SomeCompany.Rmd

---
title: "SomeCompany4"
author: "Sanjiv"
date: ""
output:
  pdf_document: default
  html_document: default
editor_options:
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

#### Loading libraries and data 

```{r message=FALSE, warning=FALSE, echo=FALSE, include=TRUE, fig.align = 'center', out.width = "100%"}

library(tidyverse)
library(RColorBrewer)
library(grid)
library(gtable)
library(ggthemes)
library(cowplot)

SomeCompanyData <- read_csv("data.csv")
str(SomeCompanyData)
class(SomeCompanyData)
summary(SomeCompanyData)

SomeCompanyData[] <- lapply(SomeCompanyData, factor)
data <- SomeCompanyData[c(-1, -2)]
data$RedeemVoucher <- as.numeric(levels(data$RedeemVoucher))[data$RedeemVoucher]

# Changing sequence of the factor label for EatingOutside
data$EatingOutside <- factor(data$EatingOutside, 
                             levels = c("Daily", "Weekly", "Monthly", "Yearly"))

# CAUTION Changing Factor
# data$CombinedLoyltyApp <- factor(data$CombinedLoyltyApp, 
#                              levels = c("No", "Yes", "Not sure"))
data$Gender <- factor(data$Gender, 
                      levels = c("Female", "Male"))
data$Age <- factor(data$Age, 
                   levels = c("<18", "19-24", "25-29", "30-40", "41-50", "51-60"))

data$Occupation <- factor(data$Occupation, 
                         levels = c("Employed", "Unemployed", "Student") )

data$RelationshipStatus <- factor(data$RelationshipStatus, 
                                  levels = c("Single", "In a Relationship", "Sambo with Partner", "Sambo with Kids", "Married", "Married with Kids"))

data$EatingInSameRestaurant <- factor(data$EatingInSameRestaurant, 
                             levels = c("Daily", "Weekly", "Monthly", "Yearly"))

data$UseLoyaltyApps <- factor(data$UseLoyaltyApps, 
                              levels = c("No", "Yes"))

data$FreqUsageOfLoyaltyApps <- factor(data$FreqUsageOfLoyaltyApps, 
                                      levels = c("Daily", "Weekly", "Monthly", "Yearly", "Never"))

data$AreYouLoyalCustomer <- factor(data$AreYouLoyalCustomer, 
                                   levels = c("No", "Yes", "Not Sure"))

# data$AppSuggestion <- factor(data$AppSuggestion, 
#                              levels = c())

data$CombinedLoyltyApp <- factor(data$CombinedLoyltyApp, 
                                 levels = c("No", "Yes", "Not Sure"))


summary(data)
glimpse(data)

####################################################
```

#### Multiple pannel graphs 

```{r message=FALSE, warning=FALSE, echo=FALSE, include=TRUE, fig.align = 'center', out.width = "100%"}

################################################################################
# Bar Plots Multipannel 
################################################################################

# Figure 1, Gender, Age, RelationshipStatus


# Gender Count
dataGender <- data %>% 
  group_by(Gender) %>% 
  summarise(count = n())

plotGender <- ggplot(dataGender, aes(x = Gender, y = count)) +
  geom_bar(fill = "#0073C2FF", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("Gender") +
  # scale_fill_viridis_d(option = "plasma") +
  scale_colour_pander() +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) 

plotGenderFlip <- ggplot(dataGender, aes(x = Gender, y = count)) +
  geom_bar(fill = "#0073C2FF", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = 0.4, hjust = -0.4, size = 7) +
  ylab("Count") +
  xlab("Gender") +
  # scale_fill_viridis_d(option = "plasma") +
  scale_colour_pander() +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) +
  coord_flip()

# ggsave(filename = "Figures/GenderCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

# Age Count
dataAge <- data %>% 
  group_by(Age) %>% 
  summarise(count = n())

plotAge <- ggplot(dataAge, aes(x = Age, y = count)) +
  geom_bar(fill = "#D55E00", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("How old are you?") +
  # scale_fill_viridis_d(option = "plasma") +
  scale_colour_pander() +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) 

plotAgeFlip <- ggplot(dataAge, aes(x = Age, y = count)) +
  geom_bar(fill = "#D55E00", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = 0.4, hjust = -0.4, size = 7) +
  ylab("Count") +
  xlab("How old are you?") +
  # scale_fill_viridis_d(option = "plasma") +
  scale_colour_pander() +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) +
  coord_flip()

# ggsave(filename = "Figures/AgeCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

# Relationship Count
dataRelationship <- data %>% 
  group_by(RelationshipStatus) %>% 
  summarise(count = n())

plotRelationship <- ggplot(dataRelationship, aes(x = RelationshipStatus, y = count)) +
  geom_bar(fill = "#117733", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("Relationship Status") +
  #scale_fill_viridis_d(option = "plasma") +
  # scale_colour_pander() +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold"), 
        axis.text.x = element_text(size = 24)) 

plotRelationshipFlip <- ggplot(dataRelationship, aes(x = RelationshipStatus, y = count)) +
  geom_bar(fill = "#117733", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = 0.4, hjust = -0.4, size = 7) +
  ylab("Count") +
  xlab("Relationship Status") +
  #scale_fill_viridis_d(option = "plasma") +
  # scale_colour_pander() +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) +
        # axis.text.x = element_text(size = 9)) + 
  coord_flip()


