Getting started
packages = c('scales', 'viridis',
'lubridate', 'ggthemes',
'gridExtra', 'tidyverse',
'readxl', 'knitr',
'data.table', 'ViSiElse')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}
Importing Data
Read All Activity Logs Files In A list
files <- list.files('E:/data/Activity Logs/')
Find ID of Participants According to Joviality
Extract Data From Activity Logs of Participants
Importing Data from RDS file
The participant whose participantId is 113 have the highest joviality The participant whose participantId is 758 have the lowest joviality
Data Preparation
log_t <- logs %>%
mutate(date = date(timestamp)) %>%
mutate(month = month(timestamp)) %>%
mutate(week = week(timestamp)) %>%
mutate(weekday = weekdays(timestamp)) %>%
mutate(hour = hour(timestamp)) %>%
mutate(min = minute(timestamp)) %>%
mutate(time_line = hour*60+min) %>%
mutate(day = as.integer(date-min(date))+1)
Change the dataframe into the structure can be used in Visielse
hunger113_w <- log_t %>%
filter(participantId == 113) %>%
filter(weekday %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")) %>%
select(participantId, hungerStatus, day, time_line) %>%
group_by(hungerStatus,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = hungerStatus,values_from =value)
hunger113_r <- log_t %>%
filter(participantId == 113) %>%
filter(weekday %in% c("Saturday", "Sunday")) %>%
select(participantId, hungerStatus, day, time_line) %>%
group_by(hungerStatus,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = hungerStatus,values_from =value)
hunger758_w <- log_t %>%
filter(participantId == 758) %>%
filter(weekday %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")) %>%
select(participantId, hungerStatus, day, time_line) %>%
group_by(hungerStatus,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = hungerStatus,values_from =value)
hunger758_r <- log_t %>%
filter(participantId == 758) %>%
filter(weekday %in% c("Saturday", "Sunday")) %>%
select(participantId, hungerStatus, day, time_line) %>%
group_by(hungerStatus,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = hungerStatus,values_from =value)
sleep113_w <- log_t %>%
filter(participantId == 113) %>%
filter(weekday %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")) %>%
select(participantId, sleepStatus, day, time_line) %>%
group_by(sleepStatus,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = sleepStatus,values_from =value)
sleep113_r <- log_t %>%
filter(participantId == 113) %>%
filter(weekday %in% c("Saturday", "Sunday")) %>%
select(participantId, sleepStatus, day, time_line) %>%
group_by(sleepStatus,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = sleepStatus,values_from =value)
sleep758_w <- log_t %>%
filter(participantId == 758) %>%
filter(weekday %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")) %>%
select(participantId, sleepStatus, day, time_line) %>%
group_by(sleepStatus,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = sleepStatus,values_from =value)
sleep758_r <- log_t %>%
filter(participantId == 758) %>%
filter(weekday %in% c("Saturday", "Sunday")) %>%
select(participantId, sleepStatus, day, time_line) %>%
group_by(sleepStatus,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = sleepStatus,values_from =value)
mode113_w <- log_t %>%
filter(participantId == 113) %>%
filter(weekday %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")) %>%
select(participantId, currentMode, day, time_line) %>%
group_by(currentMode,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = currentMode,values_from =value)
mode113_r <- log_t %>%
filter(participantId == 113) %>%
filter(weekday %in% c("Saturday", "Sunday")) %>%
select(participantId, currentMode, day, time_line) %>%
group_by(currentMode,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = currentMode,values_from =value)
mode758_w <- log_t %>%
filter(participantId == 758) %>%
filter(weekday %in% c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")) %>%
select(participantId, currentMode, day, time_line) %>%
group_by(currentMode,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = currentMode,values_from =value)
mode758_r <- log_t %>%
filter(participantId == 758) %>%
filter(weekday %in% c("Saturday", "Sunday")) %>%
select(participantId, currentMode, day, time_line) %>%
group_by(currentMode,day) %>%
summarise(value = min(time_line)) %>%
pivot_wider(day,names_from = currentMode,values_from =value)
Participant 113 weekdays
w_113 <- mode113_w %>%
merge(hunger113_w, by = "day") %>%
merge(sleep113_w, by = "day")
p113_w <- visielse(w_113,doplot = F)
plot(p113_w,
vp0w = 0.7,
unit.tps = "min",
scal.unit.tps = 30,
main = "Participant 113 weekdays")
Participant 113 weekend
r_113 <- mode113_r %>%
merge(hunger113_r, by = "day") %>%
merge(sleep113_r, by = "day")
p113_r <- visielse(r_113,doplot = F)
plot(p113_r,
vp0w = 0.7,
unit.tps = "min",
scal.unit.tps = 30,
main = "Participant 113 weekend")
Participant 758 weekdays
w_758 <- mode758_w %>%
merge(hunger113_w, by = "day") %>%
merge(sleep113_w, by = "day")
p758_w <- visielse(w_758,doplot = F)
plot(p758_w,
vp0w = 0.7,
unit.tps = "min",
scal.unit.tps = 30,
main = "Participant 758 weekdays")
Participant 758 weekend
r_758 <- mode758_r %>%
merge(hunger113_r, by = "day") %>%
merge(sleep113_r, by = "day")
p758_r <- visielse(r_758,doplot = F)
plot(p758_r,
vp0w = 0.7,
unit.tps = "min",
scal.unit.tps = 30,
main = "Participant 758 weekend")
