r_newbie_experience <- function(day) {
# Simulates the emotional journey of learning R.
# Warning: Results may vary. Side effects include: false confidence,
# existential dread about factors, sudden urges to pipe everything,
# and an unhealthy relationship with Stack Overflow.
#
# Note: This function is 100% accurate based on extensive field research
# (aka crying over RStudio worldwide).
experiences <- list(
`1` = "2 + 2 # I AM A DATA SCIENTIST NOW!!!",
`2` = "x <- 'R is easy' # This arrow thing is cool!",
`3` = "c(1, 2, 3) # Everything is a vector? Even single numbers?!",
`4` = "data.frame(x = 1:3, y = 4:6) # Look at me making data!",
`5` = "my_data$new_column <- 'value' # Dollar signs everywhere!",
`6` = "factor(c('A', 'B', 'A')) # Why are my letters ordered weird?",
`7` = "mean(c(1, 2, NA)) # Returns NA. R is broken.",
`8` = "mean(c(1, 2, NA), na.rm = TRUE) # Oh... that's embarrassing",
`9` = "library(tidyverse) # Installed 47 packages. Is this normal?",
`10` = "data %>% filter() # What is this pipe thing?",
`12` = "ggplot(data, aes(x, y)) + geom_point() # I made a chart!",
`14` = "install.packages('everything') # Package not found. Dreams crushed.",
`15` = "subset(data, condition) # Why does everyone say this is bad?",
`18` = "data %>% select(everything()) # Pipe addiction begins",
`20` = "for(i in 1:10) print(i) # Loops work but everyone judges me",
`25` = "apply(matrix, 1, function(x) sum(x)) # Functions have functions?",
`30` = "str(object) # Everything has structure. Mind = blown",
`35` = "read.csv('file.csv') # Why are my numbers now factors?!",
`40` = "stringsAsFactors = FALSE # The magic incantation",
`45` = "data$column[data$other_column == 'value'] # Bracket hell",
`50` = "library(dplyr); data %>% filter(column == 'value') # Enlightenment",
`55` = "Error: object 'column' not found # But I can see it right there!",
`60` = "data %>% mutate(new = old * 2) # Mutate sounds violent",
`65` = "group_by(category) %>% summarise(mean = mean(value)) # I'm a wizard!",
`70` = "left_join(df1, df2, by = 'id') # Joins without SQL?",
`75` = "pivot_longer(data, cols = everything()) # Reshape magic",
`80` = "ggplot() + theme_minimal() + labs() + scale_x_continuous() # Layered grammar",
`85` = "ggsave('plot.png', width = 8, height = 6, dpi = 300) # Publication ready!",
`90` = "map(list, ~function(.x)) # Functional programming in R?",
`95` = "case_when(condition1 ~ result1, TRUE ~ default) # Vectorized if-else",
`100` = "data[data$x > 5 & data$y < 10, ] # Back to base R. Feeling dirty.",
`110` = "glue('Hello {name}!') # String interpolation discovered",
`120` = "# Started answering R questions (incorrectly) on Stack Overflow",
`130` = "options(stringsAsFactors = FALSE) # Set global defaults",
`140` = "library(here); here('data', 'file.csv') # Path management zen",
`150` = "renv::init() # Environment management. Finally!",
`180` = "Discovered R Markdown. Rewrote everything. Literate programming!",
`200` = "shiny::fluidPage() # Interactive web apps in R?!",
`250` = "data.table vs dplyr debates. Chose sides. Lost friends.",
`300` = "Rcpp::cppFunction() # C++ in R. Peak performance.",
`365` = "# Still googling 'how to exit vim' but my R analysis is solid",
`400` = "targets::tar_make() # Pipeline management. Professional level.",
`500` = "Writing R packages. DESCRIPTION file mysteries solved.",
`600` = "CRAN submission. Peer review trauma begins.",
`700` = "Dreams in dplyr. Nightmares about factors.",
`800` = "Discovered {{}}. Tidy evaluation everywhere.",
`900` = "10 years later: Still finding new CRAN packages daily",
`1000` = "Achieved enlightenment: Everything is a list (even data frames)"
)
# Special milestone messages
if (day < 0) {
return("Error: Time travel not yet implemented in R 4.x (check CRAN)")
} else if (day == 0) {
return("Day 0: Googling 'R vs Excel' (preparing for enlightenment)")
} else if (day == 42) {
return("Day 42: The answer to life, universe, and missing values")
} else if (day == 404) {
return("Day 404: Object not found. Try ls() again later.")
