![]() Anyway, the functions you’re looking for are scale_x_reverse and scale_y_reverse, which should each be added as their own layer. This is just convention but it has to do with the inverse relationship with the actual formant values and our perception of sounds. As it turns out, vowel plots typically reverse both the x- and the y-axes so that high vowels are at the top, and front vowels are on the right. The high front vowel /i/-represented by the label “FLEECE”-is in the bottom right of the plot when it traditionally is in the top left. We’ll see how to fix the order of these colors, in just a sec. Because of the nature of how color works, there are several shades of blue and green, but not very many warm colors. So, it puts the vowels in order in the legend, and then again assigns them colors based on 10 equidistant values along a rainbow wheel. In this case, because the midpoints comes prepared with a predetermined order of the vowels, ggplot2 is going to respect that order. So, with that order, ggplot goes around the color wheel from red to pink, and picks equidistant, maximally-distinct colors as you have vowels. How is this color assigned? In your dataset, the vowels will probably appear alphabetical in the legend. One subtler change is that the plotting area is actually a little bit narrower to make room for the legend. Each unique vowel in our data is now represented in this legend, and the name of the column in our spreadsheet, vowel, is the title of that legend. The most obvious thing we see is that there is now color, but there’s also a legend too. The base layer can be created by just using the ggplot function. The way things work in ggplot2 is we be build a visualization layer by layer. With that in mind, we’re ready to start making some vowel plots! Building a basic scatterplot ![]() Here I use Wells’ Lexical sets, which is typical in English dialectology. ![]() ![]() F1, F2, F3, F4: the first four formant frequencies.end: the time (in seconds) from the beginning of the recording to the vowel’s offset.start: the time (in seconds) from the beginning of the recording to vowel’s onset.vowel_id: a unique identifier for every vowel token.2477 3367 sʊz s FOOT zĪs you can see we have the following columns Vowel_id start end t F1 F2 F3 F4 word pre vowel folġ 1 2.06 2.41 2.23 368. ![]()
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