Hello folks!
I’m Özgenur Şensoy, MSc in Big Data Analytics. I graduated from MEF University from the Department of Elementary Mathematics Education. Since I did not feel like I belonged there during my internships, I switched to data analytics where I could be more productive. I want to put my data science skills into practice in a few companies that I dream of. In addition, I want to progress by nourishing my knowledge with courses on artificial intelligence.
Teaching R online with RStudio Cloud
I chose to watch the video titled “Data visualization and plotting with Shiny for Python || Carson Sievert || RStudio” The video covers how to create interactive data visualization using Shiny, Python, and Pandas. Here are the key topics:
Layout and Slider: At the beginning of the video, we learn how to lay out a user interface and add interactive elements such as sliders using Shiny. This allows users to interact with data or graphs.
Reactive Calculations :The term “Reactive calculations” refers to the ability of users to make calculations based on data. For example, you can create a structure where the calculations are automatically updated when a user changes the slider.
Render Plot: Specifies how to render and update graphics or plots to be displayed to the user. Data-driven charts can refresh automatically as users interact.
Pandas Plotting: Pandas is a widely used library for data analysis and manipulation in Python. The video could possibly also cover how to analyze data using DataFrames and how to use Pandas’ plotting capabilities.
The video explains how you can create an app with an interactive graphic. This implementation seems to change the number of bars in the calculated histogram when you change a slider. The video shows you how to layout such interactive applications using “layout sidebar”, a subpackage of Shiny. This allows users to make the data and chart more interactive. Additionally, “reactive calculations” and “render plot” topics are also discussed in the video. “Reactive calculations” appears to be a feature that defines how calculations can be performed based on input values. “Render plot” explains how to create and display graphics. This shows how you can create different types of plots interactively using plot libraries such as Matplotlib. The video also provides examples using different graphics packages, for example showing how packages such as Seaborn, Plotnine, Pandas, Hollowvies, xarray and geopandas can be used. It looks like this video might offer useful information to those interested in data visualization using Shiny and Python.
======= # | Dataset (2022-23 Salaries of NBA Players)
NBA players’ salaries dataset is the data set containing the salaries of all NBA players. Contains salary, position, team, and age information for each player. Additionally, it provides details on the minutes played by players and their success percentage per period.
Key Features:
Player Information
Per Game Statistics
Shooting Efficiency
Advanced Statistics
Salaries
Data Source: This dataset merges player per-game and advanced statistics for the NBA’s 2022-23 season with player salary data, creating a comprehensive resource for understanding the performance and financial aspects of professional basketball players. The dataset is the result of web scraping player salary information from Hoopshype, and downloading traditional per-game and advanced statistics from Basketball Reference.
Potential Uses:
Player Performance Analysis
Team Budgeting and Strategy
Player Earnings Insights
Data-Driven Decisions
Access: For access official website.
This R data visualization guide provides an overview of the various techniques, libraries, and best practices for creating visually stunning visuals that effectively communicate your data insights. You will be better equipped to explore, analyze, and present your data findings in a compelling and engaging manner if you master data visualization in R. Whether you’re a seasoned data analyst or just starting out, the world of data visualization in R offers limitless opportunities to maximize the value of your data.
This analysis focuses on recruiting data, specifically in the sales department, and examines various recruitment channels. It involves loading the dataset, giving an overview of the data, and identifying the sources used for recruitment. Attrition rates are also assessed for each recruitment source to pinpoint those with high and low turnover. The results are presented through bar charts for easy interpretation.
To evaluate the effectiveness of each recruitment channel in producing top salespeople, we calculate the average sales quota attainment for hires from each source.
```{r}
#Find the average sales quota attainment for each recruiting source
avg_sales <- recruitment %>%
group_by(recruiting_source) %>%
summarize(avg_sales_quota_pct = mean(sales_quota_pct))
#Display the result avg_sales
=======
This analysis focuses on recruiting data, specifically in the sales department, and examines various recruitment channels. It involves loading the dataset, giving an overview of the data, and identifying the sources used for recruitment. Attrition rates are also assessed for each recruitment source to pinpoint those with high and low turnover. The results are presented through bar charts for easy interpretation.
To evaluate the effectiveness of each recruitment channel in producing top salespeople, we calculate the average sales quota attainment for hires from each source.
```{r}
#Find the average sales quota attainment for each recruiting source
avg_sales <- recruitment %>%
group_by(recruiting_source) %>%
summarize(avg_sales_quota_pct = mean(sales_quota_pct))
#Display the result avg_sales
The following example shows how to use a default parameter value.If we call the function without an argument, it uses the default value:
Example
my_function <- function(country = "Norway") {
paste("I am from", country)
}
my_function("Sweden") # Output: I am from Sweden[1] "I am from Sweden"my_function("India") # Output: I am from India[1] "I am from India"my_function() # Output: I am from Norway (default value)[1] "I am from Norway"my_function("USA") # Output: I am from USA[1] "I am from USA"