Add plotly figures to scripts

_drivers-of-inequality-benin.qmd

@@ -20,6 +20,12 @@ At the same time, the country's poverty rate, even measured based on the interna
 from a relative rate of households in poverty at 18.8% in 2019, to 18.7% in 2020 and 18.3% at the end of 2021,
 with the reduction threatened to be slowed further through increased prices on food and energy [@WorldBank2022b].
 
+```{python}
+#| label: gini-ben
+#| fig-cap: "Gini index of consumption per capita for Benin. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
+plot_consumption_gini_percapita(ben)
+```
+
 <!-- poverty -->
 Based on its national poverty line, Benin's overall poverty rate is 38.5%,
 though it hides a strong spatial disparity between rural and urban households with 44.2% to 31.4% households in poverty respectively [@WorldBank2022b].

_drivers-of-inequality-djibouti.qmd

@@ -17,6 +17,12 @@ The country's GDP has averaged roughly 6% per year before the Covid-19 pandemic
 However, the country's inequality levels are some of the highest in the world and its poverty rates are extreme.
 Additionally in many cases there is a lack of data or the data itself are lacking in several dimensions which hinders creating a cohesive picture or plan.
 
+```{python}
+#| label: gini-dji
+#| fig-cap: "Gini index of consumption per capita for Djibouti. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
+plot_consumption_gini_percapita(dji)
+```
+
 <!-- poverty -->
 Poverty in Djibouti is both very high and marked by high deprivation.
 Using the national poverty line of around 2.18USD (2011 PPP) the poverty rate for the overall country by consumption is estimated at 21.1% in 2017,

_drivers-of-inequality-uganda.qmd

@@ -23,6 +23,12 @@ Additionally, he sees quintile inequalities primarily driven by the highest quin
 also finding a significantly higher coefficient for the first quintile (0.14), however.
 These inequality levels remain mostly unchanged from 2012/13 to 2019/20 but hide qualitative dimensions such as the shift out of a lower-income agricultural livelihood predominantly taking place among older men who have at least some level of formal education and are from already more well-off households [@Atamanov2022].
 
+```{python}
+#| label: gini-uga
+#| fig-cap: "Gini index of consumption per capita for Uganda. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
+plot_consumption_gini_percapita(uga)
+```
+
 <!-- poverty -->
 The World Bank [-@Atamanov2022] report goes on to examine the share of people below the poverty line in Uganda:
 around 30% of households are in a state of poverty in 2019/20,

_drivers-of-inequality-vietnam.qmd

@@ -29,6 +29,12 @@ though absolute income may be rising, with the top quintile having 9.2 times the
 Economic inequality and poverty in Vietnam thus underlies an intersectional focus, between ethnic minorities, regional situations, rural-urban divides and gendered lines,
 one which exogenous shocks can rapidly exacerbate as the example of the COVID-19 pandemic has recently shown [@Ebrahim2021].
 
+```{python}
+#| label: gini-vnm
+#| fig-cap: "Gini index of consumption per capita for Vietnam. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
+plot_consumption_gini_percapita_ruralurban(vnm)
+```
+
 <!--
 * estimated Gini coeff, overall income distribution: [@Le2021]
     * fluctuating 0.42-0.44 (2010-2018)

_quarto.yml

@@ -28,7 +28,7 @@ format:
     linestretch: 1.25
     fontfamily: lmodern
     fontsize: "12"
-    # toc: true
+    toc: true
 jupyter: python3
 title: "Drivers of Inequality"
 author:

data-prep.qmd

@@ -0,0 +1,71 @@
+---
+format:
+    html:
+        code-fold: true
+        standalone: true
+fig-cap-location: top
+---
+
+```{python}
+#| echo: false
+import matplotlib.pyplot as plt
+def prepare_plot_colors():
+    # "Tableau 20" colors as RGB.
+    colors = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
+                 (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
+                 (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
+                 (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
+                 (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
+
+    # Scale RGB values to the [0, 1] range for matplotlib
+    for i in range(len(colors)):
+        r, g, b = colors[i]
+        colors[i] = (r / 255., g / 255., b / 255.)
+    return colors
+
+colors=prepare_plot_colors()
+```
+
+```{python}
+#| echo: false
+import openpyxl
+import pandas as pd
+
+df = pd.read_csv('data/cleaned/UNU-WIDER-WIID/WIID-30JUN2022_cty-select.csv', index_col="id", parse_dates=True)
+```
+
+```{python}
+#| echo: false
+df = df.loc[df['year'] > 2000]
+ben = df.loc[df['c3'] == "BEN"]
+dji = df.loc[df['c3'] == "DJI"]
+uga = df.loc[df['c3'] == "UGA"]
+vnm = df.loc[df['c3'] == "VNM"]
+```
+
+```{python}
+# Set up the data extraction and figure drawing functions
+import plotly.express as px
+import plotly.io as pio
+
+def gini_plot(country_df):
+    if svg_render:
+        pio.renderers.default = "png"
+
+    fig = px.line(country_df, x="year", y="gini", markers=True, labels={"year": "Year", "gini": "Gini coefficient"}, template="seaborn", range_y=[30,60])
+    fig.update_traces(marker_size=10)
+    fig.show()
+
+def plot_consumption_gini_percapita(country_df):
+    gni_cnsmpt = country_df[country_df['resource'].str.contains("Consumption")]
+    gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
+    gini_plot(gni_cnsmpt_percapita)
+
+def plot_consumption_gini_percapita_ruralurban(country_df):
+    gni_cnsmpt = country_df[country_df['resource'].str.contains("Consumption")]
+    gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
+    gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['source'].str.contains("World Bank")]
+    gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['areacovr'].str.contains("All")]
+    gini_plot(gni_cnsmpt)
+```
+

index.qmd

@@ -1,3 +1,14 @@
+---
+nocite: |
+    | @UNU-WIDER2022a
+    | @UNU-WIDER2022
+---
+
+{{< include data-prep.qmd >}}
+
+```{python}
+svg_render = True
+```
 
 {{< include _drivers-of-inequality-vietnam.qmd >}}