I need to change individual identifiers that are currently alphabetical to numerical. I have created a data frame where each alphabetical identifier is associated with a number
individuals num.individuals (g4)
1 ZYO 64
2 KAO 24
3 MKU 32
4 SAG 42
What I need to replace ZYO with the number 64 in my main data frame (g3) and like wise for all the other codes.
My main data frame (g3) looks like this
SAG YOG GOG BES ATR ALI COC CEL DUN EVA END GAR HAR HUX ISH INO JUL
1 2
2 2 EVA
3 SAG 2 EVA
4 2
5 SAG 2
6 2
Now on a small scale I can write a code to change it like I did with ATR
g3$ATR <- as.character(g3$ATR)
g3[g3$target == "ATR" | g3$ATR == "ATR","ATR"] <- 2
But this is time consuming and increased chance of human error.
I know there are ways to do this on a broad scale with NAs
I think maybe we could do a for loop for this, but I am not good enough to write one myself.
I have also been trying to use this function which I feel like may work but I am not sure how to logically build this argument, it was posted on the questions board here
Fast replacing values in dataframe in R
df <- as.data.frame(lapply(df, function(x){replace(x, x <0,0)})
I have tried to work my data into this by
df <- as.data.frame(lapply(g4, function(g3){replace(x, x <0,0)})
Here is one approach using the data.table package:
First, create a reproducible example similar to your data:
require(data.table)
ref <- data.table(individuals=1:4,num.individuals=c("ZYO","KAO","MKU","SAG"),g4=c(64,24,32,42))
g3 <- data.table(SAG=c("","SAG","","SAG"),KAO=c("KAO","KAO","",""))
Here is the ref table:
individuals num.individuals g4
1: 1 ZYO 64
2: 2 KAO 24
3: 3 MKU 32
4: 4 SAG 42
And here is your g3 table:
SAG KAO
1: KAO
2: SAG KAO
3:
4: SAG
And now we do our find and replacing:
g3[ , lapply(.SD,function(x) ref$g4[chmatch(x,ref$num.individuals)])]
And the final result:
SAG KAO
1: NA 24
2: 42 24
3: NA NA
4: 42 NA
And if you need more speed, the fastmatch package might help with their fmatch function:
require(fastmatch)
g3[ , lapply(.SD,function(x) ref$g4[fmatch(x,ref$num.individuals)])]
SAG KAO
1: NA 24
2: 42 24
3: NA NA
4: 42 NA
Related
This is my first time posting to Stack Exchange, my apologies as I'm certain I will make a few mistakes. I am trying to assess false detections in a dataset.
I have one data frame with "true" detections
truth=
ID Start Stop SNR
1 213466 213468 10.08
2 32238 32240 10.28
3 218934 218936 12.02
4 222774 222776 11.4
5 68137 68139 10.99
And another data frame with a list of times, that represent possible 'real' detections
possible=
ID Times
1 32239.76
2 32241.14
3 68138.72
4 111233.93
5 128395.28
6 146180.31
7 188433.35
8 198714.7
I am trying to see if the values in my 'possible' data frame lies between the start and stop values. If so I'd like to create a third column in possible called "between" and a column in the "truth" data frame called "match. For every value from possible that falls between I'd like a 1, otherwise a 0. For all of the rows in "truth" that find a match I'd like a 1, otherwise a 0.
Neither ID, not SNR are important. I'm not looking to match on ID. Instead I wand to run through the data frame entirely. Output should look something like:
ID Times Between
1 32239.76 0
2 32241.14 1
3 68138.72 0
4 111233.93 0
5 128395.28 0
6 146180.31 1
7 188433.35 0
8 198714.7 0
Alternatively, knowing if any of my 'possible' time values fall within 2 seconds of start or end times would also do the trick (also with 1/0 outputs)
(Thanks for the feedback on the original post)
Thanks in advance for your patience with me as I navigate this system.
