Read Data, Plot Missing
minoo=read.csv("C:/Users/lfult/Desktop/Minoo/total.csv")
library(Amelia)
## Warning: package 'Amelia' was built under R version 3.5.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 3.5.3
## ##
## ## Amelia II: Multiple Imputation
## ## (Version 1.7.6, built: 2019-11-24)
## ## Copyright (C) 2005-2020 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##
library(maps)
## Warning: package 'maps' was built under R version 3.5.3
missmap(minoo)
Impute Missing (Simple)
for (i in 8:ncol(minoo)){
minoo[,i][is.na(minoo[,i])] = median(minoo[,i], na.rm=TRUE)
}
missmap(minoo)
Get Descriptives
library(psych)
## Warning: package 'psych' was built under R version 3.5.3
describe(minoo[,8:117])
## vars n mean sd median trimmed mad min max range
## Green 1 126 0.75 0.43 1 0.81 0 0 1 1
## Providence 2 126 0.36 0.48 0 0.32 0 0 1 1
## Springfield 3 126 0.25 0.43 0 0.19 0 0 1 1
## France 4 126 0.00 0.00 0 0.00 0 0 0 0
## Table 5 126 0.87 0.33 1 0.96 0 0 1 1
## Diner 6 126 0.29 0.46 0 0.25 0 0 1 1
## Fast_Food 7 126 0.19 0.39 0 0.12 0 0 1 1
## Coffee_Shop 8 126 0.17 0.38 0 0.10 0 0 1 1
## Fine_Dining 9 126 0.28 0.45 0 0.23 0 0 1 1
## Deli 10 126 0.05 0.21 0 0.00 0 0 1 1
## Takeout 11 126 0.12 0.33 0 0.03 0 0 1 1
## Casual_Dining 12 126 0.49 0.50 0 0.49 0 0 1 1
## Breakfast 13 126 0.40 0.49 0 0.38 0 0 1 1
## Lunch 14 126 0.88 0.33 1 0.97 0 0 1 1
## Dinner 15 126 0.73 0.45 1 0.78 0 0 1 1
## Snacks 16 126 0.33 0.47 0 0.29 0 0 1 1
## Other 17 126 0.06 0.23 0 0.00 0 0 1 1
## G100 18 126 0.36 0.48 0 0.32 0 0 1 1
## GE50 19 126 0.29 0.45 0 0.24 0 0 1 1
## GE25 20 126 0.22 0.42 0 0.16 0 0 1 1
## L25 21 126 0.12 0.33 0 0.03 0 0 1 1
## Plastic_Recycled 22 126 0.40 0.49 0 0.37 0 0 1 1
## Plastic_Resold 23 126 0.01 0.09 0 0.00 0 0 1 1
## Plastic_Reused 24 126 0.02 0.13 0 0.00 0 0 1 1
## Plastic_Thrownout 25 126 0.68 0.47 1 0.73 0 0 1 1
## Metal_Recycled 26 126 0.34 0.48 0 0.30 0 0 1 1
## Metal_Resold 27 126 0.02 0.15 0 0.00 0 0 1 1
## Metal_Reused 28 126 0.02 0.15 0 0.00 0 0 1 1
## Metal_Thrownout 29 126 0.48 0.50 0 0.48 0 0 1 1
## Appliances_Recycled 30 126 0.48 0.50 0 0.48 0 0 1 1
## Appliances_Resold 31 126 0.17 0.38 0 0.10 0 0 1 1
## Appliances_Reused 32 126 0.18 0.39 0 0.11 0 0 1 1
## Appliances_Thrownout 33 126 0.28 0.45 0 0.23 0 0 1 1
## Boxes_Recycled 34 126 0.71 0.46 1 0.75 0 0 1 1
## Boxes_Resold 35 126 0.00 0.00 0 0.00 0 0 0 0
## Boxes_Reused 36 126 0.00 0.00 0 0.00 0 0 0 0
## Boxes_Thrownout 37 126 0.21 0.41 0 0.15 0 0 1 1
## Glass_Recycled 38 126 0.60 0.49 1 0.62 0 0 1 1
## Glass_Resold 39 126 0.01 0.09 0 0.00 0 0 1 1
