A possible solution is this
library(tidyverse)
df = read.table(text = "
year prod value
2015 PRODA test1
2015 PRODA blue
2015 PRODA 50
2015 PRODA 66
2015 PRODA 66
2018 PRODB test2
2018 PRODB yellow
2018 PRODB 70
2018 PRODB 88.8
2018 PRODB 88.8
2018 PRODA test3
2018 PRODA red
2018 PRODA 55
2018 PRODA 88
2018 PRODA 90
", header=T, stringsAsFactors=F)
df %>%
group_by(year, prod) %>% # for each year and prod combination
mutate(id = paste0("new_col_", row_number())) %>% # enumerate rows (this will be used as column names in the reshaped version)
ungroup() %>% # forget the grouping
spread(id, value) # reshape
# # A tibble: 3 x 7
# year prod new_col_1 new_col_2 new_col_3 new_col_4 new_col_5
# <int> <chr> <chr> <chr> <chr> <chr> <chr>
# 1 2015 PRODA test1 blue 50 66 66
# 2 2018 PRODA test3 red 55 88 90
# 3 2018 PRODB test2 yellow 70 88.8 88.8