Package {unsum}

Count CLOSURE samples in advance

Description

Determine how many samples closure_generate() would find for a given set of summary statistics.

• closure_count_all() counts all CLOSURE samples that correspond to the input summary statistics, but without actually generating the samples. This is much faster than closure_generate() if there are many results.

• closure_count_initial() only counts the first round of samples, from which all other ones would be generated. Based on scale range only.

This can help predict how much time closure_generate() would take, and avoid prohibitively long runs.

Usage

closure_count_all(
  mean,
  sd,
  n,
  scale_min,
  scale_max,
  rounding = "up_or_down",
  threshold = 5
)

closure_count_initial(scale_min, scale_max)

Arguments

Value

Integer (length 1).

Examples

closure_count_all(
  mean = "3.5",
  sd = "1.7",
  n = 70,
  scale_min = 1,
  scale_max = 5
)

closure_count_initial(scale_min = 1, scale_max = 5)

Heuristic to predict CLOSURE runtime

Description

Before you run closure_generate(), you may want to get a sense of the time it will take to run. Use closure_gauge_complexity() to compute a heuristics-based complexity score. For reference, here is how it determines the messages in closure_generate():

Usage

closure_gauge_complexity(mean, sd, n, scale_min, scale_max)

Arguments

Details

The result of this function is hard to interpret. All it can do is to convey an idea about the likely runtime of CLOSURE. This is because the input parameters interact in highly dynamic ways, which makes prediction difficult.

In addition, even progress bars or updates at regular intervals (e.g., "10% complete") prove to be extremely challenging: the Rust code computes CLOSURE results in parallel, which makes it hard to get an overview of the total progress across all cores; and especially to display such information on the R level.

Value

Numeric (length 1).

Examples

# Low SD, N, and scale range:
closure_gauge_complexity(
  mean = 2.55,
  sd = 0.85,
  n = 84,
  scale_min = 1,
  scale_max = 5
)

# Somewhat higher:
closure_gauge_complexity(
  mean = 4.26,
  sd = 1.58,
  n = 100,
  scale_min = 1,
  scale_max = 7
)

# Very high:
closure_gauge_complexity(
  mean = 3.81,
  sd = 3.09,
  n = 156,
  scale_min = 1,
  scale_max = 7
)

Generate CLOSURE samples

Description

Call closure_generate() to run the CLOSURE algorithm on a given set of summary statistics.

This can take seconds, minutes, or longer, depending on the input. Wide variance and large n often lead to many samples, i.e., long runtimes. These effects interact dynamically. For example, with large n, even very small increases in sd can greatly increase runtime and number of values found. Consider specifying path in these cases; see "Writing to disk" below.

If the inputs are inconsistent, there is no solution. The function will then return empty results and throw a warning.

Usage

closure_generate(
  mean,
  sd,
  n,
  scale_min,
  scale_max,
  items = 1,
  path = NULL,
  stop_after = NULL,
  include = c("stats_and_horns", "stats_only", "all"),
  rounding = "up_or_down",
  threshold = 5,
  ask_to_proceed = TRUE
)

Arguments

Value

closure_generate() returns a named list of tibbles (data frames):

• inputs: Arguments to this function.

• metrics_main:

  • samples_all: double. Number of all samples. Equal to the number of rows in results.

  • values_all: double. Number of all individual values found. Equal to n * samples_all.

• metrics_horns:

  • mean: double. Average horns value of all samples. The horns index is a measure of dispersion for bounded scales; see horns().

  • uniform: double. The value that mean would have if all samples were uniformly distributed; see horns_uniform().

  • sd, cv, mad, min, median, max, range: double. Standard deviation, coefficient of variation, median absolute deviation, minimum, median, maximum, and range of the horns index values across all samples. Note that mad is not scaled using a constant, as stats::mad() is by default.

• frequency:

  • samples: string. Frequencies apply to one of three subsets of samples: "all" for all samples, "horns_min" for those samples with the lowest horns index among all samples, and "horns_max" for those samples with the highest horns index.

  • value: integer. Scale values derived from scale_min and scale_max.

  • f_count: double. Count of scale values in the group's medoid sample (the actual sample closest to the group centroid by EMD).

  • f_relative: double. Values' share of total values found.

• results:

  • id: integer. Runs from 1 to samples_all.

  • sample (not present by default if path was specified): list of integer vectors. Each of these vectors has length n. It is a sample (or distribution) of individual scale values found by CLOSURE.

  • horns: double. Horns index of each sample.

• directory (only present if path was specified):

  • path: string. Location of the folder in which the results were saved.

Writing to disk

Specify path if the expected runtime is very long. (In case you have trouble choosing a path, use path = "." for your current working directory.) This makes sure the results are preserved by incrementally writing them to disk. Otherwise, you might encounter an out-of-memory error because closure_generate() accumulates more data than your computer can hold in memory.

More about memory

Some output columns that contain counts, such as f_count, are doubles instead of integers. This is because doubles are able to contain much larger numbers. When counting CLOSURE results, it is possible to exceed the limit of 32-bit integers in R, which is roughly two billion. For instance, this will happen with values_all in the examples (data_high) if n is changed to 120 and scale_max to 7.

Rounding limitations

The rounding and threshold arguments are not fully implemented. For example, CLOSURE currently treats all rounding bounds as inclusive, even if the rounding value would imply otherwise. Many specifications of the two arguments will not make any difference, and those that do will most likely lead to empty results.

Printing

When printing results, you can use print() explicitly with the show argument to control which elements are shown. Set show to one of:

• "some" (the default): show some elements, hide others. The hidden ones have brief descriptions.

• "all": show all elements.

• "none": show no elements, only their descriptions.

For example: print(your_results, show = "all")

Examples

# High spread often leads to many samples --
# here, 2492.
data_high <- closure_generate(
  mean = "3.5",
  sd = "1.7",
  n = 70,
  scale_min = 1,
  scale_max = 5
)

data_high

# Get a clear picture of the distribution
# by following up with `closure_plot_bar()`:
closure_plot_bar(data_high)

# Low spread, only 4 samples, and not all
# scale values are possible.
data_low <- closure_generate(
  mean = "2.9",
  sd = "0.5",
  n = 25,
  scale_min = 1,
  scale_max = 5
)

data_low

# This can also be shown by `closure_plot_bar()`:
closure_plot_bar(data_low)

Visualize CLOSURE data in a barplot

Description

Call closure_plot_bar() to get a barplot of CLOSURE results.

For each scale value, the bars show how often this value appears in the mean samples with the minimum or maximum horns index (h). This displays the typical sample with the least or most amount of variance from among all CLOSURE samples.

As CLOSURE finds all possible samples, this plot shows the min and max possible amount of variance given the input statistics.

Usage

closure_plot_bar(
  data,
  min_max = c("both", "min", "max"),
  format = c("percent", "absolute_percent", "absolute", "relative"),
  samples = c("mean", "all"),
  overlay = c("all_avg", "none", "interval", "pointinterval", "dots"),
  facet_labels = c("Minimal variance", "Maximal variance"),
  facet_labels_parens = "h",
  bar_alpha = 0.75,
  bar_color = "#5D3FD3",
  show_text = TRUE,
  text_color = bar_color,
  text_size = 12,
  text_offset = 0.05,
  mark_thousand = ",",
  mark_decimal = "."
)

Arguments

Details

By default, both faceted plots have a label that includes their horns index (h); see horns(). You can remove the parenthesized part using facet_labels_parens = NULL or the entire label using facet_labels = NULL.

Value

A ggplot object.

See Also

closure_plot_ecdf(), an alternative visualization.

Examples

# Create CLOSURE data first:
data <- closure_generate(
  mean = "2.9",
  sd = "0.5",
  n = 70,
  scale_min = 1,
  scale_max = 5
)

# Even with minimal and maximal variance,
# the results are almost the same:
closure_plot_bar(data)

# Only show the minimum-variance panel:
closure_plot_bar(data, min_max = "min")

Visualize CLOSURE data in an ECDF plot

Description

Call closure_plot_ecdf() to visualize CLOSURE results using the data's empirical cumulative distribution function (ECDF). This can be useful to display any variation between CLOSURE samples.

See closure_plot_bar() for more intuitive visuals.

Usage

closure_plot_ecdf(
  data,
  samples = c("mean_min_max", "mean", "all"),
  pad = c("extend", "match", "stop"),
  legend_title = NULL,
  line_color_single = "#5D3FD3",
  line_color_multiple = c("royalblue4", "deeppink", "darkcyan"),
  text_size = 12,
  reference_line_alpha = 0.6,
  mark_decimal = "."
)

Arguments

Details

This function was inspired by rsprite2::plot_distributions() with its option plot_type = "ecdf". However, plot_distributions() invariably shows one line per (randomly drawn) possible dataset, and it does not support the horns index or other measures of dispersion. Some further differences exist.

