R/evaluate_paired_data.R

Defines functions vds3_wim3 vds3_wim2 vds2_wim3 vds2_wim2 vds1_wimN evaluate.paired.data

##' evaluate paired data
##'
##' This function fixed an even uglier hack done earlier for speed.
##' The logic is to sift through the names, keep what I need, discard
##' (?) what I don't wim data, needs to have wim.lanes worth of info
##'
##' @title evaluate.paired.data
##' @param df the data frame with paired data
##' @param vds.names names of vds dataframe, or minimal set of names
##'     that will generate accurate list of actual lanes at the VDS
##'     site
##'
##' @return a dataframe that equals
##'     df[,c(vds.vars.lanes,wim.vars.lanes,other.vars)] where
##'     vds.vars.lanes is the vds variables (vol, occ), wim.vars.lanes
##'     is the wim variabes (*hh, *weight,*axle, and *speed variables,
##'     see the code for the exact), and other.vars are other
##'     variables
##'
##' @author James E. Marca
##'
evaluate.paired.data <- function(df,vds.names){
    lanes_paired <- calvadrscripts::extract_unique_lanes(df)
    lanes_vds <- calvadrscripts::extract_unique_lanes(vds.names)

    ##print(lanes_paired)
    ##print(lanes_vds)
    if(is.null(lanes_vds)){
        stop(paste('no vds lanes detected in passed vds.names:',vds.names))
    }

    dfnames <-  names(df)

    ## logic.
    ## Inspect the VDS-WIM paired data in df
    ## figure out the lanes with WIM-truck data
    ## figure out the lanes with VDS data (they don't have to be the same)
    ## make the best fit from df.vds lanes and df.wim lanes
    ## to the incoming vds.names list (extracted to lanes_vds)


    ## cases

    ## VDS 1
    ## ##
    ## ## WIM 1 : just make sure no extras in WIM-VDS paired
    ## ##
    ## ## WIM 2 : ditto above.  drop non-r1 data from wim site
    ## ##
    ## ## WIM 3+:  ditto above
    ## ##
    ## In VDS1 case, just make sure no non-r1 data in paired VDS-WIM data
    ## done in vds1_wimN

    ## VDS 2
    ## ## WIM 1 : Need to return r1 data, & no extra lanes in VDS-WIM site
    ## Handled by vdsN_wim1
    ##
    ## ##
    ## ## WIM 2 : Make sure exactly l1 r1 data in WIM-VDS paired site,
    ## ## handling special cases with VDS part of paired site possibly
    ## ## having more than two lanes so using r2 as l1 rather than the
    ## ## actual l1
    ## Handled by vds2_wim2
    ##
    ## ##
    ## ## WIM 3+: make sure returned data has just two lanes, not 3,
    ## ## using the R2 lane as l1 for both the WIM and the VDS data,
    ## ## dropping lane R3+ and lane L1 from the vds-wim paired data

    ## VDS 3+
    ## ##
    ## ## WIM 1 : just make sure no non-R1 data in VDS-WIM paired site
    ## Handled by vdsN_wim1
    ##
    ## ##
    ## ## WIM 2 : rename L1 data from WIM site to R2; make sure the
    ## ## VDS half of the WIM-VDS paired data has R2 data, otherwise
    ## ## also rename L1 to R2 for the VDS lanes of the WIM-VDS site
    ## ##
    ## ## WIM 3+: make sure the returned data has equivalent numbers
    ## ## of lanes as the VDS data, and that there are no extraneous
    ## ## VDS lanes compared to the WIM lanes in the VDS-WIM data.

