SQLDataFrame-methods: SQLDataFrame methods

In Liubuntu/SQLDataFrame: Representation of database tables in DataFrame metaphor.

Description

head, tail: Retrieve the first / last n rows of the SQLDataFrame object. See ?S4Vectors::head for more details.

dim, dimnames, length, names: Retrieve the dimension, dimension names, number of columns and colnames of SQLDataFrame object.

[i, j] supports subsetting by i (for row) and j (for column) and respects ‘drop=FALSE’.

Use filter() to choose rows/cases where conditions are true.

mutate() adds new columns and preserves existing ones; It also preserves the number of rows of the input. New variables overwrite existing variables of the same name.

Usage

## S4 method for signature 'SQLDataFrame'
head(x, n = 6L)

## S4 method for signature 'SQLDataFrame'
tail(x, n = 6L)

## S4 method for signature 'SQLDataFrame'
dim(x)

## S4 method for signature 'SQLDataFrame'
dimnames(x)

## S4 method for signature 'SQLDataFrame'
length(x)

## S4 method for signature 'SQLDataFrame'
names(x)

## S4 method for signature 'SQLDataFrame,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'SQLDataFrame,SQLDataFrame'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'SQLDataFrame,list'
x[i, j, ..., drop = TRUE]

## S3 method for class 'SQLDataFrame'
filter(.data, ...)

## S3 method for class 'SQLDataFrame'
mutate(.data, ...)

Arguments

x	A SQLDataFrame object
...	In filter(): Logical predicates defined in terms of the variables in ‘.data’. Multiple conditions are combined with ‘&’. Only rows where the condition evaluates to ‘TRUE’ are kept. See ?dplyr::filter for more details.
.data	A SQLDataFrame object.
...	In mutate(): Name-value pairs of expressions, each with length 1 or the same length as the number of rows in the group (if using ‘group_by()’) or in the entire input (if not using groups). The name of each argument will be the name of a new variable, and the value will be its corresponding value. Use a ‘NULL’ value in ‘mutate’ to drop a variable. New variables overwrite existing variables of the same name.

Value

head, tail: An SQLDataFrame object with certain rows.

dim: interger vector

dimnames: A list of character vectors.

length: An integer

names: A character vector

A SQLDataFrame object or vector with realized column values (with single column subsetting and default drop=TRUE. )

filter: A SQLDataFrame object with subset rows of the input SQLDataFrame object matching conditions.

mutate: A SQLDataFrame object.

Examples

##################
## basic methods
##################

dbname <- system.file("extdata/test.db", package = "SQLDataFrame")
obj <- SQLDataFrame(dbname = dbname, dbtable = "state", dbkey = "state")
dim(obj)
dimnames(obj)
length(obj)
names(obj)

obj1 <- SQLDataFrame(dbname = dbname, dbtable = "state",
                     dbkey = c("region", "population"))
###############
## subsetting
###############

obj[1]
obj["region"]
obj$region
obj[]
obj[,]
obj[NULL, ]
obj[, NULL]

## by numeric / logical / character vectors
obj[1:5, 2:3]
obj[c(TRUE, FALSE), c(TRUE, FALSE)]
obj[c("Alabama", "South Dakota"), ]
obj1[c("South\b3615.0", "West\b3559.0"), ]
### Remeber to add `.0` trailing for numeric values. If not sure,
### check `ROWNAMES()`.

## by SQLDataFrame
obj_sub <- obj[sample(10), ]
obj[obj_sub, ]

## by a named list of key column values (or equivalently data.frame /
## tibble)
obj[data.frame(state = c("Colorado", "Arizona")), ]
obj[tibble(state = c("Colorado", "Arizona")), ]
obj[list(state = c("Colorado", "Arizona")), ]
obj1[list(region = c("South", "West"),
          population = c("3615.0", "365.0")), ]
### remember to add the '.0' trailing for numeric values. If not sure,
### check `ROWNAMES()`.

###################
## filter & mutate 
###################

obj %>% filter(region == "West" & size == "medium")
obj1 %>% filter(region == "West" & population > 10000)

obj %>% mutate(p1 = population / 10)
obj %>% mutate(s1 = size)

Liubuntu/SQLDataFrame documentation built on May 17, 2019, 7:43 a.m.