I am attempting to order a geom_bar() graph as it stands in my dataframe but when it is plotted my ordering changes completely.
Here is my data:
> fert
A tibble: 28 × 4
country value fert_import_origin country_f
<chr> <dbl> <chr> <fct>
1 DRC 36.6 Russia DRC
2 Morocco 36.4 Russia Morocco
3 Cameroon 31.6 Russia Cameroon
4 Ivory Coast 26.3 Russia Ivory Coast
5 Senegal 25.8 Russia Senegal
6 Ghana 21 Russia Ghana
7 Rwanda 20.3 Russia Rwanda
8 Nigeria 20 Russia Nigeria
9 Burkina Faso 18.5 Russia Burkina Faso
10 Mozambique 17.7 Russia Mozambique
11 Tanzania 16.9 Russia Tanzania
12 Mali 16.5 Russia Mali
13 Uganda 15.7 Russia Uganda
14 South Africa 14.7 Russia South Africa
15 DRC 63.4 Rest of World DRC
16 Morocco 63.6 Rest of World Morocco
17 Cameroon 68.4 Rest of World Cameroon
18 Ivory Coast 73.7 Rest of World Ivory Coast
19 Senegal 74.2 Rest of World Senegal
20 Ghana 79 Rest of World Ghana
21 Rwanda 79.7 Rest of World Rwanda
22 Nigeria 80 Rest of World Nigeria
23 Burkina Faso 81.5 Rest of World Burkina Faso
24 Mozambique 82.3 Rest of World Mozambique
25 Tanzania 83.1 Rest of World Tanzania
26 Mali 83.5 Rest of World Mali
27 Uganda 84.3 Rest of World Uganda
28 South Africa 85.3 Rest of World South Africa
As suggested by other answers, I created a factor for the country column to keep my levels as I'd like them to be:
fert$country_f <- NA
fert$country_f <- factor(fert$country)
Here is my code for the final plot:
ggplot(fert, aes(fill = fert_import_origin, x = country_f, y = value,
label = paste0(value, sep = "%")))
geom_bar(stat = "identity", width = 0.7)
geom_text(size = 4, position = position_stack(vjust = 0.5), family = "Lato")
scale_y_continuous(breaks = pretty_breaks(n = 6), labels = percent_format(scale = 1))
scale_fill_manual(values = c("#0C7BDC","#96426C"),
breaks = c("Russia", "Rest of World"))
labs(x = "", y = "", title = "Fertiliser import by origin in Africa (%) | 2021",
caption = paste0("Source: Trade Map, 2021\n",
"Graphic: Monique Bennett"))
coord_flip()
Here's my plot with the incorrect ordering:
CodePudding user response：
Perhaps instead of using a factor variable, you can use fct_reorder and a numerical variable. I haven't tested it yet since I couldn't reproduce your data easily.
fert$country_f <- 1:nrow(fert)
ggplot(fert, aes(fill = fert_import_origin, x = fct_reorder(country, country_f), y = value,
label = paste0(value, sep = "%")))
geom_bar(stat = "identity", width = 0.7)
geom_text(size = 4, position = position_stack(vjust = 0.5), family = "Lato")
scale_y_continuous(breaks = pretty_breaks(n = 6), labels = percent_format(scale = 1))
scale_fill_manual(values = c("#0C7BDC","#96426C"),
breaks = c("Russia", "Rest of World"))
labs(x = "", y = "", title = "Fertiliser import by origin in Africa (%) | 2021",
caption = paste0("Source: Trade Map, 2021\n",
"Graphic: Monique Bennett"))
coord_flip()
CodePudding user response：
Welcome to StackOverflow. It is difficult to evaluate your problem without a reproducible example. Neverhteless, I think your problem comes from the order of the levels of your factor type variable.
The order in your plot is the same you get running levels(country_f).
The solution for your problem is to "relevel" country_f acording to the order you want. This is done by:
fert$country_f <- factor(fert$country_f, levels = fert$country)
Then you can run what you did to make the graphical representation
CodePudding user response：
I think you just need to add the levels to the factors in your desired order:
fert <- tibble::tribble(
~country, ~value, ~fert_import_origin, ~country_f,
"DRC", 36.6, "Russia", "DRC",
"Morocco", 36.4, "Russia", "Morocco",
"Cameroon", 31.6, "Russia", "Cameroon",
"Ivory Coast", 26.3, "Russia", "Ivory Coast",
"Senegal", 25.8, "Russia", "Senegal",
"Ghana", 21, "Russia", "Ghana",
"Rwanda", 20.3, "Russia", "Rwanda",
"Nigeria", 20, "Russia", "Nigeria",
"Burkina Faso", 18.5, "Russia", "Burkina Faso",
"Mozambique", 17.7, "Russia", "Mozambique",
"Tanzania", 16.9, "Russia", "Tanzania",
"Mali", 16.5, "Russia", "Mali",
"Uganda", 15.7, "Russia", "Uganda",
"South Africa", 14.7, "Russia", "South Africa",
"DRC", 63.4, "Rest of World", "DRC",
"Morocco", 63.6, "Rest of World", "Morocco",
"Cameroon", 68.4, "Rest of World", "Cameroon",
"Ivory Coast", 73.7, "Rest of World", "Ivory Coast",
"Senegal", 74.2, "Rest of World", "Senegal",
"Ghana", 79, "Rest of World", "Ghana",
"Rwanda", 79.7, "Rest of World", "Rwanda",
"Nigeria", 80, "Rest of World", "Nigeria",
"Burkina Faso", 81.5, "Rest of World", "Burkina Faso",
"Mozambique", 82.3, "Rest of World", "Mozambique",
"Tanzania", 83.1, "Rest of World", "Tanzania",
"Mali", 83.5, "Rest of World", "Mali",
"Uganda", 84.3, "Rest of World", "Uganda",
"South Africa", 85.3, "Rest of World", "South Africa"
)
fert$country_f <- factor(fert$country, levels = rev(unique(fert$country)))
library(ggplot2)
library(scales)
ggplot(fert, aes(fill = fert_import_origin, x = country_f, y = value,
label = paste0(value, sep = "%")))
geom_bar(stat = "identity", width = 0.7)
geom_text(size = 4, position = position_stack(vjust = 0.5), family = "Lato")
scale_y_continuous(breaks = pretty_breaks(n = 6), labels = percent_format(scale = 1))
scale_fill_manual(values = c("#0C7BDC","#96426C"),
breaks = c("Russia", "Rest of World"))
labs(x = "", y = "", title = "Fertiliser import by origin in Africa (%) | 2021",
caption = paste0("Source: Trade Map, 2021\n",
"Graphic: Monique Bennett"))
coord_flip()
