# Import and plot UTI and EMA data

#### Stefano Coretta

#### 2025-09-07

The package `rticulate` facilitates import in R of the following data:

- Deep Lab Cut (DLC) spline data from Articulate Assistant Advanced™. Paper

- `.pos` files from the Carstens AG500 electro-magnetic-articulographer.

- (Legacy) fan-line spline data from Articulate Assistant Advanced™.

To use the package, attach it as usual.

    library(rticulate)

The following sections illustrate how to import the three types of data.

# Import DLC spline data from AAA

The `read_aaa()` function can read DLC spline data exported from AAA. It assumes that the X/Y coordinates are in the rightmost columns of the exported data (i.e., when exporting they should be selected last). We recommend to include a header when exporting DLC data from AAA for a smooth experience.

The example file we will read can be inspected here. This file contains a header (see the section on importing fan-line spline data below on how to import legacy fan-line data files without a header).

With `format = "wide"`, the file is read as is and a `frame_id` index column is added to index each tongue contour frame (normally, each row in the exported data is one ultrasound frame).

    # system.file() is needed here because the example files reside in the package.
    # You can just include the file path directly in read_aaa, like
    # read_aaa("~/Desktop/splines.tsv", columns)
    file_path <- system.file("extdata", "it01-dlc.tsv", package = "rticulate")

    dlc <- read_aaa(file_path, format = "wide")

    ## Rows: 521 Columns: 36
    ## ── Column specification ────────────────────────────────────────────────────────
    ## Delimiter: "\t"
    ## chr  (4): Client family name, Date Time of recording, Prompt, Annotation Title
    ## dbl (32): Time of sample in recording, Time of sample in annot, X0 DLC_Tongu...
    ##
    ## ℹ Use `spec()` to retrieve the full column specification for this data.
    ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

    dlc

    ## # A tibble: 521 × 37
    ##    `Client family name` `Date Time of recording` `Time of sample in recording`
    ##    <chr>                <chr>                                            <dbl>
    ##  1 it01                 29/11/2016 15:10:52                              0.670
    ##  2 it01                 29/11/2016 15:10:52                              0.685
    ##  3 it01                 29/11/2016 15:10:52                              0.700
    ##  4 it01                 29/11/2016 15:10:52                              0.715
    ##  5 it01                 29/11/2016 15:10:52                              0.730
    ##  6 it01                 29/11/2016 15:10:52                              0.745
    ##  7 it01                 29/11/2016 15:10:52                              0.760
    ##  8 it01                 29/11/2016 15:10:52                              0.775
    ##  9 it01                 29/11/2016 15:10:52                              0.790
    ## 10 it01                 29/11/2016 15:10:52                              0.804
    ## # ℹ 511 more rows
    ## # ℹ 34 more variables: `Time of sample in annot` <dbl>, Prompt <chr>,
    ## #   `Annotation Title` <chr>, `X0 DLC_Tongue` <dbl>, `Y0 DLC_Tongue` <dbl>,
    ## #   `X1 DLC_Tongue` <dbl>, `Y1 DLC_Tongue` <dbl>, `X2 DLC_Tongue` <dbl>,
    ## #   `Y2 DLC_Tongue` <dbl>, `X3 DLC_Tongue` <dbl>, `Y3 DLC_Tongue` <dbl>,
    ## #   `X4 DLC_Tongue` <dbl>, `Y4 DLC_Tongue` <dbl>, `X5 DLC_Tongue` <dbl>,
    ## #   `Y5 DLC_Tongue` <dbl>, `X6 DLC_Tongue` <dbl>, `Y6 DLC_Tongue` <dbl>, …

For plotting and analysis, it is useful to have the data in a long format. By default, `read_aaa()` pivots the data from wide to long. The resulting tibble has X and Y coordinates for each knot of each spline in each row. DLC knots are numbered from 0, as they are in the exported data. A further column, `displ_id`, indexes a knot displacement trajectory in time if multiple time points are exported.

    dlc <- read_aaa(file_path)

