I am trying to build a for loop which will step through each site, for that site calculate frequencies of a response, and put those results in a new data frame. Then after the loop I want to be able to combine all of the site data frames so it will look something like:
Site Genus Freq
1 A 50
1 B 30
1 C 20
2 A 70
2 B 10
2 C 20
But to do this I need my names (of vectors, dataframes) to change each time through the loop. I think I can do this using the SiteNum variable, but how do I insert it into new variable names? The way I tried (below) treats it like part of the string, doesn't insert the value for the name.
I feel like what I want to use is a placeholder %, but I don't know how to do that with variable names.
> SiteNum <- 1
> for (Site in CoralSites){
> Csub_SiteNum <- subset(dfrmC, Site==CoralSites[SiteNum])
> CGrfreq_SiteNum <- numeric(length(CoralGenera))
> for (Genus in CoralGenera){
> CGrfreq_SiteNum[GenusNum] <- mean(dfrmC$Genus == CoralGenera[GenusNum])*100
> GenusNum <- GenusNum + 1
> }
> names(CGrfreq_SiteNum) <- c(CoralGenera)
> Site_SiteNum <- c(Site)
> CG_SiteNum <- data.frame(CoralGenera,CGrfreq_SiteNum,Site_SiteNum)
> SiteNum <- SiteNum + 1
> }
Your question as stated asks how you can create a bunch of variables, e.g. CGrfreq_1, CGrfreq_2, ..., where the name of the variable indicates the site number that it corresponds to (1, 2, ...). While you can do such a thing with functions like assign, it is not good practice for a few reasons:
It makes your code to generate the variables more complicated because it will be littered with calls to assign and get and paste0.
It makes your data more difficult to manipulate afterwards -- you'll need to (either manually or programmatically) identify all the variables of a certain type, grab their values with get or mget, and then do something with them.
Instead, you'll find it easier to work with other R functions that will perform the aggregation for you. In this case you're looking to generate for each Site/Genus pairing the percentage of data points at the site with the particular genus value. This can be done in a few lines of code with the aggregate function:
# Sample data:
(dat <- data.frame(Site=c(rep(1, 5), rep(2, 5)), Genus=c(rep("A", 3), rep("B", 6), "A")))
# Site Genus
# 1 1 A
# 2 1 A
# 3 1 A
# 4 1 B
# 5 1 B
# 6 2 B
# 7 2 B
# 8 2 B
# 9 2 B
# 10 2 A
# Calculate frequencies
dat$Freq <- 1
res <- aggregate(Freq~Genus+Site, data=dat, sum)
res$Freq <- 100 * res$Freq / table(dat$Site)[as.character(res$Site)]
res
# Genus Site Freq
# 1 A 1 60
# 2 B 1 40
# 3 A 2 20
# 4 B 2 80
Related
I'd like to make a data frame using only the last computed values from a Repeat loop.
For the repeat and sample functions, I'm using this data. The numbers in Prob column are the probabilities of each number to occur.
enter image description here
b <- 1
repeat {
c <- sample(a$Plus, size=1, prob=(a$Prob))
cat(b, '\t', c, '\n')
b <- b + 1
if (c >= 10) {
{
break
}
}
}
#I'm interested in the result greater than 10 only
If I run the code above, then it will compute something like
1 4
2 8
3 13
If I run this again, it will compute different results like..
1 9
2 3
3 7
4 3
5 11
What I'd like to do is to make a data frame using only the last outputs of each loop.
For example, using the computed data above, I'd like to make a frame that looks like
Trial Result
3 13
5 11
Is there any way to repeat this loop the number of times I want to and make a data frame using only the last outputs of each repeated function?
You can use a user defined function to do this. Since you haven't given your dataframe a, I've defined it as follows:
library(tidyverse)
a <- tibble(
Plus = 1:15,
Prob = seq(from = 15, to = 1, by = -1)
)
The following function does the same thing as your repeat loop, but stores the relevant results in a tibble. I've left your variable b out of this because as far as I can see, it doesn't contribute to your desired output.
samplefun <- function(a) {
c <- sample(a$Plus, size=length(a$Plus), prob=a$Prob)
res <- tibble(
Trial = which(c >= 10)[1],
Result = c[which(c >= 10)[1]]
)
return(res)
}
Then use map_dfr to return as many samples as you like:
nsamples <- 5
map_dfr(1:nsamples, ~ samplefun(a))
Output:
# A tibble: 5 x 2
Trial Result
<int> <int>
1 4 11
2 6 14
3 5 11
4 2 10
5 4 15
I just started working with R for my master thesis and up to now all my calculations worked out as I read a lot of questions and answers here (and it's a lot of trial and error, but thats ok).
