As 2021 draws to a close, it’s time to check back in on a previous post. As a recap, Garmin Connect offered four challenges to run 3 x 505 km and 1 x 505 km, one in each of the four quarters of 2021. Completing all four would mean running 2021 km in 2021.

I used an R script to check my progress throughout the year (available here). Each quarter gave a set of results, the final set for the whole of 2021 is shown here.

I hit the overall target on 5th December, with the fourth and final challenge being met a few days later. I had got ahead of target on challenges 2 and 3, so it took some more kms to complete challenge 4 even though the overall goal was done.

The code

## The aim of this script is to load and process CSV data from the Garmin Connect website.
## It will check whether you are on track to meet a running goal, i.e. n km between date1 and date2.
## This script will load all csv files in Data/ (in current wd) and filter for Running (and Treadmill Running)
## Place one or more Garmin CSV outputs into the Data folder for inclusion. Dates for activities can be overlapping
## duplicates are dealt with, so you can just keep adding csvs with the latest data and use the script again.


require(ggplot2)
require(hms)

## Setup preferred directory structure in wd
ifelse(!dir.exists("Data"), dir.create("Data"), "Folder exists already")
ifelse(!dir.exists("Output"), dir.create("Output"), "Folder exists already")
ifelse(!dir.exists("Output/Data"), dir.create("Output/Data"), "Folder exists already")
ifelse(!dir.exists("Output/Plots"), dir.create("Output/Plots"), "Folder exists already")
ifelse(!dir.exists("Script"), dir.create("Script"), "Folder exists already")

## functions

compare2target <- function(activity,fromStr,toStr,df) {
  # filter for activity
  df_window <- subset(df,grepl(tolower(activity),tolower(df$Activity.Type)))
  # activities within the window
  fromDate <- as.Date(fromStr)
  toDate <- as.Date(toStr)
  df_window <- subset(df_window, as.Date(df_window$Date) >= fromDate & as.Date(df_window$Date) <= toDate)
  # put them in order
  df_window <- df_window[order(as.numeric(df_window$Date)),]
  df_window$Cumulative.Distance <- cumsum(df_window$Distance)
  
  return(df_window)
}

maketarget <- function(fromStr,toStr,km) {
  temp <- seq(as.Date(fromStr), as.Date(toStr), by="days")
  cumdist <- seq(km / length(temp), km, by = km / length(temp))
  df <- data.frame(Date = as.POSIXct(temp),
                   Cumulative.Distance = cumdist)

  return(df)
}

process_data <- function(activityStr,fromStr,toStr,km) {
  all_files <- list.files("Data", pattern = "*.csv", full.names = TRUE)
  df_all <- read.csv(all_files[1], header = TRUE, stringsAsFactors=FALSE)
  df_all <- subset(df_all, select = c(Activity.Type,Date,Title,Distance,Time))
  for (filename in all_files[-1]) {
      df_temp <- read.csv(filename, stringsAsFactors=FALSE)
      # subset data because Garmin can add or remove columns and we don't need them all
      df_temp <- subset(df_temp, select = c(Activity.Type,Date,Title,Distance,Time))
      df_all <- rbind(df_all, df_temp)
  }
  # remove duplicates
  df_all <- df_all[!duplicated(df_all), ]
  # format Date column to POSIXct
  df_all$Date <- as.POSIXct(strptime(df_all$Date, format = "%Y-%m-%d %H:%M:%S"))
  df_all <- compare2target(activityStr,fromStr,toStr,df_all)
  df_target <- maketarget(fromStr,toStr,km)
  # wrangle data frames to have matching date columns and then merge, then find difference
  # between the cumulative distance and the target
  df_temp <- data.frame(Date = as.Date(df_all$Date),
                        Cumulative.Distance = df_all$Cumulative.Distance)
  # add today as final row if we are within the window
  if(as.Date(toStr) >= Sys.Date()) {
    df_temp[nrow(df_temp) + 1,1] = Sys.Date()
    df_temp[nrow(df_temp),2] = df_temp[nrow(df_temp) - 1,2]
  }
  df_temp2 <- df_target
  df_temp2$Date <- as.Date(df_target$Date)
  df_merge <- merge(x = df_temp,
                    y = df_temp2,
                    by = "Date",
                    all.x = TRUE)
  df_merge$Difference <- df_merge$Cumulative.Distance.x - df_merge$Cumulative.Distance.y
  
