Quartiles and Summary

 Quartiles

A quartile is a type of quantile. The first quartile (Q1), is defined as the middle number between the smallest number and the median of the data set, the second quartile (Q2) – the median of the given data set while the third quartile (Q3), is the middle number between the median and the largest value of the data set.

Example:
# R program to illustrate
# Descriptive Analysis

# Import the data using read.csv()
myData = read.csv("CardioGoodFitness.csv", stringsAsFactors = F)

# Calculating Quartiles
quartiles = quantile(myData$Age)
print(quartiles)

Output:
0%  25%  50%  75%     100% 
 18   24       26     33       50 


Interquartile Range

The interquartile range (IQR), also called as midspread or middle 50%, or technically H-spread is the difference between the third quartile (Q3) and the first quartile (Q1). It covers the center of the distribution and contains 50% of the observations.

IQR = Q3 – Q1

# R program to illustrate
# Descriptive Analysis

# Import the data using read.csv()
myData = read.csv("CardioGoodFitness.csv", stringsAsFactors = F)

# Calculating IQR
IQR = IQR(myData$Age)
print(IQR)

Output:
[1] 9

summary() function in R

The function summary() can be used to display several statistic summaries of either one variable or an entire data frame.

Summary of a single variable:

Example:
# R program to illustrate
# Descriptive Analysis

# Import the data using read.csv()
myData = read.csv("CardioGoodFitness.csv",
stringsAsFactors = F)

# Calculating summary
summary = summary(myData$Age)
print(summary)

Output:
Min.     1st Qu.  Median    Mean     3rd Qu.    Max. 
18.00   24.00       26.00       28.79      33.00   50.00

Summary of the data frame

Example:
# R program to illustrate
# Descriptive Analysis

# Import the data using read.csv()
myData = read.csv("CardioGoodFitness.csv",
stringsAsFactors = F)

# Calculating summary
summary = summary(myData)
print(summary)

Output:
Product               Age           Gender            Education    
 Length:180         Min.   :18.00   Length:180         Min.   :12.00  
 Class :character   1st Qu.:24.00   Class :character   1st Qu.:14.00  
 Mode  :character   Median :26.00   Mode  :character   Median :16.00  
                    Mean   :28.79                      Mean   :15.57  
                    3rd Qu.:33.00                      3rd Qu.:16.00  
                    Max.   :50.00                      Max.   :21.00  
 MaritalStatus          Usage          Fitness          Income      
 Length:180         Min.   :2.000   Min.   :1.000   Min.   : 29562  
 Class :character   1st Qu.:3.000   1st Qu.:3.000   1st Qu.: 44059  
 Mode  :character   Median :3.000   Median :3.000   Median : 50597  
                    Mean   :3.456   Mean   :3.311   Mean   : 53720  
                    3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.: 58668  
                    Max.   :7.000   Max.   :5.000   Max.   :104581  
     Miles      
 Min.   : 21.0  
 1st Qu.: 66.0  
 Median : 94.0  
 Mean   :103.2  
 3rd Qu.:114.8  
 Max.   :360.0  

Comments

Post a Comment

Popular posts from this blog

Programming in R - Dr Binu V P

About me Introduction R Data Types R Variables Syntax /Elements of R program comments/keywords R Input/Output R Operators Control Statements in R     if and if else statements      switch statement     for loop- break and next statement     while loop     repeat loop Functions           Functions in R          Recursive Functions in R   Data Structures            Data Structures in R          Strings         Vectors         Lists          Matrices and Arrays         Data Frames         Factors  R Graphs and Charts         R Plots          R Line Graphs         R Scatter Plots          R Bar Plots          R Pie Charts          R Histogram          R Box Plot          R Strip Charts         Saving Plots in R R Data Manipulation           R Data Sets           R Basic Statistics         Data Handling in R         Read and write CSV files in R          Read and write xlsx files in R R Statistics          Central Tendency- Mean Median Mode         Measures of Variability- Range Variance an
Read more

Introduction

R is a free and open-source scripting language developed by Ross Ihaka and Robert Gentleman also known as “R & R” of the Statistics Department of the University of Auckland.in 1993. It's an alternative implementation of the S programming language, which was widely used in the 1980s for statistical computing. The R environment is designed to perforrm complex statistical analysis and display results using many visual graphics. The R progamming languague is written in C, Fortran, and R itself. Most R packages are written in the R programming language, but heavy computational chucks are written in C, C++, and Fortran. R allows integration with Python, C, C++, .Net, and Fortran. R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementa
Read more

R Data Types

Data types are used in computer programming to specify the kind of data that can be stored in a variable. For effective memory consumption and precise computation, the right data type must be selected. Each data type has its own set of regulations and restrictions. There are fundamentally five data types in R. Though straight forward and obvious at first glance, they have a few surprises hidden in them. These elementary data types are: Numeric Integer Complex Character Logical These data types are often combined to form data structures. Let us explore the meaning of each data type in detail. Numeric Datatype in R Decimal values are called numerics in R. It is the default R data type for numbers in R. If you assign a decimal value to a variable x as follows, x will be of numeric type. Real numbers with a decimal point are represented using this data type in R. it uses a format for double-precision floating-point numbers to represent numerical values. # A simple R program # to illustrat
Read more