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The goal of whippr is to provide a set of tools for manipulating gas exchange data from cardiopulmonary exercise testing.
whippr?The name of the package is in honor of Prof. Brian J Whipp and his invaluable contribution to the field of exercise physiology.
You can install the development version of whippr from Github with:
library(whippr)
## example file that comes with the package for demonstration purposes
path_example <- system.file("example_cosmed.xlsx", package = "whippr")
df <- read_data(path = path_example, metabolic_cart = "cosmed")
df
#> # Metabolic cart: COSMED
#> # Data status: raw data
#> # Time column: t
#> # A tibble: 754 × 119
#> t Rf VT VE VO2 VCO2 O2exp CO2exp `VE/VO2` `VE/VCO2` `VO2/Kg`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2 8.08 1.19 9.60 380. 301. 185. 52.9 25.3 31.9 4.58
#> 2 4 23.2 0.915 21.2 864. 665. 141. 40.8 24.5 31.9 10.4
#> 3 8 15.6 2.11 32.9 1317. 1075. 325. 97.2 25.0 30.6 15.9
#> 4 11 20.6 1.18 24.4 894. 714. 188. 49.2 27.3 34.1 10.8
#> 5 14 23.3 0.947 22.1 822. 647. 150. 39.4 26.9 34.1 9.90
#> 6 18 14.7 2.28 33.6 1347. 1126. 351. 108. 24.9 29.8 16.2
#> 7 23 11.2 2.32 26.1 980. 848. 364. 107. 26.6 30.7 11.8
#> 8 28 13.2 2.18 28.8 1147. 981. 336. 105. 25.2 29.4 13.8
#> 9 31 17.7 1.51 26.7 1048. 860. 234. 68.8 25.5 31.0 12.6
#> 10 35 14.2 1.68 23.8 973. 794. 257. 79.3 24.5 30.0 11.7
#> # ℹ 744 more rows
#> # ℹ 108 more variables: R <dbl>, FeO2 <dbl>, FeCO2 <dbl>, HR <dbl>,
#> # `VO2/HR` <dbl>, Load1 <dbl>, Load2 <dbl>, Load3 <dbl>, Phase <dbl>,
#> # Marker <lgl>, FetO2 <dbl>, FetCO2 <dbl>, FiO2 <dbl>, FiCO2 <dbl>, Ti <dbl>,
#> # Te <dbl>, Ttot <dbl>, `Ti/Ttot` <dbl>, IV <dbl>, PetO2 <dbl>, PetCO2 <dbl>,
#> # `P(a-et)CO2` <dbl>, SpO2 <dbl>, `VD(phys)` <dbl>, `VD/VT` <dbl>,
#> # `Env. Temp.` <dbl>, `Analyz. Temp.` <dbl>, `Analyz. Press.` <dbl>, …df %>%
interpolate()
#> # Metabolic cart: COSMED
#> # Data status: interpolated data
#> # Time column: t
#> # A tibble: 2,159 × 114
#> t Rf VT VE VO2 VCO2 O2exp CO2exp `VE/VO2` `VE/VCO2` `VO2/Kg`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2 8.08 1.19 9.60 380. 301. 185. 52.9 25.3 31.9 4.58
#> 2 3 15.6 1.05 15.4 622. 483. 163. 46.8 24.9 31.9 7.50
#> 3 4 23.2 0.915 21.2 864. 665. 141. 40.8 24.5 31.9 10.4
#> 4 5 21.3 1.21 24.1 978. 767. 187. 54.9 24.6 31.6 11.8
#> 5 6 19.4 1.51 27.1 1091. 870. 233. 69.0 24.8 31.3 13.1
#> 6 7 17.5 1.81 30.0 1204. 973. 279. 83.1 24.9 30.9 14.5
#> 7 8 15.6 2.11 32.9 1317. 1075. 325. 97.2 25.0 30.6 15.9
#> 8 9 17.3 1.80 30.1 1176. 955. 279. 81.2 25.7 31.8 14.2
#> 9 10 19.0 1.49 27.2 1035. 834. 233. 65.2 26.5 33.0 12.5
#> 10 11 20.6 1.18 24.4 894. 714. 188. 49.2 27.3 34.1 10.8
#> # ℹ 2,149 more rows
#> # ℹ 103 more variables: R <dbl>, FeO2 <dbl>, FeCO2 <dbl>, HR <dbl>,
#> # `VO2/HR` <dbl>, Load1 <dbl>, Load2 <dbl>, Load3 <dbl>, Phase <dbl>,
