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### What to say when someone gives you a gift

Well, I usually program in base R for the majority of my tasks. Almost all subsetting tasks can be accomplished with "[" and normal-data aggregations just require aggregate(), ave() or whatever.

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Q70) What are all the important R packages? Answer: Tidyverse, broom, and lubridate for most of my work in data wrangling phase. At times once the data wrangling is done, I have also moved the machine learning part to python for leveraging sckit-learn package. Q71) Where do predictions depends on?

### Fe2 testing

21 Start with Tidyverse 22 Column Renaming 23 Tidy Data – Long and Wide 24 Joining Tables 25 Nesting 26 Brief Reminder – Hypothesis Testing 27 Implement t-test On Different Categories. Dealing with Missing Values 28 Removing NAs- the ordinary way 29 Remove NAs- using ‘dplyr’ 30 Data imputation with dplyr 31 More data imputation

### Chapter 4 section 1 analyzing an economic cartoon food prices and demand answers

Advanced R 1. This class builds on “Intro to R (+data visualisation)” by providing students with powerful, modern R tools including pipes, the tidyverse, and many other packages that make coding for data analysis easier, more intuitive and more readable.

### Syd barrett

Imputation with linear models Tim Zhou contributed a step to use linear models for imputation. This is a nice, compact method for adding an imputation equation for numeric predictors into the recipe. The syntax is similar to the existing imputation steps. Here’s an example from the Ames data:

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tidyverse associates, many other R developers for providing such useful tools for free and all of the R-help participants who have kindly answered so many questions.