This is my first question, so please let me know if I made any mistakes in the ask.
I am trying to create a dataframe which has multiple columns all containing the same values in the same order, but shifted in position. Where the first value from each column is moved to the end, and everything else is shifted up.
For example, I would like to convert a data frame like this:
example = data.frame(x=c(1,2,3,4), y=c(1,2,3,4), z=c(1,2,3,4), w=c(1,2,3,4)
Which looks like this
x y z w
1 1 1 1
2 2 2 2
3 3 3 3
4 4 4 4
into this:
x y z w
1 2 3 4
2 3 4 1
3 4 1 2
4 1 2 3
In the new dataframe, the "peak" or # 4 has moved progressively up in rows.
I've seen advice on how to shift columns up and down, but just replacing the remaining values with zeroes or NA. But I don't know how to shift the column up and replace the bottom-most value with what was formerly at the top.
Thanks in advance for any help.
In base R, we can update with Map by removing the sequence of elements while appending values from the end
example[-1] <- Map(function(x, y) c(tail(x, -y),
head(x, y)), example[-1], head(seq_along(example), -1))
example
# x y z w
#1 1 2 3 4
#2 2 3 4 1
#3 3 4 1 2
#4 4 1 2 3
Or another option is embed
example[] <- embed(unlist(example), 4)[1:4, 4:1]
Related
I'd like to sum two dataframe with different size in R.
> x = data.frame(a=c(1,2,3),b=c(5,6,7))
> y = data.frame(x=c(1,1,1))
> x
a b
1 1 5
2 2 6
3 3 7
> y
x
1 1
2 1
3 1
The result I want is,
>
a b
1 2 6
2 3 7
3 4 8
How can I do this?
Maybe easiest to convert y to a vector with unlist and then perform the operation. Here, the vector in unlist(y) will be recycled over the columns of the data.frame x.
x + unlist(y)
a b
1 2 6
2 3 7
3 4 8
As a side note, data.frames are a special type of list object and sometimes performing operations on lists can be a bit more involved. On the otherhand, they tend to work fairly well with vectors as long as the dimensions line up (here, as long as the vector has the same length as the number of rows in the data.frame).
We can make the dimensions same and then get the sum
x + rep(y, ncol(x))
# a b
#1 2 6
#2 3 7
#3 4 8
Or another option is sweep
sweep(x, y$x, 1, `+`)
# a b
#1 2 6
#2 3 7
#3 4 8
I am trying to remove columns from a dataframe using a vector of numbers, with those numbers being just a part of the whole column header. What I'm looking to use is something like the wildcard "*" in unix, so that I can say that I want to remove columns with labels xxxx, xxkx, etc... To illustrate what I mean, if I have the following data:
data_test_read <- read.table("batch_1_8c9.structure-edit.tsv",sep="\t", header=TRUE)
data_test_read[1:5,1:5]
samp pop X12706_10 X14223_16 X14481_7
1 BayOfIslands_s088.fq 1 4 1 3
2 BayOfIslands_s088.fq 1 4 1 3
3 BayOfIslands_s089.fq 1 4 1 3
4 BayOfIslands_s089.fq 1 4 3 3
5 BayOfIslands_s090.fq 1 4 1 3
And I want to take out, for example, columns with headers (X12706_10, X14481_7), the following works
data_subs1=subset(data_test_read, select = -c(X12706_10, X14481_7))
data_subs1[1:4,1:4]
samp pop X14223_16 X15213_19
1 BayOfIslands_s088.fq 1 1 3
2 BayOfIslands_s088.fq 1 1 3
3 BayOfIslands_s089.fq 1 1 3
4 BayOfIslands_s089.fq 1 3 3
However, what I need is to be able to identify these columns by only the numbers, so, using (12706,14481). But, if I try this, I get the following
data_subs2=subset(data_test_read, select = -c(12706,14481))
data_subs2[1:4,1:4]
samp pop X12706_10 X14223_16
1 BayOfIslands_s088.fq 1 4 1
2 BayOfIslands_s088.fq 1 4 1
3 BayOfIslands_s089.fq 1 4 1
4 BayOfIslands_s089.fq 1 4 3
This is clearly because I haven't specified anything to do with the "x", or the "_" or what is after the underscore. I've read so many answers on using regular expressions, and I just can't seem to sort it out. Any thoughts, or pointers to what I might turn to would be appreciated.
First you can just extract the numbers from the headers
# for testing
col_names <- c("X12706_10","X14223_16","X14481_7")
# in practice, use
# col_names <- names(data_test_read)
samples <- gsub("X(\\d+)_.*","\\1",col_names)
The find the indexes of the samples you want to drop.
samples_to_drop <- c(12706, 14481)
cols_to_drop <- match(samples_to_drop, samples)
Then you can use
data_subs2 <- subset(data_test_read, select = -cols_to_drop)
to actually get rid of those columns.
