Consider this simple example
library(dplyr)
library(ggplot2)
dataframe <- data_frame(id = c(1,2,3,4),
group = c('a','b','c','c'),
value = c(200,400,120,300))
# A tibble: 4 x 3
id group value
<dbl> <chr> <dbl>
1 1 a 200
2 2 b 400
3 3 c 120
4 4 c 300
Here I want to write a function that takes the dataframe and the grouping variable as input. Ideally, after grouping and aggregating I would like to print a ggpplot
chart.
This works:
get_charts2 <- function(data, mygroup){
quo_var <- enquo(mygroup)
df_agg <- data %>%
group_by(!!quo_var) %>%
summarize(mean = mean(value, na.rm = TRUE),
count = n()) %>%
ungroup()
df_agg
}
> get_charts2(dataframe, group)
# A tibble: 3 x 3
group mean count
<chr> <dbl> <int>
1 a 200 1
2 b 400 1
3 c 210 2
Unfortunately, adding ggplot
into the function above FAILS
get_charts1 <- function(data, mygroup){
quo_var <- enquo(mygroup)
df_agg <- data %>%
group_by(!!quo_var) %>%
summarize(mean = mean(value, na.rm = TRUE),
count = n()) %>%
ungroup()
ggplot(df_agg, aes(x = count, y = mean, color = !!quo_var, group = !!quo_var)) +
geom_point() +
geom_line()
}
> get_charts1(dataframe, group)
Error in !quo_var : invalid argument type
I dont understand what is wrong here. Any ideas? Thanks!
EDIT: interesting follow-up here how to create factor variables from quosures in functions using ggplot and dplyr?
ggplot
does not yet support tidy eval syntax (you can't use the !!
). You need to use more traditional standard evaluation calls. You can use aes_q
in ggplot to help with this.
get_charts1 <- function(data, mygroup){
quo_var <- enquo(mygroup)
df_agg <- data %>%
group_by(!!quo_var) %>%
summarize(mean = mean(value, na.rm = TRUE),
count = n()) %>%
ungroup()
ggplot(df_agg, aes_q(x = quote(count), y = quote(mean), color = quo_var, group = quo_var)) +
geom_point() +
geom_line()
}
get_charts1(dataframe, group)