Avocado Pricing - Hackathon
your name here
10/01/2021
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Objective
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Descriptive statistics
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data <- read.csv("avocado_O.csv")
head(data)## date average_price total_volume type year geography Mileage
## 1 12/3/17 1.44 3577 organic 2017 Albany 2832
## 2 12/3/17 1.62 10609 organic 2017 Atlanta 2199
## 3 12/3/17 1.58 38754 organic 2017 Baltimore/Washington 2679
## 4 12/3/17 1.77 1829 organic 2017 Boise 827
## 5 12/3/17 1.88 21338 organic 2017 Boston 2998
## 6 12/3/17 1.18 7575 organic 2017 Buffalo/Rochester 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 0.69 1
## 2 0.71 1
## 3 0.72 1
## 4 0.75 1
## 5 0.77 3
## 6 0.79 2
## 7 0.80 1
## 8 0.81 1
## 9 0.82 1
## 10 0.84 3
## 11 0.85 3
## 12 0.88 2
## 13 0.89 1
## 14 0.90 4
## 15 0.91 5
## 16 0.92 4
## 17 0.93 10
## 18 0.94 11
## 19 0.95 12
## 20 0.96 9
## 21 0.97 4
## 22 0.98 11
## 23 0.99 5
## 24 1.00 6
## 25 1.01 7
## 26 1.02 11
## 27 1.03 16
## 28 1.04 16
## 29 1.05 19
## 30 1.06 16
## 31 1.07 15
## 32 1.08 18
## 33 1.09 22
## 34 1.10 27
## 35 1.11 19
## 36 1.12 17
## 37 1.13 30
## 38 1.14 47
## 39 1.15 52
## 40 1.16 40
## 41 1.17 41
## 42 1.18 44
## 43 1.19 62
## 44 1.20 61
## 45 1.21 52
## 46 1.22 54
## 47 1.23 59
## 48 1.24 66
## 49 1.25 66
## 50 1.26 61
## 51 1.27 44
## 52 1.28 56
## 53 1.29 56
## 54 1.30 51
## 55 1.31 58
## 56 1.32 66
## 57 1.33 66
## 58 1.34 75
## 59 1.35 73
## 60 1.36 81
## 61 1.37 88
## 62 1.38 86
## 63 1.39 69
## 64 1.40 77
## 65 1.41 81
## 66 1.42 85
## 67 1.43 95
## 68 1.44 80
## 69 1.45 94
## 70 1.46 104
## 71 1.47 77
## 72 1.48 86
## 73 1.49 72
## 74 1.50 82
## 75 1.51 88
## 76 1.52 73
## 77 1.53 75
## 78 1.54 75
## 79 1.55 96
## 80 1.56 76
## 81 1.57 78
## 82 1.58 73
## 83 1.59 89
## 84 1.60 78
## 85 1.61 80
## 86 1.62 72
## 87 1.63 76
## 88 1.64 74
## 89 1.65 76
## 90 1.66 75
## 91 1.67 58
## 92 1.68 60
## 93 1.69 77
## 94 1.70 58
## 95 1.71 62
## 96 1.72 56
## 97 1.73 67
## 98 1.74 46
## 99 1.75 58
## 100 1.76 54
## 101 1.77 55
## 102 1.78 52
## 103 1.79 52
## 104 1.80 59
## 105 1.81 44
## 106 1.82 49
## 107 1.83 49
## 108 1.84 44
## 109 1.85 48
## 110 1.86 45
## 111 1.87 44
## 112 1.88 43
## 113 1.89 36
## 114 1.90 41
## 115 1.91 47
## 116 1.92 40
## 117 1.93 35
## 118 1.94 35
## 119 1.95 37
## 120 1.96 34
## 121 1.97 34
## 122 1.98 31
## 123 1.99 26
## 124 2.00 23
## 125 2.01 18
## 126 2.02 33
## 127 2.03 30
## 128 2.04 25
## 129 2.05 33
## 130 2.06 23
## 131 2.07 26
## 132 2.08 22
## 133 2.09 16
## 134 2.10 18
## 135 2.11 21
## 136 2.12 16
## 137 2.13 17
## 138 2.14 17
## 139 2.15 21
## 140 2.16 24
## 141 2.17 8
## 142 2.18 16
## 143 2.19 15
## 144 2.20 8
## 145 2.21 18
## 146 2.22 11
## 147 2.23 7
## 148 2.24 9
## 149 2.25 9
## 150 2.26 8
## 151 2.27 12
## 152 2.28 6
## 153 2.29 3
## 154 2.30 4
## 155 2.31 9
## 156 2.32 6
## 157 2.33 7
## 158 2.34 5
## 159 2.35 4
## 160 2.36 6
## 161 2.37 5
## 162 2.38 5
## 163 2.39 9
## 164 2.40 5
## 165 2.41 5
## 166 2.42 2
## 167 2.43 5
## 168 2.44 6
## 169 2.45 4
## 170 2.46 4
## 171 2.48 2
## 172 2.49 3
## 173 2.50 3
## 174 2.51 1
## 175 2.52 2
## 176 2.53 1
## 177 2.54 1
## 178 2.55 2
## 179 2.56 2
## 180 2.57 1
## 181 2.59 1
## 182 2.60 1
## 183 2.62 2
## 184 2.64 2
## 185 2.66 4
## 186 2.67 1
## 187 2.69 2
## 188 2.71 3
## 189 2.72 2
## 190 2.73 1
## 191 2.78 1mean(data$average_price)## [1] 1.575117median(data$average_price)## [1] 1.55cor(data$total_volume,data$average_price)## [1] 0.08112979Price 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
## -29036 -16186 -9212 5385 471453
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13574 1836 7.392 1.63e-13 ***
## average_price 7395 1143 6.467 1.08e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 28420 on 6312 degrees of freedom
## Multiple R-squared: 0.006582, Adjusted R-squared: 0.006425
## F-statistic: 41.82 on 1 and 6312 DF, p-value: 1.075e-10coefficients(regr)## (Intercept) average_price
## 13574.048 7394.563Beta <- regr$coefficients[["average_price"]]
P <- mean(data$average_price)
Q <- mean(data$total_volume)
elasticity <-Beta*P/Q
elasticity## [1] 0.4618033Elasticity
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/