# ggsave(filename = "Figures/RelationshipCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)
# , angle = 45, hjust = 1
# place multiple plots into a grid, with labels
# plot_grid(p, p, p, p, labels = "AUTO")
toprow <- plot_grid(plotGender, plotAge, labels = c("A", "B"), label_size = 24)
bottomrow <- plot_grid(plotRelationship, labels = c("C"), label_size = 24)

Figure1 <- plot_grid(toprow, bottomrow, nrow = 2)
Figure1

# Figure1Flip <- plot_grid(plotGenderFlip, plotAgeFlip, plotRelationshipFlip, labels = c("A", "B", "C"), nrow = 1)
# 
# Figure1FlipCol1 <- plot_grid(plotGenderFlip, plotAgeFlip, plotRelationshipFlip, labels = c("A", "B", "C"), ncol = 1)

ggsave(filename = "Figures/Figure_1.jpg", width = 50, height = 40, units = "cm", dpi = 400)
### END OF FIG 1 ###########

# save_plot("Figures/Figure_1.jpg", Figure1, base_height = 4, base_width = 5)
# ggsave(filename = "Figures/Figure_1.jpg", width = 30, height = 20, units = "cm", dpi = 600)


# Occupation Count
dataOccupation <- data %>% 
  group_by(Occupation) %>% 
  summarise(count = n())

plotOccupation <- ggplot(dataOccupation, aes(x = Occupation, y = count)) +
  geom_bar(fill = "#D55E00", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("What is your current occupation?") +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) 

# ggsave(filename = "Figures/OccupationCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

# Income Count

dataIncome <- data %>% 
  group_by(Income) %>% 
  summarise(count = n())

plotIncome <- ggplot(dataIncome, aes(x = Income, y = count)) +
  geom_bar(fill = "#0072B2", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("What is your income (SEK/month)?") +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold"), 
        axis.text.x = element_text(size = 14)) 

# ggsave(filename = "Figures/IncomeCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

# toprow <- plot_grid(plotGender, plotAge, labels = c("A", "B"), label_size = 24)
# bottomrow <- plot_grid(plotRelationship, labels = c("C"), label_size = 24)

Figure2 <- plot_grid(plotOccupation, plotIncome, labels = c("A", "B"), label_size = 24)

Figure2

# Figure1Flip <- plot_grid(plotGenderFlip, plotAgeFlip, plotRelationshipFlip, labels = c("A", "B", "C"), nrow = 1)
# 
# Figure1FlipCol1 <- plot_grid(plotGenderFlip, plotAgeFlip, plotRelationshipFlip, labels = c("A", "B", "C"), ncol = 1)

ggsave(filename = "Figures/Figure_2.jpg", width = 50, height = 20, units = "cm", dpi = 400)


# EatingOutside Count

dataEatingOutside <- data %>% 
  group_by(EatingOutside) %>% 
  summarise(count = n())

plotEatingOutside <- ggplot(dataEatingOutside, aes(x = EatingOutside, y = count)) +
  geom_bar(fill = "#009E73", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("How often do you buy food from a restaurant?") +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 18), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) 

# ggsave(filename = "Figures/EatingOutsideCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

# EatingInSameRestaurant Count

dataEatingInSameRestaurant <- data %>% 
  group_by(EatingInSameRestaurant) %>% 
  summarise(count = n())

plotEatingInSameRestaurant <- ggplot(dataEatingInSameRestaurant, aes(x = EatingInSameRestaurant, y = count)) +
  geom_bar(fill = "#332288", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("How often do you go to the same restaurant, café or bar?") +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 18), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) 

# ggsave(filename = "Figures/EatingInSameRestaurantCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

# RedeemVoucher Count, CAUTION many things changed 

dataRedeemVoucher <- data %>% 
  group_by(RedeemVoucher) %>% 
  summarise(count = n())

plotRedeemVoucher <- ggplot(dataRedeemVoucher, aes(x = RedeemVoucher, y = count)) +
  geom_bar(fill = "#882255", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab(paste("Reasonable number of times for you to buy a meal (worth around 100sek)",  "\nat the same restaurant before getting a voucher (value 100sek)?")) +
  scale_x_continuous(breaks=seq(1, 10, 1)) +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 18), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) 

# ggsave(filename = "Figures/RedeemVoucherCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

# AreYouLoyalCustomer Count

dataAreYouLoyalCustomer <- data %>% 
  group_by(AreYouLoyalCustomer) %>% 
  summarise(count = n())

plotAreYouLoyalCustomer <- ggplot(dataAreYouLoyalCustomer, 
                                  aes(x = AreYouLoyalCustomer, y = count)) +
  geom_bar(fill = "#AA4499", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("Do you consider yourself to be a loyal customer to any restaurant/café/bar?") +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 18), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) 

# ggsave(filename = "Figures/AreYouLoyalCustomerCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

Figure3 <- plot_grid(plotEatingOutside, plotEatingInSameRestaurant, plotRedeemVoucher, plotAreYouLoyalCustomer, labels = c("A", "B", "C", "D"), nrow = 2, ncol = 2, label_size = 24)

Figure3

ggsave(filename = "Figures/Figure_3.jpg", width = 50, height = 40, units = "cm", dpi = 400)


# UseLoyaltyApps Count

dataUseLoyaltyApps <- data %>% 
  group_by(UseLoyaltyApps) %>% 
  summarise(count = n())

plotUseLoyaltyApps <- ggplot(dataUseLoyaltyApps, aes(x = UseLoyaltyApps, y = count)) +
