# db_compute_raster

0th

Percentile

##### Aggregates intersections of two variables

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.

Uses very generic dplyr code to aggregate data. Because of this approach, the calculations automatically run inside the database if data has a database or sparklyr connection. The class() of such tables in R are: tbl_sql, tbl_dbi, tbl_sql

##### Usage
db_compute_raster(data, x, y, fill = n(), resolution = 100,
complete = FALSE)
##### Arguments
data

A table (tbl)

x

A continuous variable

y

A continuous variable

fill

The aggregation formula. Defaults to count (n)

resolution

The number of bins created by variable. The highest the number, the more records can be potentially imported from the source

complete

Uses tidyr::complete to include empty bins. Inserts value of 0.

##### Details

There are two considerations when using a Raster plot with a database. Both considerations are related to the size of the results downloaded from the database:

- The number of bins requested: The higher the bins value is, the more data is downloaded from the database.

- How concentrated the data is: This refers to how many intersections return a value. The more intersections without a value, the less data is downloaded from the database.

##### Aliases
• db_compute_raster
##### Examples
# NOT RUN {
library(dplyr)

# Returns a 100x100 grid of record count of intersections of eruptions and waiting
faithful %>%
db_compute_raster(eruptions, waiting)

# Returns a 50x50 grid of eruption averages of intersections of eruptions and waiting
faithful %>%
db_compute_raster(eruptions, waiting, fill = mean(eruptions), resolution = 50)

# }

Documentation reproduced from package dbplot, version 0.3.0, License: GPL-3

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