Preliminary Data Analysis for Thesis
Bidhu Ghosh (z5361809)
2024-08-05
Loading in packages and reading data
library(tidyverse)
library(knitr)
data <- read_csv("uncertain100.csv") #whole dataset
print(data)## # A tibble: 98 × 191
## PID Gender Age Shock `Calibrate 0` `Calibrate 100` `Calibrate 50` A
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 1 1 18 0.25 -1 -1 0.526 pink
## 2 2 1 19 1.5 -1 -1 0.526 orange
## 3 3 2 19 1.5 -1 -1 0.526 orange
## 4 4 2 18 0.75 -1 -1 0.526 pink
## 5 5 1 18 1.5 -1 -1 0.526 orange
## 6 6 1 22 2 -1 -1 0.526 blue
## 7 7 2 23 1 -1 -1 0.526 yellow
## 8 8 2 19 2 -1 -1 0.526 blue
## 9 9 2 19 1.5 -1 -1 0.526 black
## 10 10 2 18 1.5 -1 -1 0.526 orange
## # ℹ 88 more rows
## # ℹ 183 more variables: B <chr>, C <chr>, D <chr>, E <chr>, TT1 <dbl>,
## # TT2 <dbl>, TT3 <dbl>, TT4 <dbl>, TT5 <dbl>, TT6 <dbl>, TT7 <dbl>,
## # TT8 <dbl>, TT9 <dbl>, TT10 <dbl>, TT11 <dbl>, TT12 <dbl>, TT13 <dbl>,
## # TT14 <dbl>, TT15 <dbl>, TT16 <dbl>, TT17 <dbl>, TT18 <dbl>, TT19 <dbl>,
## # TT20 <dbl>, TT21 <dbl>, TT22 <dbl>, TT23 <dbl>, TT24 <dbl>, TT25 <dbl>,
## # TT26 <dbl>, TT27 <dbl>, TT28 <dbl>, TT29 <dbl>, TT30 <dbl>, TT31 <dbl>, …Coding Awareness, IUS score, and STAI score
coded_data <- data %>%
mutate(awareness = case_when(Ccont > Bcont & Bcont > Acont & Ccont > 4 & Acont < 2 ~ "aware",
TRUE ~ "unaware"),
IUSscore = rowSums(select(., 160:171)),
STAITscore = ((3 - STq1) + STq2 + STq3 + STq4 + STq5 + (3 - STq6) + (3 - STq7) + STq8 + STq9 + STq10 + STq11 + STq12 + (3 - STq13) + (3 - STq14) + STq15 + (3 - STq16) + STq17 + STq18 + (3 - STq19) + STq20))reverse score 1, 6, 7, 10, 13, 14, 16, 19 for STAIT
median splitting
median <- coded_data %>%
select(c(193, 194)) %>%
summarise(median_IUS = median(IUSscore), median_TA = median(STAITscore))
print(median)## # A tibble: 1 × 2
## median_IUS median_TA
## <dbl> <dbl>
## 1 36 29split_data <- coded_data %>%
mutate(IUS = case_when(IUSscore > median$median_IUS ~ "high",
TRUE ~ "low"),
TA = case_when(STAITscore > median$median_TA ~ "high",
TRUE ~ "low")) %>%
select(-c(5:12))
aware_count <- split_data %>%
group_by(awareness) %>%
summarise(count = n()) %>%
ungroup()
print(aware_count)## # A tibble: 2 × 2
## awareness count
## <chr> <int>
## 1 aware 79
## 2 unaware 19aware_count_ID <- split_data %>%
group_by(awareness, TA, IUS) %>%
summarise(count = n()) %>%
ungroup
print(aware_count_ID)## # A tibble: 8 × 4
## awareness TA IUS count
## <chr> <chr> <chr> <int>
## 1 aware high high 19
## 2 aware high low 18
## 3 aware low high 12
## 4 aware low low 30
## 5 unaware high high 6
## 6 unaware high low 5
## 7 unaware low high 4
## 8 unaware low low 4Uncounterbalancing anxiety and scl data
trial_data <- split_data %>%
subset(select= c(1:40, 184, 187:188)) %>% #taking trial type only
rename_at(vars(5:40), ~as.character(1:36)) %>% #renaming columns to just be trial number
pivot_longer(cols = 5:40, names_to = "trial", values_to = "type") #pivoting to get trial number and trial type values
print(trial_data)## # A tibble: 3,528 × 9
## PID Gender Age Shock awareness IUS TA trial type
## <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl>
## 1 1 1 18 0.25 aware low low 1 6
## 2 1 1 18 0.25 aware low low 2 1
## 3 1 1 18 0.25 aware low low 3 2
## 4 1 1 18 0.25 aware low low 4 5
## 5 1 1 18 0.25 aware low low 5 5
## 6 1 1 18 0.25 aware low low 6 6
## 7 1 1 18 0.25 aware low low 7 7
## 8 1 1 18 0.25 aware low low 8 1
## 9 1 1 18 0.25 aware low low 9 2
## 10 1 1 18 0.25 aware low low 10 7
## # ℹ 3,518 more rowsanx_data <- split_data %>%
subset(select= c(1, 41:76)) %>% #taking anxiety only
rename_at(vars(2:37), ~as.character(1:36)) %>% #renaming columns to just be trial number
pivot_longer(cols = 2:37, names_to = "trial", values_to = "anx") #pivoting to get trial number and anxiety values
print(anx_data)## # A tibble: 3,528 × 3
## PID trial anx
## <dbl> <chr> <dbl>
## 1 1 1 -99
## 2 1 2 -99
## 3 1 3 -99
## 4 1 4 -99
## 5 1 5 -99
## 6 1 6 -99
## 7 1 7 -99
## 8 1 8 -99
## 9 1 9 -99
## 10 1 10 12
## # ℹ 3,518 more rowsbaseSCL_data <- split_data %>%
subset(select= c(1, 77:112)) %>% #taking anxiety only
rename_at(vars(2:37), ~as.character(1:36)) %>% #renaming columns to just be trial number
pivot_longer(cols = 2:37, names_to = "trial", values_to = "base") #pivoting to get trial number and baseline SCL values
csSCL_data <- split_data %>%
