The Crunch web app provides a number of excellent visualizations and
summary statistics to help communicate survey results to clients, but by
its nature a web interface can’t accommodate every type of plot or
analysis. As a result one of our core design principles at Crunch is to
let users access their data in as convenient a way as possible. This
lets you seamlessly integrate Crunch data into your tool of choice, and
customize how you communicate that data. This vignette goes through how
you can use ggplot2 and tidyverse tools to
analyze and display Crunch data.
First we need to load a dataset into R. If this is a new process to
you, see the crunch
vignettes.
ggplots
You can plot crunch variables using ggplot2’s
autoplot() method. This will generate a plot which looks a
lot like the plots produced on the app, but can be customized with
ggplot methods.
Crunch autoplot methods can produce three families of charts: dot
plots, bar plots, and tile plots. These methods will automatically
adjust for the dimensionality of the data which is sent into the
plotting method. For instance, categorical array variables have two
dimensions, so when they are plotted, you get a two dimensional
plot.
autoplot(ds$ImportantHiring)
autoplot(ds$ImportantHiring, "tile")
autoplot(ds$ImportantHiring, "bar")
Since the autoplot methods produce ggplot objects, you
can customize and extend them just as you would any other ggplot. For
example, you can change the theme of the plot or alter the color
palette.
p <- autoplot(ds$CompanySize)
p + theme_grey()
p +
geom_point(color = "red", size = 3) +
scale_x_log10()
autoplot(ds$ImportantHiring) +
scale_color_brewer()
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
You can also define your own themes or use themes from other
packages. This is helpful if you would like to send plots to a client
using their style guide. For examples of sample corporate themes, take a
look at the ggtech
package from AirBnb, or the ggthemes
package.
Extending ggplots
ggplot2 has a rich ecosystem of extensions which you can
use to add functionality to Crunch plots. For example, you can use patchwork to combine
several plots together, or add interactivity to your plots with plotly. For a full list
of what you can do with ggplot objects, see the ggplot website website.
Working with CrunchCubes
Crunch datasets are often large enough that you can’t easily
calculate summary statistics on your local machine. As a result we try
to keep as much computation as possible on the server and only send the
results of the calculation to your local R session. One of the main ways
this is accomplished it through CrunchCubes, which are typically used to
calculate cross-tabs between categorical variables.
CrunchCubes are n-dimensional arrays where each dimension corresponds
to one of the variables used to calculate the cross tab. For instance
crtabs(~ Professional + CompanySize + Country, ds) will
generate a three dimensional array where each entry is the number of
respondents who selected a given category along the three dimensions.
You can plot high dimensional CrunchCubes in the same way that you can
plot Crunch Variables and the additional dimensions will be assigned to
plot facets.
ds %>%
group_by(CompanySize, Professional, TabsSpaces) %>%
summarize(count = n(Country)) %>%
autoplot()
Autoplots are great, but cubes can quickly become too complex for a
general plotting method to do a good job at representing the plot. If we
include lots of dimensions in a cube, or include a dimension with a lot
of categories the plot becomes unreadable. We can still work with these
types of cubes by using the as_tibble method to convert it
into a long data frame. This lets us further process the data to produce
a readable plot.
cube <- crtabs(~ Country + Professional + TabsSpaces, ds)
cube_tbl <- as_tibble(cube)
cube_tbl
## # A tibble: 4,848 × 6
## Country Professional TabsSpaces is_missing count row_count
## <fct> <fct> <fct> <lgl> <dbl> <dbl>
## 1 Afghanistan None of these Both FALSE 0 0
## 2 Aland Islands None of these Both FALSE 0 0
## 3 Albania None of these Both FALSE 0 0
## 4 Algeria None of these Both FALSE 0 0
## 5 American Samoa None of these Both FALSE 0 0
## 6 Andorra None of these Both FALSE 0 0
## 7 Angola None of these Both FALSE 0 0
## 8 Anguilla None of these Both FALSE 0 0
## 9 Antarctica None of these Both FALSE 0 0
## 10 Antigua and Barbuda None of these Both FALSE 0 0
## # … with 4,838 more rows
The tibble contains most of the same information as the cube data
structure, but it is easier to work with using tidy tools. The first few
columns will always correspond to the dimensions of the cube. The
is_missing column represents whether any of the constituent
categories are missing, which lets you easily filter out missing
elements from the table. The last columns contain the cube measures
which will usually be count and row_count. The
row_count column contains the unweighted counts, and the
count column contains the weighted counts. If there is no
weighting applied to the dataset, then count and
row_count will be the same.
The cube output is now in the correct format to feed into tidyverse
tools. Here we filter the dataset to produce a readable chart.
cube_tbl %>%
filter(
!is_missing,
Professional == "Professional developer") %>%
arrange(desc(count)) %>%
top_n(10) %>%
ggplot(aes(y = Country, x = count, color = TabsSpaces)) +
geom_point() +
theme_crunch()
## Selecting by row_count
This lets you combine the speed and efficiency of the Crunch
computation infrastructure with the flexibility of the tidyverse. You
can let Crunch do the memory intensive counting operation, but still get
a tidy dataframe representation that is easy to work with using other
packages.
Array Variables in tibbles
Array variables are treated slightly differently from regular Crunch
variables both in the cube itself and the tbl_df which is produced by
as_tibble. Array variables add two dimensions to the
tibble, in the case of categorical array variables these will be the
subvariables and their categories, and for multiple response this will
be the options and whether or not each option was selected.
cube <- crtabs(~ WantWorkMR + ImportantHiring, ds)
cube_tbl <- as_tibble(cube)
cube_tbl
## # A tibble: 6,120 × 7
## WantWorkMR_items WantWorkMR_selections ImportantHiring_items ImportantHiring…
## <fct> <lgl> <fct> <fct>
## 1 VBA TRUE ImportantHiringAlgor… Not at all impo…
## 2 TypeScript TRUE ImportantHiringAlgor… Not at all impo…
## 3 Java TRUE ImportantHiringAlgor… Not at all impo…
## 4 Scala TRUE ImportantHiringAlgor… Not at all impo…
## 5 JavaScript TRUE ImportantHiringAlgor… Not at all impo…
## 6 Perl TRUE ImportantHiringAlgor… Not at all impo…
## 7 Lua TRUE ImportantHiringAlgor… Not at all impo…
## 8 Matlab TRUE ImportantHiringAlgor… Not at all impo…
## 9 Erlang TRUE ImportantHiringAlgor… Not at all impo…
## 10 Assembly TRUE ImportantHiringAlgor… Not at all impo…
## # … with 6,110 more rows, and 3 more variables: is_missing <lgl>, count <dbl>,
## # row_count <dbl>
In the tibble output, each array variable is decomposed into two
columns. The multiple response variable has an _items
column which specifies the multiple response options, and a
_selections column which specifies whether each option was
selected (TRUE), not-selected (FALSE) or not
answered (NA). The categorical array variable gets an
_items column for the subvariable names, and a
_categories column for their categories. Take note that by
default the multiple response selection status is hidden from the array
representation of CrunchCubes, but is included in the tibble
representation. You can filter the tibble to select cases where the
multiple response *_selctions variables are
TRUE to get the entries which are displayed when
CrunchCubes are printed to the console.