TUD FOR WEEKDAYS
Kakau with minor amendments from Ben :-)
Last run at: 2020-05-18 12:10:16
# packages ----
library(plyr)
library(stringr)
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library(lubridate)##
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# parameters ----
dPath <- "/Volumes/hum-csafe/Research Projects/FoliakiPhD/tudData/" # edit for your set up
dataF <- path.expand(paste0(dPath, "TUD Weekdays 050520.csv"))1 Time Use Diary
Importing Weekends dataframe
The dataframe is a wide format. I had to clean the data by correcting typo and translating some of the data into english. So the file is not as original as it is when export from qualtrics.
# read in my dataframe
weekdays <- read.csv(dataF, na.strings=c(""), check.names = FALSE, header = TRUE, as.is=TRUE)
head(weekdays)## Village Household ID Day 3:00 AM 3:30 AM 4:00 AM 4:30 AM
## 1 Fasi 17 Monday Sleeping Sleeping Sleeping Sleeping
## 2 Fasi 17 Monday <NA> <NA> <NA> <NA>
## 3 Fanga 2 Monday Sleeping Sleeping Sleeping Sleeping
## 4 Fanga 2 Monday <NA> <NA> <NA> <NA>
## 5 Fangaloto 7 Wednesday Sleeping Sleeping Sleeping Sleeping
## 6 Fangaloto 7 Wednesday <NA> <NA> <NA> <NA>
## 5:00 AM 5:30 AM 6:00 AM 6:30 AM 7:00 AM
## 1 Sleeping Sleeping Woke up Showering Breakfast
## 2 <NA> <NA> <NA> Getting dressed <NA>
## 3 Sleeping Sleeping Sleeping Sleeping Woke up
## 4 <NA> <NA> <NA> <NA> Showering
## 5 Sleeping Sleeping Sleeping Sleeping Woke up
## 6 <NA> <NA> <NA> <NA> Listen to music/radio
## 7:30 AM 8:00 AM 8:30 AM 9:00 AM
## 1 Breakfast Driving (work) Work Work
## 2 <NA> <NA> <NA> <NA>
## 3 Getting dressed Walking (work) Work Work
## 4 Breakfast <NA> <NA> <NA>
## 5 Feeding animals Getting dressed Driving (work) Work
## 6 Listen to music/radio Listen to music/radio Driving (drop kids) <NA>
## 9:30 AM 10:00 AM 10:30 AM 11:00 AM 11:30 AM 12:00 PM 12:30 PM 1:00 PM
## 1 Work Work Work Work Work Work Work Work
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 Work Work Work Work Work Work Work Work
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 Work Work Work Work Work Work Work Work
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 1:30 PM 2:00 PM 2:30 PM 3:00 PM 3:30 PM 4:00 PM 4:30 PM 5:00 PM
## 1 Work Work Work Work Work Work Work Home
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 Work Work Work Work Work Work Work Walking (home)
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 Work Work Work Work Work Work Work Home
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5:30 PM 6:00 PM 6:30 PM 7:00 PM
## 1 Cooking Dinner Dinner Dinner
## 2 <NA> <NA> <NA> <NA>
## 3 Walking (home) Cooking Cooking Watching movies (TV)
## 4 <NA> Tidy up Laundry Dinner
## 5 Feeding animals Cooking Cooking Cooking
## 6 <NA> <NA> <NA> <NA>
## 7:30 PM 8:00 PM 8:30 PM
## 1 Showering Showering Mobile devices (work)
## 2 <NA> <NA> <NA>
## 3 Watching movies (TV) Watching movies (TV) Watching movies (TV)
## 4 <NA> <NA> <NA>
## 5 Dinner Family devotion Watching movies (TV)
## 6 <NA> <NA> <NA>
## 9:00 PM 9:30 PM 10:00 PM
## 1 Mobile devices (work) Mobile devices (work) Sleeping
## 2 <NA> <NA> <NA>
## 3 Watching movies (TV) Watching movies (TV) Showering
## 4 <NA> <NA> <NA>
## 5 Watching movies (TV) Watching movies (TV) Watching movies (TV)
## 6 <NA> <NA> <NA>
## 10:30 PM 11:00 PM 11:30 PM
## 1 Sleeping Sleeping Sleeping
## 2 <NA> <NA> <NA>
## 3 Showering Mobile devices (work) Reading
## 4 <NA> Listen to music/radio Listen to music/radio
## 5 Watching movies (TV) Sleeping Sleeping
## 6 <NA> <NA> <NA>
## 12:00 AM 12:30 AM 1:00 AM 1:30 AM 2:00 AM 2:30 AM
## 1 Sleeping Sleeping Sleeping Sleeping Sleeping Sleeping
## 2 <NA> <NA> <NA> <NA> <NA> <NA>
## 3 Reading Reading Sleeping Sleeping Sleeping Sleeping
## 4 <NA> <NA> <NA> <NA> <NA> <NA>
## 5 Sleeping Sleeping Sleeping Sleeping Sleeping Sleeping
## 6 <NA> <NA> <NA> <NA> <NA> <NA>Converting the wide dataframe to long dataframe for easier analysis
Ive noticed that the time is in a character format rather than date. i need to change it to date
see below
longweekdays <- weekdays %>%
gather(Time, Activity, 4:51)
head(longweekdays)## Village Household ID Day Time Activity
## 1 Fasi 17 Monday 3:00 AM Sleeping
## 2 Fasi 17 Monday 3:00 AM <NA>
## 3 Fanga 2 Monday 3:00 AM Sleeping
## 4 Fanga 2 Monday 3:00 AM <NA>
## 5 Fangaloto 7 Wednesday 3:00 AM Sleeping
## 6 Fangaloto 7 Wednesday 3:00 AM <NA>Sorting the TUD dataframe, Firstly, as you can see previously some respondents do not have a secondary activity (i.e NA). Secondly, the data is sorted by time. I think it would be easier to sorted by Household ID. So here i am sorting the data based on ID number
# sorting my dataframe in accending order based on the Household ID
weekdayOrder <- order(longweekdays$`Household ID`)
head(weekdayOrder)## [1] 3 4 181 182 359 360# Applying the sorting order into my "longweekdends" dataframe and call it sortweekends
sortweekdays <- longweekdays[weekdayOrder,]
head(sortweekdays)## Village Household ID Day Time Activity
## 3 Fanga 2 Monday 3:00 AM Sleeping
## 4 Fanga 2 Monday 3:00 AM <NA>
## 181 Fanga 2 Monday 3:30 AM Sleeping
## 182 Fanga 2 Monday 3:30 AM <NA>
## 359 Fanga 2 Monday 4:00 AM Sleeping
## 360 Fanga 2 Monday 4:00 AM <NA>names(sortweekdays)## [1] "Village" "Household ID" "Day" "Time"
## [5] "Activity"Here i am seprating the activity columm into a primary activity and a secondary activity
The issue im facing with is the date. It in character and alsoit picked up the current date (2020) rather than the date from the TUD.
BA: I can’t see a date variable. How would R know which date the diary was completed on?
Im trying to sort the date but it comes back N/A
see below
# Since both the first and second activiies are in one column, i need to separate them into two columns
#call the number of row in my dataframe for the maximum row for my sequence function
nrow(sortweekdays)## [1] 8544#create a new dataframe "newdata" for the primary activity only (all odd rows)
newdata <- sortweekdays[seq(1,8543,2),]
head(newdata)## Village Household ID Day Time Activity
## 3 Fanga 2 Monday 3:00 AM Sleeping
## 181 Fanga 2 Monday 3:30 AM Sleeping
## 359 Fanga 2 Monday 4:00 AM Sleeping
## 537 Fanga 2 Monday 4:30 AM Sleeping
## 715 Fanga 2 Monday 5:00 AM Sleeping
## 893 Fanga 2 Monday 5:30 AM Sleeping#create a new datagame "newdata1" for the secondary activity (oall even rows)
newdata1 <- sortweekdays[seq(2,8544,2),]
head(newdata1)## Village Household ID Day Time Activity
## 4 Fanga 2 Monday 3:00 AM <NA>
## 182 Fanga 2 Monday 3:30 AM <NA>
## 360 Fanga 2 Monday 4:00 AM <NA>
## 538 Fanga 2 Monday 4:30 AM <NA>
## 716 Fanga 2 Monday 5:00 AM <NA>
## 894 Fanga 2 Monday 5:30 AM <NA>#since i have separated my two acitvities, i need to merge them into one dataframe
newdata$Activity2 <- newdata1$Activity
kableExtra::kable(head(newdata), caption = "Test merged long form data") %>%
kable_styling()| Village | Household ID | Day | Time | Activity | Activity2 | |
|---|---|---|---|---|---|---|
| 3 | Fanga | 2 | Monday | 3:00 AM | Sleeping | NA |
| 181 | Fanga | 2 | Monday | 3:30 AM | Sleeping | NA |
| 359 | Fanga | 2 | Monday | 4:00 AM | Sleeping | NA |
| 537 | Fanga | 2 | Monday | 4:30 AM | Sleeping | NA |
| 715 | Fanga | 2 | Monday | 5:00 AM | Sleeping | NA |
| 893 | Fanga | 2 | Monday | 5:30 AM | Sleeping | NA |
BA: fix the time variable so it looks like and acts like HH:MM
#newdata$rawTime <- hms::parse_hm(newdata$Time) # this will only be right for AM
#newdata$AM_PM <- stringr::word(newdata$Time, 2) # get the second word of 'Time' which will be AM or PM
# better solution
# https://stackoverflow.com/questions/7883806/parse-timestamp-with-a-m-p-m
# this will make it today's date and the current time zone by default
newdata$rawTime <- as.POSIXct(newdata$Time, format="%l:%M %p")
# Lesson #1: always enter times as 24 hour clock: HH:MM !!
# Now use that to add 12 hours to rawTime whenit is actually PM
# newdata$fixedTime <- ifelse(newdata$AM_PM =="PM", # if PM
# newdata$rawTime + (12 * 60 * 60), # add 12 hours
# newdata$rawTime # otherwise don't
# )
newdata$fixedHMS <- hms::as_hms(newdata$rawTime) # make sure it is in H:M:S format
t <- table(newdata$Time, newdata$rawTime)
kableExtra::kable(t, caption = "Test time coding") %>%
kable_styling()| 2020-05-18 00:00:00 | 2020-05-18 00:30:00 | 2020-05-18 01:00:00 | 2020-05-18 01:30:00 | 2020-05-18 02:00:00 | 2020-05-18 02:30:00 | 2020-05-18 03:00:00 | 2020-05-18 03:30:00 | 2020-05-18 04:00:00 | 2020-05-18 04:30:00 | 2020-05-18 05:00:00 | 2020-05-18 05:30:00 | 2020-05-18 06:00:00 | 2020-05-18 06:30:00 | 2020-05-18 07:00:00 | 2020-05-18 07:30:00 | 2020-05-18 08:00:00 | 2020-05-18 08:30:00 | 2020-05-18 09:00:00 | 2020-05-18 09:30:00 | 2020-05-18 10:00:00 | 2020-05-18 10:30:00 | 2020-05-18 11:00:00 | 2020-05-18 11:30:00 | 2020-05-18 12:00:00 | 2020-05-18 12:30:00 | 2020-05-18 13:00:00 | 2020-05-18 13:30:00 | 2020-05-18 14:00:00 | 2020-05-18 14:30:00 | 2020-05-18 15:00:00 | 2020-05-18 15:30:00 | 2020-05-18 16:00:00 | 2020-05-18 16:30:00 | 2020-05-18 17:00:00 | 2020-05-18 17:30:00 | 2020-05-18 18:00:00 | 2020-05-18 18:30:00 | 2020-05-18 19:00:00 | 2020-05-18 19:30:00 | 2020-05-18 20:00:00 | 2020-05-18 20:30:00 | 2020-05-18 21:00:00 | 2020-05-18 21:30:00 | 2020-05-18 22:00:00 | 2020-05-18 22:30:00 | 2020-05-18 23:00:00 | 2020-05-18 23:30:00 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1:00 AM | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1:30 AM | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10:00 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 |
| 10:30 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 10:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 |
| 11:00 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 |
| 11:30 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 |
| 12:00 AM | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12:30 AM | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2:00 AM | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2:30 AM | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3:00 AM | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3:30 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4:00 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4:30 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5:00 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5:30 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6:00 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6:30 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 6:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7:00 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7:30 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8:00 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8:30 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9:00 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9:00 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 |
| 9:30 AM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9:30 PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 89 | 0 | 0 | 0 | 0 |
2 Weekdays TUD
Here i am trying to seprate the data into days so i can analyse the activities.
#filter newdata dataframe by date using the dplyr
#creating a new dataframe "Monday"
monday <- dplyr::filter(newdata, Day == "Monday")
head(monday)## Village Household ID Day Time Activity Activity2
## 1 Fanga 2 Monday 3:00 AM Sleeping <NA>
## 2 Fanga 2 Monday 3:30 AM Sleeping <NA>
## 3 Fanga 2 Monday 4:00 AM Sleeping <NA>
## 4 Fanga 2 Monday 4:30 AM Sleeping <NA>
## 5 Fanga 2 Monday 5:00 AM Sleeping <NA>
## 6 Fanga 2 Monday 5:30 AM Sleeping <NA>
## rawTime fixedHMS
## 1 2020-05-18 03:00:00 03:00:00
## 2 2020-05-18 03:30:00 03:30:00
## 3 2020-05-18 04:00:00 04:00:00
## 4 2020-05-18 04:30:00 04:30:00
## 5 2020-05-18 05:00:00 05:00:00
## 6 2020-05-18 05:30:00 05:30:00#creating a new dataframe "Tuesday"
tuesday <- dplyr::filter(newdata, Day == "Tuesday")
head(tuesday)## Village Household ID Day Time Activity Activity2
## 1 Fangaloto 4 Tuesday 3:00 AM Sleeping <NA>
## 2 Fangaloto 4 Tuesday 3:30 AM Sleeping <NA>
## 3 Fangaloto 4 Tuesday 4:00 AM Sleeping <NA>
## 4 Fangaloto 4 Tuesday 4:30 AM Sleeping <NA>
## 5 Fangaloto 4 Tuesday 5:00 AM Sleeping <NA>
## 6 Fangaloto 4 Tuesday 5:30 AM Sleeping <NA>
## rawTime fixedHMS
## 1 2020-05-18 03:00:00 03:00:00
## 2 2020-05-18 03:30:00 03:30:00
## 3 2020-05-18 04:00:00 04:00:00
## 4 2020-05-18 04:30:00 04:30:00
## 5 2020-05-18 05:00:00 05:00:00
## 6 2020-05-18 05:30:00 05:30:00#creating a new dataframe "Wednesday"
wednesday <- dplyr::filter(newdata, Day == "Wednesday")
head(wednesday)## Village Household ID Day Time Activity Activity2
## 1 Fangaloto 7 Wednesday 3:00 AM Sleeping <NA>
## 2 Fangaloto 7 Wednesday 3:30 AM Sleeping <NA>
## 3 Fangaloto 7 Wednesday 4:00 AM Sleeping <NA>
## 4 Fangaloto 7 Wednesday 4:30 AM Sleeping <NA>
## 5 Fangaloto 7 Wednesday 5:00 AM Sleeping <NA>
## 6 Fangaloto 7 Wednesday 5:30 AM Sleeping <NA>
## rawTime fixedHMS
## 1 2020-05-18 03:00:00 03:00:00
## 2 2020-05-18 03:30:00 03:30:00
## 3 2020-05-18 04:00:00 04:00:00
## 4 2020-05-18 04:30:00 04:30:00
## 5 2020-05-18 05:00:00 05:00:00
## 6 2020-05-18 05:30:00 05:30:00#creating a new dataframe "Thursday"
thursday <- dplyr::filter(newdata, Day == "Thursday")
head(thursday)## Village Household ID Day Time Activity Activity2
## 1 Fangaloto 6 Thursday 3:00 AM Sleeping <NA>
## 2 Fangaloto 6 Thursday 3:30 AM Sleeping <NA>
## 3 Fangaloto 6 Thursday 4:00 AM Sleeping <NA>
## 4 Fangaloto 6 Thursday 4:30 AM Sleeping <NA>
## 5 Fangaloto 6 Thursday 5:00 AM Sleeping <NA>
## 6 Fangaloto 6 Thursday 5:30 AM Sleeping <NA>
## rawTime fixedHMS
## 1 2020-05-18 03:00:00 03:00:00
## 2 2020-05-18 03:30:00 03:30:00
## 3 2020-05-18 04:00:00 04:00:00
## 4 2020-05-18 04:30:00 04:30:00
## 5 2020-05-18 05:00:00 05:00:00
## 6 2020-05-18 05:30:00 05:30:00#creating a new dataframe "Friday"
friday <- dplyr::filter(newdata, Day == "Friday")
head(friday)## Village Household ID Day Time Activity Activity2
## 1 Fasi 15 Friday 3:00 AM Sleeping <NA>
## 2 Fasi 15 Friday 3:30 AM Sleeping <NA>
## 3 Fasi 15 Friday 4:00 AM Sleeping <NA>
## 4 Fasi 15 Friday 4:30 AM Sleeping <NA>
## 5 Fasi 15 Friday 5:00 AM Sleeping <NA>
## 6 Fasi 15 Friday 5:30 AM Sleeping <NA>
## rawTime fixedHMS
## 1 2020-05-18 03:00:00 03:00:00
## 2 2020-05-18 03:30:00 03:30:00
## 3 2020-05-18 04:00:00 04:00:00
## 4 2020-05-18 04:30:00 04:30:00
## 5 2020-05-18 05:00:00 05:00:00
## 6 2020-05-18 05:30:00 05:30:003 Frequency of Activity per weekday
So im trying to sort out the coding for Monday so i can copy it for the rest of the weekdays. I’ve did a frequency and proportion of activities for Monday.
