Reshape data in R with fixed effect information within column

Here is the reshape2 and tidyr version of achieving this:

library(tidyr)
library(reshape2)

my.data <- data.frame(
  ID=c( "", "","C8477","C5273","C5566"),
  LR=c("2012Y","State:FL",5,6,8),
  LR=c("2012Y","State:AZ",5,8,10),
  LR=c("2011Y","State:FL",7,2,1)
)

# Combine first two rows as column names
colnames(my.data) <- paste(unlist(my.data[2, ]), unlist(my.data[1, ]), sep = "|")
# Remove first two rows from data
my.data <- my.data[-c(1:2), ] # negative index removes rows

# Melt data
my.data.long <- melt(
  my.data, 
  id.vars = 1L, # would be better to have explicit col name
  value.name = "LR" 
)
colnames(my.data.long) <- c("ID", "state_year", "LR")

# Split state_year column into two columns:
my.data.long <- separate(
  my.data.long, 
  state_year, 
  into = c("State", "Year"), 
  sep = "\\|" # note this is a regex
)

Idea was borrowed here.

This is a tidyverse approach:

my.data <- data.frame(
  ID=c( "", "","C8477","C5273","C5566"),
  LR=c("2012Y","State:FL",5,6,8),
  LR=c("2012Y","State:AZ",5,8,10),
  LR=c("2011Y","State:FL",7,2,1)
)

my code:

library(tidyverse)
year <- as.matrix(my.data[1, -1])
year <- str_split(year, "Y", simplify = T)[,1]
state <-as.matrix(my.data[2, -1])
both<-paste(state, year, sep = "_")
mydata1<-my.data[-c(1, 2), ]
colnames(mydata1) <-c("ID", both)
long <-pivot_longer(mydata1, 
             cols = starts_with("state"),
             names_to = "State_year",
             values_to = "LR")
long %>%
  transmute(
    ID, LR, 
    state = str_split(State_year, "_", simplify = T)[, 1],
    state = str_split(state, ":", simplify = T)[, 2], 
    year = str_split(State_year, "_", simplify = T)[, 2]
)

We get:

  ID    LR    state year 
1 C8477 5     FL    2012 
2 C8477 5     AZ    2012 
3 C8477 7     FL    2011 
4 C5273 6     FL    2012 
5 C5273 8     AZ    2012 
6 C5273 2     FL    2011 
7 C5566 8     FL    2012 
8 C5566 10    AZ    2012 
9 C5566 1     FL    2011