​

Data Visualization Tools

​

Spreadsheets

​

Advantages of Using Spreadsheets for Data Visualization

• Accessibility and Ease of Use: Spreadsheets are user-friendly and familiar to many, making it easy to start creating visualizations without extensive training.
• Integration with Data Sources: They allow for seamless data import and export, and can connect to various data sources, enhancing their functionality.
• Collaboration and Sharing: Features like real-time collaboration in Google Sheets enable easy sharing and teamwork on data visualizations.

​

Disadvantages of Using Spreadsheets for Data Visualization

• Performance Issues: Spreadsheets can experience slowdowns or crashes when dealing with very large datasets, impacting their effectiveness for extensive data analysis.
• Limited Advanced Features: They offer fewer advanced features compared to specialized data visualization tools, which may limit complex analyses and customization.
• Basic Customization: While useful for creating standard charts, they may not provide the sophisticated or interactive visualizations required for more detailed presentations.

​

Data Visualization Using Programming Languages

​

Creating Visualization in R and Python

​

R

​

Python

import pandas as pd
import datetime
from datetime import timedelta
import requests

import matplotlib.pyplot as plt

df_daily_hist = pd.DataFrame()

api_key = "CHANGE WITH YOUR OWN API KEY"

def get_date_list(start_date):
    start_date = datetime.datetime.strptime(start_date, '%Y-%m-%d')

    end_date = datetime.datetime.today()

    date_list = []

    while start_date < end_date:
        date_list.append(start_date)
        start_date += timedelta(days=90)

    date_list.append(end_date)

    return date_list

date = get_date_list("2024-04-01")

for i in ["TLKM.JK"]:
    for j in range (0,len(date)-1):
        if j==0:
            start_date = date[j]
            start_date = start_date.strftime('%Y-%m-%d')
            
            end_date = date[j+1]
            end_date = end_date.strftime('%Y-%m-%d')
        else:
            start_date = date[j]+ timedelta(days=1)
            start_date = start_date.strftime('%Y-%m-%d')
            
            end_date = date[j+1]
            end_date = end_date.strftime('%Y-%m-%d')

        url = f"https://api.sectors.app/v1/daily/{i}/?start={start_date}&end={end_date}"
        

        headers = {
            "Authorization": api_key
        }

        response = requests.get(url, headers = headers)

        if response.status_code == 200:
            data = response.json()
        else:
            # Handle error
            print(response.status_code)

        df_daily_hist = pd.concat([df_daily_hist,pd.DataFrame(data)])

        print(f"Finsih collect data for stock {i} from {start_date} to {end_date}")

df_tlkm = df_daily_hist[df_daily_hist.symbol == "TLKM.JK"]

plt.figure(figsize=(14, 10))
plt.plot(df_tlkm['date'], df_tlkm['close'], linewidth=2)

# Formatting the plot
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('TLKM.JK Daily Price Since 1 April 2024')
plt.tight_layout()

# Convert date to datetime
df_tlkm['date'] = pd.to_datetime(df_tlkm['date'])

# Define the color change dates
yellow_date = pd.Timestamp('2024-05-02')
red_date = pd.Timestamp('2024-05-19')

# Plotting the data with conditional coloring
plt.style.use('dark_background') 
plt.figure(figsize=(14, 10))

# Plot each segment with the appropriate color
for i in range(len(df_tlkm) - 1):
    start_date = df_tlkm['date'].iloc[i]
    end_date = df_tlkm['date'].iloc[i + 1]
    if end_date > red_date:
        color = 'red'
    elif end_date > yellow_date:
        color = 'yellow'
    else:
        color = 'white'
    
    plt.plot(df_tlkm['date'].iloc[i:i+2], df_tlkm['close'].iloc[i:i+2], color=color, linewidth=2)

# Formatting the plot
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('TLKM.JK Daily Price Since 1 April 2024')
plt.tight_layout()

# Create custom legend
import matplotlib.lines as mlines

blue_line = mlines.Line2D([], [], color='white', linewidth=2, label='Before Starlink Rumours')
yellow_line = mlines.Line2D([], [], color='yellow', linewidth=2, label="After First Starlink's Rumour News Emerged")
red_line = mlines.Line2D([], [], color='red', linewidth=2, label='After Starlink Official Announcement in Indonesia')

plt.legend(handles=[blue_line, yellow_line, red_line])

# Show the plot
plt.show()

​

Advantages of Using Programming Languages for Data Visualization

• Customization and Flexibility: Programming languages like Python, R, and JavaScript offer a high degree of customization, allowing users to create highly tailored visualizations that meet specific requirements.
• Advanced Features: Libraries such as Matplotlib, Seaborn, Plotly, ggplot2, and D3.js provide advanced features and capabilities, enabling complex and interactive visualizations that go beyond basic charting options.
• Integration with Data Analysis: These languages seamlessly integrate with data analysis workflows, allowing for sophisticated data manipulation and visualization in a unified environment.

