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)
#>
#> Attaching package: 'lrd'
#> The following object is masked from 'package:base':
#>
#> kappa
data("cued_recall_manuscript")
head(cued_recall_manuscript)
#> Sub.ID Trial_num Cue Target Answer
#> 1 1 1 chlorination ideological ideological
#> 2 1 2 bendy financial financial
#> 3 1 3 topography editing editing
#> 4 1 4 enquiry buzzing buzzing
#> 5 1 5 draconian statistic statistic
#> 6 1 6 speedball stopwatch stopwatch
#?cued_recall_manuscriptScoring 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)You should define the following:
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)
#> List of 2
#> $ DF_Scored :'data.frame': 120 obs. of 7 variables:
#> ..$ Trial.ID : int [1:120] 1 1 1 1 1 1 2 2 2 2 ...
#> ..$ Sub.ID : int [1:120] 1 3 5 2 4 6 6 5 2 1 ...
#> ..$ Cue : chr [1:120] "chlorination" "chlorination" "chlorination" "chlorination" ...
#> ..$ Target : chr [1:120] "ideological" "ideological" "ideological" "ideological" ...
#> ..$ Responses: chr [1:120] "ideological" "ideological" "ideological" "idological" ...
#> ..$ Answer : chr [1:120] "ideological" "ideological" "ideological" "ideological" ...
#> ..$ Scored : num [1:120] 1 1 1 1 1 0 0 0 1 1 ...
#> $ DF_Participant:'data.frame': 6 obs. of 3 variables:
#> ..$ Sub.ID : int [1:6] 1 2 3 4 5 6
#> ..$ Proportion.Correct : num [1:6] 1 0.8 0.85 0.95 0.75 0.45
#> ..$ Z.Score.Participant: num [1:6, 1] 1.026 0 0.256 0.769 -0.256 ...
#> .. ..- attr(*, "scaled:center")= num 0.8
#> .. ..- attr(*, "scaled:scale")= num 0.195We 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
#> Trial.ID Sub.ID Cue Target Responses Answer
#> 1 1 1 chlorination ideological ideological ideological
#> 2 1 3 chlorination ideological ideological ideological
#> 3 1 5 chlorination ideological ideological ideological
#> 4 1 2 chlorination ideological idological ideological
#> 5 1 4 chlorination ideological ideologicel ideological
#> 6 1 6 chlorination ideological ideological
#> 7 2 6 bendy financial money financial
#> 8 2 5 bendy financial money financial
#> 9 2 2 bendy financial financial financial
#> 10 2 1 bendy financial financial financial
#> 11 2 3 bendy financial financial financial
#> 12 2 4 bendy financial finenciel financial
#> 13 3 5 topography editing editing editing
#> 14 3 3 topography editing editting editing
#> 15 3 6 topography editing editing editing
#> 16 3 1 topography editing editing editing
#> 17 3 4 topography editing editing editing
#> 18 3 2 topography editing diting editing
#> 19 4 5 enquiry buzzing buzzing buzzing
#> 20 4 3 enquiry buzzing buzzing buzzing
#> 21 4 6 enquiry buzzing buzzing buzzing
#> 22 4 1 enquiry buzzing buzzing buzzing
#> 23 4 4 enquiry buzzing buzzing buzzing
#> 24 4 2 enquiry buzzing buzzing buzzing
#> 25 5 5 draconian statistic statistic statistic
#> 26 5 3 draconian statistic sttattisttic statistic
#> 27 5 6 draconian statistic math statistic
#> 28 5 1 draconian statistic statistic statistic
#> 29 5 4 draconian statistic stetistic statistic
#> 30 5 2 draconian statistic statistic statistic
#> 31 6 3 speedball stopwatch sttopwattch stopwatch
#> 32 6 4 speedball stopwatch stopwetch stopwatch
#> 33 6 6 speedball stopwatch watch stopwatch
#> 34 6 5 speedball stopwatch stopwatch stopwatch
#> 35 6 2 speedball stopwatch stopwatch stopwatch
#> 36 6 1 speedball stopwatch stopwatch stopwatch
#> 37 7 1 valueless did did did
#> 38 7 3 valueless did did did
#> 39 7 5 valueless did done did
#> 40 7 2 valueless did did did
#> 41 7 4 valueless did did did
#> 42 7 6 valueless did done did
#> 43 8 6 grievous numerically numerically numerically
#> 44 8 3 grievous numerically numerically numerically
#> 45 8 5 grievous numerically numerically numerically
#> 46 8 2 grievous numerically numrically numerically
#> 47 8 1 grievous numerically numerically numerically
#> 48 8 4 grievous numerically numericelly numerically
#> 49 9 6 melatonin bloated bloated bloated
#> 50 9 1 melatonin bloated bloated bloated
#> 51 9 5 melatonin bloated bloated bloated
#> 52 9 4 melatonin bloated bloeted bloated
#> 53 9 3 melatonin bloated bloatted bloated
#> 54 9 2 melatonin bloated bloatd bloated
#> 55 10 6 dose domain area domain
#> 56 10 5 dose domain area domain
