A sample of data set:
testdf <- data.frame(risk_11111 = c(0,0,1,2,3,0,1,2,3,4,0), risk_11112 = c(0,0,1,2,3,0,1,2,0,1,0))
And I need output data set which would contain new column where only maximum values of cumulative sum will be maintained:
testdf <- data.frame(risk_11111 = c(0,0,1,2,3,0,1,2,3,4,0),
risk_11111_max = c(0,0,0,0,3,0,0,0,0,4,0),
risk_11112 = c(0,0,1,2,3,0,1,2,0,1,0),
risk_11112_max = c(0,0,0,0,3,0,0,2,0,1,0))
I am guessing some logical subseting of vectors colwise with apply and extracting max value with position index, and mutate into new variables.
I dont know how to extract values for new variable.
Thanks
Something like this with base R:
lapply(testdf, function(x) {
x[diff(x) > 0] <- 0
x
})
And to have all in one data.frame:
dfout <- cbind(testdf, lapply(testdf, function(x) {
x[diff(x) > 0] <- 0
x
}))
names(dfout) <- c(names(testdf), 'risk_1111_max', 'risk_1112_max')
Output:
risk_11111 risk_11112 risk_1111_max risk_1112_max
1 0 0 0 0
2 0 0 0 0
3 1 1 0 0
4 2 2 0 0
5 3 3 3 3
6 0 0 0 0
7 1 1 0 0
8 2 2 0 2
9 3 0 0 0
10 4 1 4 1
11 0 0 0 0
Related
From a given dataframe:
# Create dataframe with 4 variables and 10 obs
set.seed(1)
df<-data.frame(replicate(4,sample(0:1,10,rep=TRUE)))
I would like to compute a substract operation between in all columns combinations by pairs, but only keeping one substact, i.e column A- column B but not column B-column A and so on.
What I got is very manual, and this tend to be not so easy when there are lots of variables.
# Result
df_result <- as.data.frame(list(df$X1-df$X2,
df$X1-df$X3,
df$X1-df$X4,
df$X2-df$X3,
df$X2-df$X4,
df$X3-df$X4))
Also the colname of the feature name should describe the operation i.e.(x1_x2) being x1-x2.
You can use combn:
COMBI = combn(colnames(df),2)
res = data.frame(apply(COMBI,2,function(i)df[,i[1]]-df[,i[2]]))
colnames(res) = apply(COMBI,2,paste0,collapse="minus")
head(res)
X1minusX2 X1minusX3 X1minusX4 X2minusX3 X2minusX4 X3minusX4
1 0 0 -1 0 -1 -1
2 1 1 0 0 -1 -1
3 0 0 0 0 0 0
4 0 0 -1 0 -1 -1
5 1 1 1 0 0 0
6 -1 0 0 1 1 0
I want to randomly insert 1's in the columns of a data frame that do not currently have 1 in them. Using different seeds for each of the variables.
Below is the code I have written so far:
# create the data frame
df <- data.frame(A = c(0,0,0,0,0,0,0,0,0,0),
B = c(0,0,0,0,0,0,0,0,0,0),
C = c(0,1,0,0,0,1,0,1,0,0),
D = c(0,0,0,0,0,0,0,0,0,0),
E = c(0,1,0,1,0,0,0,0,0,0))
# get index of columns that have 1's in them
one_index <- which(grepl(pattern = 1, df))
# function to randomly put 1's with seperate seeds
funcccs <- function(x){
i = 0
for (i in 1:ncol(x)) {
set.seed(i + 1)
x[sample(nrow(x),3)] <- 1
}}
# Apply the function to the columns that do not have 1
funcccs(df[,-one_index])
Below is the error message I get:
Error in [<-.data.frame (*tmp*, sample(nrow(x), 3), value = 1) :
new columns would leave holes after existing columns
Based on the above example, the function should randomly insert 3 values of 1 in variables 'A', 'B' and 'D', because these 3 variables do not currently have 1's in them.
Any help will be appreciated. Thanks
df <- data.frame(A = c(0,0,0,0,0,0,0,0,0,0),
B = c(0,0,0,0,0,0,0,0,0,0),
C = c(0,1,0,0,0,1,0,1,0,0),
D = c(0,0,0,0,0,0,0,0,0,0),
E = c(0,1,0,1,0,0,0,0,0,0))
one_index <- which(grepl(pattern = 1, df))
funcccs <- function(x){
i = 0
for (i in 1:ncol(x)) {
set.seed(i + 1)
x[sample(nrow(x),3),i]= 1
}
return(x)
}
df[,-one_index]=funcccs(df[,-one_index])
You where choosing the whole matrix insted of the i column.
> df
A B C D E
1 0 0 0 1 0
2 1 1 1 0 1
3 0 0 0 1 0
4 0 1 0 0 1
5 1 0 0 0 0
6 0 0 1 1 0
7 1 0 0 0 0
8 0 1 1 0 0
9 0 0 0 0 0
10 0 0 0 0 0
I have 3 dataframes
Drug<-c("ab","bc","cd","ef","gh")
Target<-c("qwewr","saff","cxzcc","sadda","sadd")
fileA<-data.frame(Drug,Target)
Drug<-c("ab","bc","cdD","efc","ghg","hj")
Target<-c("qwewr","saff","cxzccf","saddav","sadd","bn")
fileB<-data.frame(Drug,Target)
Drug<-c("abB","bcv","cdD","efc")
Target<-c("qwewrm","saff","cxzccfh","saddav")
fileC<-data.frame(Drug,Target)
As you can see each one contains a pair "Drug"-"Target". Every dataframe contains only unique pairs. But you can find exactly the same pair in the other dataframes. What I want to achieve is to create a new dataframe which will extract all the unique pairs in the first column and then in the other 3 columns will have the fileA, fileB and fileC which will be filled with 1 if the pair exists and 0 if the pair does not exist. Something like:
Pairs fileA fileB fileC
1: abqwewr 1 1 1
2: bcsaff 1 1 1
3: cdcxzcc 1 1 1
4: efsadda 1 1 1
5: ghsadd 1 1 0
6: cdDcxzccf 0 0 0
7: efcsaddav 0 0 0
8: ghgsadd 0 0 0
9: hjbn 0 0 0
10: abBqwewrm 0 0 0
11: bcvsaff 0 0 0
12: cdDcxzccfh 0 0 0
But here the dataframe is not correct since in the first column there is only the drug name and also each row should have had at least one 1.
My method:
# Create composite dataset by combining all files
compositeDataD <- rbind(fileA,fileB,fileC)
# Get unique (drug, target) pairs
# Connect Drug Names and Target Gene Symbols into one vector of pairs
compositeDataD <- na.omit(compositeDataD)
DrugTargetPairsD <- paste(compositeDataD$Drug,compositeDataD$Target,sep="")
uniquePairsD<-unique(DrugTargetPairsD)
PairsA <- DrugTargetPairsD[1:nrow(na.omit(fileA))]
PairsB <- DrugTargetPairsD[1:nrow(na.omit(fileB))]
PairsC <- DrugTargetPairsD[1:nrow(na.omit(fileC))]
# Create binary matrix for unique (drug, target) pairs
binaryA <- as.numeric(uniquePairsD %in% PairsA) # This function returns a binary value for each unique (Drug, Target) Pair compared with the content of file1
binaryB <- as.numeric(uniquePairsD %in% PairsB)
binaryC <- as.numeric(uniquePairsD %in% PairsC)
table33 <- data.table(Pairs=uniquePairsD,
fileA=binaryA,fileB=binaryB,
fileC=binaryC)
Form list L from the three objects and use lapply to paste their columns together and then stack to create a 2 column data frame with the pasted values and an indicator of which object it came from. Finally use table to provide the counts.
