I find myself writing this bit of code all the time to produce standard errors for group means ( to then use for plotting confidence intervals).
It would be nice to write my own function to do this in one line of code, though. I have read the nse vignette in dplyr on non-standard evaluation and this blog post as well. I get it somewhat, but I'm too much of a noob to figure this out on my own. Can anyone help out?
var1<-sample(c('red', 'green'), size=10, replace=T)
var2<-rnorm(10, mean=5, sd=1)
df<-data.frame(var1, var2)
df %>%
group_by(var1) %>%
summarize(avg=mean(var2), n=n(), sd=sd(var2), se=sd/sqrt(n))
You can use the function enquo to explicitly name the variables in your function call:
my_fun <- function(x, cat_var, num_var){
cat_var <- enquo(cat_var)
num_var <- enquo(num_var)
x %>%
group_by(!!cat_var) %>%
summarize(avg = mean(!!num_var), n = n(),
sd = sd(!!num_var), se = sd/sqrt(n))
}
which gives you:
> my_fun(df, var1, var2)
# A tibble: 2 x 5
var1 avg n sd se
<fctr> <dbl> <int> <dbl> <dbl>
1 green 4.873617 7 0.7515280 0.2840509
2 red 5.337151 3 0.1383129 0.0798550
and that matches the ouput of your example:
> df %>%
+ group_by(var1) %>%
+ summarize(avg=mean(var2), n=n(), sd=sd(var2), se=sd/sqrt(n))
# A tibble: 2 x 5
var1 avg n sd se
<fctr> <dbl> <int> <dbl> <dbl>
1 green 4.873617 7 0.7515280 0.2840509
2 red 5.337151 3 0.1383129 0.0798550
EDIT:
The OP has asked to remove the group_by statement from the function to add the ability to group_by more than one variables. There are two ways to go about this IMO. First, you could simply remove the group_by statement and pipe a grouped data frame into the function. That method would look like this:
my_fun <- function(x, num_var){
num_var <- enquo(num_var)
x %>%
summarize(avg = mean(!!num_var), n = n(),
sd = sd(!!num_var), se = sd/sqrt(n))
}
df %>%
group_by(var1) %>%
my_fun(var2)
Another way to go about this is to use ... and quos to allow for the function to capture multiple arguments for the group_by statement. That would look like this:
#first, build the new dataframe
var1<-sample(c('red', 'green'), size=10, replace=T)
var2<-rnorm(10, mean=5, sd=1)
var3 <- sample(c("A", "B"), size = 10, replace = TRUE)
df<-data.frame(var1, var2, var3)
# using the first version `my_fun`, it would look like this
df %>%
group_by(var1, var3) %>%
my_fun(var2)
# A tibble: 4 x 6
# Groups: var1 [?]
var1 var3 avg n sd se
<fctr> <fctr> <dbl> <int> <dbl> <dbl>
1 green A 5.248095 1 NaN NaN
2 green B 5.589881 2 0.7252621 0.5128378
3 red A 5.364265 2 0.5748759 0.4064986
4 red B 4.908226 5 1.1437186 0.5114865
# Now doing it with a new function `my_fun2`
my_fun2 <- function(x, num_var, ...){
group_var <- quos(...)
num_var <- enquo(num_var)
x %>%
group_by(!!!group_var) %>%
summarize(avg = mean(!!num_var), n = n(),
sd = sd(!!num_var), se = sd/sqrt(n))
}
df %>%
my_fun2(var2, var1, var3)
# A tibble: 4 x 6
# Groups: var1 [?]
var1 var3 avg n sd se
<fctr> <fctr> <dbl> <int> <dbl> <dbl>
1 green A 5.248095 1 NaN NaN
2 green B 5.589881 2 0.7252621 0.5128378
3 red A 5.364265 2 0.5748759 0.4064986
4 red B 4.908226 5 1.1437186 0.5114865