R: Subsetting on increasing value to max excluding the decreasing

R: Subsetting on increasing value to max excluding the decreasing - r

I have a number of trials where one variable increases to a max of interest then decreases back to a starting point. How would I go about just retaining the observations with the increasing values to max. Thanks.
For example
Trial A B C
1 2 4 1
1 4 3 2
1 3 7 3
1 3 3 2
1 4 1 1
2 4 1 1
2 6 2 2
2 3 1 3
2 1 1 2
2 7 3 1
...
So we would check max on C and retain as follows,
Trial A B C
1 2 4 1
1 4 3 2
1 3 7 3
2 4 1 1
2 6 2 2
2 3 1 3
...
Ultimately I'll have a low cut off value as well as varying perhaps what I mean by max but essentially the above is the aim.

Probably not the most efficient solution, but here is an attempt using data.table
library(data.table)
setDT(df)[, .SD[1:which.max(C)], by = Trial]
# Trial A B C
# 1: 1 2 4 1
# 2: 1 4 3 2
# 3: 1 3 7 3
# 4: 2 4 1 1
# 5: 2 6 2 2
# 6: 2 3 1 3
Or for some efficiency gain
indx <- setDT(df)[, .I[1:which.max(C)], by = Trial]
df[indx$V1]

library(dplyr)
df%>%group_by(Trial)%>%slice(1:max(C))

Related

R assign order references by other column value [duplicate]

This question already has answers here:
Create counter with multiple variables [duplicate]
(6 answers)
Closed 9 years ago.
I am trying to obtain a sequence within category.
My data are:
A B
1 1
1 2
1 2
1 3
1 3
1 3
1 4
1 4
and I want to get variable "c" such as my data look like:
A B C
1 1 1
1 2 1
1 2 2
1 3 1
1 3 2
1 3 3
1 4 1
1 4 2

Use ave with seq_along:
> mydf$C <- with(mydf, ave(A, A, B, FUN = seq_along))
> mydf
A B C
1 1 1 1
2 1 2 1
3 1 2 2
4 1 3 1
5 1 3 2
6 1 3 3
7 1 4 1
8 1 4 2
If your data are already ordered (as they are in this case), you can also use sequence with rle (mydf$C <- sequence(rle(do.call(paste, mydf))$lengths)), but you don't have that limitation with ave.
If you're a data.table fan, you can make use of .N as follows:
library(data.table)
DT <- data.table(mydf)
DT[, C := sequence(.N), by = c("A", "B")]
DT
# A B C
# 1: 1 1 1
# 2: 1 2 1
# 3: 1 2 2
# 4: 1 3 1
# 5: 1 3 2
# 6: 1 3 3
# 7: 1 4 1
# 8: 1 4 2

r repeat sequence number sequence while keeping the order of the sequence

I want repeat a sequence for specific length:
Sequence is 1:4 and I want to repeat the sequence till number of rows in a data frame.
Lets say length of the data frame is 24
I tried following:
test <- rep(1:4, each=24/4)
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
Lengthwise this is fine but i want to retain the sequence
1 2 3 4 1 2 3 4 1 2 3 4.....

You need to use times instead of each
rep(1:4, times=24/4)
[1] 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

We can just pass it without any argument and it takes the times by default
rep(1:4, 24/4)
#[1] 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

Count with table() and exclude 0's

I try to count triplets; for this I use three vectors that are packed in a dataframe:
X=c(4,4,4,4,4,4,4,4,1,1,1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3,3,3)
Y=c(1,1,1,1,1,1,1,1,1,1,1,1,2,2,3,4,2,2,2,2,3,4,1,1,2,2,3,3,4,4)
Z=c(4,4,5,4,4,4,4,4,6,1,1,1,1,1,1,1,2,2,2,2,7,2,3,3,3,3,3,3,3,3)
Count_Frame=data.frame(matrix(NA, nrow=(length(X)), ncol=3))
Count_Frame[1]=X
Count_Frame[2]=Y
Count_Frame[3]=Z
Counts=data.frame(table(Count_Frame))
There is the following problem: if I increase the value range in the vectors or use even more vectors the "Counts" dataframe quickly approaches its size limit due to the many 0-counts. Is there a way to exclude the 0-counts while generating "Counts"?

We can use data.table. Convert the 'data.frame' to 'data.table' (setDT(Count_Frame)), grouped by all the columns (.(X, Y, Z)), we get the number or rows (.N).
library(data.table)
setDT(Count_Frame)[,.N ,.(X, Y, Z)]
# X Y Z N
# 1: 4 1 4 7
# 2: 4 1 5 1
# 3: 1 1 6 1
# 4: 1 1 1 3
# 5: 1 2 1 2
# 6: 1 3 1 1
# 7: 1 4 1 1
# 8: 2 2 2 4
# 9: 2 3 7 1
#10: 2 4 2 1
#11: 3 1 3 2
#12: 3 2 3 2
#13: 3 3 3 2
#14: 3 4 3 2
Instead of naming all the columns, we can use names(Count_Frame) as well (if there are many columns)
setDT(Count_Frame)[,.N , names(Count_Frame)]

You can accomplish this with aggregate:
Count_Frame$one <- 1
aggregate(one ~ X1 + X2 + X3, data=Count_Frame, FUN=sum)
This will calculate the positive instances of table, but will not list the zero counts.

