The fibonacci series is obtained by adding together the prior two integers in the series, the Series include 1, 1, 2 , 3, 5 , 8. I used the following code to have series till 50.
y <- 50
}
fibvals <- numeric(y)
fibvals[1] <- 1
fibvals[2] <- 1
for (i in 3:y) {
fibvals[i] <- fibvals[i-1]+fibvals[i-2]
}
Now i want to add the numbers at even position, i.e 1, 3, 8 till 50th number? please help?
Try using seq to select the even vector indices from 2 to 50, like this:
sum(fibvals[seq(2, 50, by = 2)])
Also: there are R libraries to make working with series easier. You could use the numbers package for example, to get the first 50 Fibonacci numbers:
fibvals <- sapply(1:50, numbers::fibonacci)
Related
Trying to understand how the value of "traded" is 34
available <- c(10,4,7,10,12)
desired <- c(12,5,2,6,14)
traded <- sum(mapply(function(x,y) min(x,y), available, desired))
Correct value for traded is 34. Just not sure why this is the case. I thought the value would be 6 as the minimum values from each vector (4 and 2) summed together =6
This is answered in the comments, but I wanted to add this breakdown since it helps me to visualize each step.
mapply(function(x,y) min(x,y)): Maps min(x,y) to each item in vectors x and y , so the function is doing this:
min(10,12)
min(4,5)
min(7,2)
min(10,6)
min(12,14)
and outputs = (10, 4, 2, 6, 12)
sum(mapply(...)): Which "sees" the output above and computes 10+4+2+6+12 = 34
This is my first post here and I couldn't find the answer I was looking for.
I'm currently taking edX course on Probability in Data Science, but I got stuck on section 1.
The task asks you to simulate a series of 6 games with random, independent outcomes of either a loss (0) or win(1), and then use the sum function to determine whether a simulated series contained at least 4 wins.
Here's what I did:
l <- list(0:1)
n <- 6
games <- expand.grid(rep(l, n))
games <- paste (games$Var1, games$Var2, games$Var3, games$Var4, games$Var5, games$Var6)
sample (game, 1, replace = TRUE)
but I can't seem to use the sum function to sum the result of '''sample''' and check if there's a series of at least 4 games. I've been trying to use
sum(sample (game, 1, replace = TRUE))
but can't seem to get anywhere with it.
Any light would be greatly appreciated!
Thanks a lot!
This is what one simulated series look like
sample(c(0, 1), 6, replace = TRUE)
To count number of wins (i.e 1) you could use sum like
sum(sample(c(0, 1), 6, replace = TRUE)) >= 4
Now you could generate such series n times with replicate.
n <- 1000
replicate(n, sum(sample(c(0, 1), 6, replace = TRUE)) >= 4)
If you have to use games to calculate you can use rowSums to count number of 1's
sum(rowSums(games) >= 4)
#[1] 22
I'm trying to create a new column in my matrix that is the rate of change from one point in time to the next. Using the the following matrix, this is a 3 step process.
n <- 20
data <- matrix(rnorm(2 * n), nrow = n)
1) Focusing on column 1, I want to divide row 2 by row 1.
2) I want to create a new column to hold the answer in row 2
3) repeat this process down the rows (3/2, 4/3,6/5, etc.)
I'm assuming a simple function like the following would be involved in step 1
y<-data[1,1]
z<-data[2,1]
roc<- function(x){(z/y)}
Step 2 is simple
data$ROC[data[1,] >= 0]<- roc
But I'm at a loss for step 3, and I'm not 100% sure that the function is correctly written.
Complete answer based off Ryan's comment.
####data matrix####
n <- 20
data <- matrix(rnorm(2 * n), nrow = n)
####math####
y<-data[,1]/data.table::shift(data[,1])
####new column####
data$ROC[data[1,] >= 0]<- y
I am new to R and I have an R code that uses the for loop to calculate the y = m*i + b. The starting value is a negative and I want to use that in my calculation and store it in the first occurrence of the Trend.Line and so forth.
I am not getting the results that I'm expecting. If my starting is a positive number no matter what number it is, I still want to store it in the first occurrence.
For example, if start= -5, I would like to store this calculated value in Y <= m*i + b in Trend.Line[1], the -4 calculated value to Trend.Line[2]. Now if start = 6, I would like to store calculated value in Trend.Line[1], the 7 calculated value to Trend.Line[2]
Thanks for looking into this.
Here is my code:
Trend.Line <- numeric(0)
start <- -5
end <- 12
m <- 345.72
b <- 54454
for(i in start:end){
y <- m*(i) + b
Trend.Line[i] <- y
}
Trend.Line
How about just doing
Trend.Line <- start:end
m * Trend.Line + b
It returns a numeric vector with everything at the index you want. It also makes use of the vectorization of functions in R. So multiplication and addition work on all elements of the vector Trend.Line.
I would like to perform two things to my fairly large data set about 10 K x 50 K . The following is smaller set of 200 x 10000.
First I want to generate 5% missing values, which perhaps simple and can be done with simple trick:
# dummy data
set.seed(123)
# matrix of X variable
xmat <- matrix(sample(0:4, 2000000, replace = TRUE), ncol = 10000)
colnames(xmat) <- paste ("M", 1:10000, sep ="")
rownames(xmat) <- paste("sample", 1:200, sep = "")
Generate missing values at 5% random places in the data.
N <- 2000000*0.05 # 5% random missing values
inds_miss <- round ( runif(N, 1, length(xmat)) )
xmat[inds_miss] <- NA
Now I would like to generate error (means that different value than what I have in above matrix. The above matrix have values of 0 to 4. So what I would like to do:
(1) I would like to replace x value with another value that is not x (for example 0 can be replaced by a random sample of that is not 0 (i.e. 1 or 2 or 3 or 4), similarly 1 can be replaced by that is not 1 (i.e. 0 or 2 or 3 or 4). Indicies where random value can be replaced can be simply done with:
inds_err <- round ( runif(N, 1, length(xmat)) )
If I randomly sample 0:4 values and replace with the indices, this will sometime replace same value with same value ( 0 with 0, 1 with 1 and so on) without creating error.
errorg <- sample(0:4, length(inds_err), replace = TRUE)
xmat[inds_err] <- errorg
(2) So what I would like to do is introduce error in xmat with missing values, However I do not want NA generated in above step be replaced with a value (0 to 4). So ind_err should not be member of vector inds_miss.
So summary rules :
(1) The missing values should not be replaced with error values
(2) The existing value must be replaced with different value (which is definition of error here)- in random sampling this 1/5 probability of doing this.
How can it be done ? I need faster solution that can be used in my large dataset.
You can try this:
inds_err <- setdiff(round ( runif(2*N, 1, length(xmat)) ),inds_miss)[1:N]
xmat[inds_err]<-(xmat[inds_err]+sample(4,N,replace=TRUE))%%5
With the first line you generate 2*N possible error indices, than you subtract the ones belonging to inds_miss and then take the first N. With the second line you add to the values you want to change a random number between 1 and 4 and than take the mod 5. In this way you are sure that the new value will be different from the original and stil in the 0-4 range.
Here's an if/else solution that could work for you. It is a for loop so not sure if that will be okay for you. Possibly vectorize it is some way to make it faster.
# vector of options
vec <- 0:4
# simple logic based solution if just don't want NA changed
for(i in 1:length(inds_err){
if(is.na(xmat[i])){
next
}else{
xmat[i] <- sample(vec[-xmat[i]], 1)
}
}