cannot visualise using ggplot with NA values - r

Question

I am trying to create a stacked bar chart showing % frequency of occurrences by group
library(dplyr)
library(ggplot2)
brfss_2013 %>%
group_by(incomeLev, mentalHealth) %>%
summarise(count_mentalHealth=n()) %>%
group_by(incomeLev) %>%
mutate(count_inc=sum(count_mentalHealth)) %>%
mutate(percent=count_mentalHealth / count_inc * 100) %>%
ungroup() %>%
ggplot(aes(x=forcats::fct_explicit_na(incomeLev),
y=count_mentalHealth,
group=mentalHealth)) +
geom_bar(aes(fill=mentalHealth),
stat="identity") +
geom_text(aes(label=sprintf("%0.1f%%", percent)),
position=position_stack(vjust=0.5))
However, this is the traceback I receive:
1. dplyr::group_by(., incomeLev, mentalHealth)
8. plyr::summarise(., count_mentalHealth = n())
9. [ base::eval(...) ] with 1 more call
11. dplyr::n()
12. dplyr:::from_context("..group_size")
13. `%||%`(...)
In addition: Warning message:
Factor `incomeLev` contains implicit NA, consider using `forcats::fct_explicit_na`
>
Here is a sample of my data
brfss_2013 <- structure(list(incomeLev = structure(c(2L, 3L, 3L, 2L, 2L, 3L,
NA, 2L, 3L, 1L, 3L, NA), .Label = c("$25,000-$35,000", "$50,000-$75,000",
"Over $75,000"), class = "factor"), mentalHealth = structure(c(3L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L), .Label = c("Excellent",
"Ok", "Very Bad"), class = "factor")), row.names = c(NA, -12L
), class = "data.frame")
Update:
Output of str(brfss_2013):
'data.frame': 491775 obs. of 9 variables:
$ mentalHealth: Factor w/ 5 levels "Excellent","Good",..: 5 1 1 1 1 1 3 1 1 1 ...
$ pa1min_ : int 947 110 316 35 429 120 280 30 240 260 ...
$ bmiLev : Factor w/ 6 levels "Underweight",..: 5 1 3 2 5 5 2 3 4 3 ...
$ X_drnkmo4 : int 2 0 80 16 20 0 1 2 4 0 ...
$ X_frutsum : num 413 20 46 49 7 157 150 67 100 58 ...
$ X_vegesum : num 53 148 191 136 243 143 216 360 172 114 ...
$ sex : Factor w/ 2 levels "Male","Female": 2 2 2 2 1 2 2 2 1 2 ...
$ X_state : Factor w/ 55 levels "0","Alabama",..: 2 2 2 2 2 2 2 2 2 2 ...
$ incomeLev : Factor w/ 4 levels "$25,000-$35,000",..: 2 4 4 2 2 4 NA 2 4 1 ...

Answer 1

First of all, your code works incredibly well when you transform everything into character. So you could just do
brfss_2013[c("incomeLev", "mentalHealth")] <-
lapply(brfss_2013[c("incomeLev", "mentalHealth")], as.character)
and then just run your code as you figured it out.
But, let's do it with factors (don't run the lapply(.) line in this case!).
You want a "missing" category, which you can obtain by adding a new level "missing" for the NAs.
levels(brfss_2013$incomeLev) <- c(levels(brfss_2013$incomeLev), "missing")
brfss_2013$incomeLev[is.na(brfss_2013$incomeLev)] <- "missing"
Then, your aggregation (in a base R way).
b1 <- with(brfss_2013, aggregate(list(count_mentalHealth=incomeLev),
by=list(mentalHealth=mentalHealth, incomeLev=incomeLev),
length))
b2 <- aggregate(mentalHealth ~ ., brfss_2013, length)
names(b2)[2] <- "count_inc"
brfss_2013.agg <- merge(b1, b2)
rm(b1, b2) # just to clean up
Add the "percent" column.
brfss_2013.agg$percent <- with(brfss_2013.agg, count_mentalHealth / count_inc)
Plot.
library(ggplot2)
ggplot(brfss_2013.agg, aes(x=incomeLev, y=count_mentalHealth, group=mentalHealth)) +
geom_bar(aes(fill=mentalHealth), stat="identity") +
geom_text(aes(label=sprintf("%0.1f%%", percent)),
position=position_stack(vjust=0.5))
Result

Answer 2

So your code actually works fine for me. It looks like it might be an issue with package versions because it seems odd that you're using the plyr summarise function.
However, here's a slightly more concise way to create that graph (and hopefully this is helpful for whatever you want to add to this plot)
brfss_2013 %>%
# Add count of income levels first (note this only adds a variable)
add_count(incomeLev) %>%
rename(count_inc = n) %>%
# Count observations per group (this transforms data)
count(incomeLev, mentalHealth, count_inc) %>%
rename(count_mentalHealth = n) %>%
mutate(percent= count_mentalHealth / count_inc) %>%
ggplot(aes(x= incomeLev,
y= count_mentalHealth,
# Technically you don't need this group here but groups can be handy
group= mentalHealth)) +
geom_bar(aes(fill=mentalHealth),
stat="identity")+
# Using the scales package does the percent formatting for you
geom_text(aes(label = scales::percent(percent)), vjust = 1)+
theme_minimal()