I am trying to add a new column to multiple data frames, and then replace the original data frame with the new one. This is how I am creating the new data frames:
df1 <- data.frame(X1=c(1,2,3),X2=c(1,2,3))
df2 <- data.frame(X1=c(4,5,6),X2=c(4,5,6))
groups <- list(df1,df2)
groups <- lapply(groups,function(x) cbind(x,X3=x[,1]+x[,2]))
groups
[[1]]
X1 X2 X3
1 1 1 2
2 2 2 4
3 3 3 6
[[2]]
X1 X2 X3
1 4 4 8
2 5 5 10
3 6 6 12
I'm satisfied with how the new data frames have been created. What I'm stuck on is then breaking up my groups list and then saving the list elements back into their respective original data frames.
Desired Output
Essentially, I want to do something like df1,df2 <- groups[[1]],groups[[2]] but that is of course not syntatically valid. I have more than 2 data frames, which is why I'm hoping for a more programmatic approach than simply typing out N lines of code.
for (i in 1:length(groups)){
assign(paste("df",i,sep=""),as.data.frame(groups[[i]]))
}
should do it. Try it out, please.
#Rockbar led me to a general solution as well:
for(i in 1:length(groups)){
assign(names(groups)[i],as.data.frame(groups[[i]]))
}
> df1
X1 X2 X3
1 1 1 2
2 2 2 4
3 3 3 6
> df2
x1 X3 X3
1 4 4 8
2 5 5 10
3 6 6 12
I should note that this only works if the objects in the list are all named. Thank you again #Rockbar for guiding me to this.
Related
My data frame looks like this
X0 <- c(11,2,3,4)
X1 <- c(10,2,3,4)
X2 <- c(8,2,3,4)
X3 <- c(4,6,3,4)
test <- data.frame(X0,X1,X2,X3)
X0 X1 X2 X3
1 11 10 8 4
2 2 2 2 6
3 3 3 3 3
4 4 4 4 4
I would like to rename the first three columns using the character "t" and the series from 1:3.
I want my data frame to look like this
t0 t1 t2 X3
1 11 10 8 4
2 2 2 2 6
3 3 3 3 3
4 4 4 4 4
EDIT
It works like this
test %>%
rename_at(vars(X0:X2), list(~paste0("t", 0:2)))
Or using rename_with
library(dplyr)
library(stringr)
test %>%
rename_with(~ str_c('t', 0:2), X0:X2)
Here is a data.table option with setnames
setnames(setDT(test),1:3,function(v) gsub("X","t",v))
x_names <-c("x1","x2","x3")
data <- c(1,2,3,4)
fake <- c(2,3,4,5)
for (i in x_names)
{
x = fake
data = as.data.frame(cbind(data,x))
#data <- data %>% rename(x_names = x)
}
I made a toy example. This code will generate a data frame with 1 column called data, and 3 columns called x. Instead of calling the columns x, I want them with the name x1, x2, x3 (stored in x_names). I put the x_name in the code (comment out), but it does not work. Could you help me with it?
We can also use map_dfc from tidyverse:
library(tidyverse)
cbind(data, map_dfc(x_names, ~ tibble(!!.x := fake)))
Output:
data x1 x2 x3
1 1 2 2 2
2 2 3 3 3
3 3 4 4 4
4 4 5 5 5
We can avoid the for loop and use replicate to repeat fake data using setNames to name the dataframe with x_names.
cbind(data, setNames(data.frame(replicate(length(x_names), fake)), x_names))
# data x1 x2 x3
#1 1 2 2 2
#2 2 3 3 3
#3 3 4 4 4
#4 4 5 5 5
Ideally one should avoid growing objects in a loop, however one way to solve OP's problem in loop is
for (i in seq_along(x_names)) {
data = cbind.data.frame(data, fake)
names(data)[i + 1] <- x_names[i]
}
An option is just to assign the 'fake' to create the new columns in base R
data[x_names] <- fake
data
# data x1 x2 x3
#1 1 2 2 2
#2 2 3 3 3
#3 3 4 4 4
#4 4 5 5 5
EDIT: Based on comments from #avid_useR
data
data <- data.frame(data)
When you exchange your out-commented line
#data <- data %>% rename(x_names = x)
with
colnames(data)[ncol(data)] <- i
it should set the right colnames.
I would like to get rid of duplicated records in each row of my df:
df <- data.frame(a=c(1,3,5), b =c(1,2,4), c=c(2,3,7))
X1 X2 X3
1 1 1 2
2 3 2 3
3 5 4 7
I want to get this:
X1 X2 X3
1 1 NA 2
2 3 2 NA
3 5 4 7
Now, I can achieve this using apply:
data.frame(t(apply(df,1, function(row) ifelse(!duplicated(row), row, NA))))
but it seems unlikely that there isn't a more compact (and perhaps efficient) way of achieving this.
Am I missing a command or package here?
I have a univariate contingency table that I would like to convert to a data frame.
>t <- table(c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4))
>t
1 2 3 4
4 4 4 4
But converting t to a data frame yields something I dont need:
>data.frame(t)
Var1 Freq
1 1 4
2 2 4
3 3 4
4 4 4
I would like a data frame that looks exactly like the table t, with 4 columns named 1, 2, 3 and 4 (or X1, X2, X3, X4), and one row. Any help I can find, using things like as.data.frame.matrix() return errors for me, I think because my data is univariate and not multivariate.
We can use as.data.frame.list()
tbl <- table(rep(1:4, 4))
as.data.frame.list(tbl)
# X1 X2 X3 X4
# 1 4 4 4 4
Or to use the original names, add optional = TRUE
as.data.frame.list(tbl, optional = TRUE)
# 1 2 3 4
# 1 4 4 4 4
I have a data set with the following row-naming scheme:
a.X.V
where:
a is a fixed-length core ID
X is a variable-length string that subsets a, which means I should keep X
V is a variable-length ID which specifies the individual elements of a.X to be averaged
. is one of {-,_}
What I am trying to do is take column averages of all the a.X's. A sample:
sampleList <- list("a.12.1"=c(1,2,3,4,5), "b.1.23"=c(3,4,1,4,5), "a.12.21"=c(5,7,2,8,9), "b.1.555"=c(6,8,9,0,6))
sampleList
$a.12.1
[1] 1 2 3 4 5
$b.1.23
[1] 3 4 1 4 5
$a.12.21
[1] 5 7 2 8 9
$b.1.555
[1] 6 8 9 0 6
Currently I am manually gsubbing out the .Vs to get a list of general :
sampleList <- t(as.data.frame(sampleList))
y <- rowNames(sampleList)
y <- gsub("(\\w\\.\\d+)\\.d+", "\\1", y)
Is there a faster way to do this?
This is one half of 2 issues I've encountered in a workflow. The other half was answered here.
You can use a vector of patterns to find the locations of the columns you want to group. I included a pattern I knew wouldn't match anything in order to show that the solution is robust to that situation.
# A *named* vector of patterns you want to group by
patterns <- c(a.12="^a.12",b.12="^b.12",c.12="^c.12")
# Find the locations of those patterns in your list
inds <- lapply(patterns, grep, x=names(sampleList))
# Calculate the mean of each list element that matches the pattern
out <- lapply(inds, function(i)
if(l <- length(i)) Reduce("+",sampleList[i])/l else NULL)
# Set the names of the output
names(out) <- names(patterns)
Perhaps you could consider messing with your data structure to make it easier to apply some standard tools:
sampleList <- list("a.12.1"=c(1,2,3,4,5),
"b.1.23"=c(3,4,1,4,5), "a.12.21"=c(5,7,2,8,9),
"b.1.555"=c(6,8,9,0,6))
library(reshape2)
m1 <- melt(do.call(cbind,sampleList))
m2 <- cbind(m1,colsplit(m1$Var2,"\\.",c("coreID","val1","val2")))
The results looks like this:
head(m2)
Var1 Var2 value coreID val1 val2
1 1 a.12.1 1 a 12 1
2 2 a.12.1 2 a 12 1
3 3 a.12.1 3 a 12 1
Then you can more easily do something like this:
aggregate(value~val1,mean,data=subset(m2,coreID=="a"))
R is poised to do this stuff if you would just move to data.frames instead of lists. Make Your 'a', 'X', and 'V' into their own columns. Then you can use ave, by, aggregate, subset, etc.
data.frame(do.call(rbind, sampleList),
do.call(rbind, strsplit(names(sampleList), '\\.')))
# X1 X2 X3 X4 X5 X1.1 X2.1 X3.1
# a.12.1 1 2 3 4 5 a 12 1
# b.1.23 3 4 1 4 5 b 1 23
# a.12.21 5 7 2 8 9 a 12 21
# b.1.555 6 8 9 0 6 b 1 555