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exp3analysis.Rmd
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exp3analysis.Rmd
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---
title: "experiment 3 analysis"
output: html_document
date: "2023-10-16"
---
Setup
```{r setup, include=FALSE}
## packages
library(tidyverse)
library(effectsize)
library(broom)
library(circular)
library(BayesFactor)
## load data
data = read_csv('./exp3.csv')
save = FALSE ## set to TRUE if you want to output a folder with all the plots from the paper
## helper function for significance stars
sig_stars = function(p) {
if (p <= 0.005) {
stars = 3
} else if (p <= 0.01) {
stars = 2
} else if (p <= 0.05) {
stars = 1
} else {
stars = 0
}
return(stars)
}
################ image formatting ##############
## for testing/changing formatting, see block at the end of this script
## colors
sns_light = c("#8DBAB4", "#E8A896")
sns_dark = c("#5E8B8A", "#BF7666")
# sns_colors = c("#76a39f","#e39784") #d48f7e
sns_colors = c("#00B3A1","#F9A900" )
combo_color = "#395FFF" #"#B9A8CD"
combo_dark = "#395FFF" #"#8E809D" #"#b72ece"
double_combo = c("#AFA7DF", "#9D758B" )
lines = "#766B83"
washcolors =c("#74A4C9", "#DEA278", "#699E9B", "#B55E52")
ndot_colors = c("#FF7D62", "#BC63E2")
## size
img_w = 5
img_h = 5
ln_size = 1
pt_size = 3
ax_size = 0.5
err_size = 1
err_width = 0
err_alpha = 1
pd = position_dodge(width = 0.15)
## shapes
ss_shapes = c(1,16)
## theme
mytheme = theme(
text = element_text(family = "Arial", size = 18),
aspect.ratio = 9/10,
plot.title = element_text(hjust = 0.5),
panel.background = element_rect("transparent"),
panel.grid.major = element_blank(),
panel.grid.minor= element_blank(),
axis.ticks = element_line(size = ax_size),
axis.ticks.length = unit(ax_size, "cm"),
axis.text = element_text(size = 18, color = "black"),
axis.line.x.bottom = element_line(size = ax_size, color = "black"),
axis.line.y = element_line(size = ax_size, color = "black"),
legend.position = "none"
)
```
Figure 3b - Visuospatial Load x Visuospatial Precision
```{r figure 3b}
## within pt
fig3b = data %>%
group_by(id, vsp_setsize) %>%
summarize(pt_prec = 1/sd.circular(vsp_ang_err*(pi/180)), .groups = 'drop')
## across pt
fig3b = fig3b %>%
group_by(vsp_setsize) %>%
summarize(mean_prec = mean(pt_prec), sd = sd(pt_prec), n = n(), .groups = 'drop') %>%
mutate(sem = sd/ sqrt(n))
## main plot
vsp3b = ggplot(data = fig3b, aes(x = factor(vsp_setsize), y = mean_prec, fill = factor(vsp_setsize))) +
geom_bar(stat = "identity", position = position_dodge()) +
scale_fill_manual(values = ndot_colors) +
geom_errorbar(aes(ymin = mean_prec - sem,
ymax = mean_prec + sem),
size = ln_size, width = err_width, position = position_dodge(0.9)) +
labs(
# title = "Visual Target Recall \n Angular Precision \n",
y = "Mean Angular Precision \n 1/cicular sd \n",
x = "\n Number of Visual Targets") +
mytheme
vsp3b
if (save == TRUE) { ggsave(file= '../all_plots/vsp3b.svg', plot = vsp3b) }
```
```{r figure 3b stats}
fig3bstats = data %>%
mutate(setsize = paste0('n', vsp_setsize)) %>%
group_by(id, setsize) %>%
summarize(pt_prec = 1/sd.circular(vsp_ang_err*(pi/180)), .groups = 'drop') %>%
spread(setsize, pt_prec) %>%
do({ttest = broom::tidy(t.test(.$n1, .$n3, paired = TRUE))
cd = effectsize::cohens_d(.$n1, .$n3, paired = TRUE)
bind_cols(ttest, cd)}) %>%
summarize(condition = paste0("Load effects on VSP recall precision"), df = parameter, t = statistic, p = p.value, stars = sig_stars(p.value), cohens_d = Cohens_d)
print(fig3bstats)
```
Figure 3c - Visuospatial Load x Movement Recall Distance Error
```{r figure 3c}
## within pt
fig3c = data %>%
group_by(id, switch_hands, vsp_setsize) %>%
summarize(pt_mean = mean(dist_err), .groups = 'drop')
## across pt
fig3c = fig3c %>%
group_by(vsp_setsize, switch_hands) %>%
summarize(mean_err = mean(pt_mean), sd = sd(pt_mean), n = n(), .groups = 'drop') %>%
mutate(sem = sd/ sqrt(n))
## main plot
vsp3c = ggplot(data = fig3c, aes(x = vsp_setsize, y = mean_err, color = factor(switch_hands))) +
geom_line(size = ln_size, position = pd) +
geom_point(size = pt_size, position = pd) +
scale_color_manual(values = sns_colors) +
geom_errorbar(aes(ymin = mean_err - sem,
ymax = mean_err + sem),
size = ln_size, width = err_width, position = pd) +
scale_x_continuous(limits = c(0.8,3.2), breaks = c(1,3)) +
# scale_y_continuous(limits = c(2.9,4.6), breaks = c(3, 3.5, 4, 4.5)) +
scale_y_continuous(limits = c(3.1,5), breaks = c(3.2, 3.7, 4.2, 4.7)) +
labs(
# title = "Movement Error x Visual Task SS ",
y = "Movement Error \n (Distance from target, cm) \n",
x = "\n Number of Visual Targets") +
mytheme
if (save == TRUE) {ggsave(file= '../all_plots/vsp3c.svg', plot = vsp3c)}
vsp3c
```
```{r figure 3c stats}
## df setup
fig3cstats = data %>%
mutate(setsize = paste0('n', vsp_setsize)) %>%
mutate(switch_cond = paste0('sw', switch_hands)) %>%
group_by(id, switch_cond, setsize) %>%
summarize(pt_mean = mean(dist_err), .groups = 'drop')
##### NO SWITCH - SWITCH (stars in graph above) ######
fig3c_n1 = fig3cstats %>%
filter(setsize == 'n1') %>% ## 1 vsp target only
spread(switch_cond, pt_mean) %>%
do({ttest = broom::tidy(t.test(.$sw1, .$sw0, paired = TRUE))
cd = effectsize::cohens_d(.$sw1, .$sw0, paired = TRUE)
bind_cols(ttest, cd)}) %>%
summarize(condition = paste0("1 VSP target "), df = parameter, t = statistic, p = p.value, stars = sig_stars(p.value), cohens_d = Cohens_d)
print(fig3c_n1)
fig3c_n3 = fig3cstats %>%
filter(setsize == 'n3') %>% ## 3 vsp targets only
spread(switch_cond, pt_mean) %>%
do({ttest = broom::tidy(t.test(.$sw1, .$sw0, paired = TRUE))
cd = effectsize::cohens_d(.$sw1, .$sw0, paired = TRUE)
bind_cols(ttest, cd)}) %>%
summarize(condition = paste0("3 VSP target "), df = parameter, t = statistic, p = p.value, stars = sig_stars(p.value), cohens_d = Cohens_d)
print(fig3c_n3)
##### 1 vs 3 VSP TARGETS ######
fig3c_noswitch = fig3cstats %>%
filter(switch_cond == 'sw0') %>% ## noswitch trials only
spread(setsize, pt_mean) %>%
do({ttest = broom::tidy(t.test(.$n1, .$n3, paired = TRUE))
cd = effectsize::cohens_d(.$n1, .$n3, paired = TRUE)
bind_cols(ttest, cd)}) %>%
summarize(condition = paste0("Noswitch (1 - 3 VSP targets)"), df = parameter, t = statistic, p = p.value, stars = sig_stars(p.value), cohens_d = Cohens_d)
print(fig3c_noswitch)
fig3c_switch = fig3cstats %>%
filter(switch_cond == 'sw1') %>% ## noswitch trials only
spread(setsize, pt_mean) %>%
do({ttest = broom::tidy(t.test(.$n1, .$n3, paired = TRUE))
cd = effectsize::cohens_d(.$n1, .$n3, paired = TRUE)
bind_cols(ttest, cd)}) %>%
summarize(condition = paste0("Switch (1 - 3 VSP targets)"), df = parameter, t = statistic, p = p.value, stars = sig_stars(p.value), cohens_d = Cohens_d)
print(fig3c_switch)
#### ANOVA ######
fig3c_aov = fig3cstats %>%
mutate(id = factor(id))
fig3c_aov = aov(pt_mean ~ switch_cond * setsize + Error(id/(switch_cond * setsize)), data = fig3c_aov)
print(summary(fig3c_aov))
fig3c_aov_eta = eta_squared(fig3c_aov)
print(fig3c_aov_eta)
```
Figure 3d - Visuospatial Load x Movement Recall Switch Cost - Distance Error
```{r figure 3d}
## within pt
fig3d = data %>%
mutate(switch_cond = ifelse(switch_hands == 0, 'noswitch', 'switch')) %>%
group_by(id, switch_cond, vsp_setsize) %>%
summarize(pt_mean = mean(dist_err), .groups = 'drop') %>%
spread(switch_cond, pt_mean) %>%
mutate(cost = switch - noswitch ) %>%
select(id, vsp_setsize, cost)
## across pt
fig3d = fig3d %>%
group_by(vsp_setsize) %>%
summarize(mean_cost = mean(cost), sd = sd(cost), n = n(), .groups = 'drop') %>%
mutate(sem = sd/ sqrt(n))
## main plot
vsp3d = ggplot(data = fig3d, aes(x = vsp_setsize, y = mean_cost)) +
geom_line(size = ln_size, position = pd, color = combo_dark) +
geom_point(size = pt_size, position = pd, color = combo_dark) +
scale_color_manual(values = sns_colors) +
geom_errorbar(aes(ymin = mean_cost - sem,
ymax = mean_cost + sem),
size = ln_size, width = err_width, position = pd, color = combo_dark) +
scale_x_continuous(limits = c(0.8,3.2), breaks = c(1,3)) +
scale_y_continuous(limits = c(0.5,1.3), breaks = c(0.6, 0.8, 1.0, 1.2)) +
labs(
# title = "Movement recall error switch cost",
y = "Switch Cost \n (Switch - No-switch, cm) \n",
x = "\n Number of Visual Targets") +
mytheme
vsp3d
if (save == TRUE) {ggsave(file= '../all_plots/vsp3d.svg', plot = vsp3d)}
```
```{r figure 3d stats}
## NORMAL T-TEST
fig3dstats = data %>%
mutate(switch_cond = paste0('sw', switch_hands)) %>%
mutate(vsp_setsize = paste0('n', vsp_setsize)) %>%
group_by(id, switch_cond, vsp_setsize) %>%
summarize(pt_mean = mean(dist_err), .groups = 'drop') %>%
spread(switch_cond, pt_mean) %>%
mutate(cost = sw1 - sw0 ) %>%
select(id, vsp_setsize, cost) %>%
spread(vsp_setsize, cost) %>%
do({ttest = broom::tidy(t.test(.$n1, .$n3, paired = TRUE))
cd = effectsize::cohens_d(.$n1, .$n3, paired = TRUE)
bind_cols(ttest, cd)}) %>%
summarize(condition = paste0("Motor Error Switch Cost"), df = parameter, t = statistic, p = p.value, stars = sig_stars(p.value), cohens_d = Cohens_d)
print(fig3dstats)
## BAYES FACTOR ANALYSIS
fig3dbayes = data %>%
mutate(switch_cond = paste0('sw', switch_hands)) %>%
mutate(vsp_setsize = paste0('n', vsp_setsize)) %>%
group_by(id, switch_cond, vsp_setsize) %>%
summarize(pt_mean = mean(dist_err), .groups = 'drop') %>%
spread(switch_cond, pt_mean) %>%
mutate(cost = sw1 - sw0) %>%
select(id, vsp_setsize, cost) %>%
spread(vsp_setsize, cost)
fig3dbayes = ttestBF(fig3dbayes$n1, fig3dbayes$n3, paired = TRUE)
print(summary(fig3dbayes))
```
Figure 3e - Visuospatial Load x Movement Recall Switch Cost - Angular Error
```{r figure 3e}
## within pt
fig3e = data %>%
mutate(switch_cond = ifelse(switch_hands == 0, 'noswitch', 'switch')) %>%
group_by(id, switch_cond, vsp_setsize) %>%
summarize(pt_ang_mean = mean(sd.circular(ang_err*(pi/180))), .groups = 'drop') %>%
spread(switch_cond, pt_ang_mean) %>%
mutate(cost = switch - noswitch ) %>%
select(id, vsp_setsize, cost)
## across pt
fig3e = fig3e %>%
group_by(vsp_setsize) %>%
summarize(mean_ang_cost = mean(cost), sd = sd(cost), n = n(), .groups = 'drop') %>%
mutate(sem = sd/ sqrt(n))
## main plot
vsp3e = ggplot(data = fig3e, aes(x = vsp_setsize, y = mean_ang_cost)) +
geom_line(size = ln_size, position = pd, color = combo_dark) +
geom_point(size = pt_size, position = pd, color = combo_dark) +
scale_color_manual(values = sns_colors) +
geom_errorbar(aes(ymin = mean_ang_cost - sem,
ymax = mean_ang_cost + sem),
size = ln_size, width = err_width, position = pd, color = combo_dark) +
scale_x_continuous(limits = c(0.8,3.2), breaks = c(1,3)) +
scale_y_continuous(limits = c(0.02,0.13), breaks = c(0.03,0.06,0.09, 0.12)) +
labs(
# title = "Movement Angle Switch Cost",
y = "Movement Circular SD\n",
x = "\n Number of Visual Targets" ) +
mytheme
vsp3e
if (save == TRUE) {ggsave(file= '../all_plots/vsp3e.svg', plot = vsp3e)}
```
```{r figure 3e stats}
## within pt
fig3estats = data %>%
mutate(switch_cond = paste0('sw', switch_hands)) %>%
mutate(vsp_setsize = paste0('n', vsp_setsize)) %>%
group_by(id, switch_cond, vsp_setsize) %>%
summarize(pt_ang_mean = mean(sd.circular(ang_err*(pi/180))), .groups = 'drop') %>%
spread(switch_cond, pt_ang_mean) %>%
mutate(cost = sw1 - sw0 ) %>%
select(id, vsp_setsize, cost) %>%
spread(vsp_setsize, cost) %>%
do({ttest = broom::tidy(t.test(.$n1, .$n3, paired = TRUE))
cd = effectsize::cohens_d(.$n1, .$n3, paired = TRUE)
bind_cols(ttest, cd)}) %>%
summarize(condition = paste0("Motor Error Switch Cost"), df = parameter, t = statistic, p = p.value, stars = sig_stars(p.value), cohens_d = Cohens_d)
print(fig3estats)
```