th4

  Load the daily event log data of participant 1 and 2  

log1<-read_csv('data/daily1.csv')

## Rows: 128660 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr  (3): currentMode, hungerStatus, sleepStatus
## dttm (1): timestamp
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

log2<-read_csv('data/daily2.csv')

## Rows: 128660 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr  (3): currentMode, hungerStatus, sleepStatus
## dttm (1): timestamp
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

Preprocessing

  Extract the date and time of the timestamp and save the date as index and time as a new numerical column.  

log1$index<-date(log1$timestamp)
log1$time<-60*hour(log1$timestamp)+minute(log1$timestamp)
log2$index<-date(log2$timestamp)
log2$time<-60*hour(log2$timestamp)+minute(log2$timestamp)

  Use pivot to transform the data in the format that can be processed with visilie, keep only the start time of a currentMode and try plotting it.

daily1<-log1 %>% group_by(index,currentMode)%>% summarise(start_time=min(time)) %>% arrange(index,start_time)
daily1 <- daily1 %>% pivot_wider(names_from = currentMode, values_from = start_time)
daily1$index <- 1:nrow(daily1)
visielse(daily1)

## -parameters 
##  method           :  global 
##  grwithin         :   
##  quantity         :  N 
##  informer         :  median 
##  tests            :  FALSE 
##  threshold.test   :  0.01 
##  pixel            :  20 
##  t_0              :  0 
## -MATp             : 5 x 61 sparse Matrix of class "dgCMatrix" 
## -L                : 0 x 0  data.frame 
## -idsort           : 0 x 0 matrix 
## -MATpsup          : 0 x 0 sparse Matrix of class "dgCMatrix" 
## -idsup            : length  0 vector 
## -Lsup             : 0 x 0  data.frame 
## -colvect          : 1 x 1 matrix 
## -BZL              : 0 x 0 sparse Matrix of class "dgCMatrix" 
## -book             : 5 x 6  ViSibook 
## -group            : length  0 factor 
## -vect_tps         : length  61 vector 
## -testsP           : length  0 vector 
## -informers        : 3 x 5 matrix

  The result was not good, since the participant’s events are very uncertain and there are no consistent sequences of these events. One event may start multiple times in a day.   Try again with ggplot

mode_levels <- c('AtHome', 'Transport', 
                  'AtWork', 'AtRestaurant', 
                  'AtRecreation')
time_levels <- 0:288
time_levels <-time_levels*5
daily1<-log1 %>% select(currentMode,time) %>% mutate(currentMode=factor(currentMode,levels=mode_levels),time=factor(time,levels=time_levels))

daily2<-log2 %>% select(currentMode,time) %>% mutate(currentMode=factor(currentMode,levels=mode_levels),time=factor(time,levels=time_levels))

grouped <- daily1 %>% 
  count(currentMode, time) %>% 
  ungroup() %>%
  na.omit()

grouped2 <- daily2 %>% 
  count(currentMode, time) %>% 
  ungroup() %>%
  na.omit()

p1<-ggplot(grouped, 
       aes(time, 
           currentMode, 
           fill = n)) + 
geom_tile(color = "white", 
          size = 0.1) + 
#theme_tufte(base_family = "Helvetica") + 
#coord_equal() +
scale_fill_gradient(name = "# of days",
                    low = "sky blue", 
                    high = "dark blue") +
labs(x = NULL, 
     y = NULL, 
     title = "Participant 1") +
theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )

p2<-ggplot(grouped2, 
       aes(time, 
           currentMode, 
           fill = n)) + 
geom_tile(color = "white", 
          size = 0.1) + 
#theme_tufte(base_family = "Helvetica") + 
#coord_equal() +
scale_fill_gradient(name = "# of days",
                    low = "sky blue", 
                    high = "dark blue") +
labs(x = NULL, 
     y = NULL, 
     title = "Participant 2") +
theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )

p1/p2