Quick way to add annotations to your ggplot graphs

Lately, some of the graphs I have been working on have “strange/erratic” values, so I thought to plot those values in a different color and rather than adding an extra legend line I have decided to add a note to explain the difference. Among the many options available, I have found a very quick and harmless way to add annotations to ggplot graphs. It uses the library “gridExtra”, which employs user-level functions that work with “grid” graphics and draw tables.

1.load ggplot2 library
library(ggplot2)
2. and save your graph as “my_graph”:
my_graph<- qplot(wt, mpg, data = mtcars)
3. load gridExtra and add the text to the graph. Note that x, hjust and vjust give the position of the text in the outer margins. If you want to annotate INSIDE the graph, use annotate:
library(gridExtra)
g <- arrangeGrob(p, sub = textGrob("I pledge my life and honor to the Night's Watch, \nfor this night and all the nights to come.", x=0, hjust=-0.1, vjust=0.1,gp = gpar(fontface = "italic", fontsize = 10)))

5. save the graph
ggsave("my_graph_with_note.pdf", g, width=5,height=5)

my_graph_with_note

 

 

 

 

 

here is the graph I have been working on:

MORAN_I_MAC

Moran plots in ggplot2

Moran plots are one of the many way to depict spatial autocorrelation:
moran.test(varofint,listw)
where “varofint” is the variable we are studying, “listw” a listwise neighbourhood matrix, and the function “moran.test” performs the Moran’s test (duh!) for spatial autocorrelation and is included in the spdep funtionality. The same plot can be done using ggplo2 library. Provided that we already have our listwise matrix of neighborhood relationships listw, we first define the variable and the lagged variable under study, computing their mean and saving them into a data frame (there are a lot of datasets you can find implemented in R: afcon, columbus, syracuse, just to cite a few). The purpose is to obtain something that looks like this (I have used my own *large* set of Spanish data to obtain it):

ggplot2.moranplot1

Upload your data. Here is Anselin (1995) data on African conflicts, afcon:

data(afcon)
varofint listw varlag var.name <- "Total Conflicts"
m.varofint m.varlag
and compute the local Moran's statistic using localmoran:

lisa
and save everything into a dataframe:
df

use these variables to derive the four sectors "High-High"(red), "Low-Low"(blue), "Low-High"(lightblue), "High-Low"(pink):
df$sector significance vec =df$m.varofint & df$varlag>=df$m.varlag]  df$sector[df$varofint<df$m.varofint & df$varlag<df$m.varlag]  df$sector[df$varofint<df$m.varofint & df$varlag>=df$m.varlag]  =df$m.varofint & df$varlag<df$m.varlag]

df$sec.data

df$sector.col[df$sec.data==1] <- "red"
df$sector.col[df$sec.data==2] <- "blue"
df$sector.col[df$sec.data==3] <- "lightblue"
df$sector.col[df$sec.data==4] <- "pink"
df$sector.col[df$sec.data==0] <- "white"

df$sizevar df$sizevar 0.1)
df$FILL df$BORDER
to get the ggplot graph:
p 0.05", "High-High", "Low-Low","Low-High","High-Low"))+
scale_x_continuous(name=var.name)+
scale_y_continuous(name=paste("Lagged",var.name))+
theme(axis.line=element_line(color="black"),
axis.title.x=element_text(size=20,face="bold",vjust=0.1),
axis.title.y=element_text(size=20,face="bold",vjust=0.1),
axis.text= element_text(colour="black", size=20, angle=0,face = "plain"),
plot.margin=unit(c(0,1.5,0.5,2),"lines"),
panel.background=element_rect(fill="white",colour="black"),
panel.grid=element_line(colour="grey"),
axis.text.x  = element_text(hjust=.5, vjust=.5),
axis.text.y  = element_text(hjust=1, vjust=1),
strip.text.x  = element_text(size = 20, colour ="black", angle = 0),
plot.title= element_text(size=20))+
stat_smooth(method="lm",se=F,colour="black", size=1)+
geom_vline(xintercept=m.varofint,colour="black",linetype="longdash")+
geom_hline(yintercept=m.varlag,colour="black",linetype="longdash")+
theme(legend.background =element_rect("white"))+
theme(legend.key=element_rect("white",colour="white"),
legend.text =element_text(size=20))

Check out the interactive shiny version on pracademic