I am struggling with converting several characters to vectors and making them as a list in R.
The converting rule is as follows:
Assign a number to each character. ex. A=1, B=2, C=3,...
Make a vector when the length of characters is ">=2". ex. AB = c(1,2), ABC = c(1,2,3)
Make lists containing several vectors.
For example, suppose that there is ex object with three components. For each component, I want to make it to list objects list1, list2, and list3.
ex = c("(A,B,C,D)", "(AB,BC,CD)","(AB,C)")
# 3 lists to be returned from ex object
list1 = "list(1,2,3,4)" # from (A,B,C,D)
list2 = "list(c(1,2), c(2,3), c(3,4))" # from (AB,BC,CD)
list3 = "list(c(1,2), c(3))" # from (AB,C)
Please let me know a good R function to solve the example above.
* The minor change is reflected.
lookUpTable = as.numeric(1:4) #map numbers to their respective strings
names(lookUpTable) = LETTERS[1:4]
step1<- #get rid of parentheses and split by ",".
strsplit(gsub("[()]", "", ex), ",")
result<- #split again to make things like "AB" into "A", "B", also convert the strings to numbers acc. to lookUpTable
lapply(step1, function(x){ lapply(strsplit(x, ""), function(u) unname(lookUpTable[u])) })
# assign to the global environment.
invisible(
lapply(seq_along(result), function(x) {assign(paste0("list", x), result[[x]], envir = globalenv()); NULL})
)
# get it as strings:
invisible(
lapply(seq_along(result), function(x) {assign(paste0("list_string", x), capture.output(dput(result[[x]])), envir = globalenv()); NULL})
)
data:
ex = c("(A,B,C,D)", "(AB,BC,CD)","(AB,C)")
tips and tricks:
I make use of regular expressions in gsub (and strsplit). Learn regex!
I made a lookUpTable that maps the individual strings to numbers. Make sure your lookUpTable is set up analogously.
Have a look at apply functions like in that case ?lapply.
lastly I assign the result to the global environment. I dont recommend this step but its what you have requested.
Related
I want to create a dataframe with 3 columns.
#First column
name_list = c("ABC_D1", "ABC_D2", "ABC_D3",
"ABC_E1", "ABC_E2", "ABC_E3",
"ABC_F1", "ABC_F2", "ABC_F3")
df1 = data.frame(C1 = name_list)
These names in column 1 are a bunch of named results of the cor.test function. The second column should consist of the correlation coefficents I get by writing ABC_D1$estimate, ABC_D2$estimate.
My problem is now that I dont want to add the $estimate manually to every single name of the first column. I tried this:
df1$C2 = paste0(df1$C1, '$estimate')
But this doesnt work, it only gives me this back:
"ABC_D1$estimate", "ABC_D2$estimate", "ABC_D3$estimate",
"ABC_E1$estimate", "ABC_E2$estimate", "ABC_E3$estimate",
"ABC_F1$estimate", "ABC_F2$estimate", "ABC_F3$estimate")
class(df1$C2)
[1] "character
How can I get the numeric result for ABC_D1$estimate in my dataframe? How can I convert these characters into Named num? The 3rd column should constist of the results of $p.value.
As pointed out by #DSGym there are several problems, including the it is not very convenient to have a list of character names, and it would be better to have a list of object instead.
Anyway, I think you can get where you want using:
estimates <- lapply(name_list, function(dat) {
dat_l <- get(dat)
dat_l[["estimate"]]
}
)
cbind(name_list, estimates)
This is not really advisable but given those premises...
Ok I think now i know what you need.
eval(parse(text = paste0("ABC_D1", '$estimate')))
You connect the two strings and use the functions parse and eval the get your results.
