DATA SCIENCE PROGRAMMING

DESCRIPTIVE VISUALITAZIONS

1 Dataset vizualitasions

library(dplyr)
library(DT)
# Membaca file data_sales.csv
data_sales <- read.csv("C:/Users/NABILA ASWA HIDAYATI/Downloads/8 Descriptive Visualizations – Data Science Programming.csv", stringsAsFactors = FALSE)
# Menampilkan data sebagai tabel interaktif
datatable(
  data_sales,
  options = list(
    pageLength = 10,
    scrollCollapse = TRUE,
    autoWidth = TRUE
  ),
  rownames = FALSE,
  caption = htmltools::tags$caption(
    style = 'caption-side: top; text-align: left;
             font-size: 18px; font-weight: bold;',
    'Data Sales: Tabel Interaktif dari File CSV'
  ),
  class = 'stripe hover compact'
)

2 Heatmap

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ readr     2.1.5
## ✔ ggplot2   3.5.1     ✔ stringr   1.5.1
## ✔ lubridate 1.9.4     ✔ tibble    3.2.1
## ✔ purrr     1.0.2     ✔ tidyr     1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ 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(ggplot2)
library(reshape2)

## 
## Attaching package: 'reshape2'
## 
## The following object is masked from 'package:tidyr':
## 
##     smiths

data_sales <- read.csv("C:/Users/NABILA ASWA HIDAYATI/Downloads/8 Descriptive Visualizations – Data Science Programming.csv", stringsAsFactors = FALSE)

# Buat data agregasi
heatmap_data <- data_sales %>%
  group_by(Region, Product_Category) %>%
  summarise(Total_Sales = sum(Total_Price, na.rm = TRUE)) %>%
  ungroup()

## `summarise()` has grouped output by 'Region'. You can override using the
## `.groups` argument.

# Tidak perlu reshape2: pakai pivot_wider dan ubah jadi matrix
heatmap_matrix <- heatmap_data %>%
  pivot_wider(names_from = Product_Category, values_from = Total_Sales, values_fill = 0) %>%
  column_to_rownames("Region") %>%
  as.matrix()

# Visualisasi
ggplot(as.data.frame(as.table(heatmap_matrix)), aes(Var2, Var1, fill = Freq)) +
  geom_tile(color = "white") +
  scale_fill_gradient(low = "lightyellow", high = "red") +
  labs(
    title = "Heatmap Total Penjualan per Region dan Kategori Produk",
    x = "Kategori Produk",
    y = "Region",
    fill = "Total Penjualan"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

3 Relationship

3.1 scatter plot

ggplot(data_sales, aes(x = Unit_Price, y = Discount)) +
  geom_point(color = "red", alpha = 0.5) +
  labs(title = "Relationship: Unit Price vs Discount",
       x = "Unit Price",
       y = "Discount") +
  theme_minimal()

3.2 bubble chart

library(ggplot2)

ggplot(data_sales, aes(x = Quantity, 
                       y = Total_Price, 
                       size = Discount, 
                       color = Product_Category)) +
  geom_point(alpha = 0.6) +
  labs(title = "Bubble Chart: Quantity vs Total Price (Bubble Size = Discount)",
       x = "Quantity",
       y = "Total Price",
       size = "Discount",
       color = "Product Category") +
  theme_minimal()

3.3 correlation matrix

# Install dan load ggcorrplot
if (!require(ggcorrplot)) install.packages("ggcorrplot")
library(ggcorrplot)

# Ambil kolom numerik dari dataset
numeric_data <- data_sales[sapply(data_sales, is.numeric)]

# Hitung matriks korelasi
cor_matrix <- cor(numeric_data, use = "complete.obs")

# Visualisasi korelasi TANPA clustering (aman dari error)
ggcorrplot(cor_matrix,
           hc.order = FALSE,         # Tidak pakai clustering (menghindari error)
           type = "lower",           
           lab = TRUE,               
           lab_size = 3,             
           colors = c("red", "white", "blue"),
           title = "Correlation Matrix: Numerical Variables in data_sales")

4 time series

4.1 line chart time series

# Load library
library(ggplot2)
library(dplyr)
library(lubridate)

# Ubah Transaction_Date jadi format Date
data_sales$Transaction_Date <- as.Date(data_sales$Transaction_Date)

# Agregasi: total penjualan per bulan
sales_by_month <- data_sales %>%
  mutate(Month = floor_date(Transaction_Date, "month")) %>%
  group_by(Month) %>%
  summarise(Total_Sales = sum(Total_Price, na.rm = TRUE))

# Buat line chart
ggplot(sales_by_month, aes(x = Month, y = Total_Sales)) +
  geom_line(color = "steelblue", size = 1) +
  geom_point(color = "darkred") +
  labs(title = "Time Series: Total Sales per Month",
       x = "Month",
       y = "Total Sales") +
  theme_minimal()

4.2 Area chart

# Load library
library(ggplot2)
library(dplyr)
library(lubridate)

# Pastikan Transaction_Date dalam format Date
data_sales$Transaction_Date <- as.Date(data_sales$Transaction_Date)

# Agregasi data penjualan per bulan
sales_by_month <- data_sales %>%
  mutate(Month = floor_date(Transaction_Date, "month")) %>%
  group_by(Month) %>%
  summarise(Total_Sales = sum(Total_Price, na.rm = TRUE))

# Buat area chart
ggplot(sales_by_month, aes(x = Month, y = Total_Sales)) +
  geom_area(fill = "skyblue", alpha = 0.6) +
  geom_line(color = "steelblue", size = 1) +
  labs(title = "Time Series Area Chart: Total Sales per Month",
       x = "Month",
       y = "Total Sales") +
  theme_minimal()

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