Rennie Meyers, in her Math 241 Final Project did some work with the RIDE data set, mainly creating maps and exploring the data with different overlays.
Here I want to walk through some of what she did. (Most of the code is adapted from Meyers' project, which can be found here.)
Data sources:
# Load the data library(RJSONIO) library(dplyr) library(ggplot2) library(rgdal) library(ggmap) #load data PDX <- fromJSON("data/RIDE/trips.json")
##ride data # Extract information for first ride orig.coordinates <- PDX[["features"]][[1]]$geometry$coordinates %>% unlist() %>% matrix(ncol=2, byrow=TRUE) coordinates <- data.frame( lat = orig.coordinates[, 1], long = orig.coordinates[, 2], activity_type = PDX[["features"]][[1]]$properties["activity_type"], rating = PDX[["features"]][[1]]$properties["rating"], group = 1 ) # Create master data.frame master.coordinates <- coordinates # Append information for 2nd thru last ride for(i in 2:length(PDX[["features"]])){ orig.coordinates <- PDX[["features"]][[i]]$geometry$coordinates %>% unlist() %>% matrix(ncol=2, byrow=TRUE) coordinates <- data.frame( lat = orig.coordinates[, 1], long = orig.coordinates[, 2], activity_type = PDX[["features"]][[i]]$properties["activity_type"], rating = PDX[["features"]][[i]]$properties["rating"], group = i ) master.coordinates <- bind_rows(master.coordinates, coordinates) }
ggplot(master.coordinates, aes(x=long, y=lat, group=group)) + geom_path(alpha=0.3, aes(col=rating), lineend = "butt") + scale_color_gradient2(low = "yellow", mid = "green", high = "red") + scale_x_continuous(limits=c(-122.7, -122.6)) + scale_y_continuous(limits=c(45.475, 45.55)) + coord_map() + xlab("Longitude") + ylab("Latitude") + ggtitle("RIDE Data for PDX by Rating") + labs(color = "Ride Rating")
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