Package: quest 0.2.0

quest: Prepare Questionnaire Data for Analysis

Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.

Authors:David Disabato [aut, cre]

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quest.pdf |quest.html
quest/json (API)

# Install 'quest' in R:
install.packages('quest', repos = c('https://ddisab01.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.94 score 11 scripts 193 downloads 8 mentions 124 exports 84 dependencies

Last updated 11 months agofrom:1568a5217f. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winNOTEOct 31 2024
R-4.5-linuxNOTEOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:.cronbach.cronbachs.gtheory.gtheorysadd_sigadd_sig_coraggagg_dfmaggsamd_biamd_multiamd_uniauto_byave_dfmboot_ciby2centercenter_bycenterscenters_bychangechange_bychangeschanges_bycolMeans_ifcolNAcolSums_ifcompositecompositesconfint2confint2.bootconfint2.defaultcor_bycor_misscor_mlcorpcorp_bycorp_misscorp_mlcovs_testcronbachcronbachsdecomposedecomposesdeffdeffsdescribe_mldum2nomfreqfreq_byfreqsfreqs_bygtheorygtheory_mlgtheorysgtheorys_mlicc_11icc_all_byiccs_11length_bylengths_bylong2widemake.dummymake.dumNAmake.fun_ifmake.latentmake.productmean_changemean_comparemean_diffmean_ifmean_testmeans_changemeans_comparemeans_diffmeans_testmode2n_comparencasesncases_byncases_descncases_mlngrpnhstnom2dumnrow_bynrow_mlpartial.casespomppompsprop_compareprop_diffprop_testprops_compareprops_diffprops_testrecode2otherrecodesrenamesreordersrevalidrevalidsreversereversesrowMeans_ifrowNArowsNArowSums_ifscorescoresshiftshift_byshiftsshifts_bysum_ifsummary_ucfatapply2ucfavalid_testvalids_testvecNAwide2longwinsorwinsors

