/
global.R
executable file
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global.R
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library(dplyr)
library(shiny)
library(FNN)
library(sp)
options(shiny.port = 2410)
# allzips <- readRDS("data/superzip.rds")
allzips <- accidents <- read.csv("data/berlin_bike_accidents_neukoelln_2002_2015.csv")
coordinates(accidents) <- c("X_SOLDNER", "Y_SOLDNER")
proj4string(accidents) <- CRS("+init=epsg:3068")
CRS.new <- CRS("+init=epsg:4326")
accidents.coords <- spTransform(accidents, CRS.new)
accidentsSp <- SpatialPoints(accidents.coords)
cc <- coordinates(accidentsSp) %>% data.frame
xing <- read.csv("data/re_vms_detailnetz.csv")
names(xing) <- c("long", "lat", "xid")
allzips$lat <- cc$Y_SOLDNER
allzips$long <- cc$X_SOLDNER
randomdata <- function(n,allzips, xing) {
xing <- head(xing, round(n/3))
xings <- ceiling(runif(n) * nrow(xing))
xing2 <- xing[xings,]
accs <- ceiling(runif(n) * nrow(accidents))
accidents <- allzips[accs,]
accidents$lat <- jitter(xing2$lat)
accidents$long <- jitter(xing2$long)
return(accidents)
}
#allzips <- randomdata(2000, allzips, xing)
r <- get.knnx(
xing %>% select(long, lat) %>% data.matrix,
allzips %>% select(long, lat) %>% data.matrix,
1
)
allzips <- allzips %>% mutate(
B1URSACHE1 = B1URSACHE1 %>% gsub("\"", "", .),
latitude = jitter(lat),
longitude = jitter(long),
college = B2ALTER * 100,
severity = BETEILIGTE + 2*LEICHTVERL + 5*SCHWERVERL + 10*GETOETETE,
date = as.Date(DATUM),
year=as.numeric(substr(DATUM,1,4)),
total_injured = LEICHTVERL + SCHWERVERL + GETOETETE,
month=as.numeric(substr(allzips$DATUM,6,7)),
xing = xing[r$nn.index,"xid"],
xingdist = r$nn.dist[,1],
bike = (B1VERKEHRS == "Radfahrer" & B1URS1 > 0) | (B2VERKEHRS == "Radfahrer" & B2URS1 > 0),
car = (B1VERKEHRS != "Radfahrer" & B1URS1 > 0) | (B2VERKEHRS != "Radfahrer" & B2URS1 > 0)
)
nicons <- function(ic, n) {
i <- icon(ic) %>% as.character
res <- paste(replicate(n, i), collapse = "")
return(HTML(res))
}
#allzips <- allzips %>% filter(xing == 5655 & xingdist < 0.0005)
cleantable <- allzips