#Monday
#frequency of Activities
freqA <- table(monday$Activity, exclude=NULL)
freqA##
## Breakfast
## 16
## Charging mobile devices
## 1
## Church
## 6
## Church (social activities)
## 2
## Cooking
## 31
## Cooking (baby)
## 1
## Cooking (business)
## 3
## Cooking (lunch)
## 1
## Dinner
## 36
## Driving (church)
## 1
## Driving (drop kids)
## 2
## Driving (drop relatives)
## 1
## Driving (farm)
## 1
## Driving (gym)
## 4
## Driving (home)
## 23
## Driving (pick up kids)
## 9
## Driving (visiting relatives)
## 10
## Driving (work)
## 21
## Exercise (Jog, walk)
## 4
## Family devotion
## 12
## Family time (chatting, games etc)
## 4
## Farm
## 2
## Feeding animals
## 3
## Getting dressed
## 17
## Getting dressed (kids)
## 5
## Gym
## 14
## Home
## 24
## Homework (kids)
## 23
## Ironing
## 7
## Kids bedtime
## 6
## Laundry
## 2
## Light meal/snack
## 1
## Listen to music/radio
## 2
## Mobile device (movies)
## 26
## Mobile devices (browsing, social media)
## 18
## Mobile devices (work)
## 12
## Prepare kids bag for school
## 1
## Public transport (home)
## 3
## Public transport (work)
## 2
## Reading
## 10
## Rest
## 18
## Showering
## 32
## Sleeping
## 467
## Tidy up
## 13
## Turning off appliances/Lights
## 2
## Walking (home)
## 2
## Walking (work)
## 4
## Watching movies (TV)
## 49
## Woke up
## 32
## Work
## 550dim(freqA)## [1] 50#proportion of Activities
propA <- prop.table(freqA)*100
propA <- round(propA, digits=2)
propA##
## Breakfast
## 1.04
## Charging mobile devices
## 0.07
## Church
## 0.39
## Church (social activities)
## 0.13
## Cooking
## 2.02
## Cooking (baby)
## 0.07
## Cooking (business)
## 0.20
## Cooking (lunch)
## 0.07
## Dinner
## 2.34
## Driving (church)
## 0.07
## Driving (drop kids)
## 0.13
## Driving (drop relatives)
## 0.07
## Driving (farm)
## 0.07
## Driving (gym)
## 0.26
## Driving (home)
## 1.50
## Driving (pick up kids)
## 0.59
## Driving (visiting relatives)
## 0.65
## Driving (work)
## 1.37
## Exercise (Jog, walk)
## 0.26
## Family devotion
## 0.78
## Family time (chatting, games etc)
## 0.26
## Farm
## 0.13
## Feeding animals
## 0.20
## Getting dressed
## 1.11
## Getting dressed (kids)
## 0.33
## Gym
## 0.91
## Home
## 1.56
## Homework (kids)
## 1.50
## Ironing
## 0.46
## Kids bedtime
## 0.39
## Laundry
## 0.13
## Light meal/snack
## 0.07
## Listen to music/radio
## 0.13
## Mobile device (movies)
## 1.69
## Mobile devices (browsing, social media)
## 1.17
## Mobile devices (work)
## 0.78
## Prepare kids bag for school
## 0.07
## Public transport (home)
## 0.20
## Public transport (work)
## 0.13
## Reading
## 0.65
## Rest
## 1.17
## Showering
## 2.08
## Sleeping
## 30.40
## Tidy up
## 0.85
## Turning off appliances/Lights
## 0.13
## Walking (home)
## 0.13
## Walking (work)
## 0.26
## Watching movies (TV)
## 3.19
## Woke up
## 2.08
## Work
## 35.81dim(propA)## [1] 50#creating table with binding
tableMon <- cbind(freqA, propA)
tableMon <- cbind(Gender = rownames(tableMon), tableMon)
rownames(tableMon) <- NULL
tableMon## Gender freqA propA
## [1,] "Breakfast" "16" "1.04"
## [2,] "Charging mobile devices" "1" "0.07"
## [3,] "Church" "6" "0.39"
## [4,] "Church (social activities)" "2" "0.13"
## [5,] "Cooking" "31" "2.02"
## [6,] "Cooking (baby)" "1" "0.07"
## [7,] "Cooking (business)" "3" "0.2"
## [8,] "Cooking (lunch)" "1" "0.07"
## [9,] "Dinner" "36" "2.34"
## [10,] "Driving (church)" "1" "0.07"
## [11,] "Driving (drop kids)" "2" "0.13"
## [12,] "Driving (drop relatives)" "1" "0.07"
## [13,] "Driving (farm)" "1" "0.07"
## [14,] "Driving (gym)" "4" "0.26"
## [15,] "Driving (home)" "23" "1.5"
## [16,] "Driving (pick up kids)" "9" "0.59"
## [17,] "Driving (visiting relatives)" "10" "0.65"
## [18,] "Driving (work)" "21" "1.37"
## [19,] "Exercise (Jog, walk)" "4" "0.26"
## [20,] "Family devotion" "12" "0.78"
## [21,] "Family time (chatting, games etc)" "4" "0.26"
## [22,] "Farm" "2" "0.13"
## [23,] "Feeding animals" "3" "0.2"
## [24,] "Getting dressed" "17" "1.11"
## [25,] "Getting dressed (kids)" "5" "0.33"
## [26,] "Gym" "14" "0.91"
## [27,] "Home" "24" "1.56"
## [28,] "Homework (kids)" "23" "1.5"
## [29,] "Ironing" "7" "0.46"
## [30,] "Kids bedtime" "6" "0.39"
## [31,] "Laundry" "2" "0.13"
## [32,] "Light meal/snack" "1" "0.07"
## [33,] "Listen to music/radio" "2" "0.13"
## [34,] "Mobile device (movies)" "26" "1.69"
## [35,] "Mobile devices (browsing, social media)" "18" "1.17"
## [36,] "Mobile devices (work)" "12" "0.78"
## [37,] "Prepare kids bag for school" "1" "0.07"
## [38,] "Public transport (home)" "3" "0.2"
## [39,] "Public transport (work)" "2" "0.13"
## [40,] "Reading" "10" "0.65"
## [41,] "Rest" "18" "1.17"
## [42,] "Showering" "32" "2.08"
## [43,] "Sleeping" "467" "30.4"
## [44,] "Tidy up" "13" "0.85"
## [45,] "Turning off appliances/Lights" "2" "0.13"
## [46,] "Walking (home)" "2" "0.13"
## [47,] "Walking (work)" "4" "0.26"
## [48,] "Watching movies (TV)" "49" "3.19"
## [49,] "Woke up" "32" "2.08"
## [50,] "Work" "550" "35.81"#rename column names
colnames(tableMon)[colnames(tableMon) == "freqA"] <- "Frequency"
colnames(tableMon)[colnames(tableMon) == "propA"] <- "Proportion (%)"
tableMon## Gender Frequency Proportion (%)
## [1,] "Breakfast" "16" "1.04"
## [2,] "Charging mobile devices" "1" "0.07"
## [3,] "Church" "6" "0.39"
## [4,] "Church (social activities)" "2" "0.13"
## [5,] "Cooking" "31" "2.02"
## [6,] "Cooking (baby)" "1" "0.07"
## [7,] "Cooking (business)" "3" "0.2"
## [8,] "Cooking (lunch)" "1" "0.07"
## [9,] "Dinner" "36" "2.34"
## [10,] "Driving (church)" "1" "0.07"
## [11,] "Driving (drop kids)" "2" "0.13"
## [12,] "Driving (drop relatives)" "1" "0.07"
## [13,] "Driving (farm)" "1" "0.07"
## [14,] "Driving (gym)" "4" "0.26"
## [15,] "Driving (home)" "23" "1.5"
## [16,] "Driving (pick up kids)" "9" "0.59"
## [17,] "Driving (visiting relatives)" "10" "0.65"
## [18,] "Driving (work)" "21" "1.37"
## [19,] "Exercise (Jog, walk)" "4" "0.26"
## [20,] "Family devotion" "12" "0.78"
## [21,] "Family time (chatting, games etc)" "4" "0.26"
## [22,] "Farm" "2" "0.13"
## [23,] "Feeding animals" "3" "0.2"
## [24,] "Getting dressed" "17" "1.11"
## [25,] "Getting dressed (kids)" "5" "0.33"
## [26,] "Gym" "14" "0.91"
## [27,] "Home" "24" "1.56"
## [28,] "Homework (kids)" "23" "1.5"
## [29,] "Ironing" "7" "0.46"
## [30,] "Kids bedtime" "6" "0.39"
## [31,] "Laundry" "2" "0.13"
## [32,] "Light meal/snack" "1" "0.07"
## [33,] "Listen to music/radio" "2" "0.13"
## [34,] "Mobile device (movies)" "26" "1.69"
## [35,] "Mobile devices (browsing, social media)" "18" "1.17"
## [36,] "Mobile devices (work)" "12" "0.78"
## [37,] "Prepare kids bag for school" "1" "0.07"
## [38,] "Public transport (home)" "3" "0.2"
## [39,] "Public transport (work)" "2" "0.13"
## [40,] "Reading" "10" "0.65"
## [41,] "Rest" "18" "1.17"
## [42,] "Showering" "32" "2.08"
## [43,] "Sleeping" "467" "30.4"
## [44,] "Tidy up" "13" "0.85"
## [45,] "Turning off appliances/Lights" "2" "0.13"
## [46,] "Walking (home)" "2" "0.13"
## [47,] "Walking (work)" "4" "0.26"
## [48,] "Watching movies (TV)" "49" "3.19"
## [49,] "Woke up" "32" "2.08"
## [50,] "Work" "550" "35.81"#using kable extra to create better table format
tableMonday <- kable(tableMon)%>%
kable_styling(bootstrap_options = "striped", full_width = F)
tableMonday| Gender | Frequency | Proportion (%) |
|---|---|---|
| Breakfast | 16 | 1.04 |
| Charging mobile devices | 1 | 0.07 |
| Church | 6 | 0.39 |
| Church (social activities) | 2 | 0.13 |
| Cooking | 31 | 2.02 |
| Cooking (baby) | 1 | 0.07 |
| Cooking (business) | 3 | 0.2 |
| Cooking (lunch) | 1 | 0.07 |
| Dinner | 36 | 2.34 |
| Driving (church) | 1 | 0.07 |
| Driving (drop kids) | 2 | 0.13 |
| Driving (drop relatives) | 1 | 0.07 |
| Driving (farm) | 1 | 0.07 |
| Driving (gym) | 4 | 0.26 |
| Driving (home) | 23 | 1.5 |
| Driving (pick up kids) | 9 | 0.59 |
| Driving (visiting relatives) | 10 | 0.65 |
| Driving (work) | 21 | 1.37 |
| Exercise (Jog, walk) | 4 | 0.26 |
| Family devotion | 12 | 0.78 |
| Family time (chatting, games etc) | 4 | 0.26 |
| Farm | 2 | 0.13 |
| Feeding animals | 3 | 0.2 |
| Getting dressed | 17 | 1.11 |
| Getting dressed (kids) | 5 | 0.33 |
| Gym | 14 | 0.91 |
| Home | 24 | 1.56 |
| Homework (kids) | 23 | 1.5 |
| Ironing | 7 | 0.46 |
| Kids bedtime | 6 | 0.39 |
| Laundry | 2 | 0.13 |
| Light meal/snack | 1 | 0.07 |
| Listen to music/radio | 2 | 0.13 |
| Mobile device (movies) | 26 | 1.69 |
| Mobile devices (browsing, social media) | 18 | 1.17 |
| Mobile devices (work) | 12 | 0.78 |
| Prepare kids bag for school | 1 | 0.07 |
| Public transport (home) | 3 | 0.2 |
| Public transport (work) | 2 | 0.13 |
| Reading | 10 | 0.65 |
| Rest | 18 | 1.17 |
| Showering | 32 | 2.08 |
| Sleeping | 467 | 30.4 |
| Tidy up | 13 | 0.85 |
| Turning off appliances/Lights | 2 | 0.13 |
| Walking (home) | 2 | 0.13 |
| Walking (work) | 4 | 0.26 |
| Watching movies (TV) | 49 | 3.19 |
| Woke up | 32 | 2.08 |
| Work | 550 | 35.81 |
#Tueday
#frequency of Activities
freqTue <- table(tuesday$Activity, exclude=NULL)
freqTue##
## Breakfast
## 5
## Church (choir practice)
## 2
## Church (social activities)
## 5
## Cooking
## 23
## Dinner
## 24
## Driving (drop kids)
## 7
## Driving (drop relatives)
## 2
## Driving (free time)
## 2
## Driving (home)
## 8
## Driving (pick up kids)
## 12
## Driving (work)
## 12
## Exercise (Jog, walk)
## 2
## Family devotion
## 1
## Family time (chatting, games etc)
## 5
## Feeding animals
## 3
## Getting dressed
## 16
## Getting dressed (kids)
## 2
## Home
## 18
## Homework (kids)
## 2
## Ironing
## 6
## Kava
## 13
## Light meal/snack
## 2
## Listen to music/radio
## 3
## Lunch
## 2
## Mobile device (movies)
## 7
## Mobile devices (browsing, social media)
## 12
## Mobile devices (games)
## 4
## Mobile devices (work)
## 21
## Public transport (home)
## 5
## Public transport (school)
## 2
## Reading
## 4
## Rest
## 7
## School
## 23
## Showering
## 14
## Sleeping
## 263
## Studying
## 9
## Tidy up
## 13
## Turning off appliances/Lights
## 1
## Turning on appliances/lights
## 1
## Walking (home)
## 2
## Walking (Kava)
## 1
## Walking (work)
## 4
## Watching movies (TV)
## 53
## Woke up
## 19
## Work
## 270dim(freqTue)## [1] 45#proportion of Activities
propTue <- prop.table(freqTue)*100
propTue <- round(propTue, digits=2)
propTue##
## Breakfast
## 0.55
## Church (choir practice)
## 0.22
## Church (social activities)
## 0.55
## Cooking
## 2.52
## Dinner
## 2.63
## Driving (drop kids)
## 0.77
## Driving (drop relatives)
## 0.22
## Driving (free time)
## 0.22
## Driving (home)
## 0.88
## Driving (pick up kids)
## 1.32
## Driving (work)
## 1.32
## Exercise (Jog, walk)
## 0.22
## Family devotion
## 0.11
## Family time (chatting, games etc)
## 0.55
## Feeding animals
## 0.33
## Getting dressed
## 1.75
## Getting dressed (kids)
## 0.22
## Home
## 1.97
## Homework (kids)
## 0.22
## Ironing
## 0.66
## Kava
## 1.43
## Light meal/snack
## 0.22
## Listen to music/radio
## 0.33
## Lunch
## 0.22
## Mobile device (movies)
## 0.77
## Mobile devices (browsing, social media)
## 1.32
## Mobile devices (games)
## 0.44
## Mobile devices (work)
## 2.30
## Public transport (home)
## 0.55
## Public transport (school)
## 0.22
## Reading
## 0.44
## Rest
## 0.77
## School
## 2.52
## Showering
## 1.54
## Sleeping
## 28.84
## Studying
## 0.99
## Tidy up
## 1.43
## Turning off appliances/Lights
## 0.11
## Turning on appliances/lights
## 0.11
## Walking (home)
## 0.22
## Walking (Kava)
## 0.11
## Walking (work)
## 0.44
## Watching movies (TV)
## 5.81
## Woke up
## 2.08
## Work
## 29.61dim(propTue)## [1] 45#creating table with binding
tableTue <- cbind(freqTue, propTue)
tableTue <- cbind(Gender = rownames(tableTue), tableTue)
rownames(tableTue) <- NULL
tableTue## Gender freqTue propTue
## [1,] "Breakfast" "5" "0.55"
## [2,] "Church (choir practice)" "2" "0.22"
## [3,] "Church (social activities)" "5" "0.55"
## [4,] "Cooking" "23" "2.52"
## [5,] "Dinner" "24" "2.63"
## [6,] "Driving (drop kids)" "7" "0.77"
## [7,] "Driving (drop relatives)" "2" "0.22"
## [8,] "Driving (free time)" "2" "0.22"
## [9,] "Driving (home)" "8" "0.88"
## [10,] "Driving (pick up kids)" "12" "1.32"
## [11,] "Driving (work)" "12" "1.32"
## [12,] "Exercise (Jog, walk)" "2" "0.22"
## [13,] "Family devotion" "1" "0.11"
## [14,] "Family time (chatting, games etc)" "5" "0.55"
## [15,] "Feeding animals" "3" "0.33"
## [16,] "Getting dressed" "16" "1.75"
## [17,] "Getting dressed (kids)" "2" "0.22"
## [18,] "Home" "18" "1.97"
## [19,] "Homework (kids)" "2" "0.22"
## [20,] "Ironing" "6" "0.66"
## [21,] "Kava" "13" "1.43"
## [22,] "Light meal/snack" "2" "0.22"
## [23,] "Listen to music/radio" "3" "0.33"
## [24,] "Lunch" "2" "0.22"
## [25,] "Mobile device (movies)" "7" "0.77"
## [26,] "Mobile devices (browsing, social media)" "12" "1.32"
## [27,] "Mobile devices (games)" "4" "0.44"
## [28,] "Mobile devices (work)" "21" "2.3"
## [29,] "Public transport (home)" "5" "0.55"
## [30,] "Public transport (school)" "2" "0.22"
## [31,] "Reading" "4" "0.44"
## [32,] "Rest" "7" "0.77"
## [33,] "School" "23" "2.52"
## [34,] "Showering" "14" "1.54"
## [35,] "Sleeping" "263" "28.84"
## [36,] "Studying" "9" "0.99"
## [37,] "Tidy up" "13" "1.43"
## [38,] "Turning off appliances/Lights" "1" "0.11"
## [39,] "Turning on appliances/lights" "1" "0.11"
## [40,] "Walking (home)" "2" "0.22"
## [41,] "Walking (Kava)" "1" "0.11"
## [42,] "Walking (work)" "4" "0.44"
## [43,] "Watching movies (TV)" "53" "5.81"
## [44,] "Woke up" "19" "2.08"
## [45,] "Work" "270" "29.61"#rename column names
colnames(tableTue)[colnames(tableTue) == "freqTue"] <- "Frequency"