Now i need to process a more sophisticated code and i can't find a way to do this.
Thats the situation: I have multiple sub-data-sets with a lot of entries, but they are all structured in the same way. In one of them (50000 entries) I want to change only one value every row. The new value should be the amount of the existing entry plus a few values from another sub-data-set (140000 entries) where the 'ID'-variable is the same.
As this is the third day I'm trying to solve this, I already found and tested for and apply but both are running for hours (canceled after three hours).
Here is an example of one of my attempts (with for):
for (i in 1:50000) {
Entry_ID <- Sub02[i,4]
SUM_Entries <- sum(Sub03$Source==Entry_ID)
Entries_w_ID <- subset(Sub03, grepl(Entry_ID, Sub03$Source)) # The Entry_ID/Source is a character
Value1 <- as.numeric(Entries_w_ID$VAL1)
SUM_Value1 <- sum(Value1)
Value2 <- as.numeric(Entries_w_ID$VAL2)
SUM_Value2 <- sum(Value2)
OLD_Val1 <- Sub02[i,13]
OLD_Val <- as.numeric(OLD_Val1)
NEW_Val <- SUM_Entries + SUM_Value1 + SUM_Value2 + OLD_Val
Sub02[i,13] <- NEW_Val
}
I know this might be a silly code, but thats the way I tried it as a beginner. I would be very grateful if someone could help me out with this so I can get along with my thesis.
Thank you!
Here's an example of my data-structure:
Text VAL0 Source ID VAL1 VAL2 VAL3 VAL4 VAL5 VAL6 VAL7 VAL8 VAL9
XXX 12 456335667806925_1075080942599058 10153901516433434_10153902087098434 4 1 0 0 4 9 4 6 8
ABC 8 456335667806925_1057045047735981 10153677787178434_10153677793613434 6 7 1 1 5 3 6 8 11
DEF 8 456747267806925_2357045047735981 45653677787178434_94153677793613434 5 8 2 1 5 4 1 1 9
The output I expect is an updated value 'VAL9' in every row.
From what I understood so far, you need 2 things:
sum up some values in one dataset
add them to another dataset, using an ID variable
Besides what #yoland already contributed, I would suggest to break it down in two separate tasks. Consider these two datasets:
a = data.frame(x = 1:2, id = letters[1:2], stringsAsFactors = FALSE)
a
# x id
# 1 1 a
# 2 2 b
b = data.frame(values = as.character(1:4), otherid = letters[1:2],
stringsAsFactors = FALSE)
sapply(b, class)
# values otherid
# "character" "character"
Values is character now, we need to convert it to numeric:
b$values = as.numeric(b$values)
sapply(b, class)
# values otherid
# "numeric" "character"
Then sum up the values in b (grouped by otherid):
library(dplyr)
b = group_by(b, otherid)
b = summarise(b, sum_values = sum(values))
b
# otherid sum_values
# <chr> <dbl>
# 1 a 4
# 2 b 6
Then join it with a - note that identifiers are specified in c():
ab = left_join(a, b, by = c("id" = "otherid"))
ab
# x id sum_values
# 1 1 a 4
# 2 2 b 6
We can then add the result of the sum from b to the variable x in a:
ab$total = ab$x + ab$sum_values
ab
# x id sum_values total
# 1 1 a 4 5
# 2 2 b 6 8
(Updated.)
From what I understand you want to create a new variable that uses information from two different data sets indexed by the same ID. The easiest way to do this is probably to join the data sets together (if you need to safe memory, just join the columns you need). I found dplyr's join functions very handy for these cases (explained neatly here) Once you joined the data sets into one, it should be easy to create the new columns you need. e.g.: df$new <- df$old1 + df$old2
I have Valence Category for word stimuli in my psychology experiment.
1 = Negative, 2 = Neutral, 3 = Positive
I need to sort the thousands of stimuli with a pseudo-randomised condition.
