My Activities in 2018 with R and ShinyApp

January 4, 2019
By

(This article was first published on R – Networkx, and kindly contributed to R-bloggers)

What better way to analyze your activities data from Apple Health and Runkeeper into R and generating some visualizations and counters. After that I will wrapping it together into a Shiny App.

Want do I want to achieve for now?

  • Number of activities, steps, kilometers ect.
  • Heatmap of last X years number of activities colored by amount.

Loading and have a look at the data

Export Runkeeper data, the option is available after login > account settings > export data > download your data.

For export and convert your Apple Health data follow my previous post Analysing your Apple Health Data with Splunk.

runkeeper <- read.csv("data/cardioActivities.csv", stringsAsFactors=FALSE)
steps <- read.csv("data/StepCount.csv", stringsAsFactors = FALSE)
dim(runkeeper)

## [1] 676  14

dim(steps)

## [1] 47790     9

head(runkeeper)

##                            Activity.Id                Date         Type
## 1 47f59049-9bd5-4b9a-a63c-d51fca7e0bf8 2018-12-31 10:22:33      Running
## 2 7099bd74-7685-453e-a962-62ee711dc18e 2018-12-27 20:24:09      Running
## 3 c2250f5b-8567-47ef-97d7-504b174809e1 2018-12-23 13:28:04      Running
## 4 787c8438-d279-49fb-8bab-d1f348935264 2018-12-18 21:07:42 Boxing / MMA
## 5 2c41d494-204b-4be3-9b4e-d7acac5a3187 2018-12-16 13:04:44      Running
## 6 86b6429a-13a8-4122-b200-c3aadef271f5 2018-12-04 20:24:27 Boxing / MMA
##   Route.Name Distance..km. Duration Average.Pace Average.Speed..km.h.
## 1                     8.12    50:49         6:15                 9.59
## 2                    15.99  1:27:36         5:29                10.95
## 3                     8.31    50:04         6:01                 9.96
## 4                     0.00  1:20:00                                NA
## 5                    10.51    56:47         5:24                11.10
## 6                     0.00  1:00:00                                NA
##   Calories.Burned Climb..m. Average.Heart.Rate..bpm. Friend.s.Tagged Notes
## 1           772.0        83                      134                      
## 2          1485.0        57                      147                      
## 3           788.0        88                      134                      
## 4          1032.4         0                       NA                      
## 5           979.0        41                      145                      
## 6           774.3         0                       NA                      
##                GPX.File
## 1 2018-12-31-102233.gpx
## 2 2018-12-27-202409.gpx
## 3 2018-12-23-132804.gpx
## 4                      
## 5 2018-12-16-130444.gpx
## 6

head(steps)

##      sourceName sourceVersion
## 1 John’s iPhone        10.1.1
## 2 John’s iPhone        10.1.1
## 3 John’s iPhone        10.1.1
## 4 John’s iPhone        10.1.1
## 5 John’s iPhone        10.1.1
## 6 John’s iPhone        10.1.1
##                                                                                                          device
## 1 <<HKDevice: 0x2836d9310>, name:iPhone, manufacturer:Apple, model:iPhone, hardware:iPhone8,1, software:10.1.1>
## 2 <<HKDevice: 0x2836db070>, name:iPhone, manufacturer:Apple, model:iPhone, hardware:iPhone8,1, software:10.1.1>
## 3 <<HKDevice: 0x2836d9d10>, name:iPhone, manufacturer:Apple, model:iPhone, hardware:iPhone8,1, software:10.1.1>
## 4 <<HKDevice: 0x2836dad50>, name:iPhone, manufacturer:Apple, model:iPhone, hardware:iPhone8,1, software:10.1.1>
## 5 <<HKDevice: 0x2836da080>, name:iPhone, manufacturer:Apple, model:iPhone, hardware:iPhone8,1, software:10.1.1>
## 6 <<HKDevice: 0x2836dabc0>, name:iPhone, manufacturer:Apple, model:iPhone, hardware:iPhone8,1, software:10.1.1>
##        type  unit              creationDate                 startDate
## 1 StepCount count 2016-11-24 09:13:55 +0100 2016-11-24 08:55:10 +0100
## 2 StepCount count 2016-11-24 09:13:55 +0100 2016-11-24 09:10:09 +0100
## 3 StepCount count 2016-11-24 09:13:55 +0100 2016-11-24 09:11:11 +0100
## 4 StepCount count 2016-11-24 09:13:55 +0100 2016-11-24 09:12:13 +0100
## 5 StepCount count 2016-11-24 09:49:04 +0100 2016-11-24 09:13:19 +0100
## 6 StepCount count 2016-11-24 09:49:04 +0100 2016-11-24 09:33:57 +0100
##                     endDate value
## 1 2016-11-24 08:55:33 +0100    26
## 2 2016-11-24 09:11:11 +0100   114
## 3 2016-11-24 09:12:13 +0100   105
## 4 2016-11-24 09:13:19 +0100    25
## 5 2016-11-24 09:22:42 +0100   108
## 6 2016-11-24 09:42:33 +0100    89

