Sum value by Combine all variable using R - r

Can somebody help me with data manipulation using R? i have data (data.train) like this
datex <- rep(c(rep("01/01/17",6),rep("02/01/17",6),rep("03/01/17",6)),1)
datex <- as.Date(datex, "%d/%m/%y")
Ax <- rep("A1",18)
Bx <- rep(c(rep("B1",3),rep("B2",3)),3)
Cx <- rep(c("C1","C2","C3"),6)
valx <- 100
for(i in 1:17){valx[i+1] <- valx[i]+1}
data.train <- data.frame(datex, Ax, Bx, Cx, valx)
i need all combination from variable and the final form is like this
I have tried this code:
### Library
library(dplyr)
## datex
datex <- rep(c(rep("01/01/17",6),rep("02/01/17",6),rep("03/01/17",6)),1)
datex <- as.Date(datex, "%d/%m/%y")
Ax <- rep("A1",18)
Bx <- rep(c(rep("B1",3),rep("B2",3)),3)
Cx <- rep(c("C1","C2","C3"),6)
valx <- 100
for(i in 1:17){valx[i+1] <- valx[i]+1}
data.train <- data.frame(datex, Ax, Bx, Cx, valx)
names.group <- names(data.train)[1:length(data.train)-1]
data.group <- Map(combn, list(names.group), seq_along(names.group), simplify = F) %>% unlist(recursive = F)
find.index <- sapply(data.group, function(x, find.y){
any(find.y %in% x)
}, find.y = c("datex"))
index.group <- NULL
for(i in 2:length(find.index)){
if(find.index[i] == "TRUE"){
index.group[i] <- i
}
}
index.group[is.na(index.group)] <- 0
for(i in 1:length(data.group)){
if(index.group[i] == 0){
data.group[[i]] <- 0
} else {
data.group[[i]] <- data.group[[i]]
}
}
data.group2 <- data.group[sapply(data.group, function(x) any(x != 0))]
combination.result <- lapply(data.group2, FUN = function(x) {
do.call(what = group_by_, args = c(list(data.train), x)) %>% summarise(sumVar = sum(valx))
})
combination.result
but i don't produce what i want. Thanks

You can generate for combinations of length 1 then for combinations of length 2. Use paste to create your Variable column. Then rbindlist all your results to get the final output.
library(data.table)
setDT(data.train)
sumCombi <- function(x, mySep="_") {
data.train[ , sum(Val), by=c("Date", x)][,
list(Date,
Variable=do.call(paste, c(.SD[,x,with=FALSE], list(sep=mySep))),
SumVal=V1)]
}
rbindlist(c(
#combinations with 1 element in each combi
lapply(c("A", "B", "C"), sumCombi)
,
#combinations with 2 elements in each combi
lapply(combn(c("A","B","C"), 2, simplify=FALSE), sumCombi)
), use.names=FALSE)
or more generically/programmatically:
#assuming that your columns are in the middle of the columns while excl. first and last columns
myCols <- names(data.train)[-c(1, ncol(data.train))]
rbindlist(unlist(
lapply(seq_along(myCols), function(n)
combn(myCols, n, sumCombi, simplify=FALSE)
), recursive=FALSE),
use.names=FALSE)

