本文介绍了在R中线性估算缺失值的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个缺少值的数据框:
I have a data frame with missing values:
X Y Z
54 57 57
100 58 58
NA NA NA
NA NA NA
NA NA NA
60 62 56
NA NA NA
NA NA NA
69 62 62
我想根据已知值线性推算NA值，以使数据框看起来像这样:
I want to impute the NA values linearly from the known values so that the dataframe looks:
X Y Z
54 57 57
100 58 58
90 59 57.5
80 60 57
70 61 56.5
60 62 56
63 62 58
66 62 60
69 60 62
谢谢
推荐答案
Base R的approxfun()返回一个函数，该函数将线性插值处理的数据.
Base R's approxfun() returns a function that will linearly interpolate the data it is handed.
## Make easily reproducible data
df <- read.table(text="X Y Z
54 57 57
100 58 58
NA NA NA
NA NA NA
NA NA NA
60 62 56
NA NA NA
NA NA NA
69 62 62", header=T)
## See how this works on a single vector
approxfun(1:9, df$X)(1:9)
# [1] 54 100 90 80 70 60 63 66 69
## Apply interpolation to each of the data.frame's columns
data.frame(lapply(df, function(X) approxfun(seq_along(X), X)(seq_along(X))))
# X Y Z
# 1 54 57 57.0
# 2 100 58 58.0
# 3 90 59 57.5
# 4 80 60 57.0
# 5 70 61 56.5
# 6 60 62 56.0
# 7 63 62 58.0
# 8 66 62 60.0
# 9 69 62 62.0
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