From 7fae45af9455b8bed98281d12f14d7a7861eea16 Mon Sep 17 00:00:00 2001
From: Marty Oehme <marty.oehme@gmail.com>
Date: Sat, 20 Aug 2022 16:44:25 +0200
Subject: [PATCH] Add plotly figures to scripts

---
 _drivers-of-inequality-benin.qmd    |  6 +++
 _drivers-of-inequality-djibouti.qmd |  6 +++
 _drivers-of-inequality-uganda.qmd   |  6 +++
 _drivers-of-inequality-vietnam.qmd  |  6 +++
 _quarto.yml                         |  2 +-
 data-prep.qmd                       | 71 +++++++++++++++++++++++++++++
 index.qmd                           | 11 +++++
 7 files changed, 107 insertions(+), 1 deletion(-)
 create mode 100644 data-prep.qmd

diff --git a/_drivers-of-inequality-benin.qmd b/_drivers-of-inequality-benin.qmd
index ef90194..563410c 100644
--- a/_drivers-of-inequality-benin.qmd
+++ b/_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].
diff --git a/_drivers-of-inequality-djibouti.qmd b/_drivers-of-inequality-djibouti.qmd
index 96e06e0..6dda52d 100644
--- a/_drivers-of-inequality-djibouti.qmd
+++ b/_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,
diff --git a/_drivers-of-inequality-uganda.qmd b/_drivers-of-inequality-uganda.qmd
index f4529ad..0298375 100644
--- a/_drivers-of-inequality-uganda.qmd
+++ b/_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,
diff --git a/_drivers-of-inequality-vietnam.qmd b/_drivers-of-inequality-vietnam.qmd
index e1c2ec1..345adc1 100644
--- a/_drivers-of-inequality-vietnam.qmd
+++ b/_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)
diff --git a/_quarto.yml b/_quarto.yml
index ca79b49..3e4242c 100644
--- a/_quarto.yml
+++ b/_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:
diff --git a/data-prep.qmd b/data-prep.qmd
new file mode 100644
index 0000000..1763ce2
--- /dev/null
+++ b/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)
+```
+
diff --git a/index.qmd b/index.qmd
index bc7017d..38439e7 100644
--- a/index.qmd
+++ b/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 >}}