How to reorder x-axis based on y-axis values in R ggplot2

Question

I am trying to reorder (I don't mind whether it is ascending or descending order) the x-axis on my errorplot based on the mean values of the y-axis. I have applied a solution based on this post , however for some reason it seems to be ignoring the reorder command. Any ideas what is happening here?

#Import data.
df <- structure(list(X_Variable = c(4L, 4L, 13L, 18L, 12L, 3L, 15L, 
                                    NA, 18L, 4L, 17L, NA, 3L, 15L, 4L, 6L, 12L, NA, 2L, 1L, NA, 15L, 
                                    1L, 6L, 1L, 12L, NA, 6L, NA, 15L, NA, 1L, 7L, 15L, 11L, NA, NA, 
                                    1L, 1L, 7L, 2L, 2L, 12L, 11L, 15L, 17L, 1L, 4L, 11L, 15L, 2L, 
                                    3L, 13L, 17L, 15L, 6L, 3L, 14L, 12L, 8L, 12L, 11L, NA, 2L, 11L, 
                                    NA, 4L, 8L, 15L, 4L, 7L, 8L, 15L, 15L, 15L, 6L, 3L, 6L, 8L, 15L, 
                                    4L, 2L, 1L, 1L, 7L, 17L, 15L, 1L, NA, 5L, 13L, 1L, 15L, 4L, 15L, 
                                    13L, 18L, 1L, 15L, 6L, NA, 6L, NA, 6L, 1L, 16L, 4L, 1L, NA, 2L, 
                                    12L, NA, 7L, 2L, 15L, 13L, 13L, 16L, NA, 7L, 2L, 4L, 15L, 11L, 
                                    15L, 2L, 5L, 13L, 2L, 9L, 7L, 6L, 15L, 15L, 11L, 3L, 15L, 13L, 
                                    NA, 4L, 8L, NA, 4L, 8L, 18L, 4L, 1L, 8L, 5L, 18L), Y_Variable = c(6L, 
                                                                                                      4L, 5L, 4L, 4L, 3L, 7L, 1L, 1L, 7L, 4L, NA, 5L, 1L, 6L, 1L, 6L, 
                                                                                                      3L, 6L, 4L, NA, 4L, 6L, 5L, 1L, 4L, 1L, 1L, 6L, 3L, 4L, 1L, 1L, 
                                                                                                      2L, 3L, 4L, 4L, 2L, 2L, 2L, 4L, 1L, 1L, 5L, 4L, 1L, 4L, 4L, 3L, 
                                                                                                      3L, 2L, 2L, 1L, 3L, NA, 2L, 4L, 1L, 2L, 2L, 6L, 3L, NA, 2L, 2L, 
                                                                                                      NA, 4L, 2L, 3L, 6L, 5L, 4L, 1L, 5L, 3L, 1L, 4L, 6L, 1L, 5L, 4L, 
                                                                                                      2L, 1L, 5L, 4L, 3L, 2L, NA, 4L, 2L, NA, 4L, 5L, 5L, 4L, 2L, 1L, 
                                                                                                      5L, 2L, 2L, 4L, 1L, 4L, 1L, 5L, 2L, 1L, 3L, NA, 2L, 2L, 2L, 5L, 
                                                                                                      1L, 1L, 4L, 2L, 2L, NA, 3L, 5L, 7L, 1L, 1L, 1L, 1L, 4L, 1L, 2L, 
                                                                                                      2L, 3L, 3L, 3L, 4L, 1L, 4L, 3L, 4L, 3L, 6L, 1L, 5L, 4L, 2L, 5L, 
                                                                                                      2L, 3L, 1L, 1L, 2L)), row.names = c(NA, -150L), class = "data.frame")

#Error plot ordered by Y-Variable.
ggplot(df, aes(x=reorder(X_Variable, Y_Variable, FUN=mean), y=Y_Variable))+ 
  geom_point(stat="summary", fun.y="mean")+ 
  geom_errorbar(stat="summary", fun.data="mean_se", fun.args=list(mult=1.96), width=0.1)

Answer 1

I only removed missing values first. The minus sign works fine on my machine.

df1<-df %>% 
  filter(!is.na(X_Variable), !is.na(Y_Variable))  

ggplot(df1, aes(x=reorder(X_Variable, -Y_Variable, FUN=mean), y=Y_Variable))+ 
  geom_point(stat="summary", fun.y="mean")+ 
  geom_errorbar(stat="summary", fun.data="mean_se", fun.args=list(mult=1.96), width=0.1)

Edit: Because of missing values, X_Variable 1, 13, and 15 ranked last. Hope this helps.

df %>% group_by(X_Variable) %>% 
  summarise(
    Y_Variable = mean(Y_Variable)) %>% 
  arrange(Y_Variable)

# A tibble: 18 x 2
   X_Variable Y_Variable
        <int>      <dbl>
 1          4       4.71
 2          3       3.67
 3         12       3.57
 4          7       3.29
 5         17       2.75
 6         18       2.6 
 7         11       2.57
 8          2       2.55
 9          5       2.33
10          6       2.3 
11          9       2   
12         16       2   
13          8       1.86
14         14       1   
15          1      NA   
16         13      NA   
17         15      NA   
18         NA      NA   
>