means <- data.frame("State" = character(0), "Mean" = numeric(0))

for (state in unique(data$State)){
  means <- rbind(means, c("state", 4))
}

44: In `[<-.factor`(`*tmp*`, ri, value = structure(c(1L, NA,  ... :
  invalid factor level, NA generated
45: In `[<-.factor`(`*tmp*`, ri, value = structure(c(1L, NA,  ... :
  invalid factor level, NA generated

[1] "Arizona"
[1] "California"
[1] "Colorado"
[1] "District Of Columbia"
[1] "Florida"
[1] "Illinois"
[1] "Indiana"
[1] "Kansas"
[1] "Kentucky"
[1] "Louisiana"
[1] "Michigan"
[1] "Missouri"
[1] "New Jersey"
[1] "New York"
[1] "North Carolina"
[1] "Oklahoma"
[1] "Pennsylvania"
[1] "Texas"
[1] "Virginia"
[1] "Massachusetts"
[1] "Nevada"
[1] "New Hampshire"
[1] "Tennessee"
[1] "South Carolina"
[1] "Connecticut"
[1] "Iowa"
[1] "Maine"
[1] "Maryland"
[1] "Wisconsin"
[1] "Country Of Mexico"
[1] "Arkansas"
[1] "Oregon"
[1] "Wyoming"
[1] "North Dakota"
[1] "Idaho"
[1] "Ohio"
[1] "Georgia"
[1] "Delaware"
[1] "Hawaii"
[1] "Minnesota"
[1] "New Mexico"
[1] "Rhode Island"
[1] "South Dakota"
[1] "Utah"
[1] "Alabama"
[1] "Washington"
[1] "Alaska"

map_df(states, function(state) { means %>% add_row(State = state, Mean = 4)})

system.time(
  for (i in 1:100000) {
    df <- rbind(df, data.frame(x = i, y = toString(i)))
  }
)
    user   system  elapsed
1466.087  355.579 1827.724


system.time(
  map_df(1:100000, function(x) { df %>% add_row(x = x, y = toString(x)) })
)
   user  system elapsed
 76.035   0.259  76.489