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Getting Started
Crunch.io provides a cloud-based
data store and analytic engine. It has a web client for interactive data
exploration and visualization. The crunch package for R
allows analysts to interact with and manipulate Crunch datasets from
within R. Importantly, this allows technical researchers to collaborate
naturally with team members, managers, and clients who prefer a
point-and-click interface. Because every user connects to the same
dataset in the cloud, there is no need to email files back and forth
continually to share results.
Both crunch and the Crunch web application
communicate with the same application programming interface (API),
served over secure HTTP. Within an R script or interactive session, the
crunch package allows you to interact with your data in
Crunch with expressive, idiomatic R. Functions in crunch handle the
translation between R objects and API requests and responses, so you can
typically interact with your datasets as if they were local
data.frames (with some additional metadata), not data on a
remote server.
Authentication
All work with data in Crunch requires authentication with an API key,
which you can create from the web application, by following the
instructions in the help
center.
The crunch R package will use the key automatically if it is stored
in environment variable R_CRUNCH_API_KEY. To store your key
there, you can run the following code from your R console:
if (!require("usethis")) install.packages("usethis")
# Note: After running this command, R may ask you one or two questions.
# While you likely *do* want to update the dependency packages, you likely
# *do not* want to install any package from source, so you can answer something
# like "update all packages from CRAN" if it asks which packages to update,
# and "No" if it asks you to install from source.
usethis::edit_r_environ()
This will open a file where you can put the following (replacing
your-secret-key with the key from the web app, and
api-url with the API for your company, generally something
like "https://your-brand.crunch.io/api/"):
R_CRUNCH_API=api-url
R_CRUNCH_API_KEY=your-secret-key
After you save the file R will read these environment variables in
during startup. However, because you already have R open, it doesn’t
immediately take effect. If you don’t want to restart your R session,
you can run the following command from your R console:
readRenviron("~/.Renviron")
(The next time you start R, you won’t need to run this command, it is
run automatically.)
Now the crunch R package will use your token and connect
to the right API.
If you’re having trouble connecting, use crunch_sitrep()
to get the situation report on the current status of the
crunch package.
## crunch API: https://your-brand.crunch.io/api/
## (found in environment variable `R_CRUNCH_API`)
## key: abcdefghi***************************************
## (found in environment variable `R_CRUNCH_API_KEY`)
Datasets
The Crunch data store is built around datasets, which contain
variables. Crunch datasets and variables include more metadata than R
data.frames and vectors and if possible the
functions and methods in crunch work to keep all of the
data on the server, only pulling metadata and aggregations when
requested. That way, you can explore datasets much larger than you can
comfortably load on your laptop.
To get started, we’re going to import a dataset from R to Crunch. If
you already have a dataset—perhaps someone has invited you to
collaborate on one already—see listDatasets() or
loadDataset() for information on how to find and load
it.
Importing data to Crunch
There are multiple ways to create a new dataset. In the web
application, you can upload files to create datasets using the file
loader. From crunch, there are two methods for creating
datasets: one for data.frames and one for files of other
(non-R) formats.
We’ve included with the package a sample from the 2017 Stack Overflow
developer survey which is filtered on those respondents who reported
having been R users and selecting a handful of variables.
## [1] 1634 23
You can create a dataset from any data.frame you have in
your R session with newDataset. Let’s use that sample
dataset:
ds <- newDataset(SO_survey, name="Stack Overflow Developer Survey 2017")
dim(ds)
## [1] 1634 23
newDataset takes a data.frame as it’s input
and translates R data types into their analogous types in Crunch.
- character –> Text Variable
- numeric, integer –> Numeric Variable
- factor –> Categorical Variable
- Date –> Datetime Variable
If you have an SPSS or CSV file, you can upload it with that without
first reading it into R by giving newDataset the file name
or URL. In this case, it essentially does what you would do in the web
application: uploads your file and creates a dataset from it.
Dataset properties
Datasets allow you to provide a human-readable name is
specified when the dataset is uploaded, and a
description.
## [1] "Stack Overflow Developer Survey 2017"
## [1] ""
Both can be set with <- assignment. Let’s give our
dataset an informative description:
description(ds) <- "Subset of the main survey, restricted to self-reported R users"
description(ds)
## [1] "Subset of the main survey, restricted to self-reported R users"
Variables
Just as a data.frame has columns you can access with the
$ and [ operators, you can get variables from
a Crunch dataset the same way.