L <- mget(ls(pattern = "file"))
s <- stack(lapply(L, function(x) paste0(x[[1]], x[[2]])))
table(s)
giving:
ind
values fileA fileB fileC
abBqwewrm 0 0 1
abqwewr 1 1 0
bcsaff 1 1 0
bcvsaff 0 0 1
cdcxzcc 1 0 0
cdDcxzccf 0 1 0
cdDcxzccfh 0 0 1
efcsaddav 0 1 1
efsadda 1 0 0
ghgsadd 0 1 0
ghsadd 1 0 0
hjbn 0 1 0
A variation of this is to express it as this pipeline:
library(magrittr)
mget(ls(pattern = "file")) %>%
lapply(function(x) paste0(x[[1]], x[[2]])) %>%
stack %>%
table
You can first create the Pairs and then merge on them, while carrying a column where the data came from:
Create the indicator column in each file:
fileA$fileA <- 1
fileB$fileB <- 1
fileC$fileC <- 1
Create the pairs in each file:
fileA$DrugTargetPair <- paste0(fileA$Drug, fileA$Target)
fileB$DrugTargetPair <- paste0(fileB$Drug, fileB$Target)
fileC$DrugTargetPair <- paste0(fileC$Drug, fileC$Target)
Select only the indicator column and the Pairs colum :
fileA <- fileA[, c("DrugTargetPair", "fileA")]
fileB <- fileB[, c("DrugTargetPair", "fileB")]
fileC <- fileC[, c("DrugTargetPair", "fileC")]
Merge on the Pairs column, kepp all Pairs with all = T:
file_new <- merge(fileA, fileB, by = "DrugTargetPair", all = T)
file_new <- merge(file_new, fileC, by = "DrugTargetPair", all = T)
file_new[is.na(file_new)] <- 0
file_new
DrugTargetPair fileA fileB fileC
1 abBqwewrm 0 0 1
2 abqwewr 1 1 0
3 bcsaff 1 1 0
4 bcvsaff 0 0 1
5 cdcxzcc 1 0 0
6 cdDcxzccf 0 1 0
7 cdDcxzccfh 0 0 1
8 efcsaddav 0 1 1
9 efsadda 1 0 0
10 ghgsadd 0 1 0
11 ghsadd 1 0 0
12 hjbn 0 1 0
data:
Drug<-c("ab","bc","cd","ef","gh")
Target<-c("qwewr","saff","cxzcc","sadda","sadd")
fileA<-data.frame(I(Drug),I(Target))
Drug<-c("ab","bc","cdD","efc","ghg","hj")
Target<-c("qwewr","saff","cxzccf","saddav","sadd","bn")
fileB<-data.frame(I(Drug),I(Target))
Drug<-c("abB","bcv","cdD","efc")
Target<-c("qwewrm","saff","cxzccfh","saddav")
fileC<-data.frame(I(Drug),I(Target))
code:
all_list <- list(fileA, fileB, fileC)
all1 <- rbind(fileA,fileB,fileC)
all1 <- as.data.frame(unique(all1))
ans <- t(apply(all1, 1, function(drgT){ sapply(all_list, function(x) {(list(drgT) %in% unlist(apply(x,1,list), recursive = F))*1} ) }))
ans[rowSums(ans) == 1,] <- 0
cbind(all1, ans)
result:
# Drug Target 1 2 3
#1 ab qwewr 1 1 0
#2 bc saff 1 1 0
#3 cd cxzcc 0 0 0
#4 ef sadda 0 0 0
#5 gh sadd 0 0 0
#8 cdD cxzccf 0 0 0
#9 efc saddav 0 1 1
#10 ghg sadd 0 0 0
#11 hj bn 0 0 0
#12 abB qwewrm 0 0 0
#13 bcv saff 0 0 0
#14 cdD cxzccfh 0 0 0
please note:
please revise your example data/ desired outcome.
please E D U C A T E yourself on stringsAsFactors.
As I am new to R, this question may seem to you piece of a cake.
I have a data in txt format. The first column has Cluster Number and the second column has names of different organisms.
For example:
0 org4|gene759
1 org1|gene992
2 org1|gene1101
3 org4|gene757
4 org1|gene1702
5 org1|gene989
6 org1|gene990
7 org1|gene1699
9 org1|gene1102
10 org4|gene2439
10 org1|gene1374
I need to re-arrange/reshape the data in following format.
