问题
Okay, so I have this data.frame:
A B C
1 yellow purple <NA>
2 <NA> <NA> yellow
3 orange yellow <NA>
4 orange <NA> brown
5 <NA> brown purple
6 yellow purple pink
7 purple green pink
8 yellow pink green
9 purple orange <NA>
10 purple <NA> brown
And I am interested in taking all the missing values from the first columns and replace them with the values over from the other columns, as an example with rows 2, 4, 5 and 10.
A B C
1 yellow purple <NA>
2 yellow <NA> <NA>
3 orange yellow <NA>
4 orange brown <NA>
5 brown purple <NA>
6 yellow purple pink
7 purple green pink
8 yellow pink green
9 purple orange <NA>
10 purple brown <NA>
My idea was to loop over the columns to grab the rows with the missing values and replace them with the values in the column to the right but that is also potentially flawed because what if there were 4 columns and two values in columns 2 and 3 were NA. Does anyone have an idea of an algorithm that may work?
回答1:
We can loop over the rows and concatenate the non-NA elements followed by the NA elements and assign it back to the dataset
df[] <- t(apply(df, 1, function(x) c(x[!is.na(x)], x[is.na(x)])))
df
# A B C
#1 yellow purple <NA>
#2 yellow <NA> <NA>
#3 orange yellow <NA>
#4 orange brown <NA>
#5 brown purple <NA>
#6 yellow purple pink
#7 purple green pink
#8 yellow pink green
#9 purple orange <NA>
#10 purple brown <NA>
data
df <- structure(list(A = c("yellow", NA, "orange", "orange", NA, "yellow",
"purple", "yellow", "purple", "purple"), B = c("purple", NA,
"yellow", NA, "brown", "purple", "green", "pink", "orange", NA
), C = c(NA, "yellow", NA, "brown", "purple", "pink", "pink",
"green", NA, "brown")), .Names = c("A", "B", "C"), row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10"), class = "data.frame")
来源：https://stackoverflow.com/questions/49079789/using-r-to-shift-values-to-the-left-of-data-frame