Strava.r

# Basic script to open the activities excel file and mess with it.
library(xlsx)
library(broom)
library(psych)
library("ggplot2")

# All data
data_types <- c('character', 'numeric', 'numeric', 'numeric', 'numeric', 'numeric', 'numeric', 'character', 
                'character', 'character', 'numeric', 'numeric', 'numeric', 'numeric', 'numeric', 'numeric')

strava_data <- read.xlsx("./all_data.xlsx", 1, colClasses = data_types, header=TRUE)

# Only ride data
ride_data <- subset(strava_data, (type == 'VirtualRide' | type == 'Ride') & average_watts > 0)

perf_analysis <- function(field_label, data, field, bins) {
  gg <- ggplot(data, aes(field)) + geom_histogram(bins=bins) + 
     stat_function(fun=dnorm, args=list(mean = mean(field, na.rm=TRUE), sd = sd(field, na.rm=TRUE)), 
                   color='black', size=1) +
     ggtitle(field_label) + xlab(field_label) + ylab('Count')
  gg <- ggplot(data, aes(field)) + geom_histogram()
  print(gg)
  qq <- qplot(sample=field)
  print(qq)
   
  d <- describe(field)
  print(d)
   
  kurtosis <- d$kurtosis
  if (round(kurtosis, 2) > 0) {
    print(paste('Kurtosis is', round(kurtosis, 2),  '. Since it\'s greater than zero, there may be',
               'a heavily-tailed distribution. Ideally, this should be zero.'))
  } else if (round(kurtosis, 2) < 0) {
    print(paste('Kurtosis is', round(kurtosis, 2),  '. Since it\'s less than zero, there may be',
                'a light-tailed distribution. Ideally, this should be zero.'))
  } else {
    print('The Kurtosis is 0, which indicates a normal distribution.')
  }
   
  skew <- d$skew
  if (round(skew, 2) > 0) {
    print(paste('Skew is', round(skew, 2),  '. Since it\'s greater than zero, there may be ',
                 'a pile up of scores on the left of the distribution. Ideally, this should be zero.'))
  } else if (round(skew, 2) < 0) {
    print(paste('Skew is', round(skew, 2),  '. Since it\'s less than zero, there may be ',
                 'a pile up of scores on the right of the distribution. Ideally, this should be zero.'))
  } else {
    print('The Skew is 0, which indicates a normal distribution.')
  }
}

a <- perf_analysis('Average Speed (MPH)', ride_data, ride_data$average_speed_mph, 15)
a <- perf_analysis('Distance (Miles)', ride_data, ride_data$distance_mi, 15)
a <- perf_analysis('Moving Time (Minutes)', ride_data, ride_data$moving_time, 15)
a <- perf_analysis('Elevation Gain (Feet)', ride_data, ride_data$elevation_gain_ft, 15)
a <- perf_analysis('Average Power (Watts)', ride_data, ride_data$average_watts, 15)
a <- perf_analysis('Average Heart Rate', ride_data, ride_data$average_heartrate, 15)
 

ggplot(ride_data, aes(x = distance_mi, y = average_speed_mph), color = factor(gear_id)) + 
      geom_point()
 
# boxplot with bikes
ggplot(data = ride_data, aes(x = factor(gear_id), y = average_speed_mph)) +
        geom_boxplot()
 
# Simple lm model - how distance affects speed
lm_speed_dist_gear_id <- lm(average_speed_mph ~ distance_mi + factor(gear_id), data = ride_data)
summary(lm_speed_dist_gear_id)
lm_speed_dist_gear_id
 
# try parallel slopes
# Augment the model
augmented_bikes <- augment(lm_speed_dist_gear_id)
# scatterplot, with color
lm_plot <- ggplot(augmented_bikes, aes(x = distance_mi, y = average_speed_mph, color = factor.gear_id.)) + 
 geom_point()
# single call to geom_line()
lm_plot + geom_line(aes(y = .fitted))
lm_plot.Show()