探索と集約

R was created primarily as a statistical environment, so it provides many built-in functions to analyze, summarize, and understand the distribution of data in vectors or tables.

Basic Statistical Functions

R allows you to easily calculate the main measures of center and spread on numeric vectors:

• mean(x): Calculates the arithmetic mean of the elements.
• median(x): Calculates the median (the middle value).
• sd(x): Calculates the standard deviation (a measure of how spread out the data is).
• min(x) and max(x): Return the minimum and maximum values respectively.

values <- c(10, 12, 15, 18, 20, 22)

avg <- mean(values)
dispersion <- sd(values)

cat("Mean:", avg, "- Std Dev:", round(dispersion, 2), "\n")

Handling Missing Data (NA)

In the real world, datasets often contain missing values, represented in R by the special entity NA (Not Available).

If you try to calculate the mean or any other aggregate on a vector that contains even a single NA, R will return NA as the result. To ignore missing values, you must pass the logical argument na.rm = TRUE (NA remove):

temperatures <- c(18, 21, NA, 24, 19)

# This will return NA
print(mean(temperatures))

# This will ignore the NA and perform the calculation on valid values
print(mean(temperatures, na.rm = TRUE))

Global Summary with summary()

The summary() function is a powerful tool that provides a complete statistical overview of a vector or an entire Data Frame, showing min, first quartile, median, mean, third quartile, and max:

prices <- c(5, 10, 15, 20, 100)
print(summary(prices))

Aggregating Data with aggregate

When working with data frames, it is common to calculate summary statistics (such as mean or sum) for specific groups or categories. The aggregate() function in R simplifies this process:

df <- data.frame(
  department = c("HR", "IT", "HR", "IT"),
  salary = c(3000, 4500, 3200, 4800)
)

# Calcola la media dei salari per dipartimento
avg_salaries <- aggregate(salary ~ department, data = df, FUN = mean)
print(avg_salaries)

The syntax salary ~ department means "analyze the salary variable based on the department variable". The FUN = mean argument specifies that we want to calculate the arithmetic mean for each group.

Advanced Filtering with subset

The subset() function provides an intuitive and elegant way to extract subsets of rows and columns from a data frame, avoiding the use of complex square brackets:

employees <- data.frame(
  name = c("Alice", "Bob", "Charlie"),
  age = c(25, 34, 29),
  active = c(TRUE, FALSE, TRUE)
)

# Filtra solo i dipendenti attivi con età superiore a 28
active_employees <- subset(employees, active == TRUE & age > 28)

Try it yourself