Related

How to simplifying this R Code to detect repeated sequence

I found this below function to detect repeated sequence. I integrate the function into Monte Carlo Simulation to calculate the probability. The function I have is too long and takes too much time during the simulation. I would appreciate if anyone can help to simply the function and in turn fasten any simulation depends on it.
V1 <- c(68,71,72,69,80,78,80,81,84,82,67,73,65,68,66,70,69,72,74,73,68,75,70,72,75,73,69,75,74,79,80,78,80,81,79,82,69,73,67,66,70,72,69,72,75,80,68,69,71,77,70,73)
Check_repeat_Seq <- function(vector){
k <- 2:25
Lall <- setNames(lapply(k, function(i) table(zoo::rollapply(vector, width = i, toString))), k)
L <- Filter(length, lapply(Lall, function(x) x[x == max(x) & x > 1]))
dat <- data.frame(seq_rep=sapply(L, length))
dat$repeat_length <- as.numeric(rownames(dat))
return(max(dat$repeat_length))
}
Check_repeat_Seq(V1)
#### Can you please simplify the following also to calculate the sum of repeated.####
Check_repeat_Seq_no_overlap_sum <- function(vector){
k <- 2:25
Lall <- setNames(lapply(k, function(i) table(zoo::rollapply(vector, width = i, toString))), k)
L <- Filter(length, lapply(Lall, function(x) x[x == max(x) & x > 1]))
vec <- unlist(unname(L))
nms <- names(vec)
is_le <- function(i) any(grepl(nms[i], tail(nms, -i)) & (vec[i] <= tail(vec, -i)))
LL <- vec[ ! sapply(seq_along(nms), is_le) ]
dat <- data.frame(seq_rep=sapply(L, length))
dat$repeat_length <- as.numeric(rownames(dat))
dat$total_repeat <- dat$seq_rep*dat$repeat_length
return(sum(dat$total_repeat))
}
##### the original function should return data Frame as follows
Check_All_repeat_Seq<- function(vector){
k <- 2:25
Lall <- setNames(lapply(k, function(i) table(zoo::rollapply(vector, width = i, toString))), k)
L <- Filter(length, lapply(Lall, function(x) x[x == max(x) & x > 1]))
vec <- unlist(unname(L))
nms <- names(vec)
is_le <- function(i) any(grepl(nms[i], tail(nms, -i)) & (vec[i] <= tail(vec, -i)))
LL <- vec[ ! sapply(seq_along(nms), is_le) ]
dat <- data.frame(seq_rep=sapply(L, length))
dat$repeat_length <- as.numeric(rownames(dat))
dat$total_repeat <- dat$seq_rep*dat$repeat_length
return(sum(dat))
}
please help simplifying the code with the same output
Update
An even faster iterative approach leveraging the Cantor pairing function:
allDup <- function(x) {
duplicated(x) | duplicated(x, fromLast = TRUE)
