I've got a data frame with this structure:
> df
modifications
13-MOD:0057
13-MOD:0046
13-MOD:0051,13-MOD:0076
13-MOD:0036,13-MOD:0076,13-MOD:0016
13-MOD:0256,13-MOD:0156,13-MOD:0956,13-MOD:0125
13-MOD:0014 13-MOD:0156, 13-MOD:0956,13-MOD:0125...n
13-MOD:0012 ... n
To split the data I used this code:
df2 <- data.frame(str_split_fixed(df$modifications, ",", 20))
Basically, I get this data.
> df2
x1 | x2 | x3 | empty |
13-MOD:0057 | empty | empty | empty |
13-MOD:0046 | emply | empty | empty |
13-MOD:0051 | 13-MOD:0076 | empty | empty |
13-MOD:0036 | 13-MOD:0076 | 13-MOD:0016 | empty |
13-MOD:0256 | 13-MOD:0156 | 13-MOD:0956 | 13-MOD:0125
13-MOD:0014 | 13-MOD:0156 | 13-MOD:0956 | 13-MOD:0125 | ... n
13-MOD:0012 | ... | ...n
What I want is remove the empty values and stack the data from columns X2,X3, X4 ... n to the first one X1.
To do that I was using this:
df3 <- melt(setDT(df2), # set df to a data.table
measure.vars = list(c(1:20)), # set column groupings
value.name = 'V')[ # set output name scheme
, -1, with = F]
To remove the empty values:
df3[df3==""] <- NA
histo3 = subset(df3, V1 != 'NA')
But I don't know why I get an error about the length of the column in melt function. Do you know any way to make this easier?.
Reproducible example:
df <- data.frame(modifications=c("UNIMOD:108,UNIMOD:108","UNIMOD:108","UNIMOD:108","UNIMOD:108,UNIMOD:108,UNIMOD:108","UNIMOD:108,UNIMOD:108,UNIMOD:108,UNIMOD:108,UNIMOD:108,UNIMOD:108","UNIMOD:108"))
could it be something like this?
library(stringr)
# input dataset
s <- c('13-MOD:0057', '13-MOD:0046', '13-MOD:0051,13-MOD:0076', '13-MOD:0036,13-MOD:0076,13-MOD:0016', '13-MOD:0256,13-MOD:0156,13-MOD:0956,13-MOD:0125')
s
[1] "13-MOD:0057"
[2] "13-MOD:0046"
[3] "13-MOD:0051,13-MOD:0076"
[4] "13-MOD:0036,13-MOD:0076,13-MOD:0016"
[5] "13-MOD:0256,13-MOD:0156,13-MOD:0956,13-MOD:0125"
# get the individual lengths
lengths <- sapply(str_split(s,','), function(x){ length(x) })
# create the dataframe splitting in N columns
as.data.frame(str_split_fixed(s, ',', max(lengths)))
V1 V2 V3 V4
1 13-MOD:0057
2 13-MOD:0046
3 13-MOD:0051 13-MOD:0076
4 13-MOD:0036 13-MOD:0076 13-MOD:0016
5 13-MOD:0256 13-MOD:0156 13-MOD:0956 13-MOD:0125
UPDATE 1
To stack all the non-empty cells into a single column
# create the dataframe splitting in N columns
first.matrix <- str_split_fixed(s, ',', max(lengths))
# select only the cells != ""
first.matrix[which(first.matrix!="")]
[1] "13-MOD:0057" "13-MOD:0046" "13-MOD:0051" "13-MOD:0036" "13-MOD:0256" "13-MOD:0076"
[7] "13-MOD:0076" "13-MOD:0156" "13-MOD:0016" "13-MOD:0956" "13-MOD:0125"
Related
This is row selection problem in R.
I want to get only one row in each dataset based on the minimum value of the variable fifteen. I have tried two approaches below, none of them returning the desired output.