str(runkeeper)

## 'data.frame':    676 obs. of  14 variables:
##  $ Activity.Id             : chr  "47f59049-9bd5-4b9a-a63c-d51fca7e0bf8" "7099bd74-7685-453e-a962-62ee711dc18e" "c2250f5b-8567-47ef-97d7-504b174809e1" "787c8438-d279-49fb-8bab-d1f348935264" ...
##  $ Date                    : chr  "2018-12-31 10:22:33" "2018-12-27 20:24:09" "2018-12-23 13:28:04" "2018-12-18 21:07:42" ...
##  $ Type                    : chr  "Running" "Running" "Running" "Boxing / MMA" ...
##  $ Route.Name              : chr  "" "" "" "" ...
##  $ Distance..km.           : num  8.12 15.99 8.31 0 10.51 ...
##  $ Duration                : chr  "50:49" "1:27:36" "50:04" "1:20:00" ...
##  $ Average.Pace            : chr  "6:15" "5:29" "6:01" "" ...
##  $ Average.Speed..km.h.    : num  9.59 10.95 9.96 NA 11.1 ...
##  $ Calories.Burned         : num  772 1485 788 1032 979 ...
##  $ Climb..m.               : int  83 57 88 0 41 0 62 0 0 0 ...
##  $ Average.Heart.Rate..bpm.: int  134 147 134 NA 145 NA 145 NA NA NA ...
##  $ Friend.s.Tagged         : chr  "" "" "" "" ...
##  $ Notes                   : chr  "" "" "" "" ...
##  $ GPX.File                : chr  "2018-12-31-102233.gpx" "2018-12-27-202409.gpx" "2018-12-23-132804.gpx" "" ...

str(steps)

## 'data.frame':    47790 obs. of  9 variables:
##  $ sourceName   : chr  "John’s iPhone" "John’s iPhone" "John’s iPhone" "John’s iPhone" ...
##  $ sourceVersion: chr  "10.1.1" "10.1.1" "10.1.1" "10.1.1" ...
##  $ device       : chr  "<<HKDevice: 0x2836d9310>, name:iPhone, manufacturer:Apple, model:iPhone, hardware:iPhone8,1, software:10.1.1>" "<<HKDevice: 0x2836db070>, name:iPhone, manufacturer:Apple, model:iPhone, hardware:iPhone8,1, software:10.1.1>" "<<HKDevice: 0x2836d9d10>, name:iPhone, manufacturer:Apple, model:iPhone, hardware:iPhone8,1, software:10.1.1>" "<<HKDevice: 0x2836dad50>, name:iPhone, manufacturer:Apple, model:iPhone, hardware:iPhone8,1, software:10.1.1>" ...
##  $ type         : chr  "StepCount" "StepCount" "StepCount" "StepCount" ...
##  $ unit         : chr  "count" "count" "count" "count" ...
##  $ creationDate : chr  "2016-11-24 09:13:55 +0100" "2016-11-24 09:13:55 +0100" "2016-11-24 09:13:55 +0100" "2016-11-24 09:13:55 +0100" ...
##  $ startDate    : chr  "2016-11-24 08:55:10 +0100" "2016-11-24 09:10:09 +0100" "2016-11-24 09:11:11 +0100" "2016-11-24 09:12:13 +0100" ...
##  $ endDate      : chr  "2016-11-24 08:55:33 +0100" "2016-11-24 09:11:11 +0100" "2016-11-24 09:12:13 +0100" "2016-11-24 09:13:19 +0100" ...
##  $ value        : int  26 114 105 25 108 89 329 284 89 18 ...