Perhaps put this all in a function to make it easier to use
sample_subset <- function(x, drop) {
samples <- gsub("X(\\d+)_.*","\\1", names(x))
subset(x, select = -match(drop, samples))
}
sample_subset(data_test_read, c(12706, 14481))
I'm trying to remove duplicate rows from a data frame, based only on the previous row. The duplicate and unique functions will remove all duplicates, leaving you only with unique rows, which is not what I want.
I've illustrated the problem here with a loop. I need to vectorize this because my actual data set is much to large to use a loop on.
x <- c(1,1,1,1,3,3,3,4)
y <- c(1,1,1,1,3,3,3,4)
z <- c(1,2,1,1,3,2,2,4)
xy <- data.frame(x,y,z)
xy
x y z
1 1 1 1
2 1 1 2
3 1 1 1
4 1 1 1 #this should be removed
5 3 3 3
6 3 3 2
7 3 3 2 #this should be removed
8 4 4 4
# loop that produces desired output
toRemove <- NULL
for (i in 2:nrow(xy)){
test <- as.vector(xy[i,] == xy[i-1,])
if (!(FALSE %in% test)){
toRemove <- c(toRemove, i) #build a vector of rows to remove
}
}
xy[-toRemove,] #exclude rows
x y z
1 1 1 1
2 1 1 2
3 1 1 1
5 3 3 3
6 3 3 2
8 4 4 4
I've tried using dplyr's lag function, but it only works on single columns, when I try to run it over all 3 columns it doesn't work.
ifelse(xy[,1:3] == lag(xy[,1:3],1), NA, xy[,1:3])
Any advice on how to accomplish this?
Looks like we want to remove if the row is same as above:
# make an index, if cols not same as above
ix <- c(TRUE, rowSums(tail(xy, -1) == head(xy, -1)) != ncol(xy))
# filter
xy[ix, ]
Why don't you just iterate the list while keeping track of the previous row to compare it to the next row?
If this is true at some point: remember that row position and remove it from the list then start iterating from the beginning of the list.
Don't delete row while iterating because you will get concurrent modification error.
I am trying to add a new column to the beginning of my data frame in R. Right now I have something that looks like
a b c d
1 2 3 4
1 2 3 4
4 1 6 3
and I want to add a new column, z, that adds by 5 in each row to get something like
z a b c d
5 1 2 3 4
10 1 2 3 4
15 4 1 6 3
Try
z<- seq(5, length.out=nrow(df1), by=5)
Or
z <- 5*seq_len(nrow(df1))
cbind(z, df1)
# z a b c d
#1 5 1 2 3 4
#2 10 1 2 3 4
#3 15 4 1 6 3
declare your new z vector by say z <- c(5,10,15) or using another way if it follows a particular pattern. After initialization use the cbind function to merge it with the original dataframe.
cbind(df,z) adds the new vector at the end and cbind(z,df) adds in the beginning. since u want it at the beginning u can use cbind(z,df)
So here is what my problem is. I have a really big data.frame woth two columns, first one represents x coordinates (rows) and another one y coordinates (columns), for example:
x y
1 1
2 3
3 1
4 2
3 4
In another frame I have some data (numbers actually):
a b c d
8 7 8 1
1 2 3 4
5 4 7 8
7 8 9 7
1 5 2 3
I would like to add a third column in first data.frame with data from second data.frame based on coordinates from first data.frame. So the result should look like this:
x y z
1 1 8
2 3 3
3 1 5
4 2 8
3 4 8
Since my data.frames are really big the for loops are too slow. I think there is a way to do this with apply loop family, but I can't find how. Thanks in advance (and sorry for ugly message layout, this is my first post here and I don't know how to produce this nice layout with code and proper data.frames like in another questions).
This is a simple indexing question. No need in external packages or *apply loops, just do
df1$z <- df2[as.matrix(df1)]
df1
# x y z
# 1 1 1 8
# 2 2 3 3
# 3 3 1 5
# 4 4 2 8
# 5 3 4 8
A base R solution: (df1 and df2 are coordinates and numbers as data frames):
df1$z <- mapply(function(x,y) df2[x,y], df1$x, df1$y )
It works if the last y in the first data frame is corrected from 5 to 4.
I guess it was a typo since you don't have 5 columns in the second data drame.
Here's how I would do this.
First, use data.table for fast merging; then convert your data frames (I'll call them dt1 with coordinates and vals with values) to data.tables.
dt1<-data.table(dt)
vals<-data.table(vals)
Second, put vals into a new data.table with coordinates:
vals_dt<-data.table(x=rep(1:dim(vals)[1],dim(vals)[2]),
y=rep(1:dim(vals)[2],each=dim(vals)[1]),
z=matrix(vals,ncol=1)[,1],key=c("x","y"))
Now merge:
setkey(dt1,x,y)[vals_dt,z:=z]
You can also try the data.table package and update df1 by reference
library(data.table)
setDT(df1)[, z := df2[cbind(x, y)]][]
# x y z
# 1: 1 1 8
# 2: 2 3 3
# 3: 3 1 5
# 4: 4 2 8
# 5: 3 4 8