quarterly_goal_progress_report-4-22-17.md

Quarterly Goal Progress Report

Mikey Saugstad
April 22, 2017

Intro

This document assesses the progress that I am making towards my quarterly goals for the 13-week quarter 2017-3-5 to 2017-6-3. The goals who's progress can be measured using the data available are those on my frequency of exercise, eating raw fruit/veggies, and journaling.

Data Prep

Reading Data

#
# READING SLEEP DATA
#

sleep.data <- read.csv("data/sleep_data-subset.csv")


#
# READING SELF-RECORDED DATA
#

# If having trouble with loading xlsx or rJava, try running rstudio as root
#library(xlsx)

# The read.xlsx function has not been very good at coercing the data when the
# correct types are passed in (most of them are booleans), so I coerce almost
# everything to character, and then do some extra work when cleaning to get
# it all to the correct types.
classes <- c("Date", replicate(47, "character"))
self.data <- read.csv(file = "data/self_data-subset.csv", colClasses = classes)

Cleaning Data

#
# CLEANING SLEEP DATA
#

# Convert to correct data types
sleep.data$End <- as.Date(sleep.data$End, format = "%F %T")
sleep.data$Sleep.quality <- as.numeric(sub("%","",sleep.data$Sleep.quality))
sleep.data$Time.in.bed <- as.difftime(as.character(sleep.data$Time.in.bed), format = "%R")

# Use the date when I woke up to figure out which night I went to bed; I may
# go to sleep after midnight, so start date may not always give the correct
# date, but I always wake up past midnight.
sleep.data$Date <- sleep.data$End - 1


#
# CLEANING SELF-RECORDED DATA
#

# Optionally combine the drank coffee and took caffeine supplement
#mydata$coffee <- as.logical(as.numeric(mydata$coffee)) | as.logical(as.numeric(mydata$caffeine))

# Coerce remaining cols to correct types.
# TODO figured out a way to do this using col names instead of indices
binary_cols <- c(3:14,16:20,22:28,30:39,41:48)
numeric_cols <- c(2,15,29)
factor_cols <- c(21,40)
for(i in binary_cols){self.data[,i] <- as.factor(as.logical(as.numeric(self.data[,i])))}
for(i in numeric_cols){self.data[,i] <- as.numeric(self.data[,i])}
for(i in factor_cols){self.data[,i] <- as.factor(self.data[,i])}

Merging Data

#
# MERGING DATASETS
#

my.data <- merge(self.data, sleep.data, by.x = "Date", by.y = "Date", all = TRUE)

Subsetting Data

# subsetting for the current quarter
start.date <- as.Date("2017-3-5")
mid.date <- as.Date("2017-4-22")
end.date <- as.Date("2017-6-3")
current.date <- my.data$Date[nrow(my.data)]
#date.to.use <- mid.date
date.to.use <- current.date
#date.to.use <- end.date
weeks.passed <- (as.numeric(date.to.use - start.date) + 1.0) / 7.0
weeks.in.quart <- 13.0

quarter.data <- my.data[which(my.data$Date == start.date):which(my.data$Date == date.to.use),]

Progress Report

# How many days per week have I been eating raw fruit on average (goal is >= 5)?
expected.fruit.freq <- 5.0
fruit.count <- unname(summary(quarter.data$fruit)[2])
fruit.count/weeks.passed

## [1] 7

# Am I currently on track?
fruit.on.track <- fruit.count/weeks.passed >= expected.fruit.freq
fruit.on.track

## [1] TRUE

# If not on track, show frequency needed to meet goal
if(!fruit.on.track && date.to.use < end.date)
{
  (expected.fruit.freq * weeks.in.quart - fruit.count) /
    (weeks.in.quart - weeks.passed)
}

# How many days per week have I been exercising on average (goal is >= 3)?
expected.workout.freq <-3.0
workout.count <- unname(summary(quarter.data$ex)[2])
workout.count/weeks.passed

## [1] 1.272727

# Am I currently on track?
workouts.on.track <- workout.count/weeks.passed >= expected.workout.freq
workouts.on.track

## [1] FALSE

# If not on track, show frequency needed to meet goal
if(!workouts.on.track && date.to.use < end.date)
{
  (expected.workout.freq * weeks.in.quart - workout.count) /
    (weeks.in.quart - weeks.passed)
}

## [1] 3.2375

# How many days per week have I been journaling on average (goal is >= 5)?
expected.journal.freq <- 5.0
journal.count <- unname(summary(quarter.data$jour)[2])
journal.count/weeks.passed

## [1] 2.545455

# Am I currently on track?
journal.on.track <- journal.count/weeks.passed >= expected.journal.freq
journal.on.track

## [1] FALSE

# If not on track, show frequency needed to meet goal
if(!journal.on.track && date.to.use < end.date)
{
  (expected.journal.freq * weeks.in.quart - journal.count) /
    (weeks.in.quart - weeks.passed)
}

## [1] 5.3375