# dbplot

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:
install.packages("devtools")
devtools::install_github("edgararuiz/dbplot")


## Example

In addition to database connections, the functions work with sparklyr. A Spark DataFrame will be used for the examples in this README.

library(sparklyr)
sc <- spark_connect(master = "local")
spark_flights <- copy_to(sc, nycflights13::flights, "flights")


## ggplot

### Histogram

By default dbplot_histogram() creates a 30 bin histogram

library(ggplot2)

spark_flights %>%
dbplot_histogram(distance)


Use binwidth to fix the bin size

spark_flights %>%
dbplot_histogram(distance, binwidth = 400)


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

spark_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()
spark_flights %>%
dbplot_raster(sched_dep_time, sched_arr_time)


• Pass an aggregation formula that can run inside the database
spark_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
spark_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
spark_flights %>%
dbplot_bar(origin)


• Pass a formula that will be operated for each value in the discrete variable
spark_flights %>%
dbplot_bar(origin, mean(dep_delay))

## Warning: Missing values are always removed in SQL.
## Use mean(x, na.rm = TRUE) to silence this warning
## This warning is displayed only once per session.


### Line plot

• dbplot_line() defaults to a tally() of each value in a discrete variable
spark_flights %>%
dbplot_line(month)


• Pass a formula that will be operated for each value in the discrete variable
spark_flights %>%
dbplot_line(month, mean(dep_delay))


### Boxplot

• It expects a discrete variable to group by, and a continuous variable to calculate the percentiles and IQR. It doesn’t calculate outliers. Currently, this feature works with sparklyr and Hive connections.
spark_flights %>%
dbplot_boxplot(origin, dep_delay)


## 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
spark_flights %>%
db_compute_bins(arr_delay)

## # A tibble: 28 x 2
##    arr_delay  count
##        <dbl>  <dbl>
##  1      4.53  79784
##  2    -40.7  207999
##  3     95.1    7890
##  4     49.8   19063
##  5    819.        8
##  6    140.     3746
##  7    321.      232
##  8    231.      921
##  9    -86      5325
## 10    186.     1742
## # ... with 18 more rows


The data can be piped to a plot

spark_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)

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

## # Source: spark<?> [?? x 2]
##          x      n
##      <dbl>  <dbl>
##  1    4.53  79784
##  2  -40.7  207999
##  3   95.1    7890
##  4   49.8   19063
##  5  819.        8
##  6  140.     3746
##  7  321.      232
##  8  231.      921
##  9  -86      5325
## 10  186.     1742
## # ... with more rows

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


spark_disconnect(sc)

## NULL