I have a data frame with two columns. The first one "V1" indicates the objects on which the different items of the second column "V2" are found, e.g.:
V1 <- c("A", "A", "A", "A", "B", "B", "B", "C", "C", "C", "C")
V2 <- c("a","b","c","d","a","c","d","a","b","d","e")
df <- data.frame(V1, V2)
"A" for example contains "a", "b", "c", and "d". What I am looking for is a three dimensional array with dimensions of length(unique(V2)) (and the names "a" to "e" as dimnames).
For each unique value of V1 I want all possible combinations of three V2 items (e.g. for "A" it would be c("a", "b", "c"), c("a", "b", "d", and c("b", "c", "d").
Each of these "three-item-co-occurrences" should be regarded as a coordinate in the three-dimensional array and therefore be added to the frequency count which the values in the array should display. The outcome should be the following array
ar <- array(data = c(0,0,0,0,0,0,0,1,2,1,0,1,0,2,0,0,2,2,0,1,0,1,0,1,0,
0,0,1,2,1,0,0,0,0,0,1,0,0,1,0,2,0,1,0,1,1,0,0,1,0,
0,1,0,2,0,1,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,
0,2,2,0,1,2,0,1,0,1,2,1,0,0,0,0,0,0,0,0,1,1,0,0,0,
0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0),
dim = c(5, 5, 5),
dimnames = list(c("a", "b", "c", "d", "e"),
c("a", "b", "c", "d", "e"),
c("a", "b", "c", "d", "e")))
I was wondering about the 3D symmetry of your result. It took me a while to understand that you want to have all permutations of all combinations.
library(gtools) #for the permutations
foo <- function(x) {
#all combinations:
combs <- combn(x, 3, simplify = FALSE)
#all permutations for each of the combinations:
combs <- do.call(rbind, lapply(combs, permutations, n = 3, r = 3))
#tabulate:
do.call(table, lapply(asplit(combs, 2), factor, levels = letters[1:5]))
}
#apply grouped by V1, then sum the results
res <- Reduce("+", tapply(df$V2, df$V1, foo))
#check
all((res - ar)^2 == 0)
#[1] TRUE
I used to use the crossjoin CJ() to retain the pairwise count of all combinations of two different V2 items
res <- setDT(df)[,CJ(unique(V2), unique(V2)), V1][V1!=V2,
.N, .(V1,V2)][order(V1,V2)]
This code creates a data frame res with three columns. V1 and V2 contain the respective items of V2 from the original data frame df and N contains the count (how many times V1 and V2 appear with the same value of V1 (from the original data frame df).
Now, I found that I could perform this crossjoin with three 'dimensions' as well by just adding another unique(V2) and adapting the rest of the code accordingly.
The result is a data frame with four columns. V1, V2, and V3 indicate the original V2 items and N again shows the number of mutual appearances with the same original V1 objects.
res <- setDT(df)[,CJ(unique(V2), unique(V2), unique(V2)), V1][V1!=V2 & V1 != V3 & V2 != V3,
.N, .(V1,V2,V3)][order(V1,V2,V3)]
The advantage of this code is that all empty combinations (those which do not appear at all) are not considered. It worked with 1,000,000 unique values in V1 and over 600 unique items in V2, which would have otherwise caused an extremely large array of 600 x 600 x 600
I want to subset a large matrix (columns and rows) based on a list input (which will change dynamically). Example (see reproducible example below): I have a symmetric matrix (x) and a list containing the rows and column I want to have in my subset (categories). How do I subset rows and columns so that my results only shows rows & columns for a and c (see desired output)
categories = c("a", "c")
a = c(2,3,4)
b = c(1,9,8)
c = c(5,6,7)
x = cbind(a,b,c)
rownames(x) <- c("a", "b", "c")
x = as.matrix(x)
# attempt:
result = x[x %in% categories == TRUE]
desired output
a = c(2,4)
c = c(5,7)
y = cbind(a,c)
rownames(y) <- c("a", "c")
y = as.matrix(y)
You may also subset for names.
y <- x[c("a", "c"), c("a", "c")]
y
# a c
# a 2 5
# c 4 7
Or, using subset
y <- subset(x, colnames(x) %in% c("a", "c"),
rownames(x) %in% c("a", "c"))
y
# a c
# a 2 5
# c 4 7
I want to check if values (in example below "letters") in 1 dataframe appear in another dataframe. And if that is the case, I want a value (in example below "ranking") which is specific for that value from the first dataframe to be added to the second dataframe... What I have now Is the following:
Df1 <- data.frame(c("A", "C", "E"), c(1:3))
colnames(Df1) <- c("letters", "ranking")
Df2 <- data.frame(c("A", "B", "C", "D", "E"))
colnames(Df2) <- c("letters")
Df2$rank <- ifelse(Df2$letters %in% Df1$letters, 1, 0)
However... Instead of getting a '1' when the letters overlap, I want to get the specific 'ranking' number from Df1.
Thanks!
What you're looking for is called a merge:
merge(Df2, Df1, by="letters", all.x=TRUE)
Also, fun fact, you can create a dataframe and name the columns at the same time (and you'll usually want to "turn off" strings as factors):
df1 <- data.frame(
letters = c("a", "b", "c"),
ranking = 1:3,
stringsAsFactors = FALSE)
dplyr package is best for this.
Df2 <- Df2 %>%
left_join(Df1,by = "letters")
this will show a NA for "D" if you want to keep it.
Otherwise you can do semi_join
DF2 <- Df2 %>%
semi_join(Df1, by = "letters")
And this will only keep the ones they have in common (intersection)
I'm struggling with having the subset() function use a range (i.e. 4:7) that is being called as a character from a variable.
Is there a way for me to coerce the input, which is the variable DayVar and has different days I want the function to subset, to be numeric while avoiding the following issues:
1.) keeping the 4:7 as such instead of as 4, 5, 6, 7, and
2.) converting the character "1:4" into numeric format that the subset evaluation can use as though it were 1:4.
Here is a sample data frame:
DayVar = c("1", "2", "3", "4:7")
a <- c("a", "b", "c", "d", "e", "f", "g", "h", "i", "j")
b <- c(61:70)
Day <- c(1:10)
df <- data.frame("a" = a, "b" = b, "Day" = Day)
Subset <- list()
for(i in 1:length(DayVar)){
Subset[[i]] = subset(df, Day %in% DayVar[i])
}
As thelatemail suggested the list works but you have to change the DayVar quotes to get the list index:
DayVar <- list(1,2,3,4:7)
Subset <- list()
for(i in 1:length(DayVar)){
Subset[[i]] = subset(df, Day %in% DayVar[[i]])
}
I would like to make a subset of a data frame in R that is based on one OR another value in a column of factors but it seems I cannot use | with factor values.
Example:
# fake data
x <- sample(1:100, 9)
nm <- c("a", "a", "a", "b", "b", "b", "c", "c", "c")
fake <- cbind(as.data.frame(nm), as.data.frame(x))
# subset fake to only rows with name equal to a or b
fake.trunk <- fake[fake$nm == "a" | "b", ]
produces the error:
Error in fake$nm == "a" | "b" :
operations are possible only for numeric, logical or complex types
How can I accomplish this?
Obviously my actual data frame has more than 3 values in the factor column so just using != "c" won't work.
You need fake.trunk <- fake[fake$nm == "a" | fake$nm == "b", ]. A more concise way of writing that (especially with more than two conditions) is:
fake[ fake$nm %in% c("a","b"), ]
Another approach would be to use subset() and write
fake.trunk = subset(fake, nm %in% c('a', 'b'))