scripts/select_infra/test-busyance.R

In cyipt/cyipt: Cycling Infrastructure Prioritisation Toolkit

# evaluate buysance effect on propensity

library(sf)
library(dplyr)
library(ggplot2)

pct.all <- readRDS("../cyipt-securedata/pct-routes-all.Rds")

pct.all$total <- pct.all$pct.census + pct.all$onfoot + pct.all$motorvehicle + pct.all$publictransport + pct.all$other
pct.all$pcycle <- round(pct.all$pct.census / pct.all$total * 100, 1)


plot(pct.all$pcycle, pct.all$busyness, xlim = c(0,30000))
loess_fit <- loess(pcycle ~ busyness, pct.all)
lines(pct.all$busyness, predict(loess_fit), col = "blue")



# http://www.cookbook-r.com/Manipulating_data/Summarizing_data/
## Summarizes data.
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   groupvars: a vector containing names of columns that contain grouping variables
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
  library(plyr)

  # New version of length which can handle NA's: if na.rm==T, don't count them
  length2 <- function (x, na.rm=FALSE) {
    if (na.rm) sum(!is.na(x))
    else       length(x)
  }

  # This does the summary. For each group's data frame, return a vector with
  # N, mean, and sd
  datac <- ddply(data, groupvars, .drop=.drop,
                 .fun = function(xx, col) {
                   c(N    = length2(xx[[col]], na.rm=na.rm),
                     mean = mean   (xx[[col]], na.rm=na.rm),
                     sd   = sd     (xx[[col]], na.rm=na.rm)
                   )
                 },
                 measurevar
  )

  # Rename the "mean" column
  datac <- rename(datac, c("mean" = measurevar))

  datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean

  # Confidence interval multiplier for standard error
  # Calculate t-statistic for confidence interval:
  # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
  ciMult <- qt(conf.interval/2 + .5, datac$N-1)
  datac$ci <- datac$se * ciMult

  return(datac)
}

pct.summary <- summarySE(pct.all, measurevar="pcycle", groupvars= c("busyness"))

pd <- position_dodge(0.1) # move them .05 to the left and right

ggplot(pct.summary, aes(x=busyness, y=pcycle), xmax = 30000) +
  geom_errorbar(aes(ymin=pcycle-ci, ymax=pcycle+ci), colour="black", width=.1, position=pd) +
  geom_line(position=pd) +
  geom_point(position=pd, size=3)