This it how to do it for your whole data.frame:
name_list = c("ABC_D1", "ABC_D2", "ABC_D3",
"ABC_E1", "ABC_E2", "ABC_E3",
"ABC_F1", "ABC_F2", "ABC_F3")
df1 = data.frame(C1 = name_list)
df1$C2 <- map_dbl(paste0(df1$C1, '$estimate'), function(x) eval(parse(text = x)))
Assume a character vector like the following
file1_p1_analysed_samples.txt
file1_p1_raw_samples.txt
f2_file2_p1_analysed_samples.txt
f3_file3_p1_raw_samples.txt
Desired output:
file1_p1_analysed
file1_p1_raw
file2_p1_analysed
file3_p1_raw
I would like to compare the elements and remove parts of the string from start and end as much as possible but keep them unique.
The above one is just an example. The parts to be removed are not common to all elements. I need a general solution independent of the strings in the above example.
So far I have been able to chuck off parts that are common to all elements, provided the separator and the resulting split parts are of same length. Here is the function,
mf <- function(x,sep){
xsplit = strsplit(x,split = sep)
xdfm <- as.data.frame(do.call(rbind,xsplit))
res <- list()
for (i in 1:ncol(xdfm)){
if (!all(xdfm[,i] == xdfm[1,i])){
res[[length(res)+1]] <- as.character(xdfm[,i])
}
}
res <- as.data.frame(do.call(rbind,res))
res <- apply(res,2,function(x) paste(x,collapse="_"))
return(res)
}
Applying the above function:
a = c("a_samples.txt","b_samples.txt")
mf(a,"_")
V1 V2
"a" "b"
2.
> b = c("apple.fruit.txt","orange.fruit.txt")
> mf(b,sep = "\\.")
V1 V2
"apple" "orange"
If the resulting split parts are not same length, this doesn't work.
What about
files <- c("file1_p1_analysed_samples.txt", "file1_p1_raw_samples.txt", "f2_file2_p1_analysed_samples.txt", "f3_file3_p1_raw_samples.txt")
new_files <- gsub('_samples\\.txt', '', files)
new_files
... which yields
[1] "file1_p1_analysed" "file1_p1_raw" "f2_file2_p1_analysed" "f3_file3_p1_raw"
This removes the _samples.txt part from your strings.
Why not:
strings <- c("file1_p1_analysed_samples.txt",
"file1_p1_raw_samples.txt",
"f2_file2_p1_analysed_samples.txt",
"f3_file3_p1_raw_samples.txt")
sapply(strings, function(x) {
pattern <- ".*(file[0-9].*)_samples\\.txt"
gsub(x, pattern = pattern, replacement = "\\1")
})
Things that match between ( and ) can be called back as a group in the replacement with backwards referencing. You can do this with \\1. You can even specify multiple groups!
Seeing your comment on Jan's answer. Why not define your static bits and paste together a pattern and always surround them with parentheses? Then you can always call \\i in the replacement of gsub.
I have written a function which finds the intersection between two strings. I want to use this function in apply and find out all the intersections in the given data frame. I am using below code.
Function:-
common <- function(first_str,second_str)
{
a <- unlist(strsplit(first_str," "))
b <- unlist(strsplit(second_str," "))
com <- intersect(a,b)
return((length(com)/length(union(a,b)))*100)
}
Data frame:-
str1 <- c("One Two Three","X Y Z")
str2 <- c("One Two Four", "X Y A")
df <- data.frame(str1, str2)
When use apply I get argument "second_str" is missing with no default error
apply(df, 1, common)
Could you please help me out with the solution?
apply() will only pass a single vector to the function you provide. With margin=1 it will call your function once per each row with a single vector containing all the values for the "current" row. It will not split up those values into multiple parameters to your function.
You could instead re-write your function to
common2 <- function(x) {
first_str <- x[1]
second_str <- x[2]
a <- unlist(strsplit(first_str," "))
b <- unlist(strsplit(second_str," "))
com <- intersect(a,b)
return((length(com)/length(union(a,b)))*100)
}
Although that doesn't scale well for multiple parameters. YOu could also use Map or mapply to iterate over multiple vectors at a time
If your original function you can do
with(df, Map(common, str1, str2))
I have five dataframes (a-f), each of which has a column 'nq'. I want to find the max, min and average of the nq columns
classes <- c("a","b","c","d","e","f")
for (i in classes){
format(max(i$nq), scientific = TRUE)
format(min(i$nq), scientific = TRUE)
format(mean(i$nq), scientific = TRUE)
}
But the code is not working. Can you please help?