} else if (day == 418) {
return("Day 418: I'm a teapot. Also, discovered Easter eggs in R")
} else if (day == 314) {
return(paste0("Day ", day, ": pi # Built-in constant. Math rocks!"))
} else if (as.character(day) %in% names(experiences)) {
return(experiences[[as.character(day)]])
} else if (day > 1000) {
return(paste0("Day ", day, ": Realized the real R was the data we wrangled along the way"))
} else if (day > 365) {
return(paste0("Day ", day, ": Still discovering that everything is a vector (even confusion)"))
} else {
states <- c(
"Alternating between 'I'm a data wizard' and 'I know nothing'",
"Debugging code that worked yesterday without changes",
"Adding print() statements everywhere like breadcrumbs",
"Copy-pasting from Stack Overflow with pride",
"Explaining to rubber duck why dplyr should work",
"Discovering the bug was a missing comma. Again.",
"Refactoring working code because 'tidyverse is cleaner'",
"Breaking production on a Friday at 4:59 PM"
)
return(paste0("Day ", day, ": ", sample(states, 1)))
}
}
# Test the journey with milestone days
cat("=== The R Newbie Journey: Extended Director's Cut ===\n\n")
test_days <- c(0, 1, 4, 9, 30, 42, 60, 100, 365, 404, 418, 500, 1000)
for (day in test_days) {
cat(sprintf("Day %4d: %s\n", day, r_newbie_experience(day)))
}Introduction to R—IND215
A curated set of R materials for analytics students
Welcome to IND215
This interactive website provides a comprehensive four-part introduction to R programming specifically designed for students in analytics curricula. The course emphasizes R’s language fundamentals, the tidyverse ecosystem, and data manipulation for statistical computing and data science.
Why R for Analytics?
R has been the gold standard for statistical computing and data analysis for good reasons:
🌟 Statistical Foundation
- Created by statisticians for statistical analysis
- Standard tool in academia, research, and data science
- Preferred language for statistical modeling and hypothesis testing
💪 Rich Ecosystem
- Comprehensive statistical libraries (stats, MASS)
- Elegant data manipulation (tidyverse: dplyr, tidyr, ggplot2)
- Advanced visualization capabilities (ggplot2, plotly, shiny)
- Machine learning and predictive modeling (caret, randomForest)
🎯 Data-First Design
- Built-in support for statistical data types (factors, time series)
- Vectorized operations for efficient data processing
- Seamless integration with databases and file formats
- Interactive data exploration and analysis
- Publication-ready graphics and reports
The R Learning Journey: A Humorous Reality Check
Learning R is an adventure filled with “aha!” moments, head-scratching confusion, and the occasional existential crisis about why your data frame suddenly became a factor (again). Every R programmer has traveled this path, from their first 2 + 2 to their thousandth Google search about why their ggplot won’t cooperate.
Below is a humorous, fictitious R function that captures the universal experiences every R learner encounters. As you progress through this course, you’ll gradually understand why each of these moments is both frustrating and funny. Consider it a preview of coming attractions – if you don’t find it amusing now, bookmark it and return after a few weeks of R. You’ll be laughing (or crying) in recognition!
The Universal R Learning Timeline
This function documents real phenomena you’ll encounter: - Days 1-10: The honeymoon phase where everything seems logical until you meet factors - Days 10-100: The valley of despair where you discover R’s “quirks” - Days 100-365: The plateau of productivity where you can actually wrangle data (while still Googling constantly) - Days 365+: The eternal journey where you realize the tidyverse keeps evolving
Ready to Begin?
Start your R journey by:
- 📚 Reading the Course Syllabus
- 📅 Reviewing the Course Schedule
- 💻 Setting up R and RStudio with our Getting Started Guide