spdplyr (version 0.4.0)

dplyr-Spatial: Dplyr verbs for Spatial

Description

Direct application of the dplyr verbs to Spatial objects. There is no need for a conversion from and to Spatial with this approach. Not all verbs are supported, see Details.

Usage

# S3 method for Spatial
mutate(.data, ...)
# S3 method for Spatial
transmute(.data, ...)
# S3 method for Spatial
summarise(.data, ...)
# S3 method for Spatial
group_by(.data, ...)
# S3 method for Spatial
filter(.data, ...)
# S3 method for Spatial
arrange(.data, ...)
# S3 method for Spatial
slice(.data, ...)
# S3 method for Spatial
select(.data, ...)
# S3 method for Spatial
rename(.data, ...)
# S3 method for Spatial
distinct(.data, ..., .keep_all = FALSE)
# S3 method for Spatial
left_join(x, y, by = NULL, copy = FALSE, ...)
# S3 method for Spatial
inner_join(x, y, by = NULL, copy = FALSE, ...)

Arguments

.data

A tbl.

...

Name-value pairs of expressions. See mutate_

.keep_all

argument for distinct, we have to set it to TRUE

x

A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.

y

tbl to join

by

A character vector of variables to join by.

If NULL, the default, *_join() will perform a natural join, using all variables in common across x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly.

To join by different variables on x and y, use a named vector. For example, by = c("a" = "b") will match x$a to y$b.

To join by multiple variables, use a vector with length > 1. For example, by = c("a", "b") will match x$a to y$a and x$b to y$b. Use a named vector to match different variables in x and y. For example, by = c("a" = "b", "c" = "d") will match x$a to y$b and x$c to y$d.

To perform a cross-join, generating all combinations of x and y, use by = character().

copy

If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.

Warning

`distinct` uses behaviour identical to `duplicated`, by coercing all the relevant values to text and determining uniqueness from those. `dplyr::distinct` uses a different internal method that will give different results for some cases of numeric data.

Details

mutate, transmute, filter, arrange, slice, select, rename, distinct all work with attributes on the "data" slot and leave the geometry unchanged.

summarise collapses to a grouped geometries by listing all subgeometries together, it does not perform any topological union or merge, and it takes no account of the calculations done on attributes. This is a brutal collapse of all the data, and is identical to what is seen with spplot(x, "group"). The behaviour of geometric collapse like this is touch and go anyway, see the examples for a what `rgeos::gUnion` does.

summarise for points and multipoints, ... todo single Multipoint for multiple points

Examples

# NOT RUN {
library(sp)
library(maptools)
data(wrld_simpl)
library(spdplyr)
library(raster)
wrld_simpl %>% mutate(NAME = "allthesame", REGION = row_number())
wrld_simpl %>% transmute(alpha = paste0(FIPS, NAME))
wrld_simpl %>% filter(NAME %in% c("New Zealand", "Australia", "Fiji"))
# }
# NOT RUN {
wrld_simpl %>% arrange(LON)
wrld_simpl %>% slice(c(9, 100))
wrld_simpl %>% dplyr::select(UN, FIPS)
wrld_simpl %>% rename(`TM_WORLD_BORDERS_SIMPL0.2NAME` = NAME)
wrld_simpl %>% distinct(REGION, .keep_all = TRUE) %>%
   arrange(REGION)  ## first alphabetically in REGION
wrld_simpl %>% arrange(REGION, desc(NAME)) %>% distinct(REGION, .keep_all = TRUE) ## last
# }
# NOT RUN {
## we don't need to use piping
slice(filter(mutate(wrld_simpl, likepiping = FALSE), abs(LON - 5) < 35 & LAT > 50), 4)


## works with Lines
#as(wrld_simpl, "SpatialLinesDataFrame") %>%
 # mutate(perim = rgeos::gLength(wrld_simpl, byid = TRUE))

# }
# NOT RUN {
## summarise/ze can be used after group_by, or without
wrld_simpl %>% filter(REGION == 150) %>% summarize(max(AREA))
wrld_simpl %>% group_by(REGION) %>% summarize(max(AREA)) %>%
plot(col = rainbow(nlevels(factor(wrld_simpl$REGION)), alpha = 0.3))
# }
# NOT RUN {
## group_by and summarize

# }
# NOT RUN {
g <- wrld_simpl  %>% group_by(REGION)  %>%
 summarize(alon = mean(LON), mxlat = max(LAT), mxarea = max(AREA))
g %>% mutate(ar = factor(REGION)) %>% spplot("ar")
w <- wrld_simpl
w$ar <- factor(w$REGION)
spplot(w, "ar")
# }
# NOT RUN {
# compare what rgeos gives
##spplot(rgeos::gUnionCascaded(w, id = w$ar))  ## good grief, is this compelling . . .
## this is hardly a clean dissolve
##plot(rgeos::gUnionCascaded(w, id = w$ar), col = rainbow(nlevels(factor(w$ar)), alpha = 0.5))
# }