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+---
+title: "Key Global Partners in U.S. Trade for 2023"
+description: |
+ Worldwide U.S. Trade in Billions by country in 2023
+categories: '2024'
+author: Jason Brunner
+date: "`r Sys.Date()`"
+output:
+ distill::distill_article:
+ self_contained: false
+ toc: true
+---
+
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE, fig.width= 14, fig.height= 8,
+ fig.align='center')
+```
+
+## Introduction
+
+The U.S. totaled over $5.1 trillion in trade for the year 2023. This is a truly
+staggering amount and it poses the interesting question, which the major trade
+partners are. By looking at trade through this visualization we can also see
+how our world today is globalized and connected, even in times of rising global
+tensions.
+
+The original visualization can be found following this link https://www.voronoiapp.com/economy/-Top-Global-Trading-Partners-of-the-US-in-2023-2172).
+
+{.external}
+
+## The Visualization
+### The Data
+#### Getting the data and downloading the packages
+
+First we are going to start by loading the packages. For the Flagon package we
+have to take a little detour, since it is not available through the regular
+R function. Therefore, we will download and load it at a later step.
+
+```{r , message=FALSE}
+library(ggplot2)
+library(rnaturalearth)
+library(showtext)
+library(sysfonts)
+library(readxl)
+library(dplyr)
+library(ggpattern)
+library(devtools)
+library(patchwork)
+library(ggtext)
+```
+
+Luckily, the data for this visualization was available and only had to be
+downloaded and then transformed to be imported into R. The original data source
+is cited as comtrade, U.S. Census and WTO.
+
+```{r }
+trade_data <- read_excel("Data Final Project.xlsx")
+```
+
+The dataset is already tidy data and is ready to be used for our visualization.
+The *raw* trade data which we have imported before now has to be modified to fit
+the trade categories displayed in our visualization and later it will be
+merged with our main world dataset.
+
+You might wonder why they are split in these specific categories. The reason
+is, that later on when we create the legend we can't use a straight forward
+approach and will need to become a bit creative. More about this will follow
+once we reach the legend.
+
+```{r}
+trade_data <- trade_data %>%
+ mutate(trade_category = case_when(
+ `U.S. Trade (Billion US$)` < 0.1 ~ "0",
+ `U.S. Trade (Billion US$)` < 1 ~ "0.1",
+ `U.S. Trade (Billion US$)` < 10 ~ "1",
+ `U.S. Trade (Billion US$)` < 100 ~ "10",
+ `U.S. Trade (Billion US$)` < 1000 ~ "100",
+ TRUE ~ "1000"))
+```
+### The map
+
+#### Creating the basic plot
+The next step will be to focus on the actual world map. For this we take
+advantage of the rnaturalearth package which provides us with a comprehensive
+dataset about the world. We will also need to perform a few modifications to
+better fit the original plot. As you will also notice, we filter the United
+States out of the dataset. This is because the United States are filled with
+the US Flag and therefore we need to exclude it momentarily from our base
+dataset to be able to do this modification later.
+
+As mentioned earlier, we will now in the same step merge our trade data with the
+world data we have created so we get our base dataset to then start with our
+plot. Lastly we also define the colours used in the visualization. They have
+been obtained by using an online tool to get the colour codes from any colour
+you provide it.
+
+Additionally we also need to download the font. Using an online tool we were
+able to determine the font as "montserrat". Luckily it is also available as a
+free download and we can use it for our visualization.
+
+```{r}
+world <- ne_countries()
+
+excluded_countries <- c("French Southern and Antarctic Lands","Antarctica",
+ "Falkland Islands / Malvinas")
+
+#removing the excluded countries and the United States
+world <- world %>%
+ filter(!name_long %in% excluded_countries) %>%
+ filter(!name_long %in% "United States")
+
+#incorporate the trade data into to world dataset
+world <- left_join(world, trade_data, by = c("name_long" = "Country"))
+
+#defining the colours used for the map
+colors <- c("#bdbcfe", "#9494cd", "#6d6f9c", "#49476a", "#21213c")
+
+font_add_google("Montserrat", "montserrat")
+showtext_auto()
+
+```
+
+Now we have everything ready to actually create the first draft of our
+visualization. There is still a long way to go but we can plot the first
+draft with the help of the ggplot2 package to see the map with the exclusions
+and the colours for the trade data.
+
+```{r}
+X <- ggplot(data = world) +
+ geom_sf(aes(fill = as.character(trade_category)),
+ color = "#faf8f4",
+ linewidth = 0.05) +
+ coord_sf(crs = "+proj=robin") +
+ scale_fill_manual(values = colors)
+
+X
+```
+
+After having created our first plot we will continue enhancing it and
+getting it closer to the original one. The first thing that comes to mind, apart
+from the legend, are the gridlines present in the plot and the background.
+
+We can start to fix one of two problems easily by adding the following lines of
+code to our ggplot. In the same step we will also go ahead and add the title and
+subtitle to our plot. To make the title and subtitle look right, we also
+adjust the font corresponding to the original visualization. By defining this
+in our theme, it will automatically be adjusted for all text elements.
+
+```{r}
+X <- X + theme_void() +
+ labs(title = "Key Global Partners in U.S. Trade for 2023",
+ subtitle = paste("In 2023, U.S. trade totaled $5,192 billion. ",
+ "Trade with NAFTA partnes-Mexico ($803\nbillion ",
+ "and Canada ($784 billion)-accounted for 30.6% of the ",
+ "total. The European\nUnion was the next largest ",
+ "partner, contribution $986 billion, or 18.5% of total",
+ " trade.\nTrade with China amounted to $596 billion, ",
+ "representing 11.5% of the total.", sep = "")) +
+
+ #now the theme is being modified to replicate the title and subtitle from the
+ #original plot and in the same step we also adjust the background
+ theme(plot.title = element_text(hjust = 0.5,
+ size = 18,
+ face = "bold",
+ colour = "#042f4a"),
+ plot.background = element_rect(fill = "#faf8f4"),
+ plot.subtitle = element_text(hjust = 0.05,
+ vjust = -1),
+ text = element_text(family = "montserrat"))
+
+X
+```
+
+Now it already looks much more familiar. Let's not focus on the details like
+positioning and font size yet, but we will look at the more important things
+like the legend. First of all we need to turn the legend and make it horizontal
+and position it somewhat in the top right corner. We also need to fix the labels
+and get rid of the NA category.
