I have a data frame. Let's say it looks like this:
Input data set
I have simulated some values and put them into a vector c(4,5,8,8). I want to add these simulated values to columns a, b and c.
I have tried rbind or inserting the vector into the existing data frame, but that replaced the existing values with the simulated ones, instead of adding the simulated values below the existing ones.
x <- data.frame("a" = c(2,3,1), "b" = c(5,1,2), "c" = c(6,4,7))
y <- c(4,5,8,8)
This is the output I expect to see:
Output
Help would be greatly appreciated. Thank you.
Can do:
as.data.frame(sapply(x,
function(z)
append(z,y)))
a b c
1 2 5 6
2 3 1 4
3 1 2 7
4 4 4 4
5 5 5 5
6 8 8 8
7 8 8 8
An option is assignment
n <- nrow(x)
x[n + seq_along(y), ] <- y
x
# a b c
#1 2 5 6
#2 3 1 4
#3 1 2 7
#4 4 4 4
#5 5 5 5
#6 8 8 8
#7 8 8 8
Another option is replicate the 'y' and rbind
rbind(x, `colnames<-`(replicate(ncol(x), y), names(x)))
x[(nrow(x)+1):(nrow(x)+length(y)),] <- y
Related
I have a function that I repeat, changing the argument each time, using apply/sapply/lapply.
Works great.
I want to return a data set, where each row contains two (or more) variables from each iteration of the function.
Instead I get an unusable list.
do <-function(x){
a <- x+1
b <- x+2
cbind(a,b)
}
over <- [1:6]
final <- lapply(over, do)
Any suggestions?
Without changing your function do, you can use sapply and transpose it.
data.frame(t(sapply(over, do)))
# X1 X2
#1 2 3
#2 3 4
#3 4 5
#4 5 6
#5 6 7
#6 7 8
If you want to use do in current form with lapply, we can do
do.call(rbind.data.frame, lapply(over, do))
You could also try
as.data.frame(Reduce(rbind, final))
# a b
# 1 2 3
# 2 3 4
# 3 4 5
# 4 5 6
# 5 6 7
# 6 7 8
See ?Reduce and ?rbind for information about what they'll do.
You could also modify your final expression as
final <- as.data.frame(Reduce(rbind, lapply(over, do)))
#final
# a b
# 1 2 3
# 2 3 4
# 3 4 5
# 4 5 6
# 5 6 7
# 6 7 8
Lets say I have a data frame with the following structure:
> DF <- data.frame(x=1:5, y=6:10)
> DF
x y
1 1 6
2 2 7
3 3 8
4 4 9
5 5 10
I need to build a new data frame with overlapping observations from the first data frame to be used as an input for building the A matrix for the Rglpk optimization library. I would use n-length observation windows, so that if n=2 the resulting data frame would join rows 1&2, 2&3, 3&4, and so on. The length of the resulting data frame would be
(numberOfObservations-windowSize+1)*windowSize
The result for this example with windowSize=2 would be a structure like
x y
1 1 6
2 2 7
3 2 7
4 3 8
5 3 8
6 4 9
7 4 9
8 5 10
I could do a loop like
DFResult <- NULL
numBlocks <- nrow(DF)-windowSize+1
for (i in 1:numBlocks) {
DFResult <- rbind(DFResult, DF[i:(i+horizon-1), ])
}
But this seems vey inefficient, especially for very large data frames.
I also tried
rollapply(data=DF, width=windowSize, FUN=function(x) x, by.column=FALSE, by=1)
x y
[1,] 1 6
[2,] 2 7
[3,] 2 7
[4,] 3 8
where I was trying to repeat a block of rows without applying any aggregate function. This does not work since I am missing some rows
I am a bit stumped by this and have looked around for similar problems but could not find any. Does anyone have any better ideas?
We could do a vectorized approach
i1 <- seq_len(nrow(DF))
res <- DF[c(rbind(i1[-length(i1)], i1[-1])),]
row.names(res) <- NULL
res
# x y
#1 1 6
#2 2 7
#3 2 7
#4 3 8
#5 3 8
#6 4 9
#7 4 9
#8 5 10
I have an automated script that produces a standard formula (i.e., y~x1+x2) and I would like to screen my data out based on those variables.
