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lactater

The goal of lactater is to provide tools for making it easier to analyze lactate thresholds.

Installation

You can install the released version of lactater from CRAN with:

install.packages("lactater")

You can install the development version of lactater from Github with:

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

Demo data

library(lactater)

demo_data
#>   step length intensity lactate heart_rate
#> 1    0      0         0    0.93         96
#> 2    1      3        50    0.98        114
#> 3    2      3        75    1.23        134
#> 4    3      3       100    1.88        154
#> 5    4      3       125    2.80        170
#> 6    5      3       150    4.21        182
#> 7    6      3       175    6.66        193
#> 8    7      2       191    8.64        198

Usage

With lactater you can easily estimate lactate thresholds using one or multiple methods:

results_overall <- lactate_threshold(
  .data = demo_data, 
  intensity_column = "intensity", 
  lactate_column = "lactate", 
  heart_rate_column = "heart_rate",
  method = c("Log-log", "OBLA", "Bsln+", "Dmax", "LTP", "LTratio"),
  fit = "3rd degree polynomial", 
  include_baseline = TRUE, 
  sport = "cycling",
  plot = TRUE
)

#> # A tibble: 17 × 7
#>    method_category method           fitting   intensity lactate heart_rate plot 
#>    <fct>           <fct>            <chr>         <dbl>   <dbl>      <dbl> <lis>
#>  1 Log-log         Log-log          3rd degr…      83.4    1.4         140 <gg> 
#>  2 OBLA            OBLA 2.0         3rd degr…     105.     2           153 <gg> 
#>  3 OBLA            OBLA 2.5         3rd degr…     118.     2.5         160 <gg> 
#>  4 OBLA            OBLA 3.0         3rd degr…     129      3           167 <gg> 
#>  5 OBLA            OBLA 3.5         3rd degr…     137      3.5         171 <gg> 
#>  6 OBLA            OBLA 4.0         3rd degr…     145      4           176 <gg> 
#>  7 Bsln+           Bsln + 0.5       3rd degr…      82.5    1.43        139 <gg> 
#>  8 Bsln+           Bsln + 1.0       3rd degr…     104.     1.93        152 <gg> 
#>  9 Bsln+           Bsln + 1.5       3rd degr…     117.     2.43        159 <gg> 
#> 10 Dmax            Dmax             3rd degr…     132.     3.1         168 <gg> 
#> 11 Dmax            ModDmax          3rd degr…     140.     3.6         173 <gg> 
#> 12 Dmax            Exp-Dmax         Exponent…     135.     3.3         170 <gg> 
#> 13 Dmax            Log-Poly-ModDmax 3rd degr…     143      3.8         175 <gg> 
#> 14 Dmax            Log-Exp-ModDmax  Exponent…     146.     4           177 <gg> 
#> 15 LTP             LTP1             3rd degr…      88.9    1.5         143 <gg> 
#> 16 LTP             LTP2             3rd degr…     148.     4.1         178 <gg> 
#> 17 LTratio         LTratio          B-Spline…      71.2    1.2         132 <gg>

You can also choose one method:

Log-log

results_loglog <- lactate_threshold(
  .data = demo_data, 
  intensity_column = "intensity", 
  lactate_column = "lactate", 
  heart_rate_column = "heart_rate",
  method = "Log-log",
  fit = "3rd degree polynomial", 
  include_baseline = TRUE, 
  sport = "cycling",
  plot = TRUE
)

#> # A tibble: 1 × 7
#>   method_category method  fitting             intensity lactate heart_rate plot 
#>   <fct>           <fct>   <chr>                   <dbl>   <dbl>      <dbl> <lis>
#> 1 Log-log         Log-log 3rd degree polynom…      83.4     1.4        140 <gg>

OBLA

results_obla <- lactate_threshold(
  .data = demo_data, 
  intensity_column = "intensity", 
  lactate_column = "lactate", 
  heart_rate_column = "heart_rate",
  method = "OBLA",
  fit = "3rd degree polynomial", 
  include_baseline = TRUE, 
  sport = "cycling",
  plot = TRUE
)

