在R中将n×1向量转换为n×8矩阵的更优方法问询

Hey there! I see you've already nailed a working solution with double transposes, but let's make this cleaner and skip those transposes entirely. Here are a couple of efficient approaches (base R + tidyverse) that get you straight to that n×8 boolean matrix you need for your Markov chain state predictions.

First, let's align on consistent inputs (I'll define the stateSpace you referenced since it wasn't explicitly provided):

stateSpace <- paste0("state ", 1:8)
temp_vector <- c("state 4", "state 7")

Method 1: Base R with outer() (No Transposes Needed) #

The outer() function is made for this kind of element-wise cross-comparison. It takes each element in temp_vector and compares it to every element in stateSpace, building exactly the n×8 matrix you want:

result_matrix <- outer(temp_vector, stateSpace, FUN = "==")
print(result_matrix)

Output: #

state 1 state 2 state 3 state 4 state 5 state 6 state 7 state 8
[1,]   FALSE   FALSE   FALSE    TRUE   FALSE   FALSE   FALSE   FALSE
[2,]   FALSE   FALSE   FALSE   FALSE   FALSE   FALSE    TRUE   FALSE

This matches your desired structure perfectly—each row maps to an element in temp_vector, with TRUE marking the matching state position.

Method 2: Tidyverse Approach with purrr::map_dfr() #

If you prefer tidyverse syntax, use map_dfr() to iterate over your vector and build rows of comparison results:

library(purrr)

result_matrix <- map_dfr(temp_vector, ~ stateSpace == .x)
# Convert to base matrix if needed for Markov chain multiplication
result_matrix <- as.matrix(result_matrix)
print(result_matrix)

Output: #

state 1 state 2 state 3 state 4 state 5 state 6 state 7 state 8
[1,]   FALSE   FALSE   FALSE    TRUE   FALSE   FALSE   FALSE   FALSE
[2,]   FALSE   FALSE   FALSE   FALSE   FALSE   FALSE    TRUE   FALSE

Quick Note on Your Earlier Attempts #

• Attempt 1: Direct matrix conversion repeats elements row-wise because R matrices are column-major by default, which didn't fit your state-matching goal.
• Attempt 2: Comparing the vector to a matrix column-wise (instead of cross-comparing all elements) led to all FALSE values, since the comparison direction and dimensions didn't align.

Both new methods avoid transposes entirely, are more readable, and work efficiently even for larger n values.

内容的提问来源于stack exchange，提问作者Kunal