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The goal of lactater is to provide tools for making it easier to analyze lactate thresholds.
You can install the released version of lactater from CRAN with:
You can install the development version of lactater from Github with:
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 198With 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>
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>
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>
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>
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>
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>
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>
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
#>
#> $heart_rate_response
#> # A tibble: 1,411 × 2
#> intensity heart_rate
#> <dbl> <dbl>
#> 1 50 120.
#> 2 50.1 120.
#> 3 50.2 120.
#> 4 50.3 120.
#> 5 50.4 120.
#> 6 50.5 120.
#> 7 50.6 120.
#> 8 50.7 120.
#> 9 50.8 120.
#> 10 50.9 120.
#> # ℹ 1,401 more rowsAnd then you easily produce plots like the following:
library(ggplot2)
ggplot() +
geom_path(data = data_lactate_curve$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.