Introduction to Dplyr package for Data Manipulation

Dplyr package is part of Tidyverse meta-package designed to perform the Data Analysis work efficiently. All the packages in tidyverse share a common design philosophy.

Dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges. This video series will guide you on, how you can use the Dplyr functions to achieve the results of traditional R data manipulation, but faster.

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Data analysis often involves many steps. A typical journey from raw data to results might involve filtering cases, transforming values, summarizing data, and then running a statistical test. But how can we link all these steps together, while keeping our code efficient and readable?

Enter the pipe, R’s most important operator for data processing.

What does the pipe do?
The pipe operator, is a longstanding feature of the magrittr package for R. It takes the output of one function and passes it into another function as an argument. This allows us to link a sequence of analysis steps.

In this video, we will look at three different options of writing a complicated piece of code. The first two options shall be without using pipe operator. The last one will be with using pipe operator. You can then compare the difference in code readability among the three options.
The option 3, writing a code with pipe operator, is clearly emerged out as a winner!

The Rmarkdown file can be found at Github Repo:

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We can select or remove columns with the select ( ) function, using the name or index of the column
There are many ways in which you can use the select ( ) function to select the columns. In this Part-1 video, we will learn the basic usage of select function in selecting the columns.

In Part-2 we will dig deeper into select ( ) function , and use some advance features such as selecting the columns using regular expressions.

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The selection helpers select columns according to given character pattern. There are six selection helpers which give added flexibility in selecting the columns in wide (data sets having more than 20 columns) data set, such as Genomic Data.

starts_with(): Starts with an exact prefix.

ends_with(): Ends with an exact suffix.

contains(): Contains a literal string.

matches(): Matches a regular expression.

num_range(): Matches a numerical range like x01, x02, x03.

In this video, we will see how to effectively use the selection helpers to select the columns. We will use datasets from "nycflights13" package, and "billboard" data set that comes pre-loaded with tidyverse package.

Part-1 video link:
https://youtu.be/BJ0-ozlpVGU

The Rmarkdown file can be found at Github Repo:
https://github.com/siddharth-sahasrabudhe/Youtube-video-files/blob/main/Dplyr%20-%20selection%20helpers.Rmd

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filter () function in dplyr allows you to subset the rows based on their values. The filter() function takes two arguments:
- The data frame you are operating on
- A conditional expression that evaluates to TRUE or FALSE

Under the hood, dplyr filter works by testing each row against your conditional expression and mapping the results to TRUE and FALSE. It then selects all rows that evaluate to TRUE.

In this video, we will work upon Flights data set from nycflights13 package. We will use various conditional statements to filter out rows from the data.

The Rmarkdown file used in this video can be found at Github repo:
https://github.com/siddharth-sahasrabudhe/Youtube-video-files/commit/e3cd56d2173a2e576b77a6e90dd788c724c6a863

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Frequently, you’ll want to arrange the rows in a data frame in a specified order.
arrange() function from dplyr package, orders the rows of a data frame by the values of selected columns.

in this video, we will learn how to arrange the observations in ascending/descending order.
arrange ( ) function also orders the rows using multiple variables. We will work upon the data sets and try out multiple ways in which arrange ( ) function works:
- arrange the rows in ascending or descending order using single variable
- arrange the rows in ascending or descending order using multiple variables
- arrange the rows with NA values in ascending or descending order

The gapminder, flights and alcohol consumption data sets are used to illustrate the concepts.

The Rmarkdown file along with the alcohol consumption data set can be found at Github repo:
https://github.com/siddharth-sahasrabudhe/Dplyr-Package-Videos

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slice ( ) lets you index rows by their (integer) locations. It allows you to select, remove, and duplicate rows. It is accompanied by a number of helpers for common use cases:

slice_head ( ) and slice_tail ( ) select the first or last rows.

slice_sample ( ) randomly selects rows.

slice_min ( ) and slice_max ( ) select rows with the smallest or largest values of a variable.

The Rmarkdown file used in this video can be found at Github Repo:
https://github.com/siddharth-sahasrabudhe/Dplyr-Package-Videos/blob/main/slice%20function.Rmd

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dplyr package provides the group_by ( ) function to group data based on specific variables and the summarize ( ) function to calculate summary statistics for each group.

group_by ( ) function lets you group data by one or more variables. By itself, it may not seem very useful, but it’s great when you start manipulating and summarizing data. That’s because many of the functions applied to data after you use group_by are done group-wise.

summarise ( ) function generates the data summary using various statistical functions. Useful summary functions such as min ( ), max ( ), median ( ), sd ( ) etc. are used to get insights on central tendency of the data-set.

The data-set used in this video is available on Github Repo:
https://github.com/siddharth-sahasrabudhe/Dplyr-Package-Videos/blob/main/IMDB%20Movies.csv

The Rmarkdown file used in this video is available at:
https://github.com/siddharth-sahasrabudhe/Dplyr-Package-Videos/blob/main/sumamrise%20and%20group%20by%20functions.Rmd

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mutate ( ) creates new columns that are functions of existing variables. It can also modify (if the name is the same as an existing column) and delete columns (by setting their value to NULL).

A data frame’s existing variables are preserved when new variables are added using the mutate ( ) function. As well as adding new variables, you can use mutate ( ) to remove variables and modify existing variables. The mutate operation may yield different results on grouped tibbles because the expressions are computed within groups.

The R-markdown file is available on Github repo at:
https://github.com/siddharth-sahasrabudhe/Dplyr-Package-Videos/blob/main/mutate%20function.Rmd

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