This archive contains code and data for reproducing the analysis for “Replication Data for Revisiting ‘The Rise and Decline’ in a Population of Peer Production Projects”. Depending on what you hope to do with the data you probabbly do not want to download all of the files. Depending on your computation resources you may not be able to run all stages of the analysis. The code for all stages of the analysis, including typesetting the manuscript and running the analysis, is in code.tar. If you only want to run the final analysis or to play with datasets used in the analysis of the paper, you want intermediate_data.7z or the uncompressed tab and csv files. The data files are created in a four-stage process. The first stage uses the program “wikiq” to parse mediawiki xml dumps and create tsv files that have edit data for each wiki. The second stage generates all.edits.RDS file which combines these tsvs into a dataset of edits from all the wikis. This file is expensive to generate and at 1.5GB is pretty big. The third stage builds smaller intermediate files that contain the analytical variables from these tsv files. The fourth stage uses the intermediate files to generate smaller RDS files that contain the results. Finally, knitr and latex typeset the manuscript. A stage will only run if the outputs from the previous stages do not exist. So if the intermediate files exist they will not be regenerated. Only the final analysis will run. The exception is that stage 4, fitting models and generating plots, always runs. If you only want to replicate from the second stage onward, you want wikiq_tsvs.7z. If you want to replicate everything, you want wikia_mediawiki_xml_dumps.7z.001 wikia_mediawiki_xml_dumps.7z.002, and wikia_mediawiki_xml_dumps.7z.003. These instructions work backwards from building the manuscript using knitr, loading the datasets, running the analysis, to building the intermediate datasets. Building the manuscript using knitr This requires working latex, latexmk, and knitr installations. Depending on your operating system you might install these packages in different ways. On Debian Linux you can run apt install r-cran-knitr latexmk texlive-latex-extra. Alternatively, you can upload the necessary files to a project on Overleaf.com. Download code.tar. This has everything you need to typeset the manuscript. Unpack the tar archive. On a unix system this can be done by running tar xf code.tar. Navigate to code/paper_source. Install R dependencies. In R. run install.packages(c("data.table","scales","ggplot2","lubridate","texreg")) On a unix system you should be able to run make to build the manuscript generalizable_wiki.pdf. Otherwise you should try uploading all of the files (including the tables, figure, and knitr folders) to a new project on Overleaf.com. Loading intermediate datasets The intermediate datasets are found in the intermediate_data.7z archive. They can be extracted on a unix system using the command 7z x intermediate_data.7z. The files are 95MB uncompressed. These are RDS (R data set) files and can be loaded in R using the readRDS. For example newcomer.ds <- readRDS("newcomers.RDS"). If you wish to work with these datasets using a tool other than R, you might prefer to work with the .tab files. Running the analysis Fitting the models may not work on machines with less than 32GB of RAM. If you have trouble, you may find the functions in lib-01-sample-datasets.R useful to create stratified samples of data for fitting models. See line 89 of 02_model_newcomer_survival.R for an example. Download code.tar and intermediate_data.7z to your working folder and extract both archives. On a unix system this can be done with the command tar xf code.tar && 7z x intermediate_data.7z. Install R dependencies. install.packages(c("data.table","ggplot2","urltools","texreg","optimx","lme4","bootstrap","scales","effects","lubridate","devtools","roxygen2")). On a unix system you can simply run regen.all.sh to fit the models, build the plots and create the RDS files. Generating datasets Building the intermediate files The intermediate files are generated from all.edits.RDS. This process requires about 20GB of memory. Download all.edits.RDS, userroles_data.7z,selected.wikis.csv, and code.tar. Unpack code.tar and userroles_data.7z. On a unix system this can be done using tar xf code.tar && 7z x userroles_data.7z. Install R dependencies. In R run install.packages(c("data.table","ggplot2","urltools","texreg","optimx","lme4","bootstrap","scales","effects","lubridate","devtools","roxygen2")). Run 01_build_datasets.R. Building all.edits.RDS The intermediate RDS files used in the analysis are created from all.edits.RDS. To replicate building all.edits.RDS, you only need to run 01_build_datasets.R when the int... Visit https://dataone.org/datasets/sha256%3Acfa4980c107154267d8eb6dc0753ed0fde655a73a062c0c2f5af33f237da3437 for complete metadata about this dataset.