  # make dataframe to show more granular "balance" of km
  df_debit <- data.frame(Date = df_target$Date,
                         Distance = -(km / nrow(df_target)))
  df_credit <- data.frame(Date = df_all$Date,
                          Distance = df_all$Distance)
  df_balance <- rbind(df_debit,df_credit)
  df_balance <- df_balance[order(as.numeric(df_balance$Date)),]
  df_balance <- subset(df_balance, as.Date(df_balance$Date) <= as.Date(Sys.Date()))
  df_balance$Cumulative.Distance <- cumsum(df_balance$Distance)
  df_balance$Date <- as.Date(df_balance$Date, format = "%Y-%m-%d %H:%M:%S")
  
  # save data
  write.table(df_all, file = paste0("Output/Data/alldata_",fromStr,"_",toStr,".txt"), sep="\t", row.names=FALSE, col.name=TRUE)
  write.table(df_merge, file = paste0("Output/Data/mergedata_",fromStr,"_",toStr,".txt"), sep="\t", row.names=FALSE, col.name=TRUE)
  write.table(df_target, file = paste0("Output/Data/targetdata_",fromStr,"_",toStr,".txt"), sep="\t", row.names=FALSE, col.name=TRUE)
  write.table(df_balance, file = paste0("Output/Data/balancedata_",fromStr,"_",toStr,".txt"), sep="\t", row.names=FALSE, col.name=TRUE)
  
  # plot out cumulative distance over time compared to target
  p1 <- ggplot(data = df_all, aes(x = Date, y = Cumulative.Distance)) + 
    geom_line(colour = "blue", size = 1) +
    geom_line(data = df_target, linetype = 2) +
    labs(x = "Date", y = "Cumulative Distance (km)")
  # plot out how it's going wrt to target
  p2 <- ggplot(data = df_merge, aes(x = Date, y = Difference)) + 
    geom_line(colour = "blue", size = 1) +
    geom_hline(yintercept = 0, linetype = 2) +
    xlim(as.Date(fromStr), as.Date(toStr)) +
    ylim(-max(abs(df_merge$Difference)),max(abs(df_merge$Difference))) +
    labs(x = "Date", y = "Difference (km)")
  # more accurate "balance" graph
  p3 <- ggplot(data = df_balance, aes(x = Date, y = Cumulative.Distance)) + 
    geom_line(colour = "blue", size = 1) +
    geom_hline(yintercept = 0, linetype = 2) +
    xlim(as.Date(paste0(fromStr," 00:00:00"), format = "%Y-%m-%d %H:%M:%S"), as.Date(paste0(toStr," 23:59:59"), format = "%Y-%m-%d %H:%M:%S")) +
    ylim(-max(abs(df_balance$Cumulative.Distance)),max(abs(df_balance$Cumulative.Distance))) +
    labs(x = "Date", y = "Balance (km)")
  
  # save all plots
  ggsave(paste0("Output/Plots/progress_",fromStr,"_",toStr,".png"), plot = p1, width = 8, height = 4, dpi = "print")
  ggsave(paste0("Output/Plots/difference_",fromStr,"_",toStr,".png"), plot = p2, width = 8, height = 4, dpi = "print")
  ggsave(paste0("Output/Plots/balance_",fromStr,"_",toStr,".png"), plot = p3, width = 8, height = 4, dpi = "print")
  
  # report distances
  print(paste0("Last ",activityStr," activity on: ", toString(df_all[nrow(df_all),2]),". Today is ", toString(Sys.Date())))
  print(paste0("Total ",activityStr," distance between ", fromStr," and ",toStr, " is ", toString(df_merge[nrow(df_merge),2])," km. Goal is ", km, " km."))
  if(df_merge[nrow(df_merge),4] < 0) {
    print(paste0(toString(df_merge[nrow(df_merge),4] * -1)," km behind target."))
  } else {
    print(paste0(toString(df_merge[nrow(df_merge),4])," km ahead of target."))
  }
  if(km > df_merge[nrow(df_merge),2]) {
    print(paste0(toString(km - df_merge[nrow(df_merge),2]), " km to go!"))
  } else {
    print("You did it!")
  }
  print(paste0("In this period you have run ",sum(floor(df_all$Distance / 21.1))," half-marathons."))
}

