Descriptive Visualizations
Data Science Programming
1. Visualizations Combo
1.1 Heatmap
## Warning: package 'tidyverse' was built under R version 4.4.3## Warning: package 'readr' was built under R version 4.4.3## Warning: package 'purrr' was built under R version 4.4.3## Warning: package 'forcats' was built under R version 4.4.3## Warning: package 'lubridate' was built under R version 4.4.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── 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 errorslibrary(ggplot2)
library(readr)
# Baca data
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read_csv("Data_Business.csv")## New names:
## Rows: 500 Columns: 25
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (8): Transaction_ID, Customer_ID, Product_Category, Product_ID, Region... dbl
## (15): ...1, Quantity, Unit_Price, Discount, Delivery_Time, Total_Price,... lgl
## (1): ID_HasPattern date (1): Transaction_Date
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Totalin dulu
pivot_df <- df %>%
group_by(Product_Category, Region) %>%
summarise(Total_Sales = sum(Total_Price, na.rm = TRUE), .groups = "drop")
# Bikin heatmap-nya
ggplot(pivot_df, aes(x = Region, y = Product_Category, fill = Total_Sales)) +
geom_tile(color = "white") +
scale_fill_gradientn(colours = rev(RColorBrewer::brewer.pal(9, "BuPu")),
name = "Total Sales (Rp)") +
geom_text(aes(label = round(Total_Sales, 0)), color = "black", size = 3) +
labs(title = "🔥 Heatmap Total Sales by Product Category and Region",
x = "Region", y = "Product Category") +
theme_minimal(base_size = 12)
2. Relationship
2.1 Scatter Plot
library(ggplot2)
library(readr)
# Load data
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read_csv("Data_Business.csv")## New names:
## Rows: 500 Columns: 25
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (8): Transaction_ID, Customer_ID, Product_Category, Product_ID, Region... dbl
## (15): ...1, Quantity, Unit_Price, Discount, Delivery_Time, Total_Price,... lgl
## (1): ID_HasPattern date (1): Transaction_Date
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Plot
ggplot(df, aes(x = Quantity, y = Total_Price)) +
geom_point(color = "darkorange", fill = "orange", alpha = 0.6, shape = 21, size = 2.5, stroke = 0.3) +
labs(
title = "Visualisasi Relasi Quantity & Total Price",
x = "Jumlah Produk (Quantity)",
y = "Total Harga (Rp)"
) +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold", hjust = 0),
panel.grid.major = element_line(linetype = "dashed", color = "grey80")
)
2.2 Bubble Chart
library(ggplot2)
library(readr)
library(dplyr)
# Load data
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read_csv("Data_Business.csv")## New names:
## Rows: 500 Columns: 25
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (8): Transaction_ID, Customer_ID, Product_Category, Product_ID, Region... dbl
## (15): ...1, Quantity, Unit_Price, Discount, Delivery_Time, Total_Price,... lgl
## (1): ID_HasPattern date (1): Transaction_Date
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Aggregasi
df_grouped <- df %>%
group_by(Product_Category, Region) %>%
summarise(
Quantity = sum(Quantity),
Total_Price = sum(Total_Price),
.groups = 'drop'
)
# Visualisasi bubble
ggplot(df_grouped, aes(x = Product_Category, y = Quantity)) +
geom_point(aes(size = Total_Price, fill = Region), shape = 21, alpha = 0.6, color = "grey30", stroke = 0.4) +
geom_text(aes(label = Product_Category), vjust = 0.5, fontface = "bold", size = 3.5) +
scale_size_continuous(range = c(5, 20)) +
scale_fill_brewer(palette = "Pastel1") +
labs(
title = "Creative 4D Bubble Chart:\nCategory vs Quantity (Size = Total Price, Color = Region)",
x = "Product Category",
y = "Total Quantity",
size = "Total Price",
fill = "Region"
