Essential R Programming Syntax and Data Analysis Techniques

Posted on May 29, 2026 in Statistics

R Programming Fundamentals

  • n <- (x): Store a value in a variable.
  • c(): Combine values into a vector.
  • as.type(c()): Change data type (e.g., as.numeric, as.character).
  • rm(variable): Remove from memory; rm(list=ls()) deletes everything.
  • ls(): List all variables in memory.
  • ==: Equality operator.
  • NaN: Not a Number (undefined mathematical operation).
  • NA: Missing value.
  • Vector indexing: Use [x] for a single value or [x:y] for a range.

Conditional Logic and Loops

Marketing Campaign Classification

conversion <- sample(0:5, 100, replace=TRUE)
resultados <- character(length(conversion))
for (i in 1:length(conversion)) {
  if (conversion[i] >= 3) {
    resultados[i] <- "campaña exitosa"
  } else {
    resultados[i] <- "campaña mejorar"
  }
}
Evalcampaña <- cbind(conversion, resultados)

Social Media Network Classification

n <- 25
redes <- sample(c("Insta", "Facebook", "Linkedin", "TikTok", "Otras"), n, replace=TRUE)
descripcion_redes <- character(n)
for(i in 1:n){
  if(redes[i] == "Linkedin"){
    descripcion_redes[i] <- "Red profesional"
  } else if(redes[i] == "TikTok"){
    descripcion_redes[i] <- "Red entretenimiento"
  } else {
    descripcion_redes[i] <- "Red social"
  }
}

Counter Example: Age Verification

Edades <- c(12, 18, 24, 45, 14, 67, 52, 7, 81)
NumMayoresEdad <- 0
for(i in 1:length(Edades)){
  if(Edades[i] >= 18){
    NumMayoresEdad <- NumMayoresEdad + 1
  }
}
print(paste("El numero de clientes mayores de edad es:", NumMayoresEdad))

Data Frames and External Files

Creating and importing datasets in R:

df_clientes <- data.frame(Num_Cliente=1:5, Nombre=c("Ana", "Nolan", "Pol", "Alex", "Yegor"), Edad=c(56, 34, 76, 89, 12))
df_titanic <- read.csv("titanic_simple.csv", header=TRUE, sep=",")

Statistical Analysis and Visualization

  • Summary: summary(df) provides descriptive statistics.
  • Tables: table(df$Sex) for frequency counts.
  • Proportions: prop.table(table(df$Sex)) * 100.
  • Aggregation: aggregate(Fare ~ Survived, data=df, mean).
  • Visualization: barplot(prop.table(table(df$Survived)) * 100, main="Supervivencia Titanic").

Exam Preparation Cheat Sheet

  • Inspection: head(), tail(), str(), summary(), View().
  • Filtering: df[df$Edad >= 18, ].
  • Math: mean(), max(), min().
  • Sampling: sample(vector, size, replace=TRUE).