Val_Category cannot have more than 2 of the same valence stimuli in a row i.e. no more than 2x negative stimuli in a row.
for example - 2, 2, 2 = not acceptable
2, 2, 1 = ok
I can't sequence the data i.e. decide the whole experiment will be 1,3,2,3,1,3,2,3,2,2,1 because I'm not allowed to have a pattern.
I tried various packages like dylpr, sample, order, sort and nothing so far solves the problem.
I think there's a thousand ways to do this, none of which are probably very pretty. I wrote a small function that takes care of the ordering. It's a bit hacky, but it appeared to work for what I tried.
To explain what I did, the function works as follows:
Take the vector of valences and samples from it.
If sequences are found that are larger than the desired length, then, (for each such sequence), take the last value of that sequence at places it "somewhere else".
Check if the problem is solved. If so, return the reordered vector. If not, then go back to 2.
# some vector of valences
val <- rep(1:3,each=50)
pseudoRandomize <- function(x, n){
# take an initial sample
out <- sample(val)
# check if the sample is "bad" (containing sequences longer than n)
bad.seq <- any(rle(out)$lengths > n)
# length of the whole sample
l0 <- length(out)
while(bad.seq){
# get lengths of all subsequences
l1 <- rle(out)$lengths
# find the bad ones
ind <- l1 > n
# take the last value of each bad sequence, and...
for(i in cumsum(l1)[ind]){
# take it out of the original sample
tmp <- out[-i]
# pick new position at random
pos <- sample(2:(l0-2),1)
# put the value back into the sample at the new position
out <- c(tmp[1:(pos-1)],out[i],tmp[pos:(l0-1)])
}
# check if bad sequences (still) exist
# if TRUE, then 'while' continues; if FALSE, then it doesn't
bad.seq <- any(rle(out)$lengths > n)
}
# return the reordered sequence
out
}
Example:
The function may be used on a vector with or without names. If the vector was named, then these names will still be present on the pseudo-randomized vector.
# simple unnamed vector
val <- rep(1:3,each=5)
pseudoRandomize(val, 2)
# gives:
# [1] 1 3 2 1 2 3 3 2 1 2 1 3 3 1 2
# when names assigned to the vector
names(val) <- 1:length(val)
pseudoRandomize(val, 2)
# gives (first row shows the names):
# 1 13 9 7 3 11 15 8 10 5 12 14 6 4 2
# 1 3 2 2 1 3 3 2 2 1 3 3 2 1 1
This property can be used for randomizing a whole data frame. To achieve that, the "valence" vector is taken out of the data frame, and names are assigned to it either by row index (1:nrow(dat)) or by row names (rownames(dat)).
# reorder a data.frame using a named vector
dat <- data.frame(val=rep(1:3,each=5), stim=rep(letters[1:5],3))
val <- dat$val
names(val) <- 1:nrow(dat)
new.val <- pseudoRandomize(val, 2)
new.dat <- dat[as.integer(names(new.val)),]
# gives:
# val stim
# 5 1 e
# 2 1 b
# 9 2 d
# 6 2 a
# 3 1 c
# 15 3 e
# ...
I believe this loop will set the Valence Category's appropriately. I've called the valence categories treat.
#Generate example data
s1 = data.frame(id=c(1:10),treat=NA)
#Setting the first two rows
s1[1,"treat"] <- sample(1:3,1)
s1[2,"treat"] <- sample(1:3,1)
#Looping through the remainder of the rows
for (i in 3:length(s1$id))
{
s1[i,"treat"] <- sample(1:3,1)
#Check if the treat value is equal to the previous two values.
if (s1[i,"treat"]==s1[i-1,"treat"] & s1[i-1,"treat"]==s1[i-2,"treat"])
#If so draw one of the values not equal to that value
{
a = 1:3
remove <- s1[i,"treat"]
a=a[!a==remove]
s1[i,"treat"] <- sample(a,1)
}
}
This solution is not particularly elegant. There may be a much faster way to accomplish this by sorting several columns or something.
I am trying to group a column of my data.frame/data.table into three groups, all with equal sums.
The data is first ordered from smallest to largest, such that group one would be made up of a large number of rows with small values, and group three would have a small number of rows with large values. This is accomplished in spirit with:
test <- data.frame(x = as.numeric(1:100000))
store <- 0
total <- sum(test$x)
for(i in 1:100000){
store <- store + test$x[i]
if(store < total/3){
test$y[i] <- 1
} else {
if(store < 2*total/3){
test$y[i] <- 2
} else {
test$y[i] <- 3
}
}
}
While successful, I feel like there must be a better way (and maybe a very obvious solution that I am missing).
