https://github.com/erictleung/2017-new-coder-survey

:beginner: Code to help clean and format the 2017 New Coder Survey by freeCodeCamp

https://github.com/erictleung/2017-new-coder-survey

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (7.4%) to scientific vocabulary

Keywords

coder-survey data data-cleaning dplyr freecodecamp
Last synced: 4 months ago · JSON representation

Repository

:beginner: Code to help clean and format the 2017 New Coder Survey by freeCodeCamp

Basic Info
  • Host: GitHub
  • Owner: erictleung
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 50.8 KB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 2
  • Releases: 0
Topics
coder-survey data data-cleaning dplyr freecodecamp
Created almost 9 years ago · Last pushed over 8 years ago
Metadata Files
Readme

README.md

Cleaning freeCodeCamp's 2017 New Coder Survey Data

Code to help clean and format the 2017 New Coder Survey by freeCodeCamp.

Table of Contents

Introduction

The survey data is cleaned and the metadata (i.e. the data dictionary) is in the datapackage.json file, which follows closely to the specifications described by the data packages format. The format was generated with the help of Data Packagist.

The cleaning script clean-data-2017.R should be run with the working directory in R being the root of this repository.

Prerequisites for data cleaning

  • R (>= 3.3.3)
  • dplyr (>= 0.5.0)

Download survey data

The raw survey data will be downloaded into the raw-data/ directory.

git clone https://github.com/erictleung/2017-new-coder-survey.git cd 2017-new-coder-survey make download

Run script to reproduce clean data

The clean and combined data will appear in the clean-data/ directory.

make cleaning

Read in combined data after cleaning

From within R, you can run the following to read in the clean data.

r library(dplyr) setwd("clean-data") # Change this accordingly survey <- read.csv("2017-fCC-New-Coders-Survey-Data.csv", stringsAsFactors = FALSE) %>% tbl_df()

Repository structure

``` . ├── clean-data │   ├── clean-data-2017.R │   └── datapackage.json ├── Makefile └── README.md

2 directories, 4 files ```

Owner

  • Name: Eric Leung
  • Login: erictleung
  • Kind: user
  • Location: New York, NY

Data science generalist. Sharing knowledge and optimizing tools for learning and growth. Open-source and open-data advocate. Community learner.

GitHub Events

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Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 52
  • Total Committers: 1
  • Avg Commits per committer: 52.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Eric Leung e****c@e****m 52
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 16
  • Total pull requests: 5
  • Average time to close issues: 11 days
  • Average time to close pull requests: less than a minute
  • Total issue authors: 1
  • Total pull request authors: 1
  • Average comments per issue: 0.19
  • Average comments per pull request: 0.0
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • erictleung (16)
Pull Request Authors
  • erictleung (5)
Top Labels
Issue Labels
non-trivial (6) in progress (2) low priority (2)
Pull Request Labels