}
fPair <- function(i, j) {
# Cantor pairing function
k <- j + (i + j)*(i + j + 1L)/2L
match(k, unique(k))
}
Check_repeat_Seq3 <- function(v) {
v <- match(v, unique(v))
vPair <- fPair(head(v, -1), tail(v, -1))
blnKeep <- allDup(vPair)
idx <- which(blnKeep)
len <- 1L
while (length(idx)) {
len <- len + 1L
vPair <- fPair(vPair[blnKeep], v[idx + len])
blnKeep <- allDup(vPair)
idx <- idx[blnKeep]
}
return(len)
}
# benchmark against the rollaply solution
V1 <- c(68,71,72,69,80,78,80,81,84,82,67,73,65,68,66,70,69,72,74,73,68,75,70,72,75,73,69,75,74,79,80,78,80,81,79,82,69,73,67,66,70,72,69,72,75,80,68,69,71,77,70,73)
Check_repeat_Seq <- function(vector){
k <- 2:25
Lall <- setNames(lapply(k, function(i) table(zoo::rollapply(vector, width = i, toString))), k)
L <- Filter(length, lapply(Lall, function(x) x[x == max(x) & x > 1]))
dat <- data.frame(seq_rep=sapply(L, length))
dat$repeat_length <- as.numeric(rownames(dat))
return(max(dat$repeat_length))
}
Check_repeat_Seq(V1)
#> [1] 4
Check_repeat_Seq3(V1)
#> [1] 4
microbenchmark::microbenchmark(Check_repeat_Seq(V1), Check_repeat_Seq3(V1))
#> Unit: microseconds
#> expr min lq mean median uq max neval
#> Check_repeat_Seq(V1) 38445.7 40860.95 43153.058 42249.25 44051.15 60593.2 100
#> Check_repeat_Seq3(V1) 103.9 118.65 150.713 149.05 160.05 465.2 100
Original Solution
Check_repeat_Seq2 <- function(v) {
m <- matrix(c(head(v, -1), tail(v, -1)), ncol = 2)
idx <- which(duplicated(m) | duplicated(m, fromLast = TRUE))
len <- 2L
while (length(idx)) {
len <- len + 1L
m <- matrix(v[sequence(rep(len, length(idx)), idx)], ncol = len, byrow = TRUE)
idx <- idx[duplicated(m) | duplicated(m, fromLast = TRUE)]
}
return(len - 1L)
}
UPDATE 2
This should return your dat data.frame:
Check_repeat_Seq3 <- function(v) {
v1 <- match(v, unique(v))
vPair <- fPair(head(v1, -1), tail(v1, -1))
blnKeep <- allDup(vPair)
idx <- which(blnKeep)
if (length(idx)) {
len <- 1L
seq_rep <- integer(length(v)/2)
while (length(idx)) {
len <- len + 1L
vPair <- fPair(vPair[blnKeep], v1[idx + len])
blnKeep <- allDup(vPair)
seq_rep[len] <- nrow(unique(matrix(v[sequence(rep(len, length(blnKeep)), idx)], ncol = len, byrow = TRUE)))
idx <- idx[blnKeep]
}
len <- 2:len
return(data.frame(seq_rep = seq_rep[len], repeat_length = len, total_repeat = seq_rep[len]*len))
} else {
return(data.frame(seq_rep = integer(0), repeat_length = integer(0), total_repeat = integer(0)))
}
}

