Avocado Pricing - Hackathon
Estevan Mireles
10/01/2021
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Objective
Do a b c and d
Descriptive statistics
Our variables are listed below
data <- read.csv("avocado_2020.csv")
head(data)## date average_price total_volume type year geography
## 1 12/3/2017 1.39 139970 conventional 2017 Albany
## 2 12/3/2017 1.44 3577 organic 2017 Albany
## 3 12/3/2017 1.07 504933 conventional 2017 Atlanta
## 4 12/3/2017 1.62 10609 organic 2017 Atlanta
## 5 12/3/2017 1.43 658939 conventional 2017 Baltimore/Washington
## 6 12/3/2017 1.58 38754 organic 2017 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 308
## 2 Atlanta 308
## 3 Baltimore/Washington 308
## 4 Boise 308
## 5 Boston 308
## 6 Buffalo/Rochester 308
## 7 Charlotte 308
## 8 Chicago 308
## 9 Cincinnati/Dayton 308
## 10 Columbus 308
## 11 Dallas/Ft. Worth 308
## 12 Denver 308
## 13 Detroit 308
## 14 Grand Rapids 308
## 15 Harrisburg/Scranton 308
## 16 Hartford/Springfield 308
## 17 Houston 308
## 18 Indianapolis 308
## 19 Jacksonville 308
## 20 Las Vegas 308
## 21 Los Angeles 308
## 22 Louisville 308
## 23 Miami/Ft. Lauderdale 308
## 24 Nashville 308
## 25 New Orleans/Mobile 308
## 26 New York 308
## 27 Orlando 308
## 28 Philadelphia 308
## 29 Phoenix/Tucson 308
## 30 Pittsburgh 308
## 31 Portland 308
## 32 Raleigh/Greensboro 308
## 33 Richmond/Norfolk 308
## 34 Sacramento 308
## 35 San Diego 308
## 36 San Francisco 308
## 37 Seattle 308
## 38 Spokane 308
## 39 St. Louis 308
## 40 Syracuse 308
## 41 Tampa 308count(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 11
## 19 0.70 21
## 20 0.71 21
## 21 0.72 24
## 22 0.73 32
## 23 0.74 34
## 24 0.75 26
## 25 0.76 36
## 26 0.77 32
## 27 0.78 35
## 28 0.79 26
## 29 0.80 40
## 30 0.81 47
## 31 0.82 38
## 32 0.83 40
## 33 0.84 55
## 34 0.85 51
## 35 0.86 48
## 36 0.87 55
## 37 0.88 63
## 38 0.89 78
## 39 0.90 74
## 40 0.91 86
## 41 0.92 85
## 42 0.93 94
## 43 0.94 91
## 44 0.95 109
## 45 0.96 94
## 46 0.97 100
## 47 0.98 112
## 48 0.99 105
## 49 1.00 110
## 50 1.01 126
## 51 1.02 93
## 52 1.03 130
## 53 1.04 132
## 54 1.05 106
## 55 1.06 116
## 56 1.07 106
## 57 1.08 112
## 58 1.09 136
## 59 1.10 141
## 60 1.11 107
## 61 1.12 113
## 62 1.13 137
## 63 1.14 183
## 64 1.15 161
## 65 1.16 159
## 66 1.17 156
## 67 1.18 148
## 68 1.19 164
## 69 1.20 169
## 70 1.21 133
## 71 1.22 141
## 72 1.23 135
## 73 1.24 142
## 74 1.25 148
## 75 1.26 143
## 76 1.27 134
## 77 1.28 142
## 78 1.29 153
## 79 1.30 119
## 80 1.31 138
## 81 1.32 141
## 82 1.33 120
## 83 1.34 137
## 84 1.35 144
## 85 1.36 151
## 86 1.37 149
## 87 1.38 155
## 88 1.39 129
## 89 1.40 131
## 90 1.41 141
## 91 1.42 146
## 92 1.43 