#Remember to install packages before loading them with library()

library(tidyverse) ## A set of tools for Data manipulation and visualization
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library(lubridate) ## for date time manipulation
library(scales) ## Formatting numbers and values
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library(dplyr)
library(magrittr)   # optional, but gives you the pipe
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#Q1 - view data

sales <- read.csv("sales.csv")

glimpse(sales)
## Rows: 1,000
## Columns: 20
## $ Invoice.ID              <chr> "750-67-8428", "226-31-3081", "631-41-3108", "…
## $ Branch                  <chr> "A", "C", "A", "A", "A", "C", "A", "C", "A", "…
## $ City                    <chr> "Yangon", "Naypyitaw", "Yangon", "Yangon", "Ya…
## $ Customer.type           <chr> "Member", "Normal", "Normal", "Member", "Norma…
## $ Gender                  <chr> "Female", "Female", "Male", "Male", "Male", "M…
## $ Product.line            <chr> "Health and beauty", "Electronic accessories",…
## $ Unit.price              <dbl> 74.69, 15.28, 46.33, 58.22, 86.31, 85.39, 68.8…
## $ Quantity                <int> 7, 5, 7, 8, 7, 7, 6, 10, 2, 3, 4, 4, 5, 10, 10…
## $ Tax.5.                  <dbl> 26.1415, 3.8200, 16.2155, 23.2880, 30.2085, 29…
## $ Total                   <dbl> 548.9715, 80.2200, 340.5255, 489.0480, 634.378…
## $ Date                    <chr> "1/5/2019", "3/8/2019", "3/3/2019", "1/27/2019…
## $ Time                    <chr> "13:08", "10:29", "13:23", "20:33", "10:37", "…
## $ Payment                 <chr> "Ewallet", "Cash", "Credit card", "Ewallet", "…
## $ cogs                    <dbl> 522.83, 76.40, 324.31, 465.76, 604.17, 597.73,…
## $ gross.margin.percentage <dbl> 4.761905, 4.761905, 4.761905, 4.761905, 4.7619…
## $ gross.income            <dbl> 26.1415, 3.8200, 16.2155, 23.2880, 30.2085, 29…
## $ Rating                  <dbl> 9.1, 9.6, 7.4, 8.4, 5.3, 4.1, 5.8, 8.0, 7.2, 5…
## $ hour_digit              <int> 13, 10, 13, 20, 10, 18, 14, 11, 17, 13, 18, 17…
## $ order_date              <chr> "2019-01-05", "2019-03-08", "2019-03-03", "201…
## $ weekday                 <chr> "Sat", "Fri", "Sun", "Sun", "Fri", "Mon", "Mon…
library(dplyr)
library(lubridate)

sales <- read.csv("sales.csv")

sales <- sales %>%
  mutate(
    # 1) Hour digit from the Time column ("13:08" → 13)
    hour_digit = as.integer(substr(Time, 1, 2)),
    
    # 2) Proper date column (Date → real Date)
    order_date = mdy(Date),
    
    # 3) Weekday (Mon, Tue, Wed…)
    weekday = wday(order_date, label = TRUE, abbr = TRUE)
  )

glimpse(sales)
## Rows: 1,000
## Columns: 20
## $ Invoice.ID              <chr> "750-67-8428", "226-31-3081", "631-41-3108", "…
## $ Branch                  <chr> "A", "C", "A", "A", "A", "C", "A", "C", "A", "…
## $ City                    <chr> "Yangon", "Naypyitaw", "Yangon", "Yangon", "Ya…
## $ Customer.type           <chr> "Member", "Normal", "Normal", "Member", "Norma…
## $ Gender                  <chr> "Female", "Female", "Male", "Male", "Male", "M…
## $ Product.line            <chr> "Health and beauty", "Electronic accessories",…
## $ Unit.price              <dbl> 74.69, 15.28, 46.33, 58.22, 86.31, 85.39, 68.8…
## $ Quantity                <int> 7, 5, 7, 8, 7, 7, 6, 10, 2, 3, 4, 4, 5, 10, 10…
## $ Tax.5.                  <dbl> 26.1415, 3.8200, 16.2155, 23.2880, 30.2085, 29…
## $ Total                   <dbl> 548.9715, 80.2200, 340.5255, 489.0480, 634.378…
## $ Date                    <chr> "1/5/2019", "3/8/2019", "3/3/2019", "1/27/2019…
## $ Time                    <chr> "13:08", "10:29", "13:23", "20:33", "10:37", "…
## $ Payment                 <chr> "Ewallet", "Cash", "Credit card", "Ewallet", "…
## $ cogs                    <dbl> 522.83, 76.40, 324.31, 465.76, 604.17, 597.73,…
## $ gross.margin.percentage <dbl> 4.761905, 4.761905, 4.761905, 4.761905, 4.7619…
## $ gross.income            <dbl> 26.1415, 3.8200, 16.2155, 23.2880, 30.2085, 29…
## $ Rating                  <dbl> 9.1, 9.6, 7.4, 8.4, 5.3, 4.1, 5.8, 8.0, 7.2, 5…
## $ hour_digit              <int> 13, 10, 13, 20, 10, 18, 14, 11, 17, 13, 18, 17…
## $ order_date              <date> 2019-01-05, 2019-03-08, 2019-03-03, 2019-01-2…
## $ weekday                 <ord> Sat, Fri, Sun, Sun, Fri, Mon, Mon, Sun, Thu, W…
library(ggplot2)
library(dplyr)

sales_summary <- sales %>%
  group_by(weekday) %>%
  summarise(total_sales = sum(Total, na.rm = TRUE))

ggplot(sales_summary, aes(x = weekday, y = total_sales, fill = weekday)) +
  geom_col() +
  
  # Labels inside bars — now black
  geom_text(aes(label = round(total_sales, 0)),
            color = "black",
            size = 4,
            position = position_stack(vjust = 0.5)) +
  
  # Horizontal orientation
  coord_flip() +
  
  # Color palette
  scale_fill_brewer(palette = "Set2") +
  
  # Titles & labels
  labs(
    title = "Total Sales Breakdown by Weekday",
    x = "Weekday",
    y = "Total Sales"
  ) +
  
  theme_minimal()