Fruit_And_Vegetables_Prices_Analysis
AJ
2024-09-07
Introduction
- Introduction to Fruit and Vegetable Prices Analysis
The increasing cost of fruits and vegetables has become a pressing concern, particularly for households with limited budgets. Understanding the factors influencing these price fluctuations is crucial to addressing the affordability of healthy diets.
- Show Dataset
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.1## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
fruit_and_veg_prices <- read.csv("Fruit-Prices-2022.csv")
colnames(fruit_and_veg_prices)## [1] "Fruit" "Form" "RetailPrice"
## [4] "RetailPriceUnit" "Yield" "CupEquivalentSize"
## [7] "CupEquivalentUnit" "CupEquivalentPrice"str(fruit_and_veg_prices)## 'data.frame': 62 obs. of 8 variables:
## $ Fruit : chr "Apples" "Apples, applesauce" "Apples, ready-to-drink" "Apples, frozen concentrate" ...
## $ Form : chr "Fresh" "Canned" "Juice" "Juice" ...
## $ RetailPrice : num 1.854 1.171 0.87 0.609 3.616 ...
## $ RetailPriceUnit : chr "per pound" "per pound" "per pint" "per pint" ...
## $ Yield : num 0.9 1 1 1 0.93 1 0.65 1 0.64 1 ...
## $ CupEquivalentSize : num 0.242 0.54 8 8 0.364 ...
## $ CupEquivalentUnit : chr "pounds" "pounds" "fluid ounces" "fluid ounces" ...
## $ CupEquivalentPrice: num 0.5 0.632 0.435 0.304 1.415 ...head(fruit_and_veg_prices)## Fruit Form RetailPrice RetailPriceUnit Yield
## 1 Apples Fresh 1.8541 per pound 0.90
## 2 Apples, applesauce Canned 1.1705 per pound 1.00
## 3 Apples, ready-to-drink Juice 0.8699 per pint 1.00
## 4 Apples, frozen concentrate Juice 0.6086 per pint 1.00
## 5 Apricots Fresh 3.6162 per pound 0.93
## 6 Apricots, packed in juice Canned 1.8645 per pound 1.00
## CupEquivalentSize CupEquivalentUnit CupEquivalentPrice
## 1 0.2425 pounds 0.4996
## 2 0.5401 pounds 0.6323
## 3 8.0000 fluid ounces 0.4349
## 4 8.0000 fluid ounces 0.3043
## 5 0.3638 pounds 1.4145
## 6 0.5401 pounds 1.0071summary(fruit_and_veg_prices)## Fruit Form RetailPrice RetailPriceUnit
## Length:62 Length:62 Min. : 0.382 Length:62
## Class :character Class :character 1st Qu.: 1.364 Class :character
## Mode :character Mode :character Median : 2.159 Mode :character
## Mean : 2.995
## 3rd Qu.: 4.117
## Max. :10.303
## Yield CupEquivalentSize CupEquivalentUnit CupEquivalentPrice
## Min. :0.4600 Min. :0.1232 Length:62 Min. :0.2429
## 1st Qu.:0.7225 1st Qu.:0.3225 Class :character 1st Qu.:0.6393
## Median :0.9800 Median :0.3638 Mode :character Median :1.0083
## Mean :0.8761 Mean :1.7050 Mean :1.0651
## 3rd Qu.:1.0000 3rd Qu.:0.5401 3rd Qu.:1.3535
## Max. :1.0000 Max. :8.0000 Max. :3.5558Exploratory Data Analysis
- Summary
summary(fruit_and_veg_prices$RetailPrice)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.382 1.364 2.159 2.995 4.117 10.303summary(fruit_and_veg_prices$Form)## Length Class Mode
## 62 character charactersummary(fruit_and_veg_prices$Fruit)## Length Class Mode
## 62 character character- Data Manipulation
fruit_and_veg_prices <- fruit_and_veg_prices %>%
drop_na(RetailPrice, Form)
fruit_and_veg_prices <- fruit_and_veg_prices %>%
drop_na(Fruit, RetailPrice)
fruit_and_veg_prices <- fruit_and_veg_prices %>%
mutate(Form = as.factor(Form))
fruit_and_veg_prices <- fruit_and_veg_prices %>%
mutate(Fruit = as.factor(Fruit))- Plot
ggplot(fruit_and_veg_prices, aes(x = Form, y = RetailPrice)) +
geom_boxplot() +
labs(title = "Retail Price by Form")
fruit_avg_prices <- fruit_and_veg_prices %>%
group_by(Fruit) %>%
summarize(avg_price = mean(RetailPrice))
ggplot(fruit_avg_prices, aes(x = Fruit, y = avg_price)) +
geom_bar(stat = "identity") +
labs(title = "Average Retail Price by Fruit")
ggplot(fruit_and_veg_prices, aes(x = RetailPrice)) +
geom_histogram() +
labs(title = "Distribution of Retail Prices")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Hypothesis Testing
- Null Hypothesis: There is no significant difference in retail prices among different forms of fruits and vegetables.
Alternative Hypothesis: There is a significant difference in retail prices among different forms of fruits and vegetables.
anova_test1<- aov(RetailPrice ~ Form, data = fruit_and_veg_prices)
print(summary(anova_test1))## Df Sum Sq Mean Sq F value Pr(>F)
## Form 4 188.8 47.19 21.45 7.77e-11 ***
## Residuals 57 125.4 2.20
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1- Null Hypothesis: There are no significant differences in retail prices among different fruit categories.
Alternative Hypothesis: There are significant differences in retail prices among different fruit categories.
anova_test2 <- aov(RetailPrice~ Fruit, data = fruit_and_veg_prices)
summary(anova_test2)## Df Sum Sq Mean Sq F value Pr(>F)
## Fruit 52 223.38 4.296 0.426 0.974
## Residuals 9 90.78 10.087Conclusion
There is a significant difference in retail prices among different forms of fruits and vegetables. Reject the null hypothesis and Accept the Alternative Hypothesis.
There are no significant differences in retail prices among different fruit categories. Accept the Null hypothesis and Reject the Alternative Hypothesis.