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strint_manuscript_lh_test_FC.Rmd
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strint_manuscript_lh_test_FC.Rmd
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---
title: "Structural Interference (Dis. Aim 3) force channel"
output: html_document
---
```{r setup, include=FALSE}
#knitr::opts_chunk$set(echo = TRUE)
```
## Install new packages
```{r}
#install.packages('plotrix')
#install.packages("grid")
#install.packages("reshape")
#install.packages("ez")
#install.packages("Cairo")
```
## Load packages
```{r Load packages}
library(plyr)
library(ggplot2)
library(plotrix)
library(dplyr)
library(grid)
library(gridExtra)
library(lattice)
library(reshape)
library(ez)
```
## Column rearranger
```{r}
##arrange df vars by position
##'vars' must be a named vector, e.g. c("var.name"=1)
arrange.vars <- function(data, vars){
##stop if not a data.frame (but should work for matrices as well)
stopifnot(is.data.frame(data))
##sort out inputs
data.nms <- names(data)
var.nr <- length(data.nms)
var.nms <- names(vars)
var.pos <- vars
##sanity checks
stopifnot( !any(duplicated(var.nms)),
!any(duplicated(var.pos)) )
stopifnot( is.character(var.nms),
is.numeric(var.pos) )
stopifnot( all(var.nms %in% data.nms) )
stopifnot( all(var.pos > 0),
all(var.pos <= var.nr) )
##prepare output
out.vec <- character(var.nr)
out.vec[var.pos] <- var.nms
out.vec[-var.pos] <- data.nms[ !(data.nms %in% var.nms) ]
stopifnot( length(out.vec)==var.nr )
##re-arrange vars by position
data <- data[ , out.vec]
return(data)
}
```
## Data import
```{r Load in FC data}
setwd("/Volumes/mnl/Data/Adaptation/structural_interference/Post_Step_3_test_FC") #Mac
#setwd("Z:\\Data\\Adaptation\\structural_interference\\Post_Step_3_test_FC") #PC
# FORCE SENSOR DATA
fcData.fs = read.delim('lh_raw_fc_fs.csv',header = FALSE, sep = ",", na.strings = 'NaN')
numSub = nrow(fcData.fs)/48
sample = seq(1,1000)
trial = rep(1:48,numSub)
colnames(fcData.fs) = c('subID', 'upBool', 'wrongTrial', 'group', sample)
fcData.fs$trial = trial
fcData.fs = arrange.vars(fcData.fs,vars = c("trial"=5))
factors = c('subID', 'wrongTrial', 'group')
fcData.fs[,factors] = lapply(fcData.fs[,factors], factor)
# FORCE COMMAND DATA
fcData.cmd = read.delim('lh_raw_fc_cmd.csv',header = FALSE, sep = ",", na.strings = 'NaN')
numSub = nrow(fcData.cmd)/48
sample = seq(1,1000)
trial = rep(1:48,numSub)
colnames(fcData.cmd) = c('subID', 'upBool', 'wrongTrial', 'group', sample)
fcData.cmd$trial = trial
fcData.cmd = arrange.vars(fcData.cmd,vars = c("trial"=5))
factors = c('subID', 'wrongTrial','group')
fcData.cmd[,factors] = lapply(fcData.cmd[,factors], factor)
```
## Data Wrangling for FORCE SENSOR
```{r Group means and group sem}
# Need to subtract off the mean of kin basline from all trials, by subject
temp = fcData.fs %>% select(-c(wrongTrial, group)) %>% filter(trial %in% 7:12) %>% group_by(subID) %>% summarise_all(funs(mean(.,na.rm = TRUE)))
temp2 = data.frame(matrix(ncol = 1000, nrow = 48*numSub))
