# install.packages("remotes")remotes::install_github("fmmattioni/whippr")

library(whippr)## example file that comes with the package for demonstration purposespath_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-averagesdf%>%interpolate()%>%perform_average(type ="bin",bins =30)#> # Metabolic cart: COSMED#> # Data status: averaged data - 30-s bins#> # Time column: t#> # A tibble: 72 × 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    30  16.3  1.75  26.5 1032.  852.  272.   80.5     25.7      31.4     12.4#>  2    60  19.0  1.39  25.1 1046.  822.  211.   65.0     24.1      30.7     12.6#>  3    90  16.6  1.76  28.1 1164.  949.  268.   85.0     24.3      29.7     14.0#>  4   120  17.8  1.93  25.7 1054.  853.  296.   92.5     24.6      30.5     12.7#>  5   150  15.4  1.68  24.6  993.  823.  257.   80.4     24.8      29.9     12.0#>  6   180  18.1  1.38  25.1 1058.  833.  209.   65.4     24.0      30.4     12.7#>  7   210  22.3  1.37  29.1 1122.  935.  213.   63.4     26.0      31.3     13.5#>  8   240  16.6  1.91  24.9  966.  825.  301.   89.5     25.8      30.2     11.6#>  9   270  16.8  1.64  26.2 1044.  896.  252.   79.7     25.2      29.4     12.6#> 10   300  14.5  2.09  27.2 1097.  945.  322.  103.      24.6      28.8     13.2#> # ℹ 62 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-averagesdf%>%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 baselinework_rate_magic =TRUE,## produce a work rate columnbaseline_intensity =20,## baseline was performed at 20 Wramp_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           3514.            45.8    303   193   1.13 <gg>

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