More base R functions

This section contains additional functions and tools that we will use in later sections during the EpidemiaR Training workshop.

Concatenating strings and printing messages

In R, paste() and paste0() are functions used to concatenate strings, while message() is used to print messages to the console. Understanding these functions helps in creating more informative and readable outputs, as well as in debugging and providing user feedback.

paste() Function

The paste() function concatenates strings with a specified separator.

Syntax:

paste(..., sep = " ", collapse = NULL)
  • ...: One or more R objects to be concatenated.
  • sep: A string to separate the terms (default is a space).
  • collapse: An optional string to separate the results when concatenating vectors.

Examples:

  1. Concatenating strings with spaces:

    str1 <- "Hello"
str2 <- "World"
result <- paste(str1, str2)
print(result)
# Output: "Hello World"

  2. Using a different separator:

    result <- paste("A", "B", "C", sep = "-")
print(result)
# Output: "A-B-C"

  3. Collapsing a vector into a single string:

    words <- c("apple", "banana", "cherry")
result <- paste(words, collapse = ", ")
print(result)
# Output: "apple, banana, cherry"

paste0() Function

The paste0() function is a shortcut for paste(..., sep = ""). It concatenates strings without any separator.

Syntax:

paste0(...)
  • ...: One or more R objects to be concatenated.

Examples:

  1. Concatenating strings without spaces:

    str1 <- "Hello"
str2 <- "World"
result <- paste0(str1, str2)
print(result)
# Output: "HelloWorld"

  2. Concatenating multiple strings:

    result <- paste0("A", "B", "C")
print(result)
# Output: "ABC"

message() Function

The message() function is used to print a message to the console. Unlike print(), it sends its output to the standard message stream and is often used for warnings, informational messages, and debugging.

Syntax:

message(...)
  • ...: One or more R objects to be printed.

Examples:

  1. Printing a simple message:

    message("This is an informational message.")
# Output: This is an informational message.

  2. Combining paste() with message():

    name <- "John"
age <- 30
message(paste("Name:", name, "Age:", age))
# Output: Name: John Age: 30

  3. Combining paste0() with message():

    prefix <- "ID_"
id <- 1234
message(paste0("Generated ID: ", prefix, id))
# Output: Generated ID: ID_1234

Loading objects and data files

R objects and data

R provides several functions for saving and loading data, allowing you to preserve your workspace and share data with others. The save() and load() functions save and restore entire R workspaces, while saveRDS() and readRDS() handle individual R objects.

save() and load() Functions

The save() function saves R objects to a specified file, which can be loaded back into the R environment using the load() function.

save() Syntax:

save(..., file)
  • ...: R objects to be saved.
  • file: A character string naming the file to save the data to.

load() Syntax:

load(file)
  • file: A character string naming the file to load the data from.

Examples:

  1. Saving multiple objects:

    x <- 1:10
y <- letters[1:10]
save(x, y, file = "data.RData")

  2. Loading the saved objects:

    load("data.RData")
print(x)
print(y)

saveRDS() and readRDS() functions

The saveRDS() function saves a single R object to a file, and readRDS() restores it. Unlike save(), saveRDS() does not save the object name, so you can assign it any name when you load it.

saveRDS() Syntax:

saveRDS(object, file)
  • object: The R object to be saved.
  • file: A character string naming the file to save the object to.

readRDS() Syntax:

readRDS(file)
  • file: A character string naming the file to read the object from.

Examples:

  1. Saving a single object:

    z <- matrix(1:9, nrow = 3)
saveRDS(z, file = "matrix.RDS")

  2. Loading the saved object:

    my_matrix <- readRDS("matrix.RDS")
print(my_matrix)

Using load(), save(), saveRDS(), and readRDS() functions in R enables you to efficiently save and restore your data. Whether you need to save entire workspaces or individual objects, these functions provide flexible options for data persistence and sharing.

CSV and Excel files

Loading CSV and Excel Files into R

Reading data from CSV and Excel files is a common task in data analysis. R provides functions to easily load these files into your workspace for analysis.

Loading CSV Files

To load CSV files, use the read.csv() or readr::read_csv() functions. The readr package’s read_csv() is often preferred for its speed and efficiency.

read.csv() Syntax:

data <- read.csv(file, header = TRUE, sep = ",")
  • file: Path to the CSV file.
  • header: Logical value indicating if the file contains a header row.
  • sep: Character separating the values (default is a comma).

Example:

data <- read.csv("path/to/yourfile.csv")
print(head(data))

readr::read_csv() Syntax:

library(readr)
data <- read_csv("path/to/yourfile.csv")
print(head(data))

Loading Excel Files

To load Excel files, use the readxl package, which provides the read_excel() function.

Syntax:

library(readxl)
data <- read_excel(path, sheet = 1)
  • path: Path to the Excel file.
  • sheet: Sheet number or name to read from (default is the first sheet).

Example:

library(readxl)
data <- read_excel("path/to/yourfile.xlsx", sheet = "Sheet1")
print(head(data))

Using read.csv(), readr::read_csv(), and read_excel() functions, you can easily load CSV and Excel files into R for analysis. These functions provide a straightforward way to import your data and begin your analysis quickly.

Source an entire R script

Sometimes you may want to run an entire script all at once. This can be useful when using multiple scripts that must be run in a structured order, or when storing all of your custom functions in a separate script.

We can use the source() function to execute an entire script at once.

Example:

script_file <- file.path("path/to/yourscript.R")
source(script_file)

This will run all of the operations in that script in your active R sessions. All libraries, variables, and functions that are loaded or created in your sourced script will then be available in your session.

You can use the exists() function to create to make sure that the path you provide leads to an existing script. This function will return either TRUE or FALSE. This is also useful for checking if a data file is present in an expected location before loading and execute additional functions, especially when combined in an if statement.

A few things to watch out for: 1. Loading libraries may cause name conflicts for functions with shared names. 2. Similarly, sourcing a script will overwrite existing variables with the same name. 3. Sourcing script may take a long time depending on the complexity of the script.

Additional base R functions

Here are some additional functions that you may encounter in during the EpidemiaR Training sections.

  • Sys.Date()/Sys.Time() will return the active data or time.
  • nrow() will return the number of rows in a dataframe.
  • length() will return the number of objects in a vector, or the number of columns in a dataframe.
  • unique() will return all of the unique entries in a vector.