​

Disadvantages of Using Programming Languages for Data Visualization

• Steeper Learning Curve: Programming languages require a certain level of expertise and familiarity, which can be challenging for users without a coding background.
• Development Time: Creating custom visualizations can be time-consuming, especially when developing complex or highly interactive graphics.
• Maintenance and Debugging: Code-based visualizations may require ongoing maintenance and debugging, which can be demanding and may require technical skills to resolve issues effectively.

​

Business Intelligence Tools

​

Advantages of Using BI Tools for Data Visualization

• User-Friendly Interfaces: BI tools offer intuitive drag-and-drop interfaces and pre-built templates that make it easy for users with varying levels of technical expertise to create effective visualizations.
• Integration and Connectivity: They integrate seamlessly with various data sources, including databases, spreadsheets, and cloud services, facilitating real-time data updates and comprehensive analysis.
• Interactive and Collaborative Features: BI tools provide interactive dashboards and collaboration features that allow users to explore data dynamically and share insights with team members effortlessly.

​

Disadvantages of Using BI Tools for Data Visualization

• Cost: Many BI tools come with significant licensing or subscription fees, which can be a barrier for smaller organizations or individuals.
• Limited Customization: While BI tools offer many pre-built options, they may not provide the same level of customization as programming languages for highly specialized or unique visualizations.
• Learning Curve: Although user-friendly, BI tools still require a learning curve to master their full range of features and functionalities, which may necessitate training or onboarding for effective use.

​

Introduction to Tableau

​

Overview of Tableau

​

Different Types of Tableau

• Features:
  • Connects to multiple data sources, including databases, spreadsheets, and cloud services.
  • Provides advanced analytics and visualization capabilities.
  • Offers extensive customization options for creating detailed and tailored visualizations.
  • Allows users to create and publish dashboards to Tableau Server or Tableau Online for sharing.

• Features:
  • Facilitates centralized data management and collaboration.
  • Allows users to interact with dashboards and reports via a web browser.
  • Provides robust security and governance features to control access and permissions.
  • Supports real-time data connectivity and automatic data refresh.

• Features:
  • Provides the same collaboration and sharing capabilities as Tableau Server.
  • Simplifies setup and maintenance with a cloud-based infrastructure.
  • Allows users to access dashboards and reports from anywhere with an internet connection.
  • Offers scalability to accommodate growing data needs without additional hardware investments.

• Features:
  • Offers a free platform for creating and sharing visualizations with the public.
  • Visualizations are hosted on Tableau’s public server and accessible to anyone.
  • Ideal for journalists, bloggers, and anyone interested in sharing data insights publicly.
  • Limited to public data only, with no option for private data storage or sharing.

• Features:
  • Provides a visual interface for data cleaning and transformation.
  • Supports data blending from multiple sources.
  • Allows users to create repeatable data preparation workflows.
  • Integrates seamlessly with Tableau Desktop for analysis and visualization.

​

Creating First Visualization in Tableau Public

​

Importing Data

Once you’ve installed Tableau Public, we can open it and load the data. There are severals data type that we can load to Tableau Public. However, since we only use three free version of Tableau, there are some limitation in load data options. If you want to import csv file, you’ll need to first download the CSV file, and then save it as a text file, click ‘Text File’ in the left menu and open your text file. The new page shows the data preview and the corresponding fields of the dataset.

​

Create Visualization in Worksheet

Tableau has a relatively gentle learning curve. At the top of Tableau, there are areas for columns and rows. As the name suggests, we may drag the variables we wish to see in this area. I drag the sub_sector in rows area and sum of the market cap into the marks area to show a table of total market capitalization per sub sector.

We can change the type of visualization using the “Show Me” button in the top right of the menu. And in this case, I want to change it into treemap chart. Moreover, you can also change the color, the label size, and other aesthetic setting in this section.

Once you finished creating all the chart you want to make, you can create a new dashboard as a canvas or place to combine all the visualization you’ve made. In the dashboard, you can customize the size of the canvas, and select all the sheets (worksheet or visualization) that you want to show in that dashboard.

In our case we only have one visualization and we only select that chart to be shown in the dashboard. However, you can add multiple chart/visualization here, and you can also customize the size of each chart and add some text in the dashboard.

​

Hands-On practice

​

Author