subset(select= c(1, 113:148)) %>% #taking anxiety only
rename_at(vars(2:37), ~as.character(1:36)) %>% #renaming columns to just be trial number
pivot_longer(cols = 2:37, names_to = "trial", values_to = "cs") #pivoting to get trial number and cs SCL values
comb_data <- cbind(trial_data, anx = anx_data$anx, base = baseSCL_data$base, cs = csSCL_data$cs) #combining dataframes into one
comb_data$trial <- as.numeric(comb_data$trial)
head(comb_data)## PID Gender Age Shock awareness IUS TA trial type anx base cs
## 1 1 1 18 0.25 aware low low 1 6 -99 -0.4910 2.5879
## 2 1 1 18 0.25 aware low low 2 1 -99 2.3634 1.3574
## 3 1 1 18 0.25 aware low low 3 2 -99 -0.3407 -0.6572
## 4 1 1 18 0.25 aware low low 4 5 -99 2.2691 1.5259
## 5 1 1 18 0.25 aware low low 5 5 -99 -0.6053 0.7061
## 6 1 1 18 0.25 aware low low 6 6 -99 9.0449 7.5666clean_data <- comb_data %>%
mutate(SCLchange = (cs - base)) %>%
filter(trial > 10)
SCLadjust <- clean_data %>%
filter(trial > 11) %>%
group_by(PID) %>%
summarise(min = min(SCLchange)) %>%
slice(rep(1:n(), each = 26))Collating data for plotting
plot_data <- clean_data %>%
mutate(typev2 = case_when(type == 1 ~ "zero",
type == 2 | type == 3 ~ "fifty",
type == 4 ~ "hundred",
TRUE ~ "amb")) %>%
group_by(PID, typev2) %>%
mutate(exptrial = row_number()) %>%
ungroup() %>%
mutate(anx2 = case_when(anx == -99 ~ 50,
TRUE ~ anx),
SCL2 = SCLchange - SCLadjust$min)
plot_data$typev2 <- fct_relevel(plot_data$typev2, c("zero", "fifty", "hundred", "amb"))Exclusions: - Participant 18 (failed to used anxiety dial)
Plots
Plotting overall acquisition
overall_acq_data <- plot_data %>%
filter(trial < 29, PID != 18) %>%
group_by(typev2, exptrial) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
overall_anx_acq_plot <- ggplot(data = overall_acq_data, mapping = aes(x = exptrial, y = mean_anx, col = typev2)) +
geom_line() +
geom_point() +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
labs(x = "Trial",
y = "Anxiety") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(overall_anx_acq_plot)
overall_SCL_acq_plot <- ggplot(data = overall_acq_data, mapping = aes(x = exptrial, y = mean_SCL, col = typev2)) +
geom_line() +
geom_point() +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
labs(x = "Trial",
y = "SCL") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(overall_SCL_acq_plot)
overall test anxiety
overall_anx_test_data <- plot_data %>%
filter(trial > 26 & trial < 33, PID != 18) %>%
group_by(typev2) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
overall_anx_test_plot <- ggplot(data = overall_anx_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2)) +
geom_col() +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
coord_cartesian(ylim = c(0, 60)) +
scale_y_continuous(breaks = c(0, 20, 40, 60)) +
labs(x = "CS",
y = "Anxiety") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(overall_anx_test_plot)
Overall Expectancy
overall_exp_test_data <- plot_data %>%
filter(trial > 32, PID != 18) %>%
group_by(typev2) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
overall_exp_test_plot <- ggplot(data = overall_exp_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2)) +
geom_col() +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Expectancy") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(overall_exp_test_plot)
Overall test SCL
overall_SCL_test_data <- plot_data %>%
filter(trial > 26, PID != 18) %>%
group_by(typev2) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
overall_SCL_test_plot <- ggplot(data = overall_SCL_test_data, mapping = aes(x = typev2, y = mean_SCL, fill = typev2)) +
geom_col() +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "SCL") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(overall_SCL_test_plot)
aware acquisition
aware_acq_data <- plot_data %>%
filter(trial < 29, PID != 18) %>%
group_by(typev2, exptrial, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
aware_anx_acq_plot <- ggplot(data = aware_acq_data, mapping = aes(x = exptrial, y = mean_anx, col = typev2)) +
geom_line(mapping = aes(linetype = awareness)) +
geom_point(mapping = aes(shape = awareness)) +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
scale_linetype_discrete(labels = c("aware" = "Aware", "unaware" = "Unaware")) +
scale_shape_discrete(labels = c("aware" = "Aware", "unaware" = "Unaware")) +
labs(x = "Trial",
y = "Anxiety") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(aware_anx_acq_plot)
aware_SCL_acq_plot <- ggplot(data = aware_acq_data, mapping = aes(x = exptrial, y = mean_SCL, col = typev2)) +
geom_line(mapping = aes(linetype = awareness)) +
geom_point(mapping = aes(shape = awareness)) +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
scale_linetype_discrete(labels = c("aware" = "Aware", "unaware" = "Unaware")) +
scale_shape_discrete(labels = c("aware" = "Aware", "unaware" = "Unaware")) +
labs(x = "Trial",
y = "Anxiety") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(aware_SCL_acq_plot)
aware test anxiety
aware_anx_test_data <- plot_data %>%
filter(trial > 26 & trial < 33, PID != 18) %>%
group_by(typev2, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
aware_anx_test_plot <- ggplot(data = aware_anx_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2)) +
facet_wrap(~awareness) +
geom_col() +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Anxiety") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(aware_anx_test_plot)
aware expectancy
aware_exp_test_data <- plot_data %>%
filter(trial > 32, PID != 18) %>%
group_by(typev2, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
aware_exp_test_plot <- ggplot(data = aware_exp_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2)) +
facet_wrap(~awareness) +
geom_col() +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Expectancy") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(aware_exp_test_plot)
aware test SCL
aware_SCL_test_data <- plot_data %>%
filter(trial > 26, PID != 18) %>%
group_by(typev2, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
aware_SCL_test_plot <- ggplot(data = aware_SCL_test_data, mapping = aes(x = typev2, y = mean_SCL, fill = typev2)) +
facet_wrap(~awareness) +
geom_col() +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "SCL") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(aware_SCL_test_plot)
Trait Anxiety Acquisition
TA_acq_data <- plot_data %>%
filter(trial < 29, PID != 18) %>%
group_by(typev2, exptrial, TA) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
TA_anx_acq_plot <- ggplot(data = TA_acq_data, mapping = aes(x = exptrial, y = mean_anx, col = typev2)) +
geom_line(mapping = aes(linetype = TA)) +
geom_point(mapping = aes(shape = TA)) +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
scale_linetype_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
scale_shape_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
labs(x = "Trial",
y = "Anxiety") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(TA_anx_acq_plot)
TA_SCL_acq_plot <- ggplot(data = TA_acq_data, mapping = aes(x = exptrial, y = mean_SCL, col = typev2)) +
geom_line(mapping = aes(linetype = TA)) +
geom_point(mapping = aes(shape = TA)) +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
scale_linetype_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
scale_shape_discrete(labels = c("high" = "High Anxious", ";ow" = "Low Anxious")) +
labs(x = "Trial",
y = "SCL") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(TA_SCL_acq_plot)
TA test anxiety
TA_anx_test_data <- plot_data %>%
filter(trial > 26 & trial < 33, PID != 18) %>%
group_by(typev2, TA) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
TA_anx_test_plot <- ggplot(data = TA_anx_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2, linetype = TA)) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Anxiety") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(TA_anx_test_plot)
TA expectancy
TA_exp_test_data <- plot_data %>%
filter(trial > 32, PID != 18) %>%
group_by(typev2, TA) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
TA_exp_test_plot <- ggplot(data = TA_exp_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2, linetype = TA)) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Expectancy") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(TA_exp_test_plot)
TA SCL
TA_SCL_test_data <- plot_data %>%
filter(trial > 26, PID != 18) %>%
group_by(typev2, TA) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
TA_SCL_test_plot <- ggplot(data = TA_SCL_test_data, mapping = aes(x = typev2, y = mean_SCL, fill = typev2, linetype = TA)) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "SCL") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(TA_SCL_test_plot)
IUS acquisition
IUS_acq_data <- plot_data %>%
filter(trial < 29, PID != 18) %>%
