Avocado Pricing - Hackathon
Group 5
10/10/2021
R Markdown
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Objective
Your explanation here
Descriptive statistics
Your explanation here
data <- read.csv("avocado_1718.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 1.44 3577 organic 2017 Albany
## 3 12/3/17 1.07 504933 conventional 2017 Atlanta
## 4 12/3/17 1.62 10609 organic 2017 Atlanta
## 5 12/3/17 1.43 658939 conventional 2017 Baltimore/Washington
## 6 12/3/17 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 108
## 2 Atlanta 108
## 3 Baltimore/Washington 108
## 4 Boise 108
## 5 Boston 108
## 6 Buffalo/Rochester 108
## 7 Charlotte 108
## 8 Chicago 108
## 9 Cincinnati/Dayton 108
## 10 Columbus 108
## 11 Dallas/Ft. Worth 108
## 12 Denver 108
## 13 Detroit 108
## 14 Grand Rapids 108
## 15 Harrisburg/Scranton 108
## 16 Hartford/Springfield 108
## 17 Houston 108
## 18 Indianapolis 108
## 19 Jacksonville 108
## 20 Las Vegas 108
## 21 Los Angeles 108
## 22 Louisville 108
## 23 Miami/Ft. Lauderdale 108
## 24 Nashville 108
## 25 New Orleans/Mobile 108
## 26 New York 108
## 27 Orlando 108
## 28 Philadelphia 108
## 29 Phoenix/Tucson 108
## 30 Pittsburgh 108
## 31 Portland 108
## 32 Raleigh/Greensboro 108
## 33 Richmond/Norfolk 108
## 34 Sacramento 108
## 35 San Diego 108
## 36 San Francisco 108
## 37 Seattle 108
## 38 Spokane 108
## 39 St. Louis 108
## 40 Syracuse 108
## 41 Tampa 108count(data, 'average_price')## average_price freq
## 1 0.50 1
## 2 0.51 1
## 3 0.53 1
## 4 0.54 1
## 5 0.56 3
## 6 0.57 1
## 7 0.58 2
## 8 0.59 3
## 9 0.61 2
## 10 0.62 1
## 11 0.64 4
## 12 0.66 3
## 13 0.67 6
## 14 0.68 4
## 15 0.69 3
## 16 0.70 1
## 17 0.71 7
## 18 0.72 7
## 19 0.73 12
## 20 0.74 10
## 21 0.75 10
## 22 0.76 8
## 23 0.77 11
## 24 0.78 11
## 25 0.79 8
## 26 0.80 8
## 27 0.81 12
## 28 0.82 4
## 29 0.83 10
## 30 0.84 14
## 31 0.85 12
## 32 0.86 7
## 33 0.87 16
## 34 0.88 17
## 35 0.89 16
## 36 0.90 8
## 37 0.91 16
## 38 0.92 24
## 39 0.93 20
## 40 0.94 18
## 41 0.95 26
## 42 0.96 31
## 43 0.97 27
## 44 0.98 31
## 45 0.99 44
## 46 1.00 30
## 47 1.01 46
## 48 1.02 30
## 49 1.03 43
## 50 1.04 43
## 51 1.05 33
## 52 1.06 32
## 53 1.07 37
## 54 1.08 40
## 55 1.09 51
## 56 1.10 45
## 57 1.11 31
## 58 1.12 42
## 59 1.13 54
## 60 1.14 64
## 61 1.15 59
## 62 1.16 78
## 63 1.17 54
## 64 1.18 53
## 65 1.19 54
## 66 1.20 58
## 67 1.21 45
## 68 1.22 45
## 69 1.23 55
## 70 1.24 51
## 71 1.25 63
## 72 1.26 51
## 73 1.27 63
## 74 1.28 60
## 75 1.29 59
## 76 1.30 49
## 77 1.31 53
## 78 1.32 60
## 79 1.33 36
## 80 1.34 53
## 81 1.35 56
## 82 1.36 64
## 83 1.37 56
## 84 1.38 65
## 85 1.39 57
## 86 1.40 55
## 87 1.41 63
## 88 1.42 57
## 89 1.43 61
## 90 1.44 50
## 91 1.45 59
## 92 1.46 51
## 93 1.47 37
## 94 1.48 45
## 95 1.49 32
## 96 1.50 44
## 97 1.51 38
## 98 1.52 40
## 99 1.53 46
## 100 1.54 36
## 101 1.55 45
## 102 1.56 47
## 103 1.57 38
## 104 1.58 29
## 105 1.59 47
## 106 1.60 41
## 107 1.61 30
## 108 1.62 31
## 109 1.63 34
## 110 1.64 41
## 111 1.65 39
## 112 1.66 38
## 113 1.67 32
## 114 1.68 26
## 115 1.69 33
## 116 1.70 25
## 117 1.71 19
## 118 1.72 25
## 119 1.73 22
## 120 1.74 21
## 121 1.75 23
## 122 1.76 22
## 123 1.77 22
## 124 1.78 20
## 125 1.79 25
## 126 1.80 23
## 127 1.81 20
## 128 1.82 20
## 129 1.83 27
## 130 1.84 15
## 131 1.85 18
## 132 1.86 14
## 133 1.87 9
## 134 1.88 14
## 135 1.89 6
## 136 1.90 12
## 137 1.91 12
## 138 1.92 17
## 139 1.93 11
## 140 1.94 12
## 141 1.95 13
## 142 1.96 5
## 143 1.97 5
## 144 1.98 4
## 145 1.99 7
## 146 2.00 6
## 147 2.01 6
## 148 2.02 11
## 149 2.03 7
## 150 2.04 9
## 151 2.05 9
## 152 2.06 8
## 153 2.07 9
## 154 2.08 7
## 155 2.09 7
## 156 2.10 4
## 157 2.11 8
## 158 2.12 3
## 159 2.13 4
## 160 2.14 7
## 161 2.15 5
## 162 2.16 8
## 163 2.17 1
## 164 2.18 4
## 165 2.19 2
## 166 2.20 3
## 167 2.21 4
## 168 2.22 3
## 169 2.23 2
## 170 2.24 4
## 171 2.25 6
## 172 2.26 3
## 173 2.27 4
## 174 2.28 1
## 175 2.30 3
## 176 2.31 2
## 177 2.32 1
## 178 2.33 1
## 179 2.35 1
## 180 2.39 1
## 181 2.40 1
## 182 2.41 1
## 183 2.43 1
## 184 2.48 1
## 185 2.49 1
## 186 2.50 1
## 187 2.52 1
## 188 2.56 1
## 189 2.60 1
## 190 2.66 1
## 191 2.71 1mean(data$average_price)## [1] 1.367866median(data$average_price)## [1] 1.35cor(data$total_volume,data$average_price)## [1] -0.4822304Price 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
## -734315 -258668 -106296 115217 4288802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1330796 28850 46.13 <2e-16 ***
## average_price -752078 20537 -36.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 437300 on 4426 degrees of freedom
## Multiple R-squared: 0.2325, Adjusted R-squared: 0.2324
## F-statistic: 1341 on 1 and 4426 DF, p-value: < 2.2e-16coefficients(regr)## (Intercept) average_price
## 1330796.1 -752077.7Beta <- regr$coefficients[["average_price"]]
P <- mean(data$average_price)
Q <- mean(data$total_volume)
elasticity <-Beta*P/Q
elasticity## [1] -3.405812Elasticity
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/