+
+In order to get the labels of the scale to their correct position, there were
+some tricks needed. By default the labels would be centered under each category
+and by moving them to the left or right they wouldn't center, since they
+all have a different length.
+
+On a side note: If you look closely and compare the visualization and the
+original one, you will note a difference in the colourization of Libya.
+In the original plot the categorization of Libya does not correspond with the
+reported trade value from the dataset.
+
+```{r, warning=FALSE, message=FALSE}
+X <- X + theme(legend.direction = "horizontal",
+ legend.position = c(0.82, 0.99),
+ legend.title = element_text(size = 14,
+ face = "bold",
+ color = "#030001"),
+ legend.text = element_text(size = 12,
+ color = "#030001"),
+ legend.margin = margin(t = 0, r = 0, b = 0, l = 0),
+ panel.margin = margin(t = 50, r = 50, b = 50, l = 50)) +
+
+ scale_fill_manual(values = colors, name = "U.S. Trade (Billion US$)",
+ breaks = c("0", "0.1", "1", "10", "100", "1000"),
+ labels = c(" 0 ",
+ " 0.1 ",
+ " 1 ",
+ " 10 ",
+ "100 ",
+ "1'000")) +
+
+ guides(fill = guide_legend(
+ title.position = "top",
+ title.hjust = 0,
+ label.position = "bottom",
+ label.hjust = -1,
+ label.vjust = 0.5,
+ keywidth = unit(1.5, "cm"),
+ keyheight = unit(0.4, "cm")))
+
+X
+```
+
+#### The flag
+After creating the basic map, we can start adding the details. Most
+notably, the flag is still missing. For this we are going to use the flagon
+package to get the image of the flag and the spData package for the isolated
+U.S. mainland and Alaska multipolygon. The flagon package is not available to
+be downloaded through the regular R function, therefore we must take a slightly
+different approach and download it directly from Github.
+
+For Alaska it took a bit of a trial and error process, to figure out the
+appropriate scale parameter, so that we get the portion of the flag we were
+looking for.
+
+```{r, message=FALSE}
+
+usa <- spData::us_states |> sf::st_union()
+alaska <- spData::alaska
+
+
+install_github("coolbutuseless/flagon")
+library(flagon)
+flag <- flags("US", filetype = "png")
+
+X1 <- X +
+ geom_sf_pattern(data = usa,
+ pattern = 'image',
+ pattern_filename = flag,
+ pattern_type = "expand",
+ color = NA) +
+ geom_sf_pattern(data = alaska,
+ pattern = 'image',
+ pattern_filename = flag,
+ pattern_type = "none",
+ pattern_scale= 0.3,
+ pattern_gravity = "southwest",
+ color = NA)+
+ coord_sf(crs = "+proj=robin")
+
+
+```
+#### The EU-27 subplot
+
+Having fitted the flag we now continue by creating the EU-27 subplot.
+First we had to define a new vector with the EU-27 countries from the original
+plot. After this, the world dataset is filtered so only the EU-27 countries
+remain and we can create the new plot. After that we then only have to set the
+borders so we don't get the islands in the Atlantic.
+
+```{r}
+EU_27 <- c("Austria", "Belgium", "Bulgaria", "Croatia", "Cyprus",
+ "Czech Republic", "Denmark", "Estonia", "Finland", "France",
+ "Germany", "Greece", "Hungary", "Ireland", "Italy", "Latvia",
+ "Lithuania", "Luxembourg", "Malta", "Netherlands", "Poland",
+ "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Sweden")
+
+world_EU <- world %>%
+ filter(name_long %in% EU_27)
+
+EU <- ggplot(data = world_EU) +
+ geom_sf(fill = "#01339a",
+ color = "#faf8f4",
+ linewidth = 0.25) +
+
+ #setting the limits of the plot to cut off the Atlantic islands
+ coord_sf(xlim = c(-20,40),
+ ylim = c(35,70)) +
+ coord_sf(crs = "+proj=longlat") +
+ theme_void()
+
+
+```
+
+#### Adding them together
+
+To finalize the map of our plot we add the EU-27 plot to the main plot. There
+is a package called ggpatchwork for this, which allows us to position it and
+scale it accordingly. This will now yield our final map which only needs
+the annotations to make it complete.
+
+```{r}
+M <- X1 + inset_element(EU, left = 0.13, bottom = 0.4, right = 0.63,
+ top = 0.725)
+
+M
+```
+
+#### Annotations
+
+To finalize our plot we now need to add all the annotations. This step is a bit
+tedious, since every annotation has to be added manually. Additionally to this,
+certain elements of the annotation have to be somewhat "constructed", like the
+background of the numbers corresponding to the countries.
+
+To simplify the process of finding the correct font size and
+being able to adjust it across all annotations efficiently, the font size is
+defined by a variable. Since the annotations are hierarchical, we can layer
+shapes and texts on top of each other. This allowed the creation of the circles
+whit a number on top. There is also the instance of Germany and the Netherlands,
+where this feature of layering has been taken further advantage of. For these
+two annotations, the background has been filled with the colour of the main
+background, so we can cover up some small islands there. These were not able to
+be filtered out, since their geometrical information is stored in the
+same variable as the data for the mainland.
+
+For the legend we also use some tricks. By default R does not allow the
+creation of legends with rounded edges. To bypass this restriction, we will
+create a geom and lay it over the edges of the rectangular legend to create
+the illusion of a rounded edge.
+
+Additionaly, we used a special form of annotation, taking full advantage of the
+ggtext package. As you can see, certain annotations are performed using the
+richtext geom parameter, which gives us a lot of options to then modify the
+text. Only with this we were able to define custom breaks and also make
+certain parts of the annotation bold without any other restrictions.