    ## so methods:

    ## trim WIM lanes to a certain number...1 or 3+

    ## trim or rename WIM lanes in two-lane cases
    ## IF VDS==2 & WIM >2, trim to two lanes
    ## IF VDS >2 & WIM==2, rename L1 to R2

    ## trim or rename VDS lanes in two-lane cases
    ## IF VDS==2 & VDSW >2, Drop r3+, l1; rename r2 to l1
    ## IF VDS >2 & VDSW==2, rename L1 to R2


    ## match up VDS and WIM lanes to 1 or 3+
    ## given a number, drop all extra lane data
    ## does not do renaming.
    ## drops all L1 data.
    ##

    ## identify other data in the df, unrelated to lanes (things like
    ## time of day, nubmer of observations, time stamp, etc)
    laned_pattern <- paste(lanes_paired,'$',sep='',collapse='|')
    unlaned_vars <- grep(pattern=laned_pattern,x=dfnames,
                         perl=TRUE,value=TRUE,invert=TRUE)


    ## extract the WIM lanes...those lanes with Truck data
    nhh_pattern <- 'not_heavyheavy'
    df_wim_laned_vars <- grep(pattern=nhh_pattern,x=dfnames,
                          perl=TRUE,value=TRUE,invert=FALSE)
    wim_unique_lanes <- calvadrscripts::extract_unique_lanes(df_wim_laned_vars)


    ## extract the vds-based lane data in df.  by construction, this
    ## wil also include all of the WIM laned data to, because the
    ## pattern I generate below includes all lanes in the df data
    df_laned_vars <- grep(pattern=laned_pattern,x=dfnames,
                       perl=TRUE,value=TRUE)

    # and this is the lanes in the DF that best match the incoming lanes
    laned_pattern <- paste(lanes_vds,'$',sep='',collapse='|')
    keep_theselaned_vars <- grep(pattern=laned_pattern,x=dfnames,
                                 perl=TRUE,value=TRUE)


    ## not sure yet why I'm doing this.  may not need anymore with
    ## this refactor

    ## keep_names <- c(laned_vars,unlaned_vars)
    ## trimmed_df <- df[,keep_names]

    ## special case handling

    ## if the VDS site has just two lanes, and the paired site has
    ## more than two lanes, then the left lane at the paired site is
    ## unlikely to have truck variables in it, other than VDS data
    ## from the pairing.
    ##
    ## In that case, I *want* to have trucks in the left lane, as they
    ## are legally allowed there etc etc, whereas in the
    ## more-than-two-lanes original site they are not allowed in the
    ## left lane.  So, what the next bit of code does is to check
    ## first that there are 2 lanes at the VDS site, and that there
    ## are more than two lanes at the truck site.  Then it copies the
    ## truck variables into the trimmed_df, renaming the lane part
    ## from _r2 to _l1

    if(length(lanes_vds) == 1){
        ## one lane cases
        return_df <- vds1_wimN(df,
                               df_laned_vars,
                               keep_theselaned_vars,
                               unlaned_vars)

        return(return_df)
    }

    if(length(wim_unique_lanes) == 1){
        ## one lane cases on the WIM side
        return_df <- vds1_wimN(df,
                               df_laned_vars,
                               keep_theselaned_vars,
                               unlaned_vars)
        return(return_df)
    }

    if(length(lanes_vds) == 2){
        ## two lane cases

        if(length(wim_unique_lanes) == 2){
            return_df <- vds2_wim2(df,
                                   df_laned_vars,
                                   keep_theselaned_vars,
                                   unlaned_vars)
            return(return_df)
        }else{
            ## vds lanes is 2, wim lanes =1 (or zero)
            return_df <- vds2_wim3(df,
                                   df_laned_vars,
                                   keep_theselaned_vars,
                                   unlaned_vars)
            ## post
            return(return_df)
        }
    }

    ## still here? other cases
    if(length(lanes_vds)>2){
        if(length(wim_unique_lanes) == 2){
            return_df <- vds3_wim2(df,
                                   df_laned_vars,
                                   keep_theselaned_vars,
                                   unlaned_vars)
            ## post
            return(return_df)

        }else{
            return_df <- vds3_wim3(df,
                                   lanes_vds,
                                   wim_unique_lanes,
                                   df_laned_vars,
                                   keep_theselaned_vars,
                                   unlaned_vars)
            ## post
            return(return_df)
        }

    }else{
        ## I don't know.  if it isn't 1, 2, or >2, what else can it be?
        stop(paste('problem value for length of VDS site lanes vector',length(lanes_vds)))
    }