## Glass_Reused 40 126 0.02 0.15 0 0.00 0 0 1 1
## Glass_Thrownout 41 126 0.37 0.48 0 0.33 0 0 1 1
## Bags_Recycled 42 126 0.11 0.32 0 0.02 0 0 1 1
## Bags_Resold 43 126 0.02 0.13 0 0.00 0 0 1 1
## Bags_Reused 44 126 0.04 0.29 0 0.00 0 0 3 3
## Bags_Thrownout 45 126 0.77 0.42 1 0.83 0 0 1 1
## GreaseOil_Recycled 46 126 0.63 0.49 1 0.66 0 0 1 1
## GreaseOil_Resold 47 126 0.17 0.37 0 0.09 0 0 1 1
## GreaseOil_Reused 48 126 0.06 0.24 0 0.00 0 0 1 1
## GreaseOil_Thrownout 49 126 0.18 0.39 0 0.11 0 0 1 1
## OfficeSupplies_Recycled 50 126 0.26 0.44 0 0.21 0 0 1 1
## OfficeSupplies_Resold 51 126 0.02 0.15 0 0.00 0 0 1 1
## OfficeSupplies_Reused 52 126 0.13 0.34 0 0.05 0 0 1 1
## OfficeSupplies_Thrownout 53 126 0.43 0.50 0 0.41 0 0 1 1
## Meatbyproducts_Recycled 54 126 0.06 0.24 0 0.00 0 0 1 1
## Meatbyproducts_Resold 55 126 0.00 0.00 0 0.00 0 0 0 0
## Meatbyproducts_Reused 56 126 0.03 0.18 0 0.00 0 0 1 1
## Meatbyproducts_Thrownout 57 126 0.71 0.46 1 0.75 0 0 1 1
## Veggies_Recycled 58 126 0.08 0.27 0 0.00 0 0 1 1
## Veggies_Resold 59 126 0.00 0.00 0 0.00 0 0 0 0
## Veggies_Reused 60 126 0.02 0.15 0 0.00 0 0 1 1
## Veggies_Thrownout 61 126 0.71 0.46 1 0.75 0 0 1 1
## Customer_Food_Recy 62 126 0.03 0.18 0 0.00 0 0 1 1
## Customer_Food_Resold 63 126 0.02 0.13 0 0.00 0 0 1 1
## Customer_Food_ThrownOut 64 126 0.98 0.15 1 1.00 0 0 1 1
## Spoiled_Food_Recy 65 126 0.04 0.20 0 0.00 0 0 1 1
## Spoiled_Food_Resold 66 126 0.00 0.00 0 0.00 0 0 0 0
## Spoiled_Food_ThrownOut 67 126 0.90 0.31 1 0.99 0 0 1 1
## Q17 68 126 2.82 0.81 3 2.94 0 0 5 5
## BoilH20_Food_Recy 69 126 0.10 0.31 0 0.01 0 0 1 1
## BoilH20_Food_Resold 70 126 0.00 0.00 0 0.00 0 0 0 0
## BoilH20_Food_ThrownOut 71 126 0.78 0.42 1 0.84 0 0 1 1
## Bread_Donated 72 126 0.12 0.33 0 0.03 0 0 1 1
## Bread_Recycled 73 126 0.08 0.27 0 0.00 0 0 1 1
## Bread_Resold 74 126 0.02 0.13 0 0.00 0 0 1 1
## Bread_Reused 75 126 0.25 0.44 0 0.20 0 0 1 1
## Bread_Thrown_Out 76 126 0.39 0.49 0 0.36 0 0 1 1
## Unused_Donated 77 126 0.12 0.33 0 0.03 0 0 1 1
## Unused_Recycled 78 126 0.08 0.27 0 0.00 0 0 1 1
## Unused_Resold 79 126 0.06 0.23 0 0.00 0 0 1 1
## Unused_Reused 80 126 0.31 0.46 0 0.26 0 0 1 1
## Unused_Thrown_Out 81 126 0.32 0.47 0 0.27 0 0 1 1
## Heating_Gas 82 126 0.36 0.48 0 0.32 0 0 1 1
## Heating_Electric 83 126 0.48 0.50 0 0.48 0 0 1 1
## Heating_Oil 84 126 0.04 0.20 0 0.00 0 0 1 1
## Heating_Solar 85 126 0.00 0.00 0 0.00 0 0 0 0
## Heating_Other 86 126 0.03 0.18 0 0.00 0 0 1 1
## Cooling_Gas 87 126 0.11 0.32 0 0.02 0 0 1 1
## Cooling_Electric 88 126 0.78 0.42 1 0.84 0 0 1 1
## Cooling_Oil 89 126 0.01 0.09 0 0.00 0 0 1 1
## Cooling_Solar 90 126 0.00 0.00 0 0.00 0 0 0 0
## Cooling_Other 91 126 0.05 0.21 0 0.00 0 0 1 1
## Finance_SA 92 126 0.37 0.48 0 0.33 0 0 1 1
## Finance_A 93 126 0.33 0.47 0 0.28 0 0 1 1
## Finance_N 94 126 0.22 0.42 0 0.16 0 0 1 1
## Finance_D 95 126 0.04 0.20 0 0.00 0 0 1 1
## Finance_SD 96 126 0.02 0.15 0 0.00 0 0 1 1
## Rep_SA 97 126 0.44 0.50 0 0.43 0 0 1 1
## Rep_A 98 126 0.31 0.46 0 0.26 0 0 1 1
## Rep_N 99 126 0.19 0.39 0 0.12 0 0 1 1
## Rep_D 100 126 0.00 0.00 0 0.00 0 0 0 0
## Rep_SD 101 126 0.02 0.13 0 0.00 0 0 1 1
## Cust_SA 102 126 0.33 0.47 0 0.28 0 0 1 1
## Cust_A 103 126 0.29 0.46 0 0.25 0 0 1 1
## Cust_N 104 126 0.32 0.47 0 0.27 0 0 1 1
## Cust_D 105 126 0.02 0.15 0 0.00 0 0 1 1
## Cust_SD 106 126 0.01 0.09 0 0.00 0 0 1 1
## Compost 107 126 0.04 0.20 0 0.00 0 0 1 1
## Have_Training 108 126 0.33 0.47 0 0.28 0 0 1 1
## Want_Training 109 126 0.44 0.50 0 0.42 0 0 1 1
## Participate_Training 110 126 0.60 0.49 1 0.62 0 0 1 1
## skew kurtosis se
## Green -1.17 -0.65 0.04
## Providence 0.59 -1.67 0.04
## Springfield 1.17 -0.65 0.04
## France NaN NaN 0.00
## Table -2.21 2.93 0.03
## Diner 0.90 -1.21 0.04
## Fast_Food 1.56 0.43 0.04
## Coffee_Shop 1.69 0.88 0.03
## Fine_Dining 0.98 -1.05 0.04
## Deli 4.20 15.75 0.02
## Takeout 2.32 3.43 0.03
## Casual_Dining 0.03 -2.01 0.04
## Breakfast 0.38 -1.87 0.04
## Lunch -2.32 3.43 0.03
## Dinner -1.02 -0.96 0.04
## Snacks 0.70 -1.52 0.04
## Other 3.83 12.80 0.02
## G100 0.59 -1.67 0.04
## GE50 0.94 -1.13 0.04
## GE25 1.32 -0.26 0.04
## L25 2.32 3.43 0.03
## Plastic_Recycled 0.42 -1.84 0.04
## Plastic_Resold 10.96 119.05 0.01
## Plastic_Reused 7.65 57.05 0.01
## Plastic_Thrownout -0.77 -1.41 0.04
## Metal_Recycled 0.66 -1.57 0.04
## Metal_Resold 6.17 36.39 0.01
## Metal_Reused 6.17 36.39 0.01
## Metal_Thrownout 0.06 -2.01 0.04
## Appliances_Recycled 0.06 -2.01 0.04
## Appliances_Resold 1.69 0.88 0.03
## Appliances_Reused 1.62 0.64 0.03
## Appliances_Thrownout 0.98 -1.05 0.04
## Boxes_Recycled -0.90 -1.21 0.04
## Boxes_Resold NaN NaN 0.00
## Boxes_Reused NaN NaN 0.00
## Boxes_Thrownout 1.38 -0.11 0.04
## Glass_Recycled -0.38 -1.87 0.04
## Glass_Resold 10.96 119.05 0.01
## Glass_Reused 6.17 36.39 0.01
## Glass_Thrownout 0.55 -1.71 0.04
## Bags_Recycled 2.45 4.01 0.03
## Bags_Resold 7.65 57.05 0.01
## Bags_Reused 8.66 80.43 0.03
## Bags_Thrownout -1.27 -0.40 0.04
## GreaseOil_Recycled -0.52 -1.74 0.04
## GreaseOil_Resold 1.77 1.13 0.03
## GreaseOil_Reused 3.54 10.60 0.02
## GreaseOil_Thrownout 1.62 0.64 0.03
## OfficeSupplies_Recycled 1.07 -0.86 0.04
