REACTION TIMES

Setup

Load libraries

library(readr)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ purrr     1.0.2
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(readxl)

## Warning: package 'readxl' was built under R version 4.4.1

Load data

MergeFlankerDec8 <- read_excel("C:/Users/John Majoubi/Downloads/MergeFlankerDec8.xlsx")
All_Jan2_April4 <- read_csv("~/All_Jan2_April4.csv")

## New names:
## Rows: 354 Columns: 236
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (16): StartDate, EndDate, IPAddress, SONA/SFSU ID...11, SONA/SFSU ID...... dbl
## (220): Index, Progress, Duration__in_seconds, Finished, LocationLatitude...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `SONA/SFSU ID` -> `SONA/SFSU ID...11`
## • `SONA/SFSU ID` -> `SONA/SFSU ID...12`

MergeSimonDec8th <- read_excel("C:/Users/John Majoubi/Downloads/MergeSimonDec8th.xlsx")
MergeStroopDec8Full <- read_excel("C:/Users/John Majoubi/Downloads/MergeStroopDec8Full.xlsx")

Data Processing

MergeFlankerDec8_c <- MergeFlankerDec8 %>% 
  dplyr::select(Subject, Session, Block, 9:13, 29:32, 80:92)

MergeSimonDec8th_c <- MergeSimonDec8th %>% 
  dplyr::select(Subject, Session, Block, 9:13, 29:32, 80:92)

MergeStroopDec8Full_c <- MergeStroopDec8Full %>% 
  dplyr::select(Subject, Session, Block, 9:13, 29:32, 80:92)

All_Jan2_April4_c <- All_Jan2_April4 %>% 
  dplyr::select(`SONA/SFSU ID...11`, `SONA/SFSU ID...12`, SFSUID, 58:233, GPA) %>% 
  mutate(`SONA/SFSU ID...11` = as.numeric(`SONA/SFSU ID...11`),
         `SONA/SFSU ID...12` = as.numeric(`SONA/SFSU ID...12`),
         SFSUID = as.numeric(SFSUID)) %>% 
  mutate(`SONA/SFSU ID...12` = ifelse(is.na(`SONA/SFSU ID...12`), SFSUID, `SONA/SFSU ID...12`))

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `SONA/SFSU ID...11 = as.numeric(`SONA/SFSU ID...11`)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.

library(readr)
library(tidyverse)
library(readxl)
library(mice)

## 
## Attaching package: 'mice'

## The following object is masked from 'package:stats':
## 
##     filter

## The following objects are masked from 'package:base':
## 
##     cbind, rbind

impute_missing_values <- function(df) {
  numeric_cols <- sapply(df, is.numeric)
  na_columns <- colnames(df)[numeric_cols & colSums(is.na(df)) > 0]
  df[na_columns] <- lapply(df[na_columns], function(x) replace(x, is.na(x), mean(x, na.rm = TRUE)))
  return(df)
}

# Apply the function to your datasets
MergeFlankerDec8_c <- impute_missing_values(MergeFlankerDec8_c)
MergeSimonDec8th_c <- impute_missing_values(MergeSimonDec8th_c)
MergeStroopDec8Full_c <- impute_missing_values(MergeStroopDec8Full_c)
All_Jan2_April4_c <- impute_missing_values(All_Jan2_April4_c)

Flanker

Data Joining

only 198 IDs matched

joined_data_F <- MergeFlankerDec8_c %>%
  inner_join(
    All_Jan2_April4_c,
    by = c("Subject" = "SONA/SFSU ID...12")
  )

## Warning in inner_join(., All_Jan2_April4_c, by = c(Subject = "SONA/SFSU ID...12")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 2775 of `x` matches multiple rows in `y`.
## ℹ Row 254 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

joined_data_F %>% 
  dim()

## [1] 8351  204

Simon

Data Joining

200 IDs matched

joined_data_S <- MergeSimonDec8th_c %>% 
  inner_join(
    All_Jan2_April4_c,
    by = c("Subject" = "SONA/SFSU ID...12")
  )