I never like resorting to loops, especially with nested ifs, when a vectorized approach is available - with even 100,000+ records this code becomes quite slow
This method would become impossibly complex to code to a larger number of groups (not necessarily the looping, but the ifs)
Requires pre-ordering of the column. Might not be able to get around this one.
As a nuance (not that it makes a difference) but the data to be summed would not always (or ever) be consecutive integers.
Maybe with cumsum:
test$z <- cumsum(test$x) %/% (ceiling(sum(test$x) / 3)) + 1
This is more or less a bin-packing problem.
Use the binPack function from the BBmisc package:
library(BBmisc)
test$bins <- binPack(test$x, sum(test$x)/3+1)
The sums of the 3 bins are nearly identical:
tapply(test$x, test$bins, sum)
1 2 3
1666683334 1666683334 1666683332
I thought that the cumsum/modulo division approach was very elegant, but it does retrun a somewhat irregular allocation:
> tapply(test$x, test$z, sum)
1 2 3
1666636245 1666684180 1666729575
> sum(test)/3
[1] 1666683333
So I though I would first create a random permutation and offer something similar:
test$x <- sample(test$x)
test$z2 <- cumsum(test$x)[ findInterval(cumsum(test$x),
c(0, 1666683333*(1:2), sum(test$x)+1))]
> tapply(test$x, test$z2, sum)
91099 116379 129539
1666676164 1666686837 1666686999
This also achieves a more even distribution of counts:
> table(test$z2)
91099 116379 129539
33245 33235 33520
> table(test$z)
1 2 3
57734 23915 18351
I must admit to puzzlement regarding the naming of the entries in z2.
Or you can just cut on the cumsum
test$z <- cut(cumsum(test$x), breaks = 3, labels = 1:3)
or use ggplot2::cut_interval instead of cut:
test$z <- cut_interval(cumsum(test$x), n = 3, labels = 1:3)
You can use fold() from groupdata2 and get an almost equal number of elements per group:
# Create data frame
test <- data.frame(x = as.numeric(1:100000))
# Use fold() to create 3 numerically balanced groups
test <- groupdata2::fold(k = 3, num_col = "x")
# Watch first 10 rows
head(test, 10)
## # A tibble: 10 x 2
## # Groups: .folds [3]
## x .folds
## <dbl> <fct>
## 1 1 1
## 2 2 3
## 3 3 2
## 4 4 1
## 5 5 2
## 6 6 2
## 7 7 1
## 8 8 3
## 9 9 2
## 10 10 3
# Check the sum and number of elements per group
test %>%
dplyr::group_by(.folds) %>%
dplyr::summarize(sum_ = sum(x),
n_members = dplyr::n())
## # A tibble: 3 x 3
## .folds sum_ n_members
## <fct> <dbl> <int>
## 1 1 1666690952 33333
## 2 2 1666716667 33334
## 3 3 1666642381 33333
I am working in R with a data frame d:
ID <- c("A","A","A","B","B")
eventcounter <- c(1,2,3,1,2)
numberofevents <- c(3,3,3,2,2)
d <- data.frame(ID, eventcounter, numberofevents)
> d
ID eventcounter numberofevents
1 A 1 3
2 A 2 3
3 A 3 3
4 B 1 2
5 B 2 2
where numberofevents is the highest value in the eventcounter for each ID.
Currently, I am trying to create an additional vector z <- c(6,6,6,3,3).
If the numberofevents == 3, it is supposed to calculate sum(1:3), equally to 3 + 2 + 1 = 6.
If the numberofevents == 2, it is supposed to calculate sum(1:2) equally to 2 + 1 = 3.
Working with a large set of data, I thought it might be convenient to create this additional vector
by using the sum function in R d$z<-sum(1:d$numberofevents), i.e.
sum(1:3) # for the rows 1-3
and
sum(1:2) # for the rows 4-5.
However, I always get this warning:
Numerical expression has x elements: only the first is used.
You can try ave
d$z <- with(d, ave(eventcounter, ID, FUN=sum))
Or using data.table
library(data.table)
setDT(d)[,z:=sum(eventcounter), ID][]
Try using apply sapply or lapply functions in R.
sapply(numberofevents, function(x) sum(1:x))
It works for me.