Difference between revisions of "Kabak: Report Data Preparation"

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* Stack data to consolidate data table in to 2 columns (Postal Code, Housing Type)  
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* Data cleaning: Household electricity consumption data
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* Remove rows with missing data
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**Stack data to consolidate data table in to 2 columns (Postal Code, Housing Type)  
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**Remove rows with missing data
 
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[[File: Kabakdatacleaning1.png|400px|center]]
 
[[File: Kabakdatacleaning1.png|400px|center]]
 
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* Concatenate all 12 months data into one consolidated data table
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* Data cleaning: Household electricity consumption data
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**Concatenate all 12 months data into one consolidated data table
 
**By the end of this phase of data cleaning, we have a total of 177,053 rows
 
**By the end of this phase of data cleaning, we have a total of 177,053 rows
 
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* Merging Private Housing Data with Public Housing Data
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* Data cleaning: Household electricity consumption data
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**Merging Private Housing Data with Public Housing Data
 
**Final consolidated data consist of 241,766 rows  
 
**Final consolidated data consist of 241,766 rows  
 
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* Geocoding of postal codes with missing latitudes and longitude via https://developers.google.com/maps/documentation/geocoding/intro
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* Geocoding: Postal codes with missing latitudes and longitude via https://developers.google.com/maps/documentation/geocoding/intro
 
**Public housing data: 223 missing data
 
**Public housing data: 223 missing data
 
**Private housing data: 338 missing data
 
**Private housing data: 338 missing data
 
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[[File: GEOCODING.PNG|400px|center]]
 
[[File: GEOCODING.PNG|400px|center]]
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* Data cleaning: Age, Gender, Ethnicity
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**Delete rows that are empty & blank so at to merge the tables into one data sheet
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[[File: Kabakdatacleaning4.png|400px|center]]
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* Data cleaning: Age, Gender, Ethnicity
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** Consolidate data through Stacking
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[[File: Kabakdatacleaning5.png|400px|center]]
 
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Latest revision as of 16:58, 22 November 2016


OVERVIEW

DATA PREPARATION

ANALYSIS


Initial Dataset

DATASET DESCRIPTION DATA USED

Average Monthly Household Electricity Consumption
by Postal Code (Public Housing) & Dwelling Type, 1H & 2H 2015

Link (1H): https://www.ema.gov.sg/cmsmedia/Publications_and_Statistics/Statistics/23RSU.xls

Link (2H): https://www.ema.gov.sg/cmsmedia/Publications_and_Statistics/Statistics/25RSU.xls

  • Average monthly household electricity consumption (kwh)
    • By month
    • By postal code
    • By public housing type
  • Total Average household electricity consumption (kwh)
    • By postal code
    • By public housing type
  • 9379 rows of raw data X 12 sheets = 112,548 rows of raw data

Average Monthly Household Electricity Consumption by Postal Code (Private Apartments), 2015

Link: https://www.ema.gov.sg/cmsmedia/Publications_and_Statistics/Statistics/2RSU.xls

  • Average monthly household electricity consumption (kwh)
    • By month
    • By postal code
  • Total Average household electricity consumption (kwh)
    • By postal code
  • 9911 rows of raw data

Basic Demographics Characteristics (2015)

Link: http://www.singstat.gov.sg/docs/default-source/default-document-library/publications/publications_and_papers/GHS/ghs2015/excel/t7-9.xls

  • Resident Population by Planning Area/Subzone
    • By age group
    • By sex
    • By ethnicity
    • By type of dwelling
  • T7 Age group
    • 378 rows of raw data
  • T8 Ethnicity
    • 378 rows of raw data


Data Cleaning

METHOD DESCRIPTION
  • Data cleaning: Household electricity consumption data
    • Stack data to consolidate data table in to 2 columns (Postal Code, Housing Type)
    • Remove rows with missing data
Kabakdatacleaning1.png
  • Data cleaning: Household electricity consumption data
    • Concatenate all 12 months data into one consolidated data table
    • By the end of this phase of data cleaning, we have a total of 177,053 rows
Kabakdatacleaning2.png
  • Data cleaning: Household electricity consumption data
    • Merging Private Housing Data with Public Housing Data
    • Final consolidated data consist of 241,766 rows
Kabakdatacleaning3.png
GEOCODING.PNG
  • Data cleaning: Age, Gender, Ethnicity
    • Delete rows that are empty & blank so at to merge the tables into one data sheet
Kabakdatacleaning4.png
  • Data cleaning: Age, Gender, Ethnicity
    • Consolidate data through Stacking
Kabakdatacleaning5.png


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