小编MCS*_*MCS的帖子

我正在尝试并行化管道。在管道中有一个 tidyr 命令（“tidyr::complete”）。一旦并行运行，这就会分解代码，因为无法识别对象类。

dplyr 中是否有替代方法可以完成？

library(dplyr)
library(tidyr)
library(zoo)


test <- tibble(year=c(1,2,3,4,5,5,1,4,5),
               var_1=c(1,1,1,1,1,1,2,2,2), 
               var_2=c(1,1,1,1,1,2,3,3,3), 
               var_3=c(0,5,NA,15,20,NA,1,NA,NA))

max_year <- max(test$year,na.rm = T)
min_year <- min(test$year,na.rm = T)

串行

test_serial <- test %>% 
  group_by(var_1,var_2) %>% 
  complete(var_1, year = seq(min_year,max_year)) %>%
  mutate(
    var_3 = na.approx(var_3,na.rm = FALSE),
    var_3 = if(all(is.na(var_3))) NA else na.spline(var_3,na.rm = FALSE))

并行（失败）

devtools::install_github("hadley/multidplyr")
library(multidplyr)

cl <- new_cluster(2)
cluster_copy(cl, c("test","max_year","min_year"))
cluster_library(cl, c("dplyr","tidyr","zoo"))

test_parallel <- test %>% group_by(var_1,var_2) %>% partition(cl)
test_parallel <- test_parallel %>% 
  dplyr::group_by(var_1,var_2) %>% 
  tidyr::complete(var_1, year = seq(min_year,max_year)) %>%
  dplyr::mutate( …

我有一个大的稀疏矩阵（“dgCMatrix”，维度 5e+5 x 1e+6）。我需要计算每列有多少个非零值，并制作一个只有 1 个非零条目的列名称列表。

我的代码适用于小型矩阵，但对于我需要处理的实际矩阵来说计算量太大。

library(Matrix)
set.seed(0)
mat <- Matrix(matrix(rbinom(200, 1, 0.10), ncol = 20))
colnames(mat) <- letters[1:20]

entries <- colnames(mat[, nrow(mat) - colSums(mat == 0) == 1])

任何建议都非常受欢迎！

我有一个sf对象，一张划分区域的地图。我想计算每个区的质心（使用st_point_on_surface），然后计算每个质心之间的相对距离，就像我可以在其上执行计算的距离矩阵（例如保留在特定半径内的矩阵）并获得每个区的 db标识符以及符合条件的标识符列表。

对于缺乏可重现的代码，提前表示歉意。最简单的方法是什么？

提前致谢

I have a df listing a number of areas (df$area) and the areas which these shares a border with (df$next_area). Starting from it i want to get a similar df but with the neighbour of its neighbour. I wrote the following, which works, but appear extremely convoluted. Was there a better solution?

library(dplyr)
library(tidyr)

df <-     data.frame(area=c("A","A","B","B","C","C","C","D"),next_area=c("B","C","A"    ,"C","A","B","D","C") )
    df <- df %>% group_by(area) %>% 
  summarize(next_area = list(sort(unique(as.character(next_area)))))
    df$next_area_exploded <- df$next_area
    for(i in 1:nrow(df)){
      for(j in …