I think this can be conceptulised as a rolling join in data.table. Take this simplified example:
truth
# id start stop
#1: 1 1 5
#2: 2 7 10
#3: 3 12 15
#4: 4 17 20
#5: 5 22 26
possible
# id times
#1: 1 3
#2: 2 11
#3: 3 13
#4: 4 28
setDT(truth)
setDT(possible)
melt(truth, measure.vars=c("start","stop"), value.name="times")[
possible, on="times", roll=TRUE
][, .(id=i.id, truthid=id, times, status=factor(variable, labels=c("in","out")))]
# id truthid times status
#1: 1 1 3 in
#2: 2 2 11 out
#3: 3 3 13 in
#4: 4 5 28 out
The source datasets were:
truth <- read.table(text="id start stop
1 1 5
2 7 10
3 12 15
4 17 20
5 22 26", header=TRUE)
possible <- read.table(text="id times
1 3
2 11
3 13
4 28", header=TRUE)
I'll post a solution that I'm pretty sure works like you want it to in order to get you started. Maybe someone else can post a more efficient answer.
Anyway, first I needed to generate some example data - next time please provide this from your own data set in your post using the function dput(head(truth, n = 25)) and dput(head(possible, n = 25)). I used:
#generate random test data
set.seed(7)
truth <- data.frame(c(1:100),
c(sample(5:20, size = 100, replace = T)),
c(sample(21:50, size = 100, replace = T)))
possible <- data.frame(c(sample(1:15, size = 15, replace = F)))
colnames(possible) <- "Times"
After getting sample data to work with; the following solution provides what I believe you are asking for. This should scale directly to your own dataset as it seems to be laid out. Respond below if the comments are unclear.
#need the %between% operator
library(data.table)
#initialize vectors - 0 or false by default
truth.match <- c(rep(0, times = nrow(truth)))
possible.between <- c(rep(0, times = nrow(possible)))
#iterate through 'possible' dataframe
for (i in 1:nrow(possible)){
#get boolean vector to show if any of the 'truth' rows are a 'match'
match.vec <- apply(truth[, 2:3],
MARGIN = 1,
FUN = function(x) {possible$Times[i] %between% x})
#if any are true then update the match and between vectors
if(any(match.vec)){
truth.match[match.vec] <- 1
possible.between[i] <- 1
}
}
#i think this should be called anyMatch for clarity
truth$anyMatch <- truth.match
#similarly; betweenAny
possible$betweenAny <- possible.between
I have a large amount of data which I would like to subset based on the values in one of the columns (dive site in this case). The data looks like this:
site weather depth_ft depth_m vis_ft vis_m coral_safety coral_deep rate
alice rain 95 NA 50 NA 2 4 9
alice over NA 25 NA 25 2 4 9
steps clear NA 27 NA 25 2 4 9
steps NA 30 NA 20 1 4 9
andrea1 clear 60 NA 60 NA 2 4 5
I would like to create a subset of the data which contains only data for one dive site at a time (e.g. one subset for alice, one for steps, one for andrea1 etc...).
I understand that I could subset each individually using
alice <- subset(reefdata, site=="alice")
But as I have over 100 different sites to subset by would like to avoid having to individually specify each subset. I think that subset is probably not flexible enough for me to ask it to subset by a list of names (or at least not to my current knowledge of R, which is growing, but still in infancy), is there another command which I should be looking into?
Thank you
This will create a list that contains the subset data frames in separate list elements.
splitdat <- split(reefdata, reefdata$site)
Then if you want to access the "alice" data you can reference it like
splitdat[["alice"]]
I would use the plyr package.
library(plyr)
ll <- dlply(df,.variables = c("site"))
Result:
>ll
$alice
site weather depth_ft depth_m vis_ft vis_m coral_safety coral_deep rate
1 alice rain 95 NA 50 NA 2 4 9
2 alice over NA 25 NA 25 2 4 9
$andrea1
site weather depth_ft depth_m vis_ft vis_m coral_safety coral_deep rate
1 andrea1 clear 60 NA 60 NA 2 4 5
$steps
site weather depth_ft depth_m vis_ft vis_m coral_safety coral_deep rate
1 steps clear NA 27 NA 25 2 4 9
2 steps <NA> 30 NA 20 1 4 9 NA
split() and dlply() are perfect one shot solutions.
