# Exploratory Data analysis In R – Use And Terminologies

## 1. Objective – R Exploratory Data Analysis

In this blog, we will learn about the exploratory data analysis in R. Also, we will discuss the basic statistical properties. Moreover, we will look at the Exploratory graph and its use. At last, we will discuss some important Terminologies of EDA.

So, let’s start Exploratory Data Analysis in R.

## 2. Introduction to Exploratory Data Analysis in R

## 3. Why do We Use Exploratory Graphs in Data Analysis?

- To understand data properties

- For finding patterns in data

- To suggest modeling strategies

- To “Debug” analyses

## 4. Terminologies in EDA

So, following are some important Terminologies in Exploratory Data Analysis in R, let’s discuss them in detail

**i. Variable**

**Types of variables**

**a. Qualitative Variables**

**Ex.**The color of a ball (e.g., red, green, blue) or the breed of a dog (e.g., collie, shepherd, terrier.

**Types of Qualitative Variables**

**1. Nominal:**Basically, it displays graphical data — all orderings are equally meaningful.

**Ex.**a student’s religion (Atheist, Christian, Muslim, Hindu, …) is nominal.

**2. Ordinal:**A categorical variable whose categories can be meaningfully ordered is called ordinal.

**Ex.**a student’s grade in an exam (A, B, C or Fail) is ordinal.

**b. Quantitative Variables **

**Ex.**Age, count of anything etc.

**Types of Quantitative Variables:**

**1. Discrete:**A discrete variable is one that cannot take on all values within the limits of the variable.

**Ex.**The number of children is a discrete numerical variable (a count). The variable cannot have the value 1.7

**2. Continuous:**In this, the variable can take on any value between two specified values.

**Ex.**age of a human: 25 years, 10 months, 2 days, 5 hours

**ii. Value**

**iii. Observation**

**iv. Tabular data**

**v. Dataset**

- Basically, a data set is represented as a matrix

- There is a row for each unit

- There is a column for each variable

- A unit is an object which we use to measure, such as a person, or a thing

- A variable is a characteristic of a unit. We use it to assign a number or a category

**a. Dimensionality of Data Sets**

**Univariate:**Measurement made on one variable per subject

**Bivariate:**Measurement made on two variables per subject

**Multivariate:**Measurement made on many variables per subject

## 5. Numerical Summaries of Data

## 6. Conclusion

**Exploratory Data Analysis (EDA).**Also, we learned about basic statistics in R. Moreover, we discussed some important terminologies in Exploratory Data Analysis in R. Furthermore, if you feel any query, feel free to ask in the comment section.