colnames(tableTue)[colnames(tableTue) == "propTue"] <- "Proportion (%)"
tableTue## Gender Frequency Proportion (%)
## [1,] "Breakfast" "5" "0.55"
## [2,] "Church (choir practice)" "2" "0.22"
## [3,] "Church (social activities)" "5" "0.55"
## [4,] "Cooking" "23" "2.52"
## [5,] "Dinner" "24" "2.63"
## [6,] "Driving (drop kids)" "7" "0.77"
## [7,] "Driving (drop relatives)" "2" "0.22"
## [8,] "Driving (free time)" "2" "0.22"
## [9,] "Driving (home)" "8" "0.88"
## [10,] "Driving (pick up kids)" "12" "1.32"
## [11,] "Driving (work)" "12" "1.32"
## [12,] "Exercise (Jog, walk)" "2" "0.22"
## [13,] "Family devotion" "1" "0.11"
## [14,] "Family time (chatting, games etc)" "5" "0.55"
## [15,] "Feeding animals" "3" "0.33"
## [16,] "Getting dressed" "16" "1.75"
## [17,] "Getting dressed (kids)" "2" "0.22"
## [18,] "Home" "18" "1.97"
## [19,] "Homework (kids)" "2" "0.22"
## [20,] "Ironing" "6" "0.66"
## [21,] "Kava" "13" "1.43"
## [22,] "Light meal/snack" "2" "0.22"
## [23,] "Listen to music/radio" "3" "0.33"
## [24,] "Lunch" "2" "0.22"
## [25,] "Mobile device (movies)" "7" "0.77"
## [26,] "Mobile devices (browsing, social media)" "12" "1.32"
## [27,] "Mobile devices (games)" "4" "0.44"
## [28,] "Mobile devices (work)" "21" "2.3"
## [29,] "Public transport (home)" "5" "0.55"
## [30,] "Public transport (school)" "2" "0.22"
## [31,] "Reading" "4" "0.44"
## [32,] "Rest" "7" "0.77"
## [33,] "School" "23" "2.52"
## [34,] "Showering" "14" "1.54"
## [35,] "Sleeping" "263" "28.84"
## [36,] "Studying" "9" "0.99"
## [37,] "Tidy up" "13" "1.43"
## [38,] "Turning off appliances/Lights" "1" "0.11"
## [39,] "Turning on appliances/lights" "1" "0.11"
## [40,] "Walking (home)" "2" "0.22"
## [41,] "Walking (Kava)" "1" "0.11"
## [42,] "Walking (work)" "4" "0.44"
## [43,] "Watching movies (TV)" "53" "5.81"
## [44,] "Woke up" "19" "2.08"
## [45,] "Work" "270" "29.61"#using kable extra to create better table format
tableTuesday <- kable(tableTue)%>%
kable_styling(bootstrap_options = "striped", full_width = F)
tableTuesday| Gender | Frequency | Proportion (%) |
|---|---|---|
| Breakfast | 5 | 0.55 |
| Church (choir practice) | 2 | 0.22 |
| Church (social activities) | 5 | 0.55 |
| Cooking | 23 | 2.52 |
| Dinner | 24 | 2.63 |
| Driving (drop kids) | 7 | 0.77 |
| Driving (drop relatives) | 2 | 0.22 |
| Driving (free time) | 2 | 0.22 |
| Driving (home) | 8 | 0.88 |
| Driving (pick up kids) | 12 | 1.32 |
| Driving (work) | 12 | 1.32 |
| Exercise (Jog, walk) | 2 | 0.22 |
| Family devotion | 1 | 0.11 |
| Family time (chatting, games etc) | 5 | 0.55 |
| Feeding animals | 3 | 0.33 |
| Getting dressed | 16 | 1.75 |
| Getting dressed (kids) | 2 | 0.22 |
| Home | 18 | 1.97 |
| Homework (kids) | 2 | 0.22 |
| Ironing | 6 | 0.66 |
| Kava | 13 | 1.43 |
| Light meal/snack | 2 | 0.22 |
| Listen to music/radio | 3 | 0.33 |
| Lunch | 2 | 0.22 |
| Mobile device (movies) | 7 | 0.77 |
| Mobile devices (browsing, social media) | 12 | 1.32 |
| Mobile devices (games) | 4 | 0.44 |
| Mobile devices (work) | 21 | 2.3 |
| Public transport (home) | 5 | 0.55 |
| Public transport (school) | 2 | 0.22 |
| Reading | 4 | 0.44 |
| Rest | 7 | 0.77 |
| School | 23 | 2.52 |
| Showering | 14 | 1.54 |
| Sleeping | 263 | 28.84 |
| Studying | 9 | 0.99 |
| Tidy up | 13 | 1.43 |
| Turning off appliances/Lights | 1 | 0.11 |
| Turning on appliances/lights | 1 | 0.11 |
| Walking (home) | 2 | 0.22 |
| Walking (Kava) | 1 | 0.11 |
| Walking (work) | 4 | 0.44 |
| Watching movies (TV) | 53 | 5.81 |
| Woke up | 19 | 2.08 |
| Work | 270 | 29.61 |
#Wednesday
#frequency of Activities
freqWed <- table(wednesday$Activity, exclude=NULL)
freqWed##
## Babysitting
## 5
## Breakfast
## 8
## Church
## 1
## Cooking
## 26
## Dinner
## 17
## Driving (gym)
## 4
## Driving (home)
## 8
## Driving (order delivery)
## 9
## Driving (work)
## 13
## Exercise (Jog, walk)
## 3
## Family devotion
## 5
## Family time (chatting, games etc)
## 19
## Feeding animals
## 6
## Gardening
## 2
## Getting dressed
## 7
## Going out
## 7
## Gym
## 12
## Home
## 14
## Homework (kids)
## 5
## Ironing
## 2
## Kids bedtime
## 1
## Laundry
## 13
## Mobile device (movies)
## 1
## Mobile devices (browsing, social media)
## 4
## Mobile devices (work)
## 2
## Reading
## 7
## Rest
## 7
## Showering
## 18
## Showering (kids)
## 1
## Sleeping
## 211
## Tidy up
## 8
## Turning off appliances/Lights
## 1
## Walking (work)
## 2
## Watching movies (TV)
## 23
## Woke up
## 14
## Work
## 234dim(freqWed)## [1] 36#proportion of Activities
propWed <- prop.table(freqWed)*100
propWed <- round(propWed, digits=2)
propWed##
## Babysitting
## 0.69
## Breakfast
## 1.11
## Church
## 0.14
## Cooking
## 3.61
## Dinner
## 2.36
## Driving (gym)
## 0.56
## Driving (home)
## 1.11
## Driving (order delivery)
## 1.25
## Driving (work)
## 1.81
## Exercise (Jog, walk)
## 0.42
## Family devotion
## 0.69
## Family time (chatting, games etc)
## 2.64
## Feeding animals
## 0.83
## Gardening
## 0.28
## Getting dressed
## 0.97
## Going out
## 0.97
## Gym
## 1.67
## Home
## 1.94
## Homework (kids)
## 0.69
## Ironing
## 0.28
## Kids bedtime
## 0.14
## Laundry
## 1.81
## Mobile device (movies)
## 0.14
## Mobile devices (browsing, social media)
## 0.56
## Mobile devices (work)
## 0.28
## Reading
## 0.97
## Rest
## 0.97
## Showering
## 2.50
## Showering (kids)
## 0.14
## Sleeping
## 29.31
## Tidy up
## 1.11
## Turning off appliances/Lights
## 0.14
## Walking (work)
## 0.28
## Watching movies (TV)
## 3.19
## Woke up
## 1.94
## Work
## 32.50dim(propWed)## [1] 36#creating table with binding
tableWed <- cbind(freqWed, propWed)
tableWed <- cbind(Gender = rownames(tableWed), tableWed)
rownames(tableWed) <- NULL
tableWed## Gender freqWed propWed
## [1,] "Babysitting" "5" "0.69"
## [2,] "Breakfast" "8" "1.11"
## [3,] "Church" "1" "0.14"
## [4,] "Cooking" "26" "3.61"
## [5,] "Dinner" "17" "2.36"
## [6,] "Driving (gym)" "4" "0.56"
## [7,] "Driving (home)" "8" "1.11"
## [8,] "Driving (order delivery)" "9" "1.25"
## [9,] "Driving (work)" "13" "1.81"
## [10,] "Exercise (Jog, walk)" "3" "0.42"
## [11,] "Family devotion" "5" "0.69"
## [12,] "Family time (chatting, games etc)" "19" "2.64"
## [13,] "Feeding animals" "6" "0.83"
## [14,] "Gardening" "2" "0.28"
## [15,] "Getting dressed" "7" "0.97"
## [16,] "Going out" "7" "0.97"
## [17,] "Gym" "12" "1.67"
## [18,] "Home" "14" "1.94"
## [19,] "Homework (kids)" "5" "0.69"
## [20,] "Ironing" "2" "0.28"
## [21,] "Kids bedtime" "1" "0.14"
## [22,] "Laundry" "13" "1.81"
## [23,] "Mobile device (movies)" "1" "0.14"
## [24,] "Mobile devices (browsing, social media)" "4" "0.56"
## [25,] "Mobile devices (work)" "2" "0.28"
## [26,] "Reading" "7" "0.97"
## [27,] "Rest" "7" "0.97"
## [28,] "Showering" "18" "2.5"
## [29,] "Showering (kids)" "1" "0.14"
## [30,] "Sleeping" "211" "29.31"
## [31,] "Tidy up" "8" "1.11"
## [32,] "Turning off appliances/Lights" "1" "0.14"
## [33,] "Walking (work)" "2" "0.28"
## [34,] "Watching movies (TV)" "23" "3.19"
## [35,] "Woke up" "14" "1.94"
## [36,] "Work" "234" "32.5"#rename column names
colnames(tableWed)[colnames(tableWed) == "freqWed"] <- "Frequency"
colnames(tableWed)[colnames(tableWed) == "propWed"] <- "Proportion (%)"
tableWed## Gender Frequency Proportion (%)
## [1,] "Babysitting" "5" "0.69"
## [2,] "Breakfast" "8" "1.11"
## [3,] "Church" "1" "0.14"
## [4,] "Cooking" "26" "3.61"
## [5,] "Dinner" "17" "2.36"
## [6,] "Driving (gym)" "4" "0.56"
## [7,] "Driving (home)" "8" "1.11"
## [8,] "Driving (order delivery)" "9" "1.25"
## [9,] "Driving (work)" "13" "1.81"
## [10,] "Exercise (Jog, walk)" "3" "0.42"
## [11,] "Family devotion" "5" "0.69"
## [12,] "Family time (chatting, games etc)" "19" "2.64"
## [13,] "Feeding animals" "6" "0.83"
## [14,] "Gardening" "2" "0.28"
## [15,] "Getting dressed" "7" "0.97"
## [16,] "Going out" "7" "0.97"
## [17,] "Gym" "12" "1.67"
## [18,] "Home" "14" "1.94"
## [19,] "Homework (kids)" "5" "0.69"
## [20,] "Ironing" "2" "0.28"
## [21,] "Kids bedtime" "1" "0.14"
## [22,] "Laundry" "13" "1.81"
## [23,] "Mobile device (movies)" "1" "0.14"
## [24,] "Mobile devices (browsing, social media)" "4" "0.56"
## [25,] "Mobile devices (work)" "2" "0.28"
## [26,] "Reading" "7" "0.97"
## [27,] "Rest" "7" "0.97"
## [28,] "Showering" "18" "2.5"
## [29,] "Showering (kids)" "1" "0.14"
## [30,] "Sleeping" "211" "29.31"
## [31,] "Tidy up" "8" "1.11"
## [32,] "Turning off appliances/Lights" "1" "0.14"
## [33,] "Walking (work)" "2" "0.28"
## [34,] "Watching movies (TV)" "23" "3.19"
## [35,] "Woke up" "14" "1.94"
## [36,] "Work" "234" "32.5"#using kable extra to create better table format
tableWednesday <- kable(tableWed)%>%
kable_styling(bootstrap_options = "striped", full_width = F)
tableWednesday| Gender | Frequency | Proportion (%) |
|---|---|---|
| Babysitting | 5 | 0.69 |
| Breakfast | 8 | 1.11 |
| Church | 1 | 0.14 |
| Cooking | 26 | 3.61 |
| Dinner | 17 | 2.36 |
| Driving (gym) | 4 | 0.56 |
| Driving (home) | 8 | 1.11 |
| Driving (order delivery) | 9 | 1.25 |
| Driving (work) | 13 | 1.81 |
| Exercise (Jog, walk) | 3 | 0.42 |
| Family devotion | 5 | 0.69 |
| Family time (chatting, games etc) | 19 | 2.64 |
| Feeding animals | 6 | 0.83 |
| Gardening | 2 | 0.28 |
| Getting dressed | 7 | 0.97 |
| Going out | 7 | 0.97 |
| Gym | 12 | 1.67 |
| Home | 14 | 1.94 |
| Homework (kids) | 5 | 0.69 |
| Ironing | 2 | 0.28 |
| Kids bedtime | 1 | 0.14 |
| Laundry | 13 | 1.81 |
| Mobile device (movies) | 1 | 0.14 |
| Mobile devices (browsing, social media) | 4 | 0.56 |
| Mobile devices (work) | 2 | 0.28 |
| Reading | 7 | 0.97 |
| Rest | 7 | 0.97 |
| Showering | 18 | 2.5 |
| Showering (kids) | 1 | 0.14 |
| Sleeping | 211 | 29.31 |
| Tidy up | 8 | 1.11 |
| Turning off appliances/Lights | 1 | 0.14 |
| Walking (work) | 2 | 0.28 |
| Watching movies (TV) | 23 | 3.19 |
| Woke up | 14 | 1.94 |
| Work | 234 | 32.5 |
#Thursday
#frequency of Activities
freqThu <- table(thursday$Activity, exclude=NULL)
freqThu##
## Breakfast
## 4
## Church (social activities)
## 7
## Cooking
## 6
## Cooking (business)
## 3
## Cooking (lunch)
## 1
## Dinner
## 8
## Driving (church)
## 1
## Driving (drop kids)
## 1
## Driving (gym)
## 2
## Driving (home)
## 4
## Driving (order delivery)
## 5
## Driving (pick up kids)
## 1
## Driving (work)
## 4
## Exercise (Jog, walk)
## 3
## Family devotion
## 3
## Family time (chatting, games etc)
## 1
## Food prep (business)
## 3
## Gardening
## 2
## Getting dressed
## 6
## Getting dressed (kids)
## 1
## Gym
## 6
## Home
## 8
## Homework (kids)
## 3
## Laundry
## 2
## Listen to music/radio
## 9
## Lunch
## 1
## Mobile device (movies)
## 5
## Mobile devices (browsing, social media)
## 5
## Mobile devices (work)
## 6
## Reading
## 1
## Rest
## 9
## School
## 5
## Scripture study
## 1
## Showering
## 7
## Showering (kids)
## 2
## Sleeping
## 108
## Tidy up
## 11
## Walking (work)
## 1
## Woke up
## 8
## Work
## 120dim(freqThu)## [1] 40#proportion of Activities
propThu <- prop.table(freqThu)*100
propThu <- round(propThu, digits=2)
propThu##
## Breakfast
## 1.04
## Church (social activities)
## 1.82
## Cooking
## 1.56
## Cooking (business)
## 0.78
## Cooking (lunch)
## 0.26
## Dinner
## 2.08
## Driving (church)
## 0.26
## Driving (drop kids)
## 0.26
## Driving (gym)
## 0.52
## Driving (home)
## 1.04
## Driving (order delivery)
## 1.30
## Driving (pick up kids)
## 0.26
## Driving (work)
## 1.04
## Exercise (Jog, walk)
## 0.78
## Family devotion
## 0.78
## Family time (chatting, games etc)
## 0.26
## Food prep (business)
## 0.78
## Gardening
## 0.52
## Getting dressed
## 1.56
## Getting dressed (kids)
## 0.26
## Gym
## 1.56
## Home
## 2.08
## Homework (kids)
## 0.78
## Laundry
## 0.52
## Listen to music/radio
## 2.34
## Lunch
## 0.26
## Mobile device (movies)
## 1.30
## Mobile devices (browsing, social media)
## 1.30
## Mobile devices (work)
## 1.56
## Reading
## 0.26
## Rest
## 2.34
## School
## 1.30
## Scripture study
## 0.26
## Showering
## 1.82
## Showering (kids)
## 0.52
## Sleeping
## 28.12
## Tidy up
## 2.86
## Walking (work)
## 0.26
## Woke up
## 2.08
## Work
## 31.25dim(propThu)## [1] 40#creating table with binding
tableThu <- cbind(freqThu, propThu)
