Running Through the Data: Half Marathon Goal by RunTracker

In the later half of 2020, I set a new goal for myself: run 13.1 miles by the end of the year. Earlier in the year I had completed the couch to 5k program and later set the goal to improve my time to under 30 minutes. Given the extra time at home thanks to a Global Pandemic, I set my sights on the half marathon distance. Since I was already familiar with the RunTracker app, I decided to stick with that and used their “Half Marathon Goal” training plan.

The Runtracker app, made by the Fitness 22 company, features a series of running plans tailored to individuals’ current fitness levels and goals. The “Half Marathon Goal” running plan consisted of four runs per week for a total of twelve weeks, with a consistent structure throughout most of the program. After a series of base runs in the first week, the next ten weeks featured a base run on Tuesdays, segments on Thursdays, intervals on Fridays, and long run on Sundays. Duration of workouts increase steadily over the course of the first ten weeks before tapering in the final two weeks of the program.

My experience with this running plan was great once I got used to the structure. Previously, the most I had run was three days a week, while this program requires four. This means there would be runs on consecutive days, which I was not used to. Having just finished a training plan geared towards speed work, I quickly learned I would need to slow down if I was going to keep from getting inured. Once I got settled into the format, mileage built progressively and speed eventually followed. By the end of the twelve-week program, I was able to confidently run 13.1 miles using my usual training route, which coincidentally looked like a shoe:

Distance & Pace

Since my goal was to complete a half marathon, the primary variable of interest was obviously distance. Like most runners, I also tend to focus on times, so average running pace served as the secondary variable of interest. Distances ran throughout the training program ranged from 2.14 to 13.12 miles per run, with a mean of 4.85 miles per run. Running paces ranged from 5.16 to 6.1 miles per run (11:38 to 9:50 min/mile ), with a mean of 5.54 miles per hour ( 10:50 min / mile). The distributions of my runs by distance and speed for this program can be seen in the density plots below:

Comparing Workouts

When taking a closer look at these distributions by workout type, we can see some clear patterns in the data. Distances for base runs, interval sessions, and segments, remained relatively close to one another, ranging from 2.14 to 6.02 miles per run. The long runs on Sundays though lived up to their name, ranging from 5.7 to 13.12, with an average of 9.16. Running pace for all workout types were somewhat consistent between groups, with each workout type averaging between 5.5 and 5.6 miles per hour. Distributions by workout type for distance and pace can be seen in the box plots below:

Training Progress

Given that there is an ordered component to training, we can look at these data linearly (i.e. regression). Below are scatter plots of distances covered and running speeds over the course of the 46 training runs in the program. We see a slightly positive association with trainings volume (mileage), while intensity (pace) remained relatable stable throughout the training program. When you take a closer look at the distance plot, we can see how the majority of volume is gained in training through the long runs on weekends, which is typical of most long distance training programs:

Cadence & Heart Rate

Two important considerations for runners are heart rate and cadence. When runners let their heart rates get too high, they tire much quicker. So, distance runners constantly work to keep their heart rate down while still running quickly. This can be aided by increasing cadence to the rate of approximately 180 beats per minute. Increasing cadence allows runners to develop better efficiency in their technique – typically by shortening the stride – which over time can lead to a lower heart rate. This translates into better performance with respect to both speed and endurance. In the plot below we can see that both cadence and heart rate are positively associated with running pace, with a clear interaction between these two variables as speed increases, represented by the slopes crossing one another:

Final Thoughts

The “Half Marathon Goal” plan on the RunTracker app is geared towards regular runners who are ready to tackle the 13.1 distance. The training structure consists of three runs per week with a base run, a session of mile repeats, an interval session, and one long run on the weekend. The variety of workouts in the program are designed primarily to build the strength and endurance to run a half marathon, with some speed work included to build anaerobic capacity as well. For anyone who has been running for a while and is ready to tackle longer distances, this program could be an excellent option.

Below are some links related on running a first half marathon, along with the raw data and code used to create the charts and analysis.

Thanks for reading!

Resources & Code

# FRONT MATTTER

### Note: The HM_1.xlxs file will need to be converted to HM_1.csv to read in correctly. Also, all packages can be downloaded using the install.packages() function. This only needs to be done once before loading. 

