我尝试使用sparkyr在sparkDataframe中插入和填充缺失的值。该代码使用普通的Dataframe工作，但我将感谢帮助转换代码工作在Spark。这就是我尝试过的

library(tidyverse)
library(sparklyr)    
ts <- tibble(timestamp=seq(as.POSIXct('2020/01/01'), as.POSIXct('2020/01/10'), by="1 sec"))
    loco_list <- 1:25
    n_obs <- 1e3

    #R dataframe version------------------------------------------------
    #create dummy data
    data <- tibble(
      timestamp=sample(ts$timestamp,n_obs),
      loco=sample(loco_list,n_obs,replace=TRUE),
      mp=runif(n=n_obs,min=10,max=25),
      speed=runif(n=n_obs,min=23,max=88),
      section=as.character(sample(letters,n_obs,replace=TRUE))
    )

    #create a grid to use for filling in mising values
    grid <- expand.grid(timestamp=ts$timestamp,loco=loco_list)

    #join and interpolate/fill missing data
    data_fill <- grid %>% 
      left_join(data,by=c("loco","timestamp")) %>% 
      group_by(loco) %>%
      arrange(loco,timestamp) %>% 
      mutate(
        section_fill=as.character(section),
        mp_fill=as.character(mp),
        speed_fill=na_interpolation(speed)
      ) %>% 
      fill(section_fill,.direction = "down") %>% 
      fill(mp_fill,.direction = "down")

    #spark version--------------------------------------------------------------------------------------
    sc <- spark_connect(master="local")
    data_sp <- copy_to(sc,data)
    ts_sp <- copy_to(sc,ts)

    #very slow - large dataset, would prefer to create this is spark
    grid_sp <- copy_to(sc,grid)

    #preferable, but code is wrong
    grid_sp <- spark_apply(
      expand.grid(timestamp=ts_sp$timestamp,loco=unique(data_sp$loco))
    )

    #join and interpolate/fill missing data
    data_fill_sp <- grid_sp %>% 
      left_join(data,by=c("loco","timestamp")) %>% 
      group_by(loco) %>%
      arrange(loco,timestamp) %>% 
      mutate(
        section_fill=as.character(section),
        mp_fill=as.character(mp),
        speed_fill=na_interpolation(speed)
      ) %>% 
      fill(section_fill,.direction = "down") %>% 
      fill(mp_fill,.direction = "down")