From the Runkeeper data we need Date and from Apple Health StepCount we need the endDate (thats when your step is done). Both has type chr. I could convert it as date, but I leave the data what it is and will do convert it when necessary.

Create new variables

First we load the lubridate package.

library(lubridate)

I will create some new variables and convert Date and endDate as Date so I can extract the year with the year function of the lubridate package.

runkeeper$year <- year(as.Date(runkeeper$Date, origin = '1900-01-01')) 
steps$year <- year(as.Date(steps$endDate, origin = '1900-01-01')) 
summary(runkeeper$year)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2010    2013    2016    2015    2017    2018

summary(steps$year)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2016    2017    2017    2017    2018    2019

As you can see in the above summary of both dataframes, Runkeeper has more years than steps. That doesn’t matter becouse we are now only looking for 2018 in the Shiny App and I will group it by year. In the app I create a year slider.

I will parse the period with hour, minuts ans seconds of the variable Duration. And I will calculate the duration in minutus. Some of the Duration variables has no leading zero for the hour and I cannot use it with the function hms.

Add a leading zero if not exists.

runkeeper$Duration[1:5]

## [1] "50:49"   "1:27:36" "50:04"   "1:20:00" "56:47"

runkeeper$Duration <- ifelse(nchar(runkeeper$Duration) < 6, 
                             paste0("0:", runkeeper$Duration), runkeeper$Duration)
runkeeper$Duration[1:5]

## [1] "0:50:49" "1:27:36" "0:50:04" "1:20:00" "0:56:47"

Now I can parse the Duration with hms.

runkeeper$lub <- hms(runkeeper$Duration)
runkeeper$time_minutes <- hour(runkeeper$lub)*60 + 
  minute(runkeeper$lub) + 
  second(runkeeper$lub)/60
runkeeper$lub[1:5]

## [1] "50M 49S"    "1H 27M 36S" "50M 4S"     "1H 20M 0S"  "56M 47S"

runkeeper$time_minutes[1:5]

## [1] 50.81667 87.60000 50.06667 80.00000 56.78333

Group the data by year

First we load the package dplyr.

library(dplyr)

Group both dataframes by year and do some summarises like count and sum of kilometers, climb, calories and duration

grouped_runkeeper_year <- runkeeper %>%
  group_by(year) %>%
  summarise(cnt = n(), 
            km = sum(Distance..km.),
            climb = sum(Climb..m.),
            calories = sum(Calories.Burned),
            duration = sum(time_minutes, na.rm = TRUE))

grouped_steps_year <- steps %>%
  group_by(year) %>%
  summarise(cnt = sum(value))

grouped_runkeeper_year

## # A tibble: 9 x 6
##    year   cnt     km climb calories duration
##   <dbl> <int>  <dbl> <int>    <dbl>    <dbl>
## 1  2010     3   10.8     0     901      71.8
## 2  2011    96  657.   1951   56333    3864. 
## 3  2012    46  410.   1458   36876    2278. 
## 4  2013    46  258.    488   22654    1456. 
## 5  2014    24  175.    698   14382     992. 
## 6  2015   117 1202.   5910   98518    7537. 
## 7  2016    93  661.   3076   65275.   5397. 
## 8  2017   128  408.   1569   89872.   7404. 
## 9  2018   123  366.   1818   86792.   7062.

grouped_steps_year

## # A tibble: 4 x 2
##    year     cnt
##   <dbl>   <int>
## 1  2016  328759
## 2  2017 3360951
## 3  2018 4366289
## 4  2019    3692