+
+```{r}
+font_size <- 3
+
+X2 <- X1 +
+ geom_point(x = -9700000, y = 2100000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = -9700000, y = 2100000,
+ label = "1", color = "white") +
+ annotate("richtext", x = -9700000, y = 1400000,
+ label = "Mexico $803B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = -8200000 , y = 5800000 + 700000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = -8200000 , y = 5800000 + 700000,
+ label = "2", color = "white") +
+ annotate("richtext", x= -8200000, y = 5800000,
+ label = "Canada $784B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+ geom_point(x = 9100000 -800000, y = 3700000 + 180000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 9100000 - 800000, y = 3700000 + 180000,
+ label = "3", color = "white") +
+ annotate("richtext", x= 9100000, y = 3700000,
+ label = "China $596B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+ annotate("richtext", x= 1690000, y = 7800000,
+ label = "Germany $240B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = "#faf8f4",
+ label.color = NA) +
+ geom_point(x = 1680000, y = 7800000 + 700000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 1680000, y = 7800000 + 700000,
+ label = "4", color = "white") +
+
+ geom_point(x = 13000000, y = 3900000 + 700000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 13000000, y = 3900000 + 700000,
+ label = "5", color = "white") +
+ annotate("richtext", x= 13000000, y = 3900000,
+ label = "Japan $228B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = 12600000 -1000000, y = 3000000 + 180000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 12600000 -1000000, y = 3000000 + 180000,
+ label = "6", color = "white") +
+ annotate("richtext", x= 12600000, y = 3000000,
+ label = "S.Korea $185B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ annotate("richtext", x= 70000, y = 7800000,
+ label = "Netherlands $122B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = "#faf8f4",
+ label.color = NA) +
+
+ geom_point(x = -300000 -370000, y = 6550000 + 600000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = -300000 -370000, y = 6550000 + 600000,
+ label = "7", color = "white") +
+ annotate("richtext", x= -300000, y = 6550000,
+ label = "UK $140B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = 12200000 -900000, y = 2350000 + 180000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 12200000 -900000, y = 2350000 + 180000,
+ label = "8", color = "white") +
+ annotate("richtext", x= 12200000, y = 2350000,
+ label = "Taiwan $130B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = 10750000 , y = 1500000 + 700000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 10750000 , y = 1500000 + 700000,
+ label = "9", color = "white") +
+ annotate("richtext", x= 10750000, y = 1500000,
+ label = "Vietnam $129B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = 7450000 -390000, y = 2400000 + 400000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 7450000 -390000, y = 2400000 + 400000,
+ label = "10", color = "white") +
+ annotate("richtext", x= 7450000, y = 2200000,
+ label = "India $127B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+ annotate("richtext", x= 7300000, y = 6500000,
+ label = "Russia $5.5B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+
+ annotate("richtext", x= -4000000, y = 4000000,
+ label = "EU-27 $960B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ annotate("richtext", x= -4800000, y = -850000,
+ label = "Brazil $86B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+ annotate("richtext", x= 3250000, y = -4000000,
+ label = "South Africa $21B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ annotate("richtext", x= 12500000, y = -2500000,
+ label = "Australia $50B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+ annotate("richtext", x= -900000, y = 5000000,
+ label = "France $104B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ annotate("richtext", x= -1400000, y = 4250000,
+ label = "Spain $49B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ annotate("richtext", x= 1500000, y = 3500000,
+ label = "Italy $104B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = 7500000, y = 9029500,
+ size = 4.6,
+ color = "#bdbcfe") +
+ geom_point(x = 16100000, y = 9029500,
+ size = 4.6,
+ color = "#21213c") +
+
+ annotate("text", label = "1,000",
+ x = 16300000, y = 8400000,
+ family = "montserrat",
+ size = 4.24)
+
+```
+
+### The final replication
+
+Now that we have added all the annotations to the plot, adjusted the legend
+as well as created our EU subplot, we can bring everything together and
+present the final visualization
+
+
+```{r, preview = TRUE}
+
+M <- X2 + inset_element(EU, left = 0.13, bottom = 0.4, right = 0.63,
+ top = 0.725)
+
+M
+
+```
+## The Improvement
+
+Let's look at an idea on how to improve the visualization. One thing which
+always seemed a bit intriguing whilst looking at the map was, that the
+categorization makes many countries look the same, or very close to each other,
+even though their trade levels are vastly different. With the improved version
+this issue should be resolved.
+
+### Adjusting the scales
+
+First we want to get rid of the categorization of the trade data. The visual
+difference from a $2 billion and a $595 billion country is just too small, and
+for countries like India and China ($127 billion vs. $595 billion) it is even
+non existent.
+
+Since we still have to deal with a vast array of different trade levels we will
+keep the idea of a logarithmic scale, but this time we will make it continuous
+and not discrete as in the case before. For this we will need an additional
+package, so we can define the scales in a better way.
+
+To further enhance the readability, the plotting of the dataset has been
+modified, so that we only take values from 0.025 to 800 in to account.
+This means, that the values below and above these two barriers are going to be
+plotted as values on the border.
+
+
+```{r}
+
+library(scales)
+
+A <- ggplot(data = world) +
+ geom_sf(aes(fill = world$`U.S. Trade (Billion US$)`),
+ color = "#faf8f4",
+ linewidth = 0.05,
+ show.legend = TRUE) +
+ coord_sf(crs = "+proj=robin") +
+
+ labs(title = "Key Global Partners in U.S. Trade for 2023",
+ subtitle = paste("In 2023, U.S. trade totaled $5,192 billion. ",
+ "Trade with NAFTA partnes-Mexico ($803\nbillion ",
+ "and Canada ($784 billion)-accounted for 30.6% of the ",
+ "total. The European\nUnion was the next largest ",
+ "partner, contribution $986 billion, or 18.5% of total",
+ " trade.\nTrade with China amounted to $596 billion, ",
+ "representing 11.5% of the total.", sep = "")) +
+
+ scale_fill_gradient(name = "U.S. Trade (Billion US$)",
+ low = "#E4DFF0",
+ high = "#240083",
+ trans = "log",
+ breaks = c(0.1,50,800),
+ labels = c("0.1","50","800"),
+ limits = c(0.025,800),
+ oob = scales::squish,
+
+ guide = guide_colorbar(
+ title.position = "top",
+ title.hjust = 0.5,
+ label.position = "bottom",
+ barwidth = unit(5,"cm"),
+ barheight = unit(0.5,"cm")
+ )) +
+
+
+ theme_void() +
+ theme(plot.title = element_text(hjust = 0.5,
+ size = 18,
+ face = "bold",
+ colour = "#042f4a"),
+ plot.subtitle = element_text(hjust = 0.05,
+ vjust = -1),
+ plot.background = element_rect(fill = "#faf8f4"),
+ legend.direction = "horizontal",
+ legend.position = c(0.82,0.98))
+
+```
+
+After these adjustments in the code, the rest of it will be very similar to the
+former code. Therefore we will add all the annotations and the U.S. Flag. One of
+the differences is, that we don't need to make all the manual adjustments to
+the legend.