## Warning in inner_join(., All_Jan2_April4_c, by = c(Subject = "SONA/SFSU ID...12")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 13657 of `x` matches multiple rows in `y`.
## ℹ Row 28 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

joined_data_S %>% 
  dim()

## [1] 17550   204

Stroop

Data Joining

199 IDs matched

joined_data_St <- MergeStroopDec8Full_c %>% 
  inner_join(
    All_Jan2_April4_c,
    by = c("Subject" = "SONA/SFSU ID...12")
  )

## Warning in inner_join(., All_Jan2_April4_c, by = c(Subject = "SONA/SFSU ID...12")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 8581 of `x` matches multiple rows in `y`.
## ℹ Row 28 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

joined_data_St %>% 
  dim()

## [1] 11253   204

# Load necessary libraries
library(dplyr)
library(tidyr)
library(readr)

# Convert Task.RT to numeric and remove zero values
joined_data_F <- joined_data_F %>%
  mutate(Task.RT = as.numeric(as.character(Task.RT))) %>%  # Ensure Task.RT is numeric
  dplyr::filter(Task.RT > 00, FinalScore > 0)  # Remove rows where Task.RT is zero

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Task.RT = as.numeric(as.character(Task.RT))`.
## Caused by warning:
## ! NAs introduced by coercion

# Proceed with previous steps if necessary to recheck data types or handle NAs
sum(is.na(joined_data_S$Task.RT))  # Check for NAs introduced by conversion

## [1] 0

# Convert Task.RT to numeric and remove zero values
joined_data_S <- joined_data_S %>%
  mutate(Task.RT = as.numeric(as.character(Task.RT))) %>%  # Ensure Task.RT is numeric
  dplyr::filter(Task.RT > 0, FinalScore > 0)  # Remove rows where Task.RT is zero

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Task.RT = as.numeric(as.character(Task.RT))`.
## Caused by warning:
## ! NAs introduced by coercion

# Proceed with previous steps if necessary to recheck data types or handle NAs
sum(is.na(joined_data_S$Task.RT))  # Check for NAs introduced by conversio

## [1] 0

joined_data_F %>% distinct(Subject)

## # A tibble: 177 × 1
##    Subject
##      <dbl>
##  1   94558
##  2   94789
##  3  104065
##  4  102454
##  5  104068
##  6  104161
##  7  103141
##  8  104323
##  9  103669
## 10  104320
## # ℹ 167 more rows

joined_data_S %>% distinct(Subject)

## # A tibble: 200 × 1
##    Subject
##      <dbl>
##  1   89223
##  2  984514
##  3  659709
##  4  955615
##  5  793920
##  6   83029
##  7  765061
##  8  715947
##  9  486783
## 10  675062
## # ℹ 190 more rows

Quick EDA

joined_data_F %>% 
  select(Subject, Task.RT, FinalScore) %>% 
  mutate(Task.RT = as.numeric(Task.RT)) %>% 
  group_by(Subject) %>% 
  summarise(Task.RT_Mean = mean(Task.RT, na.rm = TRUE),
            FinalScore_Mean = mean(FinalScore, na.rm = TRUE)) %>% 
  ggplot(aes(x = Task.RT_Mean, y = FinalScore_Mean)) +
  geom_point() +
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

library(DataExplorer)

joined_data_F %>% 
  select(Subject, Task.RT, FinalScore) %>% 
  mutate(Task.RT = as.numeric(Task.RT)) %>% 
  group_by(Subject) %>% 
  summarise(Task.RT_Mean = mean(Task.RT, na.rm = TRUE),
            FinalScore_Mean = mean(FinalScore, na.rm = TRUE)) %>% 
  plot_correlation()

