I have a for-loop that initializes 3 vectors (launch_2012, amount, and one_week_bf) and creates a data frame. Then, it predicts a single week's of data and inserts it into vectors (amount and one_week_bf), and recreates the data.frame again; this process is looped 8 times. However, I can't seem to get the data.frame to update the new amounts. Would anyone be able to assist please?
for (i in 1:8) {
launch_2012 <- c(rep('bf', 5), 'launch', rep('af', 7))
amount <- c(7946, 6641, 5975, 5378, 5217, NA, NA, NA, NA, NA, NA, NA, NA)
one_week_bf <- c(NA, 7946, 6641, 5975, 5378, 5217, NA, NA, NA, NA, NA, NA, NA)
newdata <- data.frame(amount = amount, one_week_bf = one_week_bf, launch = launch_2012, week = week)
predicted <- predict(model0a, newdata)
amount[i+5] <- predicted[i+5]
one_week_bf[i+6] <- predicted[i+5]
View(newdata)
}
It's difficult to be sure since your example is not reproducible, but note that predict.lm(...) by default has na.action=na.pass, which means that any rows in newdata that have any NA values by default generate NA for the prediction. Since your first pass of newdata has NA in rows 6-13, predicted will have NA in those same elements. This means that amounts and one_week_bf will have NA in those elements, which in turn will generate the same newdata each time.
None of this should be in a for loop.
x <- data.frame("launch_2012" = c(rep('bf', 5), 'launch', rep('af', 7)),
"amount"=c(7946, 6641, 5975, 5378, 5217, NA, NA, NA, NA, NA, NA, NA, NA),
"one_week_bf"=c(NA, 7946, 6641, 5975, 5378, 5217, NA, NA, NA, NA, NA, NA, NA))
x$new_amount <- #the replacement from your predict vector
x$new_one_week_bf <- #the replacement from your predict vector
Note I have no idea what model0a does, so just gave what the new columns should be as whatever the resulting vector is from your predict function. This will add the new data as new columns
Related
I have a dataframe which contains many triplicate (3 columns set). And I have grouped the dataframe into each triplicate as a seperate group of list.
The example dataset is,
example_data <- structure(list(`1_3ng` = c(69648445400, 73518145600, NA, NA,
73529102400, 75481088000, NA, 73545910600, 74473949200, 77396199900
), `2_3ng` = c(71187990600, 70677690400, NA, 73675407400, 73215342700,
NA, NA, 69996254800, 69795686400, 76951318300), `3_3ng` = c(65032022000,
71248214000, NA, 72393058300, 72025550900, 71041067000, 73604692000,
NA, 73324202000, 75969608700), `4_7-5ng` = c(NA, 65845061600,
75009245100, 64021237700, 66960666600, 69055643600, NA, 64899540900,
NA, NA), `5_7-5ng` = c(65097201700, NA, NA, 69032126500, NA,
70189899800, NA, 74143529100, 69299087400, NA), `6_7-5ng` = c(71964413900,
69048485800, NA, 71281569700, 71167596500, NA, NA, 68389822800,
69322289200, NA), `7_10ng` = c(71420403700, 67552276500, 72888076300,
66491357100, NA, 68165019600, 70876631000, NA, 69174190100, 63782945300
), `8_10ng` = c(NA, 71179401200, 68959365100, 70570182700, 73032738800,
NA, 74807496700, NA, 71812102100, 73855098500), `9_10ng` = c(NA,
70403756100, NA, 70277421000, 69887731700, 69818871800, NA, 71353886700,
NA, 74115466700), `10_15ng` = c(NA, NA, 68487581700, NA, NA,
69056997400, NA, 67780479400, 66804467800, 72291939500), `11_15ng` = c(NA,
63599643700, NA, NA, 60752029700, NA, NA, 63403655600, NA, 64548492900
), `12_15ng` = c(NA, 67344750600, 61610182700, 67414425600, 65946654700,
66166118400, NA, 70830837700, 67288305700, 69911451300)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -10L)
And after grouping I got the four lists, since the above example dataset contains 4 groups. I have used the following R code for grouping the data,
grouping_data<-function(df){ #df= dataframe
df_col<-ncol(df) #calculates no. of columns in dataframe
groups<-sort(rep(0:((df_col/3)-1),3)) #creates user determined groups
id<-list() #creates empty list
for (i in 1:length(unique(groups))){
id[[i]]<-which(groups == unique(groups)[i])} #creates list of groups
names(id)<-paste0("id",unique(groups)) #assigns group based names to the list "id"
data<-list() #creates empty list
for (i in 1:length(id)){
data[[i]]<-df[,id[[i]]]} #creates list of dataframe columns sorted by groups
names(data)<-paste0("data",unique(groups)) #assigns group based names to the list "data"
return(data)}
group_data <-grouping_data(example_data)
Please suggest useful R code for do a particular function for all the lists at a same time.