  geom_bar(fill = "#E69F00", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("Do you currently use a customer loyalty app/stamp cards?") +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) 

# ggsave(filename = "Figures/UseLoyaltyAppsCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

# FreqUsageOfLoyaltyApps Count

dataFreqUsageOfLoyaltyApps <- data %>% 
  group_by(FreqUsageOfLoyaltyApps) %>% 
  summarise(count = n())

plotFreqUsageOfLoyaltyApps <- ggplot(dataFreqUsageOfLoyaltyApps, aes(x = FreqUsageOfLoyaltyApps, y = count)) +
  geom_bar(fill = "#117733", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("How often do you use customer loyalty apps/stamp cards?") +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) 

# ggsave(filename = "Figures/FreqUsageOfLoyaltyAppsCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

# CombinedLoyltyApp Count 
dataCombinedLoyltyApp <- data %>% 
  group_by(CombinedLoyltyApp) %>% 
  summarise(count = n())

plotCombinedLoyltyApp <- ggplot(dataCombinedLoyltyApp, aes(x = CombinedLoyltyApp, y = count)) +
  geom_bar(fill = "#AA4499", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab(paste("Would you be interested in an app for all restaurants, bars and", "\n cafeés that combines loyalty purchases in one?")) +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold")) 

# ggsave(filename = "Figures/CombinedLoyltyAppCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

# AppSuggestion Count

dataAppSuggestion <- data %>% 
  group_by(AppSuggestion) %>% 
  summarise(count = n())

plotAppSuggestion <- ggplot(dataAppSuggestion, aes(x = AppSuggestion, y = count)) +
  geom_bar(fill = "#999933", stat = "identity", width = 0.6) +
  geom_text(aes(label = count), vjust = -0.3, hjust = 0.5, size = 7) +
  ylab("Count") +
  xlab("How did you discover the most recent App you downloaded?") +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=24, face = "bold"),
        axis.text.x = element_text(size = 12)) 

# ggsave(filename = "Figures/AppSuggestionCount.jpg", width = 30, height = 20, units = "cm", dpi = 600)

Figure4 <- plot_grid(plotUseLoyaltyApps, plotFreqUsageOfLoyaltyApps, plotCombinedLoyltyApp, plotAppSuggestion, labels = c("A", "B", "C", "D"), nrow = 2, ncol = 2, label_size = 24)

Figure4

ggsave(filename = "Figures/Figure_4.jpg", width = 50, height = 40, units = "cm", dpi = 400)


################################################################################
# End of Bar Plots Multipannel
################################################################################

```

#### Final Plots Selected with facet labels

```{r message=FALSE, warning=FALSE, echo=FALSE, include=TRUE, fig.align = 'center', out.width = "100%"}

####################################################
# Final Plots Selected with facet labels 
####################################################

# EatingOutside vs ...
p <- ggplot(data, aes(x=Age, y=Income, color=RelationshipStatus, shape=Gender)) +
  geom_point(size = 4, alpha = 1, stroke = 2) +
  facet_grid(EatingOutside ~ AppSuggestion) +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=16, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1), 
        strip.text.x = element_text(size = 11)) 

# strip.text.x = element_text(size = 8, colour = "red", angle = 90)
# Labels 
labelR = "How often do you buy food from a restaurant?"
labelT = "The most recent app you have downloaded, how did you discover it?"

# Get the ggplot grob
z <- ggplotGrob(p)

# Get the positions of the strips in the gtable: t = top, l = left, ...
posR <- subset(z$layout, grepl("strip-r", name), select = t:r)
posT <- subset(z$layout, grepl("strip-t", name), select = t:r)

# Add a new column to the right of current right strips, 
# and a new row on top of current top strips
width <- z$widths[max(posR$r)]    # width of current right strips
height <- z$heights[min(posT$t)]  # height of current top strips

z <- gtable_add_cols(z, width, max(posR$r))  
z <- gtable_add_rows(z, height, min(posT$t)-1)

# Construct the new strip grobs
stripR <- gTree(name = "Strip_right", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelR, rot = -90, gp = gpar(fontsize = 16, col = "grey10"))))

stripT <- gTree(name = "Strip_top", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelT, gp = gpar(fontsize = 16, col = "grey10"))))

# Position the grobs in the gtable
z <- gtable_add_grob(z, stripR, t = min(posR$t)+1, 
                     l = max(posR$r) + 1, b = max(posR$b)+1, name = "strip-right")
z <- gtable_add_grob(z, stripT, t = min(posT$t), 
                     l = min(posT$l), r = max(posT$r), name = "strip-top")


# Add small gaps between strips
z <- gtable_add_cols(z, unit(1/5, "line"), max(posR$r))
z <- gtable_add_rows(z, unit(1/5, "line"), min(posT$t))


# Draw it
grid.newpage()
grid.draw(z)

ggsave(filename = "Figures/EatingOutside_AppSuggestion_GridLabel.jpg", width = 40, height = 20, units = "cm", dpi = 600, plot = z)

#### 

p <- ggplot(data, aes(x=Age, y=Income, color=RelationshipStatus, shape=Gender)) +
  geom_point(size = 4, alpha = 1, stroke = 2) +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=16, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(EatingOutside ~ EatingInSameRestaurant)

# strip.text.x = element_text(size = 8, colour = "red", angle = 90)
# Labels 
labelR = "How often do you buy food from a restaurant?"
labelT = "How often do you go to the same restaurant, café or bar?"

# Get the ggplot grob
z <- ggplotGrob(p)

# Get the positions of the strips in the gtable: t = top, l = left, ...
posR <- subset(z$layout, grepl("strip-r", name), select = t:r)
posT <- subset(z$layout, grepl("strip-t", name), select = t:r)

# Add a new column to the right of current right strips, 
# and a new row on top of current top strips
width <- z$widths[max(posR$r)]    # width of current right strips
height <- z$heights[min(posT$t)]  # height of current top strips

z <- gtable_add_cols(z, width, max(posR$r))  
z <- gtable_add_rows(z, height, min(posT$t)-1)

# Construct the new strip grobs
stripR <- gTree(name = "Strip_right", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelR, rot = -90, gp = gpar(fontsize = 16, col = "grey10"))))

stripT <- gTree(name = "Strip_top", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelT, gp = gpar(fontsize = 16, col = "grey10"))))

# Position the grobs in the gtable
z <- gtable_add_grob(z, stripR, t = min(posR$t)+1, 
                     l = max(posR$r) + 1, b = max(posR$b)+1, name = "strip-right")
z <- gtable_add_grob(z, stripT, t = min(posT$t), 
                     l = min(posT$l), r = max(posT$r), name = "strip-top")


# Add small gaps between strips
z <- gtable_add_cols(z, unit(1/5, "line"), max(posR$r))
z <- gtable_add_rows(z, unit(1/5, "line"), min(posT$t))


# Draw it
grid.newpage()
grid.draw(z)


ggsave(filename = "Figures/EatingOutside_EatingInSameRestaurant_GridLabel.jpg", width = 40, height = 20, units = "cm", dpi = 600, plot = z)

#### 
p <- ggplot(data, aes(x=Age, y=Income, color=RelationshipStatus, shape=Gender)) +
  geom_point(size = 4, alpha = 1, stroke = 2) +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=16, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(EatingOutside ~ Occupation)

# Labels 
labelR = "How often do you buy food from a restaurant?"
labelT = "What is your current occupation?"

# Get the ggplot grob
z <- ggplotGrob(p)

# Get the positions of the strips in the gtable: t = top, l = left, ...
posR <- subset(z$layout, grepl("strip-r", name), select = t:r)
posT <- subset(z$layout, grepl("strip-t", name), select = t:r)

# Add a new column to the right of current right strips, 
# and a new row on top of current top strips
width <- z$widths[max(posR$r)]    # width of current right strips
height <- z$heights[min(posT$t)]  # height of current top strips

z <- gtable_add_cols(z, width, max(posR$r))  
z <- gtable_add_rows(z, height, min(posT$t)-1)

# Construct the new strip grobs
stripR <- gTree(name = "Strip_right", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelR, rot = -90, gp = gpar(fontsize = 16, col = "grey10"))))

stripT <- gTree(name = "Strip_top", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelT, gp = gpar(fontsize = 16, col = "grey10"))))

# Position the grobs in the gtable
z <- gtable_add_grob(z, stripR, t = min(posR$t)+1, 
                     l = max(posR$r) + 1, b = max(posR$b)+1, name = "strip-right")
z <- gtable_add_grob(z, stripT, t = min(posT$t), 
                     l = min(posT$l), r = max(posT$r), name = "strip-top")


# Add small gaps between strips
z <- gtable_add_cols(z, unit(1/5, "line"), max(posR$r))
z <- gtable_add_rows(z, unit(1/5, "line"), min(posT$t))


# Draw it
grid.newpage()
grid.draw(z)


ggsave(filename = "Figures/EatingOutside_Occupation_GridLabel.jpg", width = 40, height = 20, units = "cm", dpi = 600, plot = z)

####
### CombinedLoyltyApp vs ...

p <- ggplot(data, aes(x=Age, y=Income, color=RelationshipStatus, shape=Gender)) +
  geom_point(size = 4, alpha = 1, stroke = 2) +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=16, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(CombinedLoyltyApp ~ EatingInSameRestaurant)

# Labels 
labelR = paste("Would you be interested in an app for all restaurants, bars and cafeés", "\nthat combines loyalty purchases in one?")
labelT = "How often do you go to the same restaurant, café or bar?"

# Get the ggplot grob
z <- ggplotGrob(p)

# Get the positions of the strips in the gtable: t = top, l = left, ...
posR <- subset(z$layout, grepl("strip-r", name), select = t:r)
posT <- subset(z$layout, grepl("strip-t", name), select = t:r)

# Add a new column to the right of current right strips, 
# and a new row on top of current top strips
width <- z$widths[max(posR$r)]    # width of current right strips
height <- z$heights[min(posT$t)]  # height of current top strips

z <- gtable_add_cols(z, width, max(posR$r))  
z <- gtable_add_rows(z, height, min(posT$t)-1)

# Construct the new strip grobs
stripR <- gTree(name = "Strip_right", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelR, rot = -90, gp = gpar(fontsize = 10, col = "grey10"))))

stripT <- gTree(name = "Strip_top", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelT, gp = gpar(fontsize = 16, col = "grey10"))))

# Position the grobs in the gtable
z <- gtable_add_grob(z, stripR, t = min(posR$t)+1, 
                     l = max(posR$r) + 1, b = max(posR$b)+1, name = "strip-right")
z <- gtable_add_grob(z, stripT, t = min(posT$t), 
                     l = min(posT$l), r = max(posT$r), name = "strip-top")

# Add small gaps between strips
z <- gtable_add_cols(z, unit(1/5, "line"), max(posR$r))
z <- gtable_add_rows(z, unit(1/5, "line"), min(posT$t))

# Draw it
grid.newpage()
grid.draw(z)

ggsave(filename = "Figures/CombinedLoyltyApp_EatingInSameRestaurant_GridLabel.jpg", width = 40, height = 20, units = "cm", dpi = 600, plot = z)

####
p <- ggplot(data, aes(x=Age, y=Income, color=RelationshipStatus, shape=Gender)) +
  geom_point(size = 4, alpha = 1, stroke = 2) +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=16, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(CombinedLoyltyApp ~ EatingOutside)

# Labels 
labelR = paste("Would you be interested in an app for all restaurants, bars and cafeés", "\nthat combines loyalty purchases in one?")
labelT = "How often do you buy food from a restaurant?"

# Get the ggplot grob
z <- ggplotGrob(p)

# Get the positions of the strips in the gtable: t = top, l = left, ...
posR <- subset(z$layout, grepl("strip-r", name), select = t:r)
posT <- subset(z$layout, grepl("strip-t", name), select = t:r)

# Add a new column to the right of current right strips, 
# and a new row on top of current top strips
width <- z$widths[max(posR$r)]    # width of current right strips
height <- z$heights[min(posT$t)]  # height of current top strips

z <- gtable_add_cols(z, width, max(posR$r))  
z <- gtable_add_rows(z, height, min(posT$t)-1)

# Construct the new strip grobs
stripR <- gTree(name = "Strip_right", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelR, rot = -90, gp = gpar(fontsize = 10, col = "grey10"))))

stripT <- gTree(name = "Strip_top", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelT, gp = gpar(fontsize = 16, col = "grey10"))))

# Position the grobs in the gtable
z <- gtable_add_grob(z, stripR, t = min(posR$t)+1, 
                     l = max(posR$r) + 1, b = max(posR$b)+1, name = "strip-right")
z <- gtable_add_grob(z, stripT, t = min(posT$t), 
                     l = min(posT$l), r = max(posT$r), name = "strip-top")

# Add small gaps between strips
z <- gtable_add_cols(z, unit(1/5, "line"), max(posR$r))
z <- gtable_add_rows(z, unit(1/5, "line"), min(posT$t))

# Draw it
grid.newpage()
grid.draw(z)

ggsave(filename = "Figures/CombinedLoyltyApp_EatingOutside_GridLabel.jpg", width = 40, height = 20, units = "cm", dpi = 600, plot = z)

####
p <- ggplot(data, aes(x=Age, y=Income, color=RelationshipStatus, shape=Gender)) +
  geom_point(size = 4, alpha = 1, stroke = 2) +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 24), 
        axis.text = element_text(angle=0, vjust=0.5, size=16, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  facet_grid(CombinedLoyltyApp ~ UseLoyaltyApps)

# Labels 
labelR = paste("Would you be interested in an app for all restaurants, bars and cafeés", "\nthat combines loyalty purchases in one?")
labelT = "Do you currently use a customer loyalty app/stamp cards? (McDonald's Rewards Program, Pressbyran Stämpelkort, Wolt Credit, etc.)"

# Get the ggplot grob
z <- ggplotGrob(p)

# Get the positions of the strips in the gtable: t = top, l = left, ...
posR <- subset(z$layout, grepl("strip-r", name), select = t:r)
posT <- subset(z$layout, grepl("strip-t", name), select = t:r)

# Add a new column to the right of current right strips, 
# and a new row on top of current top strips
width <- z$widths[max(posR$r)]    # width of current right strips
height <- z$heights[min(posT$t)]  # height of current top strips

z <- gtable_add_cols(z, width, max(posR$r))  
z <- gtable_add_rows(z, height, min(posT$t)-1)

# Construct the new strip grobs
stripR <- gTree(name = "Strip_right", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelR, rot = -90, gp = gpar(fontsize = 10, col = "grey10"))))

