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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:
- 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.
- laptops %>% filter(Company=="Dell")
Here, we are using the pipe operator to extract all the Dell laptops from the “laptops” dataframe.
- 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:
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:
- 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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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%>%fwhich translates to
Here is a simple example, where I create a vector of values, take the root square of every number and then compute the sum:
- c(1,2,3,4) %>% Map(sqrt, .) %>% Reduce(sum, .)
- # The output:  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:
- x %>% impute %>% shuffle %>% pivot
versus the alternative:
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 package:
I hope this helps.
To read more about weird looking programming symbols, please consider following me: Yassine Alouini
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.
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think of it as meaning “THEN”
- Very calm, almost too unnaturally calm
- They usually just smile or smirk at what an average person would lol about
- They look depressed most of the time
- They’re excellent at pointing out flaws(a negative trait)
- They get bored easily
- They love reading, not necessarily books though
- They understand easily and quickly
- Excellent problem-solving skills
- Above-average deduction skills
- Enjoy solitude
yep, pretty much it
There was a beautiful girl in my class who intrigued me. She spoke a lot and she was very intelligent but we only had this time together because we were not friends.
She was older than me, and we never had 1 on 1 time so it was not the ideal situation to just ask “do you like me?”.
So I tested some psychological tricks…
Eye Contact – I made strong eye contact while smiling when I first entered the room. She always smiled back!
Proximity – I made sure I picked a chair way in the front of the room and close to where she was sitting. I noticed she began to pay more attention to me!
Food – I brought her an apple because one day she forgot her lunch, and I wrote a note on a paper. “Do you like me?”
The next day we had a big test and I was really nervous because the girl of my dreams had still not responded to my note… after taking the test I sat there patiently twirling in my desk and trying not to look at her.
Finally when I received my test back, I was relieved! At least I got an A+ on the test so today wasn’t a complete failure.
I flipped the test over and there was a note on the back!
“Of course I like you! You are the most positive student in the class and always smiling. You sit in the front and pay great attention, and you are very thoughtful. Thanks for the apple; it was delicious. Don’t forget to study for your exam next week!”
Yep, that’s right. She liked me.
In R, custom operators are surrounded by percent signs. It’s convenient to have a special syntax for this, because parsing operators can be tricky. The percent signs help make it clear what’s an operator and what isn’t.
The ‘greater than’ symbol looks like a forward arrow and is therefore suggestive of what the operator does: forward the value returned by the preceding expression to the function following the operator.
This concept will be easier to understand if you are aware of Classes & Structures as in other languages like Java/C/C++ etc.
In R, you can create an S4 object (typically acts like a class object) and its’ instances. According to the link given by Jeremy Miles,
You create a class (or an S4 object) with:
> setClass(“Person”, slots = list(name = “character”, age = “numeric”))
and create an instance of a class (or S4 object) with:
> alice <- new("Person", name = "Alice", age = 40)
Now as you access the variables of a dataframe using ‘$’, similarly you can access the slots of the S4 object using ‘@’.
Here, you have an object “alice” and you can access the slots “name” & “age” using: