R Language Basics

The post is about data in R language. Learn how to save and read data in R with this comprehensive guide. Discover methods like write.csv(), saveRDS(), and read.table(), understand keyboard input using readline(), scan(), and master file-based data loading for matrices and datasets. Perfect for beginners and intermediate R users!

How can you Save the Data in R Language?

To save data in R Language, there are many ways. The easiest way of saving data in R is to click Data –> Active Data Set –> Export Active Data. A dialogue box will appear. Click OK in the dialogue box. The data will be saved. The other ways to save data in R are:

Saving to CSV Files

# Base R package
write.csv(Your_DataFrae, "path/to/file.csv", row.names = FALSE)

#readr (tidyverse) Package
library(readr)
write_csv(your_DataFrame, "path/to/file.csv")

Saving to MS Excel Files

To save data to Excel files, the writexl or openxlsx package can be used

library(writexl)
write_xlsx(your_DataFrame, "path/to/file.xlsx")

Saving to R’s Native Formats

Single or Multiple objects can be saved to a single file, such as RData

# .RData file
save(object1, object2, file = "path/to/data.RData")

# .rds file
saveRDS(your_DataFrame, "path/to/data.rds")

Saving to Text Files

Data can be saved to text files using the following commands:

# Using Base R Package
write.table(your_DataFrame, "path/to/file.txt", sep = "\t", row.names = FALSE)

# using readr Package
write_delim(your_DataFrame, "path/to/file.txt", delim = "\t")

Saving to JSON File Format

The data can be saved to a JSON file format using the jsonlite package.

write_json(your_DataFrame, "path/to/file.json")

Saving Data to Databases

Write data to SQL databases (for example, SQLite, PostgreSQL), for example

library(DBI)
library(RSQLIte)

# Create a database connect
con <- dbConnect(RSQLite::SQLite(), "path/to/database.db")

# Write a data frame to the database
dbWriteTable(con, "table_name", your_DataFrame)

# Disconnect when done
dbDisconnect(con)

Saving Data to Other Statistical Software Formats

The haven package can be used to save data for SPSS, Stata, or SAS. For example

library(haven)
write_sav(your_DataFrame, "path/to/file.sav")  # SPSS file format
write_dta(your_DataFrame, "path/to/file.dta")  # STATA file format

It is important to note that

• File Paths: Use absolute file paths, for example, D:/projects/data.csv, or relative paths such as data/file.csv.
• Overwriting: By default, R will overwrite existing files. Add checks to avoid accidental loss, for example,
  if (!file.exists("file.csv")){
write.csv(your_DataFrame, "file.csv")
}

How to Read Data from the Keyboard?

To read the data from the keyboard, one can use the following functions

• scan(): read data by directly pressing keyboard keys
• deadline(): read text lines from a file connection
• print(): used to display specified keystrokes on the display/monitor.

Explain How to Read Data or a Matrix from a File?

• read.table(): usually read.table() function is used to read data. The default value of a header is set to “FALSE,” and hence, when we do not have a header, we need not use this argument.
• Use read.csv() or read.table() function to import/read spreadsheet exported files in which columns are separated by commas instead of white spaces. For MS Excel file use, read.xls() function.
• When you read in a matrix using read.table(), the resultant object will become a data frame, even when all the entries got to be numeric. The as.matrix() function can be used to read it into a matrix form like this
  as.matrix(x, nrow = 5, byrow=T)

What is scan() Function in R?

The scan() function is used to read data into a vector or list from the console or a file.

z <- scan()
1: 12 5
3: 5
4:
Read 3 items

z
### Output
12 5 5

What is readline() Function in R?

The readline() function is used to read text lines from a connection. The readline() function is used for inputting a line from the keyboard in the form of a string. For example,

w <- readline()
xyz vw u

w

## Output

xyz vw u

Statistics and Data Analysis

R is a powerful language for statistical computing and data analysis. While working with data, one may encounter special values in R Language. There are several special values in R Language (such as NA, NULL, Inf, and NaN) representing missing data, undefined results, or mathematical operations. Understanding their differences and how to handle them correctly is crucial for effective R programming. Misunderstanding these special values can lead to bugs in your R programming code or incorrect analysis.