+
+```{r}
+
+#adding the annotations
+font_size <- 3
+
+ A1 <- A +
+ geom_point(x = -9700000, y = 2100000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = -9700000, y = 2100000,
+ label = "1", color = "white") +
+ annotate("richtext", x = -9700000, y = 1400000,
+ label = "Mexico $803B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = -8200000 , y = 5800000 + 700000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = -8200000 , y = 5800000 + 700000,
+ label = "2", color = "white") +
+ annotate("richtext", x= -8200000, y = 5800000,
+ label = "Canada $784B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+ geom_point(x = 9100000 -800000, y = 3700000 + 180000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 9100000 - 800000, y = 3700000 + 180000,
+ label = "3", color = "white") +
+ annotate("richtext", x= 9100000, y = 3700000,
+ label = "China $596B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+ annotate("richtext", x= 1690000, y = 7800000,
+ label = "Germany $240B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = "#faf8f4",
+ label.color = NA) +
+ geom_point(x = 1680000, y = 7800000 + 700000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 1680000, y = 7800000 + 700000,
+ label = "4", color = "white") +
+
+ geom_point(x = 13000000, y = 3900000 + 700000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 13000000, y = 3900000 + 700000,
+ label = "5", color = "white") +
+ annotate("richtext", x= 13000000, y = 3900000,
+ label = "Japan $228B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = 12600000 -1000000, y = 3000000 + 180000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 12600000 -1000000, y = 3000000 + 180000,
+ label = "6", color = "white") +
+ annotate("richtext", x= 12600000, y = 3000000,
+ label = "S.Korea $185B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ annotate("richtext", x= 70000, y = 7800000,
+ label = "Netherlands $122B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = "#faf8f4",
+ label.color = NA) +
+
+ geom_point(x = -300000 -370000, y = 6550000 + 600000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = -300000 -370000, y = 6550000 + 600000,
+ label = "7", color = "white") +
+ annotate("richtext", x= -300000, y = 6550000,
+ label = "UK $140B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = 12200000 -900000, y = 2350000 + 180000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 12200000 -900000, y = 2350000 + 180000,
+ label = "8", color = "white") +
+ annotate("richtext", x= 12200000, y = 2350000,
+ label = "Taiwan $130B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = 10750000 , y = 1500000 + 700000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 10750000 , y = 1500000 + 700000,
+ label = "9", color = "white") +
+ annotate("richtext", x= 10750000, y = 1500000,
+ label = "Vietnam $129B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ geom_point(x = 7450000 -390000, y = 2400000 + 400000,
+ size = 6.5, stroke = 1, shape = 21,
+ color = "violet", fill = "black") +
+ annotate("text", x = 7450000 -390000, y = 2400000 + 400000,
+ label = "10", color = "white") +
+ annotate("richtext", x= 7450000, y = 2200000,
+ label = "India $127B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+ annotate("richtext", x= 7300000, y = 6500000,
+ label = "Russia $5.5B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+
+ annotate("richtext", x= -4000000, y = 4000000,
+ label = "EU-27 $960B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ annotate("richtext", x= -4800000, y = -850000,
+ label = "Brazil $86B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+ annotate("richtext", x= 3250000, y = -4000000,
+ label = "South Africa $21B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ annotate("richtext", x= 12500000, y = -2500000,
+ label = "Australia $50B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA,
+ color = "white") +
+
+ annotate("richtext", x= -900000, y = 5000000,
+ label = "France $104B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ annotate("richtext", x= -1400000, y = 4250000,
+ label = "Spain $49B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA) +
+
+ annotate("richtext", x= 1500000, y = 3500000,
+ label = "Italy $104B",
+ size = font_size,
+ lineheight = 0.8,
+ fill = NA,
+ label.color = NA)
+
+
+#adding the U.S. Flag
+usa <- spData::us_states |> sf::st_union()
+alaska <- spData::alaska
+
+
+A1 <- A1 +
+ geom_sf_pattern(data = usa,
+ pattern = 'image',
+ pattern_filename = flag,
+ pattern_type = "expand",
+ color = NA) +
+ geom_sf_pattern(data = alaska,
+ pattern = 'image',
+ pattern_filename = flag,
+ pattern_type = "none",
+ pattern_scale= 0.3,
+ pattern_gravity = "southwest",
+ color = NA)+
+ coord_sf(crs = "+proj=robin")
+
+
+
+#adding the EU plot
+EU1 <- ggplot(data = world_EU) +
+ geom_sf(fill = "#240083",
+ color = "#faf8f4",
+ linewidth = 0.25) +
+ coord_sf(xlim = c(-20,40),
+ ylim = c(35,70)) +
+ coord_sf(crs = "+proj=longlat") +
+ theme_void()
+```
+
+Now that we have added all the key elements from before again, we are just left
+with joining the main plot and the EU subplot. This is done easily again
+with the same function from the patchwork package.
+
+
+```{r}
+Im <- A1 + inset_element(EU1, left = 0.13, bottom = 0.4, right = 0.63,
+ top = 0.725)
+Im
+
+```
+
+#### Limitations
+
+One thing that differs from both the replication and the improved version is,
+that the footnotes from the original visualization are not included. This has
+been done on purpose, since they don't provide any benefit or information
+regarding the visualization. In the improved version the new scale is a
+continuous logarithmic scale, that allowed us to visualize a dataset with
+values ranging from 0.0001 to 960. This therefore comes at a small price of the
+legend breaks not being evenly spaced. An idea could be, to disproportionately
+stretch the legend to make the breaks more evenly spaced. Currently this is not
+possible in R, it is also questionable if it would even generate any benefit or
+make the legend harder to read.
+
+Another limitation of this visualization is, that we do not see how the trade
+is structured. We only see that the U.S. trades with Mexico, but we don't know
+if they import more or export more. For a further project this could be an
+interesting and challenging idea, to incorporate this additional information
+in to the visualization.
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+ Key Global Partners in U.S. Trade for 2023
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Key Global Partners in U.S. Trade for 2023
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2024
+
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+
Worldwide U.S. Trade in Billions by country in 2023
+
+
+
+
+ Jason Brunner
+
+ 2025-01-12
+
+
+
+
+
+
+
Introduction
+
The U.S. totaled over $5.1 trillion in trade for the year 2023. This is a truly
+staggering amount and it poses the interesting question, which the major trade
+partners are. By looking at trade through this visualization we can also see
+how our world today is globalized and connected, even in times of rising global
+tensions.
First we are going to start by loading the packages. For the Flagon package we
+have to take a little detour, since it is not available through the regular
+R function. Therefore, we will download and load it at a later step.
Luckily, the data for this visualization was available and only had to be
+downloaded and then transformed to be imported into R. The original data source
+is cited as comtrade, U.S. Census and WTO.
The dataset is already tidy data and is ready to be used for our visualization.
+The raw trade data which we have imported before now has to be modified to fit
+the trade categories displayed in our visualization and later it will be
+merged with our main world dataset.
+
You might wonder why they are split in these specific categories. The reason
+is, that later on when we create the legend we can’t use a straight forward
+approach and will need to become a bit creative. More about this will follow
+once we reach the legend.
The next step will be to focus on the actual world map. For this we take
+advantage of the rnaturalearth package which provides us with a comprehensive
+dataset about the world. We will also need to perform a few modifications to
+better fit the original plot. As you will also notice, we filter the United
+States out of the dataset. This is because the United States are filled with
+the US Flag and therefore we need to exclude it momentarily from our base
+dataset to be able to do this modification later.
+
As mentioned earlier, we will now in the same step merge our trade data with the
+world data we have created so we get our base dataset to then start with our
+plot. Lastly we also define the colours used in the visualization. They have
+been obtained by using an online tool to get the colour codes from any colour
+you provide it.
+
Additionally we also need to download the font. Using an online tool we were
+able to determine the font as “montserrat”. Luckily it is also available as a
+free download and we can use it for our visualization.
+
+
+
world<-ne_countries()
+
+excluded_countries<-c("French Southern and Antarctic Lands","Antarctica",
+"Falkland Islands / Malvinas")
+
+#removing the excluded countries and the United States
+world<-world%>%
+filter(!name_long%in%excluded_countries)%>%
+filter(!name_long%in%"United States")
+
+#incorporate the trade data into to world dataset
+world<-left_join(world, trade_data, by =c("name_long"="Country"))
+
+#defining the colours used for the map
+colors<-c("#bdbcfe", "#9494cd", "#6d6f9c", "#49476a", "#21213c")
+
+font_add_google("Montserrat", "montserrat")
+showtext_auto()
+
+
+
Now we have everything ready to actually create the first draft of our
+visualization. There is still a long way to go but we can plot the first
+draft with the help of the ggplot2 package to see the map with the exclusions
+and the colours for the trade data.
After having created our first plot we will continue enhancing it and
+getting it closer to the original one. The first thing that comes to mind, apart
+from the legend, are the gridlines present in the plot and the background.
+
We can start to fix one of two problems easily by adding the following lines of
+code to our ggplot. In the same step we will also go ahead and add the title and
+subtitle to our plot. To make the title and subtitle look right, we also
+adjust the font corresponding to the original visualization. By defining this
+in our theme, it will automatically be adjusted for all text elements.
+
+
+
X<-X+theme_void()+
+labs(title ="Key Global Partners in U.S. Trade for 2023",
+ subtitle =paste("In 2023, U.S. trade totaled $5,192 billion. ",
+"Trade with NAFTA partnes-Mexico ($803\nbillion ",
+"and Canada ($784 billion)-accounted for 30.6% of the ",
+"total. The European\nUnion was the next largest ",
+"partner, contribution $986 billion, or 18.5% of total",
+" trade.\nTrade with China amounted to $596 billion, ",
+"representing 11.5% of the total.", sep =""))+
+
+#now the theme is being modified to replicate the title and subtitle from the
+#original plot and in the same step we also adjust the background
+theme(plot.title =element_text(hjust =0.5,
+ size =18,
+ face ="bold",
+ colour ="#042f4a"),
+ plot.background =element_rect(fill ="#faf8f4"),
+ plot.subtitle =element_text(hjust =0.05,
+ vjust =-1),
+ text =element_text(family ="montserrat"))
+
+X
+
+
+
+
Now it already looks much more familiar. Let’s not focus on the details like
+positioning and font size yet, but we will look at the more important things
+like the legend. First of all we need to turn the legend and make it horizontal
+and position it somewhat in the top right corner. We also need to fix the labels
+and get rid of the NA category.
+
In order to get the labels of the scale to their correct position, there were
+some tricks needed. By default the labels would be centered under each category
+and by moving them to the left or right they wouldn’t center, since they
+all have a different length.
+
On a side note: If you look closely and compare the visualization and the
+original one, you will note a difference in the colourization of Libya.
+In the original plot the categorization of Libya does not correspond with the
+reported trade value from the dataset.
After creating the basic map, we can start adding the details. Most
+notably, the flag is still missing. For this we are going to use the flagon
+package to get the image of the flag and the spData package for the isolated
+U.S. mainland and Alaska multipolygon. The flagon package is not available to
+be downloaded through the regular R function, therefore we must take a slightly
+different approach and download it directly from Github.
+
For Alaska it took a bit of a trial and error process, to figure out the
+appropriate scale parameter, so that we get the portion of the flag we were
+looking for.
Having fitted the flag we now continue by creating the EU-27 subplot.
+First we had to define a new vector with the EU-27 countries from the original
+plot. After this, the world dataset is filtered so only the EU-27 countries
+remain and we can create the new plot. After that we then only have to set the
+borders so we don’t get the islands in the Atlantic.
+
+
+
EU_27<-c("Austria", "Belgium", "Bulgaria", "Croatia", "Cyprus",
+"Czech Republic", "Denmark", "Estonia", "Finland", "France",
+"Germany", "Greece", "Hungary", "Ireland", "Italy", "Latvia",
+"Lithuania", "Luxembourg", "Malta", "Netherlands", "Poland",
+"Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Sweden")
+
+world_EU<-world%>%
+filter(name_long%in%EU_27)
+
+EU<-ggplot(data =world_EU)+
+geom_sf(fill ="#01339a",
+ color ="#faf8f4",
+ linewidth =0.25)+
+
+#setting the limits of the plot to cut off the Atlantic islands
+coord_sf(xlim =c(-20,40),
+ ylim =c(35,70))+
+coord_sf(crs ="+proj=longlat")+
+theme_void()
+
+
+
Adding them together
+
To finalize the map of our plot we add the EU-27 plot to the main plot. There
+is a package called ggpatchwork for this, which allows us to position it and
+scale it accordingly. This will now yield our final map which only needs
+the annotations to make it complete.
+
+
+
M<-X1+inset_element(EU, left =0.13, bottom =0.4, right =0.63,
+ top =0.725)
+
+M
+
+
+
+
Annotations
+
To finalize our plot we now need to add all the annotations. This step is a bit
+tedious, since every annotation has to be added manually. Additionally to this,
+certain elements of the annotation have to be somewhat “constructed”, like the
+background of the numbers corresponding to the countries.
