Introductory Steps

As a first step, I will download data from the Banks Association of Turkey. When you click on the previous highlighted link, you will see that there are 3 main columns, namely: Geographical Regions and Provinces, Periods, Parameters. Please choose Select All in the Geographical Regions and Provinces part by excluding all rows until Istanbul. In the Periods, we can select only 2010 and 2020 since we will only use these. Finally, in the Parameters part, I kindly ask you to select Number of Foreign Currency Deposit Accounts since this is the data we will use in this post. After that, you can click on Report and, by clicking on the Excel icon on the new page, you can download the data onto your computer automatically.

In a similar manner, let’s move on the GADM website and please select Turkey from the country list, as well as click on Shapefile and download the data.

Now, let’s load the required packages and the data into our working space and start exploring the data.

Installing Libraries and the data into R

options(scipen=999) # to prevent scientific notation 
library(readxl) # to read excel files
library(tidyverse) # for data analysis purposes and visulization -> tidyverse mainly contains ggplot2 and dplyr
library(viridis) # to make visualisations
library(sf)      # to read geospatial data

Now, let’s install data in R and look at the head of Number of Foreign Currency Deposit Accounts data.

As can be seen from the output, the data frame format for the Number of Foreign Currency Deposit Accounts is not convenient to conduct further analysis. So, we will need a brief data cleaning effort here. Please also note that we specifically choose the gadm41_TUR_1.shp file to read into the working space as it contains geospatial data on the boundaries of the Turkish cities.

Let us revise what we have done so far. We first renamed the column names in our data set to as it would not lead to further misunderstandings. Then, by using select function from the dplyr package, we selected 3 crucial columns for our analysis from the main dataset. They are, namely, cities, 2010, and 2020. Following that, as we wanted the years to be stored in a new column named years, and the values corresponding to them in a new column named account_numbers, we used the powerful pivot_longer function from the dplyr package.

!"cities" told R not to touch this column. names_to told R to store 2010, and 2020 in a new column named years and the corresponding values went to the new account_numbers column by using values_to argument. Before moving on, if you want to have a further look at pivot_longer() function, you can try this website.

Now, let’s focus on the tur_spatial data and view the data.

Now, as a second step, we need to merge Number of Foreign Currency Deposit Accounts data with tur_spatial data so that we can have corresponding values stored for the relevant cities to make the map we are aiming for.

Merging Datasets

This step has another tricky part as it requires a careful examination with regards to Turkish cities’ names due to the punctuation differences between the two datasets we are working on. While this problem occurs because of the nature of the Turkish language, with a careful examination, we can handle this problem.

The above code solved the punctuation differences between the two datasets we are working on and merged these two datasets accordingly. To elaborate on what we have done so far, we first eliminated the punctuation differences between the two datasets using case_when() function and then merged them into the final_data we will be using to make the map.².

Also, note that with the quantile() function, we got an idea on distribution of the account_numbers(), which will help us to set the boundaries for the legend when we will be making the map later.

Visualize the Data

Now, as the last step, we will visualize our data with the following code chunk;

plot <- final_data %>%
  select(years, cities, account_numbers, geometry) %>%
  # filter(years == 2010) %>%
  ggplot() +#
  geom_sf(aes(fill = account_numbers), color = "white", lwd = 0.01) +
  facet_wrap(~years, nrow = 1) +
  theme_void() +
  labs(title = "Number of Foreign Currency Deposit Accounts in Banks by Province in Turkey",
       subtitle = "2010 vs. 2020",
       caption = "Source: Banks Association of Turkey") +
  scale_fill_viridis(name="Total Account Number", trans = "log",
                     breaks = c(16087000, 214470250, 903586500, 3665321750, 735192229941), 
                     labels = c("0-16 M", "16 M- 214.5M", "214.5 M - 903.6 M", "903.6 M - 3.7 B", "3.7 B - 7.4 B"),
                     guide = guide_legend(keyheight = unit(1, units = "mm"), 
                                          keywidth=unit(6, units = "mm"), 
                                          label.position = "bottom", title.position = 'top',
                                          nrow=1)) +
  theme(plot.background = element_rect(fill = "#f5f5f2", color = NA),
        panel.background = element_rect(fill = "#f5f5f2", color = NA),
        legend.background = element_rect(fill = "#f5f5f2", color = NA),
        legend.text = element_text(size = 18),
        legend.title = element_text(size = 20),
        strip.text = element_text(size = 18),
        plot.title = element_text(size= 35, hjust = 0.5, color = "#4e4d47"),
        plot.subtitle = element_text(size= 24, hjust = 0.5, color = "#4e4d47"),
        plot.caption = element_text(size=24, color = "#4e4d47", hjust = 0.2),
        legend.position = c(0.5, 0),
        legend.direction = "horizontal") 

knitr::include_graphics("featured.png")

Voilà, here is our beautiful map that demonstrates a comparison of the number of foreign currency deposit accounts in Turkey between 2010 and 2020 by city-level. Before concluding our post, let us look at some specific arguments we made while making this plot.

• We want each city to be colored based on the corresponding number of foreign deposit accounts, so we write this fill = account_numbers. If you would like to adjust the transparency of the colors for each city, you can customize alpha argument inside geom_sf() function. 0 corresponds to full transparency, while 1 corresponds to full opacity.
• theme_void argument provides a blank background. By using scale_fill_viridis, we color our map. As the distribution of account_numbers data is not normal³, we can use trans = "log" argument to see the change between 2010 and 2020 better.
• Without going into further details, I want to lastly point out that facet_wrap() function, which helps to see the comparison between the 2010 and the 2020. If you want to look further into the specifics of these arguments, this website has wonderful tutorials for making visualisations in R that I am sure will satisfy your needs.

To conclude, in this tutorial, I tried to make a visualization that demonstrates the amount of change in foreign currency deposit accounts across cities in Turkey between 2010 and 2020. I hope this post can help you in your own endeavors. If you have further questions and suggestions, please do not hesitate to reach me via muhammetozk@icloud.com.