) +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold"),
panel.grid.major = element_line(linetype = "dotted", color = "gray80")
)
2.3 Correlation Matrix
## Warning: package 'reshape2' was built under R version 4.4.3##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smiths##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factor# Load data
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read.csv("Data_Business.csv")
# Compute correlation matrix
cor_matrix <- cor(df[sapply(df, is.numeric)], use = "complete.obs")## Warning in cor(df[sapply(df, is.numeric)], use = "complete.obs"): the standard
## deviation is zero# Melt the matrix for ggplot2
cor_melt <- melt(cor_matrix)
colnames(cor_melt) <- c("Var1", "Var2", "Correlation")
# Bubble plot
ggplot(cor_melt, aes(x = Var1, y = Var2, fill = Correlation, size = abs(Correlation))) +
geom_point(shape = 21, color = "black", alpha = 0.8) +
geom_text(aes(label = round(Correlation, 2)), size = 3, color = "black", fontface = "bold") +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +
scale_size(range = c(1, 15)) +
theme_minimal(base_size = 12) +
labs(
title = "4D Correlation Matrix (Size & Color = Strength)",
x = "Variable",
y = "Variable",
fill = "Correlation",
size = "Abs(Correlation)"
) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid = element_blank()
)## Warning: Removed 28 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 28 rows containing missing values or values outside the scale range
## (`geom_text()`).
3. Time Series
3.1 Line Chart
library(ggplot2)
library(dplyr)
# Load data
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read.csv("Data_Business.csv")
# Ubah ke format Date
df$Transaction_Date <- as.Date(df$Transaction_Date)
# Agregasi per tanggal
daily_total <- df %>%
group_by(Transaction_Date) %>%
summarise(Total_Price = sum(Total_Price))
# Plot line chart
ggplot(daily_total, aes(x = Transaction_Date, y = Total_Price)) +
geom_line(color = "steelblue", size = 1) +
geom_point(color = "darkblue") +
labs(title = "Total Harga per Hari",
x = "Tanggal", y = "Total Harga") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
3.2 Area Chart
# Load libraries
library(ggplot2)
library(dplyr)
library(readr)
# Load data (Pastikan file ada di direktori yang benar)
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read_csv("Data_Business.csv")## New names:
## Rows: 500 Columns: 25
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (8): Transaction_ID, Customer_ID, Product_Category, Product_ID, Region... dbl
## (15): ...1, Quantity, Unit_Price, Discount, Delivery_Time, Total_Price,... lgl
## (1): ID_HasPattern date (1): Transaction_Date
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Convert Transaction_Date to Date type
df$Transaction_Date <- as.Date(df$Transaction_Date)
# Agregasi total harga per tanggal, kategori, dan region
agg_df <- df %>%
group_by(Transaction_Date, Product_Category, Region) %>%
summarise(Total_Price = sum(Total_Price), .groups = 'drop')
# Plot area chart dengan facet per Region
ggplot(agg_df, aes(x = Transaction_Date, y = Total_Price, fill = Product_Category)) +
geom_area(alpha = 0.8, position = "stack") +
facet_wrap(~Region, scales = "free_y") +
labs(
title = "Area Chart Total Harga per Kategori Produk - Tiap Region",
x = "Tanggal",
y = "Total Harga"
) +
theme_minimal(base_size = 12) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
---
title: "Descriptive Visualizations"
subtitle: "Data Science Programming"
author: "Muhammad Nabil Pratama"
date: "`r format(Sys.Date(), '%B %d, %Y')`"
output:
  rmdformats::readthedown:
    self_contained: true
    thumbnails: true
    lightbox: true
    gallery: true
    lib_dir: libs
    df_print: "paged"
    code_folding: "show"
    code_download: yes
    css: "style.css"
---