## OfficeSupplies_Resold 6.17 36.39 0.01
## OfficeSupplies_Reused 2.11 2.48 0.03
## OfficeSupplies_Thrownout 0.29 -1.93 0.04
## Meatbyproducts_Recycled 3.54 10.60 0.02
## Meatbyproducts_Resold NaN NaN 0.00
## Meatbyproducts_Reused 5.28 26.07 0.02
## Meatbyproducts_Thrownout -0.90 -1.21 0.04
## Veggies_Recycled 3.08 7.52 0.02
## Veggies_Resold NaN NaN 0.00
## Veggies_Reused 6.17 36.39 0.01
## Veggies_Thrownout -0.90 -1.21 0.04
## Customer_Food_Recy 5.28 26.07 0.02
## Customer_Food_Resold 7.65 57.05 0.01
## Customer_Food_ThrownOut -6.17 36.39 0.01
## Spoiled_Food_Recy 4.66 19.87 0.02
## Spoiled_Food_Resold NaN NaN 0.00
## Spoiled_Food_ThrownOut -2.58 4.68 0.03
## Q17 -1.16 3.52 0.07
## BoilH20_Food_Recy 2.58 4.68 0.03
## BoilH20_Food_Resold NaN NaN 0.00
## BoilH20_Food_ThrownOut -1.32 -0.26 0.04
## Bread_Donated 2.32 3.43 0.03
## Bread_Recycled 3.08 7.52 0.02
## Bread_Resold 7.65 57.05 0.01
## Bread_Reused 1.12 -0.76 0.04
## Bread_Thrown_Out 0.45 -1.81 0.04
## Unused_Donated 2.32 3.43 0.03
## Unused_Recycled 3.08 7.52 0.02
## Unused_Resold 3.83 12.80 0.02
## Unused_Reused 0.81 -1.35 0.04
## Unused_Thrown_Out 0.77 -1.41 0.04
## Heating_Gas 0.59 -1.67 0.04
## Heating_Electric 0.06 -2.01 0.04
## Heating_Oil 4.66 19.87 0.02
## Heating_Solar NaN NaN 0.00
## Heating_Other 5.28 26.07 0.02
## Cooling_Gas 2.45 4.01 0.03
## Cooling_Electric -1.32 -0.26 0.04
## Cooling_Oil 10.96 119.05 0.01
## Cooling_Solar NaN NaN 0.00
## Cooling_Other 4.20 15.75 0.02
## Finance_SA 0.55 -1.71 0.04
## Finance_A 0.74 -1.47 0.04
## Finance_N 1.32 -0.26 0.04
## Finance_D 4.66 19.87 0.02
## Finance_SD 6.17 36.39 0.01
## Rep_SA 0.22 -1.97 0.04
## Rep_A 0.81 -1.35 0.04
## Rep_N 1.56 0.43 0.04
## Rep_D NaN NaN 0.00
## Rep_SD 7.65 57.05 0.01
## Cust_SA 0.74 -1.47 0.04
## Cust_A 0.90 -1.21 0.04
## Cust_N 0.77 -1.41 0.04
## Cust_D 6.17 36.39 0.01
## Cust_SD 10.96 119.05 0.01
## Compost 4.66 19.87 0.02
## Have_Training 0.74 -1.47 0.04
## Want_Training 0.25 -1.95 0.04
## Participate_Training -0.38 -1.87 0.04
Graphs by City
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.5.3
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
library(reshape2)
library(reshape)
## Warning: package 'reshape' was built under R version 3.5.2
##
## Attaching package: 'reshape'
## The following objects are masked from 'package:reshape2':
##
## colsplit, melt, recast
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.5.3
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:reshape':
##
## rename
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(plyr)