You can't use a character value as a data.frame name. The value "a" is not the same as the data.frame a.
You probably shouldn't have a bunch of data.frames lying around. You probably want to have them all in a list. Then you can lapply over them to get results.
mydata <- list(
a = data.frame(nq=runif(10)),
b = data.frame(nq=runif(10)),
c = data.frame(nq=runif(10)),
d = data.frame(nq=runif(10))
)
then you can do
lapply(mydata, function(x)
format(c(max(x$nq), min(x$nq), mean(x$nq)), scientific = TRUE)
)
to get all the values at once.
The reason it is not working is because 'i' is a character/string. As already mentioned by Mr.Flick you have to make it into a list.
Alternatively, you instead of writing i$nq in your loop you can write get(i)$nq. The get() function will search the workspace for an object by name and it will return the object itself. However, this is not as clean as making it into a list and using lapply.
I have 9880 records in a data frame, I am trying to split it into 9 groups of 1000 each and the last group will have 880 records and also name them accordingly. I used for-loop for 1-9 groups but manually for the last 880 records, but i am sure there are better ways to achieve this,
library(sqldf)
for (i in 0:8)
{
assign(paste("test",i,sep="_"),as.data.frame(final_9880[((1000*i)+1):(1000*(i+1)), (1:53)]))
}
test_9<- num_final_9880[9001:9880,1:53]
also am unable to append all the parts in one for-loop!
#append all parts
all_9880<-rbind(test_0,test_1,test_2,test_3,test_4,test_5,test_6,test_7,test_8,test_9)
Any help is appreciated, thanks!
A small variation on this solution
ls <- split(final_9880, rep(0:9, each = 1000, length.out = 9880)) # edited to Roman's suggestion
for(i in 1:10) assign(paste("test",i,sep="_"), ls[[i]])
Your command for binding should work.
Edit
If you have many dataframes you can use a parse-eval combo. I use the package gsubfn for readability.
library(gsubfn)
nms <- paste("test", 1:10, sep="_", collapse=",")
eval(fn$parse(text='do.call(rbind, list($nms))'))
How does this work? First I create a string containing the comma-separated list of the dataframes
> paste("test", 1:10, sep="_", collapse=",")
[1] "test_1,test_2,test_3,test_4,test_5,test_6,test_7,test_8,test_9,test_10"
Then I use this string to construct the list
list(test_1,test_2,test_3,test_4,test_5,test_6,test_7,test_8,test_9,test_10)
using parse and eval with string interpolation.
eval(fn$parse(text='list($nms)'))
String interpolation is implemented via the fn$ prefix of parse, its effect is to intercept and substitute $nms with the string contained in the variable nms. Parsing and evaluating the string "list($mns)" creates the list needed. In the solution the rbind is included in the parse-eval combo.
EDIT 2
You can collect all variables with a certain pattern, put them in a list and bind them by rows.
do.call("rbind", sapply(ls(pattern = "test_"), get, simplify = FALSE))
ls finds all variables with a pattern "test_"
sapply retrieves all those variables and stores them in a list
do.call flattens the list row-wise.
No for loop required -- use split
data <- data.frame(a = 1:9880, b = sample(letters, 9880, replace = TRUE))
splitter <- (data$a-1) %/% 1000
.list <- split(data, splitter)
lapply(0:9, function(i){
assign(paste('test',i,sep='_'), .list[[(i+1)]], envir = .GlobalEnv)
return(invisible())
})
all_9880<-rbind(test_0,test_1,test_2,test_3,test_4,test_5,test_6,test_7,test_8,test_9)
identical(all_9880,data)
## [1] TRUE