Scatter Plots In R

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Introduction to Scatter Plots in R Language

Scatter plots (scatter diagrams) are bivariate graphical representations for examining the relationship between two quantitative variables. Scatter plots are essential for visualizing correlations and trends in data. A scatter plot helps identify the direction and strength of the relationship between two quantitative variables. The scatter plot also helps in identifying the linear to non-linear trend in the data. If there are more than two variables in a data set, one can draw a scatter matrix diagram between all/different pairs of quantitative variables.

Scatter plots in R can be drawn in several ways. Here, we will discuss how to make several kinds of scatter plots in R.

The plot Function in R

For plot() function in R, when two numeric vectors are provided as arguments (one for horizontal and the other for vertical coordinates), the default behavior of the plot() function in R is to make a scatter diagram. For example,

library(car)
attach(Prestige)
plot(income, prestige)

will draw a simple scatterplot of prestige by income.

Usually, the interpretation of a scatterplot is often assisted by enhancing the plot with least-squares or non-parametric regression lines. For this purpose scatterplot() in car package can be used, and it will add marginal boxplots for the two variables

scatterplot(prestige ~ income, lwd = 3 )

Note that in the scatterplot, the non-parametric regression curve is drawn by a local regression smoother, where local regression works by fitting a least-square line in the neighborhood of each observation, placing greater weight on points closer to the focal observation. A fitted value for the focal observation is extracted from each local regression, and the resulting fitted values are connected to produce the non-parametric regression line.

Coded Scatterplots

The scatterplot() function can also be used to create coded scatterplots. For this purpose, a categorical variable is used for coloring or using different symbols for each category. For example, let us plot prestige by income, coded by the type of occupation

scatterplot(prestige ~ income | type)

Note that variables in the scatterplot are given in a formula-style (as y ~ x | groups).

The coded scatterplot indicates that the relationship between prestige and income may well be linear within occupation types. The slope of the relationship looks steepest for blue-collar (bc) occupations and least steep for professional and managerial occupations.

Common Plot Symbols in R

R uses numeric values to represent different symbols. The following is a list of the most commonly used plot symbols and their corresponding numbers:

SymbolCodeDescription
Circle1Solid circle (default)
Square15Solid square
Triangle2Solid triangle
Diamond18Solid diamond
Plus Sign3Plus sign
X4X marks the spot
Open Circle1Circle with no fill
Open Square0Square with no fill
Open Triangle17Triangle with no fill

Customizing Your Scatter Plots in R

One can customize the scatter plot further by adjusting the point size, color, axis labels, title, and more. For example, customized Scatter Plot with Larger Points and Color:

# Customized scatter plot
plot(x, y, 
     main="Customized Scatter Plot", 
     xlab="X Axis Label", ylab="Y Axis Label", 
     pch=17, col="red", cex=1.5, 
     xlim=c(0, 6), ylim=c(0, 12))
  • pch=17: Uses a triangle symbol for points.
  • col="red": Changes the point color to red.
  • cex=1.5: Increases the point size.
  • xlim=c(0, 6) and ylim=c(0, 12): Sets the x and y axis limits.

Jittering Scatter Plots

Jittering the data by adding a small random quantity to each coordinate serves to separate the overplotted points.

data(Vocab)
attach(Vocab)
plot(education, vocabulary) 
# without jittering
plot(jitter (education), jitter(vocabulary) )
Scatter Plots in R Language

The degree of jittering can be controlled via a factor argument. For example, specifying factor = 2 doubles the jitter.

plot(jitter(education, factor = 2), jitter(vocabulary, factor = 2))

Let’s add the least-squares and non-parametric regression line.

abline(lm(vocabulary ~ education), lwd = 3, lty = 2)
lines(lowess(education, vocabulary, f = 0.2), lwd = 3)

The lowess function (an acronym for locally weighted regression) returns coordinates for the local regression curve, which is drawn by lines. The “f” arguments set the span of the local regression to lowess.

Using these different kinds of graphical representations of relationships between variables may help to identify some hidden information (hidden due to overplotting).

FAQs about Scatter Plots in R

  1. How can one draw a scatter plot in R Language?
  2. What is the importance of scatter plots?
  3. What function can be used to draw scatter plots in R?
  4. What is the use of the scatterplot() function in R?
  5. What is meant by a coded scatter plot?
  6. What are jittering scatter plots in R?
  7. What are the important arguments of a plot() function to draw a scatter plot?
  8. What is meant by R Plot Symbols?

See more on plot() function

Summary

Scatter plots in R are essential for visualizing relationships between two continuous variables, detecting patterns, and identifying trends. You can customize the points, colors, add regression lines, and even incorporate grids for clearer insights.

https://itfeature.com, https://gmstat.com

Important MCQs R Language History & Basics 4

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The post is about MCQs R Language. The quiz about MCQS R Language covers some basics of R language, its functionality, concepts of packages, and history of R Language.

MCQs about R Language

1. which of the following is a “base” package for R language?