## Warning: package 'plyr' was built under R version 3.5.3
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:reshape':
##
## rename, round_any
## The following object is masked from 'package:maps':
##
## ozone
myplotf=function(x, t){
myt=table(x, minoo$Group)
myt
mysum=apply(myt,2,sum)
for (i in 1:3){myt[1:2,i]=100*myt[,i]/mysum[i]}
myt
mybar=barplot(myt, col=c("dark green", "dark red"),main=t, ylim=c(0,100), ylab="%", cex.lab=1.5,cex.main=1.5)
legend("center", legend=c("Not Thrown Out","Thrown Out"), fill =c("dark green", "dark red"), cex=1)
for (i in 1:3){
text(mybar[i],5, paste0(round(myt[1,i],0), " %") ,cex=1.5, col="white")
text(mybar[i],95, paste0(round(myt[2,i],0), " %") ,cex=1.5, col="white")
}
lablist<-c("Nancy", "Prov.", "Spring.")
axis(1, at=seq(1, 3, by=1), labels = FALSE)
text(seq(1, 3, by=1), par("usr")[3] -1, labels = lablist, srt = 90, pos = 2, xpd = TRUE, cex=1.5)
}
par(mfrow=c(1,3))
par(xaxt="no")
myplotf(minoo$Plastic_Thrownout, "Plastic")
myplotf(minoo$Metal_Thrownout, "Metal")
myplotf(minoo$Appliances_Thrownout, "Appliances")
myplotf(minoo$Boxes_Thrownout, "Boxes")
myplotf(minoo$Glass_Thrownout, "Glass")
myplotf(minoo$Bags_Thrownout, "Bags")
myplotf(minoo$GreaseOil_Thrownout, "Grease")
myplotf(minoo$OfficeSupplies_Thrownout, "Office Supplies")
myplotf(minoo$Meatbyproducts_Thrownout, "Meat Byproducts")
myplotf(minoo$Veggies_Thrownout, "Vegetables")
myplotf(minoo$Customer_Food_ThrownOut, "Customer Food")
myplotf(minoo$Spoiled_Food_ThrownOut, "Spoiled Food")
myplotf(minoo$BoilH20_Food_ThrownOut, "Boiled Water")
myplotf(minoo$Bread_Thrown_Out, "Bread")
myplotf(minoo$Unused_Thrown_Out, "Unused Food")
Graphs by Green Identification
myplotf2=function(x, t){
myt=table(x, minoo$Green)
myt
mysum=apply(myt,2,sum)
for (i in 1:2){myt[1:2,i]=100*myt[,i]/mysum[i]}
myt
mybar=barplot(myt, col=c("dark green", "dark red"),main=t, ylim=c(0,100), ylab="%", cex.lab=1.5,cex.main=1.5)
legend("center", legend=c("Not Thrown Out","Thrown Out"), fill =c("dark green", "dark red"), cex=1)
for (i in 1:2){
text(mybar[i],5, paste0(round(myt[1,i],0), " %") ,cex=1.5, col="white")
text(mybar[i],95, paste0(round(myt[2,i],0), " %") ,cex=1.5, col="white")
}
lablist<-c("Green", "Not Green")
axis(1, at=seq(1, 2, by=1), labels = FALSE)
text(seq(1, 2, by=1), par("usr")[3] -1, labels = lablist, srt = 45, pos = 2, xpd = TRUE, cex=1)
}
par(mfrow=c(1,3))
par(xaxt="no")
myplotf2(minoo$Boxes_Thrownout, "Cardboard Boxes")
myplotf2(minoo$Glass_Thrownout, "Glass Bottles")
myplotf2(minoo$Metal_Thrownout, "Metal Containers")
myplotf2(minoo$Plastic_Thrownout, "Plastic Bottles, Straws, etc.")