## TabsSpaces (categorical)
##
## Count
## Tabs 698
## Spaces 631
## Both 293
## Dataset "Stack Overflow Developer Survey 2017"
## Subset of the main survey, restricted to self-reported R users
##
## Contains 1634 rows of 1 variables:
##
## $CompanySize: CompanySize (categorical)
The crunch package aims to bring the minimum amount
of data down from the server. Most methods work by calculating values on
the server, and then transmitting the results to you R session instead
of transmitting all the data. In this case, the server calculates and
transmits the variable summary instead of sending the data. If you ever
want to actually access the data in the variable, you can call
as.vector(var) to pull down its column.
Crunch allows you to store additional metadata about the variables
that R doesn’t support. For instance, we can add the survey question
wording as the “description” for each variable. This question wording is
available in the schema file that comes with the Stack Overflow dataset,
in the “Question” column.
descriptions(variables(ds)) <- SO_schema$Question
description(ds$CompanySize)
## [1] "In terms of the number of employees, how large is the company or organization you work for?"
Since the R package and the web app use the same API to interact with
Crunch datasets, you can go back and forth between viewing data in R and
on the web. For convenience, the package includes a
webApp() function, which opens a dataset or a variable
within a dataset in your browser in the web application.
Array Variables
Crunch is designed for survey data, and it includes native support
for two data structures common in survey data: categorical arrays (or
“grids”) and multiple response. In R, these would typically be
represented either as multiple, separate columns in a
data.frame or as some other data structure that needs
processing before you can analyze it. Crunch makes it so you don’t have
to think about the underlying data structures in order to do exploratory
analysis.
Categorical Arrays
Surveys commonly ask sets of related questions. In the Stack Overflow
survey, there are several questions about the importance of various
factors for hiring. Conceptually these questions go together, but
because of the limitations of storing data in a table they are presented
as separate variables.
ds$ImportantHiringCompanies
## ImportantHiringCompanies (categorical)
## Congratulations! You've just been put in charge of technical recruiting at Globex, a multinational high- tech firm. This job comes with a corner office, and you have an experienced staff of recruiters at your disposal. They want to know what they should prioritize when recruiting software developers. How important should each of the following be in Globex's hiring process? Previous companies worked at
##
## Count
## Somewhat important 511
## Not very important 316
## Important 230
## Not at all important 63
## Very important 43
ds$ImportantHiringAlgorithms
## ImportantHiringAlgorithms (categorical)
## Congratulations! You've just been put in charge of technical recruiting at Globex, a multinational high- tech firm. This job comes with a corner office, and you have an experienced staff of recruiters at your disposal. They want to know what they should prioritize when recruiting software developers. How important should each of the following be in Globex's hiring process? Knowledge of algorithms and data structures
##
## Count
## Important 502
## Very important 320
## Somewhat important 281
## Not very important 50
## Not at all important 14
Crunch allows you to bundle these related questions together into a
Categorical Array. This is immensely useful when you want to
analyze or communicate information about the set of questions. You can
use the whole array in a cross-tab without having to process dozens of
individual variables.
To create a Categorical Array variable from R, we can use the
makeArray function. We’ll pass to that function a selection
of variables to bind together into an array, along with any additional
metadata we want to provide. Fortunately here, the variables we want to
select have a common naming convention, so we can grep for
them:
imphire <- grep("^ImportantHiring", names(ds), value = TRUE)
imphire
## [1] "ImportantHiringAlgorithms" "ImportantHiringTechExp" "ImportantHiringCommunication"
## [4] "ImportantHiringOpenSource" "ImportantHiringPMExp" "ImportantHiringCompanies"
## [7] "ImportantHiringTitles" "ImportantHiringEducation" "ImportantHiringRep"
## [10] "ImportantHiringGettingThingsDone"
Now we can use that selection in makeArray:
ds$ImportantHiring <- makeArray(ds[imphire], name = "Importance in Hiring Process")
The variables we included in the array have been converted into
subvariables of the categorical array, and are still accessible using
the subvariables() function.
subvariables(ds$ImportantHiring)
## Subvariables:
## $ImportantHiringAlgorithms | ImportantHiringAlgorithms
## $ImportantHiringTechExp | ImportantHiringTechExp
## $ImportantHiringCommunication | ImportantHiringCommunication
## $ImportantHiringOpenSource | ImportantHiringOpenSource
## $ImportantHiringPMExp | ImportantHiringPMExp
## $ImportantHiringCompanies | ImportantHiringCompanies
## $ImportantHiringTitles | ImportantHiringTitles
## $ImportantHiringEducation | ImportantHiringEducation
## $ImportantHiringRep | ImportantHiringRep
## $ImportantHiringGettingThingsDone | ImportantHiringGettingThingsDone
Those names aren’t that pretty. As we saw above, though, we have
better names for them appended to the question wording we read in from
the schema. Each subvariable description has the full
question wording, which really should be the description of
the parent array variable, and then after the question mark, the
response label.
