dat <- read.csv("C:/Users/evanholm/Dropbox (ASU)/Political Economy of NP Startups/data-and-analysis/data-prepped/RegressionData.csv")
dat$VulnerDist <- dat$BMS1-dat$NPO1
agg <- aggregate(VulnerDist~labels, FUN=mean, data=dat)
agg <- merge(agg, aggregate(BMS1~labels, FUN=mean, data=dat), by="labels")
agg <- merge(agg, aggregate(NPO1~labels, FUN=mean, data=dat), by="labels")
agg
## labels VulnerDist BMS1 NPO1
## 1 Agriculture -0.17659713 0.034494659 0.211091793
## 2 Animals -0.02706962 -0.100415112 -0.073345496
## 3 Arts -0.11843828 -0.097007204 0.021431074
## 4 Civil Rights -0.09997995 0.005075234 0.105055185
## 5 Community -0.22697985 0.232819978 0.459799825
## 6 Crime -0.20937727 0.112186837 0.321564107
## 7 Disease -0.07307028 -0.193037817 -0.119967534
## 8 Education -0.09877770 -0.114360970 -0.015583270
## 9 Employment -0.18486823 0.082919769 0.267787996
## 10 Environment -0.02974470 -0.160113201 -0.130368504
## 11 Health -0.05405643 -0.097824884 -0.043768459
## 12 Housing -0.23981658 0.332230299 0.572046875
## 13 Human Services -0.06065838 0.134290756 0.194949140
## 14 International 0.03143276 -0.257158831 -0.288591588
## 15 Med Research -0.10351178 -0.423165489 -0.319653711
## 16 Mental Health -0.15824604 -0.041243568 0.117002472
## 17 Mutual -0.16558922 -0.084924810 0.080664414
## 18 Philanthropy 0.06090383 -0.163128090 -0.224031923
## 19 Public Benefit -0.08184542 -0.097259850 -0.015414431
## 20 Religion -0.14564204 0.066542466 0.212184509
## 21 Safety -0.13090662 -0.136374123 -0.005467498
## 22 Science -0.08994387 -0.275475137 -0.185531271
## 23 Social Science 0.02236271 -0.195672461 -0.218035175
## 24 Sports -0.04967876 -0.178972319 -0.129293560
## 25 Unknown -0.01324096 0.102051015 0.115291979
## 26 Youth -0.09857444 0.191604231 0.290178672
plot(agg$BMS1, agg$VulnerDist, col="white")
text(agg$BMS1, agg$VulnerDist, labels =agg$labels )
plot(agg$NPO1, agg$VulnerDist, col="white")
text(agg$NPO1, agg$VulnerDist, labels =agg$labels )
