Avocado Pricing - Hackathon
Group 5
10/01/2021
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Objective
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Descriptive statistics
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data <- read.csv("avocado_1920.csv")
head(data)## date average_price total_volume type year geography
## 1 1/7/19 1.07 129222 conventional 2019 Albany
## 2 1/7/19 1.41 5006 organic 2019 Albany
## 3 1/7/19 0.92 828971 conventional 2019 Atlanta
## 4 1/7/19 1.42 16714 organic 2019 Atlanta
## 5 1/7/19 1.31 925391 conventional 2019 Baltimore/Washington
## 6 1/7/19 1.23 58619 organic 2019 Baltimore/Washington
## Mileage
## 1 2832
## 2 2832
## 3 2199
## 4 2199
## 5 2679
## 6 2679#install.packages('plyr')
library(plyr)
count(data, 'geography')## geography freq
## 1 Albany 200
## 2 Atlanta 200
## 3 Baltimore/Washington 200
## 4 Boise 200
## 5 Boston 200
## 6 Buffalo/Rochester 200
## 7 Charlotte 200
## 8 Chicago 200
## 9 Cincinnati/Dayton 200
## 10 Columbus 200
## 11 Dallas/Ft. Worth 200
## 12 Denver 200
## 13 Detroit 200
## 14 Grand Rapids 200
## 15 Harrisburg/Scranton 200
## 16 Hartford/Springfield 200
## 17 Houston 200
## 18 Indianapolis 200
## 19 Jacksonville 200
## 20 Las Vegas 200
## 21 Los Angeles 200
## 22 Louisville 200
## 23 Miami/Ft. Lauderdale 200
## 24 Nashville 200
## 25 New Orleans/Mobile 200
## 26 New York 200
## 27 Orlando 200
## 28 Philadelphia 200
## 29 Phoenix/Tucson 200
## 30 Pittsburgh 200
## 31 Portland 200
## 32 Raleigh/Greensboro 200
## 33 Richmond/Norfolk 200
## 34 Sacramento 200
## 35 San Diego 200
## 36 San Francisco 200
## 37 Seattle 200
## 38 Spokane 200
## 39 St. Louis 200
## 40 Syracuse 200
## 41 Tampa 200count(data, 'average_price')## average_price freq
## 1 0.54 1
## 2 0.56 1
## 3 0.59 1
## 4 0.60 1
## 5 0.61 2
## 6 0.62 6
## 7 0.63 3
## 8 0.64 4
## 9 0.65 8
## 10 0.66 9
## 11 0.67 9
## 12 0.68 11
## 13 0.69 8
## 14 0.70 20
## 15 0.71 14
## 16 0.72 17
## 17 0.73 20
## 18 0.74 24
## 19 0.75 16
## 20 0.76 28
## 21 0.77 21
## 22 0.78 24
## 23 0.79 18
## 24 0.80 32
## 25 0.81 35
## 26 0.82 34
## 27 0.83 30
## 28 0.84 41
## 29 0.85 39
## 30 0.86 41
## 31 0.87 39
## 32 0.88 46
## 33 0.89 62
## 34 0.90 66
## 35 0.91 70
## 36 0.92 61
## 37 0.93 74
## 38 0.94 73
## 39 0.95 83
## 40 0.96 63
## 41 0.97 73
## 42 0.98 81
## 43 0.99 61
## 44 1.00 80
## 45 1.01 80
## 46 1.02 63
## 47 1.03 87
## 48 1.04 89
## 49 1.05 73
## 50 1.06 84
## 51 1.07 69
## 52 1.08 72
## 53 1.09 85
## 54 1.10 96
## 55 1.11 76
## 56 1.12 71
## 57 1.13 83
## 58 1.14 119
## 59 1.15 102
## 60 1.16 81
## 61 1.17 102
## 62 1.18 95
## 63 1.19 110
## 64 1.20 111
## 65 1.21 88
## 66 1.22 96
## 67 1.23 80
## 68 1.24 91
## 69 1.25 85
## 70 1.26 92
## 71 1.27 71
## 72 1.28 82
## 73 1.29 94
## 74 1.30 70
## 75 1.31 85
## 76 1.32 