Cluster No. Org 1 Org 2 org3 org4
0 0 0 1
1 0 0 0
I could not figure out how to do it in R.
Thanks
We could use table
out <- cbind(ClusterNo = seq_len(nrow(df1)), as.data.frame.matrix(table(seq_len(nrow(df1)),
factor(sub("\\|.*", "", df1[[2]]), levels = paste0("org", 1:4)))))
head(out, 2)
# ClusterNo org1 org2 org3 org4
#1 1 0 0 0 1
#2 2 1 0 0 0
It is also possible that we need to use the first column to get the frequency
out1 <- as.data.frame.matrix(table(df1[[1]],
factor(sub("\\|.*", "", df1[[2]]), levels = paste0("org", 1:4))))
Reading the table into R can be done with
input <- read.table('filename.txt')
Then we can extract the relevant number from the org4|gene759 string using a regular expression, and set this to a third column of our input:
input[, 3] <- gsub('^org(.+)\\|.*', '\\1', input[, 2])
Our input data now looks like this:
> input
V1 V2 V3
1 0 org4|gene759 4
2 1 org1|gene992 1
3 2 org1|gene1101 1
4 3 org4|gene757 4
5 4 org1|gene1702 1
6 5 org1|gene989 1
7 6 org1|gene990 1
8 7 org1|gene1699 1
9 9 org1|gene1102 1
10 10 org4|gene2439 4
11 10 org1|gene1374 1
Then we need to list the possible values of org:
possibleOrgs <- seq_len(max(input[, 3])) # = c(1, 2, 3, 4)
Now for the tricky part. The following function takes each unique cluster number in turn (I notice that 10 appears twice in your example data), takes all the rows relating to that cluster, and looks at the org value for those rows.
result <- vapply(unique(input[, 1]), function (x)
possibleOrgs %in% input[input[, 1] == x, 3], logical(4)))
We can then format this result as we like, perhaps using t to transform its orientation, * 1 to convert from TRUEs and FALSEs to 1s and 0s, and colnames to title its columns:
result <- t(result) * 1
colnames (result) <- paste0('org', possibleOrgs)
rownames(result) <- unique(input[, 1])
I hope that this is what you were looking for -- it wasn't quite clear from your question!
Output:
> result
org1 org2 org3 org4
0 0 0 0 1
1 1 0 0 0
2 1 0 0 0
3 0 0 0 1
4 1 0 0 0
5 1 0 0 0
6 1 0 0 0
7 1 0 0 0
9 1 0 0 0
10 1 0 0 1
So I have a list that contains certain characters as shown below
list <- c("MY","GM+" ,"TY","RS","LG")
And I have a variable named "CODE" in the data frame as follows
code <- c("MY GM+","","LGTY", "RS","TY")
df <- data.frame(1:5,code)
df
code
1 MY GM+
2
3 LGTY
4 RS
5 TY
Now I want to create 5 new variables named "MY","GM+","TY","RS","LG"
Which takes binary value, 1 if there's a match case in the CODE variable
df
code MY GM+ TY RS LG
1 MY GM+ 1 1 0 0 0
2 0 0 0 0 0
3 LGTY 0 0 1 0 1
4 RS 0 0 0 1 0
5 TY 0 0 1 0 0
Really appreciate your help. Thank you.
Since you know how many values will be returned (5), and what you want their types to be (integer), you could use vapply() with grepl(). We can turn the resulting logical matrix into integer values by using integer() in vapply()'s FUN.VALUE argument.
cbind(df, vapply(List, grepl, integer(nrow(df)), df$code, fixed = TRUE))
# code MY GM+ TY RS LG
# 1 MY GM+ 1 1 0 0 0
# 2 0 0 0 0 0
# 3 LGTY 0 0 1 0 1
# 4 RS 0 0 0 1 0
# 5 TY 0 0 1 0 0
I think your original data has a couple of typos, so here's what I used:
List <- c("MY", "GM+" , "TY", "RS", "LG")
df <- data.frame(code = c("MY GM+", "", "LGTY", "RS", "TY"))