}

##' One VDS lane, any number WIM lanes
##'
##' In this case, the incoming VDS site has one lane.  So just pull
##' out the right lane from WIM paired data
##'
##' @title vds1_wimN
##' @param df the data frame from the vds-wim paired site
##' @param df_laned_vars all of the laned variables in the paired df,
##'     which includes the WIM data and the VDS paired data.
##' @param keep_theselaned_vars this is the laned data in the incoming
##'     VDS site, against which I am trying to match the vds-wim
##'     paired site
##' @param unlaned_vars variables in the df unrelated to lane-based
##'     data that I should keep around in the result.  Things like
##'     time stamp, number of observations, etc
##' @return a data frame with lane data matching up against the
##'     incomig VDS variables
##' @author James E. Marca
vds1_wimN <- function(df,
                      df_laned_vars,
                      keep_theselaned_vars,
                      unlaned_vars){
    ## in this case, just make sure no non-r1 data in df

    right_lane1_pattern <- 'r1' ## only WIM data uses the _ ,
                                ## plain r1 grabs both
    right_lane1_vars <- grep(pattern=right_lane1_pattern,
                                 x=df_laned_vars,
                             perl=TRUE,value=TRUE,invert=FALSE)

    return (df[,c(right_lane1_vars,unlaned_vars)])

}

##' Any number of VDS lanes, exactly one WIM lane
##'
##' In this case, the paired WIM-VDS site has just one WIM lane.  So
##' make sure there aren't extra VDS lanes, and spit that right lane
##' out
##'
##' this is an alias to VDSN_wim1, because one lane is one lane.  All
##' I'm doing is making sure that there are no extraneous lanes by
##' select just r1
##'
##' @title vdsN_wim1
##' @param df the data frame from the vds-wim paired site
##' @param df_laned_vars all of the laned variables in the paired df,
##'     which includes the WIM data and the VDS paired data.
##' @param keep_theselaned_vars this is the laned data in the incoming
##'     VDS site, against which I am trying to match the vds-wim
##'     paired site
##' @param unlaned_vars variables in the df unrelated to lane-based
##'     data that I should keep around in the result.  Things like
##'     time stamp, number of observations, etc
##' @return a data frame with lane data matching up against the
##'     incomig VDS variables
##' @author James E. Marca
vdsN_wim1 <- vds1_wimN



##' two VDS lanes, and just two WIM lanes
##'
##' VDS2 & WIM 2 : Make sure exactly l1 r1 data in WIM-VDS paired site,
##' handling special cases with VDS part of paired site possibly
##' having more than two lanes so using r2 as l1 rather than the
##' actual l1
##'
##' The WIM VDS pairing tries not to mess around with the variable
##' names when the pairing is done.  This means that you can get
##' situations like the paired VDS site has three lanes, but the paird
##' WIM site has just two, and therefore you will see things like
##' heavyheavy_r1 and heavyheavy_l1 sharing space with nr1,nr2,nl1.
##'
##' So with that in mind, this case handles the situation in which the
##' *to be analyzed* VDS site (not the one that has been paired to a
##' WIM station) has more than two lanes, the WIM site in the paired
##' set has just two lanes, but the VDS paired to the WIM site *might*
##' have more than two lanes (that sometimes happens).
##'
##' So in that case, what I want to do for the imputation of trucks at
##' the incoming VDS site is to keep the left lane for WIM
##' site (all the _l1 variables) as l1 variables, but shift the possible
##' "middle lane" (or r2) type variables from the paired VDS stations to
##' be l1 variables, and *if* it is the case that there are r2 variables,
##' drop the paired vds l1 variables.
##'
##' This function does that renaming of variables for this case
##'
##' @title two_vds_lanes_two_wim_lanes
##' @param df the data frame from the vds-wim paired site
##' @param df_laned_vars all of the laned variables in the paired df,
##'     which includes the WIM data and the VDS paired data.
##' @param keep_theselaned_vars this is the laned data in the incoming
##'     VDS site, against which I am trying to match the vds-wim
##'     paired site
##' @param unlaned_vars variables in the df unrelated to lane-based
##'     data that I should keep around in the result.  Things like
##'     time stamp, number of observations, etc
##' @return a data frame with lane data matching up against the
##'     incomig VDS variables
##' @author James E. Marca
vds2_wim2 <- function(df,
                      df_laned_vars,
                      keep_theselaned_vars,
                      unlaned_vars){

    ## borkborkbork()

    ## print(paste('vds2_wim2'
    ##            ,'just two lanes in target vds data'
    ##            ,'and more than one lane in WIM data in merged set. '
    ##            ,'Re-using second lane from right at WIM site as'
    ##            ,'artificial left lane at paired VDS site'
    ##            ,sep=' '))


    ## going to keep all r1 vars
    right_lane1_pattern <- 'r1' ## only WIM data uses the _ ,
                                    ## so be careful here to grab both
    right_lane1_vars <- grep(pattern=right_lane1_pattern,
                                 x=df_laned_vars,
                                 perl=TRUE,value=TRUE,invert=FALSE)

    ## I know there are exactly 2 WIM lanes, but perhaps 3 or more vds lanes

    ## keep the truck l1 lanes

    left_lane_pattern <- 'l1'
    left_lane_vars <- grep(pattern=left_lane_pattern,
                               x=df_laned_vars,
                               perl=TRUE,value=TRUE,invert=FALSE)

    ## if the paired VDS half of df has any R2 lanes, use them, but
    ## rename them as l1
    right_lane2_pattern <- 'r2' ## safe to skip '_' because no wim r2 data
    right_lane2_vars <- grep(pattern=right_lane2_pattern,
                             x=df_laned_vars,
                             perl=TRUE,value=TRUE,invert=FALSE)
    if(length(right_lane2_vars) > 0){
        ## print(right_lane2_vars)
        ## have r2 lanes, so rename and use as l1 data
        ##print(left_lane_vars)

        return_df <- df[,c(right_lane1_vars,
                           left_lane_vars,
                           unlaned_vars)]

        rename_lane2 <- sub(pattern='r2$',replacement='l1',
                            x=right_lane2_vars, ## or names(return_df)
                            perl=TRUE)

        return_df[,rename_lane2] <- df[,right_lane2_vars]

        return (return_df)

    }else{
        print('no right2 lanes')
        return (df[,c(right_lane1_vars,
                      left_lane_vars,
                      unlaned_vars)]
                )

    }
}


##' Two VDS lanes, and more than two WIM lanes
##'
##' The WIM VDS pairing tries not to mess around with the variable
##' names when the pairing is done.  This means that you can get
##' situations like the paired VDS site has three lanes, but the paird
##' WIM site has just two, and therefore you will see things like
##' heavyheavy_r1 and heavyheavy_l1 sharing space with nr1,nr2,nl1.
##'
##' So with that in mind, this case handles the situation in which the
##' *incoming* VDS site (not the one that has been paired to a WIM
##' station) has more than two lanes, but the WIM site in the paired
##' set has just two lanes, which is indicated by having _r1 and _l1
##' variables, but NOT _r2 variables.
##'
##' So in that case, what I want to do for the imputation of trucks at
##' the incoming VDS site is to pretend that the left lane for the WIM
##' site (all the _l1 variables) really should be _r2 variables.
##' Because in a three lane highway, heavy heavy trucks and most not
##' hh trucks are denied access to that left most lane by law, whereas
##' in a two lane case they can use the left lane to pass.
##'
##' This function does that renaming of variables for this case
##'
##' @title vds2_wim3
##' @param df the data frame from the vds-wim paired site
##' @param df_laned_vars all of the laned variables in the paired df,
##'     which includes the WIM data and the VDS paired data.
##' @param keep_theselaned_vars this is the laned data in the incoming
##'     VDS site, against which I am trying to match the vds-wim
##'     paired site
##' @param unlaned_vars variables in the df unrelated to lane-based
##'     data that I should keep around in the result.  Things like
##'     time stamp, number of observations, etc
##' @return a renamed and suitably trimmed wim_vds merged set
##' @author James E. Marca
vds2_wim3 <- function(df,
                      df_laned_vars,
                      keep_theselaned_vars,
                      unlaned_vars){

