我是在分组数据框中插入缺失值。在DF
内,Var1
和Var2
的缺失值被随机分配。
数据帧按变量Factory:MachineNum
分组。在这些分组中按Odometer
的顺序进行插补。
该代码大约可以在5-10%的时间内正常运行。它说的其他90-95%的时间;
"Error: Column Impute must be length 50 (the group size) or one, not 49".
我认为这可能与缺失值的随机性有关。也许当至少1行共享2个缺失值时。
如何使此代码更健壮?
通过多次运行整个代码,您会发现它大约可以完成5-10%的尝试,并且最终会产生Results
数据帧。
library(dplyr)
library(tidyr)
# Create dataframe with some missing values in Var1 and Var2
DF <- data.frame(Factory = c(replicate(150,"Factory_A"), replicate(150,"Factory_B")),
MachineNum = c(replicate(100,"Machine01"), replicate(100,"Machine02"), replicate(100,"Machine03")),
Odometer = c(replicate(1,sample(1:1000,100,rep=FALSE)), replicate(1,sample(5000:7000,100,rep=FALSE)), replicate(1,sample(10000:11500,100,rep=FALSE))),
Var1 =c(replicate(1, sample(c(2:10, NA), 100, rep = TRUE)), replicate(1, sample(c(15:20, NA), 100, rep = TRUE)), replicate(1, sample(c(18:24, NA), 100, rep = TRUE))),
Var2 = c(replicate(1, sample(c(110:130, NA), 100, rep = TRUE)), replicate(1, sample(c(160:170, NA), 100, rep = TRUE)), replicate(1, sample(c(220:230, NA), 100, rep = TRUE)))
)
# Variables with missing values that need imputing
cols <- grep('Var', names(DF), value = TRUE)
# Group-wise impution of missing values
library(stinepack)
Models <- DF %>%
pivot_longer(cols = starts_with('Var')) %>%
arrange(Factory, MachineNum, name, Odometer) %>%
group_by(Factory, MachineNum, name) %>%
mutate(Impute = na.stinterp(value, along = time(Odometer), na.rm = TRUE))
# Convert results from long to wide to visually inspect
Results <- Models %>%
group_by(Factory, MachineNum, name) %>%
mutate(row = row_number()) %>%
tidyr::pivot_wider(names_from = name, values_from = c(value, Impute))
当组中有NA
的前尾时,会出现erorr,并且由于您有na.rm = TRUE
,因此删除它们会使组不平衡。
如果将na.rm
保留为FALSE
,则将NA
保留为NA
,并且可以正常运行。
library(dplyr)
library(stinepack)
DF %>%
pivot_longer(cols = starts_with('Var')) %>%
arrange(Factory, MachineNum, name, Odometer) %>%
group_by(Factory, MachineNum, name) %>%
mutate(Impute = na.stinterp(value, along = time(Odometer), na.rm = FALSE))