Some simple visualizations

library(ggplot2)
library(RColorBrewer)
grouped_runkeeper_year %>%
  ggplot(aes(x=year, y=cnt )) + 
  geom_bar(stat = "identity", col="white", fill="#ee8300") +
  geom_text(aes(label=cnt), hjust=1.2, color="white", size=3.5) +
  labs(x="", y="", title="Number activities by Year") + 
  scale_x_continuous(breaks=seq(min(grouped_runkeeper_year$year),
                                max(grouped_runkeeper_year$year),1)) + 
  coord_flip() +
  theme_bw()

plot of chunk unnamed-chunk-8

grouped_steps_year %>%
  ggplot(aes(x=year, y=cnt )) + 
  geom_bar(stat = "identity", col="white", fill="#ee8300") +
  geom_text(aes(label=cnt), hjust=1.2, color="white", size=3.5) +
  labs(x="", y="", title="Number of Steps by Year") + 
  scale_x_continuous(breaks=seq(min(grouped_runkeeper_year$year),
                                max(grouped_runkeeper_year$year),1)) + 
  coord_flip() +
  theme_bw()

plot of chunk unnamed-chunk-8

Add a heatmap of number of activities last 3 years from now.

calendar_heatmap <- runkeeper %>% 
  select(Date,time_minutes,year) %>% 
  filter(year >= year(now()) - 3) %>%
  mutate(
    week = week(as.Date(Date)),
    wday = wday(as.Date(Date), week_start = 1),
    month = month(as.Date(Date), label = TRUE, abbr = TRUE),
    day = weekdays(as.Date(Date))
  )

cols <- rev(rev(brewer.pal(7,"Oranges"))[1:5])

calendar_heatmap %>%
  ggplot(aes(month, reorder(day, -wday), fill = time_minutes)) +
  geom_tile(colour = "white") +
  scale_fill_gradientn('Activity \nMinutes', colours = cols) +
  facet_wrap(~ year, ncol = 1) +
  theme_classic() +
  theme(strip.text.x = element_text(size = 16, face = "bold", colour = "orange")) +
  ylab("") + 
  xlab("")

plot of chunk unnamed-chunk-9

Screenshot Shiny App

SessionInfo

sessionInfo()
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS  10.14.2
## 
## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] RWordPress_0.2-3         knitr_1.20              
##  [3] bindrcpp_0.2.2           RColorBrewer_1.1-2      
##  [5] ggplot2_3.0.0            shinydashboardPlus_0.6.0
##  [7] shinydashboard_0.7.1     shiny_1.2.0             
##  [9] dplyr_0.7.8              lubridate_1.7.4         
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.19      prettyunits_1.0.2 ps_1.2.1         
##  [4] utf8_1.1.4        assertthat_0.2.0  rprojroot_1.3-2  
##  [7] digest_0.6.17     mime_0.5          R6_2.2.2         
## [10] plyr_1.8.4        backports_1.1.2   evaluate_0.12    
## [13] highr_0.7         pillar_1.3.0      rlang_0.3.0.1    
## [16] lazyeval_0.2.1    curl_3.2          rstudioapi_0.8   
## [19] callr_3.0.0       rmarkdown_1.10    desc_1.2.0       
## [22] labeling_0.3      devtools_2.0.1    stringr_1.3.1    
## [25] RCurl_1.95-4.11   munsell_0.5.0     compiler_3.5.1   
## [28] httpuv_1.4.5.1    pkgconfig_2.0.2   base64enc_0.1-3  
## [31] pkgbuild_1.0.2    htmltools_0.3.6   tidyselect_0.2.5 
## [34] tibble_1.4.2      XML_3.98-1.16     fansi_0.4.0      
## [37] crayon_1.3.4      withr_2.1.2       later_0.7.5      
## [40] bitops_1.0-6      grid_3.5.1        jsonlite_1.5     
## [43] xtable_1.8-3      gtable_0.2.0      magrittr_1.5     
## [46] scales_1.0.0      cli_1.0.1         stringi_1.2.4    
## [49] fs_1.2.6          promises_1.0.1    remotes_2.0.2    
## [52] testthat_2.0.0    tools_3.5.1       glue_1.3.0       
## [55] purrr_0.2.5       hms_0.4.2         processx_3.2.0   
## [58] pkgload_1.0.2     yaml_2.2.0        colorspace_1.3-2 
## [61] sessioninfo_1.1.1 memoise_1.1.0     bindr_0.1.1      
## [64] XMLRPC_0.3-1      usethis_1.4.0

Well wrap it up into a Shiny App which can be found on my GitHub.

The post My Activities in 2018 with R and ShinyApp appeared first on Networkx.

To leave a comment for the author, please follow the link and comment on their blog: R – Networkx.

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