If you want a "step by step" procedure with a loop (which is frowned upon by many R users, but I find it helpful in order to understand what's going on), try this:
# create vector with site names, assuming reefdata$site is a factor
sites <- as.character( unique( reefdata$site ) )
# create empty list to take dive data per site
dives <- list( NULL )
# collect data per site into the list
for( i in 1:length( sites ) )
{
# subset
dive <- reefdata[ reefdata$site == sites[ i ] , ]
# add resulting data.frame to the list
dives[[ i ]] <- dive
# name the list element
names( dives )[ i ] <- sites[ i ]
}
I am not sure how I can do this, but what I need is I need to form a cluster of this dataframe mydf where I want to omit the inf(infitive) values and the values greater than 50. I need to get the table that has no inf and no values greater than 50. How can I get a table that contains no inf and no value greater than 50(may be by nullifying those cells)? However, For clustering part, I don't have any problem because I can do this using mfuzz package. So the only problem I have is that I want to scale the cluster within 0-50 margin.
mydf
s.no A B C
1 Inf Inf 999.9
2 0.43 30 23
3 34 22 233
4 3 43 45
You can use NA, the built in missing data indicator in R:
?NA
By doing this:
mydf[mydf > 50 | mydf == Inf] <- NA
mydf
s.no A B C
1 1 NA NA NA
2 2 0.43 30 23
3 3 34.00 22 NA
4 4 3.00 43 45
Any stuff you do downstream in R should have NA handling methods, even if it's just na.omit
I have a panel data with "entity" and "year". I have a column "x" with values that i consider like time series. I want to create a new column "xp" where for each "entity" I give, for each "year", the value obtained from the forecast of the previous 5 years. If there are less than 5 previous values available, xp=NA.
For the sake of generality, the forecast is the output of a function built in R from a couple of predefinite functions found in some packages like "forecast". If it is easier with a specific function, let's use forecast(auto.arima(x.L5:x.L1),h=1).
For now, I use data.table in R because it is so much faster for all the other manipulations I make on my dataset.
However, what I want to do is not data.table 101 and I struggle with it.
I would so much appreciate a bit of your time to help me on that.
Thanks.
Here is an extract of what i would like to do:
entity year x xp
1 1980 21 NA
1 1981 23 NA
1 1982 32 NA
1 1983 36 NA
1 1984 38 NA
1 1985 45 42.3 =f((21,23,32,36,38))
1 1986 50 48.6 =f((23,32,36,38,45))
2 1991 2 NA
2 1992 4 NA
2 1993 6 NA
2 1994 8 NA
2 1995 10 NA
2 1996 12 12.4 =f((2,4,6,8,10))
2 1997 14 13.9 =f((4,6,8,10,12))
...
As suggested by Eddi, I found a way using rollapply:
DT <- data.table(mydata)
DT <- DT[order(entity,year)]
DT[,xp:=rollapply(.SD$x,5,timeseries,align="right",fill=NA,by="entity"]
with:
timeseries <- function(x){
fit <- auto.arima(x)
value <- as.data.frame(forecast(fit,h=1))[1,1]
return(value)
}
For a sample of mydata, it works perfectly. However, when I use the whole dataset (150k lines), after some computing time, i have the following error message:
Error in seq.default(start.at,NROW(data),by = by) : wrong sign in 'by' argument
Where does it come from?
Can it come from the "5" parameter in rollapply and from some specifities of certain entities in the dataset (not enough data...)?
Thanks again for your time and help.
I have a large data frame/.csv that is a matrix with 42 columns and 110,357,407. It was derived from the x and y coordinates for two datasets of points, one with 41 and another with 110,357,407 and the values of the rows represent the distances between these two sets of points (the distance of each point on list 1 to every single point on list 2). The first column is a list of points (from 1 to 110,357,407). An excerpt from the matrix is below.
