What does %>% sign mean in R?

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    This symbol is often used in the ‘dplyr’ package, and is useful when chaining functions together.

    Many functions work by asking for your dataframe or vector as the first parameter ie: select(your_df, column1, column2)

    with %>% you would pass the dataframe into the function like so: your_df %>% select(column1, column2)

    what makes it especially useful, is if you want to perform multiple operations on a dataframe, performing some calculation on the results of another function, without creating intermediate dataframes:

    your_df %>% select(column1, column2) %>% group_by(column1) %>% summarize(count = n(), column_sum = sum(column2))

    I find it quite useful. Do some reading about the dplyr package, it makes my life great.

    This weird looking sign is a forward-pipe operator.

    You can use it to pass the left-hand side input through the right-hand side operator. In mathematical terms, it is the following operation:

    x%>%f which translates to f(x)

    Here is a simple example, where I create a vector of values, take the root square of every number and then compute the sum:

    1. c(1,2,3,4) %>% Map(sqrt, .) %>% Reduce(sum, .) 
    2. # The output: [1] 6.146264 

    It is very useful when you need to apply many different transformations to your data and don’t want to save the intermediate results or have many opening and closing function parentheses.

    Consider writing the following:

    1. x %>% impute %>% shuffle %>% pivot 

    versus the alternative:

    1. pivot(shuffle(impute(x))) 

    I hope you get the point by now.

    Moreover, this technique is very handy when cleaning data.

    You can use it in your R session by loading the margrittr[1] package:

    If you are more familiar with Python and Pandas[2], there is a pipe[3] operator since version 0.16.2.

    I hope this helps.

    To read more about weird looking programming symbols, please consider following me: Yassine Alouini

    Footnotes

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    “%>%” is the pipe operator. It is used to connect objects/functions on the l.h.s to the objects/functions on the r.hs.

    Here are some examples of pipe(%>%) operator:

    1. laptops %>% select(1,2)  

    Here, pipe operator is used to connect the “laptops” dataframe with the select function. So, we are basically selecting the first and the second column from the laptops dataframe using the pipe operator.

    1. laptops %>% filter(Company=="Dell") 

    Here, we are using the pipe operator to extract all the Dell laptops from the “laptops” dataframe.

    1. laptops %>% select("Company","Product","Price_euros") %>% filter(Company=="Dell") 

    This is a complicated example, where we are using pipe operator to connect the laptops dataframe with the select function and the filter function.

    It is the so called “pipe” operator, replacing a traditional function chain with a more comprehensive expression.

    It is a general approach in programming (and R is no exception) to create functions, especially if they perform repetitive operations. As each function is short and specialised, a programmer can quickly end up writing hundreds of them, with a subsequent piece of code like:

    1. plot_it(summarize_it(transform_it(clean_it(read_it(x))))) 

    This is a bit difficult to read, with a complex function chain starting with the inner most one, while the pipe operator allows an expression such as:

    1. x %>% read_it %>% clean_it %>% transform_it %>% summarize_it %>% plot_it 

    The function chain is arguably now more clear, and probably more concise. In practise, this is a matter of taste, as it is very rare to have more than two or three functions in a chain. Usually, the code is split into separate steps, but this is highly programmer specific.

    There are many tutorials for the pipe operator in R, just Google for it.

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    think of it as meaning “THEN”

    It is the difference between selecting the container with its contents included versus only selecting the contents. In any complex data structure, the elements are data structures in their own rights. This comes up lots with named lists.

    test <- list(a = 5, b = 6)

    test[‘a’]

    a

    5

    test[[‘a’]]

    5

    In the former you get a vector with names, in the latter you just get the contents.

    It is actually a “pipe” operator implemented in magrittr, dplyr and other R packages. It is not part of the core.

    For more info, read the magrittr docs: Abstract

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