I’m Derya Şaşmaz. I’m currently working at Sigortam.net as CX & Process Developer. Data-driven decision making is the key for my role. I use data to prioritize the tasks and analyze cost-benefits for all projects.
Feel free to connect with me!
I choose “Data Visualization and Plotting with Shiny for Python” and “Shiny Programming Practices” videos. The main reason is my interest in data visualization, especially at work. After learning some about Shiny, I decided to watch the second video too.
In the first video, there is a discussion about the development of an interactive web application using Shiny, a web application framework for R, and its integration with various data visualization tools. It covers creating interactive applications with Shiny, explaining reactive calculations, using the render plot function for data visualization, introducing its wide compatibility with different packages, and providing practical code examples for using visualization libraries within the Shiny application.
The second video reflects on the author’s experiences building UIs using traditional event-driven methods. It mentions the concept of a “reactive graph” in frameworks like Shiny for R. He draws an analogy between functions and reactive components, explains the need for modules to be easily understandable.
“Netflix Movies and TV Shows” is a great data to analyze. Netflix is one of the most popular media and video streaming platforms. They have over 8000 movies or tv shows available on their platform.
The dataset provides details about a wide range of movies and TV shows available on Netflix, including information about their release year, rating, duration, and more. It’s a comprehensive dataset for exploring the Netflix content library.
This dataset offers an engaging and relevant way to teach data analysis, data visualization, and recommendation systems, all within the context of a popular streaming platform. I believe it allows students to work with real-world data while learning valuable data science skills.
This helps to create a simple scatter plot using the ggplot2 package to visualize data.
# Load the ggplot2 library
library(ggplot2)
# Create a sample dataset
data <- data.frame(
X = c(1, 2, 3, 4, 5),
Y = c(2, 3, 1, 4, 2)
)
# Create a scatter plot
ggplot(data, aes(x = X, y = Y)) +
geom_point(shape = 19, size = 3, color = "blue") +
labs(
title = "Simple Scatter Plot",
x = "X-axis",
y = "Y-axis"
)
This example shows how to create a basic data frame in R.
# Create a basic data frame
data <- data.frame(
Name = c("Alice", "Bob", "Charlie", "David"),
Age = c(25, 30, 22, 28),
Grade = c("A", "B", "B", "A")
)
# Print the data frame
data Name Age Grade
1 Alice 25 A
2 Bob 30 B
3 Charlie 22 B
4 David 28 ASome basic arithmetic operation examples with two variables.
# Define two variables
a <- 5
b <- 3
# Addition
sum_ab <- a + b
cat("Sum of a and b: ", sum_ab, "\n")Sum of a and b: 8 # Subtraction
diff_ab <- a - b
cat("Difference between a and b: ", diff_ab, "\n")Difference between a and b: 2 # Multiplication
product_ab <- a * b
cat("Product of a and b: ", product_ab, "\n")Product of a and b: 15 # Division
quotient_ab <- a / b
cat("Division of a by b: ", quotient_ab, "\n")Division of a by b: 1.666667 # Exponentiation
power_ab <- a^b
cat("a raised to the power of b: ", power_ab, "\n")a raised to the power of b: 125