For list 1> SPX[[1]] the date.frame is set up like this:
SPX1[[1]]
+----------------------------------+---------+------------------------+----------------+
| | stkPx | expirDate | fifteen |
+----------------------------------+---------+------------------------+----------------+
| 1 | 1461.62 | 2013-01-19 | 2 |
| 2 | 1461.25 | 2013-01-25 | 8 |
| 3 | 1461.35 | 2013-02-01 | 3 |
| . | . | . | . |
| . | . | . | . |
+----------------------------------+---------+------------------------+----------------+
The first approach is aggregating and the merge. As this has to be done for a list the code is in a loop:
df.agg<- list() # creates a list
for (l in 1:length(SPX1)){
df.agg[[l]]<- SPX1[[l]] %>%
aggregate(fifteen ~ ticker, data=SPX1[[l]], min) #Finding minimum value of fifteen for ticker
df.minSPX1 <- merge(df.agg[[l]], SPX1[[l]]) #Merge dataset so we only get row with min fifteen value
}
I get :
Error in get(as.character(FUN), mode = "function", envir = envir) :
object 'SPX1' of mode 'function' was not found
Another approach, however it just changes all the values of the first column to one, not deleting any rows when merging:
TESTER<- which.min(SPX1[[1]]$fifteen) # Finds which row has minimum value of fifteen
df.minSPX1 <- merge(TESTER, SPX1[[1]],by.x=by) #Try to merge so I only get the row with min. fifteen
I have tried reading other answers on SO but maybe because of the way the lists are set up this won't work?
I hope you can tell me where I get this wrong.
Try This
df<- lapply(SPX, function(x) x[x$fifteen==min(x$fifteen),])
df<- as.data.frame(df)
Edit:
As suggested by #Gregor-Thomas this will work when there is a tie.
df<- lapply(SPX, function(x) x[which.min(x$fifteen), ])
df<- as.data.frame(df)
I have a huge list of small data frames which I would like to meaningfully combine into one, however the logic around how to do so escapes me.
For instance, if I have a list of data frames that look something like this albeit with far more files, many of which I do not want in my data frame:
MyList = c("AthosVersusAthos.csv", "AthosVerusPorthos.csv", "AthosVersusAramis.csv", "PorthosVerusAthos.csv", "PorthosVersusPorthos.csv", "PorthosVersusAramis.csv", "AramisVersusAthos.csv", "AramisVersusPorthos.csv", "AramisVerusPothos.csv", "BobVersusMary.csv", "LostCities.txt")
What I want is to assemble these into one large data frame. Which would look like this.
| |
AthosVersusAthos | PorthosVersusAthos | AramisVersusAthos
| |
------------------------------------------------------
| |
AthosVerusPorthos | PothosVersusPorthos| AramisVersusPorthos
| |
------------------------------------------------------
| |
AthosVersusAramis | PorthosVersusAramis| AramisVersusAramis
| |
Or perhaps more correctly (with sample numbers in only one portion of the matrix):
| Athos | Porthos | Aramis
-------|------------------------------------------------------
| 10 9 5 | |
Athos | 2 10 4 | |
| 3 0 10 | |
-------|------------------------------------------------------
| | |
Porthos | | |
| | |
-------|------------------------------------------------------
| | |
Aramis | | |
| | |
-------------------------------------------------------------
What I have managed so far is:
Musketeers = c("Athos", "Porthos", "Aramis")
for(i in 1:length(Musketeers)) {
for(j in 1:length(Musketeers)) {
CombinedMatrix <- cbind (
rbind(MyList[grep(paste0("^(", Musketeers[i],
")(?=.*Versus[", Musketeers[j], "]"), names(MyList),
value = T, perl=T)])
)
}
}
What I was trying to do was combine my grep command (quite importnant given the number of files and specificity with which I need to select them) and then combine rbind and cbind so that the rows and the columns of the matrix are meaningfully concatenated.
My general plan was to merge all the data frames starting with 'Athos' into one column, and doing this once again for data frames starting with 'Porthos' and 'Aramis', and then combine those three columns, row-wise into a final dataframe.
I know I'm quite far off but I can't quite get my head around where to start.
Edit: #PierreGramme generated a useful model data set which I will add below seeing as I imagine it would have been useful to provide it originally.