Mean RT Scores

RT_Scores <- joined_data_F %>% 
  select(Subject, Task.RT, FinalScore, TrialType) %>% 
  mutate(Task.RT = as.numeric(Task.RT)) %>% 
  group_by(Subject) %>% 
  summarise(Task.RT_Mean = mean(Task.RT, na.rm = TRUE),
            Task.RT_FI = mean(Task.RT[TrialType == "FullyIncongruent"], na.rm = TRUE),
            Task.RT_SFI = mean(Task.RT[TrialType %in% c("FullyIncongruent", "StimCongruentRespIncongruent")], na.rm = TRUE),
            Task.RT_SI = mean(Task.RT[TrialType %in% c("StimCongruentRespIncongruent")], na.rm = TRUE),
            Task.RT_FC = mean(Task.RT[TrialType %in% c("FullyCongruent")], na.rm = TRUE),
            FinalScore_Mean = mean(FinalScore, na.rm = TRUE))  %>% 
  ungroup()

AllJan_scores <- joined_data_F %>% 
  dplyr::select(Subject, `Simon SQ`, `Flanker SQ`, `Stroop SQ`, Anti1MeanRT, Anti2MeanRT, SwitchMean, SwitchCostRT, MixingCostRT, MixingCostPC, NeutralPC, SwitchCostPC, Anti1PC, Anti2PC, RavenSum, RepeatPC, BSCmean, PerseveranceMean, ACSmean, PremedMean, UrgencyMeanR, RavenSum, MentalHealthMean, HappinessMean, SelfEsteemMean, GPA) %>% 
  pivot_longer(!Subject) %>% 
  group_by(Subject, name) %>% 
  summarise(Mean = mean(value, na.rm = TRUE)) %>% 
  ungroup() %>% 
  pivot_wider(names_from = name, values_from = Mean)

## `summarise()` has grouped output by 'Subject'. You can override using the
## `.groups` argument.

Merge the mean Global RT with self-ratings on Subject to align them for correlation calculation

# Load necessary libraries
library(dplyr)
library(ggplot2)
library(reshape2)

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
## 
##     smiths

# Assuming joined_data_F is already loaded and cleaned

# Compute mean Task.RT for each individual
flanker_global_rt <- joined_data_F %>%
  group_by(Subject) %>%
  summarise(`Flanker Global RT` = mean(Task.RT, na.rm = TRUE))

# Select the relevant columns for self-ratings
self_ratings <- joined_data_F %>%
  select(Subject, ACSmean, BSCmean, PerseveranceMean, UrgencyMeanR, PremedMean) %>%
  distinct()

# Merge the mean Task.RT with self-ratings on Subject to align them for correlation calculation
merged_data <- inner_join(flanker_global_rt, self_ratings, by = "Subject")

# Calculate the correlation matrix
cor_matrix <- merged_data %>%
  select(`Flanker Global RT`, ACSmean, BSCmean, PerseveranceMean, UrgencyMeanR, PremedMean) %>%
  cor(use = "complete.obs")

# Print the correlation matrix
print(cor_matrix)

##                   Flanker Global RT    ACSmean    BSCmean PerseveranceMean
## Flanker Global RT       1.000000000 -0.1130421 0.02773726       0.08874055
## ACSmean                -0.113042068  1.0000000 0.50617815       0.39630922
## BSCmean                 0.027737259  0.5061782 1.00000000       0.55956668
## PerseveranceMean        0.088740550  0.3963092 0.55956668       1.00000000
## UrgencyMeanR           -0.092515650  0.4357928 0.53545847       0.25062457
## PremedMean              0.006658044  0.1256466 0.28230265       0.26520768
##                   UrgencyMeanR  PremedMean
## Flanker Global RT  -0.09251565 0.006658044
## ACSmean             0.43579280 0.125646583
## BSCmean             0.53545847 0.282302646
## PerseveranceMean    0.25062457 0.265207678
## UrgencyMeanR        1.00000000 0.205222592
## PremedMean          0.20522259 1.000000000

# Melt the correlation matrix for visualization
cor_matrix_melt <- melt(cor_matrix)

# Create the heatmap
ggplot(cor_matrix_melt, aes(Var1, Var2, fill = value)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, limit = c(-1, 1), space = "Lab", name = "Correlation") +
  geom_text(aes(Var1, Var2, label = round(value, 2)), color = "black", size = 3) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 12, hjust = 1)) +
  coord_fixed() +
  labs(title = "Correlation Matrix Heatmap", x = "", y = "")