# to process the data
# Garmin 2021 Running - Stage 1
process_data("running","2021-01-01","2021-03-31",505)
# Garmin 2021 Running - Stage 2
process_data("running","2021-04-01","2021-06-30",505)
# Garmin 2021 Running - Stage 3
process_data("running","2021-07-01","2021-09-30",505) 
# Garmin 2021 Running - Stage 4
process_data("running","2021-10-01","2021-12-31",506) 

# Overall
process_data("running","2021-01-01","2021-12-31",2021)

The code is a bit rough and ready. I tried to connect R to the Garmin Connect API but couldn’t figure out how to do it, so it works by downloading csvs from the Garmin Connect website and feeding them to R.

Results

Progress was logged in a series of graphs which showed my progress against the projected goal and the balance and difference from that projection.

The console printed a few messages about my progress. I had a sub-goal of running a number of half-marathon distances, and so these were calculated and reported at the same time.

> # Garmin 2021 Running - Stage 1
> process_data("running","2021-01-01","2021-03-31",505)
[1] "Last running activity on: 2021-03-31 17:29:20. Today is 2021-12-23"
[1] "Total running distance between 2021-01-01 and 2021-03-31 is 505.83 km. Goal is 505 km."
[1] "0.830000000000041 km ahead of target."
[1] "You did it!"
[1] "In this period you have run 1 half-marathons."
> # Garmin 2021 Running - Stage 2
> process_data("running","2021-04-01","2021-06-30",505)
[1] "Last running activity on: 2021-06-30 17:26:44. Today is 2021-12-23"
[1] "Total running distance between 2021-04-01 and 2021-06-30 is 535.83 km. Goal is 505 km."
[1] "30.83 km ahead of target."
[1] "You did it!"
[1] "In this period you have run 11 half-marathons."
> # Garmin 2021 Running - Stage 3
> process_data("running","2021-07-01","2021-09-30",505) 
[1] "Last running activity on: 2021-09-29 06:03:46. Today is 2021-12-23"
[1] "Total running distance between 2021-07-01 and 2021-09-30 is 505.1 km. Goal is 505 km."
[1] "5.58913043478265 km ahead of target."
[1] "You did it!"
[1] "In this period you have run 11 half-marathons."
> # Garmin 2021 Running - Stage 4
> process_data("running","2021-10-01","2021-12-31",506) 
[1] "Last running activity on: 2021-12-23 06:42:38. Today is 2021-12-23"
[1] "Total running distance between 2021-10-01 and 2021-12-31 is 558.51 km. Goal is 506 km."
[1] "96.51 km ahead of target."
[1] "You did it!"
[1] "In this period you have run 8 half-marathons."
> 
> # Overall
> process_data("running","2021-01-01","2021-12-31",2021)
[1] "Last running activity on: 2021-12-23 06:42:38. Today is 2021-12-23"
[1] "Total running distance between 2021-01-01 and 2021-12-31 is 2105.27 km. Goal is 2021 km."
[1] "128.565890410959 km ahead of target."
[1] "You did it!"
[1] "In this period you have run 31 half-marathons."

Conclusion

As of today, my tally for the year is 2105.7 km with a few days left to go in 2021. I enjoyed the challenge of consistently running throughout the year, at a similar level. The keys to success were:

• staying uninjured, i.e. running sustainably
• building in weekly long runs. I ran my first 50 km (solo) this year.
• run commuting instead of bike commuting

All of this meant that I have not cycled very much this year. 2021 was easily my lowest annual cycling tally for at least fifteen years. Overall though I completed 192 hours of running or cycling in 2021 which is more than my previous big year for activities (2009, 185 hours).

—

The post title comes from “Target” by Fugazi from their Red Medicine LP