## clean up (this clears out the previous environment)
ls()

## Load Packages 
library(tidyverse)
library(wordcloud2)
library(mosaic)
library(readxl)
library(hrbrthemes)
library(viridis)

## Likert Data Packages
library(psych)
library(FSA)
library(lattice)
library(boot)
library(likert)

## Grid Extra for Multiplots
library("gridExtra")

## Multiple plot function (just copy paste code)

multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
  library(grid)

  # Make a list from the ... arguments and plotlist
  plots <- c(list(...), plotlist)

  numPlots = length(plots)

  # If layout is NULL, then use 'cols' to determine layout
  if (is.null(layout)) {
    # Make the panel
    # ncol: Number of columns of plots
    # nrow: Number of rows needed, calculated from # of cols
    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
                    ncol = cols, nrow = ceiling(numPlots/cols))
  }

 if (numPlots==1) {
    print(plots[[1]])

  } else {
    # Set up the page
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))

    # Make each plot, in the correct location
    for (i in 1:numPlots) {
      # Get the i,j matrix positions of the regions that contain this subplot
      matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))

      print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
                                      layout.pos.col = matchidx$col))
    }
  }
}


# HALF MARATHON GOAL by RUNTRACKER

## Import data from CSV, no factors

HM_1 <- read.csv("HM_1.csv", stringsAsFactors = FALSE)

HM_1 <- HM_1  %>%
  na.omit()

HM_1 


## Plot 1

p1 <- ggplot(HM_1 , aes(x=Distance)) + 
  geom_density(color="Pink", fill="Pink") + labs( x ="Distance (Miles)", y = "", title = "Running Distances",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
  theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    plot.caption = element_text(hjust = 1, face = "italic"), 
    axis.text.y=element_blank(),
    axis.ticks.y=element_blank(),
    panel.background = element_blank())


## Plot 2

p2 <- ggplot(HM_1, aes(x=Pace_MPH)) + 
  geom_density(color="light blue", fill="light blue") + 
  labs( x ="Speed (Miles per Hour)", y = "", title = "Running Pace",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
  theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    plot.caption = element_text(hjust = 1, face = "italic"), 
    axis.text.y=element_blank(),
    axis.ticks.y=element_blank(),
    panel.background = element_blank())


## Combine plots using multi-plot function:

multiplot( p1, p2, cols=1)

## Plot 3

p3 <- ggplot(HM_1 , aes(x= Session, y= Distance)) + geom_point(color="Black") +  geom_smooth(method=lm , color="Red", se=TRUE) + labs(x ="Training Session", y = "Distance (Miles)", title = "Running Distance",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
   theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"),
    panel.background = element_blank())

## Plot 4

p4<- ggplot(HM_1 , aes(x=Session, y= Pace_MPH)) + geom_point(color="Black") +  geom_smooth(method=lm , color="Blue", se=TRUE) + labs( x ="Training Session", y = "Speed (Miles per Hour)", title = "Running Pace",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"),
    panel.background = element_blank())

## Combine plots using multi-plot function
multiplot( p3, p4, cols=1)

## Summary Statistics of Distance
favstats(HM_1$Distance)

## Summary Statistics of Pace
favstats(HM_1$Pace_MPH)



## Pearson Product Correlation of Distance over Time (session)
cor.test(HM_1$Session, HM_1$Distance, method = "pearson")

## Pearson Product Correlation of Pace over Time (session)
cor.test(HM_1$Session, HM_1$Pace_MPH, method = "pearson")


## Plot
p5 <-  HM_1 %>%
  filter(Workout != "Race") %>%
  ggplot( aes(x=Workout, y= Distance, fill=Workout)) +
  geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6) +
    geom_jitter(color="Black", size=0.4, alpha=0.9) + 
  labs( x ="Workout Type", y = "Distance (Miles)", title = "Comparing Distances",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
  theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"),
    panel.background = element_blank(),
    legend.position = "none") +
    scale_fill_brewer(palette="Reds")
  