Merge the mean Global RT with `Simon SQ`, `Flanker SQ`, `Stroop SQ`, Anti1MeanRT, SwitchCostRT on Subject to align them for correlation calculation

# Load necessary libraries
library(dplyr)
library(ggplot2)
library(reshape2)

# Compute mean Task.RT for each individual as Flanker Global RT
Flanker_global_rt <- joined_data_F %>%
  group_by(Subject) %>%
  summarise(Flanker_global_rt = mean(Task.RT, na.rm = TRUE))

# Select the relevant columns for reaction times and other measures
self_ratings <- joined_data_F %>%
  select(Subject, `Simon SQ`, `Flanker SQ`, `Stroop SQ`, Anti1MeanRT,  SwitchCostRT) %>%
  distinct()

# Merge the mean Task.RT with the other measures on Subject to align them for correlation calculation
merged_data <- inner_join(Flanker_global_rt, self_ratings, by = "Subject")

# Calculate the correlation matrix
cor_matrix <- merged_data %>%
  select(Flanker_global_rt,  Anti1MeanRT, SwitchCostRT,  `Simon SQ`, `Stroop SQ`, `Flanker SQ`) %>%
  cor(use = "complete.obs")

# Print the correlation matrix
print(cor_matrix)

##                   Flanker_global_rt Anti1MeanRT SwitchCostRT   Simon SQ
## Flanker_global_rt         1.0000000   0.2635080    0.3842765 -0.5258351
## Anti1MeanRT               0.2635080   1.0000000    0.3006497 -0.4848588
## SwitchCostRT              0.3842765   0.3006497    1.0000000 -0.3002098
## Simon SQ                 -0.5258351  -0.4848588   -0.3002098  1.0000000
## Stroop SQ                -0.4053664  -0.2598792   -0.3023199  0.4120765
## Flanker SQ               -0.7940021  -0.2820028   -0.2908478  0.5420837
##                    Stroop SQ Flanker SQ
## Flanker_global_rt -0.4053664 -0.7940021
## Anti1MeanRT       -0.2598792 -0.2820028
## SwitchCostRT      -0.3023199 -0.2908478
## Simon SQ           0.4120765  0.5420837
## Stroop SQ          1.0000000  0.4836205
## Flanker SQ         0.4836205  1.0000000

# Melt the correlation matrix for visualization
cor_matrix_melt <- melt(cor_matrix)

# Create the heatmap
p <- ggplot(cor_matrix_melt, aes(Var1, Var2, fill = value)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, limit = c(-1, 1), space = "Lab", name = "Correlation") +
  geom_text(aes(Var1, Var2, label = round(value, 2)), color = "black", size = 3) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 10, hjust = 1)) +
  coord_fixed() +
  labs(title = "Correlation Matrix Heatmap", x = "", y = "")

# Display the plot
print(p)

# Save the plot with 300 DPI resolution
ggsave("GlobalRT_heatmap_plot.png", plot = p, dpi = 300, width = 8, height = 6, units = "in")

Outlier Cleaning

# Convert Task.RT to numeric
# This step also checks for non-numeric characters which will be turned into NA
joined_data_F$Task.RT <- as.numeric(joined_data_F$Task.RT)

# Check if there were any non-numeric values that got converted to NA
sum(is.na(joined_data_F$Task.RT))

## [1] 0

remove_outliers_iqr <- function(data, cols, multiplier = 1.5) {
  for (col in cols) {
    if (col %in% names(data) && is.numeric(data[[col]])) {
      Q1 <- quantile(data[[col]], 0.25, na.rm = TRUE)
      Q3 <- quantile(data[[col]], 0.75, na.rm = TRUE)
      IQR <- Q3 - Q1
      lower_bound <- Q1 - multiplier * IQR
      upper_bound <- Q3 + multiplier * IQR
      data <- data[data[[col]] >= lower_bound & data[[col]] <= upper_bound, ]
    }
  }
  return(data)
}
joined_data_F %>% distinct(Subject)

