I'm attempting to generate a column that shows persistence throughout a field. The field is sequential and numeric, but not conventionally increasing. Essentially, it goes up by 7 (when it ends in 2) and then 3 (when it ends in 9) by each ID. It's possible for an ID to miss one or more of the sequence, but then return to the same pattern. The data looks like this:
ID Col
1 0769
1 0772
1 0779
1 0782
1 0799
1 0802
1 0812
2 0769
2 0772
2 0779
3 0782
3 0799
3 0802
3 0812
What I'm trying to do is generate this:
ID Col Persistence
1 0769 1
1 0772 1
1 0779 1
1 0782 1
1 0799 2
1 0802 2
1 0812 3
2 0769 1
2 0772 1
2 0779 1
3 0782 1
3 0799 2
3 0802 2
3 0812 3
If you just want to make sure the jump is either 3 or 7, you can write a helper function to increment when a jump of a different size occurs
jumpchange <- function(x) c(0,cumsum(!diff(x) %in% c(3,7)))+1
Then you can apply this to each group most easily with dplyr
library(dplyr)
dd %>% group_by(ID) %>%
mutate(persistence = jumpchange(Col))
Or you can use transform/ave with just base R
transform(dd, persistence=ave(Col, ID, FUN=jumpchange))
Related
This question already has answers here:
How to create a consecutive group number
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I have data from an experiment that has multiple rows per item (each row has the reading time for one word of a sentence of n words), and multiple items per subject. Items can be varying numbers of rows. Items were presented in a random order, and their order in the data as initially read in reflects the sequence they saw the items in. What I'd like to do is add a column that contains the order in which the subject saw that item (i.e., 1 for the first item, 2 for the second, etc.).
Here's an example of some input data that has the relevant properties:
d <- data.frame(Subject = c(1,1,1,1,1,2,2,2,2,2),
Item = c(2,2,2,1,1,1,1,2,2,2))
Subject Item
1 2
1 2
1 2
1 1
1 1
2 1
2 1
2 2
2 2
2 2
And here's the output I want:
Subject Item order
1 2 1
1 2 1
1 2 1
1 1 2
1 1 2
2 1 1
2 1 1
2 2 2
2 2 2
2 2 2
I know I can do this by setting up a temp data frame that filters d to unique combinations of Subject and Item, adding order to that as something like 1:n() or row_number(), and then using a join function to put it back together with the main data frame. What I'd like to know is whether there's a way to do this without having to create a new data frame just to store the order---can this be done inside dplyr's mutate somehow if I group by Subject and Item, for instance?
Here's one way:
d %>%
group_by(Subject) %>%
mutate(order = match(Item, unique(Item))) %>%
ungroup()
# # A tibble: 10 x 3
# Subject Item order
# <dbl> <dbl> <int>
# 1 1 2 1
# 2 1 2 1
# 3 1 2 1
# 4 1 1 2
# 5 1 1 2
# 6 2 1 1
# 7 2 1 1
# 8 2 2 2
# 9 2 2 2
# 10 2 2 2
Here is a base R option
transform(d,
order = ave(Item, Subject, FUN = function(x) as.integer(factor(x, levels = unique(x))))
)
or
transform(d,
order = ave(Item, Subject, FUN = function(x) match(x, unique(x)))
)
both giving
Subject Item order
1 1 2 1
2 1 2 1
3 1 2 1
4 1 1 2
5 1 1 2
6 2 1 1
7 2 1 1
8 2 2 2
9 2 2 2
10 2 2 2
What I have:
test_df <- data.frame(isolate=c(1,2,3,4,1,2,3,4,5),label=c(1,1,1,1,2,2,2,2,2),alignment=c("--at","at--","--at","--at","a--","acg","a--","a--", "agg"))
> test_df
isolate label alignment
1 1 1 --at
2 2 1 at--
3 3 1 --at
4 4 1 --at
5 1 2 a--
6 2 2 acg
7 3 2 a--
8 4 2 a--
9 5 2 agg
What I want:
I'd like to explode the alignment field into two columns, position and character:
> test_df
isolate label aln_pos aln_char
1 1 1 1 -
2 1 1 2 -
3 1 1 3 a
4 1 1 4 t
...
Not all alignments are the same length, but all alignments with the same label have the same length.
What I've tried:
I was thinking I could use separate to first make each position have its own column, then use gather turn those columns into key value pairs. However, I haven't been able to get the separate part right.
Since you mentioned tidyr::gather, you could try this:
test_df <- data.frame(isolate=c(1,2,3,4,1,2,3,4,5),
label=c(1,1,1,1,2,2,2,2,2),
alignment=c("--at","at--","--at","--at","a--","acg","a--","a--", "agg"),
stringsAsFactors = FALSE)
library(tidyverse)
test_df %>%
mutate(alignment = strsplit(alignment,"")) %>%
unnest(alignment)
In base R, you can use indexing along with creation of a list with strsplit like this.