139
## 93 1.44 120
## 94 1.45 137
## 95 1.46 140
## 96 1.47 118
## 97 1.48 119
## 98 1.49 105
## 99 1.50 123
## 100 1.51 118
## 101 1.52 108
## 102 1.53 117
## 103 1.54 93
## 104 1.55 119
## 105 1.56 95
## 106 1.57 97
## 107 1.58 89
## 108 1.59 111
## 109 1.60 95
## 110 1.61 95
## 111 1.62 83
## 112 1.63 86
## 113 1.64 87
## 114 1.65 86
## 115 1.66 86
## 116 1.67 69
## 117 1.68 67
## 118 1.69 84
## 119 1.70 65
## 120 1.71 66
## 121 1.72 65
## 122 1.73 70
## 123 1.74 54
## 124 1.75 64
## 125 1.76 58
## 126 1.77 62
## 127 1.78 58
## 128 1.79 56
## 129 1.80 63
## 130 1.81 47
## 131 1.82 53
## 132 1.83 51
## 133 1.84 45
## 134 1.85 48
## 135 1.86 47
## 136 1.87 46
## 137 1.88 44
## 138 1.89 38
## 139 1.90 42
## 140 1.91 49
## 141 1.92 41
## 142 1.93 35
## 143 1.94 35
## 144 1.95 38
## 145 1.96 36
## 146 1.97 34
## 147 1.98 33
## 148 1.99 26
## 149 2.00 23
## 150 2.01 19
## 151 2.02 34
## 152 2.03 30
## 153 2.04 25
## 154 2.05 33
## 155 2.06 23
## 156 2.07 26
## 157 2.08 22
## 158 2.09 16
## 159 2.10 18
## 160 2.11 21
## 161 2.12 16
## 162 2.13 17
## 163 2.14 17
## 164 2.15 21
## 165 2.16 24
## 166 2.17 8
## 167 2.18 16
## 168 2.19 15
## 169 2.20 8
## 170 2.21 18
## 171 2.22 11
## 172 2.23 7
## 173 2.24 9
## 174 2.25 9
## 175 2.26 8
## 176 2.27 12
## 177 2.28 6
## 178 2.29 3
## 179 2.30 4
## 180 2.31 9
## 181 2.32 6
## 182 2.33 7
## 183 2.34 5
## 184 2.35 4
## 185 2.36 6
## 186 2.37 5
## 187 2.38 5
## 188 2.39 9
## 189 2.40 5
## 190 2.41 5
## 191 2.42 2
## 192 2.43 5
## 193 2.44 6
## 194 2.45 4
## 195 2.46 4
## 196 2.48 2
## 197 2.49 3
## 198 2.50 3
## 199 2.51 1
## 200 2.52 2
## 201 2.53 1
## 202 2.54 1
## 203 2.55 2
## 204 2.56 2
## 205 2.57 1
## 206 2.59 1
## 207 2.60 1
## 208 2.62 2
## 209 2.64 2
## 210 2.66 4
## 211 2.67 1
## 212 2.69 2
## 213 2.71 3
## 214 2.72 2
## 215 2.73 1
## 216 2.78 1mean(data$average_price)## [1] 1.358841median(data$average_price)## [1] 1.32cor(data$total_volume,data$average_price)## [1] -0.4169306Price 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
## -679205 -277566 -128592 118917 4901891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1179545 17117 68.91 <2e-16 ***
## average_price -628687 12198 -51.54 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 480300 on 12626 degrees of freedom
## Multiple R-squared: 0.1738, Adjusted R-squared: 0.1738
## F-statistic: 2657 on 1 and 12626 DF, p-value: < 2.2e-16coefficients(regr)## (Intercept) average_price
## 1179544.8 -628686.6Beta <- regr$coefficients[["average_price"]]
P <- mean(data$average_price)
Q <- mean(data$total_volume)
elasticity <-Beta*P/Q
elasticity## [1] -2.626474Elasticity
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/