dbplot

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Leverages dplyr to process the calculations of a plot inside a database. This package provides helper functions that abstract the work at three levels:

  1. Functions that output a ggplot2 object
  2. Functions that output a data.frame object with the calculations
  3. Functions that create formulas for calculating bins for a Histogram or a Raster plot

Installation

You can install the released version from CRAN:

Or the development version from GitHub, using the remotes package:

install.packages("remotes")
pak::pak("edgararuiz/dbplot")

Connecting to a data source

Example

The functions work with standard database connections (via DBI/dbplyr) and with Spark connections (via sparklyr). A local RSQLite database will be used for the examples in this README.

library(DBI)
library(odbc)
library(dplyr)

con <- dbConnect(RSQLite::SQLite(), ":memory:")
db_flights <- copy_to(con, nycflights13::flights, "flights")

ggplot

Histogram

By default dbplot_histogram() creates a 30 bin histogram

library(ggplot2)

db_flights |> 
  dbplot_histogram(distance)

Use binwidth to fix the bin size

db_flights |> 
  dbplot_histogram(distance, binwidth = 400)

Because it outputs a ggplot2 object, more customization can be done

db_flights |> 
  dbplot_histogram(distance, binwidth = 400) +
  labs(title = "Flights - Distance traveled") +
  theme_bw()

Raster

To visualize two continuous variables, we typically resort to a Scatter plot. However, this may not be practical when visualizing millions or billions of dots representing the intersections of the two variables. A Raster plot may be a better option, because it concentrates the intersections into squares that are easier to parse visually.

A Raster plot basically does the same as a Histogram. It takes two continuous variables and creates discrete 2-dimensional bins represented as squares in the plot. It then determines either the number of rows inside each square or processes some aggregation, like an average.

  • If no fill argument is passed, the default calculation will be count, n() 
db_flights |>
  dbplot_raster(sched_dep_time, sched_arr_time)

  • Pass an aggregation formula that can run inside the database
db_flights |>
  dbplot_raster(
    sched_dep_time, 
    sched_arr_time, 
    mean(distance, na.rm = TRUE)
    )

  • Increase or decrease for more, or less, definition. The resolution argument controls that, it defaults to 100
db_flights |>
  dbplot_raster(
    sched_dep_time, 
    sched_arr_time, 
    mean(distance, na.rm = TRUE),
    resolution = 20
    )

Bar Plot

  • dbplot_bar() defaults to a count() of each value in a discrete variable
db_flights |>
  dbplot_bar(origin)

  • Pass an aggregation formula that will be calculated for each value in the discrete variable
db_flights |>
  dbplot_bar(origin, avg_delay =  mean(dep_delay, na.rm = TRUE))

Line plot

  • dbplot_line() defaults to a count() of each value in a discrete variable
db_flights |>
  dbplot_line(month)

  • Pass a formula that will be operated for each value in the discrete variable
db_flights |>
  dbplot_line(month, avg_delay = mean(dep_delay, na.rm = TRUE))

Boxplot

It expects a discrete variable to group by, and a continuous variable to calculate the percentiles and IQR. It doesn’t calculate outliers.

Boxplot functions require database support for percentile/quantile calculations.

Supported databases:

  • Spark/Hive (via sparklyr) - uses percentile_approx()
  • SQL Server (2012+) - uses PERCENTILE_CONT()
  • PostgreSQL (9.4+) - uses percentile_cont()
  • Oracle (9i+) - uses PERCENTILE_CONT()

Not supported: SQLite, MySQL < 8.0, MariaDB (no percentile functions)

Here is an example using dbplot_boxplot() with a local data frame:

nycflights13::flights |>
  dbplot_boxplot(origin, distance)

Calculation functions

If a more customized plot is needed, the data the underpins the plots can also be accessed:

  1. db_compute_bins() - Returns a data frame with the bins and count per bin
  2. db_compute_count() - Returns a data frame with the count per discrete value
  3. db_compute_raster() - Returns a data frame with the results per x/y intersection
  4. db_compute_raster2() - Returns same as db_compute_raster() function plus the coordinates of the x/y boxes
  5. db_compute_boxplot() - Returns a data frame with boxplot calculations
db_flights |>
  db_compute_bins(arr_delay) 
#> # A tibble: 28 × 2
#>    arr_delay  count
#>        <dbl>  <int>
#>  1     NA      9430
#>  2    -86      5325
#>  3    -40.7  207999
#>  4      4.53  79784
#>  5     49.8   19063
#>  6     95.1    7890
#>  7    140.     3746
#>  8    186.     1742
#>  9    231.      921
#> 10    276.      425
#> # ℹ 18 more rows

The data can be piped to a plot

db_flights |>
  filter(arr_delay < 100 , arr_delay > -50) |>
  db_compute_bins(arr_delay) |>
  ggplot() +
  geom_col(aes(arr_delay, count, fill = count))

db_bin()

Uses ‘rlang’ to build the formula needed to create the bins of a numeric variable in an un-evaluated fashion. This way, the formula can be then passed inside a dplyr verb.

db_bin(var)
#> (((max(var, na.rm = TRUE) - min(var, na.rm = TRUE))/30) * ifelse(as.integer(floor((var - 
#>     min(var, na.rm = TRUE))/((max(var, na.rm = TRUE) - min(var, 
#>     na.rm = TRUE))/30))) == 30, as.integer(floor((var - min(var, 
#>     na.rm = TRUE))/((max(var, na.rm = TRUE) - min(var, na.rm = TRUE))/30))) - 
#>     1, as.integer(floor((var - min(var, na.rm = TRUE))/((max(var, 
#>     na.rm = TRUE) - min(var, na.rm = TRUE))/30))))) + min(var, 
#>     na.rm = TRUE)
db_flights |>
  group_by(x = !! db_bin(arr_delay)) |>
  count()
#> # Source:   SQL [?? x 2]
#> # Database: sqlite 3.51.1 [:memory:]
#> # Groups:   x
#>         x      n
#>     <dbl>  <int>
#>  1  NA      9430
#>  2 -86      5325
#>  3 -40.7  207999
#>  4   4.53  79784
#>  5  49.8   19063
#>  6  95.1    7890
#>  7 140.     3746
#>  8 186.     1742
#>  9 231.      921
#> 10 276.      425
#> # ℹ more rows
db_flights |>
  filter(!is.na(arr_delay)) |>
  group_by(x = !! db_bin(arr_delay)) |>
  count()|>
  collect() |>
  ggplot() +
  geom_col(aes(x, n))