myplotf2(minoo$Bags_Thrownout, "Plastic Bags")
#myplotf2(minoo$Appliances_Thrownout, "Appliances")
#myplotf2(minoo$GreaseOil_Thrownout, "Grease")
#myplotf2(minoo$OfficeSupplies_Thrownout, "Office Supplies")
#myplotf2(minoo$Meatbyproducts_Thrownout, "Meat Byproducts")
#myplotf2(minoo$Veggies_Thrownout, "Vegetables")
#myplotf2(minoo$Customer_Food_ThrownOut, "Customer Food")
#myplotf2(minoo$Spoiled_Food_ThrownOut, "Spoiled Food")
#myplotf2(minoo$BoilH20_Food_ThrownOut, "Boiled Water")
#myplotf2(minoo$Bread_Thrown_Out, "Bread")
#myplotf2(minoo$Unused_Thrown_Out, "Unused Food")
myt=function(x,y,z){
a=table(x,y)
print(z)
print(a)
myq=fisher.test(a)
return(myq)
}
Green vs. Non-Green
myt(minoo$Green, minoo$Metal_Thrownout, "Metal")
## [1] "Metal"
## y
## x 0 1
## 0 19 12
## 1 46 49
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.2236
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.6856554 4.2514965
## sample estimates:
## odds ratio
## 1.679598
myt(minoo$Green, minoo$Plastic_Thrownout, "Plastic")
## [1] "Plastic"
## y
## x 0 1
## 0 12 19
## 1 28 67
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.3776
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5839896 3.7918712
## sample estimates:
## odds ratio
## 1.50618
myt(minoo$Green, minoo$Unused_Thrown_Out, "Unused Food")
## [1] "Unused Food"
## y
## x 0 1
## 0 23 8
## 1 63 32
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.5077
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.5503375 4.2014392
## sample estimates:
## odds ratio
## 1.456086
#myt(minoo$Green, minoo$BoilH20_Food_Resold, "Boiled Water") constant
myt(minoo$Green, minoo$Bread_Thrown_Out, "Bread")
## [1] "Bread"
## y
## x 0 1
## 0 16 15
## 1 61 34
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.2887
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2421494 1.4724039
## sample estimates:
## odds ratio
## 0.5970536
myt(minoo$Green, minoo$OfficeSupplies_Thrownout, "Office Supplies")
## [1] "Office Supplies"
## y
## x 0 1
## 0 16 15
## 1 56 39
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.5334
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3041606 1.8265262
## sample estimates:
## odds ratio
## 0.7446448
myt(minoo$Green, minoo$Spoiled_Food_ThrownOut, "Spoiled Food")
## [1] "Spoiled Food"
## y
## x 0 1
## 0 1 30
## 1 12 83
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.1838
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.005226162 1.699429823
## sample estimates:
## odds ratio
## 0.2324228
myt(minoo$Green, minoo$Glass_Thrownout, "Glass")
## [1] "Glass"
## y
## x 0 1
## 0 19 12
## 1 61 34
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.8311
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3561264 2.2531608
## sample estimates:
## odds ratio
## 0.8834005
myt(minoo$Green, minoo$GreaseOil_Thrownout, "Grease/Oil")
## [1] "Grease/Oil"
## y
## x 0 1
## 0 24 7
## 1 79 16
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.5924
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2356884 2.2447007
## sample estimates:
## odds ratio
## 0.6965265
myt(minoo$Green, minoo$Customer_Food_ThrownOut, "Customer Food")
## [1] "Customer Food"
## y
## x 0 1
## 0 1 30
## 1 2 93
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.02543396 30.64457484
## sample estimates:
## odds ratio
## 1.54407
myt(minoo$Green, minoo$Veggies_Thrownout, "Veggies")
## [1] "Veggies"
## y
## x 0 1
## 0 10 21
## 1 27 68
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.8206
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4428621 3.0885827
## sample estimates:
## odds ratio
## 1.197513
myt(minoo$Green, minoo$Boxes_Thrownout, "Boxes")
## [1] "Boxes"
## y
## x 0 1
## 0 25 6
## 1 74 21
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.4001268 3.9872436