#> # A tibble: 5 × 7
#>   method_category method   fitting            intensity lactate heart_rate plot 
#>   <fct>           <fct>    <chr>                  <dbl>   <dbl>      <dbl> <lis>
#> 1 OBLA            OBLA 2.0 3rd degree polyno…      105.     2          153 <gg> 
#> 2 OBLA            OBLA 2.5 3rd degree polyno…      118.     2.5        160 <gg> 
#> 3 OBLA            OBLA 3.0 3rd degree polyno…      129      3          167 <gg> 
#> 4 OBLA            OBLA 3.5 3rd degree polyno…      137      3.5        171 <gg> 
#> 5 OBLA            OBLA 4.0 3rd degree polyno…      145      4          176 <gg>

Bsln+

results_bsln_plus <- lactate_threshold(
  .data = demo_data, 
  intensity_column = "intensity", 
  lactate_column = "lactate", 
  heart_rate_column = "heart_rate",
  method = "Bsln+",
  fit = "3rd degree polynomial", 
  include_baseline = TRUE, 
  sport = "cycling",
  plot = TRUE
)

#> # A tibble: 3 × 7
#>   method_category method     fitting          intensity lactate heart_rate plot 
#>   <fct>           <fct>      <chr>                <dbl>   <dbl>      <dbl> <lis>
#> 1 Bsln+           Bsln + 0.5 3rd degree poly…      82.5    1.43        139 <gg> 
#> 2 Bsln+           Bsln + 1.0 3rd degree poly…     104.     1.93        152 <gg> 
#> 3 Bsln+           Bsln + 1.5 3rd degree poly…     117.     2.43        159 <gg>

Dmax

results_dmax <- lactate_threshold(
  .data = demo_data, 
  intensity_column = "intensity", 
  lactate_column = "lactate", 
  heart_rate_column = "heart_rate",
  method = "Dmax",
  fit = "3rd degree polynomial", 
  include_baseline = TRUE, 
  sport = "cycling",
  plot = TRUE
)

#> # A tibble: 5 × 7
#>   method_category method           fitting    intensity lactate heart_rate plot 
#>   <fct>           <fct>            <chr>          <dbl>   <dbl>      <dbl> <lis>
#> 1 Dmax            Dmax             3rd degre…      132.     3.1        168 <gg> 
#> 2 Dmax            ModDmax          3rd degre…      140.     3.6        173 <gg> 
#> 3 Dmax            Exp-Dmax         Exponenti…      135.     3.3        170 <gg> 
#> 4 Dmax            Log-Poly-ModDmax 3rd degre…      143      3.8        175 <gg> 
#> 5 Dmax            Log-Exp-ModDmax  Exponenti…      146.     4          177 <gg>

LTP

results_ltp <- lactate_threshold(
  .data = demo_data, 
  intensity_column = "intensity", 
  lactate_column = "lactate", 
  heart_rate_column = "heart_rate",
  method = "LTP",
  fit = "3rd degree polynomial", 
  include_baseline = TRUE, 
  sport = "cycling",
  plot = TRUE
)

#> # A tibble: 2 × 7
#>   method_category method fitting              intensity lactate heart_rate plot 
#>   <fct>           <fct>  <chr>                    <dbl>   <dbl>      <dbl> <lis>
#> 1 LTP             LTP1   3rd degree polynomi…      88.9     1.5        143 <gg> 
#> 2 LTP             LTP2   3rd degree polynomi…     148.      4.1        178 <gg>

LTratio

results_ltratio <- lactate_threshold(
  .data = demo_data, 
  intensity_column = "intensity", 
  lactate_column = "lactate", 
  heart_rate_column = "heart_rate",
  method = "LTratio",
  fit = "3rd degree polynomial", 
  include_baseline = TRUE, 
  sport = "cycling",
  plot = TRUE
)

#> # A tibble: 1 × 7
#>   method_category method  fitting            intensity lactate heart_rate plot  
#>   <fct>           <fct>   <chr>                  <dbl>   <dbl>      <dbl> <list>
#> 1 LTratio         LTratio B-Spline (default)      71.2     1.2        132 <gg>

