library(tidyverse)
library(janitor)
survey <- read.csv("Cleaned_Truth_of_fast_fashion.csv", stringsAsFactors = FALSE)
survey <- survey %>%
clean_names()
head(survey)
## brand_willing purchase_brand purchase_frequency thrift_frequency
## 1 All H&M 2 3
## 2 Uniqlo Uniqlo 2 3
## 3 None None 1 1
## 4 All H&M 2 4
## 5 All H&M 3 1
## 6 H&M H&M 3 3
## trend_opinion labor_concern
## 1 3 3
## 2 1 4
## 3 1 1
## 4 1 5
## 5 3 4
## 6 3 1
summary(survey)
## brand_willing purchase_brand purchase_frequency thrift_frequency
## Length:17 Length:17 Min. :1.000 Min. :1.000
## Class :character Class :character 1st Qu.:2.000 1st Qu.:2.000
## Mode :character Mode :character Median :2.000 Median :3.000
## Mean :2.294 Mean :2.412
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :3.000 Max. :4.000
## trend_opinion labor_concern
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :3.000
## Mean :2.412 Mean :3.235
## 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000
survey$purchase_frequency <- as.numeric(survey$purchase_frequency)
survey$labor_concern <- as.numeric(survey$labor_concern)
survey$thrift_frequency <- as.numeric(survey$thrift_frequency)
model <- lm(purchase_frequency ~ labor_concern, data = survey)
summary(model)
##
## Call:
## lm(formula = purchase_frequency ~ labor_concern, data = survey)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3087 -0.2957 -0.2826 0.6978 0.7109
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.315217 0.405272 5.713 4.11e-05 ***
## labor_concern -0.006522 0.116706 -0.056 0.956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6071 on 15 degrees of freedom
## Multiple R-squared: 0.0002081, Adjusted R-squared: -0.06644
## F-statistic: 0.003123 on 1 and 15 DF, p-value: 0.9562
ggplot(survey, aes(x = labor_concern, y = purchase_frequency)) +
geom_point(color = "blue") +
geom_smooth(method = "lm", se = FALSE, color = "red") +
labs(
title = "Relationship Between Labor Concern and Purchase Frequency",
x = "Labor Concern (1 = Low, 5 = High)",
y = "Purchase Frequency (1 = Never, 5 = Often)"
) +
theme_minimal()
model2 <- lm(purchase_frequency ~ labor_concern + thrift_frequency, data = survey)
summary(model2)
##
## Call:
## lm(formula = purchase_frequency ~ labor_concern + thrift_frequency,
## data = survey)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3097 -0.2949 -0.2836 0.6989 0.7102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.317182 0.499672 4.637 0.000384 ***
## labor_concern -0.006243 0.126812 -0.049 0.961434
## thrift_frequency -0.001189 0.164311 -0.007 0.994328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6284 on 14 degrees of freedom
## Multiple R-squared: 0.0002119, Adjusted R-squared: -0.1426
## F-statistic: 0.001483 on 2 and 14 DF, p-value: 0.9985