Dear community
Using after_stat() I was able to visualise ggplot with standard deviations
instead of a confidence interval as seen in the R help.
p1<-ggplot(data = MS1, aes(x= Jahr, y= QI_A,color=Bio, linetype=Bio)) +
geom_smooth(aes(fill=Bio,
ymax=after_stat(y+se*sqrt(length(y))), ymin=after_stat(y-se*sqrt(y))) ,
method = "lm" , formula = y ~ x + I(x^2),linewidth=1) +
theme(panel.background = element_blank())+
theme(axis.line = element_line(colour = "black"))+
theme(axis.text=element_text(size=18))+
theme(axis.title=element_text(size=20))+
ylab("Anteil BFF an LN [%]") +xlab("Jahr")+
scale_color_manual(values=c("red","darkgreen"), labels=c("�LN", "BIO"))+
scale_fill_manual(values=c("red","darkgreen"), labels= c("�LN", "BIO"))+
theme(legend.title = element_blank())+
theme(legend.text=element_text(size=20))+
scale_linetype_manual(values=c("dashed", "solid"), labels=c("�LN", "BIO"))
p1<-p1 + expand_limits(y=c(0, 30))
When comparing the plots to the simple statistics the standard deviation do
not match. I assume it is because of the na.rm=TRUE which does not match
length(y) in the after_stat code. However I was not able to adapt the code
using NA values?
Simple statistics
> mean(MS2020[MS2020$Bio=="1",]$QI_A, na.rm=TRUE)
[1] 26.81225
> sd(MS2020[MS2020$Bio=="1",]$QI_A, na.rm=TRUE)
[1] 21.12419
> mean(MS2020[MS2020$Bio=="0",]$QI_A, na.rm=TRUE)
[1] 15.86196
> sd(MS2020[MS2020$Bio=="0",]$QI_A, na.rm=TRUE)
[1] 15.00405
Kind regards
Sibylle
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