Value

A ggplot object.

Examples

# Create CLOSURE data first:
data <- closure_generate(
  mean = "3.5",
  sd = "2",
  n = 52,
  scale_min = 1,
  scale_max = 5
)

# Visualize:
closure_plot_ecdf(data)

Write CLOSURE results to disk (and read them back in)

Description

You can use closure_write() to save the results of closure_generate() on your computer. A message will show the exact location.

The data are saved in a new folder as five separate files, one for each tibble in closure_generate()'s output.

closure_read() is the opposite: it reads those files back into R, recreating the original CLOSURE list. This is useful for later analyses if you don't want to re-run a lengthy closure_generate() call. It also works with results that closure_generate() wrote to disk itself using path = "your/path".

Usage

closure_write(data, path)

closure_read(
  path,
  include = c("stats_only", "stats_and_horns", "capped_error", "all"),
  samples_cap = NULL
)

Arguments

Details

closure_write() saves all tibbles as Parquet files. This is much faster and takes up far less disk space — roughly 1% of a CSV file with the same data. Speed and disk space can be relevant with large result sets.

Use closure_read() to import the CLOSURE list from the folder back into R. This is based on nanoparquet::read_parquet().

Value

• closure_write() returns the path to the new folder it created.

• closure_read() returns a list of the same kind as closure_generate().

Folder name

The new folder's name will contain all the inputs that determine the CLOSURE results. Dashes separate values and underscores replace decimal periods. For example:

 CLOSURE-3_5-1_0-90-1-5-up_or_down-5
 

The order is the same as in closure_generate():

 closure_generate(
   mean = "3.5",
   sd = "1.0",
   n = 90,
   scale_min = 1,
   scale_max = 5,
   rounding = "up_or_down",  # default
   threshold = 5             # default
 )

Examples

data <- closure_generate(
  mean = "2.7",
  sd = "0.6",
  n = 45,
  scale_min = 1,
  scale_max = 5
)

# Writing to a temporary folder just for this example.
# You should write to a real folder instead.
# A simple way is path = "." for your current directory.
path_new_folder <- closure_write(data, path = tempdir())

# In a later session, conveniently read the files
# back into R. This returns the original list,
# identical except for floating-point error.
# (Of course, the `path_new_folder` variable will
# no longer be available -- instead, paste the path
# to your folder here.)
closure_read(path_new_folder)

Visualize example distributions and their h values

Description

The ⁠demo_plot_*()⁠ functions are variants of ⁠closure_plot_*()⁠ that directly visualize a given frequency distribution. Their purpose is to illustrate general points about CLOSURE-type techniques and the horns index.

• demo_plot_bar() is like closure_plot_bar() except it is never faceted.

• demo_plot_horns_histogram() is like closure_plot_horns_histogram().

• demo_plot_ecdf() is like closure_plot_ecdf().

The top line shows the horns index of the given distribution (h) and the horns index of a hypothetical uniform distribution with the same number of scale points (h_u). See horns() and horns_uniform() for the corresponding functions.

Usage

demo_plot_bar(
  freqs,
  format = c("percent", "absolute_percent", "absolute", "relative"),
  bar_alpha = 0.75,
  bar_color = "#5D3FD3",
  show_text = TRUE,
  text_color = bar_color,
  text_size = 12,
  text_offset = 0.05,
  mark_thousand = ",",
  mark_decimal = "."
)

Arguments

Details

In keeping with the forensic metascience tradition of tortured backronyms, DEMO stands for "displaying examples of meticulous operation".

Value

A ggplot object.

Examples

# Zero variance: h = 0
demo_plot_bar(freqs = c(0, 40, 0, 0, 0))

# Perfect "horns of no confidence": h = 1
demo_plot_bar(freqs = c(20, 0, 0, 0, 20))

# Grouped around h = ~0.44, the uniform horns index
# for 7-point scales
demo_plot_bar(freqs = 21:27)

Horns index (h)

Description

horns() measures the dispersion in ordinal data based on scale limits or min/max values. The result is the actual variance as a proportion of the maximum possible variance. It ranges from 0 to 1:

• 0 means no variance, i.e., all observations have the same value.