## # A tibble: 177 × 1
##    Subject
##      <dbl>
##  1   94558
##  2   94789
##  3  104065
##  4  102454
##  5  104068
##  6  104161
##  7  103141
##  8  104323
##  9  103669
## 10  104320
## # ℹ 167 more rows

#Assuming remove_outliers_iqr function is defined as above and handles only numeric columns
numeric_columns <- c("Task.RT", "FinalScore", "Anti1MeanRT", "Anti2MeanRT", "SwitchCostRT", "MixingCostRT")
joined_data_F <- remove_outliers_iqr(joined_data_F, numeric_columns)

library(ggplot2)
ggplot(joined_data_F, aes(x = Task.RT)) +
geom_histogram(bins = 30, fill = 'blue') +
ggtitle("Distribution of Task.RT After Cleaning and Outlier Removal")

Convert TrialType to a factor

joined_data_F$TrialType <- as.factor(joined_data_F$TrialType)

# Print out the levels to confirm they are correct
print(levels(joined_data_F$TrialType))

## [1] "FullyCongruent"               "FullyIncongruent"            
## [3] "StimCongruentRespIncongruent" "StimIncongruentRespCongruent"

DIFFERENCE SCORES (TRADITIONAL)

Calculate the mean for combined trial types and for FullyCongruent separately

# Calculate the mean for combined trial types and for FullyCongruent separately
combined_scores <- joined_data_F %>%
  group_by(Subject) %>%
  summarise(
    FullyIncongruent = mean(Task.RT[TrialType == "FullyIncongruent"], na.rm = TRUE),
    StimCongruentRespIncongruent = mean(Task.RT[TrialType == "StimCongruentRespIncongruent"], na.rm = TRUE),
    StimIncongruentRespCongruent = mean(Task.RT[TrialType == "StimIncongruentRespCongruent"], na.rm = TRUE),
    FullyCongruent = mean(Task.RT[TrialType == "FullyCongruent"], na.rm = TRUE)
  ) %>%
  mutate(
    Combined_Mean = (FullyIncongruent + StimCongruentRespIncongruent + StimIncongruentRespCongruent) / 3,
    Difference = Combined_Mean - FullyCongruent
  ) %>%
  left_join(select(joined_data_F, Subject, `Simon SQ`, ACSmean, Anti1MeanRT, BSCmean, UrgencyMeanR, PerseveranceMean, MentalHealthMean, PremedMean), by = "Subject") %>%
  distinct()



# Calculate correlations
correlation_SimonSQ <- cor(combined_scores$Difference, combined_scores$`Simon SQ`, use = "complete.obs")
correlation_ACSmean <- cor(combined_scores$Difference, combined_scores$ACSmean, use = "complete.obs")
correlation_Anti1MeanRT <- cor(combined_scores$Difference, combined_scores$Anti1MeanRT, use = "complete.obs")
correlation_BSCmean <- cor(combined_scores$Difference, combined_scores$BSCmean, use = "complete.obs")
correlation_UrgencyMeanR <- cor(combined_scores$Difference, combined_scores$UrgencyMeanR, use = "complete.obs")
correlation_PerseveranceMean <- cor(combined_scores$Difference, combined_scores$PerseveranceMean, use = "complete.obs")
correlation_MentalHealthMean <- cor(combined_scores$Difference, combined_scores$MentalHealthMean, use = "complete.obs")
correlation_PremedMean <- cor(combined_scores$Difference, combined_scores$PremedMean, use = "complete.obs")

# Print correlations
print(correlation_SimonSQ)

## [1] -0.1669946

print(correlation_ACSmean)

## [1] -0.2530343

print(correlation_BSCmean)

## [1] -0.1578991

print(correlation_UrgencyMeanR)

## [1] -0.1403306

print(correlation_PerseveranceMean)

## [1] -0.02455753

print(correlation_PremedMean)