How to automatically create more models in my environment changing 3 parameters at a time?

A fully reproducible example.
library(forecast)
date = seq(as.Date("2019/01/01"), by = "month", length.out = 48)
productB = rep("B",48)
productB = rep("B",48)
productA = rep("A",48)
productA = rep("A",48)
subproducts1=rep("1",48)
subproducts2=rep("2",48)
subproductsx=rep("x",48)
subproductsy=rep("y",48)
b1 <- c(rnorm(30,5), rep(0,18))
b2 <- c(rnorm(30,5), rep(0,18))
b3 <-c(rnorm(30,5), rep(0,18))
b4 <- c(rnorm(30,5), rep(0,18))
Created the dataframe below
dfone <- data.frame("date"= rep(date,4),
"product"= c(rep(productB,2),rep(productA,2)),
"subproduct"=
c(subproducts1,subproducts2,subproductsx,subproductsy),
"actuals"= c(b1,b2,b3,b4))
export_df <- split(dfone[1:4], dfone[3])
Creation of data frames based off UNIQUE SUBPRODUCTS
dummy_list <- split(dfone[1:4], dfone[3]) %>% lapply( function(x)
x[(names(x) %in% c("date", "actuals"))])
dummy_list <- lapply(dummy_list, function(x) { x["date"] <- NULL; x })
list_dfs <- list()
for (i in 1:length(unique(dfone$subproduct))) {
#assign(paste0("df", i), as.data.frame(dummy_list[[i]]))
list_dfs <-append(list_dfs,dummy_list[[i]])
}
combined_dfs <- Reduce(function(x, y) merge(x, y, all = TRUE,
by='date'), list(list_dfs))
Creating the time series
list_ts <- lapply(list_dfs, function(t)
ts(t,start=c(2019,1),end=c(2021,6), frequency = 12)) %>%
lapply( function(t) ts_split(t,sample.out=(0.2*length(t)))) #
creates my train test split
list_ts <- do.call("rbind", list_ts) #Creates a list of time series
Question. This doesn't give me more than 9 models. I'd want a model for n1 =.1 n2=.99 and n3= .3 for example so we would have way more than 9 models for this.
n1 <- seq(0.1, 0.99, by = 0.1)
n2 <- seq(0.1, 0.99, by = 0.1)
n3 <- seq(0.1, 0.99, by = 0.1)
out<- lapply(seq_along(n1), function(i) {
cw_triple_holtwinters_additive <- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(ses(x,h=24,alpha =
n1[i],beta=n2[i],gamma=n3[i])))
cw_triple_holtwinters_additive <-
lapply(cw_triple_holtwinters_additive, "[", "mean")
assign(paste0("cw_triple_holtwinters_additive", i),
cw_triple_holtwinters_additive, envir = .GlobalEnv)
cw_triple_holtwinters_additive})
Additional question: for order=c(1,1,1) and order=c(0,1,0) can I create a list of values like these and loop through them both at the same time like Akrun's solution?
cw_seasonal_autoregressive_integratedmovingaverage1 <- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(arima(x,order=c(1,1,1),seasonal=list(order=c(0,1,0),
period=12)) ,h=24))
cw_seasonal_autoregressive_integratedmovingaverage1 <-
lapply(cw_seasonal_autoregressive_integratedmovingaverage1, "[",
c("mean"))
We can use expand.grid to get all the combinations
dat_n <- expand.grid(n1 = n1, n2= n2, n3 = n3)
Then, we loop over the sequence of rows of 'dat_n'
out<- lapply(seq_len(nrow(dat_n)), function(i) {
cw_triple_holtwinters_additive <- lapply(list_ts[1:
(length(list_ts)/2)], function(x)
forecast::forecast(ses(x,h=24,alpha =
dat_n$n1[i],beta=dat_n$n2[i],gamma=dat_n$n3[i])))
cw_triple_holtwinters_additive <-
lapply(cw_triple_holtwinters_additive, "[", "mean")
assign(paste0("cw_triple_holtwinters_additive", i),
cw_triple_holtwinters_additive, envir = .GlobalEnv)
cw_triple_holtwinters_additive})
-checking
ls(pattern = "cw_triple")
[1] "cw_triple_holtwinters_additive1" "cw_triple_holtwinters_additive10" "cw_triple_holtwinters_additive100" "cw_triple_holtwinters_additive101"
[5] "cw_triple_holtwinters_additive102" "cw_triple_holtwinters_additive103" "cw_triple_holtwinters_additive104" "cw_triple_holtwinters_additive105"
[9] "cw_triple_holtwinters_additive106" "cw_triple_holtwinters_additive107" "cw_triple_holtwinters_additive108" "cw_triple_holtwinters_additive109"
[13] "cw_triple_holtwinters_additive11" "cw_triple_holtwinters_additive110" "cw_triple_holtwinters_additive111" "cw_triple_holtwinters_additive112"
[17] "cw_triple_holtwinters_additive113" "cw_triple_holtwinters_additive114" "cw_triple_holtwinters_additive115" "cw_triple_holtwinters_additive116"
[21] "cw_triple_holtwinters_additive117"
...

