Asian Development Bank (Data Day)

#30DayChartChallenge Day 18

Today’s theme is a data day, so I went over to browse the interesting datasets available from the Asian Development Bank. I found this dataset that shows the inflation levels for Asian countries from 2019 to 2023, with forecasted numbers for 2024 and 2025.

As always, I enjoy analysing data from my home region as it’s less widely covered on a global news. So I thought to focus my chart on Southeast asia, in particular, I wanted to highlight the volatile inflation levels experienced by countries such as Myanmar and Laos in recent years due to political instability.

import polars as pl
from lets_plot import *
LetsPlot.setup_html()

inflation = pl.read_csv('./inflation.csv')


df = (inflation
    .filter(pl.col(' Subregion')=="Southeast Asia")
    .filter(pl.col('RegionalMember')!="Southeast Asia")
    .with_columns(pl.when(pl.col('RegionalMember')=='Myanmar').then(1)
        .when(pl.col('RegionalMember')=='Laos').then(2)
        .otherwise(0).alias('highlight')
    )
)


(ggplot(df)
    + geom_line(aes(x='Year', y='Inflation', group='RegionalMember',color='highlight'))
    + scale_color_manual(values=['#808080','#1380A1','#FAAB18',])
    + geom_label(x=2.4,y=25, label='Myanmar', fill='white', hjust=0, vjust=3, label_size=0, color='#1380A1')
    + geom_label(x=3.4,y=28, label='Laos', fill='white', hjust=0, vjust=3, label_size=0, color='#FAAB18')
    + geom_label(x=2.7,y=9, label='Rest of SEA', fill='white', hjust=0, vjust=3, label_size=0, color='#808080')
    + labs(
        y= 'Inflation (%)',
        title="Inflation levels in Southeast Asia (2019 - 2025)",
        subtitle="Myanmar and Laos have experienced the most volatile inflation levels",
        caption = '#30DayChartChallenge #Day18 Asian Development Bank\nData: Asian Development Bank\nMade by: www.ddanieltan.com'
    )
    + theme(
        plot_title=element_text(size=24, face='bold'),
        plot_subtitle=element_text(size=18),
        plot_caption=element_text(size=12, color='grey'),
        legend_position='none',
        axis_title_x=element_blank(),
    )
    +ggsize(900,700)
)

TIL

1. For geom_lines, I can use group to separate by category and color to highlight only the lines I want to draw attention to.

2. geom_labels are very useful at replacing legends and providing easy to read labels directly on the areas of interest. I originally wanted to put the labels right at the end of the chart but I had difficulty working with the right margin.