Cyclistic: Google Data Analytics Capstone Project

Cyclistic - Google Data Analytics Certification Capstone Project Moirangthem Arup Singh How Does a Bike-Share Navigate Speedy Success? Background: This project is for the Google Data Analytics Certification capstone project. I am wearing the hat of a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. Cyclistic is a bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore,my team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, my team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve the recommendations, so they must be backed up with compelling data insights and professional data visualizations. This project will be completed by using the 6 Data Analytics stages: Ask: Identify the business task and determine the key stakeholders. Prepare: Collect the data, identify how it’s organized, determine the credibility of the data. Process: Select the tool for data cleaning, check for errors and document the cleaning process. Analyze: Organize and format the data, aggregate the data so that it’s useful, perform calculations and identify trends and relationships. Share: Use design thinking principles and data-driven storytelling approach, present the findings with effective visualization. Ensure the analysis has answered the business task. Act: Share the final conclusion and the recommendations. Ask: Business Task: Recommend marketing strategies aimed at converting casual riders into annual members by better understanding how annual members and casual riders use Cyclistic bikes differently. Stakeholders: Lily Moreno: The director of marketing and my manager. Cyclistic executive team: A detail-oriented executive team who will decide whether to approve the recommended marketing program. Cyclistic marketing analytics team: A team of data analysts responsible for collecting, analyzing, and reporting data that helps guide Cyclistic’s marketing strategy. Prepare: For this project, I will use the public data of Cyclistic’s historical trip data to analyze and identify trends. The data has been made available by Motivate International Inc. under the license. I downloaded the ZIP files containing the csv files from the above link but while uploading the files in kaggle (as I am using kaggle notebook), it gave me a warning that the dataset is already available in kaggle. So I will be using the dataset cyclictic-bike-share dataset from kaggle. The dataset has 13 csv files from April 2020 to April 2021. For the purpose of my analysis I will use the csv files from April 2020 to March 2021. The source csv files are in Kaggle so I can rely on it's integrity. I am using Microsoft Excel to get a glimpse of the data. There is one csv file for each month and has information about the bike ride which contain details of the ride id, rideable type, start and end time, start and end station, latitude and longitude of the start and end stations. Process: I will use R as language in kaggle to import the dataset to check how it’s organized, whether all the columns have appropriate data type, find outliers and if any of these data have sampling bias. I will be using below R libraries

Load the tidyverse, lubridate, ggplot2, sqldf and psych libraries

library(tidyverse) library(lubridate) library(ggplot2) library(plotrix) ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

✔ ggplot2 3.3.5 ✔ purrr 0.3.4 ✔ tibble 3.1.4 ✔ dplyr 1.0.7 ✔ tidyr 1.1.3 ✔ stringr 1.4.0 ✔ readr 2.0.1 ✔ forcats 0.5.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ✖ dplyr::filter() masks stats::filter() ✖ dplyr::lag() masks stats::lag()

Attaching package: ‘lubridate’

The following objects are masked from ‘package:base’:

date, intersect, setdiff, union

Set the working directory

setwd("/kaggle/input/cyclistic-bike-share")

Import the csv files

r_202004 <- read.csv("202004-divvy-tripdata.csv") r_202005 <- read.csv("20...

***********THE REPOSITORY IS PUBLICLY AVAILABLE BUT NOT PUBLICISED YET. PLEASE DO NOT SHARE WIDELY. THE PURPOSE OF THIS REPOSITORY IS PRIMARILY TO ASSIST THE REVIEW PROCESS.*********


# Replication Package
A repository with replication material for the 2024 working paper by Prashant Garg and Thiemo Fetzer.

## Overview

This replication package contains all necessary scripts and data to replicate the main figures and tables presented in the paper.

## Folder Structure

### 1. `1_scripts`

This folder contains all scripts required to replicate the main figures and tables of the paper. The scripts are arranged in the order they should be run.

- `0_init.Rmd`: An R Markdown file that installs and loads all packages necessary for the subsequent scripts.
- `1_fig_1.Rmd`: Produces Figure 1 (Zipf's plots).
- `2_fig_2_to_4.Rmd`: Produces Figures 2 to 4 (average levels of expression).
- `3_fig_5_to_6.Rmd`: Produces Figures 5 to 6 (trends in expression).
- `4_tab_1_to_3.Rmd`: Produces Tables 1 to 3 (descriptive tables).

Expected run time for each script is under 2 minutes and requires around 4GB RAM. Script `3_fig_5_to_6.Rmd` can take up to 3-4 minutes and requires up to 6GB RAM. Installation of each package for the first time user may take around 2 minutes each, except 'tidyverse', which may take around 4 minutes.

We have not provided a demo since the actual dataset used for analysis is small enough and computations are efficient enough to be run in most systems.

Each script starts with a layperson explanation to overview the functionality of the code and a pseudocode for a detailed procedure, followed by the actual code.

### 2. `2_data`

This folder contains data used to replicate the main results. The data is called by the respective scripts automatically using relative paths.