for (i in 1:numSub){
for (j in 1:48){
temp2[(48*(i-1)+j),] = fcData.fs[(48*(i-1)+j),6:1005] - temp[i,4:1003] # This works, but takes a while...be patient :)
}
}
fcData.fs = cbind(fcData.fs[,1:5],temp2)
rm(temp, temp2)
colnames(fcData.fs)[6:1005] = c(1:1000)
fcData.fs = fcData.fs %>% select(-c(upBool))
#NOTE: 7/24/17 edit: There is no KB difference between groups...no need to do this. If we want to, make sure to change fcData.fs to fcData.fs.bc for the remainder of this code
# Calculate the group means and sem as a total dataset
vb = subset(fcData.fs, trial %in% 1:6)
kb = subset(fcData.fs, trial %in% 7:12)
bk1 = subset(fcData.fs, trial %in% 13:18)
bk2 = subset(fcData.fs, trial %in% 24:29)
bk3 = subset(fcData.fs, trial %in% 35:40)
# Vis baseline
vbmean = aggregate(vb, list(vb$group), FUN = mean, na.rm = TRUE)
vbmean$group = vbmean$Group.1
vbmean = subset(vbmean, select = -c(Group.1, subID, wrongTrial, trial))
vbse = fcData.fs %>% filter(trial %in% 1:6) %>% group_by(group) %>%
summarise_if(is.numeric, .funs = std.error, na.rm = T) %>% select(-c(trial))
vbse = as.data.frame(vbse)
temp1 = melt(vbmean, id = c("group"))
temp2 = melt(vbse, id =c("group"))
visbaseline.fs = cbind(temp1,temp2$value)
colnames(visbaseline.fs) = c("group", "sample", "mean", "se")
visbaseline.fs$sample = as.numeric(visbaseline.fs$sample)
rm(temp1, temp2, vb, vbmean, vbse)
# Kin baseline
kbmean = aggregate(kb, list(kb$group), FUN = mean, na.rm = TRUE)
kbmean$group = kbmean$Group.1
kbmean = subset(kbmean, select = -c(Group.1, subID, wrongTrial, trial))
kbse = fcData.fs %>% filter(trial %in% 7:12) %>% group_by(group) %>%
summarise_if(is.numeric, .funs = std.error, na.rm = T) %>% select(-c(trial))
kbse = as.data.frame(kbse)
temp1 = melt(kbmean, id = c("group"))
temp2 = melt(kbse, id =c("group"))
kinbaseline.fs = cbind(temp1,temp2$value)
colnames(kinbaseline.fs) = c("group", "sample", "mean", "se")
kinbaseline.fs$sample = as.numeric(kinbaseline.fs$sample)
rm(temp1, temp2, kb, kbmean, bkse)
# Block 1 (frist 6 FC trials)
bk1mean = aggregate(bk1, list(bk1$group), FUN = mean, na.rm = TRUE)
bk1mean$group = bk1mean$Group.1
bk1mean = subset(bk1mean, select = -c(Group.1, subID, wrongTrial, trial))
bk1se = fcData.fs %>% filter(trial %in% 13:18) %>% group_by(group) %>%
summarise_if(is.numeric, .funs = std.error, na.rm = T) %>% select(-c(trial))
bk1se = as.data.frame(bk1se)
temp1 = melt(bk1mean, id = c("group"))
temp2 = melt(bk1se, id =c("group"))
block1.fs = cbind(temp1,temp2$value)
colnames(block1.fs) = c("group", "sample", "mean", "se")
block1.fs$sample = as.numeric(block1.fs$sample)
rm(temp1, temp2, bk1, bk1mean, bk1se)
# Block 2
bk2mean = aggregate(bk2, list(bk2$group), FUN = mean, na.rm = TRUE)
bk2mean$group = bk2mean$Group.1
bk2mean = subset(bk2mean, select = -c(Group.1, subID, wrongTrial, trial))
bk2se = fcData.fs %>% filter(trial %in% 24:29) %>% group_by(group) %>%
summarise_if(is.numeric, .funs = std.error, na.rm = T) %>% select(-c(trial))
bk2se = as.data.frame(bk2se)
temp1 = melt(bk2mean, id = c("group"))
temp2 = melt(bk2se, id =c("group"))
block2.fs = cbind(temp1,temp2$value)
colnames(block2.fs) = c("group", "sample", "mean", "se")
block2.fs$sample = as.numeric(block2.fs$sample)
rm(temp1, temp2, bk2, bk2mean, bk2se)
# Block 3
bk3mean = aggregate(bk3, list(bk3$group), FUN = mean, na.rm = TRUE)
bk3mean$group = bk3mean$Group.1
bk3mean = subset(bk3mean, select = -c(Group.1, subID, wrongTrial, trial))
bk3se = fcData.fs %>% filter(trial %in% 35:40) %>% group_by(group) %>%
summarise_if(is.numeric, .funs = std.error, na.rm = T) %>% select(-c(trial))
bk3se = as.data.frame(bk3se)
temp1 = melt(bk3mean, id = c("group"))
temp2 = melt(bk3se, id =c("group"))
block3.fs = cbind(temp1,temp2$value)
colnames(block3.fs) = c("group", "sample", "mean", "se")
block3.fs$sample = as.numeric(block3.fs$sample)
rm(temp1, temp2, bk3, bk3mean, bk3se)
```
## Data Wrangling for FORCE COMMAND
```{r Group means and group sem}
# Need to subtract off the mean of kin basline from all trials, by subject
temp = fcData.cmd %>% select(-c(wrongTrial, group)) %>% filter(trial %in% 7:12) %>% group_by(subID) %>% summarise_all(funs(mean(.,na.rm = TRUE)))
temp2 = data.frame(matrix(ncol = 1000, nrow = 48*numSub))
for (i in 1:numSub){
for (j in 1:48){
temp2[(48*(i-1)+j),] = fcData.cmd[(48*(i-1)+j),6:1005] - temp[i,4:1003] # This works, but takes a while...be patient :)
}
}
fcData.cmd = cbind(fcData.cmd[,1:5],temp2)
rm(temp, temp2)
colnames(fcData.cmd)[6:1005] = c(1:1000)
fcData.cmd = fcData.cmd %>% select(-c(upBool))
#NOTE: 7/24/17 edit: There is no KB difference between groups...no need to do this. If we want to, make sure to change fcData.cmd to fcData.cmd.bc for the remainder of this code
# Calculate the group means and sem as a total dataset
vb = subset(fcData.cmd, trial %in% 1:6)
kb = subset(fcData.cmd, trial %in% 7:12)
bk1 = subset(fcData.cmd, trial %in% 13:18)
bk2 = subset(fcData.cmd, trial %in% 24:29)
bk3 = subset(fcData.cmd, trial %in% 35:40)
# Vis baseline
vbmean = aggregate(vb, list(vb$group), FUN = mean, na.rm = TRUE)
vbmean$group = vbmean$Group.1
vbmean = subset(vbmean, select = -c(Group.1, subID, wrongTrial, trial))
vbse = fcData.cmd %>% filter(trial %in% 1:6) %>% group_by(group) %>%
summarise_if(is.numeric, .funs = std.error, na.rm = T) %>% select(-c(trial))
vbse = as.data.frame(vbse)
temp1 = melt(vbmean, id = c("group"))
temp2 = melt(vbse, id =c("group"))
visbaseline.cmd = cbind(temp1,temp2$value)
colnames(visbaseline.cmd) = c("group", "sample", "mean", "se")
visbaseline.cmd$sample = as.numeric(visbaseline.cmd$sample)
rm(temp1, temp2, vb, vbmean, vbse)
# Kin baseline
kbmean = aggregate(kb, list(kb$group), FUN = mean, na.rm = TRUE)
kbmean$group = kbmean$Group.1
kbmean = subset(kbmean, select = -c(Group.1, subID, wrongTrial, trial))
kbse = fcData.cmd %>% filter(trial %in% 7:12) %>% group_by(group) %>%
summarise_if(is.numeric, .funs = std.error, na.rm = T) %>% select(-c(trial))
kbse = as.data.frame(kbse)
temp1 = melt(kbmean, id = c("group"))
temp2 = melt(kbse, id =c("group"))
kinbaseline.cmd = cbind(temp1,temp2$value)
