用不同的特定值替换多列中缺少的数据
我有一个巨大的数据框架,我需要替换几个缺少值,如下所示:
循环A | 周期B | 周期C | ..... |
---|---|---|---|
na | na na | na | |
na | na na | na | |
na na | na | na na | |
-1 | na | 0 | |
-1 -1 | -2 | 0 | |
na | -2 | Na | |
na | na | na | |
na | na | 1 | |
0 | -1 | 1 | |
0 | -1 | na na | |
na | na | na na | |
na | na | na | |
na na | 0 | 2 | |
1 | 0 | 2 | |
1 | na | na na | |
na | na | na na |
na na na na n na n a i需要用出现的下一个数字替换na有类似的东西:
A周期 | B | 周期C | ..... |
---|---|---|---|
-1 | -2 | 0 | |
-1 -1 | -2 | 0 | |
-1 | -2 | 0 | |
-1 -1 | -2 | 0 | |
-1 | -2 | 0 | |
0 0 | 0 -2 | -2 1 | |
0 | -1 | 1 | |
0 0 | -1 | 1 | |
0 | -1 | 1 | |
0 | -1 | 2 | |
1 | 0 | 2 1 0 | |
2 1 | 0 | 2 | |
1 | 0 | 2 | |
1 | 0 | 2 | |
1 | 1 | 3 | |
2 | 1 | 3 |
任何想法如何做? 谢谢。
I have a huge data frame with several missing value that I need to replace as follow:
Cycle A | Cycle B | Cycle C | ..... |
---|---|---|---|
na | na | na | |
na | na | na | |
na | na | na | |
-1 | na | 0 | |
-1 | -2 | 0 | |
na | -2 | na | |
na | na | na | |
na | na | 1 | |
0 | -1 | 1 | |
0 | -1 | na | |
na | na | na | |
na | na | na | |
na | 0 | 2 | |
1 | 0 | 2 | |
1 | na | na | |
na | na | na |
For each column I need to replace the NA's by the next number that appears, to have something like that:
Cycle A | Cycle B | Cycle C | ..... |
---|---|---|---|
-1 | -2 | 0 | |
-1 | -2 | 0 | |
-1 | -2 | 0 | |
-1 | -2 | 0 | |
-1 | -2 | 0 | |
0 | -2 | 1 | |
0 | -1 | 1 | |
0 | -1 | 1 | |
0 | -1 | 1 | |
0 | -1 | 2 | |
1 | 0 | 2 | |
1 | 0 | 2 | |
1 | 0 | 2 | |
1 | 0 | 2 | |
1 | 1 | 3 | |
2 | 1 | 3 |
Any idea how to do that?
Thank you.
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假设您要在所有
start_with
周期的所有列中执行替换。第一个
填充
函数将na
替换为下一个行值。突变
函数在最后一行中替换Na
是最后一个非NA值 + 1。Assume you want to perform replacement in all columns that
starts_with
Cycle.The first
fill
function replacesNA
with the next row values. Themutate
function replacesNA
in the last row to be last non-NA value + 1.首先,将转换为
na
和type.covert
的数字值。接下来,我可能错了,您是否正在寻找这种基本结构?
数据:
First, convert
"na"
toNA
andtype.covert
for numeric values.Next, I might be wrong, are you looking for this underlying structure?
Data: