Download [BETTER] Tidyverse

The Data Visualisation and Graphics for communication chapters in R for Data Science. R for Data Science is designed to give you a comprehensive introduction to the tidyverse, and these two chapters will get you up to speed with the essentials of ggplot2 as quickly as possible.

Manually installing plyr with install.package("plyr") fixes the error and tidyverse loads without problems. However, plyrfunctions sometimes mask dplyr functions, so I'd prefer to not have plyr installed.

Download Tidyverse

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The tidyverse is a collection of open source packages for the R programming language introduced by Hadley Wickham[1] and his team that "share an underlying design philosophy, grammar, and data structures" of tidy data.[2] Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping.[3][4][5]

As of November 2018, the tidyverse package and some of its individual packages comprise 5 out of the top 10 most downloaded R packages.[6] The tidyverse is the subject of multiple books and papers.[7][8][9][10] In 2019, the ecosystem has been published in the Journal of Open Source Software.[11]

Critics of the tidyverse have argued it promotes tools that are harder to teach and learn than their base-R equivalents and are too dissimilar to other programming languages.[12][13] On the other hand, some[14] have argued that tidyverse is a very effective way to introduce complete beginners into programming, as pedagogically it allows students to quickly begin doing powerful data processing tasks.[15][14]

How have we come to this point where basically everybody is expected to use tidyverse & Co. as the default? What is so "tidy" about filling your code with hundreds of %>%'s? I know it looks more like SQL but I don't want my R code to look like SQL. I want my R code to look like R!

The tidyverse is a set of packages that work in harmony because theyshare common data representations and API design. The tidyversepackage is designed to make it easy to install and load core packagesfrom the tidyverse in a single command.

The 'tidyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step. Learn more about the 'tidyverse' at .

Haven enables R to read and write various data formats used by other statistical packages by wrapping the fantastic ReadStat C library written by Evan Miller. Haven is part of the tidyverse. Currently it supports:

I have been working on this on and off for the past 4 years or so. In 2022, I have updated thecontents of the book to reflect updates introduced with R 4.1 and in several packages (especiallythose from the {tidyverse}). I have also cut some content that I think is not that useful,especially in later chapters.

This book can be useful to different audiences. If you have never used R in your life, and wantto start, start with Chapter 1 of this book. Chapter 1 to 3 are the very basics, and should beeasy to follow up to Chapter 7.Starting with Chapter 7, it gets more technical, and will be harder to follow. But I suggestyou keep on going, and do not hesitate to contact me for help if you struggle! Chapter 7is also where you can start if you are already familiar with R and the {tidyverse}, but notfunctional programming. If you are familiar with R but not the {tidyverse} (or have no cluewhat the {tidyverse} is), then you can start with Chapter 4. If you are familiar with R, the{tidyverse} and functional programming, you might still be interested in this book, especiallyChapter 9 and 10, which deal with package development and further advanced topics respectively.

The core data structure used in the tidyverse is the tibble, which is an R data frame with some small enhancements to improve the user experience. tidycensus returns tibbles by default.

A full treatment of the tidyverse and its functionality is beyond the scope of this book; however, the examples in this chapter will introduce you to several key tidyverse features using US Census Bureau data. For a more general and broader treatment of the tidyverse, I recommend the R for Data Science book (Wickham and Grolemund 2017).

Census data queries using tidycensus, combined with core tidyverse functions, are excellent ways to explore downloaded Census data. Chapter 2 covered how to download data from various Census datasets using tidycensus and return the data in a desired format. A common next step in an analytic process will involve data exploration, which is handled by a wide range of tools in the tidyverse.

Eight tidyverse packages are loaded: ggplot2, tibble (Mller and Wickham 2021), purrr, dplyr, readr, and tidyr are included along with stringr (Wickham 2019a) for string manipulation and forcats (Wickham 2021a) for working with factors. These tools collectively can be used for many core Census data analysis tasks.

The above example introduces some additional syntax common to tidyverse data analyses. The %>% operator from the magrittr R package (Bache and Wickham 2020) is a pipe operator that allows for analysts to develop analytic pipelines, which are deeply embedded in tidyverse-centric data analytic workflows. The pipe operator passes the result of a given line of code as the first argument of the code on the next line. In turn, analysts can develop data analysis pipelines of related operations that fit together in a coherent way.

Since R version 4.1, the base installation of R also includes a pipe operator, |>. It works much the same way as the magrittr pipe %>%, though %>% has some small additional features that make it work well within tidyverse analysis pipelines. In turn, %>% will be used in the examples throughout this book.

In the tidyverse, split-apply-combine is implemented with the group_by() function in the dplyr package. group_by() does the work for the analyst of splitting a dataset into groups, allowing subsequent functions used by the analyst in an analytic pipeline to be applied to each group then combined back into a single dataset. The examples that follow illustrate some common group-wise analyses.

The tidyverse approach to iteration is found in the purrr package. purrr includes a variety of functions that are designed to integrate well in workflows that require iteration and use other tidyverse tools. The map_*() family of functions iterate over values and try to return a desired result; map() returns a list, map_int() returns an integer vector, and map_chr() returns a character vector, for example. With tidycensus, the map_dfr() function is particularly useful. map_dfr() iterates over an input and applies it to a function or process defined by the user, then row-binds the result into a single data frame. The example below illustrates how this works for the years 2010 through 2019.

These margin of error functions in tidycensus can in turn be integrated into tidyverse-centric analytic pipelines to handle large margins of error around estimates. Given that the smaller age bands in the Salt Lake City dataset are characterized by too much uncertainty for our analysis, we decide in this scenario to aggregate our data upwards to represent populations aged 65 and older by sex.

The tidyverse offers a user-friendly way to view this data with the glimpse() function that is part of the tibble package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:

As you can see, tidyverse packages are very powerful tools for loading, cleaning, and inspecting data so that you can begin analyzing your data right away! And remember, you can load all of these packages at once with library(tidyverse).

Instructors must certify on a per-topic basis,just as pilots obtain ratings for different kinds of aircraft.We currently offer certification on the tidyverse and Shiny;candidates must already be familiar with one or both before taking part in training.Once certified,instructors are added to our web siteand are eligible for free licenses to RStudio professional products for use in their training.

Up to now we have been manipulating vectors by reordering and subsetting them through indexing. However, once we start more advanced analyses, the preferred unit for data storage is not the vector but the data frame. In this chapter we learn to work directly with data frames, which greatly facilitate the organization of information. We will be using data frames for the majority of this book. We will focus on a specific data format referred to as tidy and on specific collection of packages that are particularly helpful for working with tidy data referred to as the tidyverse.

We will learn how to implement the tidyverse approach throughout the book, but before delving into the details, in this chapter we introduce some of the most widely used tidyverse functionality, starting with the dplyr package for manipulating data frames and the purrr package for working with functions. Note that the tidyverse also includes a graphing package, ggplot2, which we introduce later in Chapter 8 in the Data Visualization part of the book; the readr package discussed in Chapter 5; and many others. In this chapter, we first introduce the concept of tidy data and then demonstrate how we use the tidyverse to work with data frames in this format.

The same information is provided, but there are two important differences in the format: 1) each row includes several observations and 2) one of the variables, year, is stored in the header. For the tidyverse packages to be optimally used, data need to be reshaped into tidy format, which you will learn to do in the Data Wrangling part of the book. Until then, we will use example datasets that are already in tidy format. 17dc91bb1f