+
To simplify the process of finding the correct font size and
+being able to adjust it across all annotations efficiently, the font size is
+defined by a variable. Since the annotations are hierarchical, we can layer
+shapes and texts on top of each other. This allowed the creation of the circles
+whit a number on top. There is also the instance of Germany and the Netherlands,
+where this feature of layering has been taken further advantage of. For these
+two annotations, the background has been filled with the colour of the main
+background, so we can cover up some small islands there. These were not able to
+be filtered out, since their geometrical information is stored in the
+same variable as the data for the mainland.
+
For the legend we also use some tricks. By default R does not allow the
+creation of legends with rounded edges. To bypass this restriction, we will
+create a geom and lay it over the edges of the rectangular legend to create
+the illusion of a rounded edge.
+
Additionaly, we used a special form of annotation, taking full advantage of the
+ggtext package. As you can see, certain annotations are performed using the
+richtext geom parameter, which gives us a lot of options to then modify the
+text. Only with this we were able to define custom breaks and also make
+certain parts of the annotation bold without any other restrictions.
+
+
+
font_size<-3
+
+X2<-X1+
+geom_point(x =-9700000, y =2100000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =-9700000, y =2100000,
+ label ="1", color ="white")+
+annotate("richtext", x =-9700000, y =1400000,
+ label ="Mexico<br><b>$803B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =-8200000 , y =5800000+700000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =-8200000 , y =5800000+700000,
+ label ="2", color ="white")+
+annotate("richtext", x=-8200000, y =5800000,
+ label ="Canada<br><b>$784B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+geom_point(x =9100000-800000, y =3700000+180000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =9100000-800000, y =3700000+180000,
+ label ="3", color ="white")+
+annotate("richtext", x=9100000, y =3700000,
+ label ="China<br><b>$596B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+annotate("richtext", x=1690000, y =7800000,
+ label ="Germany<br><b>$240B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill ="#faf8f4",
+ label.color =NA)+
+geom_point(x =1680000, y =7800000+700000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =1680000, y =7800000+700000,
+ label ="4", color ="white")+
+
+geom_point(x =13000000, y =3900000+700000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =13000000, y =3900000+700000,
+ label ="5", color ="white")+
+annotate("richtext", x=13000000, y =3900000,
+ label ="Japan<br><b>$228B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =12600000-1000000, y =3000000+180000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =12600000-1000000, y =3000000+180000,
+ label ="6", color ="white")+
+annotate("richtext", x=12600000, y =3000000,
+ label ="S.Korea<br><b>$185B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+annotate("richtext", x=70000, y =7800000,
+ label ="Netherlands<br><b>$122B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill ="#faf8f4",
+ label.color =NA)+
+
+geom_point(x =-300000-370000, y =6550000+600000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =-300000-370000, y =6550000+600000,
+ label ="7", color ="white")+
+annotate("richtext", x=-300000, y =6550000,
+ label ="UK<br><b>$140B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =12200000-900000, y =2350000+180000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =12200000-900000, y =2350000+180000,
+ label ="8", color ="white")+
+annotate("richtext", x=12200000, y =2350000,
+ label ="Taiwan<br><b>$130B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =10750000 , y =1500000+700000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =10750000 , y =1500000+700000,
+ label ="9", color ="white")+
+annotate("richtext", x=10750000, y =1500000,
+ label ="Vietnam<br><b>$129B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =7450000-390000, y =2400000+400000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =7450000-390000, y =2400000+400000,
+ label ="10", color ="white")+
+annotate("richtext", x=7450000, y =2200000,
+ label ="India<br><b>$127B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+annotate("richtext", x=7300000, y =6500000,
+ label ="Russia<br><b>$5.5B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+
+annotate("richtext", x=-4000000, y =4000000,
+ label ="EU-27<br><b>$960B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+annotate("richtext", x=-4800000, y =-850000,
+ label ="Brazil<br><b>$86B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+annotate("richtext", x=3250000, y =-4000000,
+ label ="South Africa<br><b>$21B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+annotate("richtext", x=12500000, y =-2500000,
+ label ="Australia<br><b>$50B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+annotate("richtext", x=-900000, y =5000000,
+ label ="France<br><b>$104B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+annotate("richtext", x=-1400000, y =4250000,
+ label ="Spain<br><b>$49B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+annotate("richtext", x=1500000, y =3500000,
+ label ="Italy<br><b>$104B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =7500000, y =9029500,
+ size =4.6,
+ color ="#bdbcfe")+
+geom_point(x =16100000, y =9029500,
+ size =4.6,
+ color ="#21213c")+
+
+annotate("text", label ="1,000",
+ x =16300000, y =8400000,
+ family ="montserrat",
+ size =4.24)
+
+
+
The final replication
+
Now that we have added all the annotations to the plot, adjusted the legend
+as well as created our EU subplot, we can bring everything together and
+present the final visualization
+
+
+
M<-X2+inset_element(EU, left =0.13, bottom =0.4, right =0.63,
+ top =0.725)
+
+M
+
+
+
+
The Improvement
+
Let’s look at an idea on how to improve the visualization. One thing which
+always seemed a bit intriguing whilst looking at the map was, that the
+categorization makes many countries look the same, or very close to each other,
+even though their trade levels are vastly different. With the improved version
+this issue should be resolved.
+
Adjusting the scales
+
First we want to get rid of the categorization of the trade data. The visual
+difference from a $2 billion and a $595 billion country is just too small, and
+for countries like India and China ($127 billion vs. $595 billion) it is even
+non existent.
+
Since we still have to deal with a vast array of different trade levels we will
+keep the idea of a logarithmic scale, but this time we will make it continuous
+and not discrete as in the case before. For this we will need an additional
+package, so we can define the scales in a better way.
+
To further enhance the readability, the plotting of the dataset has been
+modified, so that we only take values from 0.025 to 800 in to account.
+This means, that the values below and above these two barriers are going to be
+plotted as values on the border.
+
+
+
library(scales)
+
+A<-ggplot(data =world)+
+geom_sf(aes(fill =world$`U.S. Trade (Billion US$)`),
+ color ="#faf8f4",
+ linewidth =0.05,
+ show.legend =TRUE)+
+coord_sf(crs ="+proj=robin")+
+
+labs(title ="Key Global Partners in U.S. Trade for 2023",
+ subtitle =paste("In 2023, U.S. trade totaled $5,192 billion. ",
+"Trade with NAFTA partnes-Mexico ($803\nbillion ",
+"and Canada ($784 billion)-accounted for 30.6% of the ",
+"total. The European\nUnion was the next largest ",
+"partner, contribution $986 billion, or 18.5% of total",
+" trade.\nTrade with China amounted to $596 billion, ",
+"representing 11.5% of the total.", sep =""))+
+
+scale_fill_gradient(name ="U.S. Trade (Billion US$)",
+ low ="#E4DFF0",
+ high ="#240083",
+ trans ="log",
+ breaks =c(0.1,50,800),
+ labels =c("0.1","50","800"),
+ limits =c(0.025,800),
+ oob =scales::squish,
+
+ guide =guide_colorbar(
+ title.position ="top",
+ title.hjust =0.5,
+ label.position ="bottom",
+ barwidth =unit(5,"cm"),
+ barheight =unit(0.5,"cm")
+))+
+
+
+theme_void()+
+theme(plot.title =element_text(hjust =0.5,
+ size =18,
+ face ="bold",
+ colour ="#042f4a"),
+ plot.subtitle =element_text(hjust =0.05,
+ vjust =-1),
+ plot.background =element_rect(fill ="#faf8f4"),
+ legend.direction ="horizontal",
+ legend.position =c(0.82,0.98))
+
+
+
After these adjustments in the code, the rest of it will be very similar to the
+former code. Therefore we will add all the annotations and the U.S. Flag. One of
+the differences is, that we don’t need to make all the manual adjustments to
+the legend.
+
+
+
#adding the annotations
+font_size<-3
+
+A1<-A+
+geom_point(x =-9700000, y =2100000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =-9700000, y =2100000,
+ label ="1", color ="white")+
+annotate("richtext", x =-9700000, y =1400000,
+ label ="Mexico<br><b>$803B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =-8200000 , y =5800000+700000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =-8200000 , y =5800000+700000,
+ label ="2", color ="white")+
+annotate("richtext", x=-8200000, y =5800000,
+ label ="Canada<br><b>$784B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+geom_point(x =9100000-800000, y =3700000+180000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =9100000-800000, y =3700000+180000,
+ label ="3", color ="white")+
+annotate("richtext", x=9100000, y =3700000,
+ label ="China<br><b>$596B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+annotate("richtext", x=1690000, y =7800000,
+ label ="Germany<br><b>$240B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill ="#faf8f4",
+ label.color =NA)+
+geom_point(x =1680000, y =7800000+700000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =1680000, y =7800000+700000,
+ label ="4", color ="white")+
+
+geom_point(x =13000000, y =3900000+700000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =13000000, y =3900000+700000,
+ label ="5", color ="white")+
+annotate("richtext", x=13000000, y =3900000,
+ label ="Japan<br><b>$228B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =12600000-1000000, y =3000000+180000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =12600000-1000000, y =3000000+180000,
+ label ="6", color ="white")+
+annotate("richtext", x=12600000, y =3000000,
+ label ="S.Korea<br><b>$185B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+annotate("richtext", x=70000, y =7800000,
+ label ="Netherlands<br><b>$122B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill ="#faf8f4",
+ label.color =NA)+
+
+geom_point(x =-300000-370000, y =6550000+600000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =-300000-370000, y =6550000+600000,
+ label ="7", color ="white")+
+annotate("richtext", x=-300000, y =6550000,
+ label ="UK<br><b>$140B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =12200000-900000, y =2350000+180000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =12200000-900000, y =2350000+180000,
+ label ="8", color ="white")+
+annotate("richtext", x=12200000, y =2350000,
+ label ="Taiwan<br><b>$130B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =10750000 , y =1500000+700000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =10750000 , y =1500000+700000,
+ label ="9", color ="white")+
+annotate("richtext", x=10750000, y =1500000,
+ label ="Vietnam<br><b>$129B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+geom_point(x =7450000-390000, y =2400000+400000,
+ size =6.5, stroke =1, shape =21,
+ color ="violet", fill ="black")+
+annotate("text", x =7450000-390000, y =2400000+400000,
+ label ="10", color ="white")+
+annotate("richtext", x=7450000, y =2200000,
+ label ="India<br><b>$127B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+annotate("richtext", x=7300000, y =6500000,
+ label ="Russia<br><b>$5.5B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+
+annotate("richtext", x=-4000000, y =4000000,
+ label ="EU-27<br><b>$960B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+annotate("richtext", x=-4800000, y =-850000,
+ label ="Brazil<br><b>$86B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+annotate("richtext", x=3250000, y =-4000000,
+ label ="South Africa<br><b>$21B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+annotate("richtext", x=12500000, y =-2500000,
+ label ="Australia<br><b>$50B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA,
+ color ="white")+
+
+annotate("richtext", x=-900000, y =5000000,
+ label ="France<br><b>$104B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+annotate("richtext", x=-1400000, y =4250000,
+ label ="Spain<br><b>$49B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)+
+
+annotate("richtext", x=1500000, y =3500000,
+ label ="Italy<br><b>$104B</b>",
+ size =font_size,
+ lineheight =0.8,
+ fill =NA,
+ label.color =NA)
+
+
+#adding the U.S. Flag
+usa<-spData::us_states|>sf::st_union()
+alaska<-spData::alaska
+
+
+A1<-A1+
+geom_sf_pattern(data =usa,
+ pattern ='image',
+ pattern_filename =flag,
+ pattern_type ="expand",
+ color =NA)+
+geom_sf_pattern(data =alaska,
+ pattern ='image',
+ pattern_filename =flag,
+ pattern_type ="none",
+ pattern_scale=0.3,
+ pattern_gravity ="southwest",
+ color =NA)+
+coord_sf(crs ="+proj=robin")
+
+
+
+#adding the EU plot
+EU1<-ggplot(data =world_EU)+
+geom_sf(fill ="#240083",
+ color ="#faf8f4",
+ linewidth =0.25)+
+coord_sf(xlim =c(-20,40),
+ ylim =c(35,70))+
+coord_sf(crs ="+proj=longlat")+
+theme_void()
+
+
+
Now that we have added all the key elements from before again, we are just left
+with joining the main plot and the EU subplot. This is done easily again
+with the same function from the patchwork package.
