Package: ProcData 0.4.0

ProcData: Process Data Analysis

Provides tools for exploratory process data analysis. Process data refers to the data describing participants' problem-solving processes in computer-based assessments. It is often recorded in computer log files. This package provides functions to read, process, and write process data. It also implements two feature extraction methods to compress the information stored in process data into standard numerical vectors. This package also provides recurrent neural network based models that relate response processes with other binary or scale variables of interest. The functions that involve training and evaluating neural networks are wrappers of functions in 'keras'.

Authors:Xueying Tang [aut, cre], Susu Zhang [aut], Zhi Wang [aut], Jingchen Liu [aut], Zhiliang Ying [aut]

ProcData_0.4.0.tar.gz
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ProcData.pdf |ProcData.html✨
ProcData/json (API)
NEWS

# Install 'ProcData' in R:
install.packages('ProcData', repos = c('https://xytangtang.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/xytangtang/procdata/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • cc_data - Data of item CP025Q01 (climate control item 1) in PISA 2012

On CRAN:

34 exports 9 stars 1.52 score 33 dependencies 2 scripts 1.1k downloads

Last updated 3 years agofrom:2723358344. Checks:OK: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 02 2024
R-4.5-win-x86_64WARNINGSep 02 2024
R-4.5-linux-x86_64WARNINGSep 02 2024
R-4.4-win-x86_64WARNINGSep 02 2024
R-4.4-mac-x86_64WARNINGSep 02 2024
R-4.4-mac-aarch64WARNINGSep 02 2024
R-4.3-win-x86_64WARNINGSep 02 2024
R-4.3-mac-x86_64WARNINGSep 02 2024
R-4.3-mac-aarch64WARNINGSep 02 2024

Exports:action_seqs_summaryaction2entropyaseq2feature_seq2seqatseq2feature_seq2seqcalculate_dist_cppchooseK_mdschooseK_seq2seqcombine_actionscount_actionsentropy2segmentplot_subtask_seqplot_subtask_seqsprocread.seqsremove_actionremove_repeatreplace_actionsegment_functionsegment2subtaskseq_genseq_gen2seq_gen3seq2feature_mdsseq2feature_mds_largeseq2feature_mds_stochasticseq2feature_ngramseq2feature_seq2seqseqmsub_seqssubtask_analysistime_seqs_summarytseq2feature_seq2seqtseq2intervalwrite.seqs

Dependencies:backportsbase64enccliconfiggenericsglueherejsonlitekeraslatticelifecyclemagrittrMatrixpngprocessxpsR6rappdirsRcppRcppTOMLreticulaterlangrprojrootrstudioapitensorflowtfautographtfrunstidyselectvctrswhiskerwithryamlzeallot

Readme and manuals

Help Manual

Help pageTopics
ProcData: A package for process data analysisProcData-package ProcData
Summarize action sequencesaction_seqs_summary
Step 1 of Subtask Analysis: obtaining entropy sequences of action sequencesaction2entropy
Feature Extraction by action sequence autoencoderaseq2feature_seq2seq
Feature Extraction by action and time sequence autoencoderatseq2feature_seq2seq
Calculate "oss_action" dissimilarity matrix through Rcppcalculate_dist_cpp
Data of item CP025Q01 (climate control item 1) in PISA 2012cc_data
Choose the number of multidimensional scaling featureschooseK_mds
Choose the number of autoencoder featureschooseK_seq2seq
Combine consecutive actions into a single actioncombine_actions
Count action appearancescount_actions
Step 2 of Subtask Analysis: Segmenting Entropy Sequencesentropy2segment
Plot Subtask Analysis Results for One Sequenceplot_subtask_seq
Plot Subtask Analysis Results for Entire Datasetplot_subtask_seqs
Plot an subtask Objectplot.subtask
Predict method for sequence modelspredict.seqm
Print method for class '"proc"'print.proc
Print method for class '"summary.proc"'print.summary.proc
Class '"proc"' constructorproc
Reading response processes from csv filesread.seqs
Remove actions from response processesremove_action
Remove repeated actionsremove_repeat
Replace actions in response processesreplace_action
Segment an entropy sequencesegment_function
Step 3 of Subtask Analysis: Grouping Segmentssegment2subtask
Action sequence generatorseq_gen
Markov action sequence generatorseq_gen2
RNN action sequence generatorseq_gen3
Feature extraction via multidimensional scalingseq2feature_mds
Feature Extraction by MDS for Large Datasetseq2feature_mds_large
Feature extraction by stochastic mdsseq2feature_mds_stochastic
ngram feature extractionseq2feature_ngram
Feature Extraction by autoencoderseq2feature_seq2seq
Fitting sequence modelsseqm
Subset response processessub_seqs
Subtask Analysissubtask_analysis
Summary method for class '"proc"'summary.proc
Summarize timestamp sequencestime_seqs_summary
Feature Extraction by time sequence autoencodertseq2feature_seq2seq
Transform a timestamp sequence into a inter-arrival time sequencetseq2interval
Write process data to csv fileswrite.seqs