- `data_dictionary.txt`: Provides a description of all variables as they are coded in the various datasets, especially the main author by time level dataset called `repl_df.csv`.
- Processed data at individual author by time (year by month) level aggregated measures are provided, as raw data containing raw tweets cannot be shared.

## Installation Instructions

### Prerequisites

This project uses R and RStudio. Make sure you have the following installed:

- [R](https://cran.r-project.org/) (version 4.0.0 or later)
- [RStudio](https://www.rstudio.com/products/rstudio/download/)

Once installed, to ensure the correct versions of the required packages are installed, use the following R markdown script '0_init.Rmd'. This script will install the `remotes` package (if not already installed) and then install the specified versions of the required packages.

## Running the Scripts
Open 0_init.Rmd in RStudio and run all chunks to install and load the required packages.
Run the remaining scripts (1_fig_1.Rmd, 2_fig_2_to_4.Rmd, 3_fig_5_to_6.Rmd, and 4_tab_1_to_3.Rmd) in the order they are listed to reproduce the figures and tables from the paper.

# Contact
For any questions, feel free to contact Prashant Garg at prashant.garg@imperial.ac.uk.

# License

This project is licensed under the Apache License 2.0 - see the license.txt file for details.

# Replication Package for 'Political Expression of Academics on Social Media' by Prashant Garg and Thiemo Fetzer.

## Overview

This replication package contains all necessary scripts and data to replicate the main figures and tables presented in the paper.

## Folder Structure

### 1. `1_scripts`

This folder contains all scripts required to replicate the main figures and tables of the paper. The scripts are numbers with a prefix (e.g. "1_") in the order they should be run. Output will also be produced in this folder.

- `0_init.Rmd`: An R Markdown file that installs and loads all packages necessary for the subsequent scripts.
- `1_fig_1.Rmd`: Primarily produces Figure 1 (Zipf's plots) and conducts statistical tests to support underlying statistical claims made through the figure.

- `2_fig_2_to_4.Rmd`: Primarily produces Figures 2 to 4 (average levels of expression) and conducts statistical tests to support underlying statistical claims made through the figures. This includes conducting t-tests to establish subgroup differences.

The script also includes The file table_controlling_how.csv contains the full set of regression results for the analysis of subgroup differences in political stances, controlling for emotionality, egocentrism, and toxicity. This file includes effect sizes, standard errors, confidence intervals, and p-values for each stance, group variable, and confounder.

- `3_fig_5_to_6.Rmd`: Primarily produces Figures 5 to 6 (trends in expression) and conducts statistical tests to support underlying statistical claims made through the figures. This includes conducting t-tests to establish subgroup differences.

- `4_tab_1_to_2.Rmd`: Produces Tables 1 to 2, and shows code for Table A5 (descriptive tables).

Expected run time for each script is under 3 minutes and requires around 4GB RAM. Script `3_fig_5_to_6.Rmd` can take up to 3-4 minutes and requires up to 6GB RAM. Installation of each package for the first time user may take around 2 minutes each, except 'tidyverse', which may take around 4 minutes.

We have not provided a demo since the actual dataset used for analysis is small enough and computations are efficient enough to be run in most systems.

Each script starts with a layperson explanation to overview the functionality of the code and a pseudocode for a detailed procedure, followed by the actual code.

### 2. `2_data`

This folder contains all data used to replicate the main results. The data is called by the respective scripts automatically using relative paths.

- `data_dictionary.txt`: Provides a description of all variables as they are coded in the various datasets, especially the main author by time level dataset called `repl_df.csv`.
- Processed data at individual author by time (year by month) level aggregated measures are provided, as raw data containing raw tweets cannot be shared.

## Installation Instructions

### Prerequisites

This project uses R and RStudio. Make sure you have the following installed:

- [R](https://cran.r-project.org/) (version 4.0.0 or later)
- [RStudio](https://www.rstudio.com/products/rstudio/download/)

Once installed, to ensure the correct versions of the required packages are installed, use the following R markdown script '0_init.Rmd'. This script will install the `remotes` package (if not already installed) and then install the specified versions of the required packages.

## Running the Scripts
Open 0_init.Rmd in RStudio and run all chunks to install and load the required packages.
Run the remaining scripts (1_fig_1.Rmd, 2_fig_2_to_4.Rmd, 3_fig_5_to_6.Rmd, and 4_tab_1_to_2.Rmd) in the order they are listed to reproduce the figures and tables from the paper.

# Contact
For any questions, feel free to contact Prashant Garg at prashant.garg@imperial.ac.uk.

# License

This project is licensed under the Apache License 2.0 - see the license.txt file for details.