stripT <- gTree(name = "Strip_top", children = gList(
   rectGrob(gp = gpar(col = NA, fill = "grey85")),
   textGrob(labelT, gp = gpar(fontsize = 11, col = "grey10"))))

# Position the grobs in the gtable
z <- gtable_add_grob(z, stripR, t = min(posR$t)+1, 
                     l = max(posR$r) + 1, b = max(posR$b)+1, name = "strip-right")
z <- gtable_add_grob(z, stripT, t = min(posT$t), 
                     l = min(posT$l), r = max(posT$r), name = "strip-top")

# Add small gaps between strips
z <- gtable_add_cols(z, unit(1/5, "line"), max(posR$r))
z <- gtable_add_rows(z, unit(1/5, "line"), min(posT$t))

# Draw it
grid.newpage()
grid.draw(z)


ggsave(filename = "Figures/CombinedLoyltyApp_UseLoyaltyApps_GridLabel.jpg", width = 40, height = 20, units = "cm", dpi = 600, plot = z)

####
```

#### Stats

```{r}

# Gender
# Age
# Occupation
# RelationshipStatus
# Income
# EatingOutside
# EatingInSameRestaurant
# UseLoyaltyApps
# FreqUsageOfLoyaltyApps
# AreYouLoyalCustomer
# AppSuggestion
# RedeemVoucher
# CombinedLoyltyApp

# 
# a <- chisq.test(data$Gender, data$Gender)
# b <- chisq.test(data$Gender, data$Age)
# c <- chisq.test(data$Gender, data$Occupation)
# d <- chisq.test(data$Gender, data$RelationshipStatus)
# e <- chisq.test(data$Gender, data$Income)
# f <- chisq.test(data$Gender, data$EatingOutside)
# g <- chisq.test(data$Gender, data$EatingInSameRestaurant)
# h <- chisq.test(data$Gender, data$UseLoyaltyApps)
# i <- chisq.test(data$Gender, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$Gender, data$AreYouLoyalCustomer)
# k <- chisq.test(data$Gender, data$AppSuggestion)
# l <- chisq.test(data$Gender, data$RedeemVoucher)
# m <- chisq.test(data$Gender, data$CombinedLoyltyApp)
# 
# gender <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# 
# a <- chisq.test(data$Age, data$Gender)
# b <- chisq.test(data$Age, data$Age)
# c <- chisq.test(data$Age, data$Occupation)
# d <- chisq.test(data$Age, data$RelationshipStatus)
# e <- chisq.test(data$Age, data$Income)
# f <- chisq.test(data$Age, data$EatingOutside)
# g <- chisq.test(data$Age, data$EatingInSameRestaurant)
# h <- chisq.test(data$Age, data$UseLoyaltyApps)
# i <- chisq.test(data$Age, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$Age, data$AreYouLoyalCustomer)
# k <- chisq.test(data$Age, data$AppSuggestion)
# l <- chisq.test(data$Age, data$RedeemVoucher)
# m <- chisq.test(data$Age, data$CombinedLoyltyApp)
# 
# age <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# a <- chisq.test(data$Occupation, data$Gender)
# b <- chisq.test(data$Occupation, data$Age)
# c <- chisq.test(data$Occupation, data$Occupation)
# d <- chisq.test(data$Occupation, data$RelationshipStatus)
# e <- chisq.test(data$Occupation, data$Income)
# f <- chisq.test(data$Occupation, data$EatingOutside)
# g <- chisq.test(data$Occupation, data$EatingInSameRestaurant)
# h <- chisq.test(data$Occupation, data$UseLoyaltyApps)
# i <- chisq.test(data$Occupation, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$Occupation, data$AreYouLoyalCustomer)
# k <- chisq.test(data$Occupation, data$AppSuggestion)
# l <- chisq.test(data$Occupation, data$RedeemVoucher)
# m <- chisq.test(data$Occupation, data$CombinedLoyltyApp)
# 
# occupation <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# a <- chisq.test(data$RelationshipStatus, data$Gender)
# b <- chisq.test(data$RelationshipStatus, data$Age)
# c <- chisq.test(data$RelationshipStatus, data$Occupation)
# d <- chisq.test(data$RelationshipStatus, data$RelationshipStatus)
# e <- chisq.test(data$RelationshipStatus, data$Income)
# f <- chisq.test(data$RelationshipStatus, data$EatingOutside)
# g <- chisq.test(data$RelationshipStatus, data$EatingInSameRestaurant)
# h <- chisq.test(data$RelationshipStatus, data$UseLoyaltyApps)
# i <- chisq.test(data$RelationshipStatus, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$RelationshipStatus, data$AreYouLoyalCustomer)
# k <- chisq.test(data$RelationshipStatus, data$AppSuggestion)
# l <- chisq.test(data$RelationshipStatus, data$RedeemVoucher)
# m <- chisq.test(data$RelationshipStatus, data$CombinedLoyltyApp)
# 
# relationshipStatus <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# 
# a <- chisq.test(data$Income, data$Gender)
# b <- chisq.test(data$Income, data$Age)
# c <- chisq.test(data$Income, data$Occupation)
# d <- chisq.test(data$Income, data$RelationshipStatus)
# e <- chisq.test(data$Income, data$Income)
# f <- chisq.test(data$Income, data$EatingOutside)
# g <- chisq.test(data$Income, data$EatingInSameRestaurant)
# h <- chisq.test(data$Income, data$UseLoyaltyApps)
# i <- chisq.test(data$Income, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$Income, data$AreYouLoyalCustomer)
# k <- chisq.test(data$Income, data$AppSuggestion)
# l <- chisq.test(data$Income, data$RedeemVoucher)
# m <- chisq.test(data$Income, data$CombinedLoyltyApp)
# 
# income <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# 
# a <- chisq.test(data$EatingOutside, data$Gender)
# b <- chisq.test(data$EatingOutside, data$Age)
# c <- chisq.test(data$EatingOutside, data$Occupation)
# d <- chisq.test(data$EatingOutside, data$RelationshipStatus)
# e <- chisq.test(data$EatingOutside, data$Income)
# f <- chisq.test(data$EatingOutside, data$EatingOutside)
# g <- chisq.test(data$EatingOutside, data$EatingInSameRestaurant)
# h <- chisq.test(data$EatingOutside, data$UseLoyaltyApps)
# i <- chisq.test(data$EatingOutside, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$EatingOutside, data$AreYouLoyalCustomer)
# k <- chisq.test(data$EatingOutside, data$AppSuggestion)
# l <- chisq.test(data$EatingOutside, data$RedeemVoucher)
# m <- chisq.test(data$EatingOutside, data$CombinedLoyltyApp)
# 
# eatingOutside <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# 
# a <- chisq.test(data$EatingInSameRestaurant, data$Gender)
# b <- chisq.test(data$EatingInSameRestaurant, data$Age)
# c <- chisq.test(data$EatingInSameRestaurant, data$Occupation)
# d <- chisq.test(data$EatingInSameRestaurant, data$RelationshipStatus)
# e <- chisq.test(data$EatingInSameRestaurant, data$Income)
# f <- chisq.test(data$EatingInSameRestaurant, data$EatingOutside)
# g <- chisq.test(data$EatingInSameRestaurant, data$EatingInSameRestaurant)
# h <- chisq.test(data$EatingInSameRestaurant, data$UseLoyaltyApps)
# i <- chisq.test(data$EatingInSameRestaurant, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$EatingInSameRestaurant, data$AreYouLoyalCustomer)
# k <- chisq.test(data$EatingInSameRestaurant, data$AppSuggestion)
# l <- chisq.test(data$EatingInSameRestaurant, data$RedeemVoucher)
# m <- chisq.test(data$EatingInSameRestaurant, data$CombinedLoyltyApp)
# 
# eatingInSameRestaurant <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# 
# 
# a <- chisq.test(data$UseLoyaltyApps, data$Gender)
# b <- chisq.test(data$UseLoyaltyApps, data$Age)
# c <- chisq.test(data$UseLoyaltyApps, data$Occupation)
# d <- chisq.test(data$UseLoyaltyApps, data$RelationshipStatus)
# e <- chisq.test(data$UseLoyaltyApps, data$Income)
# f <- chisq.test(data$UseLoyaltyApps, data$EatingOutside)
# g <- chisq.test(data$UseLoyaltyApps, data$EatingInSameRestaurant)
# h <- chisq.test(data$UseLoyaltyApps, data$UseLoyaltyApps)
# i <- chisq.test(data$UseLoyaltyApps, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$UseLoyaltyApps, data$AreYouLoyalCustomer)
# k <- chisq.test(data$UseLoyaltyApps, data$AppSuggestion)
# l <- chisq.test(data$UseLoyaltyApps, data$RedeemVoucher)
# m <- chisq.test(data$UseLoyaltyApps, data$CombinedLoyltyApp)
# 
# useLoyaltyApps <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# a <- chisq.test(data$FreqUsageOfLoyaltyApps, data$Gender)
# b <- chisq.test(data$FreqUsageOfLoyaltyApps, data$Age)
# c <- chisq.test(data$FreqUsageOfLoyaltyApps, data$Occupation)
# d <- chisq.test(data$FreqUsageOfLoyaltyApps, data$RelationshipStatus)
# e <- chisq.test(data$FreqUsageOfLoyaltyApps, data$Income)
# f <- chisq.test(data$FreqUsageOfLoyaltyApps, data$EatingOutside)
# g <- chisq.test(data$FreqUsageOfLoyaltyApps, data$EatingInSameRestaurant)
# h <- chisq.test(data$FreqUsageOfLoyaltyApps, data$UseLoyaltyApps)
# i <- chisq.test(data$FreqUsageOfLoyaltyApps, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$FreqUsageOfLoyaltyApps, data$AreYouLoyalCustomer)
# k <- chisq.test(data$FreqUsageOfLoyaltyApps, data$AppSuggestion)
# l <- chisq.test(data$FreqUsageOfLoyaltyApps, data$RedeemVoucher)
# m <- chisq.test(data$FreqUsageOfLoyaltyApps, data$CombinedLoyltyApp)
# 
# freqUsageOfLoyaltyApps <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# 
# a <- chisq.test(data$AreYouLoyalCustomer, data$Gender)
# b <- chisq.test(data$AreYouLoyalCustomer, data$Age)
# c <- chisq.test(data$AreYouLoyalCustomer, data$Occupation)
# d <- chisq.test(data$AreYouLoyalCustomer, data$RelationshipStatus)
# e <- chisq.test(data$AreYouLoyalCustomer, data$Income)
# f <- chisq.test(data$AreYouLoyalCustomer, data$EatingOutside)
# g <- chisq.test(data$AreYouLoyalCustomer, data$EatingInSameRestaurant)
# h <- chisq.test(data$AreYouLoyalCustomer, data$UseLoyaltyApps)
# i <- chisq.test(data$AreYouLoyalCustomer, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$AreYouLoyalCustomer, data$AreYouLoyalCustomer)
# k <- chisq.test(data$AreYouLoyalCustomer, data$AppSuggestion)
# l <- chisq.test(data$AreYouLoyalCustomer, data$RedeemVoucher)
# m <- chisq.test(data$AreYouLoyalCustomer, data$CombinedLoyltyApp)
# 
# areYouLoyalCustomer <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# a <- chisq.test(data$AppSuggestion, data$Gender)
# b <- chisq.test(data$AppSuggestion, data$Age)
