In npm27/lrd: A Package for Processing Lexical Response Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Libraries and Data

Please see manuscript for a long description of the following data. We will load the example data, and you can use the ? with the dataset name to learn more about the data.

library(lrd)
data("cued_recall_manuscript")
head(cued_recall_manuscript)
#?cued_recall_manuscript

Data Cleanup

Scoring in lrd is case sensitive, so we will use tolower() to lower case all correct answers and participant answers.

cued_recall_manuscript$Target <- tolower(cued_recall_manuscript$Target)
cued_recall_manuscript$Answer <- tolower(cued_recall_manuscript$Answer)

Score the Data

You should define the following:

• data = dataframe of participant responses
• responses = column name of the participant answers
• key = column name of the answer key
• key.trial = column name of the trial id code
• id = column name of the participant id number
• id.trial = column name of the trial id within the participant data
• cutoff = the Levenshtein distance value you want to use for scoring (0 no changes exactly the same, higher numbers allow more variance in the word)
• flag = calculate z scores for outliers (TRUE/FALSE)
• group.by = column name(s) for grouping variables

Note that the answer key can be in a separate dataframe, use something like answer_key$answer for the key argument and answer_key$id_num for the trial number. Fill in answer_key with your dataframe name and the column name for those columns after the $.

cued_output <- prop_correct_cued(data = cued_recall_manuscript,
                                 responses = "Answer",
                                 key = "Target",
                                 key.trial = "Trial_num",
                                 id = "Sub.ID",
                                 id.trial = "Trial_num",
                                 cutoff = 1,
                                 flag = TRUE,
                                 group.by = NULL)

str(cued_output)

Output

We can use DF_Scored to see the original dataframe with our new scored column - also to check if our answer key and participant answers matched up correctly! The DF_Participant can be used to view a participant level summary of the data. Last, if a grouping variable is used, we can use DF_Group to see that output.

#Overall
cued_output$DF_Scored

#Participant
cued_output$DF_Participant

npm27/lrd documentation built on Dec. 11, 2021, 9:26 p.m.