• 1 means that the observations are evenly split between the extremes, with none in between.

horns_uniform() computes the value that horns() would return for a uniform distribution within given scale limits. This can be useful as a point of reference for horns().

These two functions correspond to the horns and horns_uniform columns in closure_generate().

horns_rescaled() is a version of horns() that is normalized by scale length, such that 0.5 always indicates a uniform distribution, independent of the number of scale points. It is meant to enable comparison across scales of different lengths, but it is harder to interpret for an individual scale. This makes it unlikely to be useful in most cases. Even so, the range and the meaning of 0 and 1 are the same as for horns().

Usage

horns(freqs, scale_min, scale_max)

horns_uniform(scale_min, scale_max)

horns_rescaled(freqs, scale_min, scale_max)

Arguments

Details

The horns index h is defined as:

h = \frac {\sum_{i=1}^{k} f_i (i - \bar{s})^2} {\frac{1}{4} (k - 1)^2}

where k is the number of scale points (i.e., the length of freqs here), f_i is the relative frequency of the ith point on an integer scale from 1 to k, and \bar{s} is the weighted mean frequency. The mean is derived as follows:

\bar{s} = \sum_{i=1}^{k} i \ f_i

Note that h only depends on the frequency distribution, not the actual values of the scale points. This is why both formulas invariably use a scale from 1 to k. The only reason why the horns() function still takes scale_min and scale_max arguments is safety: if freqs is misstated such that its length is different from the number of points on the scale implied by those two arguments, there will be an error.

Maximum possible variance

The "maximum possible" variance here is based on scale range only (Popoviciu 1935):

\sigma_{\max}^2 = \frac{1}{4} (k - 1)^2

It is deliberately agnostic to the mean and the sample size. In the context of CLOSURE, these statistics are only relevant for generating possible samples, which then enter the equation via the numerator; just like the standard deviation. By contrast, the number of scale points, k, is an intrinsic property of those samples. It is sensible to assess the variance of the samples by benchmarking it against the greatest variance that can occur in any sample with the same k.

Uniform distribution

Although horns_uniform() is implemented as a wrapper around horns() that constructs a perfect uniform distribution internally, an equivalent closed-form solution can be given as

h_u = \frac{k + 1}{3 (k - 1)}

"Horns of no confidence"

The term horns index was inspired by Heathers (2017) which defines the "horns of no confidence" as a reconstructed sample "where an incorrect, impossible or unlikely value set has all its constituents stacked into its highest or lowest bins to try meet a ludicrously high SD". In its purest form, this is a case where h = 1, so horns() would return 1. However, note that the implications for the plausibility of any given set of summary statistics depend on the substantive context of the data (Heathers et al. 2018).

Heathers wrote about SPRITE, but the logic applies equally to CLOSURE. Indeed, as CLOSURE is exhaustive but SPRITE is not, it is only CLOSURE that provides certainty about the set of possible samples and their horns indices.

Rust implementation

The metrics_horns tibble that is part of the output of closure_generate() is not based on the R functions presented here. Instead, it relies on efficient Rust implementations of the above formulas. These Rust functions are part of closure-core, which mainly implements CLOSURE but does not currently export horns functions for users.

Value

Numeric (length 1).

References

Popoviciu, T. (1935). Sur les équations algébriques ayant toutes leurs racines réelles. Mathematica (Cluj), 9, 129-145.

Examples

# For simplicity, all examples use a 1-5 scale and a total N of 300.

# ---- With all values at the extremes

horns(freqs = c(300, 0, 0, 0, 0), scale_min = 1, scale_max = 5)

horns(c(150, 0, 0, 0, 150), 1, 5)

horns(c(100, 0, 0, 0, 200), 1, 5)


# ---- With some values in between

horns(c(60, 60, 60, 60, 60), 1, 5)

horns(c(200, 50, 30, 20, 0), 1, 5)

horns(c(150, 100, 50, 0, 0), 1, 5)

horns(c(100, 40, 20, 40, 100), 1, 5)

Visualize horns index (h) frequencies

Description

closure_plot_horns_histogram() visualizes the distribution of horns index values found by CLOSURE.

It marks the min, max, and uniform values; see horns_uniform(). The x-axis always ranges from 0 to 1. This reveals the big picture, putting any variability among horns values into perspective.

Usage

closure_plot_horns_histogram(
  data,
  bar_color = "#5D3FD3",
  alpha = 0.75,
  binwidth = 0.01,
  show_labels = c("all", "min_max", "min_max_uniform", "min_max_bounds", "uniform",
    "uniform_bounds", "bounds", "none"),
  line_color_min_max = "red",
  line_color_reference = "grey20",
  text_limits = c(0.12, 0.88),
  text_size = 12,
  mark_thousand = ",",
  mark_decimal = "."
)

Arguments

Value

A ggplot object.

Examples

data_near_zero <- closure_generate(
  mean = "2.1",
  sd = "0.4",
  n = 35,
  scale_min = 1,
  scale_max = 7
)

# All CLOSURE samples have low horns indices:
closure_plot_horns_histogram(data_near_zero)

data_near_midpoint <- closure_generate(
  mean = "4.3",
  sd = "2.2",
  n = 35,
  scale_min = 1,
  scale_max = 7
)

# The min-max lines are beyond the midpoint,
# so all CLOSURE samples are bimodal:
closure_plot_horns_histogram(data_near_midpoint)

data_near_one <- closure_generate(
  mean = "4.3",
  sd = "2.8",
  n = 35,
  scale_min = 1,
  scale_max = 7
)

# Extremely high variance throughout; close to the upper limit.
# We can omit labels at the bounds so they don't cover the data:
closure_plot_horns_histogram(
  data_near_one,
  show_labels = "min_max_uniform"
)

# Large differences between horns values occur (only?)
# if `mean` and `sd` have no decimal places:
data_wide_spread <- closure_generate(
  mean = "3",
  sd = "2",
  n = 35,
  scale_min = 1,
  scale_max = 7
)

closure_plot_horns_histogram(data_wide_spread)

Generate SPRITE samples

Description

Call sprite_generate() to find possible samples using SPRITE (sample parameter reconstruction via iterative techniques). SPRITE reconstructs possible sample distributions given summary statistics and multi-item scale information.

stop_after is required when items > 1 to prevent overflow errors in the current implementation.

Usage

sprite_generate(
  mean,
  sd,
  n,
  scale_min,
  scale_max,
  items = 1,
  path = NULL,
  stop_after = NULL,
  include = c("stats_and_horns", "stats_only", "all"),
  rounding = "up_or_down",
  threshold = 5,
  ask_to_proceed = TRUE
)

Arguments

Value

sprite_generate() returns a named list of tibbles (data frames):

• inputs: Arguments to this function.

• metrics_main:

  • samples_all: double. Number of all samples. Equal to the number of rows in results.

  • values_all: double. Number of all individual values found. Equal to n * samples_all.

• metrics_horns:

  • mean: double. Average horns value of all samples. The horns index is a measure of dispersion for bounded scales; see horns().

  • uniform: double. The value that mean would have if all samples were uniformly distributed; see horns_uniform().

  • sd, cv, mad, min, median, max, range: double. Standard deviation, coefficient of variation, median absolute deviation, minimum, median, maximum, and range of the horns index values across all samples. Note that mad is not scaled using a constant, as stats::mad() is by default.

• frequency:

  • samples: string. Frequencies apply to one of three subsets of samples: "all" for all samples, "horns_min" for those samples with the lowest horns index among all samples, and "horns_max" for those samples with the highest horns index.

  • value: integer. Scale values derived from scale_min and scale_max.

  • f_count: double. Count of scale values in the group's medoid sample (the actual sample closest to the group centroid by EMD).

  • f_relative: double. Values' share of total values found.

• results:

  • id: integer. Runs from 1 to samples_all.

  • sample (not present by default if path was specified): list of integer vectors. Each of these vectors has length n. It is a sample (or distribution) of individual scale values found by SPRITE.

  • horns: double. Horns index of each sample.

• directory (only present if path was specified):

  • path: string. Location of the folder in which the results were saved.

Writing to disk

Specify path if the expected runtime is very long. (In case you have trouble choosing a path, use path = "." for your current working directory.) This makes sure the results are preserved by incrementally writing them to disk. Otherwise, you might encounter an out-of-memory error because sprite_generate() accumulates more data than your computer can hold in memory.

More about memory

Some output columns that contain counts, such as f_count, are doubles instead of integers. This is because doubles are able to contain much larger numbers. When counting SPRITE results, it is possible to exceed the limit of 32-bit integers in R, which is roughly two billion.

Rounding limitations

The rounding and threshold arguments are not fully implemented. For example, SPRITE currently treats all rounding bounds as inclusive, even if the rounding value would imply otherwise. Many specifications of the two arguments will not make any difference, and those that do will most likely lead to empty results.

Printing

When printing results, you can use print() explicitly with the show argument to control which elements are shown. Set show to one of:

• "some" (the default): show some elements, hide others. The hidden ones have brief descriptions.

• "all": show all elements.

• "none": show no elements, only their descriptions.

For example: print(your_results, show = "all")

Examples

# Basic example with 2 items
## Not run: 
data_simple <- sprite_generate(
  mean = "3.0",
  sd = "1.0",
  n = 120,
  scale_min = 1,
  scale_max = 5,
  stop_after = 150
)

# Larger example - use stop_after to avoid overflow
sprite_generate(
  mean = "3.5",
  sd = "1.7",
  n = 1000,
  items = 5,
  scale_min = 1,
  scale_max = 5,
  stop_after = 1000
)

## End(Not run)

Visualize SPRITE data in a barplot

Description

Call sprite_plot_bar() to get a barplot of SPRITE results.

For each scale value, the bars show how often this value appears in the mean samples with the minimum or maximum horns index (h). This displays the typical sample with the least or most amount of variance from among all SPRITE samples.

Usage

sprite_plot_bar(
  data,
  min_max = c("both", "min", "max"),
  format = c("percent", "absolute_percent", "absolute", "relative"),
  samples = c("mean", "all"),
  overlay = c("all_avg", "none", "interval", "pointinterval", "dots"),
  facet_labels = c("Minimal variance", "Maximal variance"),
  facet_labels_parens = "h",
  bar_alpha = 0.75,
  bar_color = "royalblue1",
  show_text = TRUE,
  text_color = bar_color,
  text_size = 12,
  text_offset = 0.05,
  mark_thousand = ",",
  mark_decimal = "."
)

Arguments

Value

A ggplot object.

Examples

# Run SPRITE first
data <- sprite_generate(
  mean = "3.0",
  sd = "1.0",
  n = 120,
  scale_min = 1,
  scale_max = 5,
  stop_after = 150
)

sprite_plot_bar(data)

Write SPRITE results to disk (and read them back in)

Description

You can use sprite_write() to save the results of sprite_generate() on your computer. A message will show the exact location.

The data are saved in a new folder as five separate files, one for each tibble in sprite_generate()'s output.

sprite_read() is the opposite: it reads those files back into R, recreating the original SPRITE list. This is useful for later analyses if you don't want to re-run a lengthy sprite_generate() call. It also works with results that sprite_generate() wrote to disk itself using path = "your/path".

Usage

sprite_write(data, path)

sprite_read(
  path,
  include = c("stats_only", "stats_and_horns", "capped_error", "all"),
  samples_cap = NULL
)

Arguments

Details

sprite_write() saves all tibbles as Parquet files. This is much faster and takes up far less disk space — roughly 1% of a CSV file with the same data. Speed and disk space can be relevant with large result sets.

Use sprite_read() to import the SPRITE list from the folder back into R. This is based on nanoparquet::read_parquet().

Value

• sprite_write() returns the path to the new folder it created.

• sprite_read() returns a list of the same kind as sprite_generate().

Folder name

The new folder's name will contain all the inputs that determine the SPRITE results. Dashes separate values and underscores replace decimal periods. For example:

 SPRITE-3_5-1_0-90-1-5-up_or_down-5
 

The order is the same as in sprite_generate():

 sprite_generate(
   mean = "3.5",
   sd = "1.0",
   n = 90,
   scale_min = 1,
   scale_max = 5,
   rounding = "up_or_down",  # default
   threshold = 5             # default
 )

Examples

data <- sprite_generate(
  mean = "2.7",
  sd = "0.6",
  n = 45,
  scale_min = 1,
  scale_max = 5,
  stop_after = 1000
)

# Writing to a temporary folder just for this example.
# You should write to a real folder instead.
# A simple way is path = "." for your current directory.
path_new_folder <- sprite_write(data, path = tempdir())

# In a later session, conveniently read the files
# back into R. This returns the original list,
# identical except for floating-point error.
# (Of course, the `path_new_folder` variable will
# no longer be available -- instead, paste the path
# to your folder here.)
sprite_read(path_new_folder)