From 455ec3b2cfe82ec90d596706afd82fb2daff2624 Mon Sep 17 00:00:00 2001 From: Matthew Gaughan Date: Thu, 9 Nov 2023 23:18:34 -0600 Subject: [PATCH] data is consolidated into one file (inc up_mean) --- R/calculatePower.R | 4 +- R/powerAnalysis.R | 4 +- consolidate_data.py | 29 ++- power_data_111023_mmt_ufm.csv | 329 ++++++++++++++++++++++++++++++++++ 4 files changed, 360 insertions(+), 6 deletions(-) create mode 100644 power_data_111023_mmt_ufm.csv diff --git a/R/calculatePower.R b/R/calculatePower.R index 58371dd..79a7338 100644 --- a/R/calculatePower.R +++ b/R/calculatePower.R @@ -19,13 +19,13 @@ library(readr) # (1) - Get the pilot data and clean it #source('~/Research/tor_wikipedia_edits/handcoded_edits/inter_coder_reliability_ns0.R') #source ('/data/users/mgaughan/kkex_data_110823_3') -data1 <- read_csv('../power_data_110923_mmt.csv',show_col_types = FALSE) +data1 <- read_csv('../power_data_111023_mmt_ufm.csv',show_col_types = FALSE) #d$nd <- to_logical(d$not.damaging, custom_true=c("Y")) #levels(d$source) <- c("IP-based Editors", "New Editors", "Registered Editors", "Tor-based Editors") # (2) - Run the model on the pilot data -pilotM <- glm(up_fac_mean ~ ((mmt)/ (milestones/age)), # give the anticipated regression a try +pilotM <- glm(up.fac.mean ~ ((mmt) / (milestones/age)), # give the anticipated regression a try family=gaussian(link='identity'), data=data1) summary(pilotM) #we expect effect sizes on this order diff --git a/R/powerAnalysis.R b/R/powerAnalysis.R index 16b261f..fdb60dc 100644 --- a/R/powerAnalysis.R +++ b/R/powerAnalysis.R @@ -24,7 +24,7 @@ makeDataNew <- function(n) { sample(1.610638: 1.684438, 1, replace = FALSE), sample(-0.1961401:-0.1647757, 1, replace = FALSE) ) - colnames(sDF) <- c('age', 'contributors', 'collaborators', 'milestones', 'mmt', 'up_fac_mean') + colnames(sDF) <- c('age', 'contributors', 'collaborators', 'milestones', 'mmt', 'up.fac.mean') return(sDF) } @@ -38,7 +38,7 @@ powerCheck <- function(n, nSims) { #run a power calculation on the dataset given for (s in 1:nSims) { # repeatedly we will.... simData <- makeDataNew(n) # make some data #have updated for kkex through here, now need to look at the underproduction work - m1.sim <- glm(up_fac_mean ~ ((mmt)/ (milestones/age)), # give the anticipated regression a try + m1.sim <- glm(up.fac.mean ~ ((mmt)/ (milestones/age)), # give the anticipated regression a try family=gaussian(link='identity'), data=simData) p0 <- coef(summary(m1.sim))[1,4] p1 <- coef(summary(m1.sim))[2,4] diff --git a/consolidate_data.py b/consolidate_data.py index 5648d4f..65e23cc 100644 --- a/consolidate_data.py +++ b/consolidate_data.py @@ -1,10 +1,34 @@ import json import os import csv +import pandas -directory = '/data/users/mgaughan/kkex_data_110823_3/' +#directory = '/data/users/mgaughan/kkex_data_110823_3/' +path_to_file = 'power_data_110923_mmt.csv' +csv_1 = pandas.read_csv(path_to_file) +print(csv_1) +csv_2 = pandas.read_csv('/data_ext/users/kcz1100/kaylea_dissertation/collabnetXS/raw_data/inst_all_packages_full_results.csv') +print(csv_2['pkg']) + +up_fac_mean = [] +for index, row in csv_1.iterrows(): + temporary_proj_name = row['repo'][:-12].split('_')[1] + proj_name = "" + if '.git' in temporary_proj_name: + proj_name = temporary_proj_name[:-4] + else: + proj_name = temporary_proj_name + row = csv_2.loc[csv_2['pkg'] == proj_name] + print(proj_name + row['pkg']) + print(row['up.fac.mean']) + up_fac_mean.append(row['up.fac.mean']) +csv_1['up.fac.mean'] = up_fac_mean +print(csv_1) +csv1.to_csv('power_data_111023_mmt_ufm.csv') + +''' with open('/data/users/mgaughan/power_data_110923.csv', "w") as csv_file: csv_header = ['repo', 'age', 'contributors', 'collaborators', 'milestones'] writer = csv.writer(csv_file, delimiter=',') @@ -21,4 +45,5 @@ with open('/data/users/mgaughan/power_data_110923.csv', "w") as csv_file: new_row.append(data['perceval_obj']['contributors']) 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