<img src="C:/Users/M Nabil Pratama/OneDrive/Pictures/Screenshots/Gambar WhatsApp 2024-12-06 pukul 13.33.15_c3fd0fc7.jpg" alt="Logo" id="isi_nama" style="width:200px; display: block; margin: auto;"/>

# **1. Visualizations Combo**
## **1.1 Heatmap**

```{r}
library(tidyverse)
library(ggplot2)
library(readr)

# Baca data
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read_csv("Data_Business.csv")

# Totalin dulu
pivot_df <- df %>%
  group_by(Product_Category, Region) %>%
  summarise(Total_Sales = sum(Total_Price, na.rm = TRUE), .groups = "drop")

# Bikin heatmap-nya
ggplot(pivot_df, aes(x = Region, y = Product_Category, fill = Total_Sales)) +
  geom_tile(color = "white") +
  scale_fill_gradientn(colours = rev(RColorBrewer::brewer.pal(9, "BuPu")),
                       name = "Total Sales (Rp)") +
  geom_text(aes(label = round(Total_Sales, 0)), color = "black", size = 3) +
  labs(title = "🔥 Heatmap Total Sales by Product Category and Region",
       x = "Region", y = "Product Category") +
  theme_minimal(base_size = 12)

```

# **2. Relationship**

## **2.1 Scatter Plot**
```{r}
library(ggplot2)
library(readr)

# Load data
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read_csv("Data_Business.csv")

# Plot
ggplot(df, aes(x = Quantity, y = Total_Price)) +
  geom_point(color = "darkorange", fill = "orange", alpha = 0.6, shape = 21, size = 2.5, stroke = 0.3) +
  labs(
    title = "Visualisasi Relasi Quantity & Total Price",
    x = "Jumlah Produk (Quantity)",
    y = "Total Harga (Rp)"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0),
    panel.grid.major = element_line(linetype = "dashed", color = "grey80")
  )
```

## **2.2 Bubble Chart**
```{r}
library(ggplot2)
library(readr)
library(dplyr)

# Load data
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read_csv("Data_Business.csv")

# Aggregasi
df_grouped <- df %>%
  group_by(Product_Category, Region) %>%
  summarise(
    Quantity = sum(Quantity),
    Total_Price = sum(Total_Price),
    .groups = 'drop'
  )

# Visualisasi bubble
ggplot(df_grouped, aes(x = Product_Category, y = Quantity)) +
  geom_point(aes(size = Total_Price, fill = Region), shape = 21, alpha = 0.6, color = "grey30", stroke = 0.4) +
  geom_text(aes(label = Product_Category), vjust = 0.5, fontface = "bold", size = 3.5) +
  scale_size_continuous(range = c(5, 20)) +
  scale_fill_brewer(palette = "Pastel1") +
  labs(
    title = "Creative 4D Bubble Chart:\nCategory vs Quantity (Size = Total Price, Color = Region)",
    x = "Product Category",
    y = "Total Quantity",
    size = "Total Price",
    fill = "Region"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold"),
    panel.grid.major = element_line(linetype = "dotted", color = "gray80")
  )
```

## 2.3 Correlation Matrix

```{r}
# Load packages
library(ggplot2)
library(reshape2)
library(scales)

# Load data
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read.csv("Data_Business.csv")

# Compute correlation matrix
cor_matrix <- cor(df[sapply(df, is.numeric)], use = "complete.obs")

# Melt the matrix for ggplot2
cor_melt <- melt(cor_matrix)
colnames(cor_melt) <- c("Var1", "Var2", "Correlation")

# Bubble plot
ggplot(cor_melt, aes(x = Var1, y = Var2, fill = Correlation, size = abs(Correlation))) +
  geom_point(shape = 21, color = "black", alpha = 0.8) +
  geom_text(aes(label = round(Correlation, 2)), size = 3, color = "black", fontface = "bold") +
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +
  scale_size(range = c(1, 15)) +
  theme_minimal(base_size = 12) +
  labs(
    title = "4D Correlation Matrix (Size & Color = Strength)",
    x = "Variable",
    y = "Variable",
    fill = "Correlation",
    size = "Abs(Correlation)"
  ) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid = element_blank()
  )
```

# **3. Time Series**
## **3.1 Line Chart**

```{r}
library(ggplot2)
library(dplyr)

# Load data
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read.csv("Data_Business.csv")

# Ubah ke format Date
df$Transaction_Date <- as.Date(df$Transaction_Date)

# Agregasi per tanggal
daily_total <- df %>%
  group_by(Transaction_Date) %>%
  summarise(Total_Price = sum(Total_Price))

# Plot line chart
ggplot(daily_total, aes(x = Transaction_Date, y = Total_Price)) +
  geom_line(color = "steelblue", size = 1) +
  geom_point(color = "darkblue") +
  labs(title = "Total Harga per Hari",
       x = "Tanggal", y = "Total Harga") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

## **3.2 Area Chart**

```{r}
# Load libraries
library(ggplot2)
library(dplyr)
library(readr)

# Load data (Pastikan file ada di direktori yang benar)
setwd("C:/Users/M Nabil Pratama/Downloads/dsp")
df <- read_csv("Data_Business.csv")

# Convert Transaction_Date to Date type
df$Transaction_Date <- as.Date(df$Transaction_Date)

# Agregasi total harga per tanggal, kategori, dan region
agg_df <- df %>%
  group_by(Transaction_Date, Product_Category, Region) %>%
  summarise(Total_Price = sum(Total_Price), .groups = 'drop')

# Plot area chart dengan facet per Region
ggplot(agg_df, aes(x = Transaction_Date, y = Total_Price, fill = Product_Category)) +
  geom_area(alpha = 0.8, position = "stack") +
  facet_wrap(~Region, scales = "free_y") +
  labs(
    title = "Area Chart Total Harga per Kategori Produk - Tiap Region",
    x = "Tanggal",
    y = "Total Harga"
  ) +
  theme_minimal(base_size = 12) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

