ANLY 512 51 Late Fall Final Project Quantified Self Hanwen Zhang

output: flexdashboard::flex_dashboard: orientation: columns storyboard: true vertical_layout: fill social: menu source_code: embed —

library(readxl)
expense<- read_excel("C:/Users/supre/Desktop/Daily Expense.xlsx")
sleep <- read_excel("C:/Users/supre/Desktop/sleep.xlsx")

Develop a visualiation dashboard based on a series of data about my own life.

Questions

• Which area did i spend most my money?
• When did i spend most my money?
• Is there any different expenditure pattern on each day of the week?
• Did i walk more than 5 miles per day?
• Which day of the week did i walk less than others?
• Did i sleep more than 6 hours per day?
• Is there any relationship between sleeping duration and steps?

Categories (expenditure)

X <- ggplot(expense, aes(x = categories, y = categoryamount)) + 
  geom_bar(stat="identity") +
  ggtitle("Categories (expenditure)") +
  scale_y_reverse() +  
  theme_gdocs()
ggplotly(X)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

## Warning: Removed 14 rows containing missing values (position_stack).

---

Where did i spend most my money?

• According to the above graph, first, the most significant expense for me is from my bank checking account, since I use it for some cryptocurrency investments in January. Then, because I have a business trip from 01/08 to 01/15 in Los Angeles and San Francisco, so the secondary largest expense is hotel cost, although will be reimbursed later.

Daily (expenditure)

Y <- ggplot(expense, aes(x = Date, y = amount, color=categories)) + 
  geom_bar(stat="identity") +
  ggtitle("Daily (expenditure)") +
  scale_y_reverse() +  
  theme_gdocs()
ggplotly(Y)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

---

When did i spend most my money?

• Through the graph generated above, the first peak time would be around 01/05, since I do some crypocurrency investment at that time. Plus, from 01/08 to 01/15, because of my business trip, I spent much on hotel, flight, transportation, etc.

Day of the week (expenditure)

expense$wday <- weekdays(as.Date(expense$Date,'%m/%d/%Y'))

## Warning in as.POSIXlt.POSIXct(x, tz = tz): unknown timezone '%m/%d/%Y'

Z <- ggplot(expense, aes(x = factor(expense$wday, levels= c("Sunday", "Monday", 
    "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")), y = amount, color=categories)) + 
  stat_summary(fun.y="mean",aes(shape="mean"), geom="point", color="red") +
  scale_shape_manual("", values=c("mean"="x")) +
  geom_bar(stat="identity") +
  ggtitle("Day of the week (expenditure)") +
  scale_y_reverse() +  
  theme_gdocs()
ggplotly(Z)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

## Warning in if (nchar(axisTitleText) > 0) {: the condition has length > 1
## and only the first element will be used

---

Was there any different expenditure pattern on each day of the week?

• Through the above graph, we can see I always have a larger expense on Friday, since after a busy and tired week, I usually have some drinks or films with my friends to relax on Friday.

Daily (steps)

XX <- ggplot(sleep, aes(x = date, y = steps)) + 
  geom_point(color="yellow") + 
  ggtitle("Daily (steps)") +
  theme_gdocs()
ggplotly(XX)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

---

Did i walk more than 5 miles per day?

• According to the graph created above, we can conclude most of my daily walk ranges from 3 to 5 miles. I still walk around 6 miles on three days, first, on 01/05, I hang out with my friends after work; then on 01/14, after my business trip in San Francisco, I had a road trip through part of Route 1 there and walked a lot; then on 01/25, I walked to a business meeting and was busy with all kinds of documents preparation.

Day of the week (steps)

sleep$wday <- weekdays(as.Date(sleep$date,'%m/%d/%Y'))

## Warning in as.POSIXlt.POSIXct(x, tz = tz): unknown timezone '%m/%d/%Y'

YY <- ggplot(sleep, aes(x = factor(sleep$wday, levels= c("Sunday", "Monday", 
    "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")), y = steps)) + 
  geom_boxplot(color="grey50",fill="lightgreen")+
  stat_summary(fun.y="mean", aes(shape="mean"), geom="point", color="blue") +
  scale_shape_manual("", values=c("mean"="x")) +
  xlab('day of the week') +
  ggtitle("Day of the week (steps)") +
  coord_flip() +
  theme_gdocs()
ggplotly(YY)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

---

Which day of the week did i walk less?

• As the graph shown above, I usually walk less on weekends.

Sleep (activity)

durationhours<-sleep$hours
ZZ <- ggplot(sleep, aes(x = factor(sleep$wday, levels= c("Sunday", "Monday", 
    "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")), y = durationhours)) + 
  stat_summary(fun.y="mean", aes(shape="mean"), geom="point", color="blue") +
  scale_shape_manual("", values=c("mean"="x")) +
  xlab('day of the week') +
  ggtitle("Sleep (activity)") +
  theme_gdocs()
ggplotly(ZZ)

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

---

Did i sleep more than 6 hours per day?

• As this graph shows, I ususally sleep more than 6 hours on Thursday and Sunday. The reason for Thursday I guess is because the next day is Friday, the last work day of a week, I feel more relaxed and can sleep more hours very well.

interactive graph between steps and sleeping duration (activity)

p <-plot_ly(x = sleep$date, y = sleep$durationhours, type="scatter", mode="markers+lines", name="sleeping") %>%
   add_trace(x = sleep$date, y=sleep$step, type="scatter", mode="markers+lines", name="step", yaxis="y2") %>%
  layout(
    xaxis = list(title="date"),
    yaxis = list(title="hours"), 
    yaxis2 = list(overlaying = "y", side = "right",title="steps"),
    title = "How long sleeping V.S. How many steps - Interactive Graph"
    )

## Warning: Unknown or uninitialised column: 'durationhours'.

## Warning: Unknown or uninitialised column: 'step'.

ggplotly(p)

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Is there any relationship betwwen sleeping duration and steps?

• Through the graph, we can see the more miles I had on one day, the more hours I slept on that day.