81
## 77 1.33 84
## 78 1.34 84
## 79 1.35 88
## 80 1.36 87
## 81 1.37 93
## 82 1.38 90
## 83 1.39 72
## 84 1.40 76
## 85 1.41 78
## 86 1.42 89
## 87 1.43 78
## 88 1.44 70
## 89 1.45 78
## 90 1.46 89
## 91 1.47 81
## 92 1.48 74
## 93 1.49 73
## 94 1.50 79
## 95 1.51 80
## 96 1.52 68
## 97 1.53 71
## 98 1.54 57
## 99 1.55 74
## 100 1.56 48
## 101 1.57 59
## 102 1.58 60
## 103 1.59 64
## 104 1.60 54
## 105 1.61 65
## 106 1.62 52
## 107 1.63 52
## 108 1.64 46
## 109 1.65 47
## 110 1.66 48
## 111 1.67 37
## 112 1.68 41
## 113 1.69 51
## 114 1.70 40
## 115 1.71 47
## 116 1.72 40
## 117 1.73 48
## 118 1.74 33
## 119 1.75 41
## 120 1.76 36
## 121 1.77 40
## 122 1.78 38
## 123 1.79 31
## 124 1.80 40
## 125 1.81 27
## 126 1.82 33
## 127 1.83 24
## 128 1.84 30
## 129 1.85 30
## 130 1.86 33
## 131 1.87 37
## 132 1.88 30
## 133 1.89 32
## 134 1.90 30
## 135 1.91 37
## 136 1.92 24
## 137 1.93 24
## 138 1.94 23
## 139 1.95 25
## 140 1.96 31
## 141 1.97 29
## 142 1.98 29
## 143 1.99 19
## 144 2.00 17
## 145 2.01 13
## 146 2.02 23
## 147 2.03 23
## 148 2.04 16
## 149 2.05 24
## 150 2.06 15
## 151 2.07 17
## 152 2.08 15
## 153 2.09 9
## 154 2.10 14
## 155 2.11 13
## 156 2.12 13
## 157 2.13 13
## 158 2.14 10
## 159 2.15 16
## 160 2.16 16
## 161 2.17 7
## 162 2.18 12
## 163 2.19 13
## 164 2.20 5
## 165 2.21 14
## 166 2.22 8
## 167 2.23 5
## 168 2.24 5
## 169 2.25 3
## 170 2.26 5
## 171 2.27 8
## 172 2.28 5
## 173 2.29 3
## 174 2.30 1
## 175 2.31 7
## 176 2.32 5
## 177 2.33 6
## 178 2.34 5
## 179 2.35 3
## 180 2.36 6
## 181 2.37 5
## 182 2.38 5
## 183 2.39 8
## 184 2.40 4
## 185 2.41 4
## 186 2.42 2
## 187 2.43 4
## 188 2.44 6
## 189 2.45 4
## 190 2.46 4
## 191 2.48 1
## 192 2.49 2
## 193 2.50 2
## 194 2.51 1
## 195 2.52 1
## 196 2.53 1
## 197 2.54 1
## 198 2.55 2
## 199 2.56 1
## 200 2.57 1
## 201 2.59 1
## 202 2.62 2
## 203 2.64 2
## 204 2.66 3
## 205 2.67 1
## 206 2.69 2
## 207 2.71 2
## 208 2.72 2
## 209 2.73 1
## 210 2.78 1mean(data$average_price)## [1] 1.353968median(data$average_price)## [1] 1.31cor(data$total_volume,data$average_price)## [1] -0.3882737Price 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
## -655706 -290480 -136151 114574 4927991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1118605 21201 52.76 <2e-16 ***
## average_price -576686 15117 -38.15 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 500600 on 8198 degrees of freedom
## Multiple R-squared: 0.1508, Adjusted R-squared: 0.1507
## F-statistic: 1455 on 1 and 8198 DF, p-value: < 2.2e-16coefficients(regr)## (Intercept) average_price
## 1118604.9 -576686.2Beta <- regr$coefficients[["average_price"]]
P <- mean(data$average_price)
Q <- mean(data$total_volume)
elasticity <-Beta*P/Q
elasticity## [1] -2.31154Elasticity
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/