I’ve found myself on Winston Chang’s cookbook-r website a number of times before. My most recent visit was to refresh my memory on the strategy for plotting means with error bars with `ggplot2`

:

http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)

The recipe makes use of `summarySE()`

a function that is described in detail in another post that details strategies for summarizing data.

`summarySE()`

is a custom function that computes the mean, standard deviation, count, standard error, confidence interval for a variable (“measurevar”) within defined grouping variables (“groupvars”).

Below is the code for the function, along with a working example of how to use it with the built-in “ToothGrowth” dataset (`?ToothGrowth`

).

```
## Summarizes data.
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
## data: a data frame.
## measurevar: the name of a column that contains the variable to be summarized
## groupvars: a vector containing names of columns that contain grouping variables
## na.rm: a boolean that indicates whether to ignore NA's
## conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
library(plyr)
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
```

`head(ToothGrowth)`

```
## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5
```

```
summarySE(ToothGrowth,
measurevar="len",
groupvars=c("supp","dose"),
conf.interval = 0.9)
```

```
## supp dose N len sd se ci
## 1 OJ 0.5 10 13.23 4.459709 1.4102837 2.585209
## 2 OJ 1.0 10 22.70 3.910953 1.2367520 2.267106
## 3 OJ 2.0 10 26.06 2.655058 0.8396031 1.539087
## 4 VC 0.5 10 7.98 2.746634 0.8685620 1.592172
## 5 VC 1.0 10 16.77 2.515309 0.7954104 1.458077
## 6 VC 2.0 10 26.14 4.797731 1.5171757 2.781154
```

`tidyeval`

approach

The cookbook-r site includes several solutions to the “Summary SE” problem. The `summarySE()`

function (above) works well, and includes comments explaining each parameter. However, given my inexperience with `plyr`

I find the code a little hard to understand … or at least harder than if it were written with `dplyr`

. I decided to try to translate `summarySE()`

into a syntax that I could use in a pipeline with `%>%`

. To do so, I needed to learn a little about the `tidyeval`

framework and its programming paradigm.

The code for the new function (`summary_se()`

) is below, along with the same ToothGrowth example as above.

A few notes from my exploration:

`enquo()`

captures the bare variable name, and`!!`

in a subsequent`dplyr`

call will reference that variable`enquos()`

and`!!!`

work in a similar matter but can capture multiple variable names passed in via`...`

- It’s good practice to prefix argument names with
`.`

when writing functions that use`tidyeval`

… the reason being that it is not likely that a user would have an existing column name that starts with`.`

```
summary_se <- function(.data, measure_var, ..., .ci = 0.95, na.rm = FALSE) {
measure_var <- dplyr::enquo(measure_var)
group_var <- dplyr::enquos(...)
.data %>%
group_by(!!! group_var) %>%
summarise(mean = mean(!! measure_var, na.rm = na.rm),
sd = sd(!! measure_var, na.rm = na.rm),
n = n(),
se = sd/sqrt(n),
ci = se * qt(.ci/2 + 0.5, n-1)) %>%
ungroup()
}
```

```
library(dplyr)
library(ggplot2)
ToothGrowth %>%
summary_se(len, supp, dose, .ci = 0.9)
```

```
## # A tibble: 6 x 7
## supp dose mean sd n se ci
## <fct> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 OJ 0.5 13.2 4.46 10 1.41 2.59
## 2 OJ 1 22.7 3.91 10 1.24 2.27
## 3 OJ 2 26.1 2.66 10 0.840 1.54
## 4 VC 0.5 7.98 2.75 10 0.869 1.59
## 5 VC 1 16.8 2.52 10 0.795 1.46
## 6 VC 2 26.1 4.80 10 1.52 2.78
```

```
ToothGrowth %>%
summary_se(len, supp, dose, .ci = 0.9) %>%
mutate(dose = paste0("Dose: ", dose, " (mg/day)")) %>%
ggplot(aes(supp,mean)) +
geom_point() +
geom_errorbar(aes(ymin = mean - ci,
ymax = mean + ci),
width = 0.2) +
labs(x = "Vitamin C delivery method", y = "Mean length of odontoblasts (95% CI)") +
coord_flip() +
facet_wrap(~ dose, ncol = 1) +
theme_minimal()
```