    ## Rows: 521 Columns: 36
    ## ── Column specification ────────────────────────────────────────────────────────
    ## Delimiter: "\t"
    ## chr  (4): Client family name, Date Time of recording, Prompt, Annotation Title
    ## dbl (32): Time of sample in recording, Time of sample in annot, X0 DLC_Tongu...
    ##
    ## ℹ Use `spec()` to retrieve the full column specification for this data.
    ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

    dlc

    ## # A tibble: 7,815 × 12
    ##    `Client family name` `Date Time of recording` `Time of sample in recording`
    ##    <chr>                <chr>                                            <dbl>
    ##  1 it01                 29/11/2016 15:10:52                              0.670
    ##  2 it01                 29/11/2016 15:10:52                              0.670
    ##  3 it01                 29/11/2016 15:10:52                              0.670
    ##  4 it01                 29/11/2016 15:10:52                              0.670
    ##  5 it01                 29/11/2016 15:10:52                              0.670
    ##  6 it01                 29/11/2016 15:10:52                              0.670
    ##  7 it01                 29/11/2016 15:10:52                              0.670
    ##  8 it01                 29/11/2016 15:10:52                              0.670
    ##  9 it01                 29/11/2016 15:10:52                              0.670
    ## 10 it01                 29/11/2016 15:10:52                              0.670
    ## # ℹ 7,805 more rows
    ## # ℹ 9 more variables: `Time of sample in annot` <dbl>, Prompt <chr>,
    ## #   `Annotation Title` <chr>, knot <dbl>, spline <chr>, X <dbl>, Y <dbl>,
    ## #   frame_id <int>, displ_id <dbl>

You can then proceed to plot the data as you would with any other data.

    dlc |>
      ggplot(aes(X, Y)) +
      geom_point(alpha = 0.1, size = 0.5) +
      coord_fixed()

To plot tongue contours, use the `frame_id` column to group points from the same contour (we need to filter the data to include only `DLC_Tongue` data).

    dlc |>
      filter(spline == "DLC_Tongue") |>
      ggplot(aes(X, Y, group = frame_id)) +
      geom_path(alpha = 0.1, linewidth = 0.1) +
      coord_fixed()

You can also plot the X or Y displacement of the knots along time.

    dlc |>
      filter(
        `Date Time of recording` == "29/11/2016 15:10:52",
        spline == "DLC_Tongue"
      ) |>
      ggplot(aes(`Time of sample in annot`, Y, group = displ_id)) +
      geom_path() +
      facet_grid(rows = vars(knot), scales = "free_y")

Check out `vignette("kinematics", package = "rticulate")` and `vignette("filter-signals", package = "rticulate")`.

# Import `.pos` files from the Carstens AG500 EMA

You can import binary `.pos` files from the Carstens AG500 electro-magnetic articulographer using the `read_ag500_pos()` function. In most contexts, the defaults of the function will suffice. Due to CRAN not accepting binary files, the following code is not run, but you can test it by downloading the `.pos` file provided here: https://github.com/stefanocoretta/rticulate/blob/vignettes/data-raw/ema/0025.pos.

    ema <- read_ag500_pos("0025.pos")

You can plot the antero-posterior and vertical displacement of a sensor (here the tongue tip sensor, channel 5).

    ema |>
      filter(chn == 5) |>
      ggplot(aes(x, z)) +
      geom_point(size = 0.1)

Or, for example, just the vertical displacement along time.

    ema |>
      filter(chn == 5) |>
      ggplot(aes(time, z)) +
      geom_point(size = 0.1)

# (LEGACY) Import fan-line spline data from AAA

The function `read_aaa()` can import legacy fan-line spline data from AAA and transform it into a longer format (where each observation is a point on a fan line and the coordinates values are two variables, `X` and `Y`, see `?tongue` for more details).

The following code will show how to import a file without the header that has legacy fan-line spline data. You can inspect the file we will read here. The X/Y coordinates are stored in the rightmost columns.

When the file does not contain a header like in this case, you must create a vector with the column names for all columns except the X/Y coordinates columns.

    columns <- c(
        "speaker",
        "seconds",
        "rec_date",
        "prompt",
        "label",
        "TT_displacement",
        "TT_velocity",
        "TT_abs_velocity",
        "TD_displacement",
        "TD_velocity",
        "TD_abs_velocity"
    )

Now we can use `read_aaa()` to import the spline data as a tibble. The function requires a string with the file path and name, and a vector with the names of the columns. It is necessary to specify the number of fan lines with the `fan_lines` argument.

    # system.file() is needed here because the example files reside in the package.
    # You can just include the file path directly in read_aaa, like
    # read_aaa("~/Desktop/splines.tsv", columns)
    file_path <- system.file("extdata", "it01.tsv", package = "rticulate")

    tongue <- read_aaa(file_path, fan_lines = 42, column_names = columns)

    ## Rows: 11 Columns: 95
    ## ── Column specification ────────────────────────────────────────────────────────
    ## Delimiter: "\t"
    ## chr  (4): speaker, rec_date, prompt, label
    ## dbl (91): seconds, TT_displacement, TT_velocity, TT_abs_velocity, TD_displac...
    ##
    ## ℹ Use `spec()` to retrieve the full column specification for this data.
    ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
    ## ! Column `Date Time of recording` not found. Did not create a `displ_id` column.
    ## We recommend to include `Date Time of recording` when exporting data from AAA.

