cat("\014") # clean terminal
rm(list = ls()) # clean workspace
try(dev.off(), silent = TRUE) # close all plots
library(dplyr)
master_dir <- '~/Insync/OneDrive_shared/Fondecyt_Emociones_Estabilometria'
data_dir <- paste(master_dir, 'data', sep = '/')
all_data_name <- paste(data_dir, 'all_data_raw.csv', sep='/')
all_data_raw <- read.csv(all_data_name, header = TRUE)
all_data_raw <- all_data_raw[(all_data_raw$Group == 'Elder' | all_data_raw$Group == 'Parkinson' | all_data_raw$Group == 'Young'), ]
all_data_raw <- all_data_raw[(all_data_raw$Paradigm == 'New'), ]
all_data_raw$Group <- factor(all_data_raw$Group, levels = c("Parkinson", "Elder", "Young"))
all_data_raw$Task <- factor(all_data_raw$Task, levels = c("Single", "Neutral", "Pleasant", "Unpleasant", "UnpleasantCar", "Visualization"))
all_data_raw$Emotion <- factor(all_data_raw$Emotion)
all_data_raw$ID <- factor(all_data_raw$ID)
all_data_raw$num_ID <- factor(all_data_raw$num_ID)
all_data_raw$Paradigm <- factor(all_data_raw$Paradigm)
all_data_raw$log10_area <- log10(all_data_raw$area)
General
Description
options(width = 100)
summary(all_data_raw[c("Group", "Paradigm", "Task", "Emotion", "ID", "area", "heart_rate", "ave_phasic_eda")])
Group Paradigm Task Emotion ID area
Parkinson:237 New:735 Single :145 No :195 FOJO_04: 6 Min. : 1.285
Elder :239 Neutral :146 Yes:540 FOJO_05: 6 1st Qu.: 114.131
Young :259 Pleasant :147 FOJO_06: 6 Median : 190.325
Unpleasant :147 FOJO_07: 6 Mean : 287.050
UnpleasantCar:100 EP_201 : 5 3rd Qu.: 305.294
Visualization: 50 EP_203 : 5 Max. :4001.387
(Other):701 NA's :20
heart_rate ave_phasic_eda
Min. : 48.39 Min. : -0.0031
1st Qu.: 69.42 1st Qu.: 0.0350
Median : 77.44 Median : 0.1478
Mean : 77.90 Mean : 1.3530
3rd Qu.: 85.72 3rd Qu.: 0.4842
Max. :117.80 Max. :639.0436
NA's :44 NA's :43
Valid recordings
options(width = 100)
xtabs(~ Task + Group, data = all_data_raw)
Group
Task Parkinson Elder Young
Single 46 48 51
Neutral 48 47 51
Pleasant 48 48 51
Unpleasant 48 48 51
UnpleasantCar 47 48 5
Visualization 0 0 50
COP
options(width = 100)
knitr::kable(aggregate(area ~ Task + Group, # Count rows of all groups
data = all_data_raw,
FUN = length))
| Single |
Parkinson |
44 |
| Neutral |
Parkinson |
48 |
| Pleasant |
Parkinson |
48 |
| Unpleasant |
Parkinson |
48 |
| UnpleasantCar |
Parkinson |
47 |
| Single |
Elder |
47 |
| Neutral |
Elder |
47 |
| Pleasant |
Elder |
48 |
| Unpleasant |
Elder |
48 |
| UnpleasantCar |
Elder |
48 |
| Single |
Young |
49 |
| Neutral |
Young |
47 |
| Pleasant |
Young |
46 |
| Unpleasant |
Young |
47 |
| UnpleasantCar |
Young |
5 |
| Visualization |
Young |
48 |
ECG
options(width = 100)
knitr::kable(aggregate(heart_rate ~ Task + Group, # Count rows of all groups
data = all_data_raw,
FUN = length))
| Single |
Parkinson |
42 |
| Neutral |
Parkinson |
43 |
| Pleasant |
Parkinson |
42 |
| Unpleasant |
Parkinson |
41 |
| UnpleasantCar |
Parkinson |
43 |
| Single |
Elder |
45 |
| Neutral |
Elder |
44 |
| Pleasant |
Elder |
46 |
| Unpleasant |
Elder |
46 |
| UnpleasantCar |
Elder |
46 |
| Single |
Young |
50 |
| Neutral |
Young |
50 |
| Pleasant |
Young |
49 |
| Unpleasant |
Young |
50 |
| UnpleasantCar |
Young |
5 |
| Visualization |
Young |
49 |
EDA
options(width = 100)
knitr::kable(aggregate(ave_phasic_eda ~ Task + Group, # Count rows of all groups
data = all_data_raw,
FUN = length))
| Single |
Parkinson |
42 |
| Neutral |
Parkinson |
41 |
| Pleasant |
Parkinson |
41 |
| Unpleasant |
Parkinson |
39 |
| UnpleasantCar |
Parkinson |
45 |
| Single |
Elder |
48 |
| Neutral |
Elder |
47 |
| Pleasant |
Elder |
48 |
| Unpleasant |
Elder |
48 |
| UnpleasantCar |
Elder |
48 |
| Single |
Young |
49 |
| Neutral |
Young |
45 |
| Pleasant |
Young |
47 |
| Unpleasant |
Young |
49 |
| UnpleasantCar |
Young |
5 |
| Visualization |
Young |
50 |
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