Quarto Tutorial 3

Author

Alier Reng

Published

April 24, 2022

1 Introduction

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

1.1 Definitions

Before getting started, let’s explain what each option in the yaml means.

  • toc adds the table of contents to your document.

  • number-sections adds number to the section headings when sets to true.

  • Latex equations are rendered using MathJax; however, you can change this to other options, as shown above.

  • highlight-style is used to style code outputs.

  • code-overflow controls the width of source code. When sets to wrap, the source code wraps around and vice versa.

There are numerous options to style and format your document, so we recommend reading the documentation on the Quarto website.

1.2 Loading the Libraries

Here we will load pandas, seaborn, and matplotlib.

# Loading packages
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Formatting
sns.set_context('notebook')
sns.set_style('white')

1.3 Importing the Dataset

# Import the data
ss_2008_census_df = pd.read_csv('../00_data/ss_2008_census_data_raw.csv')

# Inspect the first 5 rows
ss_2008_census_df.head()
Region Region Name Region - RegionId Variable Variable Name Age Age Name Scale Units 2008
0 KN.A2 Upper Nile SS-NU KN.B2 Population, Total (Number) KN.C1 Total units Persons 964353.0
1 KN.A2 Upper Nile SS-NU KN.B2 Population, Total (Number) KN.C2 0 to 4 units Persons 150872.0
2 KN.A2 Upper Nile SS-NU KN.B2 Population, Total (Number) KN.C3 5 to 9 units Persons 151467.0
3 KN.A2 Upper Nile SS-NU KN.B2 Population, Total (Number) KN.C4 10 to 14 units Persons 126140.0
4 KN.A2 Upper Nile SS-NU KN.B2 Population, Total (Number) KN.C5 15 to 19 units Persons 103804.0 
# Inspect the last 5 rows
ss_2008_census_df.tail()
Region Region Name Region - RegionId Variable Variable Name Age Age Name Scale Units 2008
448 KN.A11 Eastern Equatoria SS-EE KN.B8 Population, Female (Number) KN.C14 60 to 64 units Persons 5274.0
449 KN.A11 Eastern Equatoria SS-EE KN.B8 Population, Female (Number) KN.C22 65+ units Persons 8637.0
450 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
451 Source: National Bureau of Statistics, South Sudan NaN NaN NaN NaN NaN NaN NaN NaN
452 Download URL: http://southsudan.opendataforafrica.org/fvjqdp... NaN NaN NaN NaN NaN NaN NaN NaN

Above, we see that the three last rows contain nas (missing values). One is the data source where we obtained this dataset, and the other is the data URL.

2 Cleaning and Transforming the Data

2.1 Checking for Missing Values

Now that we have imported our dataset, we will clean and manipulate it. First, we will reconfirm the missing values and proceed with our data wrangling process.

# Check for missing values
ss_2008_census_df.isna().sum()
Region               1
Region Name          1
Region - RegionId    3
Variable             3
Variable Name        3
Age                  3
Age Name             3
Scale                3
Units                3
2008                 3
dtype: int64

2.2 Wrangling the Data Using Method Chaining

# Select desired columns
cols = ['Region Name', 'Variable Name', 'Age Name', '2008']

# Rename columns
cols_names = {'Region Name':'state', 
             'Variable Name':'gender', 
             'Age Name':'age_cat', 
             '2008':'population'}
             
# Create new age categories           
new_age_cats = {'0 to 4':'0-14', 
                '5 to 9':'0-14',
                '10 to 14':'0-14',
                '15 to 19':'15-29', 
                '20 to 24':'15-29',
                '25 to 29':'15-29',
                '30 to 34':'30-49', 
                '35 to 39':'30-49',
                '40 to 44':'30-49',
                '45 to 49':'30-49', 
                '50 to 54':'50-64', 
                '55 to 59':'50-64',
                '60 to 64':'50-64', 
                '65+':'>= 65'
                }
             

# Clean the data
df = (ss_2008_census_df
      [cols]
      .rename(columns = cols_names)
      .query('~age_cat.isna()')
      .assign(gender = lambda x:x['gender'].str.split('\s+').str[1],
             age_cat = lambda x:x['age_cat'].replace(new_age_cats),
             population = lambda x:x['population'].astype('int')
      )
      .query('gender != "Total" & age_cat != "Total"')
      # .drop(columns = 'pop_cat', axis = 'column')
      .groupby(['state', 'gender', 'age_cat'])['population']
      .sum()
      .reset_index()
     )

# Inspect the first 5 rows
df.head()
state gender age_cat population
0 Central Equatoria Female 0-14 221216
1 Central Equatoria Female 15-29 166887
2 Central Equatoria Female 30-49 101676
3 Central Equatoria Female 50-64 23460
4 Central Equatoria Female >= 65 8596

3 Summarizing Census Data

3.1 Population by State

# Calculate census data by state
st_df = (df  
         .groupby(['state'])['population']
         .sum()
         .reset_index()
         .sort_values('population', 
                      ascending=False, 
                      ignore_index=True)
         )

# Display the outpout
st_df
state population
0 Jonglei 1358602
1 Central Equatoria 1103557
2 Warrap 972928
3 Upper Nile 964353
4 Eastern Equatoria 906161
5 Northern Bahr el Ghazal 720898
6 Lakes 695730
7 Western Equatoria 619029
8 Unity 585801
9 Western Bahr el Ghazal 333431

3.2 Population by State and Gender

# Calculate census data by state and gender
gender_df = (df  
         .groupby(['state', 'gender'])['population']
         .sum()
         .reset_index()
         .sort_values('population', 
                      ascending=False, 
                      ignore_index=True)
         )

# Display the outpout
gender_df.head()
state gender population
0 Jonglei Male 734327
1 Jonglei Female 624275
2 Central Equatoria Male 581722
3 Upper Nile Male 525430
4 Central Equatoria Female 521835

3.3 Population by State, Gender, and Age Category

# Calculate census data by state, gender, and age category
age_df = (df  
         .groupby(['state', 'gender', 'age_cat'])['population']
         .sum()
         .reset_index()
         .sort_values(['state','population'], 
                      ascending = [True, False], 
                      ignore_index = True)
         )

# Display the outpout
age_df.head(5)
state gender age_cat population
0 Central Equatoria Male 0-14 242247
1 Central Equatoria Female 0-14 221216
2 Central Equatoria Male 15-29 179903
3 Central Equatoria Female 15-29 166887
4 Central Equatoria Male 30-49 119355

4 Closing Remarks

This tutorial demonstrates creating a Quarto document with various yaml options to style and format the output. We hope you will find this tutorial helpful. Please let us know if there are any topics you want us to do a tutorial on.

With that said, our next tutorial will be on R.