# c <- chisq.test(data$AppSuggestion, data$Occupation)
# d <- chisq.test(data$AppSuggestion, data$RelationshipStatus)
# e <- chisq.test(data$AppSuggestion, data$Income)
# f <- chisq.test(data$AppSuggestion, data$EatingOutside)
# g <- chisq.test(data$AppSuggestion, data$EatingInSameRestaurant)
# h <- chisq.test(data$AppSuggestion, data$UseLoyaltyApps)
# i <- chisq.test(data$AppSuggestion, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$AppSuggestion, data$AreYouLoyalCustomer)
# k <- chisq.test(data$AppSuggestion, data$AppSuggestion)
# l <- chisq.test(data$AppSuggestion, data$RedeemVoucher)
# m <- chisq.test(data$AppSuggestion, data$CombinedLoyltyApp)
# 
# appSuggestion <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# a <- chisq.test(data$RedeemVoucher, data$Gender)
# b <- chisq.test(data$RedeemVoucher, data$Age)
# c <- chisq.test(data$RedeemVoucher, data$Occupation)
# d <- chisq.test(data$RedeemVoucher, data$RelationshipStatus)
# e <- chisq.test(data$RedeemVoucher, data$Income)
# f <- chisq.test(data$RedeemVoucher, data$EatingOutside)
# g <- chisq.test(data$RedeemVoucher, data$EatingInSameRestaurant)
# h <- chisq.test(data$RedeemVoucher, data$UseLoyaltyApps)
# i <- chisq.test(data$RedeemVoucher, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$RedeemVoucher, data$AreYouLoyalCustomer)
# k <- chisq.test(data$RedeemVoucher, data$AppSuggestion)
# l <- chisq.test(data$RedeemVoucher, data$RedeemVoucher)
# m <- chisq.test(data$RedeemVoucher, data$CombinedLoyltyApp)
# 
# redeemVoucher <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# a <- chisq.test(data$CombinedLoyltyApp, data$Gender)
# b <- chisq.test(data$CombinedLoyltyApp, data$Age)
# c <- chisq.test(data$CombinedLoyltyApp, data$Occupation)
# d <- chisq.test(data$CombinedLoyltyApp, data$RelationshipStatus)
# e <- chisq.test(data$CombinedLoyltyApp, data$Income)
# f <- chisq.test(data$CombinedLoyltyApp, data$EatingOutside)
# g <- chisq.test(data$CombinedLoyltyApp, data$EatingInSameRestaurant)
# h <- chisq.test(data$CombinedLoyltyApp, data$UseLoyaltyApps)
# i <- chisq.test(data$CombinedLoyltyApp, data$FreqUsageOfLoyaltyApps)
# j <- chisq.test(data$CombinedLoyltyApp, data$AreYouLoyalCustomer)
# k <- chisq.test(data$CombinedLoyltyApp, data$AppSuggestion)
# l <- chisq.test(data$CombinedLoyltyApp, data$RedeemVoucher)
# m <- chisq.test(data$CombinedLoyltyApp, data$CombinedLoyltyApp)
# 
# combinedLoyltyApp <- rbind(a, b, c, d, e, f, g, h, i , j, k, l, m)
# 
# 
# chitestindividual <- rbind(gender, age, occupation, relationshipStatus, income, eatingOutside, eatingInSameRestaurant, useLoyaltyApps, freqUsageOfLoyaltyApps, areYouLoyalCustomer, appSuggestion, redeemVoucher, combinedLoyltyApp)
# 
# write.csv(chitestindividual, "test.csv")
```

#### Heatmap 

```{r message=FALSE, warning=FALSE, echo=FALSE, include=TRUE, fig.align = 'center', out.width = "100%"}
corrmatrixdata <- read_csv("CoorelationMatrix.csv")

corrmatrixdf <- as.data.frame(corrmatrixdata)

selecteddf <- corrmatrixdf %>% 
  pivot_longer(-Matrix)

selecteddf$Matrix <- factor(selecteddf$Matrix, 
                         levels = c("Gender", "Age", "Occupation", 
                                    "RelationshipStatus", "Income",
                                    "EatingOutside", "EatingInSameRestaurant",
                                    "UseLoyaltyApps", "FreqUsageOfLoyaltyApps",
                                    "AreYouLoyalCustomer", "AppSuggestion",
                                    "RedeemVoucher", "CombinedLoyltyApp"))
selecteddf$name <- factor(selecteddf$name, 
                         levels =  c("Gender", "Age", "Occupation",
                                     "RelationshipStatus", "Income",
                                     "EatingOutside", "EatingInSameRestaurant",
                                    "UseLoyaltyApps", "FreqUsageOfLoyaltyApps",
                                    "AreYouLoyalCustomer", "AppSuggestion",
                                    "RedeemVoucher","CombinedLoyltyApp"))

ggplot(selecteddf, aes(x = Matrix, y = name, fill = value)) + 
  geom_tile(aes(fill = value)) + 
  geom_text(aes(label = round(value, 3)), size = 6, fontface = "bold") + 
  scale_fill_gradient(low = "green", high = "red") +
  labs(x = "", y = "") +
  theme(panel.grid.major = element_line(size = 0.5, color = "grey"),
        plot.title = element_text(hjust = 0.5),
        axis.line = element_line(size = 0.7, color = "black"),
        text = element_text(size = 28), 
        axis.text = element_text(angle=0, vjust=0.5, size=16, face = "bold"), 
        axis.text.x = element_text(angle = 45, hjust = 1), 
        plot.subtitle=element_text(size=18, hjust=0.5, color="black")) 


ggsave(filename = "Figures/Correlation_Matrix.jpg", width = 40, height = 40, units = "cm", dpi = 600)
```