Descending order in ggplot bar_col - r

Question

Below is the dataset,
# A tibble: 449 x 7
`Country or Area` `Region 1` Year Rate MinCI MaxCI Average
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Afghanistan Southern Asia 2011 4.2 2.6 6.2 4.4
2 Afghanistan Southern Asia 2016 5.5 3.4 8.1 5.75
3 Aland Islands Northern Europe NA NA NA NA NA
4 Albania Southern Europe 2011 18.8 14.8 23 18.9
5 Albania Southern Europe 2016 21.7 17 26.7 21.8
6 Algeria Northern Africa 2011 24 19.9 28.4 24.2
7 Algeria Northern Africa 2016 27.4 22.5 32.7 27.6
8 American Samoa Polynesia NA NA NA NA NA
9 Andorra Southern Europe 2011 24.6 19.8 29.8 24.8
10 Andorra Southern Europe 2016 25.6 20.1 31.3 25.7
I need to draw a bar_col using the above dataset to compare the average obesity rate of each region. Further, I need to order the bar from the highest to the lowest.
I have also calculated the Average obesity rate as shown above.
Below is the code I used to generate the ggplot, but unable to figure out how to order from the highest to lowest.
region_plot <- ggplot(continent) + aes(x = continent$`Region 1`, y = continent$Average, fill = Average) +
geom_col() +
xlab("Region") + ylab("Average Obesity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("Average obesity rate of each region")
region_plot

Answer 1

After checking your data, you have multiple regions so in order to show the average per region you must to compute it and then plot. You can do that with dplyr using group_by() and summarise(). Your data is limited but for the real one, NA should not be present. Here the code using part of the shared data. Be careful with names when using your real data. reorder() function can arrange bars. Here the code:
library(dplyr)
library(ggplot2)
#Code
df %>% group_by(Region) %>%
summarise(Avg=mean(Average,na.rm=T)) %>%
filter(!is.na(Avg)) %>%
ggplot(aes(x=reorder(Region,-Avg),y=Avg,fill=Region))+
geom_col() +
xlab("Region") + ylab("Average Obesity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("Average obesity rate of each region")
Output:
Some data used:
#Data
df <- structure(list(Region = c("Southern Asia", "Southern Asia", "Northern Europe",
"Southern Europe", "Southern Europe", "Northern Africa", "Northern Africa",
"Polynesia", "Southern Europe", "Southern Europe"), Year = c(2011L,
2016L, NA, 2011L, 2016L, 2011L, 2016L, NA, 2011L, 2016L), Rate = c(4.2,
5.5, NA, 18.8, 21.7, 24, 27.4, NA, 24.6, 25.6), MinCI = c(2.6,
3.4, NA, 14.8, 17, 19.9, 22.5, NA, 19.8, 20.1), MaxCI = c(6.2,
8.1, NA, 23, 26.7, 28.4, 32.7, NA, 29.8, 31.3), Average = c(4.4,
5.75, NA, 18.9, 21.8, 24.2, 27.6, NA, 24.8, 25.7)), row.names = c(NA,
-10L), class = "data.frame")

Answer 2

The problem can be solved by preprocessing the data and sorting the result by Average. Then coerce Region 1 to factor.
library(ggplot2)
library(dplyr)
continent %>%
group_by(`Region 1`) %>%
summarise(Average = mean(Average, na.rm = TRUE)) %>%
arrange(desc(Average)) %>%
mutate(`Region 1` = factor(`Region 1`, levels = unique(`Region 1`))) %>%
ggplot(aes(x = `Region 1`, y = Average, fill = Average)) +
geom_col() +
xlab("Region") + ylab("Average Obesity") +
ggtitle("Average obesity rate of each region") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) -> region_plot
region_plot
Data
continent <- read.table(text = "
'Country or Area' 'Region 1' Year Rate MinCI MaxCI Average
1 Afghanistan 'Southern Asia' 2011 4.2 2.6 6.2 4.4
2 Afghanistan 'Southern Asia' 2016 5.5 3.4 8.1 5.75
3 'Aland Islands' 'Northern Europe' NA NA NA NA NA
4 Albania 'Southern Europe' 2011 18.8 14.8 23 18.9
5 Albania 'Southern Europe' 2016 21.7 17 26.7 21.8
6 Algeria 'Northern Africa' 2011 24 19.9 28.4 24.2
7 Algeria 'Northern Africa' 2016 27.4 22.5 32.7 27.6
8 'American Samoa' Polynesia NA NA NA NA NA
9 Andorra 'Southern Europe' 2011 24.6 19.8 29.8 24.8
10 Andorra 'Southern Europe' 2016 25.6 20.1 31.3 25.7
", header = TRUE, check.names = FALSE)