For example the below function I have done by following way,
#VSN Normalization
vsnNorm <- function(dat) {
dat<-as.data.frame(dat)
vsnNormed <- suppressMessages(vsn::justvsn(as.matrix(dat)))
colnames(vsnNormed) <- colnames(dat)
row.names(vsnNormed) <- rownames(dat)
return(as.matrix(vsnNormed))
}
And I have tried like below,
vsn.dat0 <- vsnNorm(group_data$data0)
vsn.dat1 <- vsnNorm(group_data$data1)
vsn.dat2 <- vsnNorm(group_data$data2)
vsn.dat3 <- vsnNorm(group_data$data3)
vsn.dat <- cbind (vsn.dat0,vsn.dat1,vsn.dat2,vsn.dat3)
It is working well.
But the dataset triplicate (3 columns set) value may be change from dataset to dataset. And calling all the lists everytime become will be tedious.
So kindly share some codes which will call all the resulted lists for performing a function and combine the result as a single file.
Thank you in advance.
The shortcut you are looking for is:
vsn.dat <- do.call("rbind", lapply(group_data, vsnNorm))
I would like to paste values of a certain data.frame row to other rows which have a certain attribute of a certain feature, however not a whole row just a couple of values of it. Exactly it looks like:
z <- c(NA, NA, 3,4,2,3,5)
x <- c(NA, NA, 2,5,5,3,3)
a <- c("Hank", NA, NA, NA, NA, NA, NA)
b <- c("Hank", NA, NA, NA, NA, NA, NA)
c <- c(NA, NA, NA, NA, NA, NA, NA)
d <- c("Bobby", NA, NA, NA, NA, NA, NA)
df <- as.data.frame(rbind( a, b, c, d, z, x))
Now, I would like to pass df["z",3:7] to the rows[3:7] which have V1 == "Hank", and pass df["x", 3:7] when V1== "Bobby".
Do anybody has a hint for me? I guess it should be a function with sapply or something like that. Maybe a dplyr could give a solution? Thanks for any advice!
I have a vector with either a negative value or NA and a threshold:
threshold <- -1
example <- c(NA, NA, -0.108, NA, NA, NA, NA, NA -0.601, -0.889, -1.178, -1.089, -1.401, -1.178, -0.959, -1.085, -1.483, -0.891, -0.817, -0.095, -1.305, NA, NA, NA, NA, -0.981, -0.457, -0.003, -0.358, NA, NA)
I want to identify all the data blocks with at least one value lower than the threshold and to replace by NA all the other blocks. With my example vector, I want this result:
result <- c(NA, NA, NA, NA, NA, NA, NA, NA -0.601, -0.889, -1.178, -1.089, -1.401, -1.178, -0.959, -1.085, -1.483, -0.891, -0.817, -0.095, -1.305, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)
So the first available value is the first block but -0.108 is higher than -1 so it turns into NA. The second block is kept the same because there is at least ine value lower than -1. The third block is now NA values because between the 4 available values, no one was lower than the threshold.
My first idea was to identify where were the values lower than the threshold:
val <- which(example < threshold)
But then I don't know how to say "keep all the values around this position which are not NA" because it is always a different number of values...
Try
library(data.table)#v >= 1.9.5 (devel version - install from GitHub).
