In edgararuiz/dbplot: Simplifies Plotting Data Inside Databases

dbplot

library(dplyr)
library(dbplot)
library(nycflights13)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

#knitr::opts_chunk$set(fig.height = 3.5, fig.width =  4, fig.align = 'center')

• Installation
• Connecting to a data source
• Example
• ggplot
  • Histogram
  • Raster
  • Bar Plot
  • Line plot
  • Boxplot
• Calculation functions
• db_bin()

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 ouput a ggplot2 object
2. Functions that outputs a data.frame object with the calculations
3. Creates the formula needed to calculate bins for a Histogram or a Raster plot

Installation

You can install the released version from CRAN:

# install.packages("dbplot")

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

# install.packages("remotes")
# remotes::install_github("edgararuiz/dbplot")

Connecting to a data source

• For more information on how to connect to databases, including Hive, please visit http://db.rstudio.com

• To use Spark, please visit the sparklyr official website: http://spark.rstudio.com

Example

In addition to database connections, the functions work with 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 tally() of each value in a discrete variable

db_flights %>%
  dbplot_bar(origin)

• Pass a formula, and column name, that will be operated 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 tally() 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. It has been tested with the following connections:

• MS SQL Server
• PostgreSQL
• Oracle
• sparklyr

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) 

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)

db_flights %>%
  group_by(x = !! db_bin(arr_delay)) %>%
  tally()

db_flights %>%
  filter(!is.na(arr_delay)) %>%
  group_by(x = !! db_bin(arr_delay)) %>%
  tally()%>%
  collect %>%
  ggplot() +
  geom_col(aes(x, n))

dbDisconnect(con)

edgararuiz/dbplot documentation built on Feb. 9, 2020, 10:21 p.m.