README.Rmd

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# lactater <img src='man/figures/logo.png' align="right" height="240" />

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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](https://CRAN.R-project.org/package=lactater) with:

``` r
install.packages("lactater")
```

You can install the development version of `lactater` from [Github](https://github.com/fmmattioni/lactater) with:

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

## Demo data

```{r}
library(lactater)

demo_data
```

## Usage

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

```{r}
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
)
```

```{r echo=FALSE}
results_overall
```

```{r echo=FALSE, fig.width=16, fig.height=11}
plot_methods(results_overall$plot)
```


## You can also choose one method:

### Log-log

```{r}
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
)
```

```{r echo=FALSE}
results_loglog
```

```{r echo=FALSE}
plot_methods(results_loglog$plot)
```

### OBLA

```{r}
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
)
```

```{r echo=FALSE}
results_obla
```

```{r echo=FALSE, fig.width=10, fig.height=7}
plot_methods(results_obla$plot)
```

### Bsln+

```{r}
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
)
```

```{r echo=FALSE}
results_bsln_plus
```

```{r echo=FALSE, fig.width=6, fig.height=6}
plot_methods(results_bsln_plus$plot, ncol = 2)
```

### Dmax

```{r}
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
)
```

```{r echo=FALSE}
results_dmax
```

```{r echo=FALSE, fig.width=10, fig.height=6}
plot_methods(results_dmax$plot)
```

### LTP

```{r}
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
)
```

```{r echo=FALSE}
results_ltp
```

```{r echo=FALSE, fig.width=10, fig.height=5}
plot_methods(results_ltp$plot)
```

### LTratio

```{r}
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
)
```

```{r echo=FALSE}
results_ltratio
```

```{r echo=FALSE}
plot_methods(results_ltratio$plot)
```

## Lactate curve

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

```{r}
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
```

And then you easily produce plots like the following:

```{r}
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:

```{r}
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:

```{r}
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"
)
```


```{r}
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)
```

## Related work

* [lactate-R](http://www.uiginn.com/lactate/lactate-r.html)
* [cycleRtools](https://github.com/cran/cycleRtools)
* [exPhysR](https://github.com/dhammarstrom/exPhysR)