Analysis of Fast Fashion Survey

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

nrow(survey)

## [1] 17

survey %>%
  count(brand_willing) %>%
  ggplot(aes(x = brand_willing, y = n, fill = brand_willing)) +
  geom_col(show.legend = FALSE) +
  geom_text(aes(label = n), vjust = -0.3) +
  labs(
    title = "Brands Students Are Willing to Wear",
    x = "Brand",
    y = "Responses"
  ) +
  theme_minimal()

survey %>%
  count(purchase_brand) %>%
  ggplot(aes(x = purchase_brand, y = n, fill = purchase_brand)) +
  geom_col(show.legend = FALSE) +
  geom_text(aes(label = n), vjust = -0.3) +
  labs(
    title = "Brands Students Are Most Likely to Purchase",
    x = "Brand",
    y = "Responses"
  ) +
  theme_minimal()

survey %>%
  count(purchase_frequency) %>%
  ggplot(aes(x = factor(purchase_frequency), y = n, fill = factor(purchase_frequency))) +
  geom_col(show.legend = FALSE) +
  geom_text(aes(label = n), vjust = -0.3) +
  labs(
    title = "Fast Fashion Purchase Frequency",
    x = "Frequency Scale (1–5)",
    y = "Responses"
  ) +
  theme_minimal()

survey %>%
  count(thrift_frequency) %>%
  ggplot(aes(x = factor(thrift_frequency), y = n, fill = factor(thrift_frequency))) +
  geom_col(show.legend = FALSE) +
  geom_text(aes(label = n), vjust = -0.3) +
  labs(
    title = "Thrift / Second-Hand Clothing Frequency",
    x = "Frequency Scale (1–5)",
    y = "Responses"
  ) +
  theme_minimal()

survey %>%
  count(labor_concern) %>%
  ggplot(aes(x = factor(labor_concern), y = n, fill = factor(labor_concern))) +
  geom_col(show.legend = FALSE) +
  geom_text(aes(label = n), vjust = -0.3) +
  labs(
    title = "Concern About Labor Practices",
    x = "Concern Scale (1–5)",
    y = "Responses"
  ) +
  theme_minimal()