R ggplot bar graph has extra lines at the base of columns - r

Question

I have two main issues I could use some help getting resolved.
1.) There are odd lines at the base of my columns which I am not sure how to get rid of.
2.) I am running into overlap with the columns when I graph. (I think this has something do do with the position_dodge(width= XXX) but not totally sure).
Attached an image of an example plot, mainly because I am not sure how to describe what is happening at the base of the plot.
The following code is being used.
where_2 <- where %>%
group_by_("gender", "radio") %>%
summarise(count = n()) %>%
mutate(perc = (perc = (count / sum(count) * 100)))
gg <- ggplot(where_2, aes_string(x = names(where_2[1]), y = where_2$perc, fill = "radio"))
gg <- gg + geom_bar(aes(y = (..count..) / sum(..count..)))
gg <-gg + geom_bar(position = position_dodge(.5),stat = "identity", width = .75)
#gg <- gg + scale_y_continuous(labels = scales::percent)
gg <- gg + xlab(paste0(lab5[2, title]))
gg <- gg + scale_fill_discrete(labels = c("Yes", "No"))
print(gg)
I have been running in a wall for the past 4 days with this question any help would be appreciated.
place gender Radio
1 Male No
1 Female Yes
1 Male No
1 Female Yes
1 Male Yes
1 Male Yes
1 Female Yes
1 Female Yes
1 Male Yes
1 Female No
1 Male Yes
1 Male Yes
1 Male No
1 Female No
1 Female Yes
1 Female Yes
1 Female No
1 Male Yes
1 Female No
1 Female Yes
1 Female No
1 Female Yes
1 Male No
1 Male No
1 Female No
1 Male No
1 Female No
1 Female No
1 Female No
1 Male Yes
1 Female No
1 Female No
1 Female Yes
1 Male No
1 Male Yes
1 Female No
2 Male Yes
2 Male Yes
2 Female No
2 Female No
2 Male Yes
2 Female No
2 Male No
2 Male Yes
2 Female No
2 Female No
2 Female No
2 Male No
2 Female No
2 Male No
2 Female Yes
2 Female Yes
2 Male Yes
2 Male No
2 Male Yes
3 Female No
3 Male Yes
3 Female No
3 Male No
3 Male Yes
3 Female No
3 Female Yes
3 Male No
3 Male Yes
3 Female Yes
3 Male No
3 Female No
3 Female Yes
3 Female No
3 Female Yes
3 Female No
3 Male Yes
3 Female No
3 Female No
4 Male Yes
4 Female No
4 Female Yes
4 Female Yes
4 Male Yes
4 Female No
4 Female No
4 Male No
4 Female No
4 Female No
4 Female No
4 Male Yes
4 Male Yes
4 Female Yes
4 Female No
4 Male Yes
4 Male Yes
4 Male Yes
4 Female No
4 Female No
4 Female No

Answer 1

Try this:
gg <- ggplot(where2,
aes(x = gender, y = perc, fill = Radio)) +
geom_col(position = "dodge", width = .75)
print(gg)
Explanation below:
You are right that the "feet" are indeed caused by geom_bar(aes(y = (..count..) / sum(..count..))). I'm not sure why you included it in the first place, but here's why it created the "feet":
Good chart
p <- ggplot(where2, aes(x = gender, y = perc, fill = Radio))
p + geom_col(position = position_dodge(0.5), width = 0.75)
Above is the chart you want to get (I assume). geom_col() is equivalent to geom_bar(stat = "identity") with less typing, so I used that instead.
Usually people set the same value in position_dodge() and width =, which would avoid the overlapped look. I've retained it for now to contrast with the "feet" below.
Notice also the values on the y-axis. They range from 0 to 60+.
Bad chart
p + geom_bar(aes(y = (..count..) / sum(..count..)))
Above is the chart of the "feet", now occupying the entire plot's height. Here, ..count.. returns the number of rows for each combination of gender & Radio, while sum(..count..) returns the total number of rows in the data frame. The data frame, where2, has 4 rows, one for each combination, so the y value associated with each bar is 0.25, and the stacked height of each gender's two bars is 0.5.
I consider this the bad chart, because the visualisation is useless. When you have already counted the number of rows in your dataset yourself (going from where to where2), it's not necessary for ggplot to do it again.
Good chart + bad chart = weird chart
p +
geom_col(position = position_dodge(0.5), width = 0.75) +
geom_bar(aes(y = (..count..) / sum(..count..)))
Above is the combined chart with both layers. Now the bad chart's bars are squeezed all the way to the bottom, since their combined height is only 0.5, while the good chart's bars stretch all the way to 60+.
data used:
> dput(where)
structure(list(place = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L), gender = structure(c(2L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L), .Label = c("Female",
"Male"), class = "factor"), Radio = structure(c(1L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L), .Label = c("No",
"Yes"), class = "factor")), .Names = c("place", "gender", "Radio"
), class = "data.frame", row.names = c(NA, -95L))
where2 <- where %>%
group_by(gender, Radio) %>%
summarise(count = n()) %>%
mutate(perc = (perc = (count / sum(count) * 100)))
> where2
# A tibble: 4 x 4
# Groups: gender [2]
gender Radio count perc
<fctr> <fctr> <int> <dbl>
1 Female No 37 67.3
2 Female Yes 18 32.7
3 Male No 15 37.5
4 Male Yes 25 62.5