#library(devtools)
#install_github("Rdatatable/data.table", build_vignettes = FALSE)
as.data.table(example)[, res:=(NA | (min(example)< -1))*example, by=rleid(is.na(example))][, res]
Another way, with the suggestion of OlliJ :
example <- c(NA, NA, -0.108, NA, NA, NA, NA, NA -0.601, -0.889, -1.178, -1.089, -1.401, -1.178, -0.959, -1.085, -1.483, -0.891, -0.817, -0.095, NA, NA, NA, NA, -0.981, -0.457, -0.003, -0.358, NA, NA)
test <- !(is.na(example))
len <- rle(test)$lengths
val <- rle(test)$values
##Matrix with the beginning and the end of each group
ind <- matrix(,nrow=length(which(val)),ncol=2)
ind[,1] <- (cumsum(len)[which(val==T)-1])+1
ind[,2] <- (cumsum(len))[val==T]
result <- rep(NA, length=length(example))
apply(ind, 1, function(x)
{
if(any(example[x[1]:x[2]] < -1))
{
result[x[1]:x[2]] <- example[x[1]:x[2]]
}
})
I have a 10x10 matrix in R, called run_off. I would like to convert this matrix to a data frame that contains the entries of the matrix (the order doesn't really matter, although I'd prefer it to be filled by row) as well as the row and columns numbers of the entries as separate columns in the data frame, so that for instance element run_off[2,3] has a row in the data frame with 3 columns, the first containing the element itself, the second containing 2 and the third containing 3.
This is what I have so far:
run_off <- matrix(data = c(45630, 23350, 2924, 1798, 2007, 1204, 1298, 563, 777, 621,
53025, 26466, 2829, 1748, 732, 1424, 399, 537, 340, NA,
67318, 42333, -1854, 3178, 3045, 3281, 2909, 2613, NA, NA,
93489, 37473, 7431, 6648, 4207, 5762, 1890, NA, NA, NA,
80517, 33061, 6863, 4328, 4003, 2350, NA, NA, NA, NA,
68690, 33931, 5645, 6178, 3479, NA, NA, NA, NA, NA,
63091, 32198, 8938, 6879, NA, NA, NA, NA, NA, NA,
64430, 32491, 8414, NA, NA, NA, NA, NA, NA, NA,
68548, 35366, NA, NA, NA, NA, NA, NA, NA, NA,
76013, NA, NA, NA, NA, NA, NA, NA, NA, NA)
, nrow = 10, ncol = 10, byrow = TRUE)
df <- data.frame()
for (i in 1:nrow(run_off)) {
for (k in 1:ncol(run_off)) {
claim <- run_off[i,k]
acc_year <- i
dev_year <- k
df[???, "claims"] <- claim # Problem here
df[???, "acc_year"] <- acc_year # and here
df[???, "dev_year"] <- dev_year # and here
}
}
dev_year refers to the column number of the matrix entry and acc_yearto the row number. My problem is that I don't know the proper index to use for the data frame.
I am assuming you are not interested in the NA elements? You can use which and the arr.ind = TRUE argument to return a two column matrix of array indices for each value and cbind this to the values, excluding the NA values:
# Get array indices
ind <- which( ! is.na(run_off) , arr.ind = TRUE )
# cbind indices to values
out <- cbind( run_off[ ! is.na( run_off ) ] , ind )
head( as.data.frame( out ) )
# V1 row col
#1 45630 1 1
#2 53025 2 1
#3 67318 3 1
#4 93489 4 1
#5 80517 5 1
#6 68690 6 1
Use t() on the matrix first if you want to fill by row, e.g. which( ! is.na( t( run_off ) ) , arr.ind = TRUE ) (and when you cbind it).
The following works, giving 3 points connected with a line.
plot(c(1,7,12), c(0,0,2),type="b")
However this does not work (it plots the points but without connecting line and without any warning or error)
t<-data.frame(x=1:20,y=c(0,NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA))
plot(t$x, t$y,type="b")
Why is that? Is it because of the NA in the data frame? I can't seem to find any reference on this.
That is the intended, documented, and (when you need to plot lines with breaks in them) desirable behavior of R's lines() function. From the ?lines help page:
The coordinates can contain ‘NA’ values. If a point contains ‘NA’
in either its ‘x’ or ‘y’ value, it is omitted from the plot, and
lines are not drawn to or from such points. Thus missing values
can be used to achieve breaks in lines.
To get lines between the points, simply remove the points for which y has a value of NA:
d <- data.frame(x=1:20,
y=c(0,NA,NA,NA,NA,NA,0,NA,NA,NA,NA,2,NA,NA,NA,NA,NA,NA,NA,NA))
plot(y ~ x, data = d, type = "b", subset = !is.na(y))
R doesn't plot points that are NA, nor does it plot connections between points that have NAs separating them.
If you have NAs in your vector and you want R to skip them, one solution is to get rid of those rows:
d<-data.frame(x=1:20,y=c(0,NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA))
d2 <- d[!(is.na(df$y)), ]
plot(d2$x, d2$y, type="b")
(Also, t is not a good name for an R variable since it is already a function in R, one that transposes matrices).