multiple data frames with similar names

I needed to generate array or many data frames from other data frames which only varied in names. This required me to do a lot of copy-paste works. Is it possible that I can make it cleaner but not keep copying and pasting? Follows are two examples from many similar cases of the analysis I am doing now (I will provide codes for reproduction at the end of the question), which I think may be able to make them cleaner with the same approach.
case 1, create an array with data from per_d1,per_d1,per_d3,per_d4,per_d5
perd <- array(dim=c(7,15,5))
perd [,,1] <- as.matrix(per_d$per_d1)
perd [,,2] <- as.matrix(per_d$per_d2)
perd [,,3] <- as.matrix(per_d$per_d3)
perd [,,4] <- as.matrix(per_d$per_d4)
perd [,,5] <- as.matrix(per_d$per_d5)
case 2, create multiple data frames from data with similar names.
dataplot <- dfmak (per_d$per_d1,ge$per_d1$g1,ge$per_d1$g2,ge$per_d1$g3,ge$per_d1$g4,ge$per_d1$g5)
dataplot2 <- dfmak (per_d$per_d2,ge$per_d2$g1,ge$per_d2$g2,ge$per_d2$g3,ge$per_d2$g4,ge$per_d2$g5)
dataplot3 <- dfmak (per_d$per_d3,ge$per_d3$g1,ge$per_d3$g2,ge$per_d3$g3,ge$per_d3$g4,ge$per_d3$g5)
dataplot4 <- dfmak (per_d$per_d4,ge$per_d4$g1,ge$per_d4$g2,ge$per_d4$g3,ge$per_d4$g4,ge$per_d4$g5)
dataplot5 <- dfmak (per_d$per_d5,ge$per_d5$g1,ge$per_d5$g2,ge$per_d5$g3,ge$per_d5$g4,ge$per_d5$g5)
codes for reproduction
N <- 1
CS <- 10.141
S <- seq (7.72,13,0.807)
t <- 15
l <- length (S)
m0 <- 100
exps <- c(0.2, 0.5, 0.9, 1.5, 2)
sd <- c(0.2, 0.5, 0.8, 1.3, 1.8)
names(sd) <- paste("per", seq_along(sd), sep = "")
per <- lapply(sd, function(x){
per <- matrix(nrow = length(S)*N, ncol = t+1)
for (i in 1:dim(per)[1]) {
for (j in 1:t+1){
per [,1] <- replicate (n = N, S)
per [i,j] <- round (abs (rnorm (1, mean = per[i,1], sd =x)),digits=3)
colnames(per) <- c('physical',paste('t', 1:15, sep = ""))
per <- as.data.frame (per)
}
}
per <- per [,-1]
return(per)
}
)
per_d <- lapply(per, function(x){
per_d <- abs (x - 10.141)
}
)
names(per_d) <- paste("per_d", seq_along(sd), sep = "")
gefun <- function (i){
res <- lapply(exps, function(x){
g <- as.matrix (m0 * exp (-x * i))
for (i in 1:l) {
for (j in 1:t){
g [i,j] <- abs((round (rnorm(1,mean = g[i,j],sd=3), digits = 3)))
colnames(g) <- paste('t', 1:ncol(g), sep = "")
g <- as.data.frame(g)
}}
return(g)
}
)
}
ge <- lapply(per_d, gefun)
for (i in 1:length(ge)){
names(ge[[i]]) <- paste("g", seq_along(ge), sep = "")
}
dfmak <- function(df1,df2,df3,df4,df5,df6){
data.frame(stimulus = c (paste0('S',1:3),'CS+',paste0('S',5:7)),
phy_dis = S,
per_dis = c(df1$t1,df1$t2,df1$t3,df1$t4,df1$t5,df1$t6,df1$t7,df1$t8,df1$t9,df1$t10,df1$t11,df1$t12,df1$t13,df1$t14,df1$t15),
trials = rep(1:15, each = 7),
response_0.2 = c (df2$t1,df2$t2,df2$t3,df2$t4,df2$t5,df2$t6,df2$t7,df2$t8,df2$t9,df2$t10,df2$t11,df2$t12,df2$t13,df2$t14,df2$t15),
response_0.5 = c (df3$t1,df3$t2,df3$t3,df3$t4,df3$t5,df3$t6,df3$t7,df3$t8,df3$t9,df3$t10,df3$t11,df3$t12,df3$t13,df3$t14,df3$t15),
response_0.9 = c (df4$t1,df4$t2,df4$t3,df4$t4,df4$t5,df4$t6,df4$t7,df4$t8,df4$t9,df4$t10,df4$t11,df4$t12,df4$t13,df4$t14,df4$t15),
response_1.5 = c (df5$t1,df5$t2,df5$t3,df5$t4,df5$t5,df5$t6,df5$t7,df5$t8,df5$t9,df5$t10,df5$t11,df5$t12,df5$t13,df5$t14,df5$t15),
response_2 = c (df6$t1,df6$t2,df6$t3,df6$t4,df6$t5,df6$t6,df6$t7,df6$t8,df6$t9,df6$t10,df6$t11,df6$t12,df6$t13,df6$t14,df6$t15)
)
}
You can try the followings. But the codes, unfortunately, are not short.
Case 1
a <- lapply(per_d, as.matrix)
b <- c(a, recursive = TRUE)
pred <- array(b, dim = c(7,15,5))
Case 2
The data frames will be stored in a list. You still have to extract them using $ or [[]].
# create empty lists to store the outputs
out <- list()
name <- list()
for(i in 1:5) {
a <- per_d[[i]]
b <- ge[[i]][[1]]
c <- ge[[i]][[2]]
d <- ge[[i]][[3]]
e <- ge[[i]][[4]]
f <- ge[[i]][[5]]
arg <- list(a, b, c, d, e, f)
name[[i]] <- paste0("df_", i)
out[[i]] <- do.call(dfmak, arg)
}
out <- setNames(out, name)

