我有多個資料框（sf物件）。每個資料框都包含一個geometry,name和probability列。它們都具有相同的范圍，但僅在某些區域重疊。
以下是使用兩個資料框的一些示例資料：
nc<-st_read(
system.file("gpkg/nc.gpkg",
package="sf"),
quiet=TRUE)
a<-nc %>%
select(c('geom')) %>%
slice(1:60) %>%
mutate(probability=runif(n=60,
min=1,
max=100)) %>%
mutate(name='A')
b<-nc %>%
select(c('geom')) %>%
slice(50:100) %>%
mutate(probability=runif(n=51,
min=1,
max=100)) %>%
mutate(name='B')
我想merge/join這兩個資料幀（a和b），但在它們重疊的區域，我只想保留最高的name地方。probability新資料框應包含nameand probability。
我將如何開始這樣的問題？
uj5u.com熱心網友回復：
在大多數情況下，您可以將 sf 物件作為常規 data.frames / tibbles 處理，并具有空間連接的額外好處。以下示例用于st_equals將匹配限制為僅來自（ ncnc[50:60,]重疊中的相鄰多邊形，因此匹配的數量會更高）的匹配，對于您的真實資料，您可能想使用其他東西（如 default ），檢查替代方案。st_intersectsst_intersects?st_join
library(dplyr)
library(sf)
library(ggplot2)
# by default st_join uses st_intersects predicate & left_join
# switching to st_equals & inner join
ab <- st_join(a, b, join = st_equals, suffix = c("_a", "_b"), left = F) %>%
mutate(
name = if_else(probability_a > probability_b, name_a, name_b),
probability = pmax(probability_a, probability_b)
# uncomment to only keep unused columns
# , .keep = "unused"
)
ggplot()
geom_sf(data = a, aes(alpha = probability), color = "gray80", fill = "red")
geom_sf(data = b, aes(alpha = probability), color = "gray80", fill = "green")
geom_sf(data = ab, aes(alpha = probability), color = "gray80", fill = "blue")
geom_sf_label(data = ab, aes(label = name), alpha = .5)
theme_void()
結果SF：
ab
#> Simple feature collection with 11 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -83.9547 ymin: 35.18983 xmax: -75.45698 ymax: 36.22926
#> Geodetic CRS: NAD27
#> First 10 features:
#> probability_a name_a probability_b name_b geom
#> 50 69.580424 A 91.374717 B MULTIPOLYGON (((-80.29824 3...
#> 51 48.284343 A 30.066734 B MULTIPOLYGON (((-77.47388 3...
#> 52 86.259738 A 46.447507 B MULTIPOLYGON (((-80.96143 3...
#> 53 44.371614 A 33.907073 B MULTIPOLYGON (((-82.2581 35...
#> 54 25.234930 A 65.436176 B MULTIPOLYGON (((-78.53874 3...
#> 55 7.997226 A 26.543661 B MULTIPOLYGON (((-82.74389 3...
#> 56 10.847150 A 48.375980 B MULTIPOLYGON (((-75.78317 3...
#> 57 32.310899 A 76.864756 B MULTIPOLYGON (((-77.10377 3...
#> 58 52.344792 A 9.340445 B MULTIPOLYGON (((-83.33182 3...
#> 59 66.538503 A 87.656812 B MULTIPOLYGON (((-77.80518 3...
#> name probability
#> 50 B 91.37472
#> 51 A 48.28434
#> 52 A 86.25974
#> 53 A 44.37161
#> 54 B 65.43618
#> 55 B 26.54366
#> 56 B 48.37598
#> 57 B 76.86476
#> 58 A 52.34479
#> 59 B 87.65681
輸入：
set.seed(1)
nc <- st_read(
system.file("gpkg/nc.gpkg",
package = "sf"
),
quiet = TRUE
)
a <- nc %>%
select(c("geom")) %>%
slice(1:60) %>%
mutate(probability = runif(
n = 60,
min = 1,
max = 100
)) %>%
mutate(name = "A")
b <- nc %>%
select(c("geom")) %>%
slice(50:100) %>%
mutate(probability = runif(
n = 51,
min = 1,
max = 100
)) %>%
mutate(name = "B")
創建于 2022-11-21，使用reprex v2.0.2
加入超過 2 個 sf 物件
一個有點愚蠢的例子，只是為了展示如何使用 reduce() 連接多個 sf 物件，從寬到長，按“某物”分組，然后從每組中選擇具有最大概率的行。按原樣，它可能不太實用。以下只是擴展了以前的代碼。
library(purrr)
library(tidyr)
# add c and d
c <- nc %>%
select(c("geom")) %>% slice(45:65) %>%
mutate(probability = runif(n = 21, min = 1,max = 100)) %>%
mutate(name = "C")
d <- nc %>%
select(c("geom")) %>% slice(40:70) %>%
mutate(probability = runif(n = 31, min = 1,max = 100)) %>%
mutate(name = "D")
# collect sf objects into list
abcd <- list("a" = a,
"b" = b,
"c" = c,
"d" = d)
abcd_ijoin <- abcd %>%
# rename columns in each sf, skip geom col
imap(function(sf_, name_) sf_ %>% rename_with(~ paste(.x, name_, sep = '_'), -geom)) %>%
# $a
# geom probability_a name_a
# 1 MULTIPOLYGON (((-81.47276 3... 27.285358 A
# ...
# "Reduce a list to a single value by iteratively applying a binary function"
# basically st_join(a,b) %>% st_join(c) %>% st_join(d)
# or st_join(st_join(st_join(a,b),c),d)
# join = st_equals, left = F are passed to each st_join() call,
# each call adds 2 columns
reduce(st_join, join = st_equals, left = F) %>%
# probability_a name_a probability_b name_b probability_c name_c probability_d name_d geom
# 50 69.580424 A 91.374717 B 63.51060 C 13.78653 D MULTIPOLYGON (((-80.29824 3...
# 51 48.284343 A 30.066734 B 39.61781 C 26.38039 D MULTIPOLYGON (((-77.47388 3...
# remove name_* columns
select(!starts_with("name")) %>%
# probability_a probability_b probability_c probability_d geom
# 50 69.580424 91.374717 63.51060 13.78653 MULTIPOLYGON (((-80.29824 3...
# 51 48.284343 30.066734 39.61781 26.38039 MULTIPOLYGON (((-77.47388 3...
# pivot from wide to long format,
# probability_a, probability_b, .. values are collected into single "probability" column and
# name-part(a,b,c,..) of column name ends up in "name" column
pivot_longer(starts_with("probability"), names_pattern = "probability_(.*)", values_to = "probability") %>%
# A tibble: 44 × 3
# geom name probability
# <MULTIPOLYGON [°]> <chr> <dbl>
# 1 (((-80.29824 35.4949, -80.72652 35.50757, -80.766... a 69.6
# 2 (((-80.29824 35.4949, -80.72652 35.50757, -80.766... b 91.4
# group by some feature (by geom only works because objects were joined with st_equals)
# and get row with max probabilty from each group
group_by(geom) %>%
slice_max(probability) %>%
ungroup()
# A tibble: 11 × 3
# geom name probability
# <MULTIPOLYGON [°]> <chr> <dbl>
# 1 (((-80.29824 35.4949, -80.72652 35.50757, -80.766... b 91.4
# 2 (((-77.47388 35.42153, -77.50456 35.48483, -77.50... a 48.3
ggplot()
geom_sf(data = bind_rows(abcd), color = "gray80", alpha = .1)
geom_sf(data = abcd_ijoin, aes(fill = name, alpha = probability), color = "gray80")
theme_void()
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