Lactate curve

In case you would like to retrieve the data for producing your own plots, you can use the lactate_curve() function:

data_lactate_curve <- lactate_curve(
  .data = demo_data,
  intensity_column = "intensity",
  lactate_column = "lactate",
  heart_rate_column = "heart_rate",
  fit = "3rd degree polynomial",
  include_baseline = FALSE,
  sport = "cycling"
)

data_lactate_curve
#> $data
#> # A tibble: 8 × 3
#>   intensity lactate heart_rate
#>       <int>   <dbl>      <int>
#> 1        25    0.93         96
#> 2        50    0.98        114
#> 3        75    1.23        134
#> 4       100    1.88        154
#> 5       125    2.8         170
#> 6       150    4.21        182
#> 7       175    6.66        193
#> 8       191    8.64        198
#> 
#> $lactate_curve
#> # A tibble: 1,411 × 2
#>    intensity lactate
#>        <dbl>   <dbl>
#>  1      50     0.957
#>  2      50.1   0.959
#>  3      50.2   0.960
#>  4      50.3   0.961
#>  5      50.4   0.963
#>  6      50.5   0.964
#>  7      50.6   0.965
#>  8      50.7   0.966
#>  9      50.8   0.968
#> 10      50.9   0.969
#> # ℹ 1,401 more rows
#> 
#> $heart_rate_response
#> # A tibble: 1,411 × 2
#>    intensity heart_rate
#>        <dbl>      <dbl>
#>  1      50         120.
#>  2      50.1       120.
#>  3      50.2       120.
#>  4      50.3       120.
#>  5      50.4       120.
#>  6      50.5       120.
#>  7      50.6       120.
#>  8      50.7       120.
#>  9      50.8       120.
#> 10      50.9       120.
#> # ℹ 1,401 more rows

And then you easily produce plots like the following:

library(ggplot2)

ggplot() +
  geom_path(data = data_lactate_curve$lactate_curve, aes(intensity, lactate)) +
  geom_point(data = data_lactate_curve$data, aes(intensity, lactate), size = 4) +
  theme_light()


ggplot() +
  geom_path(data = data_lactate_curve$heart_rate_response, aes(intensity, heart_rate)) +
  geom_point(data = data_lactate_curve$data, aes(intensity, heart_rate), size = 4) +
  theme_light()

You can also combine the results from the lactate_threshold() function to plot the results:

ggplot() +
  geom_path(data = data_lactate_curve$lactate_curve, aes(intensity, lactate)) +
  geom_point(data = data_lactate_curve$data, aes(intensity, lactate), size = 4) +
  geom_point(data = results_overall, aes(intensity, lactate, color = method), size = 3) +
  theme_light()

You can also compare the lactate curves after a training period, for example:

data_after_training <- tibble::tribble(
  ~step, ~length, ~intensity, ~lactate, ~heart_rate,
     0L,      0L,         0L,     0.93,         88L,
     1L,      3L,        50L,     0.98,        108L,
     2L,      3L,        75L,     1.12,        126L,
     3L,      3L,       100L,     1.6,         138L,
     4L,      3L,       125L,      2.1,        156L,
     5L,      3L,       150L,     3.75,        169L,
     6L,      3L,       175L,     4.98,        182L,
     7L,      3L,       200L,     7.78,        190L,
     8L,      3L,       225L,     10.12,       199L
  )

data_lactate_curve_after_training <- lactate_curve(
  .data = data_after_training,
  intensity_column = "intensity",
  lactate_column = "lactate",
  heart_rate_column = "heart_rate",
  fit = "3rd degree polynomial",
  include_baseline = FALSE,
  sport = "cycling"
)

ggplot() +
  geom_path(data = data_lactate_curve$lactate_curve, aes(intensity, lactate, color = "before training")) +
  geom_point(data = data_lactate_curve$data, aes(intensity, lactate, color = "before training"), size = 4) +
  geom_path(data = data_lactate_curve_after_training$lactate_curve, aes(intensity, lactate, color = "after training")) +
  geom_point(data = data_lactate_curve_after_training$data, aes(intensity, lactate, color = "after training"), size = 4) +
  theme_light() +
  labs(color = NULL)


ggplot() +
  geom_path(data = data_lactate_curve$heart_rate_response, aes(intensity, heart_rate, color = "before training")) +
  geom_point(data = data_lactate_curve$data, aes(intensity, heart_rate, color = "before training"), size = 4) +
  geom_path(data = data_lactate_curve_after_training$heart_rate_response, aes(intensity, heart_rate, color = "after training")) +
  geom_point(data = data_lactate_curve_after_training$data, aes(intensity, heart_rate, color = "after training"), size = 4) +
  theme_light() +
  labs(color = NULL)

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.