Take Home Exercise 4

Take Home Exercise 4 is to reveal the daily routines of two selected participant of the city of Engagement, Ohio USA.
ViSIElse, a graphical tool to visualize behavioural observations, and other appropriate visual analytics methods will be used.

Loading R packages

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)
}

1. Explore Recreatinal Pattern

1.1 Data Wrangling for Recreation Pattern Across Weekday and Hour

Note: only the first 10 activity logs files are combined and analyzed here.

logs_recreation <- read_csv(“data/logs1_10.csv”) %>% select(timestamp, participantId, currentMode) %>% filter(currentMode == “AtRecreation”) %>% mutate(wkdays = weekdays(ymd_hms(timestamp, quiet = TRUE))) %>% mutate(hour = hour(ymd_hms(timestamp, quiet = TRUE))) %>% count(wkdays, hour) %>% ungroup() %>% na.omit()

write_csv(logs_recreation,‘data/logs_recreation.csv’ )

1.2. Plot Calendar Heat Map for Recreation Pattern

logs_recreation <- read_csv("data/logs_recreation.csv")
ggplot(logs_recreation, aes(hour, wkdays, fill = n))+
  geom_tile(color = "white", size = 0.1)+
  theme_tufte(base_family = "Helvetica")+
  coord_equal()+
  scale_fill_gradient(name = "# of Recreation",
                       low = "sky blue",
                       high = "dark blue")+
  labs(x = NULL, Y= NULL, 
       title = "Recreation by Weekday and Time of the Day")+
  theme(axis.ticks = element_blank(),
      plot.title = element_text(hjust = 0.5),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 6) )

2. Visualizing Daily Life on 1 March 2022

2.1 Data Wrangling for Recreation Pattern Across Weekday and Hour for participantId = 0

logs_sleep <- read_csv(“data/logs1_10.csv”) %>% select(timestamp, participantId, sleepStatus) %>% filter (participantId == 0) %>% mutate(date = as.IDate(ymd_hms(timestamp, quiet = TRUE))) %>% mutate(minutes = hour(ymd_hms(timestamp))*60+minute(ymd_hms(timestamp))) %>% group_by(date, sleepStatus) %>% summarise(time = min(minutes)) %>% pivot_wider(names_from = sleepStatus, values_from = time)

logs_mode <- read_csv(“data/logs1_10.csv”) %>% select(timestamp, participantId, currentMode) %>% filter (participantId == 0) %>% mutate(date = as.IDate(ymd_hms(timestamp, quiet = TRUE))) %>% mutate(minutes = hour(ymd_hms(timestamp))*60+minute(ymd_hms(timestamp))) %>% group_by(date, currentMode) %>% summarise(time = min(minutes)) %>% pivot_wider(names_from = currentMode, values_from = time)

logs_hungry <- read_csv(“data/logs1_10.csv”) %>% select(timestamp, participantId, hungerStatus) %>% filter (participantId == 0) %>% mutate(date = as.IDate(ymd_hms(timestamp, quiet = TRUE))) %>% mutate(minutes = hour(ymd_hms(timestamp))*60+minute(ymd_hms(timestamp))) %>% group_by(date,hungerStatus) %>% summarise(time = min(minutes)) %>% pivot_wider(names_from = hungerStatus, values_from = time)

daily_life_p <- left_join(logs_sleep, logs_mode) daily_life <- left_join(daily_life_p, logs_hungry) write_csv(daily_life, “data/daily_life.csv”)

2.2 Plot Daily Routine for participantId = 0 with visielse()

daily_life <- read_csv("data/daily_life.csv")
visielse(daily_life)

-parameters 
 method           :  global 
 grwithin         :   
 quantity         :  N 
 informer         :  median 
 tests            :  FALSE 
 threshold.test   :  0.01 
 pixel            :  20 
 t_0              :  0 
-MATp             : 13 x 67 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             : 13 x 6  ViSibook 
-group            : length  0 factor 
-vect_tps         : length  67 vector 
-testsP           : length  0 vector 
-informers        : 3 x 13 matrix 

2.3 Plot Daily Routine for participantId = 500 with visielse()

Note that daily_life1.csv was created by using same code in 2.1 with participantId set to 500.

daily_life1 <- read_csv("data/daily_life1.csv")
visielse(daily_life1)

-parameters 
 method           :  global 
 grwithin         :   
 quantity         :  N 
 informer         :  median 
 tests            :  FALSE 
 threshold.test   :  0.01 
 pixel            :  20 
 t_0              :  0 
-MATp             : 13 x 70 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             : 13 x 6  ViSibook 
-group            : length  0 factor 
-vect_tps         : length  70 vector 
-testsP           : length  0 vector 
-informers        : 3 x 13 matrix