decode_vehicles.R

# Import the dataset you want to decode as a dataframe 'vehicles' 
# (e.g. vehicles <- read.csv("C:/path/to/file/vehicle2015.csv"))

vehicles$v_agegroup <- factor(vehicles$v_agegroup, 
                               levels = c(1:9), 
                               labels = c("Under 10", "10-16", "17-24", "25-34", 
                                          "35-44", "45-54", "55-64", "65+", "Unknown"))

# Sort out the other value:labels
vehicles$v_type <- factor(vehicles$v_type, 
                          levels = c(0:25), 
                          labels = c("Unknown", "Pedal cycle", "Motorcycle - moped", "No label 1", "Motorcycle (<125cc)", "Motorcycle (>=125cc)", 
                                     "Invalid vehicle/3-wheeler", "Car - hackney taxi", "Car", "Motor caravan", "No label2 ", 
                                     "No label3 ", "No label 4", "Tractor", "Other motor", "Goods <=3.5 tonnes", 
                                     "Goods >3.5 tonnes <7.5 tonnes", "Goods >=7.5 tonnes", "Car - taxi", "Minibus", 
                                     "Bus or coach", "Ridden horse", "Other non motor", "Motorcycle (size unknown)", 
                                     "Goods vehicles (weight unknown)", "Agri vehicle"))

vehicles$v_tow <- factor(vehicles$v_tow, 
                         levels = c(1:6), 
                         labels = c("No towing/artic", "Artic vehicle", "Double/multiple trailer",
                                    "Caravan", "Single trailer", "Other tow"))

vehicles$v_man <- factor(vehicles$v_man, 
                         levels = c(1:19), 
                         labels = c("Reversing", "Parking", "About to go", 
                                    "Slowing/stopping", "Moving off", "U-turn", 
                                    "Left turn", "Waiting to turn left", "Right turn", 
                                    "Waiting to turn right", "Change lane to left", 
                                    "Change lane to right", "Overtake moving offside", 
                                    "Overtake stationary offside", "Overtake nearside", 
                                    "Left-hand bend", "Right-hand bend", "Ahead other", "Other/not known"))

vehicles$v_loc <- factor(vehicles$v_loc, 
                         levels = c(1:13), 
                         labels = c("Leaving main road", 
                                    "Entering main road", "On main road", "On minor road", NA, "Lay-by/hard shoulder", 
                                    "Entering lay-by", "Leaving lay-by", "On cycle lane/way", "Pedestrian and vehicle shared precinct", 
                                    "Other public place", "Bus lane/way", "Footpath"))

vehicles$v_junc <- factor(vehicles$v_junc, 
                          levels = c(1:7), 
                          labels = c("Not at junction", 
                                     "Approaching/waiting/parked at junction", "Vehicle in middle junction", "Cleared junction or waiting/parked at exit", 
                                     "Did not impact", "Entering slip road", "Exiting slip road"))

vehicles$v_skid <- factor(vehicles$v_skid, 
                          levels = c(1:6), 
                          labels = c("No skid", 
                                     "Skidded", "Skidded and overturned", "Jack-knifed", "Jack-knifed and overturned", 
                                     "Overturned"))

vehicles$v_hit <- factor(vehicles$v_hit, 
                         levels = c(1:15), 
                         labels = c("None", "Previous collision", 
                                    "Roadworks", "Parked vehicle (lit)", "Parked vehicle (unlit)", "Bridge (roof)", 
                                    "Bridge side", "Bollard/refuge", "Open door", "Central island roundabout", "Kerb", 
                                    NA, "Other", "Animal except ridden horse", "Parked vehicle"))

vehicles$v_leave <- factor(vehicles$v_leave, 
                           levels = c(1:11), 
                           labels = c("None", 
                                      "Road sign/signal", "Lamp post", "Telegraph pole", "Tree/fence/boundary", "Bus stop", 
                                      "Central crash barrier", "Near/offside barrier", "Submerged in water", "Entered ditch", 
                                      "Other perm object"))

vehicles$v_hitoff <- factor(vehicles$v_hitoff, 
                            levels = c(1:11), 
                            labels = c("None", 
                                       "Road sign/signal", "Lamp post", "Telegraph pole", "Tree/fence/boundary", "Bus stop", 
                                       "Central crash barrier", "Near/offside barrier", "Submerged in water", "Entered ditch", 
                                       "Other perm object"))

vehicles$v_impact <- factor(vehicles$v_impact, 
                            levels = c(1:5), 
                            labels = c("Did not impact", 
                                       "Front", "Back", "Nearside", "Offside"))

vehicles$v_sex <- factor(vehicles$v_sex, 
                         levels = c(1:3), 
                         labels = c("Male", "Female", 
                                    "Unknown"))

vehicles$v_hitr <- factor(vehicles$v_hitr, 
                          levels = c(1:3), 
                          labels = c("Other", "Hit and run", 
                                     "Non-stop, not hit"))

vehicles$v_forreg <- factor(vehicles$v_forreg,
                            levels = c(1:4),
                            labels = c("Not foreign registered vehicle", "Foreign registered vehicle -left hand drive",
                                       "Foreign registered vehicle -right hand drive", "Foreign registered vehicle -two wheeler"))

write.csv(vehicles, file.choose(), row.names=F) # Save the file as 'vehicles2015.csv' for example