Reputation: 898
I have the following dataset.
And want to plot it using ggplot2
, but plotting the boxplots in a continous scale.
However, I could not find a way to do that. This is the furthest I could get:
ggplot(t, aes(x=treatment, y=loss)) +
scale_fill_manual(values=cbPalette)+
geom_boxplot(fill="white") +
geom_point(col="blue")+
scale_x_discrete(labels=c("35.9°C\nmedian\nPBT","40.8°C\n95perc \nPBT","42.6°C\n median\nVMT"))+
ylab("weight lost(g)")+
xlab("")+
theme(panel.background = element_rect(fill='transparent', colour='black'),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(colour="black", size=14),
axis.text.x = element_text(angle=0, vjust=0.5,colour="black", size=14),
axis.title.y = element_text(colour="black", size=14),
axis.text.y = element_text(angle=0, vjust=0.5,colour="black", size=14)
)+
theme(legend.position="none")+
annotate("text", size=5, x = 0.5,y= 25, label = "A")
Upvotes: 0
Views: 68
Reputation: 1754
Column loss
is missing from the dataset: I presume it is the difference between pre and post weights (which are expressed in Kg in the dataset). I also presume that by "continuous scale" you mean that the treatment is represented as a real number, not as a factor on the x-axis. If that is the case, then it is enough to add a group
statement.
## add loss (in gr)
t <- t %>% mutate(loss=-1e3*(post.weight-pre.weight))
p <- ggplot(t, aes(x=treatment, y=loss, group=treatment)) +
scale_fill_manual(values=cbPalette)+
geom_boxplot(fill="white") +
geom_point(col="blue")+
ylab("weight lost(g)")+
xlab("")+
theme(panel.background = element_rect(fill='transparent', colour='black'),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_text(colour="black", size=14),
axis.text.x = element_text(angle=0, vjust=0.5,colour="black", size=14),
axis.title.y = element_text(colour="black", size=14),
axis.text.y = element_text(angle=0, vjust=0.5,colour="black", size=14)
)+
theme(legend.position="none")
Upvotes: 1