R：使用突变（）逐行应用自定义功能

Question

我创建了一个函数，该函数使用sf软件包中使用st_join（）来从一组纬度和经度坐标中提取国会区（polygon），并使用不同的shapefile根据指定的“国会”论点确定国会区。 （这是必要的，因为区域会定期重新绘制，因此边界会随着时间的推移而发生变化。）下一步是通过行将功能行应用到包含多行坐标（以及相关的“国会”值）的数据框架上，以便国会给定行的值确定要使用的shapefile，然后将提取的区分配给新变量。

我在应用此功能逐行时遇到麻烦。我首先尝试使用rowwise（）和mutate（）从dplyr中函数，但获得了“必须是大小1”的错误。基于此问题的评论，我将list（）围绕mutate（）函数分配的变量，但这导致新变量是列表一个字符串。

我非常感谢帮助弄清楚（i）修改函数的方法，以便可以使用rowwise（） and mutate（）或（ ii）以其他方式应用我的功能。

可重现的代码如下；您只需要从

library(tidyverse)
library(sf)

districts_104 <- st_read("districts104.shp")
districts_111 <- st_read("districts111.shp")

congress <- c(104, 111)
latitude <- c(37.32935, 37.32935)
longitude <- c(-122.00954, -122.00954)
df_test <- data.frame(congress, latitude, longitude)

point_geo_test <- st_as_sf(df_test,
                             coords = c(x = "longitude", y = "latitude"),
                             crs = st_crs(districts_104)) # prep for st_join()

sf_use_s2(FALSE) # preempt evaluation error that would otherwise pop up when using the st_join function

extract_district <- function(points, cong) {
  shapefile <- get(paste0("districts_", cong))
  st_join_results <- st_join(points, shapefile, join = st_within)
  paste(st_join_results$STATENAME, st_join_results$DISTRICT, sep = "-")
}

point_geo_test <- point_geo_test %>%
  rowwise %>%
  mutate(district = list(extract_district(points = point_geo_test, cong = congress)))

Answer 1

Edit 7 July:

From your comments I understand you were looking for something different, the assumption I made about why your function was giving multiple values was wrong. Hence this new answer from scratch:

---

The custom function you've written doesn't lend itself to row-by-row application, because it already processes all rows at once:

Given the following input:

congress <- c(104, 111, 104, 111, 104, 111)
latitude <- c(37.32935, 37.32935, 41.1134016, 41.1134016, 42.1554948, 42.1554948)
longitude <- c(-122.00954, -122.00954, 73.720356, 73.720356, -87.868850502543, -87.868850502543)

point_geo_test contains these values:

> point_geo_test
[...]
  congress                   geometry
1      104 POINT (-122.0095 37.32935)
2      111 POINT (-122.0095 37.32935)
3      104   POINT (73.72036 41.1134)
4      111   POINT (73.72036 41.1134)
5      104 POINT (-87.86885 42.15549)
6      111 POINT (-87.86885 42.15549)

and extract_district() returns this:

> extract_district(point_geo_test, 104)
[...]
[1] "California-14" "California-14" "NA-NA"         "NA-NA"         "Illinois-10"   "Illinois-10"

This is already a result for each row.唯一的问题是，虽然它们是每行的坐标的正确结果，但它们仅在国会104期间仅用于这些坐标的 name 。因此，这些值仅是有效的for the rows in point_geo_test where congress == 104.

Extracting correct values for all rows

We will create a function that returns the correct data for all rows, eg the correct name for the在相关国会期间进行坐标。

我已经稍微简化了您的代码：df_test不再是中间数据框架，而是直接定义在point_geo_test的创建中。我提取的任何值，我也将保存到此数据框架中。

library(tidyverse)
library(sf)
sf_use_s2(FALSE)

districts_104 <- st_read("districts104.shp")
districts_111 <- st_read("districts111.shp")

congress <- c(104, 111, 104, 111, 104, 111)
latitude <- c(37.32935, 37.32935, 41.1134016, 41.1134016, 42.1554948, 42.1554948)
longitude <- c(-122.00954, -122.00954, 73.720356, 73.720356, -87.868850502543, -87.868850502543)

point_geo_test <- st_as_sf(data.frame(congress, latitude, longitude),
                           coords = c(x = "longitude", y = "latitude"),
                           crs = st_crs(districts_104))

To keep the code more flexible and organized, I'll create a generic function that can fetch any parameter for the given coordinates:

extract_values <- function(points, parameter) {
  # initialize return values, one for each row in `points`
  values <- rep(NA, nrow(points))
  
  # for each congress present in `points`, lookup parameter and store in the rows with matching congress
  for(cong in unique(points$congress)) {
    shapefile <- get(paste0("districts_", cong))
    st_join_results <- st_join(points, shapefile, join = st_within)
    values[points$congress == cong] <- st_join_results[[parameter]][points$congress == cong]
  }
  
  return(values)
}

Examples:

> extract_values(point_geo_test, 'STATENAME')
[1] "California" "California" NA           NA           "Illinois"   "Illinois"  
> extract_values(point_geo_test, 'DISTRICT')
[1] "14" "15" NA   NA   "10" "10"

Storing values

point_geo_test$state <- extract_values(point_geo_test, 'STATENAME')
point_geo_test$district <- extract_values(point_geo_test, 'DISTRICT')
point_geo_test$name <- paste(point_geo_test$state, point_geo_test$district, sep = "-")

Result:

> point_geo_test
Simple feature collection with 6 features and 4 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -122.0095 ymin: 37.32935 xmax: 73.72036 ymax: 42.15549
Geodetic CRS:  GRS 1980(IUGG, 1980)
  congress      state district          name                   geometry
1      104 California       14 California-14 POINT (-122.0095 37.32935)
2      111 California       15 California-15 POINT (-122.0095 37.32935)
3      104       <NA>     <NA>         NA-NA   POINT (73.72036 41.1134)
4      111       <NA>     <NA>         NA-NA   POINT (73.72036 41.1134)
5      104   Illinois       10   Illinois-10 POINT (-87.86885 42.15549)
6      111   Illinois       10   Illinois-10 POINT (-87.86885 42.15549)