@ -31,7 +31,7 @@
< a class = "navbar-brand me-2" href = "../index.html" > AMR (for R)< / a >
< small class = "nav-text text-muted me-auto" data-bs-toggle = "tooltip" data-bs-placement = "bottom" title = "" > 2.1.1.9232 < / small >
< small class = "nav-text text-muted me-auto" data-bs-toggle = "tooltip" data-bs-placement = "bottom" title = "" > 2.1.1.9233 < / small >
< button class = "navbar-toggler" type = "button" data-bs-toggle = "collapse" data-bs-target = "#navbar" aria-controls = "navbar" aria-expanded = "false" aria-label = "Toggle navigation" >
@ -134,23 +134,10 @@ package.</p>
< div class = "sourceCode" id = "cb1" > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Load required libraries< / span > < / span >
< span > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://msberends.github.io/AMR/" > AMR< / a > < / span > < span class = "op" > )< / span > < span class = "co" > # For AMR data analysis< / span > < / span >
< span > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://tidymodels.tidymodels.org" class = "external-link" > tidymodels< / a > < / span > < span class = "op" > )< / span > < span class = "co" > # For machine learning workflows, and data manipulation (dplyr, tidyr, ...)< / span > < / span >
< span > < span class = "co" > #> ── < span style = "font-weight: bold;" > Attaching packages< / span > ────────────────────────────────────── tidymodels 1.3.0 ──< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #00BB00; "> ✔ < / span > < span style = "color: #0000BB;" > broom < / span > 1.0.8 < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > recipes < / span > 1.2.1< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #00BB00; "> ✔ < / span > < span style = "color: #0000BB;" > dials < / span > 1.4.0 < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > rsample < / span > 1.2.1 < / span > < / span >
< span > < span class = "co" > #> < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > dplyr < / span > 1.1.4 < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > tibble < / span > 3.2.1< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > ggplot2 < / span > 3.5.1 < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > tidyr < / span > 1.3.1< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > infer < / span > 1.0.7 < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > tune < / span > 1.3.0< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > modeldata < / span > 1.4.0 < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > workflows < / span > 1.2.0< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > parsnip < / span > 1.3.1 < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > workflowsets< / span > 1.1.0< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > purrr < / span > 1.0.4 < span style = "color: #00BB00;" > ✔< / span > < span style = "color: #0000BB;" > yardstick < / span > 1.3.2< / span > < / span >
< span > < span class = "co" > #> ── < span style = "font-weight: bold;" > Conflicts< / span > ───────────────────────────────────────── tidymodels_conflicts() ──< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #BB0000;" > ✖< / span > < span style = "color: #0000BB;" > purrr< / span > ::< span style = "color: #00BB00;" > discard()< / span > masks < span style = "color: #0000BB;" > scales< / span > ::discard()< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #BB0000;" > ✖< / span > < span style = "color: #0000BB;" > dplyr< / span > ::< span style = "color: #00BB00;" > filter()< / span > masks < span style = "color: #0000BB;" > stats< / span > ::filter()< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #BB0000;" > ✖< / span > < span style = "color: #0000