Musketeers = c("Athos", "Porthos", "Aramis")
MyList = c("AthosVersusAthos.csv", "AthosVersusPorthos.csv", "AthosVersusAramis.csv",
"PorthosVersusAthos.csv", "PorthosVersusPorthos.csv", "PorthosVersusAramis.csv",
"AramisVersusAthos.csv", "AramisVersusPorthos.csv", "AramisVersusAramis.csv",
"BobVersusMary.csv", "LostCities.txt")
MyList = lapply(setNames(nm=MyList), function(x) matrix(rnorm(9), nrow=3, dimnames=list(c("a","b","c"), c("x","y","z"))) )
First make a reproducible example. Is it faithful? If so, I will add code to answer
Musketeers = c("Athos", "Pothos", "Aramis")
MyList = c("AthosVersusAthos.csv", "AthosVersusPothos.csv", "AthosVersusAramis.csv",
"PothosVersusAthos.csv", "PothosVersusPothos.csv", "PothosVersusAramis.csv",
"AramisVersusAthos.csv", "AramisVersusPothos.csv", "AramisVersusAramis.csv",
"BobVersusMary.csv", "LostCities.txt")
MyList = lapply(setNames(nm=MyList), function(x) matrix(rnorm(9), nrow=3, dimnames=list(c("a","b","c"), c("x","y","z"))) )
And then is it correct that you would like to concatenate 9 of these matrices into your combined matrix shaped as you described?
Edit:
Then the code solving your problem:
# Helper function to extract the relevant portion of MyList and rbind() it
makeColumns = function(n){
re = paste0("^",n,"Versus")
sublist = MyList[grep(re, names(MyList))]
names(sublist) = sub(re, "", sub("\\.csv$","", names(sublist)))
# Make sure sublist is sorted correctly and contains info on all musketeers
sublist = sublist[Musketeers]
# Change row and col names so that they are unique in the final result
sublist = lapply(names(sublist), function(m) {
res = sublist[[m]]
rownames(res) = paste0(m,"_",rownames(res))
colnames(res) = paste0(n,"_",colnames(res))
res
})
do.call(rbind, sublist)
}
lColumns = lapply(setNames(nm=Musketeers), makeColumns)
CombinedMatrix = do.call(cbind, lColumns)
Scaled down my dataframe looks like this:
+---+------------+-------------+
| | Label1 | Label2 |
+---+------------+-------------+
| 1 | T | F |
| 2 | F | F |
| 3 | T | T |
+---+------------+-------------+
I need to create a list of lists that map the column names to all the row numbers that have a false boolean as their value. For the above example it would look something like this:
{"Label1" : (2), "Label2" : (1,2)}
I am currently doing it as so:
myList = with(data.frame(which(!myDataFrame, arr.ind = TRUE)), list("colNames" = names(myDataFrame)[col], "rows" = row))
l = list()
count = 1;
for (i in myList[["colNames"]]) {
tmpRowNum = myList[["rows"]][[count]];
tmpList = l[[i]];
if (is.null(tmpList)) {
tmpList = list();
}
l[[i]] = c(tmpList, list(tmpRowNum))
count = count + 1;
}
This does work, but as I am new to R I can only assume there is a more efficient method of doing this. The with function creates two separate lists that I essentially have to combine to get the result that I am looking for.
You could try:
df <- data.frame(Label1=c("T","F","T"),Label2=c("F","F","T"))
lapply(df,function(x) which(x=="F"))
$Label1
[1] 2
$Label2
[1] 1 2
EDIT To get the same by row, use apply with margin=1:
apply(df,1,function(x) which(x=="F"))
To get a vector of the "F"s in row 2:
res <- apply(df,1,function(x) which(x=="F"))
res[[2]]
1 2
One useful way to get the row/column index is with which and arr.ind
i1 <- which(df=="F", arr.ind=TRUE)
I have a number of lists of conditions and I would like to evaluate their combinations, and then I'd like to get binary values for these logical values (True = 1, False = 0). The conditions themselves may change or grow as my project progresses, and so I'd like to have one place within the script where I can alter these conditional statements, while the rest of the script stays the same.
Here is a simplified, reproducible example:
# get the data
df <- data.frame(id = c(1,2,3,4,5), x = c(11,4,8,9,12), y = c(0.5,0.9,0.11,0.6, 0.5))
# name and define the conditions
names1 <- c("above2","above5")
conditions1 <- c("df$x > 2", "df$x >5")
names2 <- c("belowpt6", "belowpt4")
conditions2 <- c("df$y < 0.6", "df$y < 0.4")
# create an object that contains the unique combinations of these conditions and their names, to be used for labeling columns later
names_combinations <- as.vector(t(outer(names1, names2, paste, sep="_")))
condition_combinations <- as.vector(t(outer(conditions1, conditions2, paste, sep=" & ")))
# create a dataframe of the logical values of these conditions
condition_combinations_logical <- ????? # This is where I need help
# lapply to get binary values from these logical vectors
df[paste0("var_",names_combinations] <- +(condition_combinations_logical)
to get output that could look something like:
-id -- | -x -- | -y -- | -var_above2_belowpt6 -- | -var_above2_belowpt4 -- | etc.