One solution is to create a combination of the column values and count those instead:
library(tidyr)
as.data.frame(table(unite(Count_Frame, tmp, X1, X2, X3))) %>%
separate(Var1, c('X1', 'X2', 'X3'))
Resulting output is:
X1 X2 X3 Freq
1 1 1 1 3
2 1 1 6 1
3 1 2 1 2
4 1 3 1 1
5 1 4 1 1
6 2 2 2 4
7 2 3 7 1
8 2 4 2 1
9 3 1 3 2
10 3 2 3 2
11 3 3 3 2
12 3 4 3 2
13 4 1 4 7
14 4 1 5 1

Or using plyr:
library(plyr)
count(Count_Frame, colnames(Count_Frame))
output
# > count(Count_Frame, colnames(Count_Frame))
# X1 X2 X3 freq
# 1 1 1 1 3
# 2 1 1 6 1
# 3 1 2 1 2
# 4 1 3 1 1
# 5 1 4 1 1
# 6 2 2 2 4
# 7 2 3 7 1
# 8 2 4 2 1
# 9 3 1 3 2
# 10 3 2 3 2
# 11 3 3 3 2
# 12 3 4 3 2
# 13 4 1 4 7
# 14 4 1 5 1

generate sequence within group in R [duplicate]

This question already has answers here:
Create counter with multiple variables [duplicate]
(6 answers)
Closed 9 years ago.
I am trying to obtain a sequence within category.
My data are:
A B
1 1
1 2
1 2
1 3
1 3
1 3
1 4
1 4
and I want to get variable "c" such as my data look like:
A B C
1 1 1
1 2 1
1 2 2
1 3 1
1 3 2
1 3 3
1 4 1
1 4 2

Use ave with seq_along:
> mydf$C <- with(mydf, ave(A, A, B, FUN = seq_along))
> mydf
A B C
1 1 1 1
2 1 2 1
3 1 2 2
4 1 3 1
5 1 3 2
6 1 3 3
7 1 4 1
8 1 4 2
If your data are already ordered (as they are in this case), you can also use sequence with rle (mydf$C <- sequence(rle(do.call(paste, mydf))$lengths)), but you don't have that limitation with ave.
If you're a data.table fan, you can make use of .N as follows:
library(data.table)
DT <- data.table(mydf)
DT[, C := sequence(.N), by = c("A", "B")]
DT
# A B C
# 1: 1 1 1
# 2: 1 2 1
# 3: 1 2 2
# 4: 1 3 1
# 5: 1 3 2
# 6: 1 3 3
# 7: 1 4 1
# 8: 1 4 2

Combining an individual and aggregate level data sets

I've got two different data frames, lets call them "Months" and "People".
Months looks like this:
Month Site X
1 1 4
2 1 3
3 1 5
1 2 10
2 2 7
3 2 5
and People looks like this:
ID Month Site
1 1 1
2 1 2
3 1 1
4 2 2
5 2 2
6 2 2
7 3 1
8 3 2
I'd like to combine them so essentially each time an entry in "People" has a particular Month and Site combination, it's added to the appropriate aggregated data frame, so I'd get something like the following:
Month Site X People
1 1 4 2
2 1 3 0
3 1 5 1
1 2 10 1
2 2 7 3
3 2 5 1
But I haven't the foggiest idea of how to go about doing that. Any suggestions?

Using base packages
> aggregate( ID ~ Month + Site, data=People, FUN = length )
Month Site ID
1 1 1 2
2 3 1 1
3 1 2 1
4 2 2 3
5 3 2 1
> res <- merge(Months, aggdata, all.x = TRUE)
> res
Month Site X ID
1 1 1 4 2
2 1 2 10 1
3 2 1 3 NA
4 2 2 7 3
5 3 1 5 1
6 3 2 5 1
> res[is.na(res)] <- 0
> res
Month Site X ID
1 1 1 4 2
2 1 2 10 1
3 2 1 3 0
4 2 2 7 3
5 3 1 5 1
6 3 2 5 1

Assuming your data.frames are months and people, here's a data.table solution:
require(data.table)
m.dt <- data.table(months, key=c("Month", "Site"))
p.dt <- data.table(people, key=c("Month", "Site"))
# one-liner
dt.f <- p.dt[m.dt, list(X=X[1], People=sum(!is.na(ID)))]
> dt.f
# Month Site X People
# 1: 1 1 4 2
# 2: 1 2 10 1
# 3: 2 1 3 0
# 4: 2 2 7 3
# 5: 3 1 5 1
# 6: 3 2 5 1