user08041991 - 2 years ago 175

R Question

From the following question, we create some dummy data. Then it is converted into a format which

`ggplot2`

`var`

`test_data <-`

data.frame(

var0 = 100 + c(0, cumsum(runif(49, -20, 20))),

var1 = 150 + c(0, cumsum(runif(49, -10, 10))),

var2 = 120 + c(0, cumsum(runif(49, -5, 10))),

date = seq(as.Date("2002-01-01"), by="1 month", length.out=100)

)

#

library("reshape2")

library("ggplot2")

#

test_data_long <- melt(test_data, id="date") # convert to long format

ggplot(data=test_data_long,

aes(x=date, y=value, colour=variable)) +

geom_line() + theme_bw()

I want to plot the average of the three

`var`

`stat_summary()`

By adding either of the commands below, I do not obtain the average, nor a confidence interval. Any suggestions would be greatly appreciated.

`stat_summary(fun.data=mean_cl_normal)`

#stat_summary(fun.data ="mean_sdl", mult=1, geom = "smooth")

#stat_summary(fun.data = "mean_cl_boot", geom = "smooth")

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Answer Source

If i understood correctly you wanna display average of all three parameters (var0,var1 and var3) with standard deviation.

I do have for you two solutions. First one imply `dplyr`

package and calculation of the **standard deviation and average row-wise** and further display using `geom_ribbon()`

:

```
library(dplyr)
library(magrittr)
q <- test_data
q <- q %>% rowwise() %>% transmute(date, mean=mean(c(var0,var1,var2), na.rm=TRUE), sd = sd(c(var0,var1,var2), na.rm=TRUE))
eb <- aes(ymax = mean + sd, ymin = mean - sd)
ggplot(data = q, aes(x = date, y = mean)) +
geom_line(size = 2) +
geom_ribbon(eb, alpha = 0.5)
```

Second solution imply mentioned by you `stat_summary()`

, which actually works well with the code you have provided:

```
ggplot(data=test_data_long, aes(x=date, y=value)) +
stat_summary(fun.data ="mean_sdl", mult=1, geom = "smooth") + theme_bw()
```

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