I am new to R, and this is a very simple question. I've found a lot of similar things to what I want but not exactly it. Basically I have multiple data frames and I simply want to run the same function across all of them. A for-loop could work but I'm not sure how to set it up properly to call data frames. It also seems most prefer the lapply approach with R. I've played with the get function as well to no avail. I apologize if this is a duplicated question. Any help would be greatly appreciated!
我是R的新手,这是一个非常简单的问题。我发现了许多与我想要的相似的东西但不完全相同。基本上我有多个数据框,我只想在所有数据框中运行相同的功能。 for循环可以工作,但我不确定如何正确设置它来调用数据帧。它似乎也更喜欢与R的lapply方法。我玩get函数也无济于事。如果这是一个重复的问题,我道歉。任何帮助将不胜感激!
Here's my over simplified example: 2 data frames: df1, df2
这是我的简化示例:2个数据帧:df1,df2
df1
start stop ID
0 10 x
10 20 y
20 30 z
df2
start stop ID
0 10 a
10 20 b
20 30 c
what I want is a 4th column with the average of start and stop for both dfs
我想要的是第四列,两个dfs的开始和停止平均值
df1
start stop ID Avg
0 10 x 5
10 20 y 15
20 30 z 25
I can do this one data frame at a time with:
我可以一次完成一个数据框:
df1$Avg <- rowMeans(subset(df1, select = c(start, stop)), na.rm = TRUE)
but I want to run it on all of the dataframes.
但我想在所有数据帧上运行它。
3 个解决方案
#1
9
Make a list of data frames then use lapply to apply the function to them all.
创建一个数据框列表,然后使用lapply将函数应用于所有数据框。
df.list <- list(df1,df2,...)
res <- lapply(df.list, function(x) rowMeans(subset(x, select = c(start, stop)), na.rm = TRUE))
# to keep the original data.frame also
res <- lapply(df.list, function(x) cbind(x,"rowmean"=rowMeans(subset(x, select = c(start, stop)), na.rm = TRUE)))
The lapply will then feed in each data frame as x sequentially.
然后,lapply将按顺序将每个数据帧作为x输入。
#2
6
Put them into a list and then run rowMeans over the list.
将它们放入列表中,然后在列表上运行rowMeans。
df1 <- data.frame(x = rep(3, 5), y = seq(1, 5, 1), ID = letters[1:5])
df2 <- data.frame(x = rep(5, 5), y = seq(2, 6, 1), ID = letters[6:10])
lapply(list(df1, df2), function(w) { w$Avg <- rowMeans(w[1:2]); w })
[[1]]
x y ID Avg
1 3 1 a 2.0
2 3 2 b 2.5
3 3 3 c 3.0
4 3 4 d 3.5
5 3 5 e 4.0
[[2]]
x y ID Avg
1 5 2 f 3.5
2 5 3 g 4.0
3 5 4 h 4.5
4 5 5 i 5.0
5 5 6 j 5.5
#3
1
In case you want all the outputs in the same file this may help.
如果您想要同一文件中的所有输出,这可能会有所帮助。
df1 <- data.frame(x = rep(3, 5), y = seq(1, 5, 1), ID = letters[1:5])
df2 <- data.frame(x = rep(5, 5), y = seq(2, 6, 1), ID = letters[6:10])
z=list(df1,df2)
df=NULL
for (i in z) {
i$Avg=(i$x+i$y)/2
df<-rbind(df,i)
print (df)
}
> df
x y ID Avg
1 3 1 a 2.0
2 3 2 b 2.5
3 3 3 c 3.0
4 3 4 d 3.5
5 3 5 e 4.0
6 5 2 f 3.5
7 5 3 g 4.0
8 5 4 h 4.5
9 5 5 i 5.0
10 5 6 j 5.5
#1
9
Make a list of data frames then use lapply to apply the function to them all.
创建一个数据框列表,然后使用lapply将函数应用于所有数据框。
df.list <- list(df1,df2,...)
res <- lapply(df.list, function(x) rowMeans(subset(x, select = c(start, stop)), na.rm = TRUE))
# to keep the original data.frame also
res <- lapply(df.list, function(x) cbind(x,"rowmean"=rowMeans(subset(x, select = c(start, stop)), na.rm = TRUE)))
The lapply will then feed in each data frame as x sequentially.
然后,lapply将按顺序将每个数据帧作为x输入。
#2
6
Put them into a list and then run rowMeans over the list.
将它们放入列表中,然后在列表上运行rowMeans。
df1 <- data.frame(x = rep(3, 5), y = seq(1, 5, 1), ID = letters[1:5])
df2 <- data.frame(x = rep(5, 5), y = seq(2, 6, 1), ID = letters[6:10])
lapply(list(df1, df2), function(w) { w$Avg <- rowMeans(w[1:2]); w })
[[1]]
x y ID Avg
1 3 1 a 2.0
2 3 2 b 2.5
3 3 3 c 3.0
4 3 4 d 3.5
5 3 5 e 4.0
[[2]]
x y ID Avg
1 5 2 f 3.5
2 5 3 g 4.0
3 5 4 h 4.5
4 5 5 i 5.0
5 5 6 j 5.5
#3
1
In case you want all the outputs in the same file this may help.
如果您想要同一文件中的所有输出,这可能会有所帮助。
df1 <- data.frame(x = rep(3, 5), y = seq(1, 5, 1), ID = letters[1:5])
df2 <- data.frame(x = rep(5, 5), y = seq(2, 6, 1), ID = letters[6:10])
z=list(df1,df2)
df=NULL
for (i in z) {
i$Avg=(i$x+i$y)/2
df<-rbind(df,i)
print (df)
}
> df
x y ID Avg
1 3 1 a 2.0
2 3 2 b 2.5
3 3 3 c 3.0
4 3 4 d 3.5
5 3 5 e 4.0
6 5 2 f 3.5
7 5 3 g 4.0
8 5 4 h 4.5
9 5 5 i 5.0
10 5 6 j 5.5