1 | 11 | 0.5 | 1 | 0 |
2 | 4 | 0.9 | 0 | 0 |
3 | 8 | 0.11 | 1 | 1 |
etc. ....
Looks like the dreaded eval(parse()) does it (hard to think of a much easier way ...). Then use storage.mode()<- to convert from logical to integer ...
res <- sapply(condition_combinations,function(x) eval(parse(text=x)))
storage.mode(res) <- "integer"
Let's suppose I have a data frame with the following parameters
DATA <- data.frame(ROWID, ID1, NAME1, ...IDn, NAMEn)
Sample of what the data might look like:
ROWID | ID1 | NAME1 | ID2 | NAME2 | IDn | NAMEn
001 | 001 | FAS | 002 | MAS | 999 | ZOO
002 | 003 | BIN | 004 | DUN | 998 | SOO
Where I have 201 columns by 10k+ rows. What I would like to do is to reshape this data such that for each row in the original DATA, I produce a set of rows in a subsequent data frame. Each row would consist of the originating ROWID, IDa, NAMEa, IDb, NAMEb pairs such that the first is matched with all others (99 pairs containing ID1, 98 with ID2, and so on). This would occur for each row producing a large data frame of all possible combinations within rows for every row. The result would look like:
ROWID1 | ID1 | NAME1 | ID2 | NAME2
ROWID1 | ID1 | NAME1 | ID3 | NAME3
...
ROWID1 | ID2 | NAME2 | ID3 | NAME3
...
ROWID2 | ID1 | NAME1 | ID2 | NAME2
ROWID2 | ID1 | NAME1 | ID3 | NAME3
...
The code I produced to do this is as follows. It works great, but only on smaller data frames. The full data frame is painfully slow, and I am hoping to have alternatives to speed it up using functions or something else of which I am unaware. Thanks in advance!!
DATA <- data.frame(as described above)
META <- data.frame(ROWID=numeric(0),ID1=numeric(0),
BUS1=character(0),ID2=numeric(0),BUS2=character(0))
for (i in 1:length(DATA$ROWID)) {
SET <- data.frame(ROWID=numeric(0),ID1=numeric(0),
BUS1=character(0),ID2=numeric(0),BUS2=character(0))
ROWID <- DATA[i,1]
for (x in seq(3,ncol(DATA),2)) {
for (y in seq(x,ncol(DATA),2)) {
ID1 <- DATA[i,x-2]
BUS1 <- DATA[i,x]
ID2 <- DATA[i,y-2]
BUS2 <- DATA[i,y]
if (!is.na(BUS1) && !is.na(BUS2)) {
NEW <- cbind(ROWID, ID1, BUS1, ID2, BUS2)
SET <- rbind(SET, NEW)
}
}
}
META <- rbind(META, SET)
}
Here is my way to write it, which includes all the 3 optimizations I wrote as comments. Also, be careful! your code had some bugs in the addressing the columns... which I hopefully also fixed.
require('compiler')
enableJIT(3)
DATA2 = as.matrix(DATA)
META2 <- matrix(character(),ncol=5,nrow=(nrow(DATA2)*(ncol(DATA2)-2)^2/2)) # you want a matrix instead of a data.frame, and you want to pre-allocate its size
colnames(META2) = c("ROWID","ID1","BUS1","ID2","BUS2")
k=0
for (i in 1:nrow(DATA2)) {
for (x in seq(3,ncol(DATA2)-2,2)) {
for (y in seq(x+2,ncol(DATA2),2)) {
k=k+1
META2[k,] = c(DATA2[i,1],DATA2[i,x-1], DATA2[i,x], DATA2[i,y-1], DATA2[i,y]) # no need to use temporary variables
}
}
}
META2 = as.data.frame(META2) # converting back to data.frame
META2$BUS1 = as.numeric(META2$BUS1)
META2$BUS2 = as.numeric(META2$BUS2)
I will let you handle yourself the case in which BUS1 or BUS2 is NA - basically, you need to not add these lines (and not increment the variable k), and after the loops you need to crop your matrix to remove the trailing empty rows.