So far I have gotten this far, but I hit a sticking point where I can't quite figure it out:
#Example data
df <- data.frame(x=1:5, y=2:6, z=3:7, u=4:8)
df
x y z u
1 1 2 3 4
2 2 3 4 5
3 3 4 5 6
4 4 5 6 7
5 5 6 7 8
#Example formula
ex_form = "x~y+u"
#Delete the ~ and add a + sign to be consistent
step1 = gsub("~","+", ex_form)
#Remove + signs
step2 = strsplit(step1, "\\+")
#Final list of variables
step3 = unlist(step2)
Most solutions I've seen is something along the lines of:
#Create list of variables
mylist = c("x", "y", "u")
#Cut data
temp = df[ ,mylist]
temp
x y u
1 1 2 4
2 2 3 5
3 3 4 6
4 4 5 7
5 5 6 8
But this solution doesn't quite fit into the automation...so I need to jump from what I have to that outcome. Any thoughts?
Note: Tags are my guesses.
If you don't put your formula between " " it will be recognized as such, and can use all.vars() to extract variables from it.
ex_form = x~y+u #Without quotes it is a formula, check str(ex_form)
df[, all.vars(ex_form)]
# x y u
#1 1 2 4
#2 2 3 5
#3 3 4 6
#4 4 5 7
#5 5 6 8
Am I missing something or does simply doing temp <- df[,step3] return exactly what you say you want?
I was wondering if it is possible to convert 1 column into 1 variable next to eachother
i.e.:
d <- data.frame(y = 1:10)
> d
y
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
Convert this column into:
> d
1 2 3 4 5 6 7 8 9 10
We don't know how are you going to use the numbers, but I think it is unnecessary to make any transformation. You can use d$y to get the numbers applied to any map of colors. See for example.
d <- data.frame(y = 1:7)
library(RColorBrewer)
mypalette<-brewer.pal(4,"Greens")
mycol <-palette()#rainbow(7)
heatmap(matrix(1:28,ncol=4),col=mypalette[d$y[1:4]],xlab="Greens (sequential)",
ylab="",xaxt="n",yaxt="n",bty="n",RowSideColors=mycol[d$y])
Not sure what is the prupose of:
1 variable next to eachother
But there are few ways to get the desired result (again, depends on the objective). You can do either:
d$y
unname(unlist(d)) #suggested by agstudy
or, better yet, to convert your dataframe's column into a vector, do this:
v <- as.vector(d[,1])
as string:
args <- paste(d$y, sep=" ")
args<-noquote(args)
now you'll have
[1] 1 2 3 4 5 6 7 8 9 10
I am trying to simulate the OFFSET function from Excel. I understand that this can be done for a single value but I would like to return a range. I'd like to return a group of values with an offset of 1 and a group size of 2. For example, on row 4, I would like to have a group with values of column a, rows 3 & 2. Sorry but I am stumped.
Is it possible to add this result to the data frame as another column using cbind or similar? Alternatively, could I use this in a vectorized function so I could sum or mean the result?
Mockup Example:
> df <- data.frame(a=1:10)
> df
a
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
> #PROCESS
> df
a b
1 1 NA
2 2 (1)
3 3 (1,2)
4 4 (2,3)
5 5 (3,4)
6 6 (4,5)
7 7 (5,6)
8 8 (6,7)
9 9 (7,8)
10 10 (8,9)
This should do the trick:
df$b1 <- c(rep(NA, 1), head(df$a, -1))
df$b2 <- c(rep(NA, 2), head(df$a, -2))
Note that the result will have to live in two columns, as columns in data frames only support simple data types. (Unless you want to resort to complex numbers.) head with a negative argument cuts the negated value of the argument from the tail, try head(1:10, -2). rep is repetition, c is concatenation. The <- assignment adds a new column if it's not there yet.
What Excel calls OFFSET is sometimes also referred to as lag.
EDIT: Following Greg Snow's comment, here's a version that's more elegant, but also more difficult to understand:
df <- cbind(df, as.data.frame((embed(c(NA, NA, df$a), 3))[,c(3,2)]))
Try it component by component to see how it works.
Do you want something like this?
> df <- data.frame(a=1:10)
> b=t(sapply(1:10, function(i) c(df$a[(i+2)%%10+1], df$a[(i+4)%%10+1])))
> s = sapply(1:10, function(i) sum(b[i,]))
> df = data.frame(df, b, s)
> df
a X1 X2 s
1 1 4 6 10
2 2 5 7 12
3 3 6 8 14
4 4 7 9 16
5 5 8 10 18
6 6 9 1 10
7 7 10 2 12
8 8 1 3 4
9 9 2 4 6
10 10 3 5 8