在R中用相同的自定义键组合坐标
在R中用相同的自定义键组合坐标
提问于 2022-06-08 07:51:48
我取消了多家商店的定位器，现在我想找到每一家商店的所有品牌。由于每个品牌的坐标略有不同，所以我通过创建一个自定义键(四舍五入和长到4个小数点，加上商店名称的前5个字母)来组合数据，但这有明显的局限性，对我的情况来说还不够好。
我的下一个方法应该是完美的，但我被困住了。我想：
. based =‘based 2’>在每一点周围创建100米的圆(基于this answer)
- Check的st_transform和st_buffer，如果其中任何一个圆圈具有相同的前5个字母相交.group_id.
- Combine ( group_by(name_key) %>% st_overlaps)，并以某种方式获得了唯一的st_overlaps数据，以将group_id用作密钥
希望有人能帮我一把，因为我完全被困住了。
tt <- tibble(lat = c(41.38702918, 41.386601, 41.38744179, 41.3871449, 41.38896353, 41.38963478, 41.38666041, 41.38598465, 41.3867169, 41.3852132453, 41.38438262),
long = c(2.170086301, 2.16939207, 2.17080332, 2.171450558, 2.167685415, 2.16892721, 2.170950285, 2.171100009, 2.171736392, 2.171215346, 2.170955304),
name = c( "El Corte Inglés", "EL CORTE INGLÉS DIAGONAL (007)", "El Corte Inglés", "El Corte Inglés Barcelona", "Nadons", "Pops And Co", "Bitti", "BITTI", "BITTI", "Bitti", "Bitti"),
brand = c( "b", "s", "c", "e", "e", "c", "m", "c", "s", "e", "b") ) %>%
mutate(name_key = tolower(name),
name_key = iconv(name_key, from="UTF-8",to="ASCII//TRANSLIT"),
name_key = str_remove_all(name_key, '[[:digit:]]'),
name_key = str_remove_all(name_key, '[:punct:]'),
name_key = str_remove_all(name_key, '[:space:]'),
# Remove the common company types from the name
name_key = str_remove_all(name_key, 'ltda'),
name_key = str_remove_all(name_key, 'ltd'),
# Only get the first 5 letters
name_key = str_sub(name_key, end = 5L))
leaflet() %>%
# add different provider tiles
addProviderTiles(
"OpenStreetMap",
# give the layer a name
group = "OpenStreetMap") %>%
addCircleMarkers(data = tt,
radius = 4,
opacity = 0.7,
label = paste(
"Store name: ", tt$name, "<br>",
"name_id: ", tt$name_key,"<br>",
"Brand: ", tt$brand) %>%
lapply(htmltools::HTML) ) Stack Overflow用户
发布于 2022-06-09 15:38:00
这似乎起作用了。这样，您就可以完成所有想要的数据争用和group_by()摘要。
library(sf)
library(tidyverse)
tt <- tibble(lat = c(41.436921, 41.4329208, 41.3829246, 41.398841, 41.3908955, 41.3790881, 41.3876734, 41.4091834, 41.3819518, 41.3878469, 41.3941022, 41.390988469863, 41.3917335, 41.3987172, 41.43623025, 41.3868139, 41.4329513, 41.387896, 41.3913932, 41.3876928, 41.401127, 41.3826847, 41.385063, 41.408131, 41.38855346, 41.39145716, 41.3883035, 41.387676, 41.3856535, 41.39719374, 41.4408444, 41.432961, 41.4331833, 41.4083721, 41.37511991, 41.3925928, 41.4361573, 41.401058, 41.40464, 41.3878013, 41.4136467, 41.390564, 41.3868788, 41.3865356, 41.4125671, 41.3896482, 41.3850639, 41.3919671, 41.3999835, 41.386993, 41.3878405, 41.3917335, 41.3987172, 41.382774, 41.388265, 41.4091834, 41.3828898, 41.386879, 41.3917335, 41.3987302, 41.409183, 41.432923, 41.4344164, 41.3869842, 41.40922657),