#> # FetO2 <dbl>, FetCO2 <dbl>, FiO2 <dbl>, FiCO2 <dbl>, Ti <dbl>, Te <dbl>,
#> # Ttot <dbl>, `Ti/Ttot` <dbl>, IV <dbl>, PetO2 <dbl>, PetCO2 <dbl>,
#> # `P(a-et)CO2` <dbl>, SpO2 <dbl>, `VD(phys)` <dbl>, `VD/VT` <dbl>,
#> # `Env. Temp.` <dbl>, `Analyz. Temp.` <dbl>, `Analyz. Press.` <dbl>, …## example of performing 30-s bin-averages
df %>%
interpolate() %>%
perform_average(type = "bin", bins = 30)
#> # Metabolic cart: COSMED
#> # Data status: averaged data - 30-s bins
#> # Time column: t
#> # A tibble: 73 × 114
#> t Rf VT VE VO2 VCO2 O2exp CO2exp `VE/VO2` `VE/VCO2` `VO2/Kg`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 19.0 1.33 24.0 932. 744. 207. 58.9 25.8 32.5 11.2
#> 2 30 15.3 1.85 27.1 1097. 904. 284. 87.0 24.8 30.1 13.2
#> 3 60 19.6 1.47 27.1 1133. 892. 223. 69.6 24.1 30.7 13.7
#> 4 90 13.3 2.29 26.0 1043. 885. 353. 111. 24.9 29.5 12.6
#> 5 120 20.5 1.43 27.1 1107. 883. 218. 66.9 24.6 31.0 13.3
#> 6 150 14.4 1.57 22.1 928. 751. 239. 75.5 24.1 29.7 11.2
#> 7 180 23.0 1.18 26.4 1071. 849. 180. 54.4 24.8 31.3 12.9
#> 8 210 16.1 2.17 28.7 1070. 941. 342. 101. 27.0 30.6 12.9
#> 9 240 18.9 1.43 26.1 1058. 880. 219. 68.8 24.7 29.8 12.7
#> 10 270 15.1 1.65 24.5 987. 847. 253. 81.4 24.8 28.9 11.9
#> # ℹ 63 more rows
#> # ℹ 103 more variables: R <dbl>, FeO2 <dbl>, FeCO2 <dbl>, HR <dbl>,
#> # `VO2/HR` <dbl>, Load1 <dbl>, Load2 <dbl>, Load3 <dbl>, Phase <dbl>,
#> # FetO2 <dbl>, FetCO2 <dbl>, FiO2 <dbl>, FiCO2 <dbl>, Ti <dbl>, Te <dbl>,
#> # Ttot <dbl>, `Ti/Ttot` <dbl>, IV <dbl>, PetO2 <dbl>, PetCO2 <dbl>,
#> # `P(a-et)CO2` <dbl>, SpO2 <dbl>, `VD(phys)` <dbl>, `VD/VT` <dbl>,
#> # `Env. Temp.` <dbl>, `Analyz. Temp.` <dbl>, `Analyz. Press.` <dbl>, …## example of performing 30-s rolling-averages
df %>%
interpolate() %>%
perform_average(type = "rolling", rolling_window = 30)
#> # Metabolic cart: COSMED
#> # Data status: averaged data - 30-s rolling average
#> # Time column: t
#> # A tibble: 2,130 × 114
#> t Rf VT VE VO2 VCO2 O2exp CO2exp `VE/VO2` `VE/VCO2` `VO2/Kg`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 16.5 16.4 1.75 26.5 1033. 852. 271. 80.1 25.7 31.3 12.4
#> 2 17.5 16.6 1.76 27.0 1054. 870. 273. 80.7 25.7 31.3 12.7
#> 3 18.5 16.7 1.78 27.3 1067. 882. 276. 81.6 25.7 31.3 12.9
#> 4 19.5 16.4 1.80 27.4 1071. 887. 280. 82.8 25.7 31.2 12.9
#> 5 20.5 16.2 1.82 27.4 1071. 888. 282. 83.6 25.7 31.1 12.9
#> 6 21.5 16.0 1.82 27.3 1068. 885. 282. 83.8 25.7 31.1 12.9
#> 7 22.5 16.0 1.81 27.1 1062. 880. 280. 83.4 25.7 31.1 12.8
#> 8 23.5 16.0 1.78 26.9 1052. 871. 277. 82.4 25.6 31.0 12.7
#> 9 24.5 16.1 1.77 26.7 1048. 867. 274. 81.8 25.5 31.0 12.6
#> 10 25.5 16.1 1.76 26.6 1050. 868. 273. 81.9 25.4 30.8 12.6
#> # ℹ 2,120 more rows
#> # ℹ 103 more variables: R <dbl>, FeO2 <dbl>, FeCO2 <dbl>, HR <dbl>,
#> # `VO2/HR` <dbl>, Load1 <dbl>, Load2 <dbl>, Load3 <dbl>, Phase <dbl>,