# make variable a character vector
test_df$alignment <- as.character(test_df$alignment)
# get list of individual characters
myList <- strsplit(test_df$alignment, split="")
then build the data.frame
# construct data.frame
final_df <- cbind(test_df[rep(seq_len(nrow(test_df)), lengths(myList)),
c("isolate", "label")],
aln_pos=sequence(lengths(myList)),
aln_char=unlist(myList))
Here, we take the first two columns of the original data.frame and repeat the rows using rep with a vector input in its second argument telling it how many times to repeat the corresponding value in its first argument. The number of times is calculated with lengths. The second argument of cbind is a call to sequence taking the same lengths output. this produces counts from 1 to the corresponding length. The third argument is the unlisted character values.
this returns
head(final_df, 10)
isolate label aln_pos aln_char
1 1 1 1 -
1.1 1 1 2 -
1.2 1 1 3 a
1.3 1 1 4 t
2 2 1 1 a
2.1 2 1 2 t
2.2 2 1 3 -
2.3 2 1 4 -
3 3 1 1 -
3.1 3 1 2 -
I was writing a loop with if function in R. The table is like below:
ID category
1 a
1 b
1 c
2 a
2 b
3 a
3 b
4 a
5 a
I want to use the for loop with if function to add another column to count each grouped ID, like below count column:
ID category Count
1 a 1
1 b 2
1 c 3
2 a 1
2 b 2
3 a 1
3 b 2
4 a 1
5 a 1
My code is (output is the table name):
for (i in 2:nrow(output1)){
if(output1[i,1] == output[i-1,1]){
output1[i,"rn"]<- output1[i-1,"rn"]+1
}
else{
output1[i,"rn"]<-1
}
}
But the result returns as all count column values are all "1".
ID category Count
1 a 1
1 b 1
1 c 1
2 a 1
2 b 1
3 a 1
3 b 1
4 a 1
5 a 1
Please help me out... Thanks
There are packages and vectorized ways to do this task, but if you are practicing with loops try:
output1$rn <- 1
for (i in 2:nrow(output1)){
if(output1[i,1] == output1[i-1,1]){
output1[i,"rn"]<- output1[i-1,"rn"]+1
}
else{
output1[i,"rn"]<-1
}
}
With your original code, when you made this call output1[i-1,"rn"]+1 in the third line of your loop, you were referencing a row that didn't exist on the first pass. By first creating the row and filling it with the value 1, you give the loop something explicit to refer to.
output1
# ID category rn
# 1 1 a 1
# 2 1 b 2
# 3 1 c 3
# 4 2 a 1
# 5 2 b 2
# 6 3 a 1
# 7 3 b 2
# 8 4 a 1
# 9 5 a 1
With the package dplyr you can accomplish it quickly with:
library(dplyr)
output1 %>% group_by(ID) %>% mutate(rn = 1:n())
Or with data.table:
library(data.table)
setDT(output1)[,rn := 1:.N, by=ID]
With base R you can also use:
output1$rn <- with(output1, ave(as.character(category), ID, FUN=seq))
There are vignettes and tutorials on the two packages mentioned, and by searching ?ave in the R console for the last approach.
looping solution will be painfully slow for bigger data. Here is one line solution using data.table:
require(data.table)
a<-data.table(ID=c(1,1,1,2,2,3,3,4,5),category=c('a','b','c','a','b','a','b','a','a'))
a[,':='(category_count = 1:.N),by=.(ID)]
what you want is actually a column of factor level. do this
df$count=as.numeric(df$category)
this will give out put as
ID category count
1 1 a 1
2 1 b 2
3 1 c 3
4 2 a 1
5 2 b 2
6 3 a 1
7 3 b 2
8 4 a 1
9 5 a 1
provided your category is already a factor. if not first convert to factor
df$category=as.factor(df$category)
df$count=as.numeric(df$category)
Im am trying to figure out how to subset my dataset according to the repeated value of the variable s, taking also into account the id associated to the row.
Suppose my dataset is:
dat <- read.table(text = "
id s
1 2
1 2
1 1
1 3
1 3
1 3
2 3
2 3
3 2
3 2",
header=TRUE)
What I would like to do is, for each id, to keep only the first row for which s = 3. The result with dat would be:
id s
1 2
1 2
1 1
1 3
2 3
3 2
3 2
I have tried to use both duplicated() and which() for using subset() in a second moment, but I am not going anywhere. The main problem is that it is not sufficient to isolate the first row of the s = 3 "blocks", because in some cases (as here between id = 1 and id = 2) the 3's overlap between one id and another.. Which strategy would you adopt?
Like this:
subset(dat, s != 3 | s == 3 & !duplicated(dat))
# id s
# 1 1 2
# 2 1 2
# 3 1 1
# 4 1 3
# 7 2 3
# 9 3 2
# 10 3 2
Note that subset can be dangerous to work with (see Why is `[` better than `subset`?), so the longer but safer version would be:
dat[dat$s != 3 | dat$s == 3 & !duplicated(dat), ]
I have lots of little vectors like this:
id<-c(2,2,5,2,1,9,4,4,3,9,5,5)
and I want to create another vector of the same length that has the cumulative number of occurrences of each id thus far. For the above example data this would look like:
> id.count
[1] 1 2 1 3 1 1 1 2 1 2 2 3
I can not find a function that does this easily, maybe because I do not really know how to fully articulate in words what it is that I actually want (hence the slightly awkward question title). Any suggestions?
Here is another way:
ave(id,id,FUN=seq_along)
Gives:
[1] 1 2 1 3 1 1 1 2 1 2 2 3
> sapply(1:length(id), function(i) sum(id[1:i] == id[i]))
[1] 1 2 1 3 1 1 1 2 1 2 2 3
Or if you have NAs in id you should use id[1:i] %in% id[i] instead.