ML Kit launched in May of this year with APIs for text recognition, face detection, barcode scanning, image labeling, and landmark detection eventually, smart replies will be part of the collection. The AI for Smart Replies doesn't quite exist yet, but Google has said it will be part of ML Kit, a new toolkit designed to give developers easy-to-use machine learning APIs without having to learn something like TensorFlow. Now you just need a chatbot-style AI that can read the messages and generate truly worthwhile responses.
The easy part is already in Android Pie-notifications can have smart reply buttons that send pre-generated text to an app. With Android Pie, Google is taking a second swing at supplying smart replies to all apps, but this time with enough OS and cloud support to make it a real feature. Reply only used its powers for good, but it was definitely a scary proposition. After granting Reply a shocking number of permissions-like being able to read and reply to all your notifications-it basically became a man-in-the-middle notification hijacking scheme. It was a neat idea, but as an app that glommed an unauthorized new feature onto a third-party app, it was a total hack job. Google's magical AI would scan your message and generate a few custom responses based on things like the message text and sometimes even your location and the traffic. No matter what I do (in Android Messages, at least), I always get a placeholder image.Įarlier this year, Google released an experimental app called Reply, which would inject machine-learning-generated replies into an app's notifications. The one oddity with threaded messaging is that, while your contact's picture will show up, there seems to be no way to set a contact icon for yourself. The messaging notification can also show pictures in-line, so instead of a "picture," you can see a tiny thumbnail right in the notification panel. The threaded notifications look just like a mini texting app, complete with contact pictures on either side of the message.
Message notifications are now threaded, so if you have a rapid-fire texting session, the last few replies will show up in the notification. To discover more about all the things you can do in R, check out our “R” guides.Messaging notifications-the kinds generated by texting with SMS, Hangouts, WhatsApp, and other apps-get a few special features in Android Pie.
A complete list of properties and attributes can be found on the the ggplot2 webpage. There are a wide range of additional properties that can be modified in the ggplot2 package including chart and axis titles, borders, grid lines, legend, etc.
In the code above I have broken up the stages across multiple lines to help with readability, but you can typically do it all on one line The code above builds the pie chart by: You can sequence functions for modifying the plot by “adding” them, by which I mean a “+” sign is used to separate the different function calls.
I’ve generated this pie chart with a specified custom color palette. Pie = pie + theme_classic() + theme(axis.line = element_blank(), Pie = pie + labs(x = NULL, y = NULL, fill = NULL, title = "Phones - Market Share") Pie = pie + coord_polar("y", start=0) + geom_text(aes(label = paste0(round(value*100), "%")), position = position_stack(vjust = 0.5)) # Convert to pie (polar coordinates) and add labels Pie = ggplot(df, aes(x="", y=share, fill=brand)) + geom_bar(stat="identity", width=1) Creating a Pie Chartįirst we’ll load the ggplot2 package and create a bar chart using the geom_bar function. Next, we’ll use this data frame to create the pie chart using the ggplot2 package. For this example, we’ll use some sample data showing global market share for mobile phone manufacturers. We first create a data frame containing the values that we want to display in the pie chart.
All you need for a pie chart is a series of data representing counts or proportions, together with the corresponding labels.