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Exploratory Data Analysis
The document discusses exploratory data analysis (EDA) techniques in R. It explains that EDA involves analyzing data using visual methods to discover patterns. Common EDA techniques in R include descriptive statistics, histograms, bar plots, scatter plots, and line graphs. Tools like R and Python are useful for EDA due to their data visualization capabilities. The document also provides code examples for creating various graphs in R.
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Exploratory Data Analysis
- 1. Exploratory Data Analysis (EDA)in R Big Data & IoT Umair Shafique (03246441789) Scholar MS Information Technology - University of Gujrat
- 2. Exploratory Data Analysis(EDA) • Exploratory Data Analysis (EDA) is analyzing the data using visual techniques. It is used to discover trends or patterns or to check assumptions with the help of statistical summaries and graphical representations. • Why we use EDA? • Detection of mistakes. • Checking of assumptions. • Preliminary selection of appropriate models. • Determining the relationships among the exploratory variables. • Accessing the rough size and direction of relationships between exploratory and outcomes variables.
- 3. Techniques: Most EDA techniquesare graphical in nature with a few quantitative techniques. The particular graphical techniques employed in EDA are often quite simple, consisting of various techniques of: • Plotting the raw data such as histograms and probability plots. • Plotting simple statistics such as mean pots and standard deviation and box plot.
- 4. Tools: Some of themost common data science tools used to create an EDA include: • Python: An interpreted, object-oriented programming language with dynamic semantics. Its high-level, built-in data structures. Python and EDA can be used together to identify missing values in a data set, which is important so you can decide how to handle missing values for machine learning. • R: An open-source programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians in data science in developing statistical observations and data analysis.
- 5. Exploratory Data AnalysisIn R In R Language we are going to perform EDA under two broad classifications: • Descriptive Statistics, which include mean, median, mode, inter-quartile range, and so on. • Graphical Methods, which include histograms, box plots, and so on.
- 6. How to performExploratory Data Analysis In R This involves exploring a dataset in three ways: • 1. Summarizing a dataset using descriptive statistics. • 2. Visualizing a dataset using charts. • 3. Normalizing dataset
- 7. Factor in R •What is factor in R? • Factors are variables in R which take on a limited number of different values; such as variables are often referred to as categorical variables. • In a dataset we can distinguish two types of variables categorical and continuous. • In a categorical variable, the value is limited and usually based on a particular finite group. For example, a categorical variable can be countries, years, and gender. • A continuous variable however can take any value from integer to decimal. For example, we can have revenue, the price of a share.
- 8. R charts andgraphs: A pie chart is a representation of values as slices of a circle with different colors. The slices are labeled and the numbers corresponding to each slide are also represented in the chart. In R the pie chart I created using the pie () function which takes positive numbers as a vector input. The additional parameters are used to control labels, colors, titles, etc. Syntax: Pie (x, label, radius, main, col, clockwise) Following is the description of the parameters used: • X is a vector containing the numeric values used in the pie chart. • Labels are used to give a description of the slices. • Radius indicates the radius of the circle of the pie chart. • Main indicates the title of the pie chart. • The color indicates the color palette. • Clockwise is a logical value indicating if the slices are drawn clockwise or anti-clockwise.
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- 11. Bar charts: A barchart presents data in rectangular bars with the length of the bar proportional to the value of R. R uses the function bar plot () to create bar charts. R can be drawn in both vertical a horizontal bar in the bar chart. In the bar chart, each of the bars can give a different color. Syntax: The basic syntax to create a bar chart in R is: Bar plot (H, lab, lab, main, nams.org, col) Following is the description of the parameters used: • H is a vector or matrix containing numeric values used in the chart. • xlab is the label for the x-axis • ylab is the label for the y-axis. • Main I the title of the bar chart. • Names.org is a ctr of names appearing under each bar. • Col is used to give colors to the bars in the graph.
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- 14. Histograms: Histograms represent thefrequencies of a variable. Each bar in the histogram represents the height of the number of values present. R creates histogram using hist () function. This function takes a vector as input and uses some parameters to plot histograms. Syntax: Hist (v, main, xlab,ylab, xlim, ylim, breaks, col, border) • V is a vector containing numeric values in histogram. • Man indicates the title of the chart. • Col is used to set the color. • The border is used to set the border color of each bar. • xlab is used to give a description of the x-axis • ylab is used to give a description of the y-axis. • xlim is used to specify the range of values on the x-axis. • ylim is used to specify the range of values on the y-axis. • Breaks are used to mention the width of each bar.
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- 16. Line graphs: The linechart is a graph that connects a series o points by drawing segments between them. Line charts are usually used in identifying the trends in data. The plot () function in R is used to create the line graph. Syntax: Plot (v, type, col, xlab, ylab) Following is the description of the parameters used: • v is a vector containing the numeric values. • type takes the value “p” to draw only the points, “l” to draw the lines, and “o” to draw both points and lines. • xlab is used to give a description of the x-axis • ylab is used to give a description of the y-axis. • main indicates the title of the chart. • Col is used to set the color.
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- 18. Scatter plot The scatterplotshows many points plotted in the Cartesian plane. Each point represents the values of two variables. One variable is chosen on the horizontal axis and another on the vertical axis. The simple scatterplot is created using the plot () function. Syntax: Plot(x, y, main, xlab, ylab , xlim, ylim) Following is the description of the parameters used: • x is the data set whose values are the horizontal coordinates. • y is the data set whose values are the vertical coordinates. • xlab is used to give a description of the x-axis • ylab is used to give a description of the y-axis. • Xlim is used to specify the range of values on the x-axis. • ylim is used to specify the range of values on the y-axis. • main indicates the title of the chart.
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- 20. IRIS dataset: • Thedataset contains four features sepal length, sepal width, petal length, and petal width for each of the different species (Versicolor, Virginica, Setosa) of the iris flower.
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- 22. How to useR-Studio:
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