Grok all the things

grok (v): to understand (something) intuitively.


👶  Children (ELI5)

Ahoy, young explorers! Today, we're embarking on an exciting adventure into the magical world of R programming! Believe it or not, R is like a treasure chest filled with hidden gems, and it's just waiting for you to discover its secrets!

Are you ready to unlock the mysteries of R? Let's dive right in!

ğŸŽ­ An Intriguing Introduction to R

Once upon a time (well, not that long ago), in the land of statisticians and data scientists, R was brought to life. It's a programming language that helps people work with numbers and make sense of them. From counting stars in the night sky to predicting what kind of ice cream will sell best at the park , R can do it all!

R was created in 1993 by two brilliant explorers named Ross Ihaka and Robert Gentleman. Their combined powers of curiosity and knowledge gave birth to this marvelous tool. It's like having a magic wand that can transform piles of boring numbers into beautiful stories!

🧩 R's Magical Toolbox

R has a mysterious bag of tricks called "packages." These packages are like secret spells that help you do almost anything you can imagine! Some packages can read data from websites, while others can make your numbers dance like a beautifully choreographed ballet. There's even a package for making pretty pictures out of data!

Let's take a look at a few popular packages:

  1. ggplot2: With this magical spell, you can create stunning graphs and charts that help you understand your data better!
  2. dplyr: This package helps you clean and organize your data, like a friendly little helper! Just say the magic words, and it'll do the dirty work for you!
  3. shiny: Now this one is a gem – it lets you create interactive web apps, so you can share your findings with the world in the coolest way possible!

And remember, young adventurers, these are just a few examples... there are thousands of others out there, waiting to be uncovered!

📚 A Story of Variables and Functions

In the land of R, everything revolves around two main characters: variables and functions. You see, variables are like little boxes that hold important pieces of information. They can store numbers, words, or even entire sentences!

Functions, on the other hand, are like enchanted recipes. Give them the right ingredients (the data held in your variables), and they'll cook up something amazing! Sometimes, they might even create new variables or change the existing ones!

Let's meet some R variables and functions:

# This is how we create a variable called 'age' and store the number 7 inside it.
age <- 7

# Here's another variable called 'name,' holding the word 'Alice.'
name <- "Alice"

# Now let's use a magical function to print Alice's age
paste(name, "is", age, "years old")

Did you see that? We used the paste function to combine the words and numbers stored in our age and name variables. It's like a potion that glues things together!

🧙‍♀️ Casting Data Spells with R

Now that we've met variables and functions, it's time to learn some enchanting R spells! These spells will help you manipulate data in the most marvelous ways.

The Spell of Creating Data 📜

Remember those magical packages? Well, we're going to use one called tidyverse to help us create a spellbook (a dataset) of our own! First, we need to cast the installation spell:


That's it! Now, let's call upon the powers of tidyverse with a library spell:


With the tidyverse powers on our side, we can now create a spellbook with the tribble function:

# Prepare your wands, and say these magic words... 🪄
spellbook <- tribble(
  ~Spell,          ~Type,   ~Level,
  "Lumos",         "Light",  1,
  "Alohomora",     "Unlock", 1,
  "Expelliarmus",  "Disarm", 2,
  "Expecto Patronum", "Protect", 5

Behold the mighty spellbook! It has four spells, each with a name (Spell), type (Type), and level (Level). Notice how we used the tribble function to create the columns and rows of our dataset. It's like a magic tablecloth that gathers all the information in one place!

The Spell of Shuffling Data 🔀

Our quest continues with another powerful spell – arrange()! This incantation sorts your dataset based on the values of a column. For example, let's sort our spellbook by level:

spellbook <- arrange(spellbook, Level)

As if by magic, our spellbook is now organized from the lowest to the highest level!

ğŸŽ¨ Painting with Data: The Art of Visualizations

Remember the ggplot2 package we mentioned earlier? Well, it's time to unleash its powers! First, we need to summon it with the library spell:


Now, let's create a simple dataset with two variables – one for ice cream flavors, and one for their popularity:

flavors <- c("Chocolate", "Vanilla", "Strawberry", "Mint")
popularity <- c(300, 500, 200, 400)

ice_cream_data <- tibble(Flavor = flavors, Popularity = popularity)

Using the powers of ggplot2, let's create a beautiful bar chart to display this data:

ggplot(ice_cream_data, aes(x = Flavor, y = Popularity, fill = Flavor)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  labs(title = "Ice Cream Popularity", x = "Flavor", y = "Votes")

And just like that, we have a colorful masterpiece! It shows the number of votes each ice cream flavor received. Now you know which flavors are the most popular in the ice cream kingdom!

🌈 The End... Or Just the Beginning?

Congratulations! You've successfully completed your first R programming adventure! But remember, this is just the tip of the iceberg... there are countless more mysteries waiting to be discovered!

So keep exploring, and who knows, maybe one day you'll be the creator of a magical new R package!

Now, go forth and conquer the world of data, my brave adventurers! is a collection of articles on a variety of technology and programming articles assembled by James Padolsey. Enjoy! And please share! And if you feel like you can donate here so I can create more free content for you.