Dependencies:abindarmbackportsBHbootbroomcarcarDatacheckmateclicodacolorspacecowplotcpp11DerivdigestdoBydplyrfansifarverFormulagenericsggplot2glueGPArotationgtableisobandlabelinglatticelavaanlifecyclelme4magrittrMASSMatrixMatrixModelsMBESSmgcvmimicrobenchmarkminqamnormtmodelrmultilevelmunsellmvtnormnlmenloptrnnetnumDerivOpenMxpbivnormpbkrtestpillarpkgconfigplyrpsychpurrrquadprogquantregR6RColorBrewerRcppRcppEigenRcppParallelreshaperlangrpfscalessemsemToolsSparseMStanHeadersstr2strstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Pre-processing Questionnaire Dataquest-package quest
Bootstrap Function for 'cronbach()' Function.cronbach
Bootstrap Function for 'cronbachs()' Function.cronbachs
Bootstrap Function for 'gtheory()' Function.gtheory
Bootstrap Function for 'gtheorys()' Function.gtheorys
Add Significance Symbols to a (Atomic) Vector, Matrix, or Arrayadd_sig
Add Significance Symbols to a Correlation Matrixadd_sig_cor
Aggregate an Atomic Vector by Groupagg
Data Information by Groupagg_dfm
Aggregate Data by Groupaggs
Amount of Missing Data - Bivariate (Pairwise Deletion)amd_bi
Amount of Missing Data - Multivariate (Listwise Deletion)amd_multi
Amount of Missing Data - Univariateamd_uni
Autoregressive Coefficient by Groupauto_by
Repeated Group Statistics for a Data-Frameave_dfm
Bootstrapped Confidence Intervals from a Matrix of Coefficientsboot_ci
Apply a Function to Data by Groupby2
Centering and/or Standardizing a Numeric Vectorcenter
Centering and/or Standardizing a Numeric Vector by Groupcenter_by
Centering and/or Standardizing Numeric Datacenters
Centering and/or Standardizing Numeric Data by Groupcenters_by
Change Score from a Numeric Vectorchange
Change Scores from a Numeric Vector by Groupchange_by
Change Scores from Numeric Datachanges
Change Scores from Numeric Data by Groupchanges_by
Column Means Conditional on Frequency of Observed ValuescolMeans_if
Frequency of Missing Values by ColumncolNA
Column Sums Conditional on Frequency of Observed ValuescolSums_if
Composite Reliability of a Scorecomposite
Composite Reliability of Multiple Scorescomposites
Confidence Intervals from Statistical Informationconfint2
Bootstrapped Confidence Intervals from a 'boot' Objectconfint2.boot
Confidence Intervals from Parameter Estimates and Standard Errorsconfint2.default
Correlation Matrix by Groupcor_by
Point-biserial Correlations of Missingnesscor_miss
Multilevel Correlation Matricescor_ml
Bivariate Correlations with Significant Symbolscorp
Bivariate Correlations with Significant Symbols by Groupcorp_by
Point-biserial Correlations of Missingness With Significant Symbolscorp_miss
'corp_ml' decomposes correlations from multilevel data into within-group and between-group correlations as well as adds significance symbols to the end of each value. The workhorse of the function is 'statsBy'. 'corp_ml' is simply a combination of 'cor_ml' and 'add_sig_cor'.corp_ml
Covariances Test of Significancecovs_test
Cronbach's Alpha of a Set of Variables/Itemscronbach
Cronbach's Alpha for Multiple Sets of Variables/Itemscronbachs
Decompose a Numeric Vector by Groupdecompose
Decompose Numeric Data by Groupdecomposes
Design Effect from Multilevel Numeric Vectordeff
Design Effects from Multilevel Numeric Datadeffs
Multilevel Descriptive Statisticsdescribe_ml
Dummy Variables to a Nominal Variabledum2nom
Univariate Frequency Tablefreq
Univariate Frequency Table By Groupfreq_by
Multiple Univariate Frequency Tablesfreqs
Multiple Univariate Frequency Tablesfreqs_by
Generalizability Theory Reliability of a Scoregtheory
Generalizability Theory Reliability of a Multilevel Scoregtheory_ml
Generalizability Theory Reliability of Multiple Scoresgtheorys
Generalizability Theory Reliability of Multiple Multilevel Scoresgtheorys_ml
Intraclass Correlation for Multilevel Analysis: ICC(1,1)icc_11
All Six Intraclass Correlations by Groupicc_all_by
Intraclass Correlation for Multiple Variables for Multilevel Analysis: ICC(1,1)iccs_11
Length of a (Atomic) Vector by Grouplength_by
Length of Data Columns by Grouplengths_by
Reshape Multiple Scores From Long to Widelong2wide
Make Dummy Columnsmake.dummy
Make Dummy Columns For Missing Data.make.dumNA
Make a Function Conditional on Frequency of Observed Valuesmake.fun_if
Make Model Syntax for a Latent Factor in Lavaanmake.latent
Make Product Terms (e.g., interactions)make.product
Mean Change Across Two Timepoints (dependent two-samples t-test)mean_change
Mean differences for a single variable across 3+ independent groups (one-way ANOVA)mean_compare
Mean difference across two independent groups (independent two-samples t-test)mean_diff
Mean Conditional on Minimum Frequency of Observed Valuesmean_if
Test for Sample Mean Against Mu (one-sample t-test)mean_test
Mean Changes Across Two Timepoints For Multiple PrePost Pairs of Variables (dependent two-samples t-tests)means_change
Mean differences for multiple variables across 3+ independent groups (one-way ANOVAs)means_compare
Mean differences across two independent groups (independent two-samples t-tests)means_diff
Test for Multiple Sample Means Against Mu (one-sample t-tests)means_test
Statistical Mode of a Numeric Vectormode2
Test for Equal Frequency of Values (chi-square test of goodness of fit)n_compare
Number of Cases in Datancases
Number of Cases in Data by Groupncases_by
Describe Number of Cases in Data by Groupncases_desc
Multilevel Number of Casesncases_ml
Number of Groups in Datangrp
Null Hypothesis Significance Testingnhst
Nominal Variable to Dummy Variablesnom2dum
Number of Rows in Data by Groupnrow_by
Multilevel Number of Rowsnrow_ml
Find Partial Casespartial.cases
Recode a Numeric Vector to Percentage of Maximum Possible (POMP) Unitspomp
Recode Numeric Data to Percentage of Maximum Possible (POMP) Unitspomps
Proportion Comparisons for a Single Variable across 3+ Independent Groups (Chi-square Test of Independence)prop_compare
Proportion Difference for a Single Variable across Two Independent Groups (Chi-square Test of Independence)prop_diff
Test for Sample Proportion Against Pi (chi-square test of goodness of fit)prop_test
Proportion Comparisons for Multiple Variables across 3+ Independent Groups (Chi-square Tests of Independence)props_compare
Proportion Difference of Multiple Variables Across Two Independent Groups (Chi-square Tests of Independence)props_diff
Test for Multiple Sample Proportion Against Pi (Chi-square Tests of Goodness of Fit)props_test
Recode Unique Values in a Character Vector to 0ther (or NA)recode2other
Recode Datarecodes
Rename Data Columns from a Codebookrenames
Reorder Levels of Factor Datareorders
Recode Invalid Values from a Vectorrevalid
Recode Invalid Values from Datarevalids
Reverse Code a Numeric Vectorreverse
Reverse Code Numeric Datareverses
Row Means Conditional on Frequency of Observed ValuesrowMeans_if
Frequency of Missing Values by RowrowNA
Frequency of Multiple Sets of Missing Values by RowrowsNA
Row Sums Conditional on Frequency of Observed ValuesrowSums_if
Observed Unweighted Scoring of a Set of Variables/Itemsscore
Observed Unweighted Scoring of Multiple Sets of Variables/Itemsscores
Shift a Vector (i.e., lag/lead)shift
Shift a Vector (i.e., lag/lead) by Groupshift_by
Shift Data (i.e., lag/lead)shifts
Shift Data (i.e., lag/lead) by Groupshifts_by
Sum Conditional on Minimum Frequency of Observed Valuessum_if
Summary of a Unidimensional Confirmatory Factor Analysissummary_ucfa
Apply a Function to a (Atomic) Vector by Grouptapply2
Unidimensional Confirmatory Factor Analysisucfa
Test for Invalid Elements in a Vectorvalid_test
Test for Invalid Elements in Datavalids_test
Frequency of Missing Values in a VectorvecNA
Reshape Multiple Sets of Variables From Wide to Longwide2long
Winsorize a Numeric Vectorwinsor
Winsorize Numeric Datawinsors