To check the head of the tibble, just do:

    tongue

    ## # A tibble: 462 × 16
    ##    speaker seconds rec_date            prompt  label TT_displacement TT_velocity
    ##    <chr>     <dbl> <chr>               <chr>   <chr>           <dbl>       <dbl>
    ##  1 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  2 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  3 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  4 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  5 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  6 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  7 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  8 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  9 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ## 10 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ## # ℹ 452 more rows
    ## # ℹ 9 more variables: TT_abs_velocity <dbl>, TD_displacement <dbl>,
    ## #   TD_velocity <dbl>, TD_abs_velocity <dbl>, knot <dbl>, spline <chr>,
    ## #   X <dbl>, Y <dbl>, frame_id <int>

Sometimes is useful to add extra information for each prompt (like vowel, consonant place, phonation, etc.). We can do so by using functions from the `dplyr` package (`word()` is from the `stringr` package).

    stimuli <- read_csv(system.file("extdata", "stimuli.csv", package = "rticulate"))

    ## Rows: 12 Columns: 11
    ## ── Column specification ────────────────────────────────────────────────────────
    ## Delimiter: ","
    ## chr (10): word, ipa, c1, c1_phonation, vowel, anteropost, height, c2, c2_pho...
    ## dbl  (1): item
    ##
    ## ℹ Use `spec()` to retrieve the full column specification for this data.
    ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

    tongue <- mutate(tongue, word = word(prompt, 2)) %>%
        left_join(y = stimuli) %>%
        mutate_if(is.character, as.factor)

    ## Joining with `by = join_by(word)`

Let’s check `tongue` again.

    tongue

    ## # A tibble: 462 × 27
    ##    speaker seconds rec_date            prompt  label TT_displacement TT_velocity
    ##    <fct>     <dbl> <fct>               <fct>   <fct>           <dbl>       <dbl>
    ##  1 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  2 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  3 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  4 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  5 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  6 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  7 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  8 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ##  9 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ## 10 it01       1.20 29/11/2016 15:11:03 Dico p… max_…            77.9       -7.73
    ## # ℹ 452 more rows
    ## # ℹ 20 more variables: TT_abs_velocity <dbl>, TD_displacement <dbl>,
    ## #   TD_velocity <dbl>, TD_abs_velocity <dbl>, knot <dbl>, spline <fct>,
    ## #   X <dbl>, Y <dbl>, frame_id <int>, word <fct>, item <dbl>, ipa <fct>,
    ## #   c1 <fct>, c1_phonation <fct>, vowel <fct>, anteropost <fct>, height <fct>,
    ## #   c2 <fct>, c2_phonation <fct>, c2_place <fct>

# Import multiple files

To import multiple files with AAA data, simply use a list of paths with `read_aaa`, for example using `list.files`.

    tongue2 <- list.files(
        path = system.file("extdata", package = "rticulate"),
        pattern = "*\\d{2}.tsv",
        full.names = TRUE
        ) %>%
        read_aaa(., fan_lines = 42, column_names = columns)

    ## Rows: 11 Columns: 95
    ## ── Column specification ────────────────────────────────────────────────────────
    ## Delimiter: "\t"
    ## chr  (4): speaker, rec_date, prompt, label
    ## dbl (91): seconds, TT_displacement, TT_velocity, TT_abs_velocity, TD_displac...
    ##
    ## ℹ Use `spec()` to retrieve the full column specification for this data.
    ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
    ## ! Column `Date Time of recording` not found. Did not create a `displ_id` column.
    ## We recommend to include `Date Time of recording` when exporting data from AAA.
    ##
    ## Rows: 8 Columns: 95
    ## ── Column specification ────────────────────────────────────────────────────────
    ## Delimiter: "\t"
    ## chr  (4): speaker, rec_date, prompt, label
    ## dbl (91): seconds, TT_displacement, TT_velocity, TT_abs_velocity, TD_displac...
    ##
    ## ℹ Use `spec()` to retrieve the full column specification for this data.
    ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
    ## ! Column `Date Time of recording` not found. Did not create a `displ_id` column.
    ## We recommend to include `Date Time of recording` when exporting data from AAA.