Can someone describe exactly (I understand partially) what the following line does?
which(!is.na(table[1,]))
1) table[1,] = ? line 1 or column 1 or of a file called "table"?
2) !is.na = why the !? (is.na is used to eliminate the NA but why the !? Normally, ! represents negative (not equal).
If we split the function to pieces,
table[1,]
subset the first row of the dataset
is.na(table[1,])
checks whether there are NA values in the first row. It will return a vector of logical elements (TRUE for NA and FALSE for non-NA).
! is negation operator. So, it will convert the TRUE to FALSE and vice versa to give a vector of logical elements that are non NA for TRUE values
!is.na(table[1,])
and lastly the which wrapper gives the numeric index of TRUE values
To demonstrate an example, say we have a matrix
m1 <- matrix(c(NA, 0, 1, 2), 2, 2)
Then, if we follow the steps
m1[1,] #returns the 1st row as a vector
#[1] NA 1
is.na(m1[1,]) #returns TRUE for NA
#[1] TRUE FALSE
!is.na(m1[1,]) #returns TRUE for non-NA elements
#[1] FALSE TRUE
which(!is.na(m1[1,]))
#[1] 2
#or perhaps more usefully
which(is.na(m1[1,]))
#[1] 1
Related
I have a vector A, which contains a list of genera, which I want to use to subset a second vector, B. I have successfully used grepl to extract anything from B that has a partial match to the genera in A. Below is a reproducible example of what I have done.
But now I would like to get a list of which genera in A matched with something in B, and which which genera did not. I.e. the "matched" list would contain Cortinarius and Russula, and the "unmatched" list would contain Laccaria and Inocybe. Any ideas on how to do this? In reality my vectors are very long, and the genus names in B are not all in the same position amongst the other info.
# create some dummy vectors
A <- c("Cortinarius","Laccaria","Inocybe","Russula")
B <- c("fafsdf_Cortinarius_sdfsdf","sdfsdf_Russula_sdfsdf_fdf","Tomentella_sdfsdf","sdfas_Sebacina","sdfsf_Clavulina_sdfdsf")
# extract the elements of B that have a partial match to anything in A.
new.B <- B[grepl(paste(A,collapse="|"), B)]
# But now how do I tell which elements of A were present in B, and which ones were not?
We could use lapply or sapply to loop over the patterns and then get a named output
out <- setNames(lapply(A, function(x) grep(x, B, value = TRUE)), A)
THen, it is easier to check the ones returning empty elements
> out[lengths(out) > 0]
$Cortinarius
[1] "fafsdf_Cortinarius_sdfsdf"
$Russula
[1] "sdfsdf_Russula_sdfsdf_fdf"
> out[lengths(out) == 0]
$Laccaria
character(0)
$Inocybe
character(0)
and get the names of that
> names(out[lengths(out) > 0])
[1] "Cortinarius" "Russula"
> names(out[lengths(out) == 0])
[1] "Laccaria" "Inocybe"
You can use sapply with grepl to check for each value of A matching with ever value of B.
sapply(A, grepl, B)
# Cortinarius Laccaria Inocybe Russula
#[1,] TRUE FALSE FALSE FALSE
#[2,] FALSE FALSE FALSE TRUE
#[3,] FALSE FALSE FALSE FALSE
#[4,] FALSE FALSE FALSE FALSE
#[5,] FALSE FALSE FALSE FALSE
You can take column-wise sum of these values to get the count of matches.
result <- colSums(sapply(A, grepl, B))
result
#Cortinarius Laccaria Inocybe Russula
# 1 0 0 1
#values with at least one match
names(Filter(function(x) x > 0, result))
#[1] "Cortinarius" "Russula"
#values with no match
names(Filter(function(x) x == 0, result))
#[1] "Laccaria" "Inocybe"
Following is related to R language.
x1 <- c(1, 4, 3, NA, 7)
is.na(x1) <- which(x1 == 7)
I don't undertand, the LHS in last line gives you a vector of boolean and RHS is a value(index where x ==7, 5 in this case). So what does it mean to assign a boolean vector a value of 5?
is.na from the docs returns:
The default method for is.na applied to an atomic vector returns a logical vector of the same length as its argument x, containing TRUE for those elements marked NA or, for numeric or complex vectors, NaN, and FALSE otherwise.
Therefore, by making a logical vector(you're in essence saying wherever an index is TRUE, this should be an NA.
By "matching" these indices to the corresponding index from which, you're turning the latter into NAs wherever FALSE hence the change.
To put it in practice:
This is the output from is.na(x1):
is.na(x1)
[1] FALSE FALSE FALSE TRUE FALSE
The corresponding output from which(x==7):
which(x1 == 7)
[1] 5
Combining, the element at position 5 will now become an NA because it has been given the logical is.na() which returns TRUE
is.na(x1) <- which(x1 == 7)
x1
[1] 1 4 3 NA NA
The above turns the first index into an NA and appends two more NAs so as to make index 7 and NA.
This can be best seen by:
is.na(x1) <- c(1,7)
x1
[1] NA 4 3 NA 7 NA NA
Compare with this example from the docs:
(xx <- c(0:4))
is.na(xx) <- c(2, 4)
xx
[1] 0 NA 2 NA 4
From the above, it is clear that c(2,4) follows the original indices in xx hence the rest become NAs.
I have the following data frame:
df <- data.frame("Logical"=c("true",NA,"false","true","","false"),
"Numeric"=c(1,2,3,4,5,6))
unique(df$Logical)
length(df$Logical == TRUE)
I'm trying to figure out, how many TRUE-values do I have in my df$Logical column. But seems I'm missing something and length(df$Logical == TRUE) returns no of records in my logical column.
What I'm doing wrong in this particular case. Desired result should be 2 for TRUE-values in df$Logical column. Many thanks in advance.
We need to specify the string in the lower case as the values were 'true/false' and not exactly TRUE/FALSE. Also, instead of length, sum should be used. The sum gets the number of TRUE elements.
sum(df$Logical == "true")
#[1] 2
If there are NA elements in the column, use na.rm = TRUE
sum(df$Logical=='true', na.rm = TRUE)
#[1] 2
The length of a logical or any other vector would be the same as the original length/number of rows of the dataset.
length(df$Logical == "true")
#[1] 6
because it returns a logical vector of length 6.
df$Logical == "true"
#[1] TRUE FALSE FALSE TRUE FALSE FALSE
To get the counts of both true and false, we can use table
table(df$Logical)
First of all "true" and "false" as you put it into you data frame are not Booleans but simple strings.
Moreover, length(df$Logical == TRUE) will always return 6 in this example, i.e. the number of elements in the column. This is because df$Logical == TRUE returns a sequence of TRUE or FALSE. In your case it will return
FALSE NA FALSE FALSE FALSE FALSE
because the boolean expression is never true. However, the length of this will be 6 as returned by length().
To overcome the problem you might define your data frame like this
df <- data.frame("Logical"=c(TRUE,NA,FALSE,FALSE,NA,FALSE),
"Numeric"=c(1,2,3,4,5,6))
And then you can sum up the number of TRUE
sum(df$Logical == TRUE, na.rm = T)
[1] 2
na.rm = T is important here because otherwise the sum will return NA if one more more elements are NA.
Alternatively, you can work with strings to indicate true or false (and empty strings a NA)
Then you could write
df <- data.frame("Logical"=c("true",NA,"false","true","","false"),
"Numeric"=c(1,2,3,4,5,6))
sum(df$Logical == "true", na.rm = T)
[1] 2
I have a matrix that I am performing a for loop over. I want to know if the values of position i in the for loop exist anywhere else in the matrix, and if so, report TRUE. The matrix looks like this
dim
x y
[1,] 5 1
[2,] 2 2
[3,] 5 1
[4,] 5 9
In this case, dim[1,] is the same as dim[3,] and should therefore report TRUE if I am in position i=1 in the for loop. I could write another for loop to deal with this, but I am sure there are more clever and possibly vectorized ways to do this.
We can use duplicated
duplicated(m1)|duplicated(m1, fromLast=TRUE)
#[1] TRUE FALSE TRUE FALSE
The duplicated(m1) gives a logical vector of 'TRUE/FALSE' values. If there is a duplicate row, it will be TRUE
duplicated(m1)
#[1] FALSE FALSE TRUE FALSE
In this case, the third row is duplicate of first row. Suppose if we need both the first and third row, we can do the duplication from the reverse side and use | to make both positions TRUE. i.e.
duplicated(m1, fromLast=TRUE)
#[1] TRUE FALSE FALSE FALSE
duplicated(m1)|duplicated(m1, fromLast=TRUE)
#[1] TRUE FALSE TRUE FALSE
According to ?duplicated, the input data can be
x: a vector or a data frame or an array or ‘NULL’.
data
m1 <- cbind(x=c(5,2,5,5), y=c(1,2,1,9))
How to check if entire vector has no values other than NA (or NAN) in R ?
If I use is.na it returns a vector of TRUE / FALSE.
I need to check if there is single not NA element or not.
The function all(), when passed a Boolean vector, will tell you whether all of the values in it are TRUE:
> all(is.na(c(NA, NaN)))
[1] TRUE
> all(is.na(c(NA, NaN, 1)))
[1] FALSE