## Plot
p6  <-  HM_1 %>%
  filter(Workout != "Race") %>%
  ggplot( aes(x=Workout, y= Pace_MPH, fill=Workout)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6) +
    geom_jitter(color="Black", size=0.4, alpha=0.9) + 
  labs( x ="Workout Type", y = "Speed (Miles per Hour)", title = "Comparing Paces",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
  theme(plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"),
    panel.background = element_blank(),
    legend.position = "none") +
    scale_fill_brewer(palette="Blues")

## Combine plots using multi-plot function
multiplot( p5, p6, cols=2)

## Plot 7

p7 <- ggplot(HM_1 , aes(x= Cadence, y= Distance)) + geom_point(color="Black") +  geom_smooth(method=lm , color="Red", se=TRUE) + labs(x ="Average Running Cadence", y = "Distance (Miles)", title = "Cadence by Distance",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
   theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"),
    panel.background = element_blank())


## Plot 8

p8<- ggplot(HM_1 , aes(x=Cadence, y= Pace_MPH)) + geom_point(color="Black") +  geom_smooth(method=lm , color="Green", se=TRUE) + labs( x ="Average Running Cadence", y = "Speed (Miles per Hour)", title = "Cadence by Pace",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"),
    panel.background = element_blank())


## Plot 9

p9 <- ggplot(HM_1 , aes(x= Avg_Heart_Rate, y= Distance)) + geom_point(color="Black") +  geom_smooth(method=lm , color="Blue", se=TRUE) + labs(x ="Average Heart Rate", y = "Distance (Miles)", title = "Heart Rate by Distance",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
   theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"),
    panel.background = element_blank())

## Plot 10

p10<- ggplot(HM_1 , aes(x=Avg_Heart_Rate, y= Pace_MPH)) + geom_point(color="Black") +  geom_smooth(method=lm , color="Purple", se=TRUE) + labs( x ="Average Heart Rate", y = "Speed (Miles per Hour)", title = "Heart Rate by Pace",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"),
    panel.background = element_blank())

## Combine plots using multi-plot function
multiplot( p7, p8, p9, p10, cols=2)

## Pivot data from wide to long for next chart

HM_1A <- gather(HM_1, Measurement, BPM, Cadence, Avg_Heart_Rate)

HM_1A

## Plot 11

p11<- ggplot(HM_1A , aes(x=Pace_MPH, y= BPM, Color= Measurement)) +
     geom_point() +
     geom_smooth(method = "lm", alpha = .15, aes(fill = Measurement)) + labs(x ="Average Pace (Miles per Hour)", y = "Beats per Minute", title = "Heart Rate & Cadence by Pace",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"),
    panel.background = element_blank())

p11

## Plot 12

p12<- ggplot(HM_1A , aes(x=Distance, y= BPM, Color= Measurement)) +
     geom_point() +
     geom_smooth(method = "lm", alpha = .15, aes(fill = Measurement)) + labs( x ="Average Distance in Miles", y = "Beats per Minute", title = "Heart Rate & Cadence by Distance",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"),
    panel.background = element_blank())

p12

# Combine plots using multi-plot function
multiplot( p11, p12, cols=1)



## Plot 13
p13 <- ggplot(HM_1A , aes(x = Pace_MPH, y = BPM, color = Measurement) ) +
     geom_point() +
     geom_smooth(method = "lm", alpha = .15, aes(fill = Measurement)) + labs(x ="Average Pace (Miles per Hour)", y = "Beats per Minute", title = "Heart Rate & Cadence by Pace",  subtitle = "Half Marathon Goal by Runtracker", caption = "Data source: TheDataRunner.com") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 20, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12), 
    plot.caption = element_text(hjust = 1, face = "italic"))

4 thoughts on “Running Through the Data: Half Marathon Goal by RunTracker”

  1. Pingback: Running Through the Data: Tempo Run: 5K by Runtracker – The Data Runner
  2. Pingback: From Couch to Half Marathon – The Data Runner
  3. Pingback: Up & Running with R – The Data Runner
  4. Pingback: FROM COUCH TO 50K – The Data Runner

Leave a Reply

Fill in your details below or click an icon to log in:

Gravatar
WordPress.com Logo

You are commenting using your WordPress.com account. ( Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. ( Log Out /  Change )

Cancel

Connecting to %s

%d bloggers like this: