kable(head(sessionData))

# Load eye tracking data into data frame
dataFile <- here("data", "subject_info.csv")
subjectData <- read_csv2(dataFile, col_names = TRUE, progress = FALSE, col_types = cols(
  session.order = col_factor(c("first.anodal", "first.cathodal"))
))

kable(head(subjectData))

tooFast <- 50
tooSlow <- 400
badFix <- 1.8
badSacc <- 8

# Mark outliers
groupData <- mutate(groupData, outlier = "non.outlier", # fill vector for all saccades
                    outlier = ifelse(latency < tooFast, "fast", outlier), # mark too fast saccades as "fast"
                    outlier = ifelse(latency > tooSlow, "slow", outlier), # mark too slow saccades as "slow"
                    outlier = ifelse(deviation.start > badFix, "fixation", outlier), # mark bad fixations as "fixation"
                    outlier = ifelse(deviation.end.x > badSacc, "saccade", outlier), # mark inaccurate saccades as "saccade"
                    outlier = ifelse(is.na(latency), "none", outlier) # mark absence of saccade as "none"
                    )

preproc <- filter(groupData, outlier == "non.outlier")

subs2exclude <- c("S21","S25","S16","S22","S28")

preproc <- filter(preproc, !(subject %in% subs2exclude))

preproc <- preproc %>%
  mutate(leg = as.character(leg), # cannot edit leg if it's still a factor
         leg = replace(leg, leg == "post" & block <= 3, "post.1"),
         leg = replace(leg, block > 3, "post.2"),
         leg = factor(leg, levels = c("pre", "tDCS", "post.1", "post.2")) # refactor and order levels
         )

subjectData %>%
  filter(!(subject %in% c(subs2exclude, "S23"))) %>% # remove rows with these subject
  group_by(gender) %>% # for each gender
  summarise(count = n_distinct(subject)) %>%
  kable(.)# count number of subjects

subjectData %>%
  filter(!(subject %in% c(subs2exclude, "S23"))) %>%
  summarise_at(vars(age), funs(mean, min, max, sd), na.rm = TRUE) %>% # apply summary functions to age column
  kable(.)

sessionData %>%
  filter(!(subject %in% c(subs2exclude, "S23"))) %>%
  group_by(session, stimulation) %>%
  summarise(count = n_distinct(subject)) %>% 
  kable(.)

preproc %>%
  group_by(subject,stimulation,leg,direction,type) %>% # for each cell
  summarise(saccades = n()) %>% # count how many saccacdes there are left
  group_by(type) %>% 
  summarise_at(vars(saccades), funs(mean, min, max, sd)) %>% # compute summary statistics per type
  kable(.)

n_total <- nrow(filter(groupData, !(subject %in% subs2exclude)))  # total amount of saccades across all included sessions/subjects
groupData %>%
  filter(!(subject %in% subs2exclude), !(outlier %in% c("non.outlier", "none"))) %>%
  group_by(subject,stimulation,leg,direction,type,outlier) %>%
  summarize(outlier_count = n()) %>% # for each condition and subject, count how many (non)outliers there are
  group_by(outlier) %>% # for each outlier type
  summarise(percentage = sum(outlier_count) / n_total *100) %>% # calculate percentage
  kable(.)

latencyMedianLeg <- preproc %>%
  group_by(subject,stimulation,direction,type) %>% 
  summarise(baseline = median(latency[leg == "pre"]), # take average of 3 blocks, make new column
            tDCS = median(latency[leg == "tDCS"]),
            post.1 = median(latency[leg == "post.1"]),
            post.2 = median(latency[leg == "post.2"])) %>%
  gather(leg,latency,baseline,tDCS,post.1,post.2) %>% # gather new columns to use as factor
  mutate(leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))) # reorder factor levels

latencyMedianBaseline <- latencyMedianLeg %>%
  group_by(subject,stimulation,direction,type) %>% # for each condition, subtract baseline scores and make new columns
  summarise(baseline = latency[leg == "baseline"] - latency[leg == "baseline"],
           tDCS = latency[leg == "tDCS"] - latency[leg == "baseline"],
           post.1 = latency[leg == "post.1"] - latency[leg == "baseline"],
           post.2 = latency[leg == "post.2"] - latency[leg == "baseline"]) %>%
  gather(leg, latency.baseline, baseline, tDCS, post.1, post.2)  %>% # gather new columns to use as factor 
  mutate(leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))) # reorder factor levels

baselineLabelLatency <- latencyMedianLeg %>%
  group_by(stimulation,direction,type,leg) %>%
  filter(leg == "baseline") %>%
  summarise(latency.baseline = round(mean(latency)))

plot_sub_latency_lateral <- ggplot(filter(latencyMedianLeg, type == "lateral"), aes(leg, latency)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  guides(colour = FALSE) +
  scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous("median latency (ms)", breaks = seq(100,200,20), limits = c(90,220)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Lateral saccades")

plot_sub_latency_center <- ggplot(filter(latencyMedianLeg, type == "center"), aes(leg, latency)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous("median latency (ms)", breaks = seq(100,200,20), limits = c(90,220)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Center saccades")

plot_group_latency_lateral <- ggplot(filter(latencyMedianBaseline, type == "lateral"), aes(leg, latency.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelLatency, type=="lateral"), aes(y = c(2,1,4,3), label=latency.baseline, color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous("latency change from baseline (ms)", breaks = seq(-8,8,2)) +
  coord_cartesian(ylim = c(-8,8)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())

plot_group_latency_center <- ggplot(filter(latencyMedianBaseline, type == "center"), aes(leg, latency.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelLatency, type=="center"), aes(y = c(2,1,3,4), label=latency.baseline, color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete(labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous("latency change from baseline (ms)", breaks = seq(-8,8,2)) +
  coord_cartesian(ylim = c(-8,8)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())

legend_fig4 <- get_legend(plot_group_latency_lateral)
figure_4 <- plot_grid(
  plot_sub_latency_lateral, plot_sub_latency_center,
  plot_group_latency_lateral + theme(legend.position = "none"),
  plot_group_latency_center + theme(legend.position = "none"), rel_heights = c(1,.75))
figure_4 <- plot_grid(figure_4,legend_fig4,rel_widths = c(1,1/10))
figure_4

ggsave("fig/figure_4.pdf", plot = figure_4, width = 180, height = 112.5, units = "mm")
ggsave("fig/figure_4.png", plot = figure_4, width = 180, height = 112.5, units = "mm")

latencyMedianBaseline %>%
  filter(type == "lateral", stimulation == "anodal", direction == "left", leg != "baseline") %>%
  group_by(leg) %>%
  summarise_at(vars(latency.baseline), funs(mean, sd)) %>%
  kable(.)

latencyMedianLeg %>%
  filter(leg == "baseline") %>%
  group_by(direction,type) %>% # for each of these 4 pairs
  nest() %>% # create a separate data frame of all remaining columns. These data frames are stored in a list-column named "data"
  mutate(stats = map(data, ~t.test(formula = latency~stimulation, paired = TRUE, data =.))) %>% # run t-test on the data frames
  mutate(tidy_model = map(stats, tidy)) %>% # force the four test outputs to also be data frames in a list column ("tidy_model")
  unnest(tidy_model, .drop = TRUE) %>% # unpack the list-column with results, drop the list-column we used to organize the data
  kable(.)

medianLatencyLateralStats <- latencyMedianBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "lateral") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))

aovMedianLatencyLateral <- ezANOVA(data = data.frame(medianLatencyLateralStats), dv = latency.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)
kable(aovMedianLatencyLateral$ANOVA)

kable(aovMedianLatencyLateral$`Mauchly's Test for Sphericity`)

kable(aovMedianLatencyLateral$`Sphericity Corrections`)

bfMedianLatencyLateral = anovaBF(latency.baseline~stimulation*leg*direction+subject, data = data.frame(medianLatencyLateralStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000)
bfMedianLatencyLateral = sort(bfMedianLatencyLateral, decreasing = TRUE) # sort such that winning model is at the top

kable(select(extractBF(bfMedianLatencyLateral), bf)) # show only the Bayes factors in a table

kable(inclusionBF(bfMedianLatencyLateral, models = "matched"))

medianLatencyCenterStats <- latencyMedianBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "center") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))

aovMedianLatencyCenter <- ezANOVA(data = data.frame(medianLatencyCenterStats), dv = latency.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)
kable(aovMedianLatencyCenter$ANOVA)

kable(aovMedianLatencyCenter$`Mauchly's Test for Sphericity`)

kable(aovMedianLatencyCenter$`Sphericity Corrections`)

bfMedianLatencyCenter = anovaBF(latency.baseline~stimulation*leg*direction+subject, data = data.frame(medianLatencyCenterStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000)
bfMedianLatencyCenter = sort(bfMedianLatencyCenter, decreasing = TRUE) # sort such that winning model is at the top
kable(select(extractBF(bfMedianLatencyCenter), bf)) # show only the Bayes factors in a table

kable(select(extractBF(bfMedianLatencyCenter), bf)) # show only the Bayes factors in a table

kable(inclusionBF(bfMedianLatencyCenter, models = "matched"))

medianLatencyCenterNoS01 <- medianLatencyCenterStats %>%
  filter(subject != "S01") %>%
  mutate(subject = factor(subject))
bfMedianLatencyCenterNoS01 = anovaBF(latency.baseline~stimulation*leg*direction+subject, data = data.frame(medianLatencyCenterNoS01), whichModels="withmain", whichRandom = "subject", progress = FALSE, iterations = 100000) # compute Bayes Factors
bfKanaiCenterNoS01 <- sort(bfMedianLatencyCenterNoS01, decreasing = TRUE) # sort such that winning model is at the top
kable(select(extractBF(bfMedianLatencyCenterNoS01), bf)) # show only the Bayes factors in a table

kable(inclusionBF(bfMedianLatencyCenterNoS01, models = "matched"))

medianLatencyCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(latency = mean(latency.baseline)) %>% # average over all other variables (df is now still grouped per stimulation)
  summarise_if(is.numeric, funs(list(tidy(t.test(.))))) %>%  # run one-sample t-test for each stimulation condition, return tidy data frames
  unnest() %>% # unpack the list-column with data frame for each test
  kable(.)

medianLatencyCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(latency = mean(latency.baseline)) %>% # average over all other variables
  spread(stimulation, latency) %>% # make separate columns with test data
  summarise_if(is.numeric, funs(extractBF(ttestBF(.), onlybf = TRUE))) %>% # run Bayesian t-test on each column, keeping only the BF
  gather(stimulation,BF,anodal,cathodal) %>% # make row for each stimulation condition
  kable(.)

# Calculate end point deviation
accData <- preproc %>%
  group_by(subject,stimulation,leg,block,trial,direction,type) %>%
  summarise(deviation.end = sqrt(deviation.end.x^2 + deviation.end.y^2))

accMeanLeg <- accData %>%
  group_by(subject,stimulation,direction,type) %>% 
  summarise(baseline = mean(deviation.end[leg == "pre"]), # take average of 3 blocks, make new column
            tDCS = mean(deviation.end[leg == "tDCS"]),
            post.1 = mean(deviation.end[leg == "post.1"]),
            post.2 = mean(deviation.end[leg == "post.2"])) %>%
  gather(leg,deviation.end,baseline,tDCS,post.1,post.2) %>% # gather new columns to use as factor
  mutate(leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))) # reorder factor levels

accMeanBaseline <- accMeanLeg %>%
  group_by(subject,stimulation,direction,type) %>% # for each condition, subtract baseline scores and make new columns
  summarise(baseline = deviation.end[leg == "baseline"] - deviation.end[leg == "baseline"],
           tDCS = deviation.end[leg == "tDCS"] - deviation.end[leg == "baseline"],
           post.1 = deviation.end[leg == "post.1"] - deviation.end[leg == "baseline"],
           post.2 = deviation.end[leg == "post.2"] - deviation.end[leg == "baseline"]) %>%
  gather(leg, deviation.end.baseline, baseline, tDCS, post.1, post.2)  %>% # gather new columns to use as factor 
  mutate(leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))) # reorder factor levels

baselineLabelAcc <- accMeanLeg %>%
  group_by(stimulation,direction,type,leg) %>%
  filter(leg == "baseline") %>%
  summarise(deviation.end.baseline = round(mean(deviation.end), digits = 2))

plot_sub_acc_lateral <- ggplot(filter(accMeanLeg, type == "lateral"), aes(leg, deviation.end)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  guides(colour = FALSE) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("mean deviation " ( degree)), breaks = seq(0.5,2,0.5), limits = c(0.3,2.2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Lateral saccades")

plot_sub_acc_center <- ggplot(filter(accMeanLeg, type == "center"), aes(leg, deviation.end)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  guides(colour = FALSE) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("mean deviation " ( degree)), breaks = seq(0.5,2,0.5), limits = c(0.3,2.2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Center saccades")

plot_group_acc_lateral <- ggplot(filter(accMeanBaseline, type == "lateral"), aes(leg, deviation.end.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelAcc, type=="lateral"), aes(y = c(.05,.025,.1,.075), label=deviation.end.baseline, color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("deviation change from baseline " ( degree)), breaks = seq(-.2,.2,.1)) +
  coord_cartesian(ylim = c(-.22,.22)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())

plot_group_acc_center <- ggplot(filter(accMeanBaseline, type == "center"), aes(leg, deviation.end.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelAcc, type=="center"), aes(y = c(.05,.025,.1,.075), label=deviation.end.baseline, color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("deviation change from baseline " ( degree)), breaks = seq(-.2,.2,.1)) +
  coord_cartesian(ylim = c(-.22,.22)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())

legend_fig6 <- get_legend(plot_group_acc_lateral)
figure_6 <- plot_grid(
  plot_sub_acc_lateral, plot_sub_acc_center,
  plot_group_acc_lateral + theme(legend.position = "none"),
  plot_group_acc_center + theme(legend.position = "none"), rel_heights = c(1,.75))
figure_6 <- plot_grid(figure_6,legend_fig6,rel_widths = c(1,1/10))
figure_6

ggsave("fig/figure_6.pdf", plot = figure_6, width = 180, height = 112.5, units = "mm")
ggsave("fig/figure_6.png", plot = figure_6, width = 180, height = 112.5, units = "mm")

accMeanLeg %>%
  filter(leg == "baseline") %>%
  group_by(direction,type) %>% 
  nest() %>% 
  mutate(stats = map(data, ~t.test(formula = deviation.end~stimulation, paired = TRUE, data =.))) %>% # run t-test on the data frames
  mutate(tidy_model = map(stats, tidy)) %>%
  unnest(tidy_model, .drop = TRUE) %>% 
  kable(.)

accMeanLeg %>%
  filter(leg != "baseline", type == "center") %>%
  group_by(stimulation,direction,leg) %>% 
  summarise(mean = mean(deviation.end)) %>%
  kable(.)            

meanAccLateralStats <- accMeanBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "lateral") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))

aovMeanAccLateral <- ezANOVA(data = data.frame(meanAccLateralStats), dv = deviation.end.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)
kable(aovMeanAccLateral$ANOVA)

kable(aovMeanAccLateral$`Mauchly's Test for Sphericity`)

kable(aovMeanAccLateral$`Sphericity Corrections`)

bfMeanAccLateral = anovaBF(deviation.end.baseline~stimulation*leg*direction+subject, data = data.frame(meanAccLateralStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000)
bfMeanAccLateral = sort(bfMeanAccLateral, decreasing = TRUE) # sort such that winning model is at the top

kable(select(extractBF(bfMeanAccLateral), bf)) # show only the Bayes factors in a table

kable(inclusionBF(bfMeanAccLateral, models = "matched"))

meanAccCenterStats <- accMeanBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "center") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))

aovMeanAccCenter <- ezANOVA(data = data.frame(meanAccCenterStats), dv = deviation.end.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)
kable(aovMeanAccCenter$ANOVA)

kable(aovMeanAccCenter$`Mauchly's Test for Sphericity`)

kable(aovMeanAccCenter$`Sphericity Corrections`)

bfMeanAccCenter = anovaBF(deviation.end.baseline~stimulation*leg*direction+subject, data = data.frame(meanAccCenterStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000)
bfMeanAccCenter = sort(bfMeanAccCenter, decreasing = TRUE) # sort such that winning model is at the top

kable(select(extractBF(bfMeanAccCenter), bf)) # show only the Bayes factors in a table

kable(inclusionBF(bfMeanAccCenter, models = "matched"))

meanAccCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(deviation = mean(deviation.end.baseline)) %>% # average over all other variables (df is now still grouped per stimulation)
  summarise_if(is.numeric, funs(list(tidy(t.test(.))))) %>%  # run one-sample t-test for each stimulation condition, return tidy data frames
  unnest() %>% # unpack the list-column with data frame for each test
  kable(.)

meanAccCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(deviation = mean(deviation.end.baseline)) %>% # average over all other variables
  spread(stimulation, deviation) %>% # make separate columns with test data
  summarise_if(is.numeric, funs(extractBF(ttestBF(.), onlybf = TRUE))) %>% # run Bayesian t-test on each column, keeping only the BF
  gather(stimulation,BF,anodal,cathodal) %>% # make row for each stimulation condition
  kable(.)

varMeanLeg <- preproc %>%
  group_by(subject,stimulation,leg,direction,type) %>% 
  summarise(std.deviation.x = sd(deviation.end.x)) %>% # standard deviation
  ungroup() %>%
  mutate(leg = as.character(leg), # edit leg factor to match other data frames
         leg = replace(leg, leg == "pre", "baseline"),
         leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))
         )

varMeanBaseline <- varMeanLeg %>%
  group_by(subject,stimulation,direction,type) %>% # for each condition, subtract baseline scores and make new columns
  summarise(baseline = std.deviation.x[leg == "baseline"] - std.deviation.x[leg == "baseline"],
           tDCS = std.deviation.x[leg == "tDCS"] - std.deviation.x[leg == "baseline"],
           post.1 = std.deviation.x[leg == "post.1"] - std.deviation.x[leg == "baseline"],
           post.2 = std.deviation.x[leg == "post.2"] - std.deviation.x[leg == "baseline"]) %>%
  gather(leg, std.deviation.x.baseline, baseline, tDCS, post.1, post.2)  %>% # gather new columns to use as factor 
  mutate(leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))) # reorder factor levels

baselineLabelVar <- varMeanLeg %>%
  group_by(stimulation,direction,type,leg) %>%
  filter(leg == "baseline") %>%
  summarise(std.deviation.x.baseline = round(mean(std.deviation.x), digits = 2))

plot_sub_var_lateral <- ggplot(filter(varMeanLeg, type == "lateral"), aes(leg, std.deviation.x)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  guides(colour = FALSE) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("standard deviation " ( degree)), breaks = seq(0.5,2,0.5), limits = c(0,2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Lateral saccades")

plot_sub_var_center <- ggplot(filter(varMeanLeg, type == "center"), aes(leg, std.deviation.x)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  guides(colour = FALSE) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("standard deviation " ( degree)), breaks = seq(0.5,2,0.5), limits = c(0,2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Center saccades")

plot_group_var_lateral <- ggplot(filter(varMeanBaseline, type == "lateral"), aes(leg, std.deviation.x.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelVar, type=="lateral"), aes(y = c(.05,.025,.1,.075), label=(std.deviation.x.baseline), color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_y_continuous(expression("SD change from baseline " ( degree)), breaks = seq(-.2,.2,.1)) +
  coord_cartesian(ylim = c(-.2,.2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())

plot_group_var_center <- ggplot(filter(varMeanBaseline, type == "center"), aes(leg, std.deviation.x.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelVar, type=="center"), aes(y = c(.05,.025,.1,.075), label=(std.deviation.x.baseline), color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("SD change from baseline " ( degree)), breaks = seq(-.2,.2,.1)) +
  coord_cartesian(ylim = c(-.2,.2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())

legend_fig7 <- get_legend(plot_group_acc_lateral)
figure_7 <- plot_grid(
  plot_sub_var_lateral, plot_sub_var_center,
  plot_group_var_lateral + theme(legend.position = "none"),
  plot_group_var_center + theme(legend.position = "none"), rel_heights = c(1,.75))
figure_7 <- plot_grid(figure_7,legend_fig7,rel_widths = c(1,1/10))
figure_7

ggsave("fig/figure_7.pdf", plot = figure_7, width = 180, height = 112.5, units = "mm")
ggsave("fig/figure_7.png", plot = figure_7, width = 180, height = 112.5, units = "mm")

varMeanLeg %>%
  filter(leg == "baseline") %>%
  group_by(direction,type) %>% 
  nest() %>% 
  mutate(stats = map(data, ~t.test(formula = std.deviation.x~stimulation, paired = TRUE, data =.))) %>% # run t-test on the data frames
  mutate(tidy_model = map(stats, tidy)) %>%
  unnest(tidy_model, .drop = TRUE) %>% 
  kable(.)

meanVarLateralStats <- varMeanBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "lateral") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))

aovMeanVarLateral <- ezANOVA(data = data.frame(meanVarLateralStats), dv = std.deviation.x.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)
kable(aovMeanVarLateral$ANOVA)

kable(aovMeanVarLateral$`Mauchly's Test for Sphericity`)

kable(aovMeanVarLateral$`Sphericity Corrections`)

bfMeanVarLateral = anovaBF(std.deviation.x.baseline~stimulation*leg*direction+subject, data = data.frame(meanVarLateralStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000)
bfMeanVarLateral = sort(bfMeanVarLateral, decreasing = TRUE) # sort such that winning model is at the top

kable(select(extractBF(bfMeanVarLateral), bf)) # show only the Bayes factors in a table

kable(inclusionBF(bfMeanVarLateral, models = "matched"))

meanVarCenterStats <- varMeanBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "center") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))

aovMeanVarCenter <- ezANOVA(data = data.frame(meanVarCenterStats), dv = std.deviation.x.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)
kable(aovMeanVarCenter$ANOVA)

kable(aovMeanVarCenter$`Mauchly's Test for Sphericity`)

kable(aovMeanVarCenter$`Sphericity Corrections`)

bfMeanVarCenter = anovaBF(std.deviation.x.baseline~stimulation*leg*direction+subject, data = data.frame(meanVarCenterStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000)
bfMeanVarCenter = sort(bfMeanVarCenter, decreasing = TRUE) # sort such that winning model is at the top

kable(select(extractBF(bfMeanVarCenter), bf)) # show only the Bayes factors in a table

kable(inclusionBF(bfMeanVarCenter, models = "matched"))

meanVarCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(std.deviation.x. = mean(std.deviation.x.baseline)) %>% # average over all other variables (df is now still grouped per stimulation)
  summarise_if(is.numeric, funs(list(tidy(t.test(.))))) %>%  # run one-sample t-test for each stimulation condition, return tidy data frames
  unnest() %>% # unpack the list-column with data frame for each test
  kable(.)

meanVarCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(std.deviation.x = mean(std.deviation.x.baseline)) %>% # average over all other variables
  spread(stimulation, std.deviation.x) %>% # make separate columns with test data
  summarise_if(is.numeric, funs(extractBF(ttestBF(.), onlybf = TRUE))) %>% # run Bayesian t-test on each column, keeping only the BF
  gather(stimulation,BF,anodal,cathodal) %>% # make row for each stimulation condition
  kable(.)

qData <- groupData %>%
  filter(!is.na(latency)) %>% # discard missing saccades
  group_by(subject,leg,type,direction) %>% # for each condition
  nest(stimulation,latency) %>% # make a list_column "data" out of the stimulation and latency columns. Now, each group has its own data frame consisting of the stimulation and latency columns for that group
  mutate(shift = purrr::map(data, ~ shiftdhd_pbci(., formula = latency ~ stimulation, nboot = 2000))) # for each group, estimate quantiles and compute shift function, and store as a list column in "shift"

qData <- unnest(qData,shift) # unpack the list column to get the model results for each group in the original data frame

# Alternatively, instead of "nest ...", "mutate ..." and "unnest ...", you can call shiftdhd through dplyr::do (but this is basically depreated in favor of purrr:map)
# do(shiftdhd(.[,c("stimulation","latency")], formula = latency ~ stimulation, nboot = 100)) # estimate quantiles and compute shift function

qData <- read_csv(here("data", "sacc-tDCS_quantiles.csv")) %>%
  filter(!(subject %in% subs2exclude)) %>% # exclude subjects
    mutate(leg = replace(leg, leg == "pre", "baseline"), # rename and reoder levels
         leg = replace(leg, leg == "post.1", "post-1"),
         leg = replace(leg, leg == "post.2", "post-2"),
         leg = factor(leg, levels = c("baseline", "tDCS", "post-1", "post-2")))
  

qStats <- qData %>%
  group_by(subject,leg,type,direction) %>%
  mutate(deco = c(seq(1,5),seq(4,1))) %>% # add code of deciles to data frame
  group_by(leg,type,direction,q) %>% 
  mutate(anodal = mean(anodal)) %>% # mean of "anodal" quantiles OVER subjects
  group_by(leg,type,direction) %>% 
  mutate(anodal_median = median(anodal)) # median for plotting

qSig <- qData %>%
  group_by(subject,leg,type,direction,q) %>%
  mutate(significance = NA,
         # If (anodal - cathodal) quantile difference is positive, latency for anodal > latency for cathodal
         significance = replace(significance, p_value < p_crit & difference > 0, "cathodal.faster"),
         # If (anodal - cathodal) quantile difference is negative, latency for anodal < latency for cathodal
         significance = replace(significance, p_value < p_crit & difference < 0, "anodal.faster")
  )

plot_shift_lateral <- ggplot(filter(qStats, type == "lateral"), aes(anodal, difference)) +
   facet_grid(leg ~ direction) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.5) +
   geom_vline(aes(xintercept = anodal_median), linetype = "dashed", alpha = 0.5) +
   stat_summary(fun.data = mean_cl_normal, geom = "linerange", colour = "black", size = 0.5) +
   stat_summary(fun.y = mean, geom = "line", size = 1, colour = "grey50", alpha = 0.5) +
   stat_summary(fun.y = mean, geom = "point", aes(fill = deco), size = 2, colour = "black", shape = 21) +
   scale_fill_gradient(low = "white", high = "grey30", guide = FALSE) +
   scale_x_continuous("anodal deciles (ms)", limits = c(80,200), breaks = seq(100,200,25)) +
   scale_y_continuous("anodal - cathodal deciles (ms)")

plot_sig_lateral <- ggplot(filter(qSig, type == "lateral", !is.na(significance)), aes(q, fill = significance)) +
  facet_grid(leg ~ direction) +
  geom_bar(position = "stack") +
  stat_bin(binwidth = .1, geom = "text", size = mm_to_pt, aes(label = ..count..), position = position_stack(vjust = 0.5)) +
  scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_continuous("decile", breaks = seq(0.1,0.9,0.1), labels = seq(1,9,1)) +
  scale_y_continuous("number of participants with significant difference", limits = c(0, length(unique(qSig$subject))), breaks = c(0,10,20,length(unique(qSig$subject))))

plot_shift_center <- ggplot(filter(qStats, type == "center"), aes(anodal, difference)) +
   facet_grid(leg ~ direction) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.5) +
   geom_vline(aes(xintercept = anodal_median), linetype = "dashed", alpha = 0.5) +
   stat_summary(fun.data = mean_cl_normal, geom = "linerange", colour = "black", size = 0.5) +
   stat_summary(fun.y = mean, geom = "line", size = 1, colour = "grey50", alpha = 0.5) +
   stat_summary(fun.y = mean, geom = "point", aes(fill = deco), size = 2, colour = "black", shape = 21) +
   scale_fill_gradient(low = "white", high = "grey30", guide = FALSE) +
   scale_x_continuous("anodal deciles (ms)", limits = c(80,200), breaks = seq(100,200,25)) +
   scale_y_continuous("anodal - cathodal deciles (ms)")

plot_sig_center <- ggplot(filter(qSig, type == "center", !is.na(significance)), aes(q, fill = significance)) +
  facet_grid(leg ~ direction) +
  geom_bar(position = "stack") +
  stat_bin(binwidth = .1, geom = "text", size = mm_to_pt, aes(label = ..count..), position = position_stack(vjust = 0.5)) +
  scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_continuous("decile", breaks = seq(0.1,0.9,0.1), labels = seq(1,9,1)) +
  scale_y_continuous("number of participants with significant difference", limits = c(0, length(unique(qSig$subject))), breaks = c(0,10,20,length(unique(qSig$subject))))

figure_5_lateral <- plot_grid(plot_shift_lateral, plot_sig_lateral + theme(legend.position = "none"), rel_widths = c(1.33,1)) # top row
figure_5_center <- plot_grid(plot_shift_center,plot_sig_center + theme(legend.position = "none"), rel_widths = c(1.33,1)) # bottom row
#title objects
title_lateral <- ggdraw() + draw_label("Lateral saccades", fontface = 'bold', size = base_font_size)
title_center <- ggdraw() + draw_label("Center saccades", fontface = 'bold', size = base_font_size)
#combine with titles
figure_5_lateral <- plot_grid(title_lateral, figure_5_lateral, ncol = 1, rel_heights = c(0.05, 1))
figure_5_center <- plot_grid(title_center, figure_5_center, ncol = 1, rel_heights = c(0.05, 1))
figure_5 <- plot_grid(figure_5_lateral, figure_5_center, nrow = 2)
figure_5

ggsave("fig/figure_5.pdf", plot = figure_5, width = 180, height = 180, units = "mm")
ggsave("fig/figure_5.png", plot = figure_5, width = 180, height = 180 , units = "mm")

# Load the data frame
dataFile <- here("data", "tdcs_sensations.csv")
sensData <- read_csv2(dataFile, col_types = cols(
  session = col_factor(c("first","second")),
  stimulation = col_factor(c("anodal","cathodal"))
))
kable(head(sensData)) # show data frame

idxComplete <- rowSums(is.na(sensData)) != ncol(sensData) - 3 # rows that do not have all NAs (except the 3 factor columns)
# calculate number of questionnaires completed per stimulation type
nAnodal <- sum(sensData$stimulation == "anodal" & idxComplete)
nCathodal <- sum(sensData$stimulation == "cathodal" & idxComplete)

# Make long form data frame of sensation intensity
ratings <- sensData %>%
  select(everything(), -contains("conf"), -felt.more) %>% # drop other columns
  gather(sensation, rating, itching:nausea) # make long form
# Make long form data frame of sensation confidence
confidence <- sensData %>%
  select(contains("conf"), subject, session, stimulation) %>% 
  gather(sensation, confidence, conf.itching:conf.nausea) %>%
  mutate(sensation = str_replace(sensation, "conf.", "")) # get rid of "conf." prefix so it matches the sensation intensity tibble
# Join the two data frames
sensDataLong <- dplyr::full_join(ratings,confidence)

plot_sensation <- ggplot(sensDataLong, aes(rating, fill = stimulation)) +
    facet_wrap(~sensation, nrow = 1) +
    geom_bar(position = "stack") +
    stat_bin(binwidth = 1, geom = "text", size = mm_to_pt, aes(label = ..count..), position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
    xlim(0.5,4.5) + # exclude "0" ratings that were not present
    ylim(0,35) + # bound plot at max number of ratings
    ylab("number of sessions") +
  ggtitle("Sensation intensity")

plot_confidence <- ggplot(sensDataLong, aes(confidence, fill = stimulation)) +
    facet_wrap(~sensation, nrow = 1) +
    geom_bar(position = "stack") +
    stat_bin(binwidth = 1, geom = "text", size = mm_to_pt, aes(label = ..count..), position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
    xlim(0.5,4.5) +
    ylim(0,35) +
    xlab("ratings") +
    ylab("number of sessions") +
    ggtitle("Confidence")

legend_S1 <- get_legend(plot_sensation)
figure_S1 <- plot_grid(
  plot_sensation + theme(legend.position = "none"),
  plot_confidence + theme(legend.position = "none"),
  nrow = 2)
figure_S1 <- plot_grid(figure_S1,legend_S1, rel_widths = c(1,1/10))
figure_S1

ggsave("fig/figure_S1.pdf", plot = figure_S1, width = 180, height = 112.5, units = "mm")
ggsave("fig/figure_S1.png", plot = figure_S1, width = 180, height = 112.5, units = "mm")

sensationList <- c("itching", "tingling", "burning", "pain", "headache", "fatigue", "dizziness", "nausea")
senseTests <- data.frame(sensation = sensationList, p.value = NA) # initialize results data frame
for (item in sensationList) {
  testData <- sensData[[item]] # extract column with test dat
  tmp <- wilcox.test(testData[sensData$stimulation == "anodal"], testData[sensData$stimulation == "cathodal"])
  senseTests$p.value[senseTests$sensation %in% item] <- tmp$p.value # put p-value in row of results data frame
}
kable(senseTests)

dataFile <- here("data", "FEF_coords_native.csv")
nativeCoords <- read_csv2(dataFile)
nativeCoords %>% 
  select(-folder, -scan) %>% # drop columns with folder and scan names
  filter(!(subject %in% subs2exclude)) %>% # drop rows with excluded subjects
  kable(.)

dataFile <- here("data", "FEF_coords_MNI.csv")
mniCoords <- read_delim(dataFile, ";")
mniCoords <- filter(mniCoords, !(subject %in% subs2exclude)) # exclude subjects
mniCoords %>%
  select(-folder, -scan) %>% # drop columns with folder and scan names
  kable(.)

mniCoords %>%
  gather(dimension, coord, MNI_X:MNI_Z) %>%
  group_by(dimension) %>%
  summarise_at(vars(coord), funs(mean, min, max, sd)) %>% 
  kable(.)

---
title: "No evidence that frontal eye field tDCS affects latency or accuracy of prosaccades"
author: "Leon Reteig, Tomas Knapen, Richard Ridderinkhof, Heleen Slagter"
output:
  github_document:
    toc: true
    toc_depth: 3
  html_notebook:
    code_folding: hide
    highlight: pygments
    toc: true
    toc_float: true
---

```{r setup}
# Load some libraries
library(here) # file paths
library(tidyverse) # importing, transforming, and visualizing data frames
library(stringr) # manipulating strings
library(broom) # # transform model output into a data frame
library(cowplot) # formatting plots
library(knitr) # R notebook output
library(ez) # classical ANOVA
library(BayesFactor) # Bayesian statistics
library(rogme) # shift functions

# set default output and figure options
knitr::opts_chunk$set(message = FALSE, warning = FALSE, fig.width = 7, fig.asp = 0.618, out.width = "75%", fig.align = "center")

source(here("src", "lib", "InclusionBF.R"))
print(sessionInfo())
```

```{r Make plot theme}
base_font_size <- 8
mm_to_pt <- 7 * 0.35 # so geom_text size is same as axis text
base_font_family <- "Helvetica"
base_line_size <- .25
theme_custom <- theme_cowplot(font_size = base_font_size, font_family = base_font_family, line_size = base_line_size)
theme_set(theme_custom)
```

# Group analyses 

## Load data

All (meta)data are stored as `.csv` files in the `/data` folder.

### Eye tracking data

Here we load the `.csv` file with the processed eye tracking data, which was created in MATLAB. To recreate it from the raw data, run the `analysis.m` script. This scripts calls the `processEDF.m` function to parse the raw eye tracking data.

```{r Load eye tracking data}
# Load eye tracking data into data frame
dataFile <- here("data", "sacc-tDCS_data.csv")
groupData <- read_csv(dataFile, col_names = TRUE, na = "NaN", progress = FALSE, col_types = cols(
  stimulation = col_factor(c("anodal","cathodal")),
  leg = col_factor(c("pre","tDCS","post")),
  type = col_factor(c("lateral","center")),
  direction = col_factor(c("left","right")) 
))
```

```{r Show eye tracking data frame}
kable(head(groupData))
```

* __subject__: subject ID
* __stimulation__: Whether data are from the `anodal` or `cathodal` session
* __leg__: Whether data are before (`pre`), during (`tDCS`), or after (`post`) tDCS
* __block__: After each block participant had a brief break and tracker was recalibrated
* __trial__: trial number within a block
* __type__:
    * `lateral` - fixation in center of display, saccade made towards the periphery
    * `center` - fixation in periphery, saccade made back towards the center of the display
* __direction__: `left` for saccades towards the left of current fixation position; `right` for saccades to the right
* __deviation.start__ : distance (in visual angle) from saccade start point to fixation
* __deviation.end.x__: distance (in visual angle) from x-coordinate of saccade end point to x-coordinate of target location
* __deviation.end.y__: same for y-coordinate
* __amplitude__: distance (in visual angle) between saccade start and end point
* __latency__: time (in ms) from target onset to start of saccade
* __drift.x__: distance (in visual angle) between x-coordinate of average fixation position during the break to x-coordinate of fixation stimulus. This stimulus was displayed at each break in the task, so this data can be used as an estimate of offsets to do drift correction.
* __drift.y__: same for y-coordinate

### Metadata

#### Session info

```{r Load session info data}
# Load eye tracking data into data frame
dataFile <- here("data", "session_info.csv")
sessionData <- read_csv2(dataFile, col_names = TRUE, progress = FALSE, col_types = cols(
  session = col_factor(c("first","second")),
  stimulation = col_factor(c("anodal","cathodal"))
))
```

```{r Show session info data frame, results='asis'}
kable(head(sessionData))
```

* __subject__: subject ID
* __session__: Whether data are from the `first`) or `second` session
* __stimulation__: Whether data are from the `anodal` or `cathodal` session
* __date__: YY/MM/DD date the session took place
* __day__: day of the week the session took place
* __time__: time of day the session took place

#### Subject info

```{r Load subject info data}
# Load eye tracking data into data frame
dataFile <- here("data", "subject_info.csv")
subjectData <- read_csv2(dataFile, col_names = TRUE, progress = FALSE, col_types = cols(
  session.order = col_factor(c("first.anodal", "first.cathodal"))
))
```

```{r Show subject info data frame, results='asis'}
kable(head(subjectData))
```

* __subject__: subject ID
* __session.order__: Whether subject had anodal stimulation in the first session (`first.anodal`) or cathodal stimulation in the first session (`first.cathodal`)
* __gender__
* __age__: in years
* __dominant.eye__: result of eye dominance test

## Preprocess eye data

_See the `inspection.nb.html` notebook for more explanation and exploration of preprocessing, outlier saccades and subject exclusion_

### Reject outlier saccades

```{r Outlier criteria}
tooFast <- 50
tooSlow <- 400
badFix <- 1.8
badSacc <- 8
```

Criteria for outliers:

* Discard fast saccades, with a latency of `r tooFast` ms or less
* Discard slow saccades, saccades with a latency of `r tooSlow` ms or more
* Discard inaccurate fixations, with saccade starting point more than `r badFix` degrees or more away from fixation
* Discard faulty saccades, with x-coordinate of saccade end point `r badSacc` degree or more away from the target

```{r Mark saccades as outliers}
# Mark outliers
groupData <- mutate(groupData, outlier = "non.outlier", # fill vector for all saccades
                    outlier = ifelse(latency < tooFast, "fast", outlier), # mark too fast saccades as "fast"
                    outlier = ifelse(latency > tooSlow, "slow", outlier), # mark too slow saccades as "slow"
                    outlier = ifelse(deviation.start > badFix, "fixation", outlier), # mark bad fixations as "fixation"
                    outlier = ifelse(deviation.end.x > badSacc, "saccade", outlier), # mark inaccurate saccades as "saccade"
                    outlier = ifelse(is.na(latency), "none", outlier) # mark absence of saccade as "none"
                    )
```

```{r Remove outlier saccades from data}
preproc <- filter(groupData, outlier == "non.outlier")
```

### Exclude participants

* S21 and S25 were tested < 48h apart
* S16, S22 and S28 have fewer than 50 saccades per condition after trial rejection

```{r List of subjects to exclude}
subs2exclude <- c("S21","S25","S16","S22","S28")
```

```{r Remove excluded participants from data}
preproc <- filter(preproc, !(subject %in% subs2exclude))
```

### Collapse data across 15 minute intervals

Cut the post-block into two so we have four 15-minute intervals: one before, one during, and two after stimulation.

```{r Cut into 15-minute intervals}
preproc <- preproc %>%
  mutate(leg = as.character(leg), # cannot edit leg if it's still a factor
         leg = replace(leg, leg == "post" & block <= 3, "post.1"),
         leg = replace(leg, block > 3, "post.2"),
         leg = factor(leg, levels = c("pre", "tDCS", "post.1", "post.2")) # refactor and order levels
         )
```

## Subject and session descriptives

### Sample demographics

For S23, no eye data was collected because eye tracker could not be calibrated, so here we don't include their subject/session data.

__Gender of remaining participants:__

```{r subject gender}
subjectData %>%
  filter(!(subject %in% c(subs2exclude, "S23"))) %>% # remove rows with these subject
  group_by(gender) %>% # for each gender
  summarise(count = n_distinct(subject)) %>%
  kable(.)# count number of subjects
```

__Age:__

```{r subject age}
subjectData %>%
  filter(!(subject %in% c(subs2exclude, "S23"))) %>%
  summarise_at(vars(age), funs(mean, min, max, sd), na.rm = TRUE) %>% # apply summary functions to age column
  kable(.)
```

### tDCS session order

```{r session order}
sessionData %>%
  filter(!(subject %in% c(subs2exclude, "S23"))) %>%
  group_by(session, stimulation) %>%
  summarise(count = n_distinct(subject)) %>% 
  kable(.)
```

### Saccade counts

Average number of saccades per type after rejection:

```{r valid saccades per cell}
preproc %>%
  group_by(subject,stimulation,leg,direction,type) %>% # for each cell
  summarise(saccades = n()) %>% # count how many saccacdes there are left
  group_by(type) %>% 
  summarise_at(vars(saccades), funs(mean, min, max, sd)) %>% # compute summary statistics per type
  kable(.)
```

Percentage of outlier saccades per type out of all sacccades:

```{r proportion of outlier types}
n_total <- nrow(filter(groupData, !(subject %in% subs2exclude)))  # total amount of saccades across all included sessions/subjects

groupData %>%
  filter(!(subject %in% subs2exclude), !(outlier %in% c("non.outlier", "none"))) %>%
  group_by(subject,stimulation,leg,direction,type,outlier) %>%
  summarize(outlier_count = n()) %>% # for each condition and subject, count how many (non)outliers there are
  group_by(outlier) %>% # for each outlier type
  summarise(percentage = sum(outlier_count) / n_total *100) %>% # calculate percentage
  kable(.)
```

## Saccade latency

_See the `median_latency.nb.html` notebook for more explanation and exploration of the median saccade latency data_

Compute the median for each 3 blocks

```{r Compute median latency over blocks}
latencyMedianLeg <- preproc %>%
  group_by(subject,stimulation,direction,type) %>% 
  summarise(baseline = median(latency[leg == "pre"]), # take average of 3 blocks, make new column
            tDCS = median(latency[leg == "tDCS"]),
            post.1 = median(latency[leg == "post.1"]),
            post.2 = median(latency[leg == "post.2"])) %>%
  gather(leg,latency,baseline,tDCS,post.1,post.2) %>% # gather new columns to use as factor
  mutate(leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))) # reorder factor levels
```

Make a separate data frame where the median baseline latency is subtracted from subsequent blocks

```{r Subtract baseline latency}
latencyMedianBaseline <- latencyMedianLeg %>%
  group_by(subject,stimulation,direction,type) %>% # for each condition, subtract baseline scores and make new columns
  summarise(baseline = latency[leg == "baseline"] - latency[leg == "baseline"],
           tDCS = latency[leg == "tDCS"] - latency[leg == "baseline"],
           post.1 = latency[leg == "post.1"] - latency[leg == "baseline"],
           post.2 = latency[leg == "post.2"] - latency[leg == "baseline"]) %>%
  gather(leg, latency.baseline, baseline, tDCS, post.1, post.2)  %>% # gather new columns to use as factor 
  mutate(leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))) # reorder factor levels
```

### Figure 4

```{r Create baseline latency plot labels}
baselineLabelLatency <- latencyMedianLeg %>%
  group_by(stimulation,direction,type,leg) %>%
  filter(leg == "baseline") %>%
  summarise(latency.baseline = round(mean(latency)))
```

Plot median _lateral saccade_ latency per participant

```{r Median latency lateral saccades}
plot_sub_latency_lateral <- ggplot(filter(latencyMedianLeg, type == "lateral"), aes(leg, latency)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  guides(colour = FALSE) +
  scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous("median latency (ms)", breaks = seq(100,200,20), limits = c(90,220)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Lateral saccades")
```

Plot median _center saccade_ latency per participant

```{r Median latency center saccades}
plot_sub_latency_center <- ggplot(filter(latencyMedianLeg, type == "center"), aes(leg, latency)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous("median latency (ms)", breaks = seq(100,200,20), limits = c(90,220)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Center saccades")
```

Plot group mean _lateral saccade_ latency as change from baseline

```{r Group mean latency lateral saccades}
plot_group_latency_lateral <- ggplot(filter(latencyMedianBaseline, type == "lateral"), aes(leg, latency.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelLatency, type=="lateral"), aes(y = c(2,1,4,3), label=latency.baseline, color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous("latency change from baseline (ms)", breaks = seq(-8,8,2)) +
  coord_cartesian(ylim = c(-8,8)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())
```

Plot group mean _center saccade_ latency as change from baseline

```{r Group mean latency center saccades}
plot_group_latency_center <- ggplot(filter(latencyMedianBaseline, type == "center"), aes(leg, latency.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelLatency, type=="center"), aes(y = c(2,1,3,4), label=latency.baseline, color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete(labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous("latency change from baseline (ms)", breaks = seq(-8,8,2)) +
  coord_cartesian(ylim = c(-8,8)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())
```

Combine all the plots:

```{r Figure 4}
legend_fig4 <- get_legend(plot_group_latency_lateral)
figure_4 <- plot_grid(
  plot_sub_latency_lateral, plot_sub_latency_center,
  plot_group_latency_lateral + theme(legend.position = "none"),
  plot_group_latency_center + theme(legend.position = "none"), rel_heights = c(1,.75))
figure_4 <- plot_grid(figure_4,legend_fig4,rel_widths = c(1,1/10))
figure_4
```

Save the plot:

```{r Save figure 4, eval=FALSE}
ggsave("fig/figure_4.pdf", plot = figure_4, width = 180, height = 112.5, units = "mm")
ggsave("fig/figure_4.png", plot = figure_4, width = 180, height = 112.5, units = "mm")
```

### Statistics

#### Contralateral saccades in the anodal session

Means and SDs for effect of _anodal_ tDCS on the latency changes in _contralateral_ (here: left) _lateral_ saccades, as this is what [Kanai et al. (2012)](http://dx.doi.org/10.3389/fpsyt.2012.00045) specifically found:

```{r Anodal left lateral saccades}
latencyMedianBaseline %>%
  filter(type == "lateral", stimulation == "anodal", direction == "left", leg != "baseline") %>%
  group_by(leg) %>%
  summarise_at(vars(latency.baseline), funs(mean, sd)) %>%
  kable(.)
```

#### Baseline tests 

```{r t-tests of baseline difference - latency}
latencyMedianLeg %>%
  filter(leg == "baseline") %>%
  group_by(direction,type) %>% # for each of these 4 pairs
  nest() %>% # create a separate data frame of all remaining columns. These data frames are stored in a list-column named "data"
  mutate(stats = map(data, ~t.test(formula = latency~stimulation, paired = TRUE, data =.))) %>% # run t-test on the data frames
  mutate(tidy_model = map(stats, tidy)) %>% # force the four test outputs to also be data frames in a list column ("tidy_model")
  unnest(tidy_model, .drop = TRUE) %>% # unpack the list-column with results, drop the list-column we used to organize the data
  kable(.)
```

#### Lateral saccades {.tabset .tabset-fade}

##### Classical ANOVA

Repeated measures ANOVA matching [Kanai et al. (2012)](http://dx.doi.org/10.3389/fpsyt.2012.00045).

__Data__: Baseline subtracted, lateral saccades

__Dependent measure__: saccade latency

__Factors__:

* STIMULATION (anodal vs. cathodal)
* LEG (tDCS, post.1, post.2)
* DIRECTION (left vs. right)

Prepare data: 

```{r test data median latency lateral}
medianLatencyLateralStats <- latencyMedianBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "lateral") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))
```

```{r rmANOVA median latency lateral, results='asis'}
aovMedianLatencyLateral <- ezANOVA(data = data.frame(medianLatencyLateralStats), dv = latency.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)

kable(aovMedianLatencyLateral$ANOVA)
kable(aovMedianLatencyLateral$`Mauchly's Test for Sphericity`)
kable(aovMedianLatencyLateral$`Sphericity Corrections`)
```

##### Bayesian ANOVA

Same design as the classical ANOVA

Bayes factors agains the null model:

```{r Bayesian ANOVA median latency lateral}
bfMedianLatencyLateral = anovaBF(latency.baseline~stimulation*leg*direction+subject, data = data.frame(medianLatencyLateralStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000) 
bfMedianLatencyLateral = sort(bfMedianLatencyLateral, decreasing = TRUE) # sort such that winning model is at the top
```

```{r}
kable(select(extractBF(bfMedianLatencyLateral), bf)) # show only the Bayes factors in a table
```

Inclusion Bayes factor across matched models:

```{r inclusion BF ANOVA median latency lateral}
kable(inclusionBF(bfMedianLatencyLateral, models = "matched"))
```

#### Center saccades {.tabset .tabset-fade}

##### Classical ANOVA

Repeated measures ANOVA matching [Kanai et al. (2012)](http://dx.doi.org/10.3389/fpsyt.2012.00045).

__Data__: Baseline subtracted, center saccades

__Dependent measure__: saccade latency

__Factors__:

* STIMULATION (anodal vs. cathodal)
* LEG (tDCS, post.1, post.2)
* DIRECTION (left vs. right)

Prepare data: 

```{r test data median latency center}
medianLatencyCenterStats <- latencyMedianBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "center") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))
```

```{r rmANOVA median latency center, results='asis'}
aovMedianLatencyCenter <- ezANOVA(data = data.frame(medianLatencyCenterStats), dv = latency.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)

kable(aovMedianLatencyCenter$ANOVA)
kable(aovMedianLatencyCenter$`Mauchly's Test for Sphericity`)
kable(aovMedianLatencyCenter$`Sphericity Corrections`)
```

##### Bayesian ANOVA

Same design as the classical ANOVA

Bayes factors agains the null model:

```{r Bayesian ANOVA median latency center}
bfMedianLatencyCenter = anovaBF(latency.baseline~stimulation*leg*direction+subject, data = data.frame(medianLatencyCenterStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000) 
bfMedianLatencyCenter = sort(bfMedianLatencyCenter, decreasing = TRUE) # sort such that winning model is at the top

kable(select(extractBF(bfMedianLatencyCenter), bf)) # show only the Bayes factors in a table
```

```{r}
kable(select(extractBF(bfMedianLatencyCenter), bf)) # show only the Bayes factors in a table
```

Inclusion Bayes factor across matched models:

```{r inclusion BF ANOVA median latency center}
kable(inclusionBF(bfMedianLatencyCenter, models = "matched"))
```

###### Without S01

```{r BF ANOVA median latency center - without S01}
medianLatencyCenterNoS01 <- medianLatencyCenterStats %>%
  filter(subject != "S01") %>%
  mutate(subject = factor(subject))

bfMedianLatencyCenterNoS01 = anovaBF(latency.baseline~stimulation*leg*direction+subject, data = data.frame(medianLatencyCenterNoS01), whichModels="withmain", whichRandom = "subject", progress = FALSE, iterations = 100000) # compute Bayes Factors
bfKanaiCenterNoS01 <- sort(bfMedianLatencyCenterNoS01, decreasing = TRUE) # sort such that winning model is at the top
kable(select(extractBF(bfMedianLatencyCenterNoS01), bf)) # show only the Bayes factors in a table
kable(inclusionBF(bfMedianLatencyCenterNoS01, models = "matched"))
```

##### Follow-up tests

###### Main effect of stimulation

Follow-up One-sample t-test:

```{r Classical follow-up test - latency}
medianLatencyCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(latency = mean(latency.baseline)) %>% # average over all other variables (df is now still grouped per stimulation)
  summarise_if(is.numeric, funs(list(tidy(t.test(.))))) %>%  # run one-sample t-test for each stimulation condition, return tidy data frames
  unnest() %>% # unpack the list-column with data frame for each test
  kable(.)
```

Follow-up Bayesian one-sample t-test:

```{r Bayesian follow-up test - latency}
medianLatencyCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(latency = mean(latency.baseline)) %>% # average over all other variables
  spread(stimulation, latency) %>% # make separate columns with test data
  summarise_if(is.numeric, funs(extractBF(ttestBF(.), onlybf = TRUE))) %>% # run Bayesian t-test on each column, keeping only the BF
  gather(stimulation,BF,anodal,cathodal) %>% # make row for each stimulation condition
  kable(.)
```

## Saccade endpoint deviation

_See the `accuracy.nb.html` notebook for more explanation and exploration of the saccade endpoint deviation data_

Calculate endpoint deviation for each trial (Euclidian distance from target, defined by x- and y-coordinate deviations)

```{r Calculate end point deviation}
# Calculate end point deviation
accData <- preproc %>%
  group_by(subject,stimulation,leg,block,trial,direction,type) %>%
  summarise(deviation.end = sqrt(deviation.end.x^2 + deviation.end.y^2))
```

Compute the mean for each 3 blocks

```{r Compute mean deviation over blocks}
accMeanLeg <- accData %>%
  group_by(subject,stimulation,direction,type) %>% 
  summarise(baseline = mean(deviation.end[leg == "pre"]), # take average of 3 blocks, make new column
            tDCS = mean(deviation.end[leg == "tDCS"]),
            post.1 = mean(deviation.end[leg == "post.1"]),
            post.2 = mean(deviation.end[leg == "post.2"])) %>%
  gather(leg,deviation.end,baseline,tDCS,post.1,post.2) %>% # gather new columns to use as factor
  mutate(leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))) # reorder factor levels
```

Make a separate data frame where the mean deviation is subtracted from subsequent blocks

```{r Subtract baseline deviation}
accMeanBaseline <- accMeanLeg %>%
  group_by(subject,stimulation,direction,type) %>% # for each condition, subtract baseline scores and make new columns
  summarise(baseline = deviation.end[leg == "baseline"] - deviation.end[leg == "baseline"],
           tDCS = deviation.end[leg == "tDCS"] - deviation.end[leg == "baseline"],
           post.1 = deviation.end[leg == "post.1"] - deviation.end[leg == "baseline"],
           post.2 = deviation.end[leg == "post.2"] - deviation.end[leg == "baseline"]) %>%
  gather(leg, deviation.end.baseline, baseline, tDCS, post.1, post.2)  %>% # gather new columns to use as factor 
  mutate(leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))) # reorder factor levels
```

### Figure 6

```{r Create baseline deviation plot labels}
baselineLabelAcc <- accMeanLeg %>%
  group_by(stimulation,direction,type,leg) %>%
  filter(leg == "baseline") %>%
  summarise(deviation.end.baseline = round(mean(deviation.end), digits = 2))
```

Plot mean _lateral saccade_ deviation per participant

```{r Mean deviation lateral saccades}
plot_sub_acc_lateral <- ggplot(filter(accMeanLeg, type == "lateral"), aes(leg, deviation.end)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  guides(colour = FALSE) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("mean deviation " ( degree)), breaks = seq(0.5,2,0.5), limits = c(0.3,2.2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Lateral saccades")
```

Plot mean _center saccade_ deviation per participant

```{r Mean deviation center saccades}
plot_sub_acc_center <- ggplot(filter(accMeanLeg, type == "center"), aes(leg, deviation.end)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  guides(colour = FALSE) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("mean deviation " ( degree)), breaks = seq(0.5,2,0.5), limits = c(0.3,2.2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Center saccades")
```

Plot group mean _lateral saccade_ deviation as change from baseline

```{r Group mean deviation lateral saccades}
plot_group_acc_lateral <- ggplot(filter(accMeanBaseline, type == "lateral"), aes(leg, deviation.end.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelAcc, type=="lateral"), aes(y = c(.05,.025,.1,.075), label=deviation.end.baseline, color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("deviation change from baseline " ( degree)), breaks = seq(-.2,.2,.1)) +
  coord_cartesian(ylim = c(-.22,.22)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())
```

Plot group mean _center saccade_ deviation as change from baseline

```{r Group mean deviation center saccades}
plot_group_acc_center <- ggplot(filter(accMeanBaseline, type == "center"), aes(leg, deviation.end.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelAcc, type=="center"), aes(y = c(.05,.025,.1,.075), label=deviation.end.baseline, color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("deviation change from baseline " ( degree)), breaks = seq(-.2,.2,.1)) +
  coord_cartesian(ylim = c(-.22,.22)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())
```

Combine all the plots:

```{r Figure 6}
legend_fig6 <- get_legend(plot_group_acc_lateral)
figure_6 <- plot_grid(
  plot_sub_acc_lateral, plot_sub_acc_center,
  plot_group_acc_lateral + theme(legend.position = "none"),
  plot_group_acc_center + theme(legend.position = "none"), rel_heights = c(1,.75))
figure_6 <- plot_grid(figure_6,legend_fig6,rel_widths = c(1,1/10))
figure_6
```

Save the plot:

```{r Save figure 6, eval=FALSE}
ggsave("fig/figure_6.pdf", plot = figure_6, width = 180, height = 112.5, units = "mm")
ggsave("fig/figure_6.png", plot = figure_6, width = 180, height = 112.5, units = "mm")
```

### Statistics

#### Baseline tests

```{r t-tests of baseline difference - deviation}
accMeanLeg %>%
  filter(leg == "baseline") %>%
  group_by(direction,type) %>% 
  nest() %>% 
  mutate(stats = map(data, ~t.test(formula = deviation.end~stimulation, paired = TRUE, data =.))) %>% # run t-test on the data frames
  mutate(tidy_model = map(stats, tidy)) %>%
  unnest(tidy_model, .drop = TRUE) %>% 
  kable(.)
```

#### Differences after baseline

Because there might be a significant baseline difference for center saccades, also print the numerical values for the blocks after, to see whether they continue to differ

```{r time periods after baseline}
accMeanLeg %>%
  filter(leg != "baseline", type == "center") %>%
  group_by(stimulation,direction,leg) %>% 
  summarise(mean = mean(deviation.end)) %>%
  kable(.)            
```

#### Lateral saccades {.tabset .tabset-fade}

##### Classical ANOVAs

Repeated measures ANOVA matching [Kanai et al. (2012)](http://dx.doi.org/10.3389/fpsyt.2012.00045).

__Data__: Baseline subtracted, lateral saccades

__Dependent measure__: saccade endpoint deviation

__Factors__:

* STIMULATION (anodal vs. cathodal)
* LEG (tDCS, post.1, post.2)
* DIRECTION (left vs. right)

Prepare data: 

```{r test data mean deviation lateral}
meanAccLateralStats <- accMeanBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "lateral") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))
```

```{r rmANOVA mean deviation lateral, results='asis'}
aovMeanAccLateral <- ezANOVA(data = data.frame(meanAccLateralStats), dv = deviation.end.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)

kable(aovMeanAccLateral$ANOVA)
kable(aovMeanAccLateral$`Mauchly's Test for Sphericity`)
kable(aovMeanAccLateral$`Sphericity Corrections`)
```

##### Bayesian ANOVA 

Same design as the classical ANOVA (without order effect)

Bayes factors agains the null model:

```{r Bayesian ANOVA mean deviation lateral}
bfMeanAccLateral = anovaBF(deviation.end.baseline~stimulation*leg*direction+subject, data = data.frame(meanAccLateralStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000) 
bfMeanAccLateral = sort(bfMeanAccLateral, decreasing = TRUE) # sort such that winning model is at the top
```

```{r}
kable(select(extractBF(bfMeanAccLateral), bf)) # show only the Bayes factors in a table
```

Inclusion Bayes factor across matched models:

```{r inclusion BF ANOVA mean deviation lateral}
kable(inclusionBF(bfMeanAccLateral, models = "matched"))
```

#### Center saccades {.tabset .tabset-fade}

##### Classical ANOVA

Repeated measures ANOVA matching [Kanai et al. (2012)](http://dx.doi.org/10.3389/fpsyt.2012.00045).

__Data__: Baseline subtracted, center saccades

__Dependent measure__: saccade endpoint deviation

__Factors__:

* STIMULATION (anodal vs. cathodal)
* LEG (tDCS, post.1, post.2)
* DIRECTION (left vs. right)

Prepare data: 

```{r test data mean deviation center}
meanAccCenterStats <- accMeanBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "center") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))
```

```{r rmANOVA mean deviation center, results='asis'}
aovMeanAccCenter <- ezANOVA(data = data.frame(meanAccCenterStats), dv = deviation.end.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)

kable(aovMeanAccCenter$ANOVA)
kable(aovMeanAccCenter$`Mauchly's Test for Sphericity`)
kable(aovMeanAccCenter$`Sphericity Corrections`)
```

##### Bayesian ANOVA 

Same design as the classical ANOVA

Bayes factors agains the null model:

```{r Bayesian ANOVA mean deviation center}
bfMeanAccCenter = anovaBF(deviation.end.baseline~stimulation*leg*direction+subject, data = data.frame(meanAccCenterStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000) 
bfMeanAccCenter = sort(bfMeanAccCenter, decreasing = TRUE) # sort such that winning model is at the top
```

```{r}
kable(select(extractBF(bfMeanAccCenter), bf)) # show only the Bayes factors in a table
```

Inclusion Bayes factor across matched models:

```{r inclusion BF ANOVA mean deviation center}
kable(inclusionBF(bfMeanAccCenter, models = "matched"))
```

##### Follow-up tests

###### Main effect of stimulation

Follow-up One-sample t-test:

```{r Classical follow-up test - deviation}
meanAccCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(deviation = mean(deviation.end.baseline)) %>% # average over all other variables (df is now still grouped per stimulation)
  summarise_if(is.numeric, funs(list(tidy(t.test(.))))) %>%  # run one-sample t-test for each stimulation condition, return tidy data frames
  unnest() %>% # unpack the list-column with data frame for each test
  kable(.)
```

Follow-up Bayesian one-sample t-test:

```{r Bayesian follow-up test - deviation}
meanAccCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(deviation = mean(deviation.end.baseline)) %>% # average over all other variables
  spread(stimulation, deviation) %>% # make separate columns with test data
  summarise_if(is.numeric, funs(extractBF(ttestBF(.), onlybf = TRUE))) %>% # run Bayesian t-test on each column, keeping only the BF
  gather(stimulation,BF,anodal,cathodal) %>% # make row for each stimulation condition
  kable(.)
```

## Saccade endpoint variability

_See the `accuracy.nb.html` notebook for more explanation and exploration of the saccade endpoint variability data_

Calculate endpoint variability, as standard deviation of the horizontal component (x-coordinate) of saccades

```{r calculate standard deviation}
varMeanLeg <- preproc %>%
  group_by(subject,stimulation,leg,direction,type) %>% 
  summarise(std.deviation.x = sd(deviation.end.x)) %>% # standard deviation
  ungroup() %>%
  mutate(leg = as.character(leg), # edit leg factor to match other data frames
         leg = replace(leg, leg == "pre", "baseline"),
         leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))
         )
```

Make a separate data frame where the mean variability is subtracted from subsequent blocks

```{r Subtract baseline variability}
varMeanBaseline <- varMeanLeg %>%
  group_by(subject,stimulation,direction,type) %>% # for each condition, subtract baseline scores and make new columns
  summarise(baseline = std.deviation.x[leg == "baseline"] - std.deviation.x[leg == "baseline"],
           tDCS = std.deviation.x[leg == "tDCS"] - std.deviation.x[leg == "baseline"],
           post.1 = std.deviation.x[leg == "post.1"] - std.deviation.x[leg == "baseline"],
           post.2 = std.deviation.x[leg == "post.2"] - std.deviation.x[leg == "baseline"]) %>%
  gather(leg, std.deviation.x.baseline, baseline, tDCS, post.1, post.2)  %>% # gather new columns to use as factor 
  mutate(leg = factor(leg, levels = c("baseline", "tDCS", "post.1", "post.2"))) # reorder factor levels
```

### Figure 7

```{r Create baseline variability plot labels}
baselineLabelVar <- varMeanLeg %>%
  group_by(stimulation,direction,type,leg) %>%
  filter(leg == "baseline") %>%
  summarise(std.deviation.x.baseline = round(mean(std.deviation.x), digits = 2))
```

Plot mean _lateral saccade_ variability per participant

```{r Mean variability lateral saccades}
plot_sub_var_lateral <- ggplot(filter(varMeanLeg, type == "lateral"), aes(leg, std.deviation.x)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  guides(colour = FALSE) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("standard deviation " ( degree)), breaks = seq(0.5,2,0.5), limits = c(0,2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Lateral saccades")
```

Plot mean _center saccade_ variability per participant

```{r Mean variability center saccades}
plot_sub_var_center <- ggplot(filter(varMeanLeg, type == "center"), aes(leg, std.deviation.x)) +
  facet_grid(stimulation ~ direction) +
  geom_line(aes(group = subject, color = subject), alpha = 0.5) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), linetype = direction), geom = "line") +
  stat_summary(fun.y = mean, geom = "point", aes(shape = stimulation)) +
  guides(colour = FALSE) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("standard deviation " ( degree)), breaks = seq(0.5,2,0.5), limits = c(0,2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank(), legend.position = "none") +
  ggtitle("Center saccades")
```

Plot group mean _lateral saccade_ variability as change from baseline

```{r Group mean variability lateral saccades}
plot_group_var_lateral <- ggplot(filter(varMeanBaseline, type == "lateral"), aes(leg, std.deviation.x.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelVar, type=="lateral"), aes(y = c(.05,.025,.1,.075), label=(std.deviation.x.baseline), color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_y_continuous(expression("SD change from baseline " ( degree)), breaks = seq(-.2,.2,.1)) +
  coord_cartesian(ylim = c(-.2,.2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())
```

Plot group mean _center saccade_ variability as change from baseline

```{r Group mean variability center saccades}
plot_group_var_center <- ggplot(filter(varMeanBaseline, type == "center"), aes(leg, std.deviation.x.baseline)) +
  geom_hline(yintercept = 0, linetype = "dashed", size = base_line_size) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "line", position = position_dodge(width = 0.5), size = .75) + 
  stat_summary(fun.data = mean_cl_normal, aes(group = interaction(stimulation, direction), color = stimulation, linetype = direction), geom = "linerange", position = position_dodge(width = 0.5), show.legend = FALSE) +
  stat_summary(fun.y = mean, aes(group = interaction(stimulation, direction), color = stimulation, shape = stimulation), geom = "point", position = position_dodge(width = 0.5), size = 2) +
  geom_text(data = subset(baselineLabelVar, type=="center"), aes(y = c(.05,.025,.1,.075), label=(std.deviation.x.baseline), color = stimulation), position = position_dodge(width = 0.5), size = mm_to_pt) +
  scale_colour_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_discrete("time", labels = c("baseline", "tDCS", "post-1", "post-2")) +
  scale_y_continuous(expression("SD change from baseline " ( degree)), breaks = seq(-.2,.2,.1)) +
  coord_cartesian(ylim = c(-.2,.2)) +
  theme(axis.text.x = element_text(angle = 22, vjust = .5), axis.title.x = element_blank())
```

Combine all the plots:

```{r Figure 7}
legend_fig7 <- get_legend(plot_group_acc_lateral)
figure_7 <- plot_grid(
  plot_sub_var_lateral, plot_sub_var_center,
  plot_group_var_lateral + theme(legend.position = "none"),
  plot_group_var_center + theme(legend.position = "none"), rel_heights = c(1,.75))
figure_7 <- plot_grid(figure_7,legend_fig7,rel_widths = c(1,1/10))
figure_7
```

Save the plot:

```{r Save figure 7, eval=FALSE}
ggsave("fig/figure_7.pdf", plot = figure_7, width = 180, height = 112.5, units = "mm")
ggsave("fig/figure_7.png", plot = figure_7, width = 180, height = 112.5, units = "mm")
```

### Statistics 

#### Baseline tests 

```{r t-tests of baseline difference - variability}
varMeanLeg %>%
  filter(leg == "baseline") %>%
  group_by(direction,type) %>% 
  nest() %>% 
  mutate(stats = map(data, ~t.test(formula = std.deviation.x~stimulation, paired = TRUE, data =.))) %>% # run t-test on the data frames
  mutate(tidy_model = map(stats, tidy)) %>%
  unnest(tidy_model, .drop = TRUE) %>% 
  kable(.)
```

#### Lateral saccades {.tabset .tabset-fade}

##### Classical ANOVA

Repeated measures ANOVA matching [Kanai et al. (2012)](http://dx.doi.org/10.3389/fpsyt.2012.00045).

__Data__: Baseline subtracted, lateral saccades

__Dependent measure__: saccade endpoint variability (x-coordinate standard deviation)

__Factors__:

* STIMULATION (anodal vs. cathodal)
* LEG (tDCS, post.1, post.2)
* DIRECTION (left vs. right)

Prepare data: 

```{r test data mean variability lateral}
meanVarLateralStats <- varMeanBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "lateral") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))
```

```{r rmANOVA mean variability lateral, results='asis'}
aovMeanVarLateral <- ezANOVA(data = data.frame(meanVarLateralStats), dv = std.deviation.x.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)

kable(aovMeanVarLateral$ANOVA)
kable(aovMeanVarLateral$`Mauchly's Test for Sphericity`)
kable(aovMeanVarLateral$`Sphericity Corrections`)
```

##### Bayesian ANOVA

Same design as the classical ANOVA

Bayes factors agains the null model:

```{r Bayesian ANOVA mean variability lateral}
bfMeanVarLateral = anovaBF(std.deviation.x.baseline~stimulation*leg*direction+subject, data = data.frame(meanVarLateralStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000) 
bfMeanVarLateral = sort(bfMeanVarLateral, decreasing = TRUE) # sort such that winning model is at the top
```

```{r}
kable(select(extractBF(bfMeanVarLateral), bf)) # show only the Bayes factors in a table
```

Inclusion Bayes factor across matched models:

```{r inclusion BF ANOVA mean variability lateral}
kable(inclusionBF(bfMeanVarLateral, models = "matched"))
```

#### Center saccades {.tabset .tabset-fade}

##### Classical ANOVA

Repeated measures ANOVA matching [Kanai et al. (2012)](http://dx.doi.org/10.3389/fpsyt.2012.00045).

__Data__: Baseline subtracted, center saccades

__Dependent measure__: saccade endpoint variability (x-coordinate standard deviation)

__Factors__:

* STIMULATION (anodal vs. cathodal)
* LEG (tDCS, post.1, post.2)
* DIRECTION (left vs. right)

Prepare data: 

```{r test data mean variability center}
meanVarCenterStats <- varMeanBaseline %>%
  ungroup() %>%
  filter(leg != "baseline", type == "center") %>%
  select(-type) %>%
  mutate(leg = factor(leg, levels = c("tDCS", "post.1", "post.2"))) %>%
  mutate(subject = factor(subject))
```

```{r rmANOVA mean variability center, results='asis'}
aovMeanVarCenter <- ezANOVA(data = data.frame(meanVarCenterStats), dv = std.deviation.x.baseline, wid = subject, within = .(stimulation,leg,direction), type = 3)

kable(aovMeanVarCenter$ANOVA)
kable(aovMeanVarCenter$`Mauchly's Test for Sphericity`)
kable(aovMeanVarCenter$`Sphericity Corrections`)
```

##### Bayesian ANOVA

Same design as the classical ANOVA

Bayes factors agains the null model:

```{r Bayesian ANOVA mean variability center}
bfMeanVarCenter = anovaBF(std.deviation.x.baseline~stimulation*leg*direction+subject, data = data.frame(meanVarCenterStats), whichModels = "withmain", whichRandom = "subject", progress = FALSE, iterations = 100000) 
bfMeanVarCenter = sort(bfMeanVarCenter, decreasing = TRUE) # sort such that winning model is at the top
```

```{r}
kable(select(extractBF(bfMeanVarCenter), bf)) # show only the Bayes factors in a table
```

Inclusion Bayes factor across matched models:

```{r inclusion BF ANOVA mean variability center}
kable(inclusionBF(bfMeanVarCenter, models = "matched"))
```

##### Follow-up tests

###### Main effect of stimulation

Follow-up One-sample t-test:

```{r Classical follow-up test - variability}
meanVarCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(std.deviation.x. = mean(std.deviation.x.baseline)) %>% # average over all other variables (df is now still grouped per stimulation)
  summarise_if(is.numeric, funs(list(tidy(t.test(.))))) %>%  # run one-sample t-test for each stimulation condition, return tidy data frames
  unnest() %>% # unpack the list-column with data frame for each test
  kable(.)
```

Follow-up Bayesian one-sample t-test:

```{r Bayesian follow-up test - variability}
meanVarCenterStats %>%
  group_by(stimulation,subject) %>% # for each session and subject
  summarise(std.deviation.x = mean(std.deviation.x.baseline)) %>% # average over all other variables
  spread(stimulation, std.deviation.x) %>% # make separate columns with test data
  summarise_if(is.numeric, funs(extractBF(ttestBF(.), onlybf = TRUE))) %>% # run Bayesian t-test on each column, keeping only the BF
  gather(stimulation,BF,anodal,cathodal) %>% # make row for each stimulation condition
  kable(.)
```

# Quantile analysis

_See the `RT_quantiles.nb.html` notebook for more explanation and exploration of the saccade latency distribution data_

Estimate quantiles and compute shift function: 

```{r Quantiles and shift function, eval=FALSE}
qData <- groupData %>%
  filter(!is.na(latency)) %>% # discard missing saccades
  group_by(subject,leg,type,direction) %>% # for each condition
  nest(stimulation,latency) %>% # make a list_column "data" out of the stimulation and latency columns. Now, each group has its own data frame consisting of the stimulation and latency columns for that group
  mutate(shift = purrr::map(data, ~ shiftdhd_pbci(., formula = latency ~ stimulation, nboot = 2000))) # for each group, estimate quantiles and compute shift function, and store as a list column in "shift"

qData <- unnest(qData,shift) # unpack the list column to get the model results for each group in the original data frame

# Alternatively, instead of "nest ...", "mutate ..." and "unnest ...", you can call shiftdhd through dplyr::do (but this is basically depreated in favor of purrr:map)
# do(shiftdhd(.[,c("stimulation","latency")], formula = latency ~ stimulation, nboot = 100)) # estimate quantiles and compute shift function
```

Note that this takes quite a while to compute with a large number of bootstrap samples, so we will load the result from disk in the next code chunk. By default, the chunk above will not run becuase of the `eval=FALSE` statement; remove this to execute it and compute the result from scratch.

```{r Load quantile data}
qData <- read_csv(here("data", "sacc-tDCS_quantiles.csv")) %>%
  filter(!(subject %in% subs2exclude)) %>% # exclude subjects
    mutate(leg = replace(leg, leg == "pre", "baseline"), # rename and reoder levels
         leg = replace(leg, leg == "post.1", "post-1"),
         leg = replace(leg, leg == "post.2", "post-2"),
         leg = factor(leg, levels = c("baseline", "tDCS", "post-1", "post-2")))
  
```

```{r Average anodal deciles}
qStats <- qData %>%
  group_by(subject,leg,type,direction) %>%
  mutate(deco = c(seq(1,5),seq(4,1))) %>% # add code of deciles to data frame
  group_by(leg,type,direction,q) %>% 
  mutate(anodal = mean(anodal)) %>% # mean of "anodal" quantiles OVER subjects
  group_by(leg,type,direction) %>% 
  mutate(anodal_median = median(anodal)) # median for plotting
```

For each quantile, count which subjects show significant effects, and in which direction

```{r Count significance of deciles}
qSig <- qData %>%
  group_by(subject,leg,type,direction,q) %>%
  mutate(significance = NA,
         # If (anodal - cathodal) quantile difference is positive, latency for anodal > latency for cathodal
         significance = replace(significance, p_value < p_crit & difference > 0, "cathodal.faster"),
         # If (anodal - cathodal) quantile difference is negative, latency for anodal < latency for cathodal
         significance = replace(significance, p_value < p_crit & difference < 0, "anodal.faster")
  )
```

## Figure 5

Shift functions for _lateral saccades_

```{r Shift function lateral}
plot_shift_lateral <- ggplot(filter(qStats, type == "lateral"), aes(anodal, difference)) +
   facet_grid(leg ~ direction) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.5) +
   geom_vline(aes(xintercept = anodal_median), linetype = "dashed", alpha = 0.5) +
   stat_summary(fun.data = mean_cl_normal, geom = "linerange", colour = "black", size = 0.5) +
   stat_summary(fun.y = mean, geom = "line", size = 1, colour = "grey50", alpha = 0.5) +
   stat_summary(fun.y = mean, geom = "point", aes(fill = deco), size = 2, colour = "black", shape = 21) +
   scale_fill_gradient(low = "white", high = "grey30", guide = FALSE) +
   scale_x_continuous("anodal deciles (ms)", limits = c(80,200), breaks = seq(100,200,25)) +
   scale_y_continuous("anodal - cathodal deciles (ms)")
```

Number of subjects with signifcant difference per quantile for lateral saccades

```{r Significance shift function lateral}
plot_sig_lateral <- ggplot(filter(qSig, type == "lateral", !is.na(significance)), aes(q, fill = significance)) +
  facet_grid(leg ~ direction) +
  geom_bar(position = "stack") +
  stat_bin(binwidth = .1, geom = "text", size = mm_to_pt, aes(label = ..count..), position = position_stack(vjust = 0.5)) +
  scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_continuous("decile", breaks = seq(0.1,0.9,0.1), labels = seq(1,9,1)) +
  scale_y_continuous("number of participants with significant difference", limits = c(0, length(unique(qSig$subject))), breaks = c(0,10,20,length(unique(qSig$subject))))
```

Shift functions for _center saccades_

```{r Shift function center}
plot_shift_center <- ggplot(filter(qStats, type == "center"), aes(anodal, difference)) +
   facet_grid(leg ~ direction) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.5) +
   geom_vline(aes(xintercept = anodal_median), linetype = "dashed", alpha = 0.5) +
   stat_summary(fun.data = mean_cl_normal, geom = "linerange", colour = "black", size = 0.5) +
   stat_summary(fun.y = mean, geom = "line", size = 1, colour = "grey50", alpha = 0.5) +
   stat_summary(fun.y = mean, geom = "point", aes(fill = deco), size = 2, colour = "black", shape = 21) +
   scale_fill_gradient(low = "white", high = "grey30", guide = FALSE) +
   scale_x_continuous("anodal deciles (ms)", limits = c(80,200), breaks = seq(100,200,25)) +
   scale_y_continuous("anodal - cathodal deciles (ms)")
```


Number of subjects with signifcant difference per quantile for lateral saccades

```{r Significance shift function center}
plot_sig_center <- ggplot(filter(qSig, type == "center", !is.na(significance)), aes(q, fill = significance)) +
  facet_grid(leg ~ direction) +
  geom_bar(position = "stack") +
  stat_bin(binwidth = .1, geom = "text", size = mm_to_pt, aes(label = ..count..), position = position_stack(vjust = 0.5)) +
  scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
  scale_x_continuous("decile", breaks = seq(0.1,0.9,0.1), labels = seq(1,9,1)) +
  scale_y_continuous("number of participants with significant difference", limits = c(0, length(unique(qSig$subject))), breaks = c(0,10,20,length(unique(qSig$subject))))
```

```{r Figure 5, fig.width = 8}
figure_5_lateral <- plot_grid(plot_shift_lateral, plot_sig_lateral + theme(legend.position = "none"), rel_widths = c(1.33,1)) # top row
figure_5_center <- plot_grid(plot_shift_center,plot_sig_center + theme(legend.position = "none"), rel_widths = c(1.33,1)) # bottom row

#title objects
title_lateral <- ggdraw() + draw_label("Lateral saccades", fontface = 'bold', size = base_font_size)
title_center <- ggdraw() + draw_label("Center saccades", fontface = 'bold', size = base_font_size)

#combine with titles
figure_5_lateral <- plot_grid(title_lateral, figure_5_lateral, ncol = 1, rel_heights = c(0.05, 1))
figure_5_center <- plot_grid(title_center, figure_5_center, ncol = 1, rel_heights = c(0.05, 1))
figure_5 <- plot_grid(figure_5_lateral, figure_5_center, nrow = 2)
figure_5
```

Save the plot:

```{r Save figure 5, eval=FALSE}
ggsave("fig/figure_5.pdf", plot = figure_5, width = 180, height = 180, units = "mm")
ggsave("fig/figure_5.png", plot = figure_5, width = 180, height = 180 , units = "mm")
```

# Supplementary results

## tDCS adverse effect questionnaire

_See the `questionnaires.nb.html` notebook for more explanation and exploration of the questionnaire data_

```{r Load tDCS sensations data}
# Load the data frame
dataFile <- here("data", "tdcs_sensations.csv")
sensData <- read_csv2(dataFile, col_types = cols(
  session = col_factor(c("first","second")),
  stimulation = col_factor(c("anodal","cathodal"))
))
kable(head(sensData)) # show data frame
```

Participants were asked to which degree the following sensations were present during stimulation: _tingling_, _itching sensation_, _burning sensation_, _pain_, _headache_, _fatigue_, _dizziness_ and _nausea_. Each was rated on a scale from 0-4:

0. none
1. a little
2. moderate
3. strong
4. very strong

They also rated their confidence _that the sensations were caused by the stimulation_ on a scale from 0-4 (columns starting with `conf.`):

0. n/a (meaning they rated the sensation a 0 on the previous scale)
1. unlikely
2. possibly
3. likely
4. very likely

__Factors__:

* _subject_: subject ID (`S01`, `S02`, etc)
* _session_: Whether data are from the `first` or `second` session
* _stimulation_: Whether data are from the `anodal` or `cathodal` session

Calculate how many anodal and cathodal sessions were rated:

```{r Tally cases}
idxComplete <- rowSums(is.na(sensData)) != ncol(sensData) - 3 # rows that do not have all NAs (except the 3 factor columns)
# calculate number of questionnaires completed per stimulation type
nAnodal <- sum(sensData$stimulation == "anodal" & idxComplete) 
nCathodal <- sum(sensData$stimulation == "cathodal" & idxComplete)
```

### Figure S1

Prepare the sensation intensity and confidence ratings.

```{r Make long form data frame}
# Make long form data frame of sensation intensity
ratings <- sensData %>%
  select(everything(), -contains("conf"), -felt.more) %>% # drop other columns
  gather(sensation, rating, itching:nausea) # make long form

# Make long form data frame of sensation confidence
confidence <- sensData %>%
  select(contains("conf"), subject, session, stimulation) %>% 
  gather(sensation, confidence, conf.itching:conf.nausea) %>%
  mutate(sensation = str_replace(sensation, "conf.", "")) # get rid of "conf." prefix so it matches the sensation intensity tibble

# Join the two data frames
sensDataLong <- dplyr::full_join(ratings,confidence)
```

Make plot of sensation distribution:

```{r plot tDCS sensation distributions}
plot_sensation <- ggplot(sensDataLong, aes(rating, fill = stimulation)) +
    facet_wrap(~sensation, nrow = 1) +
    geom_bar(position = "stack") +
    stat_bin(binwidth = 1, geom = "text", size = mm_to_pt, aes(label = ..count..), position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
    xlim(0.5,4.5) + # exclude "0" ratings that were not present
    ylim(0,35) + # bound plot at max number of ratings
    ylab("number of sessions") +
  ggtitle("Sensation intensity")
```

Make plot of confidence distribution:

```{r plot tDCS confidence distributions}
plot_confidence <- ggplot(sensDataLong, aes(confidence, fill = stimulation)) +
    facet_wrap(~sensation, nrow = 1) +
    geom_bar(position = "stack") +
    stat_bin(binwidth = 1, geom = "text", size = mm_to_pt, aes(label = ..count..), position = position_stack(vjust = 0.5)) +
    scale_fill_manual(values = c("#F25F5C", "#4B93B1")) +
    xlim(0.5,4.5) +
    ylim(0,35) +
    xlab("ratings") +
    ylab("number of sessions") +
    ggtitle("Confidence")
```

Put together and show plot:

```{r tDCS AE plot, fig.height = 5} 
legend_S1 <- get_legend(plot_sensation)
figure_S1 <- plot_grid(
  plot_sensation + theme(legend.position = "none"),
  plot_confidence + theme(legend.position = "none"),
  nrow = 2)
figure_S1 <- plot_grid(figure_S1,legend_S1, rel_widths = c(1,1/10))
figure_S1
```

Save the plot:

```{r Save figure S1, eval=FALSE}
ggsave("fig/figure_S1.pdf", plot = figure_S1, width = 180, height = 112.5, units = "mm")
ggsave("fig/figure_S1.png", plot = figure_S1, width = 180, height = 112.5, units = "mm")
```

### Statistics

Mann-Whitney U tests for difference in sensation intensity ratings between anodal and cathodal sessions.

```{r Test sensations anodal vs. cathodal, results='asis'}
sensationList <- c("itching", "tingling", "burning", "pain", "headache", "fatigue", "dizziness", "nausea")
senseTests <- data.frame(sensation = sensationList, p.value = NA) # initialize results data frame
for (item in sensationList) {
  testData <- sensData[[item]] # extract column with test dat
  tmp <- wilcox.test(testData[sensData$stimulation == "anodal"], testData[sensData$stimulation == "cathodal"])
  senseTests$p.value[senseTests$sensation %in% item] <- tmp$p.value # put p-value in row of results data frame
}
kable(senseTests)
```

Note that the p-value for "nausea" is undefined because it was never reported (i.e. all ranks are tied at 0).

## Frontal eye field coordinates

_See the `frontal_eye_field.nb.html` notebook for more explanation and exploration of the frontal eye field coordinate data_

### Native space

These were determined for each subject's scan; see `neuronav_notes.md` for further info.

```{r Show native coords, results='asis'}
dataFile <- here("data", "FEF_coords_native.csv")
nativeCoords <- read_csv2(dataFile)
nativeCoords %>% 
  select(-folder, -scan) %>% # drop columns with folder and scan names
  filter(!(subject %in% subs2exclude)) %>% # drop rows with excluded subjects
  kable(.)
```

### MNI space (Table S1)

Load in the coordinates that were transformed to MNI space.

#### Table S1

```{r show MNI coords, results='asis'}
dataFile <- here("data", "FEF_coords_MNI.csv")
mniCoords <- read_delim(dataFile, ";")
mniCoords <- filter(mniCoords, !(subject %in% subs2exclude)) # exclude subjects
mniCoords %>%
  select(-folder, -scan) %>% # drop columns with folder and scan names
  kable(.)
```

#### Descriptives

Calculate descriptive statistics over subjects:

```{r Descriptives of MNI coordinates, results='asis'}
mniCoords %>%
  gather(dimension, coord, MNI_X:MNI_Z) %>%
  group_by(dimension) %>%
  summarise_at(vars(coord), funs(mean, min, max, sd)) %>% 
  kable(.)
```