Let’s use some regular expressions to clean that up. We can pull the
part before the question mark from one of the subvariables and add that
to the parent, and then prune the question wording from all and use that
as the “names”:
description(ds$ImportantHiring) <- sub("^(.*\\?).*$", "\\1",
descriptions(subvariables(ds$ImportantHiring))[1])
names(subvariables(ds$ImportantHiring)) <- sub("^.*\\? (.*)$", "\\1",
descriptions(subvariables(ds$ImportantHiring)))
subvariables(ds$ImportantHiring)
## Subvariables:
## $ImportantHiringAlgorithms | Knowledge of algorithms and data structures
## $ImportantHiringTechExp | Experience with specific tools (libraries, frameworks, etc.) used by the employer
## $ImportantHiringCommunication | Communication skills
## $ImportantHiringOpenSource | Contributions to open source projects
## $ImportantHiringPMExp | Experience with specific project management tools & techniques
## $ImportantHiringCompanies | Previous companies worked at
## $ImportantHiringTitles | Previous job titles held
## $ImportantHiringEducation | Educational credentials (e.g. schools attended, specific field of study, grade...
## $ImportantHiringRep | Stack Overflow reputation
## $ImportantHiringGettingThingsDone | Track record of getting things done
webApp(ds$ImportantHiring)
Multiple Response
Crunch also has a special variable type for multiple-response data.
These are survey questions that give users a set of options and allow
them to select more than one of them. An example of this from the Stack
Overflow survey is asking which language a respondent wanted to work
with. In this dataset, the responses are stored in a single column with
each language delimited by semicolon:
knitr::kable(SO_survey[1:5, "HaveWorkedLanguage", drop = FALSE], row.names = FALSE)
| Matlab; Python; R; SQL |
| R; SQL |
| C#; JavaScript; Matlab; Python; R |
| Java; JavaScript; Lua; PHP; R; SQL |
| C++; Matlab; Python; R; VBA |
To analyze this data without Crunch you would usually go through an
elaborate data cleaning process to split the delimited variable up into
its constituent parts, and then reshape it into a format which is more
amenable to analysis. With Crunch, you can make use of multiple response
variables to store it in a compact form, and analyze it just like any
other variable. Since delimited text is such a common way of storing
this kind of answer, Crunch allows you to create a variable directly
from the text. This function creates a derived variable, which
means that if you make changes to the data in the initial variable, the
derived variable will change along with it.
ds$WantWorkLanguageMR <- makeMRFromText(ds$WantWorkLanguage,
delim = "; ",
name = "Languages Desired for Work",
description = description(ds$WantWorkLanguage))
To see what this looks like, let’s do a simple table()
of the new variable and see the counts:
table(ds$WantWorkLanguageMR)
## WantWorkLanguageMR
## VBA TypeScript Java Scala JavaScript Perl Lua Matlab
## 44 170 418 257 591 65 69 120
## Erlang Assembly VB.NET Python Common Lisp Dart CoffeeScript Elixir
## 67 89 24 915 44 22 51 70
## Ruby Groovy C F# Hack C++ Objective-C R
## 143 43 255 88 18 420 65 815
## Clojure Rust Swift Julia Haskell Visual Basic 6 SQL Go
## 82 105 148 155 150 23 594 289
## PHP Smalltalk
## 149 24
Since this is multiple-response data, the sum of these numbers may be
bigger than the number of respondents because people were allowed to
select more than one.
We can reorder the subvariables to make this card easier to read.
Let’s sort them by frequency.
counts <- sort(table(ds$WantWorkLanguageMR), decreasing = TRUE)
subvariables(ds$WantWorkLanguageMR) <- subvariables(ds$WantWorkLanguageMR)[names(counts)]
Conclusion
This vignette has gone through some of the basics of working with
crunch in R including loading data, manipulating dataset properties, and
creating array variables. The crunch package provides
an extensive set of tools for working with survey data and nearly
everything that can be done in the crunch web app can be accomplished
programmatically using R. To learn more about specific use cases, check
out some of the other articles:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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