colnames(kinbaseline.cmd) = c("group", "sample", "mean", "se")
kinbaseline.cmd$sample = as.numeric(kinbaseline.cmd$sample)
rm(temp1, temp2, kb, kbmean, bkse)
# Block 1 (frist 6 FC trials)
bk1mean = aggregate(bk1, list(bk1$group), FUN = mean, na.rm = TRUE)
bk1mean$group = bk1mean$Group.1
bk1mean = subset(bk1mean, select = -c(Group.1, subID, wrongTrial, trial))
bk1se = fcData.cmd %>% filter(trial %in% 13:18) %>% group_by(group) %>%
summarise_if(is.numeric, .funs = std.error, na.rm = T) %>% select(-c(trial))
bk1se = as.data.frame(bk1se)
temp1 = melt(bk1mean, id = c("group"))
temp2 = melt(bk1se, id =c("group"))
block1.cmd = cbind(temp1,temp2$value)
colnames(block1.cmd) = c("group", "sample", "mean", "se")
block1.cmd$sample = as.numeric(block1.cmd$sample)
rm(temp1, temp2, bk1, bk1mean, bk1se)
# Block 2
bk2mean = aggregate(bk2, list(bk2$group), FUN = mean, na.rm = TRUE)
bk2mean$group = bk2mean$Group.1
bk2mean = subset(bk2mean, select = -c(Group.1, subID, wrongTrial, trial))
bk2se = fcData.cmd %>% filter(trial %in% 24:29) %>% group_by(group) %>%
summarise_if(is.numeric, .funs = std.error, na.rm = T) %>% select(-c(trial))
bk2se = as.data.frame(bk2se)
temp1 = melt(bk2mean, id = c("group"))
temp2 = melt(bk2se, id =c("group"))
block2.cmd = cbind(temp1,temp2$value)
colnames(block2.cmd) = c("group", "sample", "mean", "se")
block2.cmd$sample = as.numeric(block2.cmd$sample)
rm(temp1, temp2, bk2, bk2mean, bk2se)
# Block 3
bk3mean = aggregate(bk3, list(bk3$group), FUN = mean, na.rm = TRUE)
bk3mean$group = bk3mean$Group.1
bk3mean = subset(bk3mean, select = -c(Group.1, subID, wrongTrial, trial))
bk3se = fcData.cmd %>% filter(trial %in% 35:40) %>% group_by(group) %>%
summarise_if(is.numeric, .funs = std.error, na.rm = T) %>% select(-c(trial))
bk3se = as.data.frame(bk3se)
temp1 = melt(bk3mean, id = c("group"))
temp2 = melt(bk3se, id =c("group"))
block3.cmd = cbind(temp1,temp2$value)
colnames(block3.cmd) = c("group", "sample", "mean", "se")
block3.cmd$sample = as.numeric(block3.cmd$sample)
rm(temp1, temp2, bk3, bk3mean, bk3se)
```
## Plotting
```{r Plot mean FC data with +/- sem}
ggplot(data = block1.fs, aes(x = sample, y = mean, color = group))+
geom_point()+
geom_errorbar(aes(x = block1.fs$sample, ymin = block1.fs$mean-block1.fs$se, ymax = block1.fs$mean+block1.fs$se, color = block1.fs$group, alpha = 0.05))+
coord_cartesian(ylim = c(-0.5, 3.5))
```
```{r Plot mean FC data with +/- sem}
ggplot(data = block2.fs, aes(x = sample, y = mean, color = group))+
geom_point()+
geom_errorbar(aes(x = block2.fs$sample, ymin = block2.fs$mean-block2.fs$se, ymax = block2.fs$mean+block2.fs$se, color = block2.fs$group, alpha = 0.05))+
coord_cartesian(ylim = c(-0.5, 3.5))
```
```{r Plot mean FC data with +/- sem}
ggplot(data = block3.fs, aes(x = sample, y = mean, color = group))+
geom_point()+
geom_errorbar(aes(x = block3.fs$sample, ymin = block3.fs$mean-block3.fs$se, ymax = block3.fs$mean+block3.fs$se, color = block3.fs$group, alpha = 0.05))+
coord_cartesian(ylim = c(-0.5, 3.5))
```