rquery 1.2.0 now incorporates bquote() quasi-quotation.
In fact this is enough to allow rqdatatable to directly work the indirect column names example from our bquote() articles (1, 2).
First let’s check what packages we have available for these examples.
have_rqdatatable <- FALSE
if (requireNamespace("rqdatatable", quietly = TRUE)) {
library("rqdatatable")
have_rqdatatable <- TRUE
}
have_db <- FALSE
if (requireNamespace("RSQLite", quietly = TRUE) &&
requireNamespace("DBI", quietly = TRUE)) {
have_db <- TRUE
}library("rquery")
# define our parameters
# pretend these come from far away
# or as function arguments.
group_nm <- "am"
num_nm <- as.name("hp")
den_nm <- as.name("cyl")
derived_nm <- as.name(paste0(num_nm, "_per_", den_nm))
mean_nm <- as.name(paste0("mean_", derived_nm))
count_nm <- as.name("group_count")Immediate mode example (note we are using newer rquery 1.2.1 notation “extend()” instead of extend_nse()).
# apply a parameterized pipeline using bquote
mtcars %.>%
extend(.,
.(derived_nm) := .(num_nm)/.(den_nm)) %.>%
project(.,
.(mean_nm) := mean(.(derived_nm)),
.(count_nm) := length(.(derived_nm)),
groupby = group_nm) %.>%
orderby(.,
group_nm)## am mean_hp_per_cyl group_count
## 1 0 22.71491 19
## 2 1 23.41987 13Stored operator tree examples.
# make an abstract description of the table to start with
td <- mk_td("mtcars",
as.character(list(group_nm, num_nm, den_nm)))
# helper function to adapt to later database environemnt
count <- function(v) { length(v) }
# capture the operator pipeline
ops <- td %.>%
extend(.,
.(derived_nm) := .(num_nm)/.(den_nm)) %.>%
project(.,
.(mean_nm) := mean(.(derived_nm)),
.(count_nm) := count(.(derived_nm)),
groupby = group_nm) %.>%
orderby(.,
group_nm)# apply it to data
mtcars %.>% ops## am mean_hp_per_cyl group_count
## 1 0 22.71491 19
## 2 1 23.41987 13We can display the pipeline in various forms.
# print the operator sequence
cat(format(ops))## mk_td("mtcars", c(
## "am",
## "hp",
## "cyl")) %.>%
## extend(.,
## hp_per_cyl := hp / cyl) %.>%
## project(., mean_hp_per_cyl := mean(hp_per_cyl), group_count := count(hp_per_cyl),
## groupby = c('am')) %.>%
## order_rows(.,
## c('am'),
## reverse = c(),
## limit = NULL)# draw the pipeline
ops %.>%
op_diagram(.) %.>%
DiagrammeR::grViz(.)
The same example in a database.
# connect to a database
raw_connection <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
# build a representation of the database connection
dbopts <- rq_connection_tests(raw_connection)
db <- rquery_db_info(connection = raw_connection,
is_dbi = TRUE,
connection_options = dbopts)
print(db)## [1] "rquery_db_info(SQLiteConnection, is_dbi=TRUE, note=\"\")"# copy data to db
tr <- rquery::rq_copy_to(db, "mtcars", mtcars,
temporary = TRUE,
overwrite = TRUE)
print(tr)## [1] "mk_td(\"mtcars\", c( \"mpg\", \"cyl\", \"disp\", \"hp\", \"drat\", \"wt\", \"qsec\", \"vs\", \"am\", \"gear\", \"carb\"))"
## mpg cyl disp hp drat wt qsec vs am gear carb
## 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## 6 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
## ...# materialize result remotely (without passing through R)
res <- materialize(db, ops)
DBI::dbReadTable(raw_connection, res$table_name)## am mean_hp_per_cyl group_count
## 1 0 22.71491 19
## 2 1 23.41987 13# or execute and pull results back
execute(db, ops)## am mean_hp_per_cyl group_count
## 1 0 22.71491 19
## 2 1 23.41987 13# print the derived sql
sql <- to_sql(ops, db)
cat(sql)## SELECT * FROM (
## SELECT `am`, AVG ( `hp_per_cyl` ) AS `mean_hp_per_cyl`, count ( `hp_per_cyl` ) AS `group_count` FROM (
## SELECT
## `am`,
## `hp` / `cyl` AS `hp_per_cyl`
## FROM (
## SELECT
## `am`,
## `hp`,
## `cyl`
## FROM
## `mtcars`
## ) tsql_43672649717521417876_0000000000
## ) tsql_43672649717521417876_0000000001
## GROUP BY
## `am`
## ) tsql_43672649717521417876_0000000002 ORDER BY `am`# disconnect
DBI::dbDisconnect(raw_connection)
rm(list = c("raw_connection", "db"))