 
 
 
 

2. R Runs on the _________ operating system

 
 
 
 

3. Which of the following is the wrong statement:

 
 
 
 

4. Which of the following is used for Statistical analysis in the R language?

 
 
 
 

5. Which of the following are examples of variable names that can be used in R?

 
 
 
 

6. Which package contains most fundamental functions to run R?

 
 
 
 

7. Which of the following are best practices for creating data frames?

 
 
 
 

8. The “base” R system can be downloaded from

 
 
 
 

9. The public version of R released in 2000 was

 
 
 
 

10. The following packages are not contained in the “base” R system.

 
 
 
 

11. Advanced users of R can write _______ code to manipulate R objects directly

 
 
 
 

12. The primary source code copyright for R is held by the

 
 
 
 

13. The primary R system is available from the ______

 
 
 
 

14. In which year the R-Core Team was formed?

 
 
 
 

15. The wrong statement from the following is:

 
 
 
 

16. What is the output of getOption(“defaultPackages”) in R Studio?

 
 
 
 

17. R is published under the ______ General Public License version.

 
 
 
 

18. One limitation of R is that its functionality is based on _________

 
 
 
 

19. Which of the following is a recommended package in R

 
 
 
 

20. R functionality is divided into a number of _______

 
 
 
 

The R language is a free and open-source language developed by Ross Ihaka and Robert Gentleman in 1991 at the University of Auckland, New Zealand. The R Language is used for statistical computing and graphics to clean, analyze, and graph your data.

MCQs R Language History and Basics

MCQs R Language History and Basics Online Quiz

  • In which year the R-Core Team was formed?
  • The public version of R released in 2000 was
  • R Runs on the operating system
  • The primary source code copyright for R is held by the
  • R is published under the General Public License version.
  • The “base” R system can be downloaded from
  • The following packages are not contained in the “base” R system.
  • One limitation of R is that its functionality is based on __________.
  • The wrong statement from the following is:
  • R functionality is divided into a number of
  • The primary R system is available from the ________.
  • Which package contains the most fundamental functions to run R?
  • Which of the following is the wrong statement:
  • Which of the following is a “base” package for the R language?
  • Which of the following is a recommended package in R
  • What is the output of getOption(“defaultPackages”) in R Studio?
  • Advanced users of R can write ___________ code to manipulate R objects directly
  • Which of the following is used for Statistical analysis in the R language?
  • Which of the following are examples of variable names that can be used in R?
  • Which of the following are best practices for creating data frames?

The strengths of R programming language lie in its statistical capabilities, data visualization tools (such as ggplot2), and a vast ecosystem of packages contributed by the community. R Language remains a popular choice for statisticians and data scientists working on a wide range of projects.

Basic Statistics and Data Analysis

JSON Files in R: Reading and Writing (2019)

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Introduction to JSON Files in R

A JSON file stores simple data structures and objects in JavaScript Object Notation (JSON) format. JSON is a standard data lightweight interchange format primarily used for transmitting data between a web application and a server. The JSON file is a text file that is language-independent, self-describing, and easy to understand. In this article, we will discuss reading and writing a JSON file in R Language in detail using the R package “rjson“.

Since JSON file format is text only, it can be sent to and from a server and used as a data format by any programming language. The data in the JSON file is nested and hierarchical. Let us start reading and writing JSON files in R.

Creating JSON File

Let’s create a JSON file. Copy the following lines into a text editor such as Notepad. Save the file with a .json extension and choose the file type as all files(*.*). Let the file name be “data.json”, stored on the “D:” drive.

{ 
"ID":["1","2","3","4","5","6","7","8" ],
"Name":["Rick","Dan","Michelle","Ryan","Gary","Nina","Simon","Guru" ],
"Salary":["623.3","515.2","611","729","843.25","578","632.8","722.5" ],
"StartDate":[ "1/1/2012","9/23/2013","11/15/2014","5/11/2014","3/27/2015","5/21/2013",
"7/30/2013","6/17/2014"],
"Dept":[ "IT","Operations","IT","HR","Finance","IT","Operations","Finance"]
}
Reading and Writing JSON files in R

Installing rjson R Package

The R language can also read the JSON files using the rjson package. To read a JSON data file, First, install the rjson package. Issue the following command in the R console, to install the rjson package.

install.packages("rjson")

The rjson package needs to be loaded after installation of the package.

Reading JSON Files in R

To read a JSON file, the rjson package needs to be loaded. Use the fromJSON( ) function to read the file.

# Give the data file name to the function.
result <- fromJSON(file = "D:\\data.json")
# Print the result.
print(result)

The JSON file now can be converted to a Data Frame for further analysis using the as.data.frame() function.

# Convert JSON file to a data frame.
json_data_frame <- as.data.frame(result)
print(json_data_frame)

Writing JSON objects to .Json file

To write JSON Object to file, the toJSON() function from the rjson library can be used to prepare a JSON object and then use the write() function for writing the JSON object to a local file.

Let’s create a list of objects as follows

list1 <- vector(mode="list", length=2)
list1[[1]] <- c("apple", "banana", "rose")
list1[[2]] <- c("fruit", "fruit", "flower")

read the above list to JSON

jsonData <- toJSON(list1)

write JSON object to a file

write(jsonData, "output.json")

Read more about importing and exporting data in R: see the post

MCQs General Knowledge