This guide about special values in R Language covers:

• NA: Missing or undefined data.
• NULL: Absence of a value or object.
• Inf / -Inf: Infinity from calculations like division by zero.
• NaN: “Not a Number” for undefined math operations.

Let us explore each special value with examples.

NA – Not Available (Missing Data)

NA represents missing or unavailable data in vectors, matrices, and data frames. The key properties of special value NA are

• Used in all data types (logical, numeric, character).
• Functions like is.na() detect NA values.

x <- c(1, 2, NA, 4)
is.na(x)    # Returns FALSE FALSE TRUE FALSE

Note that Operations involving NA usually result in NA unless explicitly handled with na.rm = TRUE. NA is not the same as "NA" (a character string). Also note that type-specific NAs are NA_integer_, NA_real_, NA_complex_, NA_character_.

NULL – Absence of a Value

NULL signifies an empty or undefined object, often returned by functions expecting no result. It is different from NA because NULL means the object does not exist, while NA means a value is missing. The key properties are:

• NULL is a zero-length object, while NA has a placeholder.
• Cannot be part of a vector.
• Functions return NULL if they operate on a NULL object.
• Use is.null() to check for NULL.

y <- NULL
is.null(y) # Returns TRUE

Note that NaN is a subtype of NA (is.na(NaN) returns TRUE). Also note that it is used for invalid numerical operations.

Inf and -Inf – Infinity

Inf and -Inf represent positive and negative infinity in R. These values occur when numbers exceed the largest finite representable value. Inf arises from operations like division by zero or overflow. The key properties are:

• Often results from division by zero.
• Can be used in comparisons (Inf > 1000 returns TRUE).

1 / 0            # Returns Inf
log(0)           # Returns -Inf
is.infinite(1/0) # TRUE

Note that Infinite values can be checked with is.infinite(x). Inf and -Inf results in NaN.

NaN – Not a Number

NaN results from undefined mathematical operations, like 0/0. One can check NaN values by using is.nan() function. Let us see how to check for NaN using R example:

0 / 0 # Returns NaN
is.nan(0 / 0) # TRUE
is.na(NaN) # TRUE (NaN is a type of NA)

Note that NULL is different from NA and NaN; it means no value exists. It is commonly used for empty lists, missing function arguments, or when an object is undefined.

FALSE and TRUE (Boolean Values)

Results in logical values used in conditions and expressions.

b <- TRUE
c <- FALSE
as.numeric(b)  # 1
as.numeric(c)  # 0

Note that Logical values are stored as integers (TRUE = 1, FALSE = 0). These are useful for indexing and conditional statements.

Comparison between NA, NULL, Inf, NaN

Common Pitfalls and Best Practices

1. NA vs. NULL: Use NA for missing data in datasets; NULL for empty function returns.
2. Math with Inf/NaN: Use is.finite() to filter valid numbers.
3. Debugging Tip: Check for NA before calculations to avoid unexpected NaNs.

Handling Special Values in R

To manage special values in R efficiently, use the following functions:

• is.na(x): Check for NA values.
• is.null(x): Check for NULL values.
• is.infinite(x): Check for Inf or -Inf.
• is.nan(x): Check for NaN.

Practical Tips When Using Special Values in R Language

The following are some important practical tips when making use of special values in R Language:

1. Handling Missing Data (NA)
   • Use na.omit(x) or complete.cases(x) to remove NA values.
   • Use replace(x, is.na(x), value) to fill in missing values.
2. Avoiding NaN Issues
   • Check for potential division by zero.
   • Use ifelse(is.nan(x), replacement, x) to handle NaN.
3. Checking for Special Values in R
   • is.na(x), is.nan(x), is.infinite(x), and is.null(x) help identify special values.
4. Using Default Values with NULL
   • Set default function arguments as NULL and use if (is.null(x)) to assign a fallback value.

Summary of Special Values in R Language

Understanding special values in R is essential for data analysis and statistical computing. Properly handling NA, NULL, Inf, and NaN ensures accurate calculations and prevents errors in your R scripts. By using built-in functions, one can effectively manage these special values in R and improve the workflow.

Learn more about Statistics Software

To dive into data analysis, one of the first functions encountered is the summary() function in R Language. This versatile function as a tool is a game-changer for quickly getting and understanding the data insights, identifying patterns, and spotting potential issues. For a beginner or an experienced R user, mastering the summary() function can significantly improve not only your R language learning, R programming, and data analytics skills but may also streamline the users’ workflow. This function helps in getting many of the descriptive statistics and exploratory data analysis. In this post, we will explore what the summary() function in R does, provide real-world examples, and share actionable tips to help you get the most out of it.

What is the summary() Function in R?

The summary() function in R is a built-in function that provides a concise overview of an R object (such as a data frame, vector, or statistical model) to get a statistical summary of the data. For numeric data, it calculates key statistics like the mean, median, quartiles, and minimum/maximum values. For categorical data, it displays frequency counts. For regression models (e.g., linear regression), it offers insights into coefficients, residuals, and overall model performance.

Real-World Examples of Using summary()

1. Exploring a Dataset

Suppose you are analyzing a dataset of $mtcars$. The summary() function in R can be used to get a quick snapshot of the data:

# Load a sample dataset
data("mtcars")

# Get a summary of the dataset
summary(mtcars)

The output will show key statistics for each column, such as:

• MPG (miles per gallon): Min, 1st Quartile, Median, Mean, 3rd Quartile, Max
• Cylinders: Frequency counts for each category

The above output helps you quickly identify trends, such as the average MPG or the most common number of cylinders in the dataset.

2. Analyzing a Linear Regression Model

Suppose for a linear regression model to predict mile per gallon (mpg), you can use summary() to evaluate its performance:

# Fit a linear model
model <- lm(mpg ~ wt + hp, data = mtcars)

# Summarize the model
summary(model)

The output will include:

• Coefficients: Estimates, standard errors, and p-values
• R-squared: How well the model explains the variance in the data
• Residuals: Distribution of errors

This information is invaluable for understanding the strength and significance of your predictors.

3. Summarizing Categorical Data

For categorical data, such as survey responses, summary() function in R provides frequency counts:

# Create a factor vector
survey_responses <- factor(c("Yes", "No", "Yes", "Maybe", "No", "Yes"))

# Summarize the responses
summary(survey_responses)

## Output
Maybe    No   Yes 
    1     2     3 

The output will show:

• Counts for each category (e.g., “Yes”: 3, “No”: 2, “Maybe”: 1)

This is a quick way to understand the distribution of responses.

Actionable Tips for Using summary() Effectively

Combine with str() for a Comprehensive Overview
Use str() alongside summary() to get both the structure and summary statistics of your data. This helps you understand the data types and distributions simultaneously.

str(mtcars)
## Output
'data.frame':   32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

summary(mtcars)

Use summary() for Data Cleaning
Look for missing values (NA) in the summary output. This can help you identify columns that require imputation or removal.

Customize Output for Specific Columns
If you’re only interested in specific columns, subset your data before applying summary()

summary(mtcars$mpg)

## Output
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  10.40   15.43   19.20   20.09   22.80   33.90 

Leverage summary() for Model Diagnostics
When working with statistical models, use summary() function in R to check for significant predictors and assess model fit.

Visualize Summary Statistics
Pair summary() with visualization tools like ggplot2 or boxplot() to better understand the distribution of your data.

Conclusion: Start Using summary() Today!

The summary() function in R Language is a simple yet powerful tool that every R user should have in their toolkit. Whether one is exploring data, cleaning datasets, or evaluating models, summary() provides the insights one needs to make informed decisions. Incorporating summary() function into workflow, will save time and gain a deeper understanding of your data.

Summary Statistics using the measure of central tendency