## [1] -0.144658

# Load necessary libraries
library(dplyr)
library(tidyr)
library(ggplot2)

# Calculate the mean for combined trial types and for FullyCongruent separately
combined_scores <- joined_data_F %>%
  group_by(Subject) %>%
  summarise(
    FullyIncongruent = mean(Task.RT[TrialType == "FullyIncongruent"], na.rm = TRUE),
    StimCongruentRespIncongruent = mean(Task.RT[TrialType == "StimCongruentRespIncongruent"], na.rm = TRUE),
    StimIncongruentRespCongruent = mean(Task.RT[TrialType == "StimIncongruentRespCongruent"], na.rm = TRUE),
    FullyCongruent = mean(Task.RT[TrialType == "FullyCongruent"], na.rm = TRUE)
  ) %>%
  mutate(
    Combined_Mean = (FullyIncongruent + StimCongruentRespIncongruent + StimIncongruentRespCongruent) / 3,
    Difference = Combined_Mean - FullyCongruent
  ) %>%
  left_join(select(joined_data_F, Subject, `Simon SQ`, ACSmean, Anti1MeanRT, BSCmean, UrgencyMeanR, PerseveranceMean, MentalHealthMean, PremedMean), by = "Subject") %>%
  distinct()



# Create a correlation matrix
cor_matrix <- combined_scores %>%
  select(Difference, ACSmean, BSCmean, UrgencyMeanR, PerseveranceMean, PremedMean) %>%
  cor(use = "complete.obs")

# Melt the correlation matrix
cor_matrix_melt <- as.data.frame(as.table(cor_matrix))

# Plot the correlation matrix heatmap
Diff_Score_Heatmap <- ggplot(cor_matrix_melt, aes(Var1, Var2, fill = Freq)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1, 1), space = "Lab", 
                       name = "Correlation") +
  geom_text(aes(Var1, Var2, label = round(Freq, 2)), color = "black", size = 2) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 8, hjust = 1)) +
  coord_fixed() +
  labs(title = "Diff_Score_Heatmap", x = "", y = "")

# Save the ggplot2 heatmap
ggsave("Diff_Score_Heatmap.png", plot = Diff_Score_Heatmap, dpi = 300, width = 6, height = 4, units = "in")

# Print the plot
print(Diff_Score_Heatmap)

Calculate the difference for each N-1 FullyCongruent trial

# Load necessary libraries
library(dplyr)
library(tidyr)
library(readr)
library(ggplot2)
library(reshape2)
library(ggpubr)





# Ensure Task.RT and other necessary variables are numeric
joined_data_F <- joined_data_F %>%
  mutate(Task.RT = as.numeric(as.character(Task.RT)),
         ACSmean = as.numeric(as.character(ACSmean)),
         BSCmean = as.numeric(as.character(BSCmean)),
         PerseveranceMean = as.numeric(as.character(PerseveranceMean)),
         PremedMean = as.numeric(as.character(PremedMean)),
         UrgencyMeanR = as.numeric(as.character(UrgencyMeanR)),
         `Flanker SQ` = as.numeric(as.character(`Flanker SQ`)),
         `Simon SQ` = as.numeric(as.character(`Simon SQ`)),
         `Stroop SQ` = as.numeric(as.character(`Stroop SQ`)),
         Anti1MeanRT = as.numeric(as.character(Anti1MeanRT)),
         SwitchCostRT = as.numeric(as.character(SwitchCostRT)))

# Calculate the difference score correctly
N_1FullyCongruent <- joined_data_F %>%
  mutate(Next_Task_RT = dplyr::lead(Task.RT),  # Create a lead column for Task.RT
         Next_TrialType = dplyr::lead(TrialType)) %>%  # Create a lead column for TrialType as well
  dplyr::filter(TrialType == "FullyCongruent",  # Keep only FullyCongruent trials
                Next_TrialType != "FullyCongruent") %>%  # Exclude cases where the next trial is also FullyCongruent
  mutate(Difference_Score = Next_Task_RT - Task.RT)  # Calculate the difference as (next - current)

