Getting Started with Plotly

We need to start out by creating an account using the Create Account Button.

Next we will see an invitation to take a tour (which is worth doing) and Plotly helpfully points out that we can load files from Google Drive or Dropbox. We then select the Workspace option to begin work.

Importing Files

When you first arrive you will see a Workspace with a Grid (Plotly’s term for a table or worksheet).

In the workspace you will see an Import Icon that provides a range of options for importing data. Don’t import anything yet! You can also copy data from a file and paste it into the Grid.

The reason not to use these options at the moment is that while the data may import fine first time, in other cases it will not. Using the options on this page you will receive no information if an import fails. We also encountered problems with saving data that had been pasted into the worksheet (even where it appeared to work). To avoid potential frustration head over to Organize.

From the Organize page select the New button and then Upload. Now select your local file. When you upload the file a status message will display and if all goes well then you will see a completed message. If not a red message will display informing you that there has been a problem (how you fix these problems is unclear)

For this experiment we used two datasets from the Open Source Patent Analytics Manual data repository. When using the Github repository click on the file of interest until you see a View Raw message. Then right click to download the data file from there. You can download them for your own use directly from the following links.

1. WIPO trends application trends by year and with % change
2. Pizza patents by country and year. This is a simple dataset containing counts of patent documents containing the word pizza from WIPO Patentscope broken down by country and year.

One important point to note is that Plotly is not a data processing tool. While there are some data tools, your data will generally need to be in a form that is suitable for plotting at the time of input. In part this reflects the use of APIs which allow for users of Python, R and Matlab to send their data to Plotly directly for sharing with others. This is one of the great strengths of Plotly and we will cover this below. However, we also experienced problems in loading and graphing datasets that were easy to work with in Tableau (as a benchmark). This suggests a need to invest time in understanding the formats that Plotly understands.

We experienced a different type of problem with the simple WIPO trends data where Plotly concatenated the first row (containing labels) and the first data row into one heading row. However, in most cases import seemed to be fine. To turn a row into a heading row try right clicking the row with the headings and right clicking on use row as col headers. Then right click again to remove the original row.

Creating a graphic

We will start with the simple WIPO trends data by opening up that Grid

Note here that in the Grid we have options to select the x or y axis for plotting. There is also an Options menu that we will come back to.

The Type of plot can be changed by selecting the drop down menu as we can see below.

Sticking with a line graph, when we create the plot we can add a title and then change the theme (in this case to Catherine).

We could also add a fit line by selecting the FIT DATA menu icon. This will ask you to create a fit and then you have a range of preset functions or you can add your own. Here we have simply chosen the default Linear fit.

We can then save the plot and use the export button to save the plot in a variety of formats and sizes. It is also very easy to add annotations using the Notes icon. Confusingly, the large blue Share button only seems to save the file and despite saving the plot we were not able to locate it again. While Plotly certainly looks nice, and appears to have attractive functions it is not intuitive and the difficulties involved in importing and sharing can be frustrating and time consuming. In short, time is needed to invest in and explore the potential of this tool.

Working with Plotly in R

We are following the instructions for setting up Plotly in R here. We will be using RStudio for this experiment. Download RStudio for your operating system here. For Python try these installation instructions to get started.

In RStudio first we need to install or load the devtools package.

install.packages("devtools")

Then load the library.

library(devtools)

The we install the plotly package.

devtools::install_github("ropensci/plotly")

When we load the library it will load other required packages, note that you may need to install some of these packages if you don’t have them already. Use install.packages("ggplot2") and so on in the Console if this happens and then load the libraries.

library(plotly)  ## loads Plotly and the additional packages it needs.

Loading required package: RCurl Loading required package: bitops Loading required package: RJSONIO Loading required package: ggplot2

We now need to set our credentials for the API. Follow this link to obtain your API key (when logged in to Plotly). Note also that you can obtain a streaming API token on the same page. Streaming will update a graphic from inside RStudio.

Next save the username and key in your R profile as follows.

Sys.setenv(plotly_username = "your_plotly_username")
Sys.setenv(plotly_api_key = "your_api_key")

Next we will use a quick plot or qplot from ggplot2. First we will load the pizza patents by country and year dataset from the Github repository using readr. First you may need to install the readr package and/or lead the library. If you have the tidyverse installed then you will simply need to load the library.

install.packages("readr")

Load the library.

library(readr)

Now read in the dataset from the Github respository (if you have it downloaded already you could load by inserting the path to your local file inside the quotes).

pcy <- read_csv("https://github.com/poldham/opensource-patent-analytics/raw/master/2_datasets/pizza_medium_clean/pcy.csv")
pcy
## # A tibble: 325 x 4
##    pubcountry pubcode pubyear     n
##    <chr>      <chr>     <int> <int>
##  1 Canada     CA         1968     1
##  2 Canada     CA         1971     2
##  3 Canada     CA         1972     4
##  4 Canada     CA         1974     1
##  5 Canada     CA         1975     1
##  6 Canada     CA         1976     1
##  7 Canada     CA         1977     1
##  8 Canada     CA         1978     4
##  9 Canada     CA         1979     8
## 10 Canada     CA         1980    11
## # ... with 315 more rows

We will now make a quick plot using qplot from ggplot2. The data is for trends in patent documents mentioning pizza from WIPO Patentscope. We have set a limit to the data for 1970 to 2012 to edit out sparse data and remove the data cliff for recent years.

library(ggplot2)
pizza <- qplot(pubyear, n, data = pcy, geom = "line", colour = pubcountry, xlim = c(1970, 
    2012))
pizza

If we want to convert the ggplot into plotly we can use ggplotly.

ggplotly(pizza)

We can now hover over the graph to display the data points and also we can use the scroll to see the range of countries. Clearly in this case activity in the US is squashing the other countries down to the bottom so we would in reality want to split this up into separate graphs. For the moment however lets upload the graph to our account.

For this we just need to use api_create. It will be a good idea to check the api_create help and try out the examples to get a better feel for this. Note that you can also upload data frames with the same function.

api_create(pizza)

This will trigger your browser window.

To visit the plot just created try this link https://plot.ly/~poldham/613/

You will now see an online plot that should look like this.

If we hover over the data points we will also see the data appear by country (to add a legend, edit graph and then Legends). Note here that the data table is also provided under the Data tab.

We can also share the graph via social media, download the data, or edit the graph. Note that the default setting for a graph sent via the API appears is public. To change that use the Edit button and then select the Share. Note that you will need a paid account to share data privately.

Round Up

In this article we have provided a brief introduction to Plotly to help you get started with using this tool for patent analytics. Plotly provides visually appealing and interactive graphics that can readily be shared with colleagues, pasted into websites and shared publicly. The availability of APIs is also a key feature of Plotly for those working in Python, R or other programmatic environments.

However, Plotly can also be confusing. For example, we found it hard to understand why particular datasets would not upload correctly (when they can easily be read in Tableau). We also found it hard to understand the format that the data needed to be in to plot correctly. If we examine the data table above it is clear that Plotly has converted each country in the underlying data (which is in long format) into individual x and y axes. We experienced significant problems with making datasets that work fine in Tableau work in Plotly. So, Plotly can be somewhat frustrating although it has very considerable potential for sharing appealing graphics. As is so often the case, this will also involve significant investments in time to understand the way Plotly works and in particular the format for the data that works best with Plotly.

In this article we have only touched on the potential of Plotly. Other kinds of plots that are well worth exploring include Bubble maps, contour maps and heat maps. To experiment for yourself try the Plotly tutorials.