Here is another solution with tidy function.
library(tidyr)
library(dplyr)
global_df <- read.table("c:/users/fjcc/Documents/R/Play/Dummy.csv",
header = FALSE, sep = " ")
global_df
#> V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
#> 1 -0.384 -0.457 -0.673 -0.344 -0.311 -0.071 -0.246 -0.235 -0.380 -0.418
#> 2 -0.437 -0.150 -0.528 -0.692 -0.629 -0.363 -0.375 -0.328 -0.495 -0.646
#> 3 -0.452 -1.031 -0.643 -0.328 -0.311 -0.263 -0.248 -0.274 -0.203 -0.121
#> 4 -0.249 -0.041 -0.082 -0.172 -0.085 -0.278 -0.220 -0.132 -0.436 -0.234
#> V11 V12
#> 1 -0.670 -0.386
#> 2 -0.754 -0.137
#> 3 -0.913 -0.197
#> 4 -0.288 -0.486
colnames(global_df) <- c("M01", "M02", "M03", "M04", "M05", "M06",
"M07", "M08", "M09", "M10", "M11", "M12")
global_df <- global_df %>% mutate(Year = seq(1856, 1859))
global_df
#> M01 M02 M03 M04 M05 M06 M07 M08 M09 M10
#> 1 -0.384 -0.457 -0.673 -0.344 -0.311 -0.071 -0.246 -0.235 -0.380 -0.418
#> 2 -0.437 -0.150 -0.528 -0.692 -0.629 -0.363 -0.375 -0.328 -0.495 -0.646
#> 3 -0.452 -1.031 -0.643 -0.328 -0.311 -0.263 -0.248 -0.274 -0.203 -0.121
#> 4 -0.249 -0.041 -0.082 -0.172 -0.085 -0.278 -0.220 -0.132 -0.436 -0.234
#> M11 M12 Year
#> 1 -0.670 -0.386 1856
#> 2 -0.754 -0.137 1857
#> 3 -0.913 -0.197 1858
#> 4 -0.288 -0.486 1859
global_df <- global_df %>% gather(key = Month, value = Value, M01:M12) %>%
arrange(Year, Month)
head(global_df)
#> Year Month Value
#> 1 1856 M01 -0.384
#> 2 1856 M02 -0.457
#> 3 1856 M03 -0.673
#> 4 1856 M04 -0.344
#> 5 1856 M05 -0.311
#> 6 1856 M06 -0.071
global.ts = ts(global_df$Value, start = c(1856, 1), end = c(1859, 12), fr = 12)
plot(global.ts)

global.annual <- aggregate(global.ts, FUN = mean)
plot(global.annual)

Created on 2019-09-10 by the reprex package (v0.2.1)