## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
#  fig.path = "",
  comment = "#>",
  message = FALSE,
  warning = FALSE
)

## ----setup--------------------------------------------------------------------
library(implicitMeasures)

## -----------------------------------------------------------------------------
data("raw_data")
# explore the dataframe
str(raw_data)

# explore the levels of the blockcode variable to identify the SC-IAT blocks
levels(raw_data$blockcode)

## -----------------------------------------------------------------------------
data("raw_data")
sciat_data <- clean_sciat(raw_data, sbj_id = "Participant",
                         block_id = "blockcode",
                         latency_id = "latency",
                         accuracy_id = "correct",
                         block_sciat_1 = c("test.sc_dark.Darkbad",
                                           "test.sc_dark.Darkgood"),
                         block_sciat_2 = c("test.sc_milk.Milkbad",
                                           "test.sc_milk.Milkgood"),
                         trial_id  = "trialcode",
                         trial_eliminate = c("reminder",
                                             "reminder1"), 
                         demo_id = "blockcode", 
                         trial_demo = "demo")

## -----------------------------------------------------------------------------
str(sciat_data) # structure of the resulting List

## -----------------------------------------------------------------------------
sciat1 <- sciat_data[[1]] # extract first SC-IAT data
sciat2 <- sciat_data[[2]] # extract second SC-IAT data
demo_data <- sciat_data[[3]] # extract demographic information

head(sciat1)
head(demo_data)

## -----------------------------------------------------------------------------
# Compute the D score for the first SC-IAT
 d_sciat1 <- compute_sciat(sciat1,
                  mappingA = "test.sc_dark.Darkbad",
                  mappingB = "test.sc_dark.Darkgood",
                  non_response = "alert")

# dataframe containing the SC-IAT D score of the of the first SC-IAT
str(d_sciat1) 
 
# Compute D score for the second SC-IAT
 d_sciat2 <- compute_sciat(sciat2,
                  mappingA = "test.sc_milk.Milkbad",
                  mappingB = "test.sc_milk.Milkgood",
                  non_response = "alert")
 
 # dataframe containing the SC-IAT D score of the of the second SC-IAT
 head(d_sciat2)


## -----------------------------------------------------------------------------
summary(d_sciat1) # Data frame containing SC-IAT D scores

## ----fig.align='center', fig.width=8, fig.height=6----------------------------
 plot(d_sciat1) # Data frame containing SC-IAT D scores