#Remember to install packages before loading them with library()
library(tidyverse) ## A set of tools for Data manipulation and visualization
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## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.1 ✔ stringr 1.5.2
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
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## ✖ 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 errors
library(lubridate) ## for date time manipulation
library(scales) ## Formatting numbers and values
##
## Attaching package: 'scales'
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## The following object is masked from 'package:purrr':
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## discard
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## col_factor
#library(hrbrthemes)# For changing ggplot theme
library(extrafont) # More font options
## Registering fonts with R
#Q1 - view data
sales <- read.csv("sales.csv")
glimpse(sales)
## Rows: 1,000
## Columns: 17
## $ 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…
sales <- sales %>%
mutate(
time = as.integer(substr(Time, 1, 2)),
date = mdy(Date),
weekday = wday(date, label = TRUE)
)
library(dplyr)
library(ggplot2)
# Ensure 'weekday' is a factor with proper order (Mon → Sun)
sales$weekday <- factor(sales$weekday, levels = c("Mon","Tue","Wed","Thu","Fri","Sat","Sun"))
# Define colors for each weekday
weekday_colors <- c(
"Mon" = "#FF4D4D", # Red
"Tue" = "#FFA64D", # Orange
"Wed" = "#FFD24D", # Yellow
"Thu" = "#57D957", # Green
"Fri" = "#4D79FF", # Blue
"Sat" = "#B266FF", # Purple
"Sun" = "#FF66A3" # Pink
)
sales_by_day <- sales %>% group_by(weekday) %>%
summarise(Total_Sales=sum(Total)) %>% ungroup
sales_by_day <- sales_by_day %>%
mutate(weekday = factor(weekday, levels = rev(c("Mon","Tue","Wed","Thu","Fri","Sat","Sun"))))
ggplot(sales_by_day, aes(x = weekday, y = Total_Sales, fill = weekday)) +
geom_col(width = 0.6, color = "black") +
geom_text(aes(label = scales::comma(Total_Sales)),
hjust = 1.1, color = "white", size = 4, fontface = "bold") + # inside bars
coord_flip() +
scale_fill_manual(values = weekday_colors) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(face = "bold", size = 18, hjust = 0.5),
axis.title.y = element_blank(),
axis.title.x = element_text(size = 12),
axis.text.y = element_text(size = 12, face = "bold"),
axis.text.x = element_text(size = 11, color = "gray30"),
panel.grid.major.y = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_line(color = "gray80", linewidth = 0.4),
legend.position = "none"
) +
labs(
title = "Weekly Sales Breakdown",
y = "Total Sales"
)