Data has become an integral part of the world. Every sector, nowadays, depends on data. To understand the data, techniques of data visualization are used. To use this data to gain insights, languages such as R is used. R language is a statistical graphical tool that represents the data in an organized and structured manner.
Data visualization and Exploratory Data Analysis are essential steps in the initial analysis of data. These data strategies break down complex data. They help in understanding data and their underlying problems. R programming helps with its broad libraries and packages and provides tools to transform data. In this blog, we will delve into data visualization and EDA techniques using R programming help.
Data Visualization -Who and Why is it Used?
Data visualization is a powerful tool used by professionals across various organizations and industries. It helps to break down complex data making it easier to comprehend. It identifies patterns, correlations, and trends in the data. This allows us to make informed decisions by providing Data-driven findings. It also identifies irregularities that can be neglected in raw data.
Data visualization are used by professionals such as data analyst, marketers, engineers, educators, and corporations like NGOs or corporates. It can be used to analyze the data, troubleshoot errors, understand the customers, track campaigns, and use data for social, economic, and demographic purposes.
R packages: Understanding the packages used for Data visualization
R language is a much better tool than Python to generate visually appealing data. The packages in R programming help provide the interface and the tools for data to be interpreted in the same place.
Some of the known packages are Ploty, ggplot, and lattice. These packages cater to the needs according to the type of data. Let’s try to understand the packages a little bit.
Ploty
It is an open-source library that creates interactive graphics. The greatest feature of Ploty is that it requires no internet connection to work. You can download the package from Github or CRAN and utilize it. Ploty is easy to use and supports various chart types such as histograms, 3D charts, line charts, scatter plots, and many more. The diversity in graphics aids in creating web-based interactive dashboards.
ggplot2
ggplot2 is a versatile package for creating powerful visualization. It provides customizable themes and styles for the data to be explored. It utilizes high-level API to generate visuals such as error charts, bar graphs, pie charts, and more. A unique feature of ggplot2 is that it uses faceting. Faceting means creating multiples of plots having data arranged in a grid. This feature makes it easier to compare and contrast various data across variables.
lattice
lattice is a high-level visualization tool for implementing data in R. It is used to create Trellis graphs. These graphs show multivariate data sets at the same time for comparison. It supports various plots for exploration and is highly customizable. It integrates with other R packages allowing manipulation of data before creating conditioned plots for visualization.
Exploratory Data Analysis(EDA)
Just as the name suggests, EDA is used to explore the data to find insights on the data. It analyses and visualizes the data to find patterns and trends. Exploratory Analysis is an iterative process, i.e. you need to go back and forth between the steps to learn the data.
Features of EDA include
- It utilizes the entire structure of the raw data and tries to understand the patterns
- It looks for any abnormalities or errors that could be present in the data
- It identifies the relationship between different variables in data and generates a hypothesis based on it.
Exploratory data analysis is a crucial tool for data visualization. R is a popular language used by statisticians and analysts. It is a powerful instrument for EDA because it has a wide range of functions. This makes it easy to create custom visuals by enforcing various packages and making the data in an easy-to-read format.
Guide on Executing Exploratory Data Analysis in R
EDA is used in the initial investigation and analysis of the dataset. There is no fixed routine or steps to perform an EDA. But, these steps could be beneficial if you are starting or learning how to carry out the analysis.
Loading the Data
The first step in the analysis is to load the data into the R programming language. We can do this by using a function like read.csv() if the data is in a CSV file. If the data is available in another format, we can use other R functions to load the data.
Summarization Of Data
To have a basic understanding of the data structure and distribution, summarising the data could be beneficial. Using the summary() function, basic statistics of the data sets such as the mean, median, and mode of the variables are shown. To analyze the relationship between datasets, the table() function can be used.
Visualisation of Data
R programming helps to offer a variety of functions for data analysis. Some of the functions include:
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Histogram: The histogram provides graphics of continuous numerical data. It divides the data into “bins” with a set variable.
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Barplot: It creates a chart in both vertical and horizontal bars. It displays the relationship between different variables among various groups.
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Boxplot: Boxplot displays information using vectors and draws a distribution. The distribution is based on sets. The sets include a minimum. First quartile, median, second quartile, and maximum.
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Scatterplot: It creates graphics to depict the relationship between two numeric variables. It is a set of dotted lines using two vectors, i.e., x-axis and y-axis.
Identifying and Reviewing the Data
Once, the data is visualized according to the preference, it is reviewed. One of the main goals of EDA is to find the correlation between variables in the data set. Reviewing the data helps to identify the various anomalies and outliers in the data.
The variance could include:
- Unexpected trends and patterns in the data
- The significant difference in the data from the rest
This information could be used to generate a hypothesis that could further be tested. The next testing process could use more formal statistical techniques. Ultimately, informed decisions could be made after the interpretation of the data.
Conclusion
Data visualization is one of the most important tools in EDA. Therefore, to understand the data it becomes crucial to generate information by asking questions. EDA in R programming helps in creating initial visualisation.
This might seem like a daunting task for learners and students. But, with the help of experts in programming, all queries regarding the concepts will be cleared. The personalised solutions and availability around the clock make learning uncomplicated. If you are tight on deadlines or stuck in solving R problems, opt for R Programming Homework Help.