long = c(2.1806423, 2.1893701, 2.1274556, 2.152954, 2.1440196, 2.1278398, 2.1668985, 2.2021878, 2.1757278, 2.127962, 2.1418273, 2.1391142945205, 2.1436192, 2.1528498, 2.1814508559036, 2.1560665191209, 2.1893589, 2.1666726, 2.1704746, 2.171124, 2.203366, 2.1731507, 2.173404, 2.188301, 2.17087001, 2.17049986, 2.1677237, 2.155939, 2.1539378, 2.16165528, 2.19848871, 2.189327, 2.189431, 2.1644419, 2.13273168, 2.1464824, 2.1817558, 2.151513, 2.154447, 2.1280387, 2.1385689, 2.1438591, 2.1560561, 2.1714268, 2.2113698, 2.1659005, 2.1734034999999, 2.1757278, 2.1487078, 2.16995, 2.16675529999997, 2.1436192, 2.1528498, 2.12765960000001, 2.127915, 2.2021878, 2.1274947, 2.156056, 2.1436192, 2.1528875, 2.202188, 2.189361, 2.1544574, 2.1208967, 2.20219468),
name = c("El Corte Inglés", "Babis", "Experiencia bebe - Barcelona", "ALI-BEY nens", "Bitti", "El Corte Inglés", "Abitare Kids Barruguet", "Noari kids", "El Corte Inglés", "El Corte Inglés", "Pops And Co", "El Corte Inglés", "BITTI", "Ali Bey", "El Corte Inglés", "Tienda bebés Barcelona - Nonetes & Nou mesos", "Babis", "Abitare Kids - Barruguet", "Palacio del bebe", "El Corte Inglés", "Noari Kids", "El Món D'en Dadà", "Els Tresors de la Panera", "Paloma", "Prenatal Barcelona", "Palacio del Bebé", "El Corte Inglés Barcelona", "Rabasa", "Nonetes", "Prenatal Barcelona", "Prenatal Barcelona", "Babi's", "Nou Mesos", "BCN Bebé", "Nadons", "El Corte Inglés Barcelona", "El Corte Inglés Barcelona", "La Mama Vaca", "Kangura Portabebes ", "El Corte Inglés Barcelona", "Bitti", "Corte Ingles", "Nonetes", "Corte Ingles", "Corte Ingles", "MAINADA KIDS", "LES 4 LLUNES", "EL PALACIO DEL BEBE", "CASTELLBELL", "EL CORTE INGLÉS PLAZA CATALUñA (002)", "ABITARE KIDS BARRUGUET BARCELONA", "BITTI", "ALÍ BEY NENS", "EXPERIENCIA BEBÉ BARCELONA", "EL CORTE INGLÉS DIAGONAL (007)", "NOARI KIDS- BARCELONA", "Experiencia Bebé (Barcelona)", "Nonetes", "Bitti", "Ali Bey Nens", "Noari Kids", "Babis", "Nonetes & Nou mesos", "Nonetes & Nou mesos", "Noari Kids Barcelona"),
brand = c("b", "b", "b", "b", "b", "b", "b", "b", "b", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "e", "m", "m", "m", "m", "m", "sc", "sc", "sc", "sc", "s", "s", "s", "s", "s", "s", "s", "e", "e", "e", "e", "e", "e", "n", "n", "n") ) %>%
mutate(name_key = tolower(name),
name_key = iconv(name_key, from="UTF-8",to="ASCII//TRANSLIT"),
name_key = str_remove_all(name_key, '[[:digit:]]'),
name_key = str_remove_all(name_key, '[:punct:]'),
name_key = str_remove_all(name_key, '[:space:]'),
# Remove the common company types from the name
name_key = str_remove_all(name_key, 'ltda'),
name_key = str_remove_all(name_key, 'ltd'),
# Only get the first 5 letters
name_key = str_sub(name_key, end = 5L))
tt <- tt %>%
st_as_sf(coords = c("long", "lat"),
crs = 4326) %>%
mutate(id = row_number())
tt
tt_buffer <- tt %>%
st_buffer(100)
tt <- tt %>%
mutate(intersects = st_intersects(.,
tt_buffer))
tt_long <- unnest(tt, intersects) %>%
arrange(name, intersects)
tt <- tt %>%
st_drop_geometry() %>%
select(-intersects)
tt_long <- tt_long %>%
tidylog::left_join(.,
tt,
by = c("intersects" = "id"))页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/72541967
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