tableThu <- cbind(Gender = rownames(tableThu), tableThu)
rownames(tableThu) <- NULL
tableThu## Gender freqThu propThu
## [1,] "Breakfast" "4" "1.04"
## [2,] "Church (social activities)" "7" "1.82"
## [3,] "Cooking" "6" "1.56"
## [4,] "Cooking (business)" "3" "0.78"
## [5,] "Cooking (lunch)" "1" "0.26"
## [6,] "Dinner" "8" "2.08"
## [7,] "Driving (church)" "1" "0.26"
## [8,] "Driving (drop kids)" "1" "0.26"
## [9,] "Driving (gym)" "2" "0.52"
## [10,] "Driving (home)" "4" "1.04"
## [11,] "Driving (order delivery)" "5" "1.3"
## [12,] "Driving (pick up kids)" "1" "0.26"
## [13,] "Driving (work)" "4" "1.04"
## [14,] "Exercise (Jog, walk)" "3" "0.78"
## [15,] "Family devotion" "3" "0.78"
## [16,] "Family time (chatting, games etc)" "1" "0.26"
## [17,] "Food prep (business)" "3" "0.78"
## [18,] "Gardening" "2" "0.52"
## [19,] "Getting dressed" "6" "1.56"
## [20,] "Getting dressed (kids)" "1" "0.26"
## [21,] "Gym" "6" "1.56"
## [22,] "Home" "8" "2.08"
## [23,] "Homework (kids)" "3" "0.78"
## [24,] "Laundry" "2" "0.52"
## [25,] "Listen to music/radio" "9" "2.34"
## [26,] "Lunch" "1" "0.26"
## [27,] "Mobile device (movies)" "5" "1.3"
## [28,] "Mobile devices (browsing, social media)" "5" "1.3"
## [29,] "Mobile devices (work)" "6" "1.56"
## [30,] "Reading" "1" "0.26"
## [31,] "Rest" "9" "2.34"
## [32,] "School" "5" "1.3"
## [33,] "Scripture study" "1" "0.26"
## [34,] "Showering" "7" "1.82"
## [35,] "Showering (kids)" "2" "0.52"
## [36,] "Sleeping" "108" "28.12"
## [37,] "Tidy up" "11" "2.86"
## [38,] "Walking (work)" "1" "0.26"
## [39,] "Woke up" "8" "2.08"
## [40,] "Work" "120" "31.25"#rename column names
colnames(tableThu)[colnames(tableThu) == "freqThu"] <- "Frequency"
colnames(tableThu)[colnames(tableThu) == "propThu"] <- "Proportion (%)"
tableThu## Gender Frequency Proportion (%)
## [1,] "Breakfast" "4" "1.04"
## [2,] "Church (social activities)" "7" "1.82"
## [3,] "Cooking" "6" "1.56"
## [4,] "Cooking (business)" "3" "0.78"
## [5,] "Cooking (lunch)" "1" "0.26"
## [6,] "Dinner" "8" "2.08"
## [7,] "Driving (church)" "1" "0.26"
## [8,] "Driving (drop kids)" "1" "0.26"
## [9,] "Driving (gym)" "2" "0.52"
## [10,] "Driving (home)" "4" "1.04"
## [11,] "Driving (order delivery)" "5" "1.3"
## [12,] "Driving (pick up kids)" "1" "0.26"
## [13,] "Driving (work)" "4" "1.04"
## [14,] "Exercise (Jog, walk)" "3" "0.78"
## [15,] "Family devotion" "3" "0.78"
## [16,] "Family time (chatting, games etc)" "1" "0.26"
## [17,] "Food prep (business)" "3" "0.78"
## [18,] "Gardening" "2" "0.52"
## [19,] "Getting dressed" "6" "1.56"
## [20,] "Getting dressed (kids)" "1" "0.26"
## [21,] "Gym" "6" "1.56"
## [22,] "Home" "8" "2.08"
## [23,] "Homework (kids)" "3" "0.78"
## [24,] "Laundry" "2" "0.52"
## [25,] "Listen to music/radio" "9" "2.34"
## [26,] "Lunch" "1" "0.26"
## [27,] "Mobile device (movies)" "5" "1.3"
## [28,] "Mobile devices (browsing, social media)" "5" "1.3"
## [29,] "Mobile devices (work)" "6" "1.56"
## [30,] "Reading" "1" "0.26"
## [31,] "Rest" "9" "2.34"
## [32,] "School" "5" "1.3"
## [33,] "Scripture study" "1" "0.26"
## [34,] "Showering" "7" "1.82"
## [35,] "Showering (kids)" "2" "0.52"
## [36,] "Sleeping" "108" "28.12"
## [37,] "Tidy up" "11" "2.86"
## [38,] "Walking (work)" "1" "0.26"
## [39,] "Woke up" "8" "2.08"
## [40,] "Work" "120" "31.25"#using kable extra to create better table format
tableThursday <- kable(tableThu)%>%
kable_styling(bootstrap_options = "striped", full_width = F)
tableThursday| Gender | Frequency | Proportion (%) |
|---|---|---|
| Breakfast | 4 | 1.04 |
| Church (social activities) | 7 | 1.82 |
| Cooking | 6 | 1.56 |
| Cooking (business) | 3 | 0.78 |
| Cooking (lunch) | 1 | 0.26 |
| Dinner | 8 | 2.08 |
| Driving (church) | 1 | 0.26 |
| Driving (drop kids) | 1 | 0.26 |
| Driving (gym) | 2 | 0.52 |
| Driving (home) | 4 | 1.04 |
| Driving (order delivery) | 5 | 1.3 |
| Driving (pick up kids) | 1 | 0.26 |
| Driving (work) | 4 | 1.04 |
| Exercise (Jog, walk) | 3 | 0.78 |
| Family devotion | 3 | 0.78 |
| Family time (chatting, games etc) | 1 | 0.26 |
| Food prep (business) | 3 | 0.78 |
| Gardening | 2 | 0.52 |
| Getting dressed | 6 | 1.56 |
| Getting dressed (kids) | 1 | 0.26 |
| Gym | 6 | 1.56 |
| Home | 8 | 2.08 |
| Homework (kids) | 3 | 0.78 |
| Laundry | 2 | 0.52 |
| Listen to music/radio | 9 | 2.34 |
| Lunch | 1 | 0.26 |
| Mobile device (movies) | 5 | 1.3 |
| Mobile devices (browsing, social media) | 5 | 1.3 |
| Mobile devices (work) | 6 | 1.56 |
| Reading | 1 | 0.26 |
| Rest | 9 | 2.34 |
| School | 5 | 1.3 |
| Scripture study | 1 | 0.26 |
| Showering | 7 | 1.82 |
| Showering (kids) | 2 | 0.52 |
| Sleeping | 108 | 28.12 |
| Tidy up | 11 | 2.86 |
| Walking (work) | 1 | 0.26 |
| Woke up | 8 | 2.08 |
| Work | 120 | 31.25 |
#Friday
#frequency of Activities
freqFri <- table(friday$Activity, exclude=NULL)
freqFri##
## Breakfast
## 5
## Church
## 1
## Cooking
## 16
## Dinner
## 15
## Driving (church)
## 1
## Driving (drop relatives)
## 2
## Driving (farm)
## 4
## Driving (gym)
## 2
## Driving (home)
## 8
## Driving (Kava)
## 1
## Driving (work)
## 9
## Family devotion
## 4
## Family time (chatting, games etc)
## 8
## Farm
## 24
## Feeding animals
## 1
## Getting dressed
## 10
## Gym
## 9
## Home
## 15
## Homework (kids)
## 5
## Ironing
## 6
## Kava
## 6
## Laundry
## 1
## Mobile device (movies)
## 18
## Mobile devices (browsing, social media)
## 12
## Mobile devices (work)
## 32
## Reading
## 1
## Rest
## 2
## Scripture Reading
## 1
## showering
## 1
## Showering
## 14
## Sleeping
## 219
## Tidy up
## 12
## Watching movies (TV)
## 27
## Woke up
## 15
## Work
## 213dim(freqFri)## [1] 35#proportion of Activities
propFri <- prop.table(freqFri)*100
propFri <- round(propFri, digits=2)
propFri##
## Breakfast
## 0.69
## Church
## 0.14
## Cooking
## 2.22
## Dinner
## 2.08
## Driving (church)
## 0.14
## Driving (drop relatives)
## 0.28
## Driving (farm)
## 0.56
## Driving (gym)
## 0.28
## Driving (home)
## 1.11
## Driving (Kava)
## 0.14
## Driving (work)
## 1.25
## Family devotion
## 0.56
## Family time (chatting, games etc)
## 1.11
## Farm
## 3.33
## Feeding animals
## 0.14
## Getting dressed
## 1.39
## Gym
## 1.25
## Home
## 2.08
## Homework (kids)
## 0.69
## Ironing
## 0.83
## Kava
## 0.83
## Laundry
## 0.14
## Mobile device (movies)
## 2.50
## Mobile devices (browsing, social media)
## 1.67
## Mobile devices (work)
## 4.44
## Reading
## 0.14
## Rest
## 0.28
## Scripture Reading
## 0.14
## showering
## 0.14
## Showering
## 1.94
## Sleeping
## 30.42
## Tidy up
## 1.67
## Watching movies (TV)
## 3.75
## Woke up
## 2.08
## Work
## 29.58dim(propFri)## [1] 35#creating table with binding
tableFri <- cbind(freqFri, propFri)
tableFri <- cbind(Gender = rownames(tableFri), tableFri)
rownames(tableFri) <- NULL
tableFri## Gender freqFri propFri
## [1,] "Breakfast" "5" "0.69"
## [2,] "Church" "1" "0.14"
## [3,] "Cooking" "16" "2.22"
## [4,] "Dinner" "15" "2.08"
## [5,] "Driving (church)" "1" "0.14"
## [6,] "Driving (drop relatives)" "2" "0.28"
## [7,] "Driving (farm)" "4" "0.56"
## [8,] "Driving (gym)" "2" "0.28"
## [9,] "Driving (home)" "8" "1.11"
## [10,] "Driving (Kava)" "1" "0.14"
## [11,] "Driving (work)" "9" "1.25"
## [12,] "Family devotion" "4" "0.56"
## [13,] "Family time (chatting, games etc)" "8" "1.11"
## [14,] "Farm" "24" "3.33"
## [15,] "Feeding animals" "1" "0.14"
## [16,] "Getting dressed" "10" "1.39"
## [17,] "Gym" "9" "1.25"
## [18,] "Home" "15" "2.08"
## [19,] "Homework (kids)" "5" "0.69"
## [20,] "Ironing" "6" "0.83"
## [21,] "Kava" "6" "0.83"
## [22,] "Laundry" "1" "0.14"
## [23,] "Mobile device (movies)" "18" "2.5"
## [24,] "Mobile devices (browsing, social media)" "12" "1.67"
## [25,] "Mobile devices (work)" "32" "4.44"
## [26,] "Reading" "1" "0.14"
## [27,] "Rest" "2" "0.28"
## [28,] "Scripture Reading" "1" "0.14"
## [29,] "showering" "1" "0.14"
## [30,] "Showering" "14" "1.94"
## [31,] "Sleeping" "219" "30.42"
## [32,] "Tidy up" "12" "1.67"
## [33,] "Watching movies (TV)" "27" "3.75"
## [34,] "Woke up" "15" "2.08"
## [35,] "Work" "213" "29.58"#rename column names
colnames(tableFri)[colnames(tableFri) == "freqFri"] <- "Frequency"
colnames(tableFri)[colnames(tableFri) == "propFri"] <- "Proportion (%)"
tableFri## Gender Frequency Proportion (%)
## [1,] "Breakfast" "5" "0.69"
## [2,] "Church" "1" "0.14"
## [3,] "Cooking" "16" "2.22"
## [4,] "Dinner" "15" "2.08"
## [5,] "Driving (church)" "1" "0.14"
## [6,] "Driving (drop relatives)" "2" "0.28"
## [7,] "Driving (farm)" "4" "0.56"
## [8,] "Driving (gym)" "2" "0.28"
## [9,] "Driving (home)" "8" "1.11"
## [10,] "Driving (Kava)" "1" "0.14"
## [11,] "Driving (work)" "9" "1.25"
## [12,] "Family devotion" "4" "0.56"
## [13,] "Family time (chatting, games etc)" "8" "1.11"
## [14,] "Farm" "24" "3.33"
## [15,] "Feeding animals" "1" "0.14"
## [16,] "Getting dressed" "10" "1.39"
## [17,] "Gym" "9" "1.25"
## [18,] "Home" "15" "2.08"
## [19,] "Homework (kids)" "5" "0.69"
## [20,] "Ironing" "6" "0.83"
## [21,] "Kava" "6" "0.83"
## [22,] "Laundry" "1" "0.14"
## [23,] "Mobile device (movies)" "18" "2.5"
## [24,] "Mobile devices (browsing, social media)" "12" "1.67"
## [25,] "Mobile devices (work)" "32" "4.44"
## [26,] "Reading" "1" "0.14"
## [27,] "Rest" "2" "0.28"
## [28,] "Scripture Reading" "1" "0.14"
## [29,] "showering" "1" "0.14"
## [30,] "Showering" "14" "1.94"
## [31,] "Sleeping" "219" "30.42"
## [32,] "Tidy up" "12" "1.67"
## [33,] "Watching movies (TV)" "27" "3.75"
## [34,] "Woke up" "15" "2.08"
## [35,] "Work" "213" "29.58"#using kable extra to create better table format
tableFriday <- kable(tableFri)%>%
kable_styling(bootstrap_options = "striped", full_width = F)
tableFriday| Gender | Frequency | Proportion (%) |
|---|---|---|
| Breakfast | 5 | 0.69 |
| Church | 1 | 0.14 |
| Cooking | 16 | 2.22 |
| Dinner | 15 | 2.08 |
| Driving (church) | 1 | 0.14 |
| Driving (drop relatives) | 2 | 0.28 |
| Driving (farm) | 4 | 0.56 |
| Driving (gym) | 2 | 0.28 |
| Driving (home) | 8 | 1.11 |
| Driving (Kava) | 1 | 0.14 |
| Driving (work) | 9 | 1.25 |
| Family devotion | 4 | 0.56 |
| Family time (chatting, games etc) | 8 | 1.11 |
| Farm | 24 | 3.33 |
| Feeding animals | 1 | 0.14 |
| Getting dressed | 10 | 1.39 |
| Gym | 9 | 1.25 |
| Home | 15 | 2.08 |
| Homework (kids) | 5 | 0.69 |
| Ironing | 6 | 0.83 |
| Kava | 6 | 0.83 |
| Laundry | 1 | 0.14 |
| Mobile device (movies) | 18 | 2.5 |
| Mobile devices (browsing, social media) | 12 | 1.67 |
| Mobile devices (work) | 32 | 4.44 |
| Reading | 1 | 0.14 |
| Rest | 2 | 0.28 |
| Scripture Reading | 1 | 0.14 |
| showering | 1 | 0.14 |
| Showering | 14 | 1.94 |
| Sleeping | 219 | 30.42 |
| Tidy up | 12 | 1.67 |
| Watching movies (TV) | 27 | 3.75 |
| Woke up | 15 | 2.08 |
| Work | 213 | 29.58 |
#weekdays
#frequency of Activities
freqwkd <- table(newdata$Activity, exclude=NULL)
freqwkd##
## Babysitting
## 5
## Breakfast
## 38
## Charging mobile devices
## 1
## Church
## 8
## Church (choir practice)
## 2
## Church (social activities)
## 14
## Cooking
## 102
## Cooking (baby)
## 1
## Cooking (business)
## 6
## Cooking (lunch)
## 2
## Dinner
## 100
## Driving (church)
## 3
## Driving (drop kids)
## 10
## Driving (drop relatives)
## 5
## Driving (farm)
## 5
## Driving (free time)
## 2
## Driving (gym)
## 12
## Driving (home)
## 51
## Driving (Kava)
## 1
## Driving (order delivery)
## 14
## Driving (pick up kids)
## 22
## Driving (visiting relatives)
## 10
## Driving (work)
## 59
## Exercise (Jog, walk)
## 12
## Family devotion
## 25
## Family time (chatting, games etc)
## 37
## Farm
## 26
## Feeding animals
## 13
## Food prep (business)
## 3
## Gardening
## 4
## Getting dressed
## 56
## Getting dressed (kids)
## 8
## Going out
## 7
## Gym
## 41
## Home
## 79
## Homework (kids)
## 38
## Ironing
## 21
## Kava
## 19
## Kids bedtime
## 7
## Laundry
## 18
## Light meal/snack
## 3
## Listen to music/radio
## 14
## Lunch
## 3
## Mobile device (movies)
## 57
## Mobile devices (browsing, social media)
## 51
## Mobile devices (games)
## 4
## Mobile devices (work)
## 73
## Prepare kids bag for school
## 1
## Public transport (home)
## 8
## Public transport (school)
## 2
## Public transport (work)
## 2
## Reading
## 23
## Rest
## 43
## School
## 28
## Scripture Reading
## 1
## Scripture study
## 1
## showering
## 1
## Showering
## 85
## Showering (kids)
## 3
## Sleeping
## 1268
## Studying
## 9
## Tidy up
## 57
## Turning off appliances/Lights
## 4
## Turning on appliances/lights
## 1
## Walking (home)
## 4
## Walking (Kava)
## 1
## Walking (work)
## 11
## Watching movies (TV)
## 152
## Woke up
## 88
## Work
## 1387dim(freqwkd)## [1] 70#proportion of Activities
propwkd <- prop.table(freqwkd)*100
propwkd <- round(propwkd, digits=2)
propwkd##
