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Basic data wrangling in r

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IADA201 Basic Data Wrangling in R The Basics of Data Wrangling in R The ability to be effective when working with data begins with being able to make sense of the data, through data wrangling - the process of cleaning, transforming and summarising data. To start with data wrangling, the dpylr (Wickham et. al., 2021)1 package needs to be installed and loaded. This done using the install.packages() and library() commands, respectively. install.packages('dplyr') library(dplyr) The install.packages() is run “once-off” to install the package on the machine, the library() command, on the other hand, needs to be run in every session in which the package will be used. 1 Hadley Wickham, Romain François, Lionel Henry and Kirill Müller, 2021. dplyr: A Grammar of Data Manipulation. R package version 1.0.5. https://CRAN.R-project.org/package=dplyr The Pipe Operator in dplyr A useful operator in the dplyr package is the “pipe” operator. This operator allows the user to combine functions and commands to produce often complex tables/summaries with relative ease and in an easy to follow manner. The pipe operator in R using the dplyr package is written as %>%. If you are using RStudio the short-cut Ctrl+Shift+M will insert the pipe operator into your script. The Pipe Operator in dplyr The pipe operator works in such a way that it passes the output of one function/command into the next. That is, the output of the function/command will become the input for the following functio ...
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