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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 -1.7954621These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.