## sample estimates:
## odds ratio
## 1.180933
myt(minoo$Green, minoo$Meatbyproducts_Thrownout, "Meat Byproducts")
## [1] "Meat Byproducts"
## y
## x 0 1
## 0 11 20
## 1 26 69
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.4959
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.550382 3.720117
## sample estimates:
## odds ratio
## 1.455095
myt(minoo$Green, minoo$Appliances_Thrownout, "Appliances")
## [1] "Appliances"
## y
## x 0 1
## 0 24 7
## 1 67 28
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.4993
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.51883 4.38982
## sample estimates:
## odds ratio
## 1.428917
myt(minoo$Green, minoo$Bags_Thrownout, "Bags")
## [1] "Bags"
## y
## x 0 1
## 0 7 24
## 1 22 73
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3097988 2.7326745
## sample estimates:
## odds ratio
## 0.9680517
Heating and Cooling Systems
table(minoo$Group, minoo$Heating_Solar) #no solar
##
## 0
## Nancy 50
## Providence 45
## Springfield 31
table(minoo$Group, minoo$Cooling_Solar) #no solar
##
## 0
## Nancy 50
## Providence 45
## Springfield 31
table(minoo$Group, minoo$Heating_Gas)
##
## 0 1
## Nancy 41 9
## Providence 24 21
## Springfield 16 15
table(minoo$Group, minoo$Cooling_Gas)
##
## 0 1
## Nancy 47 3
## Providence 39 6
## Springfield 26 5
City Differences
myt(minoo$Group, minoo$Metal_Thrownout, "Metal")
## [1] "Metal"
## y
## x 0 1
## Nancy 17 33
## Providence 29 16
## Springfield 19 12
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.005594
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$Plastic_Thrownout, "Plastic")
## [1] "Plastic"
## y
## x 0 1
## Nancy 9 41
## Providence 19 26
## Springfield 12 19
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.02209
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$Unused_Thrown_Out, "Unused Food")
## [1] "Unused Food"
## y
## x 0 1
## Nancy 28 22
## Providence 35 10
## Springfield 23 8
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.05713
## alternative hypothesis: two.sided
#myt(minoo$Group, minoo$BoilH20_Food_Resold, "Boiled Water") constant
myt(minoo$Group, minoo$Bread_Thrown_Out, "Bread")
## [1] "Bread"
## y
## x 0 1
## Nancy 36 14
## Providence 25 20
## Springfield 16 15
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.1241
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$OfficeSupplies_Thrownout, "Office Supplies")
## [1] "Office Supplies"
## y
## x 0 1
## Nancy 34 16
## Providence 22 23
## Springfield 16 15
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.1357
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$Spoiled_Food_ThrownOut, "Spoiled Food")
## [1] "Spoiled Food"
## y
## x 0 1
## Nancy 8 42
## Providence 4 41
## Springfield 1 30
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.2039
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$Glass_Thrownout, "Glass")
## [1] "Glass"
## y
## x 0 1
## Nancy 36 14
## Providence 25 20
## Springfield 19 12
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.2431
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$GreaseOil_Thrownout, "Grease/Oil")
## [1] "Grease/Oil"
## y
## x 0 1
## Nancy 44 6
## Providence 35 10
## Springfield 24 7
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.3272
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$Customer_Food_ThrownOut, "Customer Food")
## [1] "Customer Food"
## y
## x 0 1
## Nancy 0 50
## Providence 2 43
## Springfield 1 30
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.3476
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$Veggies_Thrownout, "Veggies")
## [1] "Veggies"
## y
## x 0 1
## Nancy 12 38
## Providence 15 30
## Springfield 10 21
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.5437
