subset

I have a multi dimensional array in julia: julia> ac.value 3x100x3 Array{Float64,3}: [:, :, 1] = 0.29238 0.0751815 0.00843636 … -0.0143826 0.0403283 0.0225896 0.263146 0.080687 0.000462262 -0.00635778 0.0307563 0.0379104 0.992458 0.986423 0.980587 0.561173 0.55516 0.549105 [:, :, 2] = 0.362155 0.13406 0.0741124 … 0.0231614 0.0156455 0.0121797 0.325581 0.11181 0.0447847 0.0098042 0.0193873 0.0146943 0.914888 0.852297 0.796608 -0.0500265 -0.0551787 -0.0520171 [:, :, 3] = 0.269976 0.108082 0.0441809 … 0.0249861 0.0128778 0.0168318 0.218475 0.0997567 0.0532782 0.0243412 0.00742072 0.00978782 0

I have two lists of vertices: V and S . I would like to generate all possible directed graphs from V and S so, that each vertex from V has only one out-edge and exactly one in-edge, and each vertex from S can have any number of in- and out- edges. Each graph in the result should contain exactly all vertices from V and from S . The result can contain both connected and disconnected graphs. First I thought it was a powerset-related problem, but powerset has many other sets that may contain just one element (and I do not need those). My current strategy is to: find all pairs between vertices from

Super short version: I'm trying to use a user-defined function to populate a new column in a dataframe with the command: TestDF$ELN<-EmployeeLocationNumber(TestDF$Location) However, when I run the command, it seems to just apply EmployeeLocationNumber to the first row's value of Location rather than using each row's value to determine the new column's value for that row individually. Please note: I'm trying to understand R, not just perform this particular task. I was actually able to get the output I was looking for using the Apply() function, but that's irrelevant. My understanding is that

set.seed(8) df <- data.frame( A=sample(c(1:3), 10, replace=T), B=sample(c(1:3), 10, replace=T), C=sample(c(1:3), 10, replace=T), D=sample(c(1:3), 10, replace=T), E=sample(c(1:3), 10, replace=T), F=sample(c(1:3), 10, replace=T)) Would like to pass a subset of columns into a dplyr mutate() and make a row-wise calculation, for instance cor() to get correlation between column A-C and D-F, but cannot figure out how. Found SO inspiration here , here and here , but nevertheless failed to produce an acceptable code. For instance, I tried this: require(plyr) require(dplyr) df %>% rowwise() %>% mutate(c

I would like to automate a simple multiple regression for the subsets defined by the unique combinations of the grouping variables. I have a dataframe with several grouping variables df1[,1:6] and some independent variables df1[,8:10] and a response df1[,7]. This is an excerpt from the data. structure(list(Surface = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("NiAu", "Sn"), class = "factor"), Supplier = structure(c(1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"), ParticleSize = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L),