From 1a5b69851290a655221ba5d73ec34d4eea1d1f88 Mon Sep 17 00:00:00 2001
From: Kaylea Champion <khascall@gmail.com>
Date: Fri, 10 Nov 2023 11:22:58 -0800
Subject: [PATCH] demos some changes

---
 R/calculatePower.R | 39 ++++++++++++++++++++++++++++++++++++---
 1 file changed, 36 insertions(+), 3 deletions(-)

diff --git a/R/calculatePower.R b/R/calculatePower.R
index 07f0c80..6429216 100644
--- a/R/calculatePower.R
+++ b/R/calculatePower.R
@@ -15,20 +15,53 @@ rm(list=ls())
 set.seed(424242) 
 
 library(readr)
+library(ggplot2)
 
 # (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)
-data2 <- read_csv('/data_ext/users/kcz1100/kaylea_dissertation/collabnetXS/raw_data/inst_all_packages_full_results.csv')
+data2 <- read_csv('../inst_all_packages_full_results.csv')
 #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")
 
 data1$up.fac.mean <- as.numeric(data2$up.fac.mean[match(data1$pkg, data2$pkg)])
 data1$milestones <- as.numeric(data1$milestones > 0) + 1
 # (2) - Run the model on the pilot data
-pilotM <- glm(up.fac.mean ~ ((mmt) / (milestones/age)),   # give the anticipated regression a try
-                  family=gaussian(link='identity'), data=data1)
+data1$formal.score <- data1$mmt / (data1$milestones/data1$age)
+table(data1$milestones)
+hist(data1$mmt) #inequality of participation
+hist(data1$formal.score)
+hist(data1$age/365)
+kmodel1 <- lm(up.fac.mean ~ mmt, data=data1)
+summary(kmodel1)
+kmodel1 <- lm(up.fac.mean ~ formal.score, data=data1)
+summary(kmodel1)
+hist(data1$formal.score)
+cor.test(data1$formal.score, data1$up.fac.mean)
+cor.test(data1$mmt, data1$up.fac.mean)
+cor.test(data1$milestones, data1$up.fac.mean)
+cor.test(data1$age, data1$up.fac.mean)
+
+g <- ggplot(data1, aes(x=formal.score, y=up.fac.mean)) +
+  geom_point() +
+  geom_smooth()
+g
+
+data2 <- subset(data1, (data1$age / 365) < 9 )
+hist(data2$age)
+g <- ggplot(data2, aes(x=formal.score, y=up.fac.mean)) +
+  geom_point() +
+  geom_smooth()
+g
+
+data2$yearsOld <- data2$age / 365
+
+kmodel2 <- lm(up.fac.mean ~ mmt + milestones + yearsOld, data=data2)
+summary(kmodel2)
+
+#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
 
 pilot.b0 <- coef(summary(pilotM))[1,1]