## Babysitting
## 0.12
## Breakfast
## 0.89
## Charging mobile devices
## 0.02
## Church
## 0.19
## Church (choir practice)
## 0.05
## Church (social activities)
## 0.33
## Cooking
## 2.39
## Cooking (baby)
## 0.02
## Cooking (business)
## 0.14
## Cooking (lunch)
## 0.05
## Dinner
## 2.34
## Driving (church)
## 0.07
## Driving (drop kids)
## 0.23
## Driving (drop relatives)
## 0.12
## Driving (farm)
## 0.12
## Driving (free time)
## 0.05
## Driving (gym)
## 0.28
## Driving (home)
## 1.19
## Driving (Kava)
## 0.02
## Driving (order delivery)
## 0.33
## Driving (pick up kids)
## 0.51
## Driving (visiting relatives)
## 0.23
## Driving (work)
## 1.38
## Exercise (Jog, walk)
## 0.28
## Family devotion
## 0.59
## Family time (chatting, games etc)
## 0.87
## Farm
## 0.61
## Feeding animals
## 0.30
## Food prep (business)
## 0.07
## Gardening
## 0.09
## Getting dressed
## 1.31
## Getting dressed (kids)
## 0.19
## Going out
## 0.16
## Gym
## 0.96
## Home
## 1.85
## Homework (kids)
## 0.89
## Ironing
## 0.49
## Kava
## 0.44
## Kids bedtime
## 0.16
## Laundry
## 0.42
## Light meal/snack
## 0.07
## Listen to music/radio
## 0.33
## Lunch
## 0.07
## Mobile device (movies)
## 1.33
## Mobile devices (browsing, social media)
## 1.19
## Mobile devices (games)
## 0.09
## Mobile devices (work)
## 1.71
## Prepare kids bag for school
## 0.02
## Public transport (home)
## 0.19
## Public transport (school)
## 0.05
## Public transport (work)
## 0.05
## Reading
## 0.54
## Rest
## 1.01
## School
## 0.66
## Scripture Reading
## 0.02
## Scripture study
## 0.02
## showering
## 0.02
## Showering
## 1.99
## Showering (kids)
## 0.07
## Sleeping
## 29.68
## Studying
## 0.21
## Tidy up
## 1.33
## Turning off appliances/Lights
## 0.09
## Turning on appliances/lights
## 0.02
## Walking (home)
## 0.09
## Walking (Kava)
## 0.02
## Walking (work)
## 0.26
## Watching movies (TV)
## 3.56
## Woke up
## 2.06
## Work
## 32.47dim(propwkd)## [1] 70#creating table with binding
tablewkd <- cbind(freqwkd, propwkd)
tablewkd <- cbind(Gender = rownames(tablewkd), tablewkd)
rownames(tablewkd) <- NULL
tablewkd## Gender freqwkd propwkd
## [1,] "Babysitting" "5" "0.12"
## [2,] "Breakfast" "38" "0.89"
## [3,] "Charging mobile devices" "1" "0.02"
## [4,] "Church" "8" "0.19"
## [5,] "Church (choir practice)" "2" "0.05"
## [6,] "Church (social activities)" "14" "0.33"
## [7,] "Cooking" "102" "2.39"
## [8,] "Cooking (baby)" "1" "0.02"
## [9,] "Cooking (business)" "6" "0.14"
## [10,] "Cooking (lunch)" "2" "0.05"
## [11,] "Dinner" "100" "2.34"
## [12,] "Driving (church)" "3" "0.07"
## [13,] "Driving (drop kids)" "10" "0.23"
## [14,] "Driving (drop relatives)" "5" "0.12"
## [15,] "Driving (farm)" "5" "0.12"
## [16,] "Driving (free time)" "2" "0.05"
## [17,] "Driving (gym)" "12" "0.28"
## [18,] "Driving (home)" "51" "1.19"
## [19,] "Driving (Kava)" "1" "0.02"
## [20,] "Driving (order delivery)" "14" "0.33"
## [21,] "Driving (pick up kids)" "22" "0.51"
## [22,] "Driving (visiting relatives)" "10" "0.23"
## [23,] "Driving (work)" "59" "1.38"
## [24,] "Exercise (Jog, walk)" "12" "0.28"
## [25,] "Family devotion" "25" "0.59"
## [26,] "Family time (chatting, games etc)" "37" "0.87"
## [27,] "Farm" "26" "0.61"
## [28,] "Feeding animals" "13" "0.3"
## [29,] "Food prep (business)" "3" "0.07"
## [30,] "Gardening" "4" "0.09"
## [31,] "Getting dressed" "56" "1.31"
## [32,] "Getting dressed (kids)" "8" "0.19"
## [33,] "Going out" "7" "0.16"
## [34,] "Gym" "41" "0.96"
## [35,] "Home" "79" "1.85"
## [36,] "Homework (kids)" "38" "0.89"
## [37,] "Ironing" "21" "0.49"
## [38,] "Kava" "19" "0.44"
## [39,] "Kids bedtime" "7" "0.16"
## [40,] "Laundry" "18" "0.42"
## [41,] "Light meal/snack" "3" "0.07"
## [42,] "Listen to music/radio" "14" "0.33"
## [43,] "Lunch" "3" "0.07"
## [44,] "Mobile device (movies)" "57" "1.33"
## [45,] "Mobile devices (browsing, social media)" "51" "1.19"
## [46,] "Mobile devices (games)" "4" "0.09"
## [47,] "Mobile devices (work)" "73" "1.71"
## [48,] "Prepare kids bag for school" "1" "0.02"
## [49,] "Public transport (home)" "8" "0.19"
## [50,] "Public transport (school)" "2" "0.05"
## [51,] "Public transport (work)" "2" "0.05"
## [52,] "Reading" "23" "0.54"
## [53,] "Rest" "43" "1.01"
## [54,] "School" "28" "0.66"
## [55,] "Scripture Reading" "1" "0.02"
## [56,] "Scripture study" "1" "0.02"
## [57,] "showering" "1" "0.02"
## [58,] "Showering" "85" "1.99"
## [59,] "Showering (kids)" "3" "0.07"
## [60,] "Sleeping" "1268" "29.68"
## [61,] "Studying" "9" "0.21"
## [62,] "Tidy up" "57" "1.33"
## [63,] "Turning off appliances/Lights" "4" "0.09"
## [64,] "Turning on appliances/lights" "1" "0.02"
## [65,] "Walking (home)" "4" "0.09"
## [66,] "Walking (Kava)" "1" "0.02"
## [67,] "Walking (work)" "11" "0.26"
## [68,] "Watching movies (TV)" "152" "3.56"
## [69,] "Woke up" "88" "2.06"
## [70,] "Work" "1387" "32.47"#rename column names
colnames(tablewkd)[colnames(tablewkd) == "freqwkd"] <- "Frequency"
colnames(tablewkd)[colnames(tableTue) == "propwkd"] <- "Proportion (%)"
tablewkd## Gender Frequency propwkd
## [1,] "Babysitting" "5" "0.12"
## [2,] "Breakfast" "38" "0.89"
## [3,] "Charging mobile devices" "1" "0.02"
## [4,] "Church" "8" "0.19"
## [5,] "Church (choir practice)" "2" "0.05"
## [6,] "Church (social activities)" "14" "0.33"
## [7,] "Cooking" "102" "2.39"
## [8,] "Cooking (baby)" "1" "0.02"
## [9,] "Cooking (business)" "6" "0.14"
## [10,] "Cooking (lunch)" "2" "0.05"
## [11,] "Dinner" "100" "2.34"
## [12,] "Driving (church)" "3" "0.07"
## [13,] "Driving (drop kids)" "10" "0.23"
## [14,] "Driving (drop relatives)" "5" "0.12"
## [15,] "Driving (farm)" "5" "0.12"
## [16,] "Driving (free time)" "2" "0.05"
## [17,] "Driving (gym)" "12" "0.28"
## [18,] "Driving (home)" "51" "1.19"
## [19,] "Driving (Kava)" "1" "0.02"
## [20,] "Driving (order delivery)" "14" "0.33"
## [21,] "Driving (pick up kids)" "22" "0.51"
## [22,] "Driving (visiting relatives)" "10" "0.23"
## [23,] "Driving (work)" "59" "1.38"
## [24,] "Exercise (Jog, walk)" "12" "0.28"
## [25,] "Family devotion" "25" "0.59"
## [26,] "Family time (chatting, games etc)" "37" "0.87"
## [27,] "Farm" "26" "0.61"
## [28,] "Feeding animals" "13" "0.3"
## [29,] "Food prep (business)" "3" "0.07"
## [30,] "Gardening" "4" "0.09"
## [31,] "Getting dressed" "56" "1.31"
## [32,] "Getting dressed (kids)" "8" "0.19"
## [33,] "Going out" "7" "0.16"
## [34,] "Gym" "41" "0.96"
## [35,] "Home" "79" "1.85"
## [36,] "Homework (kids)" "38" "0.89"
## [37,] "Ironing" "21" "0.49"
## [38,] "Kava" "19" "0.44"
## [39,] "Kids bedtime" "7" "0.16"
## [40,] "Laundry" "18" "0.42"
## [41,] "Light meal/snack" "3" "0.07"
## [42,] "Listen to music/radio" "14" "0.33"
## [43,] "Lunch" "3" "0.07"
## [44,] "Mobile device (movies)" "57" "1.33"
## [45,] "Mobile devices (browsing, social media)" "51" "1.19"
## [46,] "Mobile devices (games)" "4" "0.09"
## [47,] "Mobile devices (work)" "73" "1.71"
## [48,] "Prepare kids bag for school" "1" "0.02"
## [49,] "Public transport (home)" "8" "0.19"
## [50,] "Public transport (school)" "2" "0.05"
## [51,] "Public transport (work)" "2" "0.05"
## [52,] "Reading" "23" "0.54"
## [53,] "Rest" "43" "1.01"
## [54,] "School" "28" "0.66"
## [55,] "Scripture Reading" "1" "0.02"
## [56,] "Scripture study" "1" "0.02"
## [57,] "showering" "1" "0.02"
## [58,] "Showering" "85" "1.99"
## [59,] "Showering (kids)" "3" "0.07"
## [60,] "Sleeping" "1268" "29.68"
## [61,] "Studying" "9" "0.21"
## [62,] "Tidy up" "57" "1.33"
## [63,] "Turning off appliances/Lights" "4" "0.09"
## [64,] "Turning on appliances/lights" "1" "0.02"
## [65,] "Walking (home)" "4" "0.09"
## [66,] "Walking (Kava)" "1" "0.02"
## [67,] "Walking (work)" "11" "0.26"
## [68,] "Watching movies (TV)" "152" "3.56"
## [69,] "Woke up" "88" "2.06"
## [70,] "Work" "1387" "32.47"#using kable extra to create better table format
tableWeekdays <- kable(tablewkd)%>%
kable_styling(bootstrap_options = "striped", full_width = F)
tableWeekdays| Gender | Frequency | propwkd |
|---|---|---|
| Babysitting | 5 | 0.12 |
| Breakfast | 38 | 0.89 |
| Charging mobile devices | 1 | 0.02 |
| Church | 8 | 0.19 |
| Church (choir practice) | 2 | 0.05 |
| Church (social activities) | 14 | 0.33 |
| Cooking | 102 | 2.39 |
| Cooking (baby) | 1 | 0.02 |
| Cooking (business) | 6 | 0.14 |
| Cooking (lunch) | 2 | 0.05 |
| Dinner | 100 | 2.34 |
| Driving (church) | 3 | 0.07 |
| Driving (drop kids) | 10 | 0.23 |
| Driving (drop relatives) | 5 | 0.12 |
| Driving (farm) | 5 | 0.12 |
| Driving (free time) | 2 | 0.05 |
| Driving (gym) | 12 | 0.28 |
| Driving (home) | 51 | 1.19 |
| Driving (Kava) | 1 | 0.02 |
| Driving (order delivery) | 14 | 0.33 |
| Driving (pick up kids) | 22 | 0.51 |
| Driving (visiting relatives) | 10 | 0.23 |
| Driving (work) | 59 | 1.38 |
| Exercise (Jog, walk) | 12 | 0.28 |
| Family devotion | 25 | 0.59 |
| Family time (chatting, games etc) | 37 | 0.87 |
| Farm | 26 | 0.61 |
| Feeding animals | 13 | 0.3 |
| Food prep (business) | 3 | 0.07 |
| Gardening | 4 | 0.09 |
| Getting dressed | 56 | 1.31 |
| Getting dressed (kids) | 8 | 0.19 |
| Going out | 7 | 0.16 |
| Gym | 41 | 0.96 |
| Home | 79 | 1.85 |
| Homework (kids) | 38 | 0.89 |
| Ironing | 21 | 0.49 |
| Kava | 19 | 0.44 |
| Kids bedtime | 7 | 0.16 |
| Laundry | 18 | 0.42 |
| Light meal/snack | 3 | 0.07 |
| Listen to music/radio | 14 | 0.33 |
| Lunch | 3 | 0.07 |
| Mobile device (movies) | 57 | 1.33 |
| Mobile devices (browsing, social media) | 51 | 1.19 |
| Mobile devices (games) | 4 | 0.09 |
| Mobile devices (work) | 73 | 1.71 |
| Prepare kids bag for school | 1 | 0.02 |
| Public transport (home) | 8 | 0.19 |
| Public transport (school) | 2 | 0.05 |
| Public transport (work) | 2 | 0.05 |
| Reading | 23 | 0.54 |
| Rest | 43 | 1.01 |
| School | 28 | 0.66 |
| Scripture Reading | 1 | 0.02 |
| Scripture study | 1 | 0.02 |
| showering | 1 | 0.02 |
| Showering | 85 | 1.99 |
| Showering (kids) | 3 | 0.07 |
| Sleeping | 1268 | 29.68 |
| Studying | 9 | 0.21 |
| Tidy up | 57 | 1.33 |
| Turning off appliances/Lights | 4 | 0.09 |
| Turning on appliances/lights | 1 | 0.02 |
| Walking (home) | 4 | 0.09 |
| Walking (Kava) | 1 | 0.02 |
| Walking (work) | 11 | 0.26 |
| Watching movies (TV) | 152 | 3.56 |
| Woke up | 88 | 2.06 |
| Work | 1387 | 32.47 |
3.1 Weekdays Activities TUD
Im trying to see the count for all the activities in the TUD
#Monday
groupbymonAct <- monday %>%
dplyr::group_by(Activity) %>%
dplyr::summarize(count = n())
groupbymonAct## # A tibble: 50 x 2
## Activity count
## <chr> <int>
## 1 Breakfast 16
## 2 Charging mobile devices 1
## 3 Church 6
## 4 Church (social activities) 2
## 5 Cooking 31
## 6 Cooking (baby) 1
## 7 Cooking (business) 3
## 8 Cooking (lunch) 1
## 9 Dinner 36
## 10 Driving (church) 1
## # … with 40 more rows#Tuesday
groupbytueAct <- tuesday %>%
dplyr::group_by(Activity) %>%
dplyr::summarize(count = n())
groupbytueAct## # A tibble: 45 x 2
## Activity count
## <chr> <int>
## 1 Breakfast 5
## 2 Church (choir practice) 2
## 3 Church (social activities) 5
## 4 Cooking 23
## 5 Dinner 24
## 6 Driving (drop kids) 7
## 7 Driving (drop relatives) 2
## 8 Driving (free time) 2
## 9 Driving (home) 8
## 10 Driving (pick up kids) 12
## # … with 35 more rows#Wednesday
groupbywedAct <- wednesday %>%
dplyr::group_by(Activity) %>%
dplyr::summarize(count = n())
groupbywedAct## # A tibble: 36 x 2
## Activity count
## <chr> <int>
## 1 Babysitting 5
## 2 Breakfast 8
## 3 Church 1
## 4 Cooking 26
## 5 Dinner 17
## 6 Driving (gym) 4
## 7 Driving (home) 8
## 8 Driving (order delivery) 9
## 9 Driving (work) 13
## 10 Exercise (Jog, walk) 3
## # … with 26 more rows#Thursday
groupbythuAct <- thursday %>%
dplyr::group_by(Activity) %>%
dplyr::summarize(count = n())
groupbythuAct## # A tibble: 40 x 2
## Activity count
## <chr> <int>
## 1 Breakfast 4
## 2 Church (social activities) 7
## 3 Cooking 6
## 4 Cooking (business) 3
## 5 Cooking (lunch) 1
## 6 Dinner 8
## 7 Driving (church) 1
## 8 Driving (drop kids) 1
## 9 Driving (gym) 2
## 10 Driving (home) 4
## # … with 30 more rows#Friday
groupbyfriAct <- friday %>%
dplyr::group_by(Activity) %>%
dplyr::summarize(count = n())
groupbyfriAct## # A tibble: 35 x 2
## Activity count
## <chr> <int>
## 1 Breakfast 5
## 2 Church 1
## 3 Cooking 16
## 4 Dinner 15
## 5 Driving (church) 1
## 6 Driving (drop relatives) 2
## 7 Driving (farm) 4
## 8 Driving (gym) 2
## 9 Driving (home) 8
## 10 Driving (Kava) 1
## # … with 25 more rows#Weekdays
groupbyWkdAct <- newdata %>%
dplyr::group_by(Activity) %>%
dplyr::summarize(count = n())
groupbyWkdAct## # A tibble: 70 x 2
## Activity count
## <chr> <int>
## 1 Babysitting 5
## 2 Breakfast 38
## 3 Charging mobile devices 1
## 4 Church 8
## 5 Church (choir practice) 2
## 6 Church (social activities) 14
## 7 Cooking 102
## 8 Cooking (baby) 1
## 9 Cooking (business) 6
## 10 Cooking (lunch) 2
## # … with 60 more rowsIm am trying to remove sleeping, rest, going to work from the data so that i can only focus on the rest of the acitivities.
The issue i am facing as the time is not sorted in accending order. Can you help with the coding
yep - see what I did :-)
tableAll <- monday %>%
#select(Time, Activity) %>%
select(fixedHMS, Activity) %>% # use the new time variable
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
#group_by(Time) %>%
group_by(fixedHMS) %>%
tally
kableExtra::kable(tableAll, caption = "Monday test (no sleep/rest/work)") %>%
kable_styling()| fixedHMS | n |
|---|---|
| 00:00:00 | 4 |
| 00:30:00 | 2 |
| 04:30:00 | 1 |
| 05:00:00 | 5 |
| 05:30:00 | 7 |
| 06:00:00 | 13 |
| 06:30:00 | 19 |
| 07:00:00 | 30 |
| 07:30:00 | 29 |
| 08:00:00 | 28 |
| 08:30:00 | 8 |
| 09:00:00 | 2 |
| 09:30:00 | 1 |
| 10:00:00 | 1 |
| 15:00:00 | 1 |
| 15:30:00 | 2 |
| 16:00:00 | 3 |
| 16:30:00 | 7 |
| 17:00:00 | 26 |
| 17:30:00 | 24 |
| 18:00:00 | 25 |
| 18:30:00 | 26 |
| 19:00:00 | 30 |
| 19:30:00 | 30 |
| 20:00:00 | 30 |
| 20:30:00 | 30 |
| 21:00:00 | 29 |
| 21:30:00 | 27 |
| 22:00:00 | 22 |
| 22:30:00 | 19 |
| 23:00:00 | 12 |
| 23:30:00 | 8 |
# plot
ggplot2::ggplot(tableAll, aes(x = fixedHMS, y = n)) +
geom_col()
3.2 Cooking
Here im am trying to look at anything related to preparing and eating a meal.
# creating dataframe for all cooking
#Monday
tableCooking <- monday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Cooking",
"Cooking (business)",
"Cooking (baby)",
"Cooking (lunch)",
"Food prep (business)",
"Lunch",
"Breakfast",
"Dinner",
"Light meal/snack")) %>%
group_by(fixedHMS) %>%
tally
tableCooking## # A tibble: 17 x 2
## fixedHMS n
## <time> <int>
## 1 06:00 3
## 2 06:30 5
## 3 07:00 7
## 4 07:30 6
## 5 08:00 3
## 6 08:30 2
## 7 09:00 1
## 8 17:00 1
## 9 17:30 4
## 10 18:00 8
## 11 18:30 9
## 12 19:00 10
## 13 19:30 13
## 14 20:00 8
## 15 20:30 6
## 16 21:00 2
## 17 21:30 1MonCook <- ggplot(tableCooking, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="blue", fill="blue") +
ggtitle("Monday Cooking") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
MonCook
#Tuesday
tableCookingTue <- tuesday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Cooking",
"Cooking (business)",
"Cooking (baby)",
"Cooking (lunch)",
"Food prep (business)",
"Lunch",
"Breakfast",
"Dinner",
"Light meal/snack")) %>%
group_by(fixedHMS) %>%
tally
tableCookingTue## # A tibble: 24 x 2
## fixedHMS n
## <time> <int>
## 1 05:00 1
## 2 05:30 1
## 3 06:00 2
## 4 06:30 2
## 5 07:00 1
## 6 07:30 1
## 7 08:00 1
## 8 09:00 1
## 9 12:30 1
## 10 13:00 1
## # … with 14 more rowsTueCook <- ggplot(tableCookingTue, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="blue", fill="blue") +
ggtitle("Tuesday Cooking") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
TueCook
#Wednesday
tableCookingWed <- wednesday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Cooking",
"Cooking (business)",
"Cooking (baby)",
"Cooking (lunch)",
"Food prep (business)",
"Lunch",
"Breakfast",
"Dinner",
"Light meal/snack")) %>%
group_by(fixedHMS) %>%
tally
tableCookingWed## # A tibble: 14 x 2
## fixedHMS n
## <time> <int>
## 1 06:00 2
## 2 06:30 2
## 3 07:00 5
## 4 07:30 6
## 5 08:00 1
## 6 17:30 1
## 7 18:00 3
## 8 18:30 4
## 9 19:00 8
## 10 19:30 8
## 11 20:00 4
## 12 20:30 4
## 13 21:00 2
## 14 21:30 1WedCook <- ggplot(tableCookingWed, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="blue", fill="blue") +
ggtitle("Wednesday Cooking") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
WedCook
#Thursday
tableCookingThu <- thursday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Cooking",
"Cooking (business)",
"Cooking (baby)",
"Cooking (lunch)",
"Food prep (business)",
"Lunch",
"Breakfast",
"Dinner",
"Light meal/snack")) %>%
group_by(fixedHMS) %>%
tally
tableCookingThu## # A tibble: 20 x 2
## fixedHMS n
## <time> <int>
## 1 06:30 1
## 2 07:30 1
## 3 08:00 2
## 4 08:30 1
## 5 09:00 1
## 6 09:30 1
## 7 10:00 1
## 8 14:30 1
## 9 17:30 2
## 10 18:00 1
## 11 18:30 3
## 12 19:00 2
## 13 19:30 1
## 14 20:00 2
## 15 20:30 1
## 16 21:00 1
## 17 21:30 1
## 18 22:00 1
## 19 22:30 1
## 20 23:00 1ThuCook <- ggplot(tableCookingThu, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="blue", fill="blue") +
ggtitle("Thursday Cooking") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
ThuCook
#Friday
tableCookingFri <- friday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Cooking",
"Cooking (business)",
"Cooking (baby)",
"Cooking (lunch)",
"Food prep (business)",
"Lunch",
"Breakfast",
"Dinner",
"Light meal/snack")) %>%
group_by(fixedHMS) %>%
tally
tableCookingFri## # A tibble: 20 x 2
## fixedHMS n
## <time> <int>
## 1 07:00 2
## 2 07:30 1
## 3 09:00 1
## 4 10:00 1
## 5 14:00 1
## 6 14:30 1
## 7 15:00 1
## 8 15:30 1
## 9 16:00 1
## 10 16:30 1
## 11 17:00 1
## 12 17:30 4
## 13 18:00 3
## 14 18:30 5
## 15 19:00 4
## 16 19:30 3
## 17 20:00 2
## 18 20:30 1
## 19 22:00 1
## 20 22:30 1FriCook <- ggplot(tableCookingFri, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="blue", fill="blue") +
ggtitle("Friday Cooking") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
FriCook
#Weekdays
tableCookingWkd <- newdata %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Cooking",
"Cooking (business)",
"Cooking (baby)",
"Cooking (lunch)",
"Food prep (business)",
"Lunch",
"Breakfast",
"Dinner",
"Light meal/snack")) %>%
group_by(fixedHMS) %>%
tally
tableCookingWkd## # A tibble: 33 x 2
## fixedHMS n
## <time> <int>
## 1 05:00 1
## 2 05:30 1
## 3 06:00 7
## 4 06:30 10
## 5 07:00 15
## 6 07:30 15
## 7 08:00 7
## 8 08:30 3
## 9 09:00 4
## 10 09:30 1
## # … with 23 more rowsWkdCook <- ggplot(tableCookingWkd, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="blue", fill="blue") +
ggtitle("Weekdays Cooking") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
WkdCook
3.3 Weekdays Tidy up
similar to the above but for tidy up/cleaning up
# creating dataframe for all cleaning up
#Monday
mondayTidyup <- monday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Tidy up",
"Laundry",
"Showering",
"Showering (kids)",
"Gardening",
"Getting dressed",
"Getting dressed (kids)",
"Prepare kids bag for school",
"Turning off applicances/Lights",
"Turning on appliances/lights",
"Ironing",
"Feeding animals")) %>%
group_by(fixedHMS) %>%
tally
mondayTidyup## # A tibble: 21 x 2
## fixedHMS n
## <time> <int>
## 1 05:30 1
## 2 06:00 3
## 3 06:30 5
## 4 07:00 7
## 5 07:30 17
## 6 08:00 7
## 7 09:00 1
## 8 09:30 1
## 9 17:00 1
## 10 17:30 2
## # … with 11 more rowsMondayTidy <- ggplot(mondayTidyup, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="red", fill="red") +
ggtitle("Monday Tidy up") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
MondayTidy
#Tuesday
tuesdayTidyup <- tuesday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Tidy up",
"Laundry",
"Showering",
"Showering (kids)",
"Gardening",
"Getting dressed",
"Getting dressed (kids)",
"Prepare kids bag for school",
"Turning off applicances/Lights",
"Turning on appliances/lights",
"Ironing",
"Feeding animals")) %>%
group_by(fixedHMS) %>%
tally
tuesdayTidyup## # A tibble: 24 x 2
## fixedHMS n
## <time> <int>
## 1 05:30 1
## 2 06:00 1
## 3 06:30 5
## 4 07:00 7
## 5 07:30 10
## 6 08:00 5
## 7 08:30 1
## 8 09:30 1
## 9 10:00 1
## 10 11:00 1
## # … with 14 more rowsTuesdayTidy <- ggplot(tuesdayTidyup, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="red", fill="red") +
ggtitle("Tuesday Tidy up") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
TuesdayTidy
#Wednesday
wednesdayTidyup <- wednesday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Tidy up",
"Laundry",
"Showering",
"Showering (kids)",
"Gardening",
"Getting dressed",
"Getting dressed (kids)",
"Prepare kids bag for school",
"Turning off applicances/Lights",
"Turning on appliances/lights",
"Ironing",
"Feeding animals")) %>%
group_by(fixedHMS) %>%
tally
wednesdayTidyup## # A tibble: 27 x 2
## fixedHMS n
## <time> <int>
## 1 00:30 1
## 2 05:30 1
## 3 06:00 1
## 4 06:30 3
## 5 07:00 3
## 6 07:30 6
## 7 08:00 6
## 8 08:30 1
## 9 09:00 1
## 10 14:00 1
## # … with 17 more rowsWednesdayTidy <- ggplot(wednesdayTidyup, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="red", fill="red") +
ggtitle("Wednesday Tidy up") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
WednesdayTidy
#Thursday
thursdayTidyup <- thursday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Tidy up",
"Laundry",
"Showering",
"Showering (kids)",
"Gardening",
"Getting dressed",
"Getting dressed (kids)",
"Prepare kids bag for school",
"Turning off applicances/Lights",
"Turning on appliances/lights",
"Ironing",
"Feeding animals")) %>%
group_by(fixedHMS) %>%
tally
thursdayTidyup## # A tibble: 18 x 2
## fixedHMS n
## <time> <int>
## 1 00:30 1
## 2 06:30 1
## 3 07:00 4
## 4 07:30 5
## 5 08:00 1
## 6 13:00 1
## 7 13:30 1
## 8 14:00 1
## 9 17:30 1
## 10 18:00 1
## 11 19:00 2
## 12 19:30 3
## 13 20:00 2
## 14 21:00 2
## 15 21:30 2
## 16 22:00 1
## 17 22:30 1
## 18 23:00 1ThursdayTidy <- ggplot(thursdayTidyup, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="red", fill="red") +
ggtitle("Thursday Tidy up") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
ThursdayTidy
#Friday
fridayTidyup <- friday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Tidy up",
"Laundry",
"Showering",
"Showering (kids)",
"Gardening",
"Getting dressed",
"Getting dressed (kids)",
"Prepare kids bag for school",
"Turning off applicances/Lights",
"Turning on appliances/lights",
"Ironing",
"Feeding animals")) %>%
group_by(fixedHMS) %>%
tally
fridayTidyup## # A tibble: 19 x 2
## fixedHMS n
## <time> <int>
## 1 00:30 1
## 2 06:30 3
## 3 07:00 4
## 4 07:30 6
## 5 08:00 6
## 6 08:30 2
## 7 09:30 2
## 8 12:00 1
## 9 12:30 1
## 10 13:00 1
## 11 13:30 1
## 12 17:00 1
## 13 17:30 4
## 14 18:00 3
## 15 18:30 2
## 16 19:30 2
## 17 21:30 1
## 18 22:00 1
## 19 22:30 2FridayTidy <- ggplot(fridayTidyup, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="red", fill="red") +
ggtitle("Friday Tidy up") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
FridayTidy
#Weekdays
weekdaysTidyup <- newdata %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Tidy up",
"Laundry",
"Showering",
"Showering (kids)",
"Gardening",
"Getting dressed",
"Getting dressed (kids)",
"Prepare kids bag for school",
"Turning off applicances/Lights",
"Turning on appliances/lights",
"Ironing",
"Feeding animals")) %>%
group_by(fixedHMS) %>%
tally
weekdaysTidyup## # A tibble: 36 x 2
## fixedHMS n
## <time> <int>
## 1 00:30 3
## 2 05:30 3
## 3 06:00 5
## 4 06:30 17
## 5 07:00 25
## 6 07:30 44
## 7 08:00 25
## 8 08:30 4
## 9 09:00 2
## 10 09:30 4
## # … with 26 more rowsWeekdaysTidy <- ggplot(weekdaysTidyup, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="red", fill="red") +
ggtitle("Weekdays Tidy up") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
WeekdaysTidy
3.4 Weekday Leisure
similar to the above but for leisure
# creating dataframe for all leisure
#Monday
mondayleisure <- monday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Babysitting",
"Charging mobile devices",
"Family devotion",
"Exercise (Jog, walk)",
"Family time (chatting, games etc)",
"Homework (kids)",
"Kids bedtime",
"Listen to music/radio",
"Mobile devices (movies)",
"Mobile devices (games)",
"Mobile devices (work)",
"Mobile devices (browsing, social media)",
"Scripture study",
"Studying",
"Reading",
"Watching movies (TV)")) %>%
group_by(fixedHMS) %>%
tally
mondayleisure## # A tibble: 18 x 2
## fixedHMS n
## <time> <int>
## 1 00:00 1
## 2 00:30 1
## 3 05:30 2
## 4 06:30 1
## 5 07:00 1
## 6 17:30 1
## 7 18:00 2
## 8 18:30 6
## 9 19:00 10
## 10 19:30 10
## 11 20:00 15
## 12 20:30 15
## 13 21:00 18
## 14 21:30 19
## 15 22:00 15
## 16 22:30 12
## 17 23:00 8
## 18 23:30 4Mondayleisure <- ggplot(mondayleisure, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="violet", fill="violet") +
ggtitle("Monday Leisure") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
Mondayleisure
#Tuesday
tuesdayleisure <- tuesday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Babysitting",
"Charging mobile devices",
"Family devotion",
"Exercise (Jog, walk)",
"Family time (chatting, games etc)",
"Homework (kids)",
"Kids bedtime",
"Listen to music/radio",
"Mobile devices (movies)",
"Mobile devices (games)",
"Mobile devices (work)",
"Mobile devices (browsing, social media)",
"Scripture study",
"Studying",
"Reading",
"Watching movies (TV)")) %>%
group_by(fixedHMS) %>%
tally
tuesdayleisure## # A tibble: 18 x 2
## fixedHMS n
## <time> <int>
## 1 00:00 5
## 2 00:30 4
## 3 03:00 1
## 4 08:00 1
## 5 15:30 1
## 6 17:30 3
## 7 18:00 2
## 8 18:30 5
## 9 19:00 8
## 10 19:30 9
## 11 20:00 8
## 12 20:30 9
## 13 21:00 10
## 14 21:30 12
## 15 22:00 13
## 16 22:30 12
## 17 23:00 8
## 18 23:30 5Tuesdayleisure <- ggplot(tuesdayleisure, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="violet", fill="violet") +
ggtitle("Tuesday Leisure") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
Tuesdayleisure
#Wednesday
wednesdayleisure <- wednesday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Babysitting",
"Charging mobile devices",
"Family devotion",
"Exercise (Jog, walk)",
"Family time (chatting, games etc)",
"Homework (kids)",
"Kids bedtime",
"Listen to music/radio",
"Mobile devices (movies)",
"Mobile devices (games)",
"Mobile devices (work)",
"Mobile devices (browsing, social media)",
"Scripture study",
"Studying",
"Reading",
"Watching movies (TV)")) %>%
group_by(fixedHMS) %>%
tally
wednesdayleisure## # A tibble: 19 x 2
## fixedHMS n
## <time> <int>
## 1 00:00 1
## 2 05:30 1
## 3 06:00 1
## 4 07:00 1
## 5 16:30 1
## 6 17:00 1
## 7 17:30 4
## 8 18:00 4
## 9 18:30 2
## 10 19:00 4
## 11 19:30 3
## 12 20:00 6
## 13 20:30 6
## 14 21:00 8
## 15 21:30 8
## 16 22:00 9
## 17 22:30 8
## 18 23:00 4
## 19 23:30 2Wednesdayleisure <- ggplot(wednesdayleisure, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="violet", fill="violet") +
ggtitle("Wednesday Leisure") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
Wednesdayleisure
#Thursday
thursdayleisure <- thursday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Babysitting",
"Charging mobile devices",
"Family devotion",
"Exercise (Jog, walk)",
"Family time (chatting, games etc)",
"Homework (kids)",
"Kids bedtime",
"Listen to music/radio",
"Mobile devices (movies)",
