Tidy Evaluation with rlang Cheatsheet Tidy Evaluation (Tidy Eval) is a framework for doing non-standard evaluation in R that makes it easier to program with tidyverse functions. Non-standard evaluation, better thought of as “delayed evaluation,” lets you capture a user’s R code to run later in a new environment or against a new data frame. A consistent, simple and easy to use set of wrappers around the fantastic stringi package. All function and argument names (and positions) are consistent, all functions deal with 'NA's and zero length vectors in the same way, and the output from one function is easy to feed into the input of another. 15.1 Why the cheatsheet. Examples for those of us who don’t speak SQL so good. There are lots of Venn diagrams re: SQL joins on the internet, but I wanted R examples. Those diagrams also utterly fail to show what’s really going on vis-a-vis rows AND columns. Other great places to read about joins: The dplyr vignette on Two-table verbs. Data Wrangling with dplyr and tidyr Cheat Sheet- RStudio. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. Data wrangling with dplyr and tidyr cheat sheet tidy data foundation for wrangling in ma ma in tidy data set: each variable is saved in its own column syntax.

1. Tidyr Cheat Sheet
2. Dplyr Cheat Sheet Pdf
3. R Dataframe Cheat Sheet

• Reshaping data using tidyr package

Previously, we described the essentials of R programming and provided quick start guides for importing data into R as well as converting your data into a tibble data format, which is the best and modern way to work with your data.

[Figure adapted from RStudio data wrangling cheatsheet (see reference section)]

A data set is called tidy when:

• each column represents a variable
• and each row represents an observation

Having your data in tidy format is crucial for facilitating the tasks of data analysis including data manipulation, modeling and visualization.

The R package tidyr, developed by Hadley Wickham, provides functions to help you organize (or reshape) your data set into tidy format. It’s particularly designed to work in combination with magrittr and dplyr to build a solid data analysis pipeline.

1. Launch RStudio as described here: Running RStudio and setting up your working directory

2. Import your data as described here: Importing data into R

The tidyr package, provides four functions to help you change the layout of your data set:

• gather(): gather (collapse) columns into rows
• spread(): spread rows into columns
• separate(): separate one column into multiple
• unite(): unite multiple columns into one

Example data sets

We’ll use the R built-in USArrests data sets. We start by subsetting a small data set, which will be used in the next sections as an example data set:

Row names are states, so let’s use the function cbind() to add a column named “state” in the data. This will make the data tidy and the analysis easier.

gather(): collapse columns into rows

1. Simplified format:

Tidyr Cheat Sheet

• data: A data frame
• key, value: Names of key and value columns to create in output
• …: Specification of columns to gather. Allowed values are:
  • variable names
  • if you want to select all variables between a and e, use a:e
  • if you want to exclude a column name y use -y
  • for more options, see: dplyr::select()

2. Examples of usage:

• Gather all columns except the column state

Note that, all column names (except state) have been collapsed into a single key column (here “arrest_attribute”). Their values have been put into a value column (here “arrest_estimate”).

• Gather only Murder and Assault columns

Note that, the two columns Murder and Assault have been collapsed and the remaining columns (state, UrbanPop and Rape) have been duplicated.

• Gather all variables between Murder and UrbanPop

The remaining state column is duplicated.

3. How to use gather() programmatically inside an R function?

You should use the function gather_() which takes character vectors, containing column names, instead of unquoted column names

The simplified syntax is as follow:

• data: a data frame
• key_col, value_col: Strings specifying the names of key and value columns to create
• gather_cols: Character vector specifying column names to be gathered together into pair of key-value columns.

As an example, type this:

spread(): spread two columns into multiple columns

1. Simplified format:

• data: A data frame
• key: The (unquoted) name of the column whose values will be used as column headings.
• value:The (unquoted) names of the column whose values will populate the cells.

2. Examples of usage:

Spread “my_data2” to turn back to the original data:

3. How to use spread() programmatically inside an R function?

You should use the function spread_() which takes strings specifying key and value columns instead of unquoted column names

The simplified syntax is as follow:

• data: a data frame.
• key_col, value_col: Strings specifying the names of key and value columns.

As an example, type this:

Dplyr Cheat Sheet Pdf

unite(): Unite multiple columns into one

1. Simplified format:

• data: A data frame
• col: The new (unquoted) name of column to add.
• sep: Separator to use between values

2. Examples of usage:

The R code below uses the data set “my_data” and unites the columns Murder and Assault

3. How to use unite() programmatically inside an R function?

You should use the function unite_() as follow.

• data: A data frame.
• col: String giving the name of the new column to be added
• from: Character vector specifying the names of existing columns to be united
• sep: Separator to use between values.

As an example, type this:

separate(): separate one column into multiple

1. Simplified format:

• data: A data frame
• col: Unquoted column names
• into: Character vector specifying the names of new variables to be created.
• sep: Separator between columns:
  • If character, is interpreted as a regular expression.
  • If numeric, interpreted as positions to split at. Positive values start at 1 at the far-left of the string; negative value start at -1 at the far-right of the string.

2. Examples of usage:

Separate the column “Murder_Assault” [in my_data4] into two columns Murder and Assault:

3. How to use separate() programmatically inside an R function?

You should use the function separate_() as follow.

• data: A data frame.
• col: String giving the name of the column to split
• into: Character vector specifying the names of new columns to create
• sep: Separator between columns (as above).

As an example, type this:

Chaining multiple operations

It’s possible to combine multiple operations using maggrittr forward-pipe operator : %>%.

For example, x %>% f is equivalent to f(x).

In the following R code:

• first, my_data is passed to gather() function
• next, the output of gather() is passed to unite() function

You should tidy your data for easier data analysis using the R package tidyr, which provides the following functions.

• Collapse multiple columns together into key-value pairs (long data format): gather(data, key, value, …)

• Spread key-value pairs into multiple columns (wide data format): spread(data, key, value)

• Unite multiple columns into one: unite(data, col, …)

• Separate one columns into multiple: separate(data, col, into)

• Previous chapters
• Next chapters

• The figures illustrating tidyr functions have been adapted from RStudio data wrangling cheatsheet
• Learn more about tidy data: Hadley Wickham. Tidy Data. Journal of Statistical Software, August 2014, Volume 59, Issue 10..

This analysis has been performed using R (ver. 3.2.3).

R Dataframe Cheat Sheet

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