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Here is the thing.
I have a bunch of vehicles GPS data points, which consist of "timestamp, longitude, latitude, fuel consumption and distance" and the data is OSM(open street map) based. I want to match the data points to OSM, divide it by road type, so that I could get road type specific data in groups (e.g, highway group, local roadway group). Then I'll could calculate the fuel efficiency corresponding to different road type.
This is a project I'm currently doing. I'm wondering if R has any packages could handle this type of task?
Any help would be greatly appreciated!
I have recently commented a similar question here on stackoverflow.
R: How to identify a road type using GPS?
You need a package that wraps an API enabling "reverse geocoding" service. In detail you need a function that extracts extra information other than the the address. See package ggmap as suggested in the other thread for example. Not sure you can extract the detail you need.. Anyway it is worth a try.
Take a look at the package Nominatim (https://github.com/hrbrmstr/nominatim) or geocodeHERE(https://github.com/corynissen/geocodeHERE) as well. And please inform us all about successes or failures :) Good luck
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I am trying to find a way to get the travel time for both walking and driving for my dataset. Using ArcGIS I was able to get the liner/planar line that connects them but I do not know how to get the travel times. After days spent trying, I am switching to a more practical solution in R!
edit: I have a dataset with 2 sets of coordinates, origin and destination, where each point has its own origin (where the journey started) and a destination (where the journey ended). What I want to do is calculate the driving time and the walking time for these points.
Thank you!
Google is perhaps the best API for driving distance, Googleway is an R package allowing you to compute distances
https://www.rdocumentation.org/packages/googleway/versions/2.7.1
I did the same kind of task but directly from Gsheet/Appscript then sending it back to R:
https://developers.google.com/apps-script/quickstart/custom-functions
I'm not sure it answers your question but hope it help.
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I just completed data science topic i want to fo a mini project in data science can anyone suggest me a project . I searched a lot in google but i not found any projects can anyonr suggest a project
The beauty of data science is that the project can be anything you're curious about. To figure out what kind of project you want to tackle, what kind of questions do you want to know the answers to?
At the top of my head, you could explore:
the various COVID related data try to find any insights or simply
the price differences of peaches around different areas of Atlanta,
GA
is there a particular skill you want to improve such as time series analysis, classification, or regression?
Find a question you want to answer or a project that will improve your skill in a certain area.
Try this project :
https://www.udemy.com/course/data-science-hands-on-1-hour-project-on-deep-learning/
The Hands on project is currently free on Udemy. It uses Image localization techniques to build, train and test deep learning model for autonomous vehicles. Try changing the data set and try something more innovative liks skin cancer detection or text detection. Try it once.
Try creating a project on no of accidents happened in your country or state, due to different conditions like natural calamities or human mistakes or due to animals and predict the no of cases for the next upcoming year.
Try creating a time series analysis project on covid - 19.
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We were going over dataframes in my statistics class today, and my instructor told us about a 'molten' type and a 'cast' type. I understand what the differences are and how to convert between the two - but why would I do this? What, if anything, makes one of them more/less useful than the other? Are there specific cases where one would be preferable to the other?
My instructor told us that "we would know when we needed to use one or the other just by looking at it"... But I have no idea what I'm even looking for. A google search for "molten vs cast in R" gave me all sorts of helpful links for if I needed to know how to do it, but not why one is preferred to the other.
In addition to modeling, I have found that long datasets can be helpful with plotting varying levels. When I am doing analysis and typically need data as 1 row per observation, I re-work my data to be wide.
In the end, there isn't a precise answer, but the beauty of packages like reshape2 is that you beat your data into whatever form you need.
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Why does Google restrict the use of the JavaScript Distance Matrix API so that it can only be used if you display the data on a Google Map?
From the Distance Matrix API documentation
Use of the Distance Matrix API must relate to the display of information on a Google Map; for example, to determine origin-destination pairs that fall within a specific driving time from one another, before requesting and displaying those destinations on a map. Use of the service in an application that doesn't display a Google map is prohibited.
I've written a simple bookmark scriptlet for personal use that allows me to select an address and click it and it then displays the driving distance and time to several points of interest. In order to comply with this seemingly arbitrary restriction, I will now have to use more of Google's resources displaying a map that I don't want to see!
Anybody know why they would have this restriction?
It is due to licensing restrictions between Google and the data providers.
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I have multiple variations of an automated strategy for trading certain investment vehicles. For each of these variations I have cross-validated backtests on historic data. I need to pick the best-performing test. There is significant variation between the tests in terms of trades per day, net position size, etc. This makes it difficult to compare one to another.
The nature of the test relies on the predictions of a multidimensional nearest-neighbor search.
Having been recently acquainted with R, I am looking for packages/functions/tests to help me analyze various elements of my strategies' performance. Particularly, I am interested in two things:
1. Packages/functions/metrics that gauge the efficacy of my predictor.
2. Packages/functions/metrics that gauge the relative "profitability" of one variation to another.
If you know something that I should take a look at, please do not hesitate to post it!
I would definitely take a look at these two R Task views:
Taskview Econometrics
Taskview Finance
They provide a broad overview of the kind of packages that are used in these fields. Googling for:
using R for financial analysis
also got me a lot of hits that are relevant for your situation. Good luck!