Data Processing

Read e-book online Big Data Analytics Made Easy PDF

By Y. Lakshmi Prasad

ISBN-10: 1946390720

ISBN-13: 9781946390721

Enormous information Analytics Made effortless is a must-read for everyone because it explains the facility of Analytics in an easy and logical manner besides an finish to finish code in R. whether you're a beginner in huge info Analytics, you are going to nonetheless be ready to comprehend the techniques defined during this publication. while you are already operating in Analytics and working with colossal information, you are going to nonetheless locate this booklet valuable, because it covers exhaustive facts Mining concepts, that are thought of to be complex themes. It covers computing device studying options and offers in-depth wisdom on unsupervised in addition to supervised studying, that is extremely important for decision-making. the hardest facts Analytics thoughts are made less complicated, It positive aspects examples from all of the domain names in order that the reader will get hooked up to the publication simply. This ebook is sort of a own coach to help you grasp the artwork of knowledge technological know-how.

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We use the assignment operator (<-) to create new variables. 3 SORTING DATA To Group a data frame in R, use the order( ) function. By default, sorting is ASCENDING. By a minus sign to indicate DESCENDING order. # Sort by Age Agesort <- Employee[order(Age),] #Sorting with Multiple Variables, sort by Gender and Age Mul_sort <- Employee[order(Gender, Age),] By executing the above code we sorted the data frame based on Gender as first preference and Age as second. 4 IDENTIFYING AND REMOVING DUPLICATED DATA We can remove duplicate data using functions duplicated() and unique() as well as the function distinct in dplyr package.

We use trimws function to deal with blanks of a string. Name <- “ Y Lakshmi Prasad “ Trimmed_Name <- trimws(Name, which = c(“both”, “left”, “right”)) substr Function: This function is used to Extract characters from string variables. The arguments to substr() specify the input vector, start character position and end character position. The last parameter is optional. When omitted, all characters after the location specified in the second space will be extracted. character(numericx) typeof(stringx) typeof(numericx) The typeof() function can be used to verify the type of an object, possible values include logical, integer, double, complex, character, raw, list, NULL, closure (function), special and built in.

Salary <- c(46000, 50000, 35000, 30000, 44800, 45000, 10200, 15000) barplot(salary) meanValue <- mean(salary) Let’s see a plot showing the mean value: abline(h=meanValue) To calculate standard deviation we use sd function. Let us call sd on the salary vector now, and assign the result to the deviation variable. deviation <- sd(salary) We’ll add a line on the plot to show one standard deviation above the mean abline(h = meanValue + deviation) Now try adding a line on the plot to show one standard deviation below the mean (the bottom of the normal range): abline(h = meanValue - deviation) 3.

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