"Mobile devices (games)",
"Mobile devices (work)",
"Mobile devices (browsing, social media)",
"Scripture study",
"Studying",
"Reading",
"Watching movies (TV)")) %>%
group_by(fixedHMS) %>%
tally
thursdayleisure## # A tibble: 15 x 2
## fixedHMS n
## <time> <int>
## 1 04:30 1
## 2 05:00 1
## 3 05:30 1
## 4 06:00 2
## 5 06:30 2
## 6 19:00 1
## 7 19:30 1
## 8 20:00 2
## 9 20:30 4
## 10 21:00 3
## 11 21:30 2
## 12 22:00 3
## 13 22:30 3
## 14 23:00 4
## 15 23:30 2Thursdayleisure <- ggplot(thursdayleisure, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="violet", fill="violet") +
ggtitle("Thursday Leisure") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
Thursdayleisure
#Friday
fridayleisure <- friday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Babysitting",
"Charging mobile devices",
"Family devotion",
"Exercise (Jog, walk)",
"Family time (chatting, games etc)",
"Homework (kids)",
"Kids bedtime",
"Listen to music/radio",
"Mobile devices (movies)",
"Mobile devices (games)",
"Mobile devices (work)",
"Mobile devices (browsing, social media)",
"Scripture study",
"Studying",
"Reading",
"Watching movies (TV)")) %>%
group_by(fixedHMS) %>%
tally
fridayleisure## # A tibble: 36 x 2
## fixedHMS n
## <time> <int>
## 1 00:00 4
## 2 00:30 2
## 3 01:00 1
## 4 01:30 1
## 5 02:00 1
## 6 02:30 1
## 7 05:30 1
## 8 06:00 1
## 9 06:30 1
## 10 10:30 1
## # … with 26 more rowsFridayleisure <- ggplot(fridayleisure, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="violet", fill="violet") +
ggtitle("Friday Leisure") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
Fridayleisure
#Weekdays
weekdaysleisure <- newdata %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Babysitting",
"Charging mobile devices",
"Family devotion",
"Exercise (Jog, walk)",
"Family time (chatting, games etc)",
"Homework (kids)",
"Kids bedtime",
"Listen to music/radio",
"Mobile devices (movies)",
"Mobile devices (games)",
"Mobile devices (work)",
"Mobile devices (browsing, social media)",
"Scripture study",
"Studying",
"Reading",
"Watching movies (TV)")) %>%
group_by(fixedHMS) %>%
tally
weekdaysleisure## # A tibble: 41 x 2
## fixedHMS n
## <time> <int>
## 1 00:00 11
## 2 00:30 7
## 3 01:00 1
## 4 01:30 1
## 5 02:00 1
## 6 02:30 1
## 7 03:00 1
## 8 04:30 1
## 9 05:00 1
## 10 05:30 5
## # … with 31 more rowsWeekdayleisure <- ggplot(weekdaysleisure, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="violet", fill="violet") +
ggtitle("Weekdays Leisure") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
Weekdayleisure
3.5 Weekdays Away from home
similar to the above but for away from home
# creating dataframe for Away from Home
#Monday
mondayAway <- monday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity %in% c("Farm",
"Relatives",
"Work",
"Kava",
"Church",
"Church (social activities)",
"Church (choir practice)",
"Exercise (Jog, walk)",
"Gym",
"Sports",
"School",
"Going out"))%>%
group_by(fixedHMS) %>%
tally
mondayAway## # A tibble: 31 x 2
## fixedHMS n
## <time> <int>
## 1 05:30 2
## 2 06:00 2
## 3 06:30 3
## 4 07:00 1
## 5 07:30 2
## 6 08:00 4
## 7 08:30 24
## 8 09:00 30
## 9 09:30 31
## 10 10:00 31
## # … with 21 more rowsMondayAwayHome <- ggplot(mondayAway, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="orange", fill="orange") +
ggtitle("Monday Away From Home") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
MondayAwayHome
#Tuesday
tuesdayAway <- tuesday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity %in% c("Farm",
"Relatives",
"Work",
"Kava",
"Church",
"Church (social activities)",
"Church (choir practice)",
"Exercise (Jog, walk)",
"Gym",
"Sports",
"School",
"Going out"))%>%
group_by(fixedHMS) %>%
tally
tuesdayAway## # A tibble: 40 x 2
## fixedHMS n
## <time> <int>
## 1 00:00 1
## 2 00:30 1
## 3 01:00 1
## 4 01:30 1
## 5 02:00 1
## 6 02:30 1
## 7 07:00 1
## 8 07:30 1
## 9 08:00 3
## 10 08:30 12
## # … with 30 more rowsTuesdayAwayHome <- ggplot(tuesdayAway, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="orange", fill="orange") +
ggtitle("Tuesday Away From Home") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
TuesdayAwayHome
#Wednesday
wednesdayAway <- wednesday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity %in% c("Farm",
"Relatives",
"Work",
"Kava",
"Church",
"Church (social activities)",
"Church (choir practice)",
"Exercise (Jog, walk)",
"Gym",
"Sports",
"School",
"Going out"))%>%
group_by(fixedHMS) %>%
tally
wednesdayAway## # A tibble: 31 x 2
## fixedHMS n
## <time> <int>
## 1 05:30 2
## 2 06:00 2
## 3 06:30 1
## 4 07:00 1
## 5 08:00 1
## 6 08:30 9
## 7 09:00 14
## 8 09:30 14
## 9 10:00 14
## 10 10:30 14
## # … with 21 more rowsWednesdayAwayHome <- ggplot(wednesdayAway, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="orange", fill="orange") +
ggtitle("Wednesday Away From Home") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
WednesdayAwayHome
#Thursday
thursdayAway <- thursday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity %in% c("Farm",
"Relatives",
"Work",
"Kava",
"Church",
"Church (social activities)",
"Church (choir practice)",
"Exercise (Jog, walk)",
"Gym",
"Sports",
"School",
"Going out"))%>%
group_by(fixedHMS) %>%
tally
thursdayAway## # A tibble: 33 x 2
## fixedHMS n
## <time> <int>
## 1 05:30 1
## 2 06:00 1
## 3 06:30 1
## 4 07:30 1
## 5 08:00 1
## 6 08:30 6
## 7 09:00 7
## 8 09:30 7
## 9 10:00 7
## 10 10:30 7
## # … with 23 more rowsThursdayAwayHome <- ggplot(thursdayAway, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="orange", fill="orange") +
ggtitle("Monday Away From Home") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
ThursdayAwayHome
#Friday
fridayAway <- friday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity %in% c("Farm",
"Relatives",
"Work",
"Kava",
"Church",
"Church (social activities)",
"Church (choir practice)",
"Exercise (Jog, walk)",
"Gym",
"Sports",
"School",
"Going out"))%>%
group_by(fixedHMS) %>%
tally
fridayAway## # A tibble: 32 x 2
## fixedHMS n
## <time> <int>
## 1 06:00 1
## 2 07:00 1
## 3 07:30 1
## 4 08:00 3
## 5 08:30 8
## 6 09:00 11
## 7 09:30 13
## 8 10:00 13
## 9 10:30 14
## 10 11:00 14
## # … with 22 more rowsFridayAwayHome <- ggplot(fridayAway, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="orange", fill="orange") +
ggtitle("Friday Away From Home") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
FridayAwayHome
#Weekday
weekdayAway <- newdata %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity %in% c("Farm",
"Relatives",
"Work",
"Kava",
"Church",
"Church (social activities)",
"Church (choir practice)",
"Exercise (Jog, walk)",
"Gym",
"Sports",
"School",
"Going out"))%>%
group_by(fixedHMS) %>%
tally
weekdayAway## # A tibble: 43 x 2
## fixedHMS n
## <time> <int>
## 1 00:00 1
## 2 00:30 1
## 3 01:00 1
## 4 01:30 1
## 5 02:00 1
## 6 02:30 1
## 7 05:30 5
## 8 06:00 6
## 9 06:30 5
## 10 07:00 4
## # … with 33 more rowsWeekdaysAwayHome <- ggplot(weekdayAway, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="orange", fill="orange") +
ggtitle("Weekdays Away From Home") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
WeekdaysAwayHome
3.6 Monday Transport
similar to the above but for transportation
BA: table breaks for some reason?
# creating dataframe for transportation
# Monday
mondayTransport <- monday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Driving (church)",
"Driving (drop kids)",
"Driving (drop relatives)",
"Driving (farm)",
"Driving (free time)",
"Driving (gym)",
"Driving (home)",
"Driving (Kava)",
"Driving (order delivery)",
"Driving (pick up kids)",
"Driving (visiting relatives)",
"Driving (work)",
"Public transport (home)",
"Public transport (work)",
"Public transport (school)",
"Walking (home)",
"Walking (work)",
"Walking (Kava)")) %>%
group_by(fixedHMS) %>%
tally
mondayTransport## # A tibble: 21 x 2
## fixedHMS n
## <time> <int>
## 1 05:00 1
## 2 07:00 2
## 3 07:30 6
## 4 08:00 17
## 5 08:30 6
## 6 10:00 1
## 7 15:00 1
## 8 15:30 2
## 9 16:00 3
## 10 16:30 5
## # … with 11 more rows#ploting Bar graph
MondayTransport <- ggplot(mondayTransport, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="purple", fill="purple") +
ggtitle("Monday Travelling") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
MondayTransport
# Tuesday
tuesdayTransport <- tuesday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Driving (church)",
"Driving (drop kids)",
"Driving (drop relatives)",
"Driving (farm)",
"Driving (free time)",
"Driving (gym)",
"Driving (home)",
"Driving (Kava)",
"Driving (order delivery)",
"Driving (pick up kids)",
"Driving (visiting relatives)",
"Driving (work)",
"Public transport (home)",
"Public transport (work)",
"Public transport (school)",
"Walking (home)",
"Walking (work)",
"Walking (Kava)")) %>%
group_by(fixedHMS) %>%
tally
tuesdayTransport## # A tibble: 21 x 2
## fixedHMS n
## <time> <int>
## 1 06:30 1
## 2 07:00 1
## 3 07:30 3
## 4 08:00 9
## 5 08:30 5
## 6 09:00 3
## 7 09:30 3
## 8 10:00 2
## 9 10:30 1
## 10 11:00 1
## # … with 11 more rows#ploting Bar graph
TuesdayTransport <- ggplot(tuesdayTransport, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="purple", fill="purple") +
ggtitle("Tuesday Travelling") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
TuesdayTransport
# Wednesday
wednesdayTransport <- wednesday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Driving (church)",
"Driving (drop kids)",
"Driving (drop relatives)",
"Driving (farm)",
"Driving (free time)",
"Driving (gym)",
"Driving (home)",
"Driving (Kava)",
"Driving (order delivery)",
"Driving (pick up kids)",
"Driving (visiting relatives)",
"Driving (work)",
"Public transport (home)",
"Public transport (work)",
"Public transport (school)",
"Walking (home)",
"Walking (work)",
"Walking (Kava)")) %>%
group_by(fixedHMS) %>%
tally
wednesdayTransport## # A tibble: 19 x 2
## fixedHMS n
## <time> <int>
## 1 05:00 1
## 2 05:30 1
## 3 07:30 1
## 4 08:00 7
## 5 08:30 5
## 6 09:30 1
## 7 10:00 1
## 8 10:30 1
## 9 11:00 1
## 10 11:30 1
## 11 12:00 1
## 12 12:30 1
## 13 13:00 1
## 14 13:30 1
## 15 16:30 2
## 16 17:00 6
## 17 17:30 2
## 18 18:30 1
## 19 20:00 1#ploting Bar graph
WednesdayTransport <- ggplot(wednesdayTransport, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="purple", fill="purple") +
ggtitle("Wednesday Travelling") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
WednesdayTransport
# Thursday
thursdayTransport <- thursday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Driving (church)",
"Driving (drop kids)",
"Driving (drop relatives)",
"Driving (farm)",
"Driving (free time)",
"Driving (gym)",
"Driving (home)",
"Driving (Kava)",
"Driving (order delivery)",
"Driving (pick up kids)",
"Driving (visiting relatives)",
"Driving (work)",
"Public transport (home)",
"Public transport (work)",
"Public transport (school)",
"Walking (home)",
"Walking (work)",
"Walking (Kava)")) %>%
group_by(fixedHMS) %>%
tally
thursdayTransport## # A tibble: 13 x 2
## fixedHMS n
## <time> <int>
## 1 07:00 1
## 2 07:30 1
## 3 08:00 4
## 4 08:30 1
## 5 10:30 1
## 6 11:00 1
## 7 11:30 1
## 8 12:00 1
## 9 12:30 1
## 10 16:30 2
## 11 17:00 2
## 12 17:30 2
## 13 18:00 1#ploting Bar graph
ThursdayTransport <- ggplot(thursdayTransport, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="purple", fill="purple") +
ggtitle("Thursday Travelling") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
ThursdayTransport
# Friday
fridayTransport <- friday %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Driving (church)",
"Driving (drop kids)",
"Driving (drop relatives)",
"Driving (farm)",
"Driving (free time)",
"Driving (gym)",
"Driving (home)",
"Driving (Kava)",
"Driving (order delivery)",
"Driving (pick up kids)",
"Driving (visiting relatives)",
"Driving (work)",
"Public transport (home)",
"Public transport (work)",
"Public transport (school)",
"Walking (home)",
"Walking (work)",
"Walking (Kava)")) %>%
group_by(fixedHMS) %>%
tally
fridayTransport## # A tibble: 14 x 2
## fixedHMS n
## <time> <int>
## 1 05:30 1
## 2 06:30 1
## 3 07:30 2
## 4 08:00 3
## 5 08:30 3
## 6 09:00 1
## 7 10:00 1
## 8 16:30 2
## 9 17:00 4
## 10 17:30 2
## 11 18:00 1
## 12 19:00 4
## 13 19:30 1
## 14 21:30 1#ploting Bar graph
FridayTransport <- ggplot(fridayTransport, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="purple", fill="purple") +
ggtitle("Friday Travelling") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
FridayTransport
# Weekday
weekdayTransport <- newdata %>%
select(fixedHMS, Activity) %>%
filter(Activity != "Sleeping") %>%
filter(Activity != "Rest") %>%
filter(Activity != "Work") %>%
filter(Activity %in% c("Driving (church)",
"Driving (drop kids)",
"Driving (drop relatives)",
"Driving (farm)",
"Driving (free time)",
"Driving (gym)",
"Driving (home)",
"Driving (Kava)",
"Driving (order delivery)",
"Driving (pick up kids)",
"Driving (visiting relatives)",
"Driving (work)",
"Public transport (home)",
"Public transport (work)",
"Public transport (school)",
"Walking (home)",
"Walking (work)",
"Walking (Kava)")) %>%
group_by(fixedHMS) %>%
tally
weekdayTransport## # A tibble: 33 x 2
## fixedHMS n
## <time> <int>
## 1 05:00 2
## 2 05:30 2
## 3 06:30 2
## 4 07:00 4
## 5 07:30 13
## 6 08:00 40
## 7 08:30 20
## 8 09:00 4
## 9 09:30 4
## 10 10:00 5
## # … with 23 more rows#ploting Bar graph
WeekdaysTransport <- ggplot(weekdayTransport, aes(x = fixedHMS, y = n)) + geom_bar(stat = "identity", position = "stack", color="purple", fill="purple") +
ggtitle("Weekdays Travelling") +
xlab("Time ") +
ylab("n") + theme(plot.title = element_text(color="Black", size=11, face="bold.italic", hjust=0.5,lineheight=0.8),
axis.title.x = element_text(color="black", size=10, face="bold"),
axis.title.y = element_text(color="black", size=10, face="bold"),
panel.background=element_rect(fill = "white"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line = element_line(colour = "black", size = 1) +
scale_x_date(labels = date_format("%I:%M %p"))
)
WeekdaysTransport
3.7 Monday Multiple plots
I wanted to put all the graphs in one page
#graphs in one page
library(grid)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(gridBase)BA: I would do this a different way by re-coding and putting all the plots in one plot:
# I would use data.table for this, not because it is the best way but because I know how to use it!
library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:lubridate':
##
## hour, isoweek, mday, minute, month, quarter, second, wday,
## week, yday, year## The following objects are masked from 'package:dplyr':
##
## between, first, last## The following object is masked from 'package:purrr':
##
## transposemondayDT <- data.table::as.data.table(monday)
mondayDT[, ba_act := ifelse(Activity %in% c("Cooking",
"Cooking (business)",
"Cooking (baby)",
"Cooking (lunch)",
"Food prep (business)",
"Lunch",
"Breakfast",
"Dinner",
"Light meal/snack"),
"Cooking", # <- new code
NA)
]
mondayDT[, ba_act := ifelse(Activity %in% c("Tidy up",
"Laundry",
"Showering",
"Showering (kids)",
"Gardening",
"Getting dressed",
"Getting dressed (kids)",
"Prepare kids bag for school",
"Turning off applicances/Lights",
"Turning on appliances/lights",
"Ironing",
"Feeding animals"),
"Tidy up",
ba_act)
]
mondayDT[, ba_act := ifelse(Activity %in% c("Babysitting",
"Charging mobile devices",
"Family devotion",
"Exercise (Jog, walk)",
"Family time (chatting, games etc)",
"Homework (kids)",
"Kids bedtime",
"Listen to music/radio",
"Mobile devices (movies)",
"Mobile devices (games)",
"Mobile devices (work)",
"Mobile devices (browsing, social media)",
"Scripture study",
"Studying",
"Reading",
"Watching movies (TV)"),
"Leisure",
ba_act)
]
mondayDT[, ba_act := ifelse(Activity %in% c("Farm",
"Relatives",
"Work",
"Kava",
"Church",
"Church (social activities)",
"Church (choir practice)",
"Exercise (Jog, walk)",
"Gym",
"Sports",
"School",
"Going out"),
"Away",
ba_act)
]
mondayDT[, ba_act := ifelse(Activity %in% c("Driving (church)",
"Driving (drop kids)",
"Driving (drop relatives)",
"Driving (farm)",
"Driving (free time)",
"Driving (gym)",
"Driving (home)",
"Driving (Kava)",
"Driving (order delivery)",
"Driving (pick up kids)",
"Driving (visiting relatives)",
"Driving (work)",
"Public transport (home)",
"Public transport (work)",
"Public transport (school)",
"Walking (home)",
"Walking (work)",
"Walking (Kava)"),
"Transport",
ba_act)
]
t <- table(mondayDT$Activity, mondayDT$ba_act, useNA = "always")
kableExtra::kable(t, caption = "Test activity coding") %>%
kable_styling()| Away | Cooking | Leisure | Tidy up | Transport | NA | |
|---|---|---|---|---|---|---|
| Breakfast | 0 | 16 | 0 | 0 | 0 | 0 |
| Charging mobile devices | 0 | 0 | 1 | 0 | 0 | 0 |
| Church | 6 | 0 | 0 | 0 | 0 | 0 |
| Church (social activities) | 2 | 0 | 0 | 0 | 0 | 0 |
| Cooking | 0 | 31 | 0 | 0 | 0 | 0 |
| Cooking (baby) | 0 | 1 | 0 | 0 | 0 | 0 |
| Cooking (business) | 0 | 3 | 0 | 0 | 0 | 0 |
| Cooking (lunch) | 0 | 1 | 0 | 0 | 0 | 0 |
| Dinner | 0 | 36 | 0 | 0 | 0 | 0 |
| Driving (church) | 0 | 0 | 0 | 0 | 1 | 0 |
| Driving (drop kids) | 0 | 0 | 0 | 0 | 2 | 0 |
| Driving (drop relatives) | 0 | 0 | 0 | 0 | 1 | 0 |
| Driving (farm) | 0 | 0 | 0 | 0 | 1 | 0 |
| Driving (gym) | 0 | 0 | 0 | 0 | 4 | 0 |
| Driving (home) | 0 | 0 | 0 | 0 | 23 | 0 |
| Driving (pick up kids) | 0 | 0 | 0 | 0 | 9 | 0 |
| Driving (visiting relatives) | 0 | 0 | 0 | 0 | 10 | 0 |
| Driving (work) | 0 | 0 | 0 | 0 | 21 | 0 |
| Exercise (Jog, walk) | 4 | 0 | 0 | 0 | 0 | 0 |
| Family devotion | 0 | 0 | 12 | 0 | 0 | 0 |
| Family time (chatting, games etc) | 0 | 0 | 4 | 0 | 0 | 0 |
| Farm | 2 | 0 | 0 | 0 | 0 | 0 |
| Feeding animals | 0 | 0 | 0 | 3 | 0 | 0 |
| Getting dressed | 0 | 0 | 0 | 17 | 0 | 0 |
| Getting dressed (kids) | 0 | 0 | 0 | 5 | 0 | 0 |
| Gym | 14 | 0 | 0 | 0 | 0 | 0 |
| Home | 0 | 0 | 0 | 0 | 0 | 24 |
| Homework (kids) | 0 | 0 | 23 | 0 | 0 | 0 |
| Ironing | 0 | 0 | 0 | 7 | 0 | 0 |
| Kids bedtime | 0 | 0 | 6 | 0 | 0 | 0 |
| Laundry | 0 | 0 | 0 | 2 | 0 | 0 |
| Light meal/snack | 0 | 1 | 0 | 0 | 0 | 0 |
| Listen to music/radio | 0 | 0 | 2 | 0 | 0 | 0 |
| Mobile device (movies) | 0 | 0 | 0 | 0 | 0 | 26 |
| Mobile devices (browsing, social media) | 0 | 0 | 18 | 0 | 0 | 0 |
| Mobile devices (work) | 0 | 0 | 12 | 0 | 0 | 0 |
| Prepare kids bag for school | 0 | 0 | 0 | 1 | 0 | 0 |
| Public transport (home) | 0 | 0 | 0 | 0 | 3 | 0 |
| Public transport (work) | 0 | 0 | 0 | 0 | 2 | 0 |
| Reading | 0 | 0 | 10 | 0 | 0 | 0 |
| Rest | 0 | 0 | 0 | 0 | 0 | 18 |
| Showering | 0 | 0 | 0 | 32 | 0 | 0 |
| Sleeping | 0 | 0 | 0 | 0 | 0 | 467 |
| Tidy up | 0 | 0 | 0 | 13 | 0 | 0 |
| Turning off appliances/Lights | 0 | 0 | 0 | 0 | 0 | 2 |
| Walking (home) | 0 | 0 | 0 | 0 | 2 | 0 |
| Walking (work) | 0 | 0 | 0 | 0 | 4 | 0 |
| Watching movies (TV) | 0 | 0 | 49 | 0 | 0 | 0 |
| Woke up | 0 | 0 | 0 | 0 | 0 | 32 |
| Work | 550 | 0 | 0 | 0 | 0 | 0 |
| NA | 0 | 0 | 0 | 0 | 0 | 0 |
# make a summary table for the plot
plotDT <- mondayDT[!is.na(ba_act), # excludes all NAs - see table to check coding
.(count = .N), keyby = .(fixedHMS, ba_act)]
ggplot2::ggplot(plotDT, aes(x = fixedHMS, y = count, fill = ba_act)) +
geom_col() +
scale_fill_discrete(name="Activity (filtered)")
4 All weekdays plotted using facets
Figure 4.1 shows counts for all days. We can see a few days are unknown. Also we have higher counts on Mondays.
# use the all days table and the same coding method but across all days
allDaysDT <- data.table::as.data.table(newdata)
allDaysDT[, dayF := factor(Day,
levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday") # sets the order for the plot
)
]
allDaysDT[, ba_act := ifelse(Activity %in% c("Cooking",
"Cooking (business)",
"Cooking (baby)",
"Cooking (lunch)",
"Food prep (business)",
"Lunch",
"Breakfast",
"Dinner",
"Light meal/snack"),
"Cooking", # <- new code
NA)
]
allDaysDT[, ba_act := ifelse(Activity %in% c("Tidy up",
"Laundry",
"Showering",
"Showering (kids)",
"Gardening",
"Getting dressed",
"Getting dressed (kids)",
"Prepare kids bag for school",
"Turning off applicances/Lights",
"Turning on appliances/lights",
"Ironing",
"Feeding animals"),
"Tidy up",
ba_act)
]
allDaysDT[, ba_act := ifelse(Activity %in% c("Babysitting",
"Charging mobile devices",
"Family devotion",
"Exercise (Jog, walk)",
"Family time (chatting, games etc)",
"Homework (kids)",
"Kids bedtime",
"Listen to music/radio",
"Mobile devices (movies)",
"Mobile devices (games)",
"Mobile devices (work)",
"Mobile devices (browsing, social media)",
"Scripture study",
"Studying",
"Reading",
"Watching movies (TV)"),
"Leisure",
ba_act)
]
allDaysDT[, ba_act := ifelse(Activity %in% c("Farm",
"Relatives",
"Work",
"Kava",
"Church",
"Church (social activities)",
"Church (choir practice)",
"Exercise (Jog, walk)",
"Gym",
"Sports",
"School",
"Going out"),
"Away",
ba_act)
]
allDaysDT[, ba_act := ifelse(Activity %in% c("Driving (church)",
"Driving (drop kids)",
"Driving (drop relatives)",
"Driving (farm)",
"Driving (free time)",
"Driving (gym)",
"Driving (home)",
"Driving (Kava)",
"Driving (order delivery)",
"Driving (pick up kids)",
"Driving (visiting relatives)",
"Driving (work)",
"Public transport (home)",
"Public transport (work)",
"Public transport (school)",
"Walking (home)",
"Walking (work)",
"Walking (Kava)"),
"Transport",
ba_act)
]
# t <- table(allDaysDT$Activity, allDaysDT$ba_act, useNA = "always")
#
# kableExtra::kable(t, caption = "Test activity coding (all days)") %>%
# kable_styling()
# make a summary table for the plot
plotDT <- allDaysDT[!is.na(ba_act), # excludes all NAs - see table to check coding
.(count = .N),
keyby = .(fixedHMS, ba_act, dayF)] # use new day factor
ggplot2::ggplot(plotDT, aes(x = fixedHMS, y = count, fill = ba_act)) +
geom_col() +
facet_grid(dayF ~ .) +
scale_fill_discrete(name="Activity (filtered)")
Figure 4.1: All days
t <- with(allDaysDT[!is.na(ba_act)], # select the ones we re-coded only
table(ba_act, dayF))
kableExtra::kable(t, caption = "Number of recorded coded acts by day (non recoded actas e.g. sleeping ignored)") %>%
kable_styling()| Monday | Tuesday | Wednesday | Thursday | Friday | |
|---|---|---|---|---|---|
| Away | 578 | 315 | 257 | 141 | 253 |
| Cooking | 89 | 56 | 51 | 26 | 36 |
| Leisure | 137 | 114 | 71 | 29 | 89 |
| Tidy up | 80 | 55 | 57 | 31 | 44 |
| Transport | 83 | 57 | 36 | 19 | 27 |
t <- allDaysDT[, .(nRespondents = uniqueN(`Household ID`)), keyby = .(dayF)]
kableExtra::kable(t, caption = "Number of unique respondents by day (all acts)") %>%
kable_styling()| dayF | nRespondents |
|---|---|
| Monday | 32 |
| Tuesday | 19 |
| Wednesday | 15 |
| Thursday | 8 |
| Friday | 15 |
There are a lot of Mondays in this data…
How many are there per village?
dt <- allDaysDT[, .(n = uniqueN(`Household ID`)),
keyby = .(Village)]
kableExtra::kable(dt, caption = "Number of respondents by village")| Village | n |
|---|---|
| Fanga | 2 |
| Fangaloto | 5 |
| Fasi | 8 |
| Fuaamotu | 8 |
| Ha’ateiho | 3 |
| Halaleva | 1 |
| Halaovave | 1 |
| Havelu | 6 |
| Hofoa | 1 |
| Houma | 1 |
| Houmakelikao | 2 |
| Kolofo’ou | 1 |
| Kolomotu’a | 7 |
| Lakepa | 2 |
| Lapaha | 2 |
| Liahona | 5 |
| Longolongo | 1 |
| Ma’ufanga | 3 |
| Mailetaha | 1 |
| Malapo | 1 |
| Matahau | 1 |
| Mataika | 1 |
| Navutoka | 1 |
| Ngele’ia | 1 |
| Niutoua | 1 |
| Nukunuku | 1 |
| Pahu | 1 |
| Popua | 4 |
| Puke | 1 |
| Sopu | 1 |
| Tatakamotonga | 1 |
| Te’ekiu | 1 |
| Tofoa | 1 |
| Tokomololo | 1 |
| Tuatakilangi | 2 |
| Umusi | 1 |
| Vaini | 6 |
| Vaololoa | 1 |
| Veitongo | 1 |
Ben, so far with the graphs not convinced its good enough, how can i use “proportion” instead of “count” in the Y axis? Given the uneven distribution of respondents per day