#> 57 10 4 dose domain domein domain
#> 58 10 3 dose domain domain domain
#> 59 10 2 dose domain domain domain
#> 60 10 1 dose domain domain domain
#> 61 11 6 dynastically steadily steadily
#> 62 11 5 dynastically steadily steadily steadily
#> 63 11 4 dynastically steadily steedily steadily
#> 64 11 3 dynastically steadily stteadily steadily
#> 65 11 2 dynastically steadily stadily steadily
#> 66 11 1 dynastically steadily steadily steadily
#> 67 12 5 staffer withdraw withdraw withdraw
#> 68 12 4 staffer withdraw withdrew withdraw
#> 69 12 3 staffer withdraw witthdraw withdraw
#> 70 12 2 staffer withdraw withdraw withdraw
#> 71 12 6 staffer withdraw withdraw withdraw
#> 72 12 1 staffer withdraw withdraw withdraw
#> 73 13 3 institutionalism beside beside beside
#> 74 13 6 institutionalism beside beside beside
#> 75 13 5 institutionalism beside beside beside
#> 76 13 2 institutionalism beside bsid beside
#> 77 13 4 institutionalism beside beside beside
#> 78 13 1 institutionalism beside beside beside
#> 79 14 1 dollhouse doodle doodle doodle
#> 80 14 3 dollhouse doodle doodle doodle
#> 81 14 5 dollhouse doodle draw doodle
#> 82 14 2 dollhouse doodle doodl doodle
#> 83 14 4 dollhouse doodle doodle doodle
#> 84 14 6 dollhouse doodle draw doodle
#> 85 15 6 bolero membrane membrane membrane
#> 86 15 5 bolero membrane membrane membrane
#> 87 15 2 bolero membrane mmbran membrane
#> 88 15 1 bolero membrane membrane membrane
#> 89 15 3 bolero membrane membrane membrane
#> 90 15 4 bolero membrane membrene membrane
#> 91 16 5 soulless unofficially unofficially unofficially
#> 92 16 3 soulless unofficially unofficially unofficially
#> 93 16 6 soulless unofficially unofficially
#> 94 16 1 soulless unofficially unofficially unofficially
#> 95 16 4 soulless unofficially unofficielly unofficially
#> 96 16 2 soulless unofficially unofficially unofficially
#> 97 17 5 uncurled vibration vibration vibration
#> 98 17 3 uncurled vibration vibrattion vibration
#> 99 17 6 uncurled vibration vibration vibration
#> 100 17 1 uncurled vibration vibration vibration
#> 101 17 4 uncurled vibration vibretion vibration
#> 102 17 2 uncurled vibration vibration vibration
#> 103 18 5 giveaway permitted permitted permitted
#> 104 18 3 giveaway permitted permitttted permitted
#> 105 18 6 giveaway permitted granted permitted
#> 106 18 1 giveaway permitted permitted permitted
#> 107 18 4 giveaway permitted permitted permitted
#> 108 18 2 giveaway permitted prmittd permitted
#> 109 19 3 origination sleek sleek sleek
#> 110 19 4 origination sleek sleek sleek
#> 111 19 6 origination sleek shiny sleek
#> 112 19 5 origination sleek shiny sleek
#> 113 19 2 origination sleek slk sleek
#> 114 19 1 origination sleek sleek sleek
#> 115 20 1 iconology ignorance ignorance ignorance
#> 116 20 3 iconology ignorance ignorance ignorance
#> 117 20 5 iconology ignorance ignorance ignorance
#> 118 20 2 iconology ignorance ignoranc ignorance
#> 119 20 4 iconology ignorance ignorence ignorance
#> 120 20 6 iconology ignorance ignorance ignorance
#> Scored
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1
#> 6 0
#> 7 0
#> 8 0
#> 9 1
#> 10 1
#> 11 1
#> 12 0
#> 13 1
#> 14 1
#> 15 1
#> 16 1
#> 17 1
#> 18 1
#> 19 1
#> 20 1
#> 21 1
#> 22 1
#> 23 1
#> 24 1
#> 25 1
#> 26 0
#> 27 0
#> 28 1
#> 29 1
#> 30 1
#> 31 0
#> 32 1
#> 33 0
#> 34 1
#> 35 1
#> 36 1
#> 37 1
#> 38 1
#> 39 0
#> 40 1
#> 41 1
#> 42 0
#> 43 1
#> 44 1
#> 45 1
#> 46 1
#> 47 1
#> 48 1
#> 49 1
#> 50 1
#> 51 1
#> 52 1
#> 53 1
#> 54 1
#> 55 0
#> 56 0
#> 57 1
#> 58 1
#> 59 1
#> 60 1
#> 61 0
#> 62 1
#> 63 1
#> 64 1
#> 65 1
#> 66 1
#> 67 1
#> 68 1
#> 69 1
#> 70 1
#> 71 1
#> 72 1
#> 73 1
#> 74 1
#> 75 1
#> 76 0
#> 77 1
#> 78 1
#> 79 1
#> 80 1
#> 81 0
#> 82 1
#> 83 1
#> 84 0
#> 85 1
#> 86 1
#> 87 0
#> 88 1
#> 89 1
#> 90 1
#> 91 1
#> 92 1
#> 93 0
#> 94 1
#> 95 1
#> 96 1
#> 97 1
#> 98 1
#> 99 1
#> 100 1
#> 101 1
#> 102 1
#> 103 1
#> 104 0
#> 105 0
#> 106 1
#> 107 1
#> 108 0
#> 109 1
#> 110 1
#> 111 0
#> 112 0
#> 113 0
#> 114 1
#> 115 1
#> 116 1
#> 117 1
#> 118 1
#> 119 1
#> 120 1
#Participant
cued_output$DF_Participant
#> Sub.ID Proportion.Correct Z.Score.Participant
#> 1 1 1.00 1.0259784
#> 2 2 0.80 0.0000000
#> 3 3 0.85 0.2564946
#> 4 4 0.95 0.7694838
#> 5 5 0.75 -0.2564946
#> 6 6 0.45 -1.7954621