Avocado Pricing - Hackathon
Group 5
10/10/2021
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Objective
Your explanation here
Descriptive statistics
Your explanation here
data <- read.csv("avocado_C.csv")
head(data)## date average_price total_volume type year geography
## 1 12/3/17 1.39 139970 conventional 2017 Albany
## 2 12/3/17 1.07 504933 conventional 2017 Atlanta
## 3 12/3/17 1.43 658939 conventional 2017 Baltimore/Washington
## 4 12/3/17 1.14 86646 conventional 2017 Boise
## 5 12/3/17 1.40 488588 conventional 2017 Boston
## 6 12/3/17 1.13 153282 conventional 2017 Buffalo/Rochester
## Mileage
## 1 2832
## 2 2199
## 3 2679
## 4 827
## 5 2998
## 6 2552#install.packages('plyr')
library(plyr)
count(data, 'geography')## geography freq
## 1 Albany 154
## 2 Atlanta 154
## 3 Baltimore/Washington 154
## 4 Boise 154
## 5 Boston 154
## 6 Buffalo/Rochester 154
## 7 Charlotte 154
## 8 Chicago 154
## 9 Cincinnati/Dayton 154
## 10 Columbus 154
## 11 Dallas/Ft. Worth 154
## 12 Denver 154
## 13 Detroit 154
## 14 Grand Rapids 154
## 15 Harrisburg/Scranton 154
## 16 Hartford/Springfield 154
## 17 Houston 154
## 18 Indianapolis 154
## 19 Jacksonville 154
## 20 Las Vegas 154
## 21 Los Angeles 154
## 22 Louisville 154
## 23 Miami/Ft. Lauderdale 154
## 24 Nashville 154
## 25 New Orleans/Mobile 154
## 26 New York 154
## 27 Orlando 154
## 28 Philadelphia 154
## 29 Phoenix/Tucson 154
## 30 Pittsburgh 154
## 31 Portland 154
## 32 Raleigh/Greensboro 154
## 33 Richmond/Norfolk 154
## 34 Sacramento 154
## 35 San Diego 154
## 36 San Francisco 154
## 37 Seattle 154
## 38 Spokane 154
## 39 St. Louis 154
## 40 Syracuse 154
## 41 Tampa 154count(data, 'average_price')## average_price freq
## 1 0.50 1
## 2 0.51 1
## 3 0.53 1
## 4 0.54 2
## 5 0.56 4
## 6 0.57 1
## 7 0.58 2
## 8 0.59 4
## 9 0.60 1
## 10 0.61 4
## 11 0.62 7
## 12 0.63 3
## 13 0.64 8
## 14 0.65 8
## 15 0.66 12
## 16 0.67 15
## 17 0.68 15
## 18 0.69 10
## 19 0.70 21
## 20 0.71 20
## 21 0.72 23
## 22 0.73 32
## 23 0.74 34
## 24 0.75 25
## 25 0.76 36
## 26 0.77 29
## 27 0.78 35
## 28 0.79 24
## 29 0.80 39
## 30 0.81 46
## 31 0.82 37
## 32 0.83 40
## 33 0.84 52
## 34 0.85 48
## 35 0.86 48
## 36 0.87 55
## 37 0.88 61
## 38 0.89 77
## 39 0.90 70
## 40 0.91 81
## 41 0.92 81
## 42 0.93 84
## 43 0.94 80
## 44 0.95 97
## 45 0.96 85
## 46 0.97 96
## 47 0.98 101
## 48 0.99 100
## 49 1.00 104
## 50 1.01 119
## 51 1.02 82
## 52 1.03 114
## 53 1.04 116
## 54 1.05 87
## 55 1.06 100
## 56 1.07 91
## 57 1.08 94
## 58 1.09 114
## 59 1.10 114
## 60 1.11 88
## 61 1.12 96
## 62 1.13 107
## 63 1.14 136
## 64 1.15 109
## 65 1.16 119
## 66 1.17 115
## 67 1.18 104
## 68 1.19 102
## 69 1.20 108
## 70 1.21 81
## 71 1.22 87
## 72 1.23 76
## 73 1.24 76
## 74 1.25 82
## 75 1.26 82
## 76 1.27 90
## 77 1.28 86
## 78 1.29 97
## 79 1.30 68
## 80 1.31 80
## 81 1.32 75
## 82 1.33 54
## 83 1.34 62
## 84 1.35 71
## 85 1.36 70
## 86 1.37 61
## 87 1.38 69
## 88 1.39 60
## 89 1.40 54
## 90 1.41 60
## 91 1.42 61
## 92 1.43 44
## 93 1.44 40
## 94 1.45 43
## 95 1.46 36
## 96 1.47 41
## 97 1.48 33
## 98 1.49 33
## 99 1.50 41
## 100 1.51 30
## 101 1.52 35
## 102 1.53 42
## 103 1.54 18
## 104 1.55 23
## 105 1.56 19
## 106 1.57 19
## 107 1.58 16
## 108 1.59 22
## 109 1.60 17
## 110 1.61 15
## 111 1.62 11
## 112 1.63 10
## 113 1.64 13
## 114 1.65 10
## 115 1.66 11
## 116 1.67 11
## 117 1.68 7
## 118 1.69 7
## 119 1.70 7
## 120 1.71 4
## 121 1.72 9
## 122 1.73 3
## 123 1.74 8
## 124 1.75 6
## 125 1.76 4
## 126 1.77 7
## 127 1.78 6
## 128 1.79 4
## 129 1.80 4
## 130 1.81 3
## 131 1.82 4
## 132 1.83 2
## 133 1.84 1
## 134 1.86 2
## 135 1.87 2
## 136 1.88 1
## 137 1.89 2
## 138 1.90 1
## 139 1.91 2
## 140 1.92 1
## 141 1.95 1
## 142 1.96 2
## 143 1.98 2
## 144 2.01 1
## 145 2.02 1mean(data$average_price)## [1] 1.142566median(data$average_price)## [1] 1.13cor(data$total_volume,data$average_price)## [1] -0.177141Price elasticity
To calculate Price Elasticity of Demand we use the formula: PE = (ΔQ/ΔP) * (P/Q) # (Iacobacci, 2015, p.134-135).
(ΔQ/ΔP) is determined by the coefficient in our regression analysis below. Here Beta represents the change in the dependent variable y with respect to x (i.e. Δy/Δx = (ΔQ/ΔP)). To determine (P/Q) we will use the average price and average sales volume (Salem, 2014).
plot(total_volume ~ average_price, data)
regr <- lm(total_volume ~ average_price, data)
abline(regr, col='red')
summary(regr)##
## Call:
## lm(formula = total_volume ~ average_price, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -668069 -397312 -181986 201191 4813886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1159708 38139 30.41 <2e-16 ***
## average_price -467729 32709 -14.30 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 604800 on 6312 degrees of freedom
## Multiple R-squared: 0.03138, Adjusted R-squared: 0.03123
## F-statistic: 204.5 on 1 and 6312 DF, p-value: < 2.2e-16coefficients(regr)## (Intercept) average_price
## 1159708.1 -467728.6Beta <- regr$coefficients[["average_price"]]
P <- mean(data$average_price)
Q <- mean(data$total_volume)
elasticity <-Beta*P/Q
elasticity## [1] -0.8546503Elasticity
Your conclusions here:
Ref: Salem, 2014. Price Elasticity with R. http://www.salemmarafi.com/code/price-elasticity-with-r/
365datascience. https://365datascience.com/trending/price-elasticity/