    ## going to keep all r1 vars
    right_lane1_pattern <- 'r1' ## only WIM data uses the _ ,
                                    ## so be careful here to grab both
    right_lane1_vars <- grep(pattern=right_lane1_pattern,
                                 x=df_laned_vars,
                                 perl=TRUE,value=TRUE,invert=FALSE)

    ## keep VDS r2 lanes, but rename to l1
    ## keep truck r2 lanes, but rename them to l1
    right_lane2_pattern <- 'r2' ## only WIM data uses the _ ,
                                ## so be careful here to grab both
    right_lane2_vars <- grep(pattern=right_lane2_pattern,
                             x=df_laned_vars,
                             perl=TRUE,value=TRUE,invert=FALSE)

    ##print(right_lane2_vars)
    ## rename step
    ## rename truck l1 to r2
    rename_lane2 <- sub(pattern='r2$',replacement='l1',
                        x=right_lane2_vars,
                        perl=TRUE)

    ## create the result

    return_df <- df[,c(right_lane1_vars,unlaned_vars)]
    return_df[,rename_lane2] <- df[,right_lane2_vars]

    return(return_df)

}


##' Three or more VDS lanes, exactly two WIM lanes
##'
##' In this case, the incoming VDS site has more than two lanes, and
##' the WIM site has two or more lanes, but maybe more paired vds
##' lanes.
##' @title vds3_wim2
##' @param df the data frame from the vds-wim paired site
##' @param df_laned_vars all of the laned variables in the paired df,
##'     which includes the WIM data and the VDS paired data.
##' @param keep_theselaned_vars this is the laned data in the incoming
##'     VDS site, against which I am trying to match the vds-wim
##'     paired site
##' @param unlaned_vars variables in the df unrelated to lane-based
##'     data that I should keep around in the result.  Things like
##'     time stamp, number of observations, etc
##' @return a data frame with lane data matching up against the
##'     incomig VDS variables
##' @author James E. Marca
vds3_wim2 <- function(df,
                      df_laned_vars,
                      keep_theselaned_vars,
                      unlaned_vars){

    ## borkborkbork()

    ## keep the truck l1 lanes, but rename them to r2

    left_lane_pattern <- '_l1' ## only get truck lanes here with the _
    left_lane_wim_vars <- grep(pattern=left_lane_pattern,
                               x=df_laned_vars,
                               perl=TRUE,value=TRUE,invert=FALSE)

    ## cut those left lane truck vars out of keep_theselaned_vars
    keep_theselaned_vars <- grep(pattern=left_lane_pattern,
                               x=keep_theselaned_vars,
                               perl=TRUE,value=TRUE,invert=TRUE)

    ## rename truck l1 to r2
    rename_wimlane2 <- sub(pattern='l1$',replacement='r2',
                           x=left_lane_wim_vars,
                           perl=TRUE)

    ## scoop up any other vds lanes that match incoming wim lanes
    ## then overwrite with the above

    return_df <- df[,c(keep_theselaned_vars,unlaned_vars)]
    return_df[,rename_wimlane2] <- df[,left_lane_wim_vars]

    return(return_df)
}

##' Three or more VDS lanes, and three or more WIM lanes
##'
##' This function does that renaming of variables for this case
##'
##' @title vds3_wim3
##' @param df the data frame from the vds-wim paired site
##' @param df_laned_vars all of the laned variables in the paired df,
##'     which includes the WIM data and the VDS paired data.
##' @param keep_theselaned_vars this is the laned data in the incoming
##'     VDS site, against which I am trying to match the vds-wim
##'     paired site
##' @param unlaned_vars variables in the df unrelated to lane-based
##'     data that I should keep around in the result.  Things like
##'     time stamp, number of observations, etc
##' @return a renamed and suitably trimmed wim_vds merged set
##' @author James E. Marca
vds3_wim3 <- function(df,
                      lanes_vds,
                      wim_unique_lanes,
                      df_laned_vars,
                      keep_theselaned_vars,
                      unlaned_vars){
    ## going to keep all r1, r2 vars as is
    right_lanes_pattern <- 'r1|r2' ## only WIM data uses the _ ,
    ## so be careful here to grab both
    right_lanes_vars <- grep(pattern=right_lanes_pattern,
                             x=df_laned_vars,
                             perl=TRUE,value=TRUE,invert=FALSE)

    ## conditionally keep all left lane vars
    left_lane_pattern <- 'l1' ## only WIM data uses the _ ,
    ## so be careful here to grab both
    left_lane_vars <- grep(pattern=left_lane_pattern,
                           x=df_laned_vars,
                           perl=TRUE,value=TRUE,invert=FALSE)


    ## pull out other lanes in VDS incoming
    ##print(lanes_vds)
    right2_and_left1 <- 'r1|r2|l1'
    otherlanes <- grep(pattern=right2_and_left1,
                       x=lanes_vds,
                       perl=TRUE,value=TRUE,
                       invert=TRUE ## notice invert = TRUE !!!
                       )

    ##
    ## print(wim_unique_lanes)
    ##print(otherlanes)

    ## now for each of these "other lanes", keep the related wim
    ## (truck) lane data, as well as any corresponding vds data in the
    ## paired set (df)

    extra_wim <- NULL
    extra_vds <- NULL
    for(lane in otherlanes){
        ##print(lane)
        ## extract corresponding lane from wim lanes, if any

        keep_wim_lane <- grep(pattern=lane,
                              x=wim_unique_lanes,
                              perl=TRUE,value=TRUE,
                              invert=FALSE)
        if(length(keep_wim_lane) == 0){
            ##print('extra lane')
            extra_vds <- c(extra_vds,lane)
        }else{

            ## this lane is in paired df.  So add all matches to
            ## right_lanes_vars
            more_right_lanes_vars <- grep(pattern=lane,
                                          x=df_laned_vars,
                                          perl=TRUE,value=TRUE,
                                          invert=FALSE)
            right_lanes_vars <- c(right_lanes_vars,more_right_lanes_vars)
            ## also extend the matching pattern
            right2_and_left1 <- paste(right2_and_left1,lane,sep='|')
        }
    }
    if(length(extra_vds) == 0){
        ## in this case, num vds lanes is not greater than num WIM lanes
        ##print('extra vds is zero')
        ## perhaps we have extra WIM data

        otherwimlanes <- grep(pattern=right2_and_left1,
                              x=wim_unique_lanes,
                              perl=TRUE,value=TRUE,
                              invert=TRUE ## notice invert = TRUE !!!
                              )
        if(length(otherwimlanes) == 0){
            ## in this case, wim lanes and vds lanes are exact
            ## so use left lane as is
            ##print('extra wim lanes is zero')
            return_df <- df[,c(right_lanes_vars,left_lane_vars,unlaned_vars)]
            return(return_df)
        }else{
            ## more WIM right lanes than vds right lanes, so rename
            ## the first extra lane to be "l1"
            ##print('extra wim lanes is not zero')
            ##print(otherwimlanes)
            minwimlane <- (sort(otherwimlanes))[1]
            ## minwimlane is the minimum extra lane from WIM.
            last_right_lane_vars <- grep(pattern=minwimlane,
                                         x=df_laned_vars,
                                         perl=TRUE,value=TRUE,
                                         invert=FALSE)

            ## set up the renaming
            rename_rightlanevars <- sub(pattern=paste(minwimlane,'$',sep=''),
                                        replacement='l1',
                                        x=last_right_lane_vars,
                                        perl=TRUE)

            return_df <- df[,c(right_lanes_vars,unlaned_vars)]
            return_df[,rename_rightlanevars] <- df[,last_right_lane_vars]
            return(return_df)

        }


    }else{
        ## in this case, num vds lanes is greater than num WIM lanes
        ## so we just use everything as is
        ## form the output
        ##print('extra vds is not zero')

        ## but it could be that while the truck lanes don't overlap,
        ## the paired data might also have extra VDS right lanes
        ## print(extra_vds)
        more_keep_vars <- grep(pattern=paste(extra_vds,sep='|'),
                               x=df_laned_vars,
                               perl=TRUE,value=TRUE,
                               invert=FALSE)
        ##print(more_keep_vars)
        return_df <- df[,c(right_lanes_vars,
                           more_keep_vars,
                           left_lane_vars,
                           unlaned_vars)]

        return(return_df)
    }

    ## why are you still here?
    stop(paste('bad condition in vds3 wim3 case:',
               paste(c(
                   lanes_vds,
                   wim_unique_lanes)
                  ,sep=', '
                   )
                   ,collapse=': '))

}



##     ## do this only if lanes at WIM == 2 and lanes at VDS > 2
## ##    if(length(lanes_vds) > 2 && length(wim_unique_lanes) == 2){
## ## similar to the above block, but in this case rename left
##         ## lane WIM data to lane r2
##         ## now, in the wim-only laned variables, are there more than 2 lanes?
##     ## incoming_df <-
##         print(paste('more than two lanes in target vds data'
##                    ,'but just two lanes in WIM data in merged set. '
##                    ,'make sure second lane in WIM merged set is renamed to r2'
##                    ,'so as to match up with VDS site'
##                    ,sep=' '))
##         ## okay, have something to do
##         ## extract all wim data from lane r2

##         ## get a list of laned variable names from trimmed_df in
##         ## right lane only
##         wim_right_lane2_pattern <- '_r2' ## only WIM data uses the _
##         wim_right_lane2_vars <- grep(pattern=wim_right_lane2_pattern,
##                                      x=dfnames,
##                                      perl=TRUE,value=TRUE,invert=FALSE)

##         wim_left_lane_pattern <- 'l1' ## only WIM data uses the _, but
##                                       ## in this case I also want to
##                                       ## snag the paired VDS data and
##                                       ## make it nr2 and or2, instead of nl1, ol1

##         wim_left_lane_vars <- grep(pattern=wim_left_lane_pattern,
##                                    x=dfnames,
##                                    perl=TRUE,value=TRUE,invert=FALSE)

##         print(paste('left lane vars',paste(wim_left_lane_vars,collapse=', '),sep=':'))
##         if(length(wim_right_lane2_vars) == 0){
##             ## no existing right 2 lane, so move left lane (l1) to right 2 (r2)
##             ## grab the rightlane2 WIM data from the merged wim/vds site
##             df_wim_left <- df[,c(wim_left_lane_vars,unlaned_vars)]
##             rename_r2_l1 <- sub(pattern='l1$',replacement='r2',
##                                 x=names(df_wim_left),
##                                 perl=TRUE)
##             ## do the rename
##             print(rename_r2_l1)
##             print(names(df_wim_left))
##             names(df_wim_left) <- rename_r2_l1
##             print(names(df_wim_left))
##             ## exclude those that already exist, which pretty much
##             ## only means wgt_spd_all_veh_speed_l1 and
##             ## count_all_veh_speed_l1 if those are already in the
##             ## trimmed_df data.frame

##             for(llv in wim_left_lane_vars){
##                 trimmed_df[,llv] <- NULL
##             }

##             non_overlapping_l1 <- setdiff(rename_r2_l1,names(trimmed_df))
##             ## print("non_overlapping_l1")
##             ## print(non_overlapping_l1)

##             expanded_df <- merge(trimmed_df,
##                                  df_wim_left[,c(unlaned_vars,
##                                                 non_overlapping_l1)],
##                                  all=TRUE)
##             trimmed_df <- expanded_df
##             ##print('trimmed names')
##             ##print(names(trimmed_df))
##         }
##     }

##     trimmed_df
## }
jmarca/calvad_merge_pairs documentation built on May 17, 2017, 11:28 p.m.