# View the result
print(N_1FullyCongruent)

## # A tibble: 1,118 × 207
##    Subject Session Block FinalScore FullyCongruentFinalScore
##      <dbl>   <dbl> <dbl>      <dbl>                    <dbl>
##  1   94558       1    14         33                        9
##  2   94558       1    17         33                        9
##  3   94558       1    24         33                        9
##  4   94558       1    27         33                        9
##  5   94558       1    30         33                        9
##  6   94558       1    34         33                        9
##  7   94558       1    38         33                        9
##  8   94558       1    42         33                        9
##  9   94558       1    45         33                        9
## 10   94789       1    10         28                        8
## # ℹ 1,108 more rows
## # ℹ 202 more variables: FullyCongruentTrialsCount <dbl>,
## #   FullyIncongruentFinalScore <dbl>, FullyIncongruentTrialsCount <dbl>,
## #   StimCongruentRespIncongruentFinalScore <dbl>,
## #   StimCongruentRespIncongruentTrialsCount <dbl>,
## #   StimIncongruentRespCongruentFinalScore <dbl>,
## #   StimIncongruentRespCongruentTrialsCount <dbl>, Task.DurationError <chr>, …

# Optionally, if you need to merge this back to the original data or perform further analysis:
joined_data_F <- joined_data_F %>%
  left_join(N_1FullyCongruent %>% select(Subject, Difference_Score), by = "Subject")

## Warning in left_join(., N_1FullyCongruent %>% select(Subject, Difference_Score), : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

# Check the updated dataset
print(head(joined_data_F))

## # A tibble: 6 × 205
##   Subject Session Block FinalScore FullyCongruentFinalS…¹ FullyCongruentTrials…²
##     <dbl>   <dbl> <dbl>      <dbl>                  <dbl>                  <dbl>
## 1   94558       1    13         33                      9                      9
## 2   94558       1    13         33                      9                      9
## 3   94558       1    13         33                      9                      9
## 4   94558       1    13         33                      9                      9
## 5   94558       1    13         33                      9                      9
## 6   94558       1    13         33                      9                      9
## # ℹ abbreviated names: ¹FullyCongruentFinalScore, ²FullyCongruentTrialsCount
## # ℹ 199 more variables: FullyIncongruentFinalScore <dbl>,
## #   FullyIncongruentTrialsCount <dbl>,
## #   StimCongruentRespIncongruentFinalScore <dbl>,
## #   StimCongruentRespIncongruentTrialsCount <dbl>,
## #   StimIncongruentRespCongruentFinalScore <dbl>,
## #   StimIncongruentRespCongruentTrialsCount <dbl>, Task.DurationError <chr>, …

# Load necessary libraries
library(dplyr)
library(tidyr)
library(ggplot2)

# Calculate the average Difference_Score for each subject
N_1FullyCongruent_avg_Score <- N_1FullyCongruent %>%
  group_by(Subject) %>%
  summarise(N_1FullyCongruent_avg_Score = mean(Difference_Score, na.rm = TRUE))

# Merge the average difference scores with ACSmean, BSCmean, PerseveranceMean, PremedMean, UrgencyMeanR, and Flanker SQ
merged_data <- joined_data_F %>%
  select(Subject, ACSmean, BSCmean, PerseveranceMean, PremedMean, UrgencyMeanR, `Flanker SQ`) %>%
  distinct() %>%
  left_join(N_1FullyCongruent_avg_Score, by = "Subject")

# Create a correlation matrix
cor_matrix <- merged_data %>%
  select(N_1FullyCongruent_avg_Score, ACSmean, BSCmean, PerseveranceMean, PremedMean, UrgencyMeanR, `Flanker SQ`) %>%
  cor(use = "complete.obs")

# Melt the correlation matrix
melted_cor_matrix <- melt(cor_matrix)

# Plot the correlation matrix heatmap
N_1_Correlation_Matrix_Heatmap <- ggplot(data = melted_cor_matrix, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1, 1), space = "Lab", 
                       name = "Correlation") +
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 8, hjust = 1)) +
  coord_fixed() +
  geom_text(aes(label = round(value, 2)), color = "black", size = 2) +
  labs(title = "N-1 Correlation Matrix Heatmap")