group_by(typev2, exptrial, IUS) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
IUS_anx_acq_plot <- ggplot(data = IUS_acq_data, mapping = aes(x = exptrial, y = mean_anx, col = typev2)) +
geom_line(mapping = aes(linetype = IUS)) +
geom_point(mapping = aes(shape = IUS)) +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
scale_linetype_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
scale_shape_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
labs(x = "Trial",
y = "Anxiety") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(IUS_anx_acq_plot)
IUS_SCL_acq_plot <- ggplot(data = IUS_acq_data, mapping = aes(x = exptrial, y = mean_SCL, col = typev2)) +
geom_line(mapping = aes(linetype = IUS)) +
geom_point(mapping = aes(shape = IUS)) +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
scale_linetype_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
scale_shape_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
labs(x = "Trial",
y = "SCL") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(IUS_SCL_acq_plot)
IUS test anxiety
IUS_anx_test_data <- plot_data %>%
filter(trial > 26 & trial < 33, PID != 18) %>%
group_by(typev2, IUS) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
IUS_anx_test_plot <- ggplot(data = IUS_anx_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2, linetype = IUS)) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Anxiety") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(IUS_anx_test_plot)
TA expectancy
IUS_exp_test_data <- plot_data %>%
filter(trial > 32, PID != 18) %>%
group_by(typev2, IUS) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
IUS_exp_test_plot <- ggplot(data = IUS_exp_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2, linetype = IUS)) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Expectancy") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(IUS_exp_test_plot)
IUS SCL
IUS_SCL_test_data <- plot_data %>%
filter(trial > 26, PID != 18) %>%
group_by(typev2, IUS) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
IUS_SCL_test_plot <- ggplot(data = IUS_SCL_test_data, mapping = aes(x = typev2, y = mean_SCL, fill = typev2, linetype = IUS)) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "SCL") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(IUS_SCL_test_plot)
Aware Trait Anxiety Acquisition
TA_acq_data <- plot_data %>%
filter(trial < 29, PID != 18) %>%
group_by(typev2, exptrial, TA, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
TA_anx_acq_plot <- ggplot(data = TA_acq_data, mapping = aes(x = exptrial, y = mean_anx, col = typev2)) +
facet_wrap(~awareness) +
geom_line(mapping = aes(linetype = TA)) +
geom_point(mapping = aes(shape = TA)) +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
scale_linetype_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
scale_shape_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
labs(x = "Trial",
y = "Anxiety") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(TA_anx_acq_plot)
TA_SCL_acq_plot <- ggplot(data = TA_acq_data, mapping = aes(x = exptrial, y = mean_SCL, col = typev2)) +
facet_wrap(~awareness) +
geom_line(mapping = aes(linetype = TA)) +
geom_point(mapping = aes(shape = TA)) +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
scale_linetype_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
scale_shape_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
labs(x = "Trial",
y = "SCL") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(TA_SCL_acq_plot)
Aware TA test anxiety
TA_anx_test_data <- plot_data %>%
filter(trial > 26 & trial < 33, PID != 18) %>%
group_by(typev2, TA, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
TA_anx_test_plot <- ggplot(data = TA_anx_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2, linetype = TA)) +
facet_wrap(~awareness) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Anxiety") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(TA_anx_test_plot)
Aware TA expectancy
TA_exp_test_data <- plot_data %>%
filter(trial > 32, PID != 18) %>%
group_by(typev2, TA, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
TA_exp_test_plot <- ggplot(data = TA_exp_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2, linetype = TA)) +
facet_wrap(~awareness) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Expectancy") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(TA_exp_test_plot)