V1 V2 V3 V4 V5 V6 V7
1 38517.05 38717.8 38840.16 38961.37 39281.06 88551.03 88422.62
2 38514.05 38714.79 38837.15 38958.34 39278 88545.48 88417.09
3 38511.05 38711.79 38834.14 38955.3 39274.94 88539.92 88411.56
4 38508.05 38708.78 38831.13 38952.27 39271.88 88534.37 88406.03
5 38505.06 38705.78 38828.12 38949.24 39268.83 88528.82 88400.5
6 38502.07 38702.78 38825.12 38946.21 39265.78 88523.27 88394.97
7 38499.08 38699.78 38822.12 38943.18 39262.73 88517.72 88389.44
8 38496.09 38696.79 38819.12 38940.15 39259.68 88512.17 88383.91
9 38493.1 38693.8 38816.12 38937.13 39256.63 88506.62 88378.38
10 38490.12 38690.8 38813.12 38934.11 39253.58 88501.07 88372.85
11 38487.14 38687.81 38810.13 38931.09 39250.54 88495.52 88367.33
12 38484.16 38684.83 38807.14 38928.07 39247.5 88489.98 88361.8
13 38481.18 38681.84 38804.15 38925.06 39244.46 88484.43 88356.28
14 38478.21 38678.86 38801.16 38922.04 39241.43 88478.88 88350.75
15 38475.23 38675.88 38798.17 38919.03 39238.39 88473.34 88345.23
16 38472.26 38672.9 38795.19 38916.03 39235.36 88467.8 88339.71
My issue is that I would like to change this matrix into just 3 columns, the first column would be similar to the first column of the matrix with the 110,357,407 rows, the second would be the 41 data points (each matched up with a distance each of the first points to all of the others) and the third would be the distance between those points. So it would look something like this
Back Pres Dist
1 1 3486
2 1 3456
3 1 3483
4 1 3456
5 1 3429
6 1 3438
7 1 3422
8 1 3427
9 1 3428
(After the distances between the back and all of the first value of pres are complete, pres will change to 2 and will eventually work its way up to 41)
I realize that this will output a hugely ridiculous number of rows, but this is the format that I need to run some processes that are outside of R.
I tried using this code
cols.Output <- data.frame(col = rep(colnames(output3), each = nrow(output3)),
row = rep(rownames(output3), ncol(output3)),
value = as.vector(output3))
But there won’t be the same number of rows for each column, so I received an error (and I don’t think it would have really worked with my pres column needs). I tried experimenting with some of the rbind.fill and cbind.fill functions (the one in plyr and ones that others have come up with in the forum). I also looked into some of the melting and reshaping but I was very confused about the functions and couldn’t figure out how to implement them appropriately (or if they even are appropriate for what I need). I would really appreciate any help on this as I’ve been struggling with it for a long time.
Edit: Just to be a little more clear about what I need. Take these two smaller data sets
back <- 1 dataset with 5 sets of x, y points
pres <- 1 dataset with 3 sets of x, y points
Calculating distances between these two data frames generates the initial matrix:
Back 1 2 3
1 3427 3444 3451
2 3432 3486 3476
3 3486 3479 3486
4 3449 3438 3484
5 3483 3486 3486
And my desired output would look like this:
Back Pres Dist
1 1 3427
2 1 3432
3 1 3486
4 1 3449
5 1 3483
1 2 3444
2 2 3486
3 2 3479
4 2 3438
5 2 3486
1 3 3451
2 3 3476
3 3 3486
4 3 3484
5 3 3486
Yes, it looks this is the kind of problem generally solved with some combination of melt and cast in the reshape2 package. That said, with 100+ million rows, I'm not sure that that's the most efficient way to go in this case.
You could do it all manually as follows. I'll assume your data frame is called df, and the distances are in columns 2 to 42. See if this works.
d <- unlist(df[-1]) # put all the distances into a vector
newdf <- cbind(expand.grid(back=seq_len(nrow(df)), pres=seq_len(ncol(df) - 1)), d)
This will probably die unless you have tons of memory. The same holds for any simple solution though, since you have > 4.2 billion elements in the vector of distances. You can work on subsets of the full dataset at a time to get around this problem.
Here's how to use melt on a small example:
require(reshape2)
a <- matrix(rnorm(9), nrow = 3)
a[, 1] <- 1:3 ## Pretending these are one set of points
rownames(a) <- a[, 1] ## We'll put them as rownames instead of a column
melt(a[, -1]) ## And omit that column when melting
If you have memory issues, you could write a for loop and do it in pieces, writing each to a file when they're completed.