When thinking about how to combine datasets to ask more complex questions it is important to determine where the overlaps in the datasets might be. As an example, The Mount Shasta Avalanche Center keeps records of seasonal weather and avalanche forecast data that I discussed in a previous post. I just made a small movement dataset from some of the ski tours I have done in the Mount Shasta area. Here are just a few summary views of the dataset below. In the next series of posts we will overlap the avalanche forecast data, weather data, and the ski touring movement data based on date, time, elevations, and aspects.
library(tidyverse)
ski_data <- read.csv("../../../static/data/SkiTouring.csv")The data set has a number of interesting variables including my movement as I skied, some speed data, some altitude data, and some biometric heart rate data. All of the tours were in the 2019-2020 or the 2020-2021 ski season on the lower slopes of Mount Shasta, Ca.
Take a quick look.
glimpse(ski_data)## Rows: 13,491
## Columns: 17
## $ timestamp <chr> "2020-01-18 18:33:31", "2020-01-18 18:33:58", "2020-01-…
## $ position_lat <dbl> 41.35011, 41.35010, 41.35012, 41.35014, 41.35016, 41.35…
## $ position_long <dbl> -122.2793, -122.2793, -122.2793, -122.2792, -122.2792, …
## $ altitude <dbl> NA, 1504.2, 1504.8, 1504.8, 1505.0, 1505.2, 1505.6, 150…
## $ heart_rate <int> NA, 55, 62, 62, 64, 64, 69, 66, 67, 76, 74, 73, 72, 70,…
## $ cadence <int> NA, 64, 48, 52, 49, 46, 46, 43, 43, 0, 48, 72, 75, 75, …
## $ temperature <int> NA, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,…
## $ distance <int> NA, NA, NA, NA, 7, 9, NA, 15, NA, NA, NA, 24, 26, 29, 3…
## $ speed <dbl> NA, NA, NA, NA, 0.5, 0.8, 1.1, 1.1, 0.7, 0.0, 0.0, 1.0,…
## $ vertical_speed <dbl> NA, 0.00, 0.02, 0.00, 0.00, 0.00, 0.00, 0.00, 0.04, 0.0…
## $ File_Path <chr> "Move_2020_01_18_10_33_29_Ski_touring.fit", "Move_2020_…
## $ activity <chr> "SkiTouring", "SkiTouring", "SkiTouring", "SkiTouring",…
## $ HRzone <chr> NA, "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "…
## $ datetime <chr> "2020-01-18 18:33:31", "2020-01-18 18:33:58", "2020-01-…
## $ year <int> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2…
## $ date <chr> "2020-01-18", "2020-01-18", "2020-01-18", "2020-01-18",…
## $ seconds <chr> "18:33:31", "18:33:58", "18:34:42", "18:34:45", "18:34:…summary(ski_data)## timestamp position_lat position_long altitude
## Length:13491 Min. :41.35 Min. :-122.3 Min. :1504
## Class :character 1st Qu.:41.36 1st Qu.:-122.2 1st Qu.:1827
## Mode :character Median :41.36 Median :-122.2 Median :2052
## Mean :41.36 Mean :-122.2 Mean :2031
## 3rd Qu.:41.36 3rd Qu.:-122.2 3rd Qu.:2262
## Max. :41.37 Max. :-122.2 Max. :2532
## NA's :8908
## heart_rate cadence temperature distance
## Min. : 42.00 Min. : 0.00 Min. :12.00 Min. : 7
## 1st Qu.: 55.00 1st Qu.: 37.00 1st Qu.:19.00 1st Qu.:1640
## Median : 71.00 Median : 48.00 Median :20.00 Median :3144
## Mean : 76.91 Mean : 49.95 Mean :20.83 Mean :3309
## 3rd Qu.: 98.00 3rd Qu.: 69.00 3rd Qu.:22.00 3rd Qu.:4668
## Max. :148.00 Max. :114.00 Max. :31.00 Max. :9317
## NA's :361 NA's :8908 NA's :8908 NA's :10905
## speed vertical_speed File_Path activity
## Min. :0.00 Min. :-1.180 Length:13491 Length:13491
## 1st Qu.:0.80 1st Qu.:-0.020 Class :character Class :character
## Median :1.00 Median : 0.040 Mode :character Mode :character
## Mean :1.48 Mean :-0.021
## 3rd Qu.:1.40 3rd Qu.: 0.100
## Max. :8.40 Max. : 0.240
## NA's :8912 NA's :8908
## HRzone datetime year date
## Length:13491 Length:13491 Min. :2020 Length:13491
## Class :character Class :character 1st Qu.:2020 Class :character
## Mode :character Mode :character Median :2020 Mode :character
## Mean :2020
## 3rd Qu.:2020
## Max. :2021
##
## seconds
## Length:13491
## Class :character
## Mode :character
##
##
##
## Make a quick plot using the latitude and longitude coordinates and color by the date of the tour.
ski_plot1 <- ggplot(ski_data, aes(x = position_long, y = position_lat, color = date)) +
coord_quickmap() + geom_point()
ski_plot1