I have two data.frames, lookup_df and values_df. For each row in lookup_df I want to lookup the closest value in the values_df that is less than or equal to an index value.
Here's my code so far:
lookup_df <- data.frame(ids = 1:10)
values_df <- data.frame(idx = c(1,3,7), values = c(6,2,8))
What I'm wanting for the result_df is the following:
> result_df
ids values
1 1 6
2 2 6
3 3 2
4 4 2
5 5 2
6 6 2
7 7 8
8 8 8
9 9 8
10 10 8
I know how to do this with SQL fairly easily but I'm curious if there is an R way that is straightforward. I could iterate the the rows of the lookup_df and then loop through the rows of the values_df but that is not computationally efficient. I'm open to using dplyr library if someone knows how to use that to solve the problem.
If values_df is sorted by idx ascending, then findInterval will work:
lookup_df <- data.frame(ids = 1:10)
values_df <- data.frame(idx = c(1,3,7), values = c(6,2,8))
lookup_df$values <- values_df$values[findInterval(lookup_df$ids,values_df$idx)]
lookup_df
> ids values
1 1 6
2 2 6
3 3 2
4 4 2
5 5 2
6 6 2
7 7 8
8 8 8
9 9 8
10 10 8
Related
I have a large dataset (8,000 obs) and about 16 lists with anywhere from 120 to 2,000 items. Essentially, I want to check to see if any of the observations in the dataset match an item in a list. If there is a match, I want to include a variable indicating the match.
As an example, if I have data that look like this:
dat <- as.data.frame(1:10)
list1 <- c(2:4)
list2 <- c(7,8)
I want to end with a dataset that looks something like this
Obs Var List
1 1
2 2 1
3 3 1
4 4 1
5 5
6 6
7 7 2
8 8 2
9 9
10 10
How do I go about doing this? Thank you!
Here is one way to do it using boolean sum and %in%. If several match, then the last one is taken here:
dat <- data.frame(Obs = 1:10)
list_all <- list(c(2:4), c(7,8))
present <- sapply(1:length(list_all), function(n) dat$Obs %in% list_all[[n]]*n)
dat$List <- apply(present, 1, FUN = max)
dat$List[dat$List == 0] <- NA
dat
> dat
Obs List
1 1 NA
2 2 1
3 3 1
4 4 1
5 5 NA
6 6 NA
7 7 2
8 8 2
9 9 NA
10 10 NA
I am generating 5 different prediction and adding those predictions to an existing data frame. My code is:
For j in i{
…
actual.predicted <- data.frame(test_data, predicted)
}
I am trying to concatenate words together to create new column names, in the loop. Specifically, I have a column named “predicted” and I am generating predictions in each iteration of the loop. So, in the first iteration, I want the new column name to be “predicted.1” and for the second iteration, the new column name should be “predicted.2” and so on.
Any thoughts would be greatly appreciated.
You may not even need to use a loop here, but assuming you do, one pattern which might work well here would be to use a list:
results <- list()
for j in i {
# do something involving j
name <- paste0("predicted.", j)
results[[name]] <- data.frame(test_data, predicted)
}
One option is to set the names after assigning new columns
actual.predicted <- data.frame(orig_col = sample(10))
for (j in 1:5){
new_col = sample(10)
actual.predicted <- cbind(actual.predicted, new_col)
names(actual.predicted)[length(actual.predicted)] <- paste0('predicted.',j)
}
actual.predicted
# orig_col predicted.1 predicted.2 predicted.3 predicted.4 predicted.5
# 1 1 4 4 9 1 5
# 2 10 2 3 7 5 9
# 3 8 6 5 4 2 3
# 4 5 9 9 10 7 7
# 5 2 1 10 8 3 10
# 6 9 7 6 6 8 6
# 7 7 8 7 2 4 2
# 8 3 3 1 1 6 8
# 9 6 10 2 3 9 4
# 10 4 5 8 5 10 1
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 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$a[(i+4)%%10+1])))
> s = sapply(1:10, function(i) sum(b[i,]))
> df = data.frame(df, b, s)
> df
a X1 X2 s
1 1 4 6 10
2 2 5 7 12
3 3 6 8 14
4 4 7 9 16
5 5 8 10 18
6 6 9 1 10
7 7 10 2 12
8 8 1 3 4
9 9 2 4 6
10 10 3 5 8
This question already has an answer here:
Closed 10 years ago.
Possible Duplicate:
Select and insert value unique number of times in R
I would like to generate 2000 random numbers between 1 and 10 such that for each random number I have the same number of instances.
In this case 200 for each number.
What should be random is the order in which it is generated.
I have the following problem:
I have an array with 2000 entries but not each with unique values, for example it starts like this:
11112233333333344445667777777777
and consists of 2000 entries.
I would like to generate random numbers and assign each UNIQUE value a separate random number but have an entry for each value
So my intended result would look like this:
original array: 11112233333333344445667777777777
random numbers: 33334466666666699991778888888888
You could do this in a few steps:
my_numbers <- rep(1:10, each=200)
my_randomizer <- sample(seq_along(my_numbers), length(my_numbers))
my_random_numbers <- my_numbers[my_randomizer]
Based on the edits:
I would use rle. It sounds like you don't have an array, but instead a vector:
my_array_rled <- rle(my_array)
my_random_numbers <- sample(1:10, length(unique(my_array)))
my_array_rled$values <- factor(my_array_rled$values)
levels(my_array_rled$values) <- my_random_numbers
my_array_randomized <- inverse.rle(my_array_rled)
If I understand you correctly you can use "rep" to replicate your random numbers 200 times and "sample" to randomize the resulting vector.
x <- sample(rep(runif(2000,1,10),200))
A non vectorized code:
# using a seed for reproducible example
set.seed(2)
original_array <- c(1,1,1,1,2,2,3,3,3,3,3,3,3,3,3,4,4,4,4,5,6,6,7,7,7,7,7,7,7,7,7,7)
random_numbers <- numeric(length=length(original_array))
rdnum <- sample(unique(original_array), length(unique(original_array)))
for ( i in 1:length(unique(original_array)))
random_numbers[original_array == i] <- rdnum[i]
random_numbers
2 2 2 2 5 5 3 3 3 3 3 3 3 3 3 1 1 1 1 6 7 7 4 4 4 4 4 4 4 4 4 4
The table function with sample comes in quite handy for this scenerio:
set.seed(1)
## ASSUMING ORIGINAL IS A VECTOR
original <- c(1, 1, 1, 1, 2,2,3,3,3,3,3,3,3,3,3,4,4,4,4,5,6,6,7,7,7,7,7,7,7,7,7,7)
## CREATE A TABLE OF ALL THE VALUES
tabl <- table(original)
## RNG is the sample range to select from. Assuming 1:10 in this example
RNG <- 1:10
## PICK VALUES RANDOMLY FROM RNG
tabl[] <- sample(RNG, length(tabl), replace=FALSE)
# note that the `names` of `tabl` will contain the values from `original`
# whereas the values of `tabl` will contain the new random value.
## ASSIGN NEW VALUES
randomNums <- original
for(i in seq(length(tabl)))
randomNums[ original==as.numeric(names(tabl))[[i]] ] <- tabl[[i]]
Results:
rbind(orig=original, rand=randomNums)
orig: 1 1 1 1 2 2 3 3 3 3 3 3 3 3 3 4 4 4 4 5 6 6 7 7 7 7 7 7 7 7 7 7
rand: 3 3 3 3 4 4 5 5 5 5 5 5 5 5 5 7 7 7 7 2 8 8 9 9 9 9 9 9 9 9 9 9