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$Boxes_Thrownout, "Boxes")
## [1] "Boxes"
## y
## x 0 1
## Nancy 37 13
## Providence 37 8
## Springfield 25 6
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.6225
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$Meatbyproducts_Thrownout, "Meat Byproducts")
## [1] "Meat Byproducts"
## y
## x 0 1
## Nancy 14 36
## Providence 12 33
## Springfield 11 20
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.7254
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$Appliances_Thrownout, "Appliances")
## [1] "Appliances"
## y
## x 0 1
## Nancy 36 14
## Providence 31 14
## Springfield 24 7
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.7437
## alternative hypothesis: two.sided
myt(minoo$Group, minoo$Bags_Thrownout, "Bags")
## [1] "Bags"
## y
## x 0 1
## Nancy 11 39
## Providence 11 34
## Springfield 7 24
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.9635
## alternative hypothesis: two.sided
Belief Structure
minoo$Finance <- minoo$Finance_SD+2*minoo$Finance_D+3*minoo$Finance_N+4*minoo$Finance_A+5*minoo$Finance_SA
myt(minoo$Group, minoo$Finance, "Financial")
## [1] "Financial"
## y
## x 0 1 2 3 4 5
## Nancy 1 1 3 11 17 17
## Providence 1 1 1 9 14 19
## Springfield 1 1 1 8 10 10
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.9952
## alternative hypothesis: two.sided
minoo$Rep <- minoo$Rep_SD+2*minoo$Rep_D+3*minoo$Rep_N+4*minoo$Rep_A+5*minoo$Rep_SA
myt(minoo$Group, minoo$Rep, "Reputation")
## [1] "Reputation"
## y
## x 0 1 3 4 5
## Nancy 1 2 13 15 19
## Providence 2 0 7 13 23
## Springfield 2 0 4 11 14
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.5896
## alternative hypothesis: two.sided
minoo$Cust <- minoo$Cust_SD+2*minoo$Cust_D+3*minoo$Cust_N+4*minoo$Cust_A+5*minoo$Cust_SA
myt(minoo$Group, minoo$Cust, "Customers")
## [1] "Customers"
## y
## x 0 1 2 3 4 5
## Nancy 2 1 3 21 12 11
## Providence 1 0 0 10 14 20
## Springfield 1 0 0 9 11 10
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.1572
## alternative hypothesis: two.sided
Training
myt(minoo$Group,minoo$Have_Training, "Have Training")
## [1] "Have Training"
## y
## x 0 1
## Nancy 45 5
## Providence 24 21
## Springfield 16 15
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 2.659e-05
## alternative hypothesis: two.sided
myt(minoo$Group,minoo$Want_Training, "Want Training")
## [1] "Want Training"
## y
## x 0 1
## Nancy 32 18
## Providence 24 21
## Springfield 15 16
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.3574
## alternative hypothesis: two.sided
myt(minoo$Group,minoo$Participate_Training, "Would Participate in Training")
## [1] "Would Participate in Training"
## y
## x 0 1
## Nancy 16 34
## Providence 20 25
## Springfield 15 16
##
## Fisher's Exact Test for Count Data
##
## data: a
## p-value = 0.2638
## alternative hypothesis: two.sided
Generalized Linear Model
mylm=glm(minoo$Have_Training~minoo$Group+minoo$Plastic_Thrownout+minoo$Bags_Thrownout, family="binomial")
summary(mylm)
##
## Call:
## glm(formula = minoo$Have_Training ~ minoo$Group + minoo$Plastic_Thrownout +
## minoo$Bags_Thrownout, family = "binomial")
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6668 -0.8771 -0.3570 1.0505 2.3600
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8618 0.6313 -1.365 0.172248
## minoo$GroupProvidence 1.9641 0.5840 3.363 0.000771 ***
## minoo$GroupSpringfield 2.0897 0.6195 3.373 0.000744 ***
## minoo$Plastic_Thrownout -0.9375 0.4429 -2.117 0.034294 *
## minoo$Bags_Thrownout -0.9218 0.4999 -1.844 0.065205 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 158.98 on 125 degrees of freedom
## Residual deviance: 128.63 on 121 degrees of freedom
## AIC: 138.63
##
## Number of Fisher Scoring iterations: 5