+
+
+
Im<-A1+inset_element(EU1, left =0.13, bottom =0.4, right =0.63,
+ top =0.725)
+Im
+
+
+
+
Limitations
+
One thing that differs from both the replication and the improved version is,
+that the footnotes from the original visualization are not included. This has
+been done on purpose, since they don’t provide any benefit or information
+regarding the visualization. In the improved version the new scale is a
+continuous logarithmic scale, that allowed us to visualize a dataset with
+values ranging from 0.0001 to 960. This therefore comes at a small price of the
+legend breaks not being evenly spaced. An idea could be, to disproportionately
+stretch the legend to make the breaks more evenly spaced. Currently this is not
+possible in R, it is also questionable if it would even generate any benefit or
+make the legend harder to read.
+
Another limitation of this visualization is, that we do not see how the trade
+is structured. We only see that the U.S. trades with Mexico, but we don’t know
+if they import more or export more. For a further project this could be an
+interesting and challenging idea, to incorporate this additional information
+in to the visualization.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_projects/2024/100543010/100543010_files/anchor-4.2.2/anchor.min.js b/_projects/2024/100543010/100543010_files/anchor-4.2.2/anchor.min.js
new file mode 100644
index 00000000..1342f5f6
--- /dev/null
+++ b/_projects/2024/100543010/100543010_files/anchor-4.2.2/anchor.min.js
@@ -0,0 +1,9 @@
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+//
+// AnchorJS - v4.2.2 - 2019-11-14
+// https://www.bryanbraun.com/anchorjs/
+// Copyright (c) 2019 Bryan Braun; Licensed MIT
+//
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function f(A){A.icon=A.hasOwnProperty("icon")?A.icon:"",A.visible=A.hasOwnProperty("visible")?A.visible:"hover",A.placement=A.hasOwnProperty("placement")?A.placement:"right",A.ariaLabel=A.hasOwnProperty("ariaLabel")?A.ariaLabel:"Anchor",A.class=A.hasOwnProperty("class")?A.class:"",A.base=A.hasOwnProperty("base")?A.base:"",A.truncate=A.hasOwnProperty("truncate")?Math.floor(A.truncate):64,A.titleText=A.hasOwnProperty("titleText")?A.titleText:""}function p(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A instanceof NodeList))throw new Error("The selector provided to AnchorJS was invalid.");e=[].slice.call(A)}return e}this.options=A||{},this.elements=[],f(this.options),this.isTouchDevice=function(){return!!("ontouchstart"in window||window.DocumentTouch&&document instanceof DocumentTouch)},this.add=function(A){var e,t,i,n,o,s,a,r,c,h,l,u,d=[];if(f(this.options),"touch"===(l=this.options.visible)&&(l=this.isTouchDevice()?"always":"hover"),0===(e=p(A=A||"h2, h3, h4, h5, h6")).length)return this;for(!function(){if(null!==document.head.querySelector("style.anchorjs"))return;var A,e=document.createElement("style");e.className="anchorjs",e.appendChild(document.createTextNode("")),void 0===(A=document.head.querySelector('[rel="stylesheet"], style'))?document.head.appendChild(e):document.head.insertBefore(e,A);e.sheet.insertRule(" .anchorjs-link { opacity: 0; text-decoration: none; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; }",e.sheet.cssRules.length),e.sheet.insertRule(" *:hover > .anchorjs-link, .anchorjs-link:focus { opacity: 1; }",e.sheet.cssRules.length),e.sheet.insertRule(" [data-anchorjs-icon]::after { content: attr(data-anchorjs-icon); }",e.sheet.cssRules.length),e.sheet.insertRule(' @font-face { font-family: "anchorjs-icons"; src: url(data:n/a;base64,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) format("truetype"); }',e.sheet.cssRules.length)}(),t=document.querySelectorAll("[id]"),i=[].map.call(t,function(A){return A.id}),o=0;o\]\.\/\(\)\*\\\n\t\b\v]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),t=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||t||!1}}});
+// @license-end
\ No newline at end of file
diff --git a/_projects/2024/100543010/100543010_files/bowser-1.9.3/bowser.min.js b/_projects/2024/100543010/100543010_files/bowser-1.9.3/bowser.min.js
new file mode 100644
index 00000000..3da61049
--- /dev/null
+++ b/_projects/2024/100543010/100543010_files/bowser-1.9.3/bowser.min.js
@@ -0,0 +1,6 @@
+/*!
+ * Bowser - a browser detector
+ * https://github.com/ded/bowser
+ * MIT License | (c) Dustin Diaz 2015
+ */
+!function(e,t,n){typeof module!="undefined"&&module.exports?module.exports=n():typeof define=="function"&&define.amd?define(t,n):e[t]=n()}(this,"bowser",function(){function t(t){function n(e){var n=t.match(e);return n&&n.length>1&&n[1]||""}function r(e){var n=t.match(e);return n&&n.length>1&&n[2]||""}function N(e){switch(e){case"NT":return"NT";case"XP":return"XP";case"NT 5.0":return"2000";case"NT 5.1":return"XP";case"NT 5.2":return"2003";case"NT 6.0":return"Vista";case"NT 6.1":return"7";case"NT 6.2":return"8";case"NT 6.3":return"8.1";case"NT 10.0":return"10";default:return undefined}}var i=n(/(ipod|iphone|ipad)/i).toLowerCase(),s=/like android/i.test(t),o=!s&&/android/i.test(t),u=/nexus\s*[0-6]\s*/i.test(t),a=!u&&/nexus\s*[0-9]+/i.test(t),f=/CrOS/.test(t),l=/silk/i.test(t),c=/sailfish/i.test(t),h=/tizen/i.test(t),p=/(web|hpw)os/i.test(t),d=/windows 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new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/_projects/2024/100543010/100543010_files/header-attrs-2.29/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
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--- /dev/null
+++ b/_projects/2024/100543010/100543010_files/jquery-3.6.0/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+ "use strict";
+
+ if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+ // For CommonJS and CommonJS-like environments where a proper `window`
+ // is present, execute the factory and get jQuery.
+ // For environments that do not have a `window` with a `document`
+ // (such as Node.js), expose a factory as module.exports.
+ // This accentuates the need for the creation of a real `window`.
+ // e.g. var jQuery = require("jquery")(window);
+ // See ticket #14549 for more info.
+ module.exports = global.document ?
+ factory( global, true ) :
+ function( w ) {
+ if ( !w.document ) {
+ throw new Error( "jQuery requires a window with a document" );
+ }
+ return factory( w );
+ };
+ } else {
+ factory( global );
+ }
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+ return arr.flat.call( array );
+} : function( array ) {
+ return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+ // Support: Chrome <=57, Firefox <=52
+ // In some browsers, typeof returns "function" for HTML
+
+
