.70
##  Max.   :2018   Max.   :999.90
temp_lisboa_yr <- mutate(temp_lisboa_yr,ta=ifelse(ta==999.9,NA,ta))

When we use the year as a variable, we do not usually convert it into a date object, however it is advisable. This allows us to use the date functions of the library lubridate and the support functions inside of ggplot2. The str_c( ) function of the library stringr, part of the collection of tidyverse, is similar to paste( ) of R Base that allows us to combine characters by specifying a separator (sep = “-”). The ymd( ) (year month day) function of the lubridate library converts a date character into a Date object. It is possible to combine several functions using the pipe operator %>% that helps to chain without assigning the result to a new object. Its use is very extended especially with the library tidyverse. If you want to know more about its use, here you have a tutorial.

temp_lisboa_yr <- mutate(temp_lisboa_yr,date=str_c(YEAR,"01-01",sep="-")%>%ymd())

## Creating the strips

First, we create the style of the graph, specifying all the arguments of the theme we want to adjust. We start with the default style of theme_minimal( ). In addition, we assign the colors from RColorBrewer to an object col_srip. More information about the colors used here.

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(vjust=3),
panel.grid.minor=element_blank(),
plot.title=element_text(size=14,face="bold")
)

col_strip <- brewer.pal(11,"RdBu")

brewer.pal.info
##          maxcolors category colorblind
## BrBG            11      div       TRUE
## PiYG            11      div       TRUE
## PRGn            11      div       TRUE
## PuOr            11      div       TRUE
## RdBu            11      div       TRUE
## RdGy            11      div      FALSE
## RdYlBu          11      div       TRUE
## RdYlGn          11      div      FALSE
## Spectral        11      div      FALSE
## Accent           8     qual      FALSE
## Dark2            8     qual       TRUE
## Paired          12     qual       TRUE
## Pastel1          9     qual      FALSE
## Pastel2          8     qual      FALSE
## Set1             9     qual      FALSE
## Set2             8     qual       TRUE
## Set3            12     qual      FALSE
## Blues            9      seq       TRUE
## BuGn             9      seq       TRUE
## BuPu             9      seq       TRUE
## GnBu             9      seq       TRUE
## Greens           9      seq       TRUE
## Greys            9      seq       TRUE
## Oranges          9      seq       TRUE
## OrRd             9      seq       TRUE
## PuBu             9      seq       TRUE
## PuBuGn           9      seq       TRUE
## PuRd             9      seq       TRUE
## Purples          9      seq       TRUE
## RdPu             9      seq       TRUE
## Reds             9      seq       TRUE
## YlGn             9      seq       TRUE
## YlGnBu           9      seq       TRUE
## YlOrBr           9      seq       TRUE
## YlOrRd           9      seq       TRUE

For the final graphic we use the geometry geom_tile( ). Since the data does not have a specific value for the Y axis, we need a dummy value, here I used 1. Also, I adjust the width of the color bar in the legend.

     ggplot(temp_lisboa_yr,
aes(x=date,y=1,fill=ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand=c(0,0))+
scale_y_continuous(expand=c(0,0))+
guides(fill=guide_colorbar(barwidth = 1))+
labs(title="LISBOA 1880-2018",
caption="Datos: GISS Surface Temperature Analysis")+
theme_strip

In case we want to get only the strips, we can use theme_void( ) and the argument show.legend = FALSE in geom_tile( ) to remove all style elements. We can also change the color for the NA values, including the argument na.value = “gray70” in the scale_fill_gradientn( ) function.

     ggplot(temp_lisboa_yr,
aes(x=date,y=1,fill=ta))+
geom_tile(show.legend = FALSE)+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand=c(0,0))+
scale_y_discrete(expand=c(0,0))+
theme_void()