Tailgating Common Measurements Analysis

# Load, examine, and clean dataset
common <- read.csv ("/Users/squishy/Dropbox/Recycling GRA/Tailgating Study/Tailgating Data/Tailgating Data - Common Bins.csv") 
str(common) # 116 observations

## 'data.frame':    52 obs. of  9 variables:
##  $ BagID      : int  101 102 103 104 105 106 107 201 202 203 ...
##  $ Lot        : Factor w/ 2 levels "Benson","Duane": 2 2 2 2 2 2 2 1 1 1 ...
##  $ Date       : int  151017 151017 151017 151017 151017 151017 151017 151017 151017 151017 ...
##  $ PreTrash   : num  17.1 19.1 34 45.7 54 ...
##  $ PreRecycle : num  22.1 15.9 15.3 20.7 38.5 ...
##  $ PostTrash  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ PostRecycle: num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Recorder   : Factor w/ 3 levels "AH","JN","XW": 2 2 2 2 2 2 2 3 3 3 ...
##  $ Comments   : Factor w/ 6 levels "","3.15 boxes + bubbles",..: 1 1 6 1 1 1 1 2 1 1 ...

commonU <- subset(common, Date=="151107") # Didn't get post measurments for day 1
str(commonU) # 29 observations

## 'data.frame':    29 obs. of  9 variables:
##  $ BagID      : int  301 302 303 304 305 306 307 308 309 310 ...
##  $ Lot        : Factor w/ 2 levels "Benson","Duane": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Date       : int  151107 151107 151107 151107 151107 151107 151107 151107 151107 151107 ...
##  $ PreTrash   : num  17.5 18.6 18.1 19.9 31.6 33 29 35.3 43.4 28.2 ...
##  $ PreRecycle : num  23.2 18.4 18.1 18.2 18.6 19.1 18.4 18.8 19 19.1 ...
##  $ PostTrash  : num  NA 19.9 18 18 25.8 32.9 24 35.3 43.6 28 ...
##  $ PostRecycle: num  24 21.5 18.1 20.1 20.4 19.1 24.5 18.6 18.6 19.4 ...
##  $ Recorder   : Factor w/ 3 levels "AH","JN","XW": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Comments   : Factor w/ 6 levels "","3.15 boxes + bubbles",..: 4 1 1 1 1 1 1 1 1 1 ...

Totals and diversion rates

# Total 
preT <- sum(commonU$PreTrash)
preT # Presorting trash weight

## [1] 744.9

preR <- sum(commonU$PreRecycle)
preR # Presorting recycling weight, aka waste diversion weight 

## [1] 584.5

preTot = preT + preR 
preTot # Total combined presorting weight

## [1] 1329.4

# Minimum and max weights
min(commonU$PreTrash)

## [1] 16.5

max(commonU$PreTrash)

## [1] 46

min(commonU$PreRecycle)

## [1] 15.2

max(commonU$PreRecycle)

## [1] 40.8

# % Diversion
preR/preTot 

## [1] 0.439672

The team measured 29 paired recycling and trash common bins in the Duane and Benson lots on day 2 (58 bins total), totaling 1329 lbs. The total pre-sorted trash weight was 745 lbs and recycling weight was 585 lbs. Trash bins weighed anywhere between 16.5 and 46 lbs. And recycling bins weighed between 15.2 to 40.8 lbs (fairly comparable to the amount of trash).

The diversion rate was 44%, very close to the diversion rate of individual tailgating groups.

Contamination rate

# Create new columns calculating the absolute difference between pre and post-sort weights
# Convert columns to numeric
commonU$PreTrash <- as.numeric(commonU$PreTrash)
commonU$PostTrash <- as.numeric(commonU$PostTrash)
commonU$PreRecycle <- as.numeric(commonU$PreRecycle)
commonU$PostRecycle <- as.numeric(commonU$PostRecycle)

# Take abs difference of pre- and post-sorting measurements
commonU$DiffT <- abs(commonU$PreTrash - commonU$PostTrash) 
commonU$DiffR <- abs(commonU$PreRecycle - commonU$PostRecycle)
str(commonU)

## 'data.frame':    29 obs. of  11 variables:
##  $ BagID      : int  301 302 303 304 305 306 307 308 309 310 ...
##  $ Lot        : Factor w/ 2 levels "Benson","Duane": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Date       : int  151107 151107 151107 151107 151107 151107 151107 151107 151107 151107 ...
##  $ PreTrash   : num  17.5 18.6 18.1 19.9 31.6 33 29 35.3 43.4 28.2 ...
##  $ PreRecycle : num  23.2 18.4 18.1 18.2 18.6 19.1 18.4 18.8 19 19.1 ...
##  $ PostTrash  : num  NA 19.9 18 18 25.8 32.9 24 35.3 43.6 28 ...
##  $ PostRecycle: num  24 21.5 18.1 20.1 20.4 19.1 24.5 18.6 18.6 19.4 ...
##  $ Recorder   : Factor w/ 3 levels "AH","JN","XW": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Comments   : Factor w/ 6 levels "","3.15 boxes + bubbles",..: 4 1 1 1 1 1 1 1 1 1 ...
##  $ DiffT      : num  NA 1.3 0.1 1.9 5.8 ...
##  $ DiffR      : num  0.8 3.1 0 1.9 1.8 ...

ContTotT <- sum(commonU$DiffT, na.rm=T) # Trash contamination total
ContTotT/preT # Trash contamination rate

## [1] 0.07517788

ContTotR <- sum(commonU$DiffR, na.rm=T) # Recycling contamination total
ContTotR/preR # Recycling contamination rate

## [1] 0.07459367

mean(commonU$DiffT, na.rm=T) # Avg trash contamination weight

## [1] 2

mean(commonU$DiffR, na.rm=T) # Avg recycling contamination weight

## [1] 1.503448

7.5% of the trash was contaminated. On average, trash bins had about 2 lbs of contamination.

7.5% of the recycling was contaminated (which is substantially lower than the 18% contamination rate in individual reycling trash bags). On average, recycling bins had 1.5 lbs of contamination.