Feedback

.gitignore

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+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+
+# testing
+/coverage
+
+# production
+/build
+
+# misc
+.DS_Store
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+.idea/

README.md

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-# final-project-starter-code
+# Project Page
+
+Title: Gender Inequality in China
+
+Team Member: Yunyi Zhu ([email protected])
+
+Summary Image: ![left: a webpage with summary, right: a webpage with visualization](https://github.com/6859-sp21/final-project-yunyi/blob/main/final-6859-data/figures/0_fig1/fig1.png)
+
+Abstract: In this project, we develop an interactive website that introduces and visualizes the gender inequality in China. It includes visualizations using multiple datasets that focus on discrimination faced by Chinese women in different life stages. For each life stage, it uses scrollytelling to display the data together with paragraphs that provides the contexts of the data. We hope that this website brings awareness to different parties on the gender inequalities faced by women living in China. 
+
+Link to Paper: https://github.com/6859-sp21/final-project-yunyi/blob/main/FinalPaper.pdf
+
+Link to Video: https://www.youtube.com/watch?v=0M9cH8rjX0w
+
+Link to Visualization Github Pages: https://6859-sp21.github.io/final-project-yunyi/#/
+
+
+# Project Process
+
+Split Work: this is a one-person project so all parts are implemented by myself. 
+
+During the data search and implementation, I made several compromises on the interactivity of the visualization. In the first sketch, there will be five subcategories: birth, education, work, marriage and inheritance. Each of the subcategories have enough non-numerical data that supports them, but they really lack numerical data. 
+
+Crawling and processing the data was harder than I estimated so I ended up using aggregated data from documents such as governmental and industrial reports. For example, I originally planned to crawl all the court pages that are related to domestic violence, classify them and create an interactive system where people can hover and see each individual case. The challenges of this plans include (1) crawling all the data, (2) classify the data and (3) translate the data to English. I crawled the data but they were all textual data, so I ended up falling back to my plan B, which is to use aggregated data.
+
+For education and work inequalities, it was even harder because the data were nowhere to be found. Originally, I wanted to find different cut offs of the college entrance exam for men and women, but those cut offs turn out to be private. I could potentially get in touch with feminist groups that collect such info, but in that case the data can be skewed, so I put this part to future work since I will need more time to find places to get data.
+

final-6859-data/assets-attribution/favicon/icon.txt

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+<a target="_blank" href="undefined/icons/set/female">Female icon</a> icon by <a target="_blank" href="">Icons8</a>

final-6859-data/assets-attribution/ink-stain/ink-stain.txt

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+chinese ink ink PNG Designed By 588ku from <a href="https://pngtree.com/">Pngtree.com</a>

final-6859-data/processed-data/1_birth/1_sex-ratio-general/sex-ratio-general-china.json

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+[
+  {
+    "sex": "boy",
+    "ratio": 1.212,
+  },
+  {
+    "sex": "girl",
+    "ratio": 1
+  }
+]

final-6859-data/processed-data/1_birth/1_sex-ratio-general/source.txt

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+http://www.stats.gov.cn/tjsj/pcsj/rkpc/6rp/indexch.htm

final-6859-data/processed-data/1_birth/2_sex-ratio-world/convert.py

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+import csv
+import json
+
+# Function to convert a CSV to JSON
+# Takes the file paths as arguments
+def make_json(csvFilePath, jsonFilePath, dataFieldMapping):
+
+    # create a dictionary
+    data = []
+
+    # Open a csv reader called DictReader
+    with open(csvFilePath) as csvf:
+        csvReader = csv.DictReader(csvf)
+
+        # Convert each row into a dictionary
+        # and add it to data
+        for row in csvReader:
+
+        	filtered_row = {}
+
+        	for key in dataFieldMapping.keys():
+        		value = row[key]
+        		new_key = dataFieldMapping[key]
+        		filtered_row[new_key] = value
+
+        	data.append(filtered_row)
+
+    # Open a json writer, and use the json.dumps()
+    # function to dump data
+    with open(jsonFilePath, 'w') as jsonf:
+        jsonf.write(json.dumps(data, indent=4))
+
+# Driver Code
+
+# Decide the two file paths according to your
+# computer system
+csvFilePath = r'source.csv'
+jsonFilePath = r'result.json'
+dataFieldMapping = {
+	"Country Code": "id",
+	"2009": "value"
+}
+
+# Call the make_json function
+make_json(csvFilePath, jsonFilePath, dataFieldMapping)