# Save the ggplot2 heatmap
ggsave("N_1_Correlation_Matrix_Heatmap.png", plot = N_1_Correlation_Matrix_Heatmap, dpi = 300, width = 6, height = 4, units = "in")

# Print the plot
print(N_1_Correlation_Matrix_Heatmap)

# Merge the average difference scores with the selected variables
merged_data <- joined_data_F %>%
  select(Subject, `Simon SQ`, `Flanker SQ`, `Stroop SQ`, Anti1MeanRT, SwitchCostRT) %>%
  distinct() %>%
  left_join(N_1FullyCongruent_avg_Score, by = "Subject")

# Create a correlation matrix
cor_matrix <- merged_data %>%
  select(N_1FullyCongruent_avg_Score, `Simon SQ`, `Flanker SQ`, `Stroop SQ`, Anti1MeanRT, SwitchCostRT) %>%
  cor(use = "complete.obs")

# Melt the correlation matrix
library(reshape2)
melted_cor_matrix <- melt(cor_matrix)

# Filter out negative scores from Simon SQ, Flanker SQ, and Stroop SQ
joined_data_F <- joined_data_F %>%
  dplyr::filter(`Simon SQ` >= 0, `Flanker SQ` >= 0, `Stroop SQ` >= 0)

# Print dimensions to see how many rows remain after filtering
#print(dim(joined_data_F))
joined_data_F %>% distinct(Subject)

## # A tibble: 130 × 1
##    Subject
##      <dbl>
##  1   94558
##  2   94789
##  3  104065
##  4  102454
##  5  104068
##  6  104161
##  7  103141
##  8  104323
##  9  103669
## 10   99631
## # ℹ 120 more rows

# Plot the correlation matrix heatmap
library(ggplot2)
p <- ggplot(data = melted_cor_matrix, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1, 1), space = "Lab", 
                       name = "Correlation") +
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 8, hjust = 1)) +
  coord_fixed() +
  geom_text(aes(label = round(value, 2)), color = "black", size = 2) +
  labs(title = "Correlation Matrix Heatmap")
print(p)

# Save the plot with 300 DPI resolution
ggsave("heatmap_plot.png", plot = p, dpi = 300, width = 6, height = 4, units = "in")

REACTION TIMES

John Mahjoubinia

2024-07-08

Setup

Load libraries

Load data

Data Processing

Flanker

Data Joining

Simon

Data Joining

Stroop

Data Joining

Quick EDA

Mean RT Scores

Merge the mean Global RT with self-ratings on Subject to align them for correlation calculation

Merge the mean Global RT with `Simon SQ`, `Flanker SQ`, `Stroop SQ`, Anti1MeanRT, SwitchCostRT on Subject to align them for correlation calculation

Outlier Cleaning

Convert TrialType to a factor

DIFFERENCE SCORES (TRADITIONAL)

Calculate the mean for combined trial types and for FullyCongruent separately

Calculate the difference for each N-1 FullyCongruent trial

REACTION TIMES

John Mahjoubinia

2024-07-08

Setup

Load libraries

Load data

Data Processing

Flanker

Data Joining

Simon

Data Joining

Stroop

Data Joining

Quick EDA

Mean RT Scores

Merge the mean Global RT with self-ratings on Subject to align them for correlation calculation

Merge the mean Global RT with Simon SQ, Flanker SQ, Stroop SQ, Anti1MeanRT, SwitchCostRT on Subject to align them for correlation calculation

Outlier Cleaning

Convert TrialType to a factor

DIFFERENCE SCORES (TRADITIONAL)

Calculate the mean for combined trial types and for FullyCongruent separately

Calculate the difference for each N-1 FullyCongruent trial

Merge the mean Global RT with `Simon SQ`, `Flanker SQ`, `Stroop SQ`, Anti1MeanRT, SwitchCostRT on Subject to align them for correlation calculation