#> # FetO2 <dbl>, FetCO2 <dbl>, FiO2 <dbl>, FiCO2 <dbl>, Ti <dbl>, Te <dbl>,
#> # Ttot <dbl>, `Ti/Ttot` <dbl>, IV <dbl>, PetO2 <dbl>, PetCO2 <dbl>,
#> # `P(a-et)CO2` <dbl>, SpO2 <dbl>, `VD(phys)` <dbl>, `VD/VT` <dbl>,
#> # `Env. Temp.` <dbl>, `Analyz. Temp.` <dbl>, `Analyz. Press.` <dbl>, …results_kinetics <- vo2_kinetics(
.data = df,
intensity_domain = "moderate",
vo2_column = "VO2",
protocol_n_transitions = 3,
protocol_baseline_length = 360,
protocol_transition_length = 360,
cleaning_level = 0.95,
cleaning_baseline_fit = c("linear", "exponential", "exponential"),
fit_level = 0.95,
fit_bin_average = 5,
fit_phase_1_length = 20,
fit_baseline_length = 120,
fit_transition_length = 240,
verbose = TRUE
)
#> ────────────────────────── * V̇O₂ kinetics analysis * ─────────────────────────
#> ✔ Detecting outliers
#> • 14 outliers found in transition 1
#> • 15 outliers found in transition 2
#> • 13 outliers found in transition 3
#> ✔ Processing data...
#> ✔ └─ Removing outliers
#> ✔ └─ Interpolating each transition
#> ✔ └─ Ensemble-averaging transitions
#> ✔ └─ Performing 5-s bin averages
#> ✔ Fitting data...
#> ✔ └─ Fitting baseline
#> ✔ └─ Fitting transition
#> ✔ └─ Calculating residuals
#> ✔ └─ Preparing plots
#> ────────────────────────────────── * DONE * ──────────────────────────────────df_incremental <- read_data(path = system.file("ramp_cosmed.xlsx", package = "whippr"), metabolic_cart = "cosmed")
vo2_max(
.data = df_incremental, ## data from `read_data()`
vo2_column = "VO2",
vo2_relative_column = "VO2/Kg",
heart_rate_column = "HR",
rer_column = "R",
detect_outliers = TRUE,
average_method = "bin",
average_length = 30,
plot = TRUE,
verbose = TRUE,
## arguments for `incremental_normalize()`
incremental_type = "ramp",
has_baseline = TRUE,
baseline_length = 240, ## 4-min baseline
work_rate_magic = TRUE, ## produce a work rate column
baseline_intensity = 20, ## baseline was performed at 20 W
ramp_increase = 25, ## 25 W/min ramp
## arguments for `detect_outliers()`
test_type = "incremental",
cleaning_level = 0.95,
method_incremental = "linear"
)
#> ──────────────────────────── * V̇O₂ max analysis * ────────────────────────────
#> ✔ Normalizing incremental data...
#> ✔ Detecting outliers
#> • 2 outlier(s) found in baseline
#> • 15 outlier(s) found in ramp
#> ✔ Filtering out outliers...
#> ✔ Interpolating from breath-by-breath into second-by-second...
#> ✔ Performing averages...
#> # A tibble: 1 × 6
#> VO2max_absolute VO2max_relative POpeak HRmax RERmax plot
#> <dbl> <dbl> <int> <dbl> <dbl> <list>
#> 1 3524. 46.0 303 193 1.13 <gg>Would you like to perform VO2 kinetics analyses but don’t know R? No problem! You can use our online app: VO2 Kinetics App
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
Icons made by monkik from www.flaticon.com
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.