Check out "Hasset"
https://play.google.com/store/apps/details?id=com.idealogic.hasset

R Programming – R Programming Language Beginners to Pro https://coursecouponclub.com/matplotlib-for-python-developers-beginners/

@code_it_now

R is a programming language and environment primarily used for statistical computing, data analysis, and graphical representation. It is widely used among statisticians, data scientists, and researchers for its powerful data manipulation capabilities and extensive libraries.

Key Features of R:

1. Statistical Analysis: R provides a wide range of statistical techniques, including linear and nonlinear modeling, time-series analysis, classification, clustering, and more.

2. Data Visualization: R has strong graphical capabilities, allowing users to create high-quality plots and visualizations. Libraries like ggplot2 are popular for creating complex graphics.

3. Data Manipulation: Packages like dplyr and tidyr make it easy to manipulate and clean data, transforming it into a format suitable for analysis.

4. Extensive Package Ecosystem: R has a rich ecosystem of packages available through CRAN (Comprehensive R Archive Network), Bioconductor (for bioinformatics), and GitHub.

5. Open Source: R is free to use and has a large community of users who contribute to its development and provide support.

6. Integration with Other Languages: R can be integrated with other programming languages like C++, Python, and Java, allowing for more complex applications.

7. Reproducible Research: Tools like R Markdown allow users to create dynamic reports that combine code, output, and narrative text, promoting reproducibility in research.

Basic Syntax:

Here are some basic examples of R syntax:

1. Creating a Vector:
  

   my_vector <- c(1, 2, 3, 4, 5)
   

2. Data Frame Creation:
  

   my_data <- data.frame(
       Name = c("Alice", "Bob", "Charlie"),
       Age = c(25, 30, 35)
   )
   

3. Basic Plotting:
  

   plot(my_data$Age, main="Age Plot", xlab="Index", ylab="Age", col="blue")
   

4. Using ggplot2 for Advanced Visualization:
  

   library(ggplot2)
   ggplot(my_data, aes(x=Name, y=Age)) + geom_bar(stat="identity")
   

5. Statistical Modeling:
  

   model <- lm(Age ~ Name, data=my_data)
   summary(model)
  

Statistics about Internet in Ethiopia

https://www.statista.com/map/africa/ethiopia/internet

@code_it_now

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Looking forward to your responses.

Gd night 🙌😁

Gd morning

Hw U doin

monday + networking final exam = 🤝

e was fine

there are still some question abt the world that science haven't yet been able to answer...............
> how did it all begin?
> how did life come abt? and what happens when we die?

good night✋

Be ashamed to die until you have scored some victory for humanity !!!

happy new year☺️☺️

#2025

Kill ur EGO🙌