Sample function in R vector combining

I need your help, I need to combine two vectors(z and Num1 or Num2), so z will 10 in final vector and Num1(Num2) was 90 in final vector.
Code that I have now:
I <- seq(1:100)
NA1<-vector()
NA2<-vector()
z <- rep(NA, 10)
Num1 <- rnorm(100)
Num2 <- rnorm(100)
vect_1 <- sample(c(Num1, z))
vect_2 <- sample(c(Num2, z))
vect_1_NA <- is.na(vect_1)
vect_2_NA <- is.na(vect_2)
for(i in I){
if(vect_1_NA[i] == TRUE)
NA1 <- append(NA1, i)
}
for(i in I){
if(vect_2_NA[i] == TRUE)
NA2 <- append(NA2, i)
}

How to do calculations on elements from a sublist in R

my code is as follows:
x <- data.frame(matrix(rnorm(20), nrow=10))
colnames(x) <- c("z", "m")
n_boot<-4
bs <- list()
for (i in 1:n_boot) {
bs[[i]] <- x[sample(nrow(x), 10, replace = TRUE), ]
}
bt<-matrix(unlist(bs), ncol = 2*n_boot, byrow = FALSE)
colnames(bt) <- rep(c("z","m"),times=n_boot)
M_to_boot <- bt[,seq(2,8,by=2)]
funct<-function(M_boot_max) {
od<-(1/((10*((10^((16-M_boot_max-25)/5))^3)/3)*((max(M_boot_max)-min(M_boot_max))/50)))
}
V_boot<-apply(M_to_boot,2,funct)
rows.combined <- nrow(M_to_boot)
cols.combined <- ncol(M_to_boot) + ncol(V_boot)
matrix.combined <- matrix(NA, nrow=rows.combined, ncol=cols.combined)
matrix.combined[, seq(1, cols.combined, 2)] <- M_to_boot
matrix.combined[, seq(2, cols.combined, 2)] <- V_boot
colnames(matrix.combined) <- rep(c("M_boot","V_boot"),times=n_boot)
df<-as.data.frame(matrix.combined)
start0 <- seq(1, by = 2, length = ncol(df) / 2)
start <- lapply(start0, function(i, df) df[i:(i+1)], df = df)
tests<-lapply(start, function(xy) split(xy, cut(xy$M_boot,breaks=5)))
Now I want to prepare some calculations on values V_boot from a sublists. To be specific I want to for each subsample calculate the sum of V_boot. So, for example I want for a bin M_boot "[[4]]$(0.811,1.25]" to have a value of sum(V_boot) for that bin. But I cannot figure out how to get to that each V_boot values.
Please help me.

Resources