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.

autoplot(ds$CompanySize)

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.

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.

cube <- crtabs(~ WantWorkMR + ImportantHiring, ds)
cube_tbl <- as_tibble(cube)
cube_tbl

## # A tibble: 6,120 × 7
##    WantWorkMR_items WantWorkMR_selections Import…¹ Impor…² is_mi…³ count row_c…⁴
##    <fct>            <lgl>                 <fct>    <fct>   <lgl>   <dbl>   <dbl>
##  1 VBA              TRUE                  Importa… Not at… FALSE       1       1
##  2 TypeScript       TRUE                  Importa… Not at… FALSE       0       0
##  3 Java             TRUE                  Importa… Not at… FALSE       2       2
##  4 Scala            TRUE                  Importa… Not at… FALSE       0       0
##  5 JavaScript       TRUE                  Importa… Not at… FALSE       6       6
##  6 Perl             TRUE                  Importa… Not at… FALSE       1       1
##  7 Lua              TRUE                  Importa… Not at… FALSE       1       1
##  8 Matlab           TRUE                  Importa… Not at… FALSE       1       1
##  9 Erlang           TRUE                  Importa… Not at… FALSE       2       2
## 10 Assembly         TRUE                  Importa… Not at… FALSE       1       1
## # … with 6,110 more rows, and abbreviated variable names
## #   ¹​ImportantHiring_items, ²​ImportantHiring_categories, ³​is_missing,
## #   ⁴​row_count

Going further

This vignette provided the basics of plotting Crunch objects, and processing CrunchCubes using tidy tools. You can use these to conduct analyses and generate custom visualizations of Crunch data, but you can also send these custom plots back into the Crunch app by including them in a Shiny app or a flexdashboard and hosting them on the Crunch shiny infrastructure.