Take a look at the following data table. It's full of pictures of vegetables and fruits, and looks fun!
The variable names are written in Japanese, but even if you don't understand Japanese, you should be able to guess what each variable represents by looking at the pictures. This data contains the daily consumption amounts of vegetables and fruits.
Data source: Household Survey (Ministry of Internal Affairs and Communications)
Emojis originated in Japan
The color in this data table is achieved by tagging variables with emojis .
Emoji, as the word suggests, originated in Japan. Emoji are now a global standard because they are included in Unicode.
The latest version , JMP 19, released last September, adds the ability to assign tags to columns, and now allows you to select emojis as tags.
Using emojis not only makes things look more colorful, but they are also useful in many practical situations.
In this article, we will first introduce the new feature "Tags," and then provide three effective examples of using emoji tags.
Set emoji as a "tag"
If we were to list three benefits of using "tags," they would be A, B, and C below.
Benefits of using tags
- Column roles can be clearly displayed
- Select and display only tagged columns
- When specifying columns on the platform, you can select columns by specifying tags.
The JMP sample data, "Lipid Data.jmp," has many tags set, so we will use this data to illustrate these benefits.
At the bottom of the column panel, there are "Tags" (the area surrounded by a red frame in the image below), and several tags have been pre-set, such as "BP Measure" and "Blood Pressure." Each column (variable) in the data table has been assigned a tag appropriate to that column. This data table also confirms Advantage A, "The role of each column can be clearly displayed."
How to set tags
Click the "+" button next to "Tags" in the Columns panel and set the tag name, style, etc.
In Styles, you can manage tags by adding color to the regular tag marks (in this example, "Total Measure" and "BP Measure"), or you can set emojis, which is the subject of this article.
The set tag can be selected from the [Tags] menu by right-clicking the column name in the column panel.
By selecting the tag you have set, you can access benefit B, "select and display only the columns with that tag." This is useful when there are many columns and you want to narrow down the data to only the columns with a specific tag.
Furthermore, you can select the function (platform) you want to analyze from the [Analysis] or [Graph] menu, and when specifying columns, you can select "Tag" itself.
In the example below, in "Bivariate Relationship," the tag "Blood Measurements" is specified for [Y, Objective Variable] and the tag "Physical Measurements" is specified for [X, Explanatory Variable]. By specifying in this way, you can specify all columns with that tag as roles.
This is Advantage C, the ability to select columns using tags when specifying columns on the platform .
Not only emojis but also regular characters (text) can be set as tags. The figure below shows an example where the tag "Y" is specified for the four response variables and "X" for the three factors for data obtained through an experimental design.
Examples of using emoji tags
In the previous section, we showed that you can specify emoji tags for columns. Here are three cases where using emojis is useful:
1. Make variables easy to visualize
The following data summarizes the annual GDP trends for each country. The columns "Country Name," "Year," and "GDP ($)" are tagged with emoji symbols that represent the variables.
The first example of vegetables and fruits also uses emoji tags to make variable names easier to imagine.
2. Identify important variables
The figure below shows an example of how it can be used to identify important variables from among many others. "Most important variables" and "important variables" are expressed with easy-to-understand pictogram tags, and the relevant variables are tagged.
When exploring factors in data with many variables, there are cases where you first narrow down the important variables using a method such as screening, and then build a detailed model using the selected variables. This is an effective way to use this tool in such situations.
3. Setting the target type for characteristic values
It is also effective for expressing goals (larger is better, smaller is better, or aiming for a specific value) for continuous value characteristics (Y) in an easy-to-understand manner.
In the data below, the data obtained using an experimental design has been tagged with emojis to clearly indicate the targets for the four response variables. Here, arrow and target emojis are used, but there are other emojis that are suitable for similar purposes.
When analyzing various data, you may forget the details of individual data over time. For example, if you add new data to the data you are currently analyzing and reanalyze it a year from now, even if you know the variable names, you may not remember the intention or meaning of creating those variables.
In such cases, setting up emoji tags in advance will allow you to proceed with your analysis more efficiently.
by Naohiro Masukawa (JMP Japan)
Naohiro Masukawa - JMP User Community