Aware TA SCL
TA_SCL_test_data <- plot_data %>%
filter(trial > 26, PID != 18) %>%
group_by(typev2, TA, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
TA_SCL_test_plot <- ggplot(data = TA_SCL_test_data, mapping = aes(x = typev2, y = mean_SCL, fill = typev2, linetype = TA)) +
facet_wrap(~awareness) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High Anxious", "low" = "Low Anxious")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "SCL") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(TA_SCL_test_plot)
Aware IU Acquisition
IUS_acq_data <- plot_data %>%
filter(trial < 29, PID != 18) %>%
group_by(typev2, exptrial, IUS, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
IUS_anx_acq_plot <- ggplot(data = IUS_acq_data, mapping = aes(x = exptrial, y = mean_anx, col = typev2)) +
facet_wrap(~awareness) +
geom_line(mapping = aes(linetype = IUS)) +
geom_point(mapping = aes(shape = IUS)) +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
scale_linetype_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
scale_shape_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
labs(x = "Trial",
y = "Anxiety") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(IUS_anx_acq_plot)
IUS_SCL_acq_plot <- ggplot(data = IUS_acq_data, mapping = aes(x = exptrial, y = mean_SCL, col = typev2)) +
facet_wrap(~awareness) +
geom_line(mapping = aes(linetype = IUS)) +
geom_point(mapping = aes(shape = IUS)) +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0.1) +
scale_color_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred")) +
scale_linetype_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
scale_shape_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
labs(x = "Trial",
y = "SCL") +
scale_x_continuous(breaks = c(1, 2, 3, 4, 5, 6)) +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x.bottom = element_line(colour = "black", size = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(IUS_SCL_acq_plot)
Aware IU test anxiety
IUS_anx_test_data <- plot_data %>%
filter(trial > 26 & trial < 33, PID != 18) %>%
group_by(typev2, IUS, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
IUS_anx_test_plot <- ggplot(data = IUS_anx_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2, linetype = IUS)) +
facet_wrap(~awareness) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Anxiety") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(IUS_anx_test_plot)
Aware TA expectancy
IUS_exp_test_data <- plot_data %>%
filter(trial > 32, PID != 18) %>%
group_by(typev2, IUS, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
IUS_exp_test_plot <- ggplot(data = IUS_exp_test_data, mapping = aes(x = typev2, y = mean_anx, fill = typev2, linetype = IUS)) +
facet_wrap(~awareness) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_anx - se_anx, max = mean_anx + se_anx), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "Expectancy") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(IUS_exp_test_plot)
Aware TA SCL
IUS_SCL_test_data <- plot_data %>%
filter(trial > 26, PID != 18) %>%
group_by(typev2, IUS, awareness) %>%
summarise(mean_anx = mean(anx2),
mean_SCL = mean(log(SCL2 + 1)),
se_anx = sd(anx2)/sqrt(n()),
se_SCL = sd(log(SCL2 + 1))/sqrt(n())) %>%
ungroup()
IUS_SCL_test_plot <- ggplot(data = IUS_SCL_test_data, mapping = aes(x = typev2, y = mean_SCL, fill = typev2, linetype = IUS)) +
facet_wrap(~awareness) +
geom_col(position = "dodge", colour = "black") +
geom_errorbar(mapping = aes(min = mean_SCL - se_SCL, max = mean_SCL + se_SCL), width = 0, position = position_dodge(.9)) +
scale_fill_manual(values = c("zero" = "#ff0000", "fifty" = "#0dca41", "hundred" = "#002fff", "amb" = "#7b21d1"),
labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
scale_linetype_discrete(labels = c("high" = "High IU", "low" = "Low IU")) +
scale_x_discrete(labels = c("zero" = "Zero", "fifty" = "Fifty", "hundred" = "Hundred", "amb" = "Ambiguous")) +
labs(x = "CS",
y = "SCL") +
theme_minimal() +
theme(axis.line.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_line(colour = "black", size = 0.5),
axis.line.x.bottom = element_line(colour = "black", linewidth = 0.5),
axis.ticks.length = unit(-0.1, "cm"),
axis.line.x = element_line(colour = "black", linewidth = 0.5),
panel.grid = element_blank(),
legend.title = element_blank(),
legend.key = element_blank())
print(IUS_SCL_test_plot)