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Source: vignettes/receptiviti.Rmd
receptiviti.Rmd

Built with R 4.3.3

Install and Load

First, download and install R for your system: Windows | Mac | Linux

You may also want to download and install RStudio for a nice interface to R.

Then, from an R console, install the package; this only needs to be done once:

install.packages("receptiviti")

Each time you start an R session, load the package:

Set Up API Credentials

You can find your API key and secret on your dashboard.

You can set these credentials up in R permanently or temporarily:

Permanent

Open or create your ~/.Renviron file:

usethis::edit_r_environ()

Then add these environment variables with your key and secret:

RECEPTIVITI_KEY=""
RECEPTIVITI_SECRET=""

Then restart R for the changes to take effect.

Temporary

Add your key and secret, and run at the start of each session:

Sys.setenv(
  RECEPTIVITI_KEY = "",
  RECEPTIVITI_SECRET = ""
)

Confirm Credentials

Check that the API is reachable, and your credentials are recognized:

receptiviti_status()
#> Status: OK
#> Message: Hello there, World!

If your credentials are not recognized, you’ll get a response like this:

receptiviti_status(key = 123, secret = 123)
#> Status: ERROR
#> Message: 401 (1411): Unrecognized API key pair. This call will not count towards your plan.

Enter Your Text

Loaded Text

If your texts are already in R, you can enter them directly.

These can be in a single character:

results <- receptiviti("texts to score")

Or a character vector:

results <- receptiviti(c("text one", "text two"))

Or from a data.frame:

data <- data.frame(texts = c("text a", "text b"))

# directly
results <- receptiviti(data$texts)

# by column name
results <- receptiviti(data, text_column = "texts")

Text in files

You can enter paths to files containing separate texts in each line:

# single
results <- receptiviti("../files/file.txt")

# multiple
results <- receptiviti(files = c("../files/file1.txt", "../files/file2.txt"))

Or to a comma delimited file with a column containing text. Here, the text_column argument specifies which column contains text:

# single
results <- receptiviti("../files/file.csv", text_column = "text")

# multiple
results <- receptiviti(
  files = c("../files/file1.csv", "../files/file2.csv"),
  text_column = "text"
)

Or you can point to a directory containing text files:

results <- receptiviti(dir = "../files")

By default .txt files will be looked for, but you can specify .csv files with the file_type argument:

results <- receptiviti(
  dir = "../files",
  text_column = "text", file_type = "csv"
)

Use Results

Returned Results

By default, results are returned as a data.frame, with a row for each text, and columns for each framework variable:

results <- receptiviti("texts to score")
results[, 1:4]
#>                          text_hash summary.word_count
#> 1 acab8277267d0efee0828f94e0919ddf                  3
#>   summary.words_per_sentence summary.sentence_count
#> 1                          3                      1

Here, the first column (text_hash) is the MD5 hash of the text, which identifies unique texts, and is stored in the main cache.

The entered text can also be included with the return_text argument:

results <- receptiviti("texts to score", return_text = TRUE)
results[, 1:3]
#>             text                        text_hash summary.word_count
#> 1 texts to score acab8277267d0efee0828f94e0919ddf                  3

You can also select frameworks before they are all returned:

results <- receptiviti("texts to score", frameworks = "liwc")
results[, 1:4]
#>                          text_hash analytical_thinking clout authentic
#> 1 acab8277267d0efee0828f94e0919ddf                0.99   0.5      0.01

By default, a single framework will have column names without the framework name, but you can retain these with framework_prefix = TRUE:

results <- receptiviti(
  "texts to score",
  frameworks = "liwc", framework_prefix = TRUE
)
results[, 1:3]
#>                          text_hash liwc.analytical_thinking liwc.clout
#> 1 acab8277267d0efee0828f94e0919ddf                     0.99        0.5

You can also have results returned as a list of frameworks, which might make it easier to work with each separately:

results <- receptiviti("texts to score", as_list = TRUE)
results$personality[, 1:4]
#>                          text_hash extraversion   active assertive
#> 1 acab8277267d0efee0828f94e0919ddf     40.60506 43.54977  32.54014

Aligning Results

Results are returned in a way that aligns with the text you enter originally, including any duplicates or invalid entries.

This means you can add the results object to original data:

data <- data.frame(id = 1:4, text = c("text a", NA, "", "text a"))
results <- receptiviti(data$text)

# combine data and results
cbind(data, results)[, 1:4]
#>   id   text                        text_hash summary.word_count
#> 1  1 text a 42ff59040f004970040f90a19aa6b3fa                  2
#> 2  2   <NA>                             <NA>                 NA
#> 3  3                                    <NA>                 NA
#> 4  4 text a 42ff59040f004970040f90a19aa6b3fa                  2

You can also provide a vector of unique IDs to be returned with results so they can be merged with other data:

results <- receptiviti(c("text a", "text b"), id = c("a", "b"))
results[, 1:3]
#>   id                        text_hash summary.word_count
#> 1  a 42ff59040f004970040f90a19aa6b3fa                  2
#> 2  b 4db2bfd2c8140dffac0060c9fb1c6d6f                  2

# merge with a new dataset
data <- data.frame(
  id = c("a1", "b1", "a2", "b2"),
  type = c("a", "b", "a", "b")
)
merge(data, results, by.x = "type", by.y = "id")[, 1:4]
#>   type id                        text_hash summary.word_count
#> 1    a a1 42ff59040f004970040f90a19aa6b3fa                  2
#> 2    a a2 42ff59040f004970040f90a19aa6b3fa                  2
#> 3    b b1 4db2bfd2c8140dffac0060c9fb1c6d6f                  2
#> 4    b b2 4db2bfd2c8140dffac0060c9fb1c6d6f                  2

Saved Results

Results can also be saved to a .csv file:

receptiviti("texts to score", "../results.csv", overwrite = TRUE)
results <- read.csv("../results.csv")
results[, 1:4]
#>                          text_hash summary.word_count
#> 1 acab8277267d0efee0828f94e0919ddf                  3
#>   summary.words_per_sentence summary.sentence_count
#> 1                          3                      1

And this can be compressed:

receptiviti(
  "texts to score", "../results.csv.xz",
  compress = TRUE, overwrite = TRUE
)
results <- read.csv("../results.csv.xz")
results[, 1:4]
#>                          text_hash summary.word_count
#> 1 acab8277267d0efee0828f94e0919ddf                  3
#>   summary.words_per_sentence summary.sentence_count
#> 1                          3                      1

In this case, the compressed file is 85% smaller.

Preserving Results

The receptiviti function tries to avoid sending texts to the API as much as possible:

  • As part of the preparation process, it excludes duplicates and invalid texts.
  • If enabled, it checks the primary cache to see if any texts have already been scored.
    • The primary cache is an Arrow database located by the cache augment.
    • Its format is determined by cache_format.
    • You can skip checking it initially while still writing results to it with cache_overwrite = TRUE.
    • It can be cleared with clear_cache = TRUE.
  • It will check for any responses to previous, identical requests.
    • Responses are stored in tempdir() as .json files named by the bundle’s MD5 hash.
    • You can avoid using this cache with request_cache = FALSE.
    • This cache is cleared when R is restarted.

If you want to make sure no texts are sent to the API, you can use make_request = FALSE. This will use the primary and request cache, but will fail if any texts are not found there.

If a call fails before results can be written to the cache or returned, all received responses will still be in the request cache, but those will be deleted if R is restarted. If you want to preserve these but need to restart R, you can move the cached responses out of tempdir(), then replace them after restarting.

Handling Big Data

The receptiviti function will handle splitting texts into bundles, so the limit on how many texts you can process at once will come down to your system’s amount of random access memory (RAM). Several thousands of texts should be fine, but getting into millions of texts, you may not be able to have all of the results loaded at once. To get around this, you can fully process subsets of your texts.

A benefit of processing more texts at once is that requests can be parallelized, but this is more RAM intensive, and the primary cache is updated less frequently (as it is updated only at the end of a complete run).

You could also parallelize your own batches, but be sure to set cores to 1 (to disable the function’s parallelization) and do not enable the primary cache (to avoid attempting to read from the cache while it is being written to by another instance).

Not using the primary cache is also more efficient, but you may want to ensure you are not sending duplicate texts between calls. The function handles duplicate texts within calls (only ever sending unique texts), but depends on the cache to avoid sending duplicates between calls.