# [ACCEPTED]-Show standard devation using geom_smooth and ggplot-ggplot2

Score: 17

hi i'm not sure if I correctly understand 3 what you want, but for example,

``````d <- data.frame(Time=rep(1:20, 4),
Value=rnorm(80, rep(1:20, 4)+rep(1:4*2, each=20)),
Run=gl(4,20))

mean_se <- function(x, mult = 1) {
x <- na.omit(x)
se <- mult * sqrt(var(x) / length(x))
mean <- mean(x)
data.frame(y = mean, ymin = mean - se, ymax = mean + se)
}

ggplot( d, aes(x=Time,y=Value) ) + geom_line( aes(group=Run) ) +
geom_smooth(se=FALSE) +
stat_summary(fun.data=mean_se, geom="ribbon", alpha=0.25)
``````

note that 2 mean_se is going to appear in the next version 1 of ggplot2.

Score: 1

The accepted answer just works if measurements 3 are aligned/discretized on x. In case of 2 continuous data you could use a rolling 1 window and add a custom ribbon

``````iris %>%
## apply same grouping as for plot
group_by(Species) %>%
## Important sort along x!
arrange(Petal.Length) %>%
## calculate rolling mean and sd
mutate(rolling_sd=rollapply(Petal.Width, width=10, sd,  fill=NA), rolling_mean=rollmean(Petal.Width, k=10, fill=NA)) %>%  # table_browser()
## build the plot
ggplot(aes(Petal.Length, Petal.Width, color = Species)) +
# optionally we could rather plot the rolling mean instead of the geom_smooth loess fit
# geom_line(aes(y=rolling_mean), color="black") +
geom_ribbon(aes(ymin=rolling_mean-rolling_sd/2, ymax=rolling_mean+rolling_sd/2), fill="lightgray", color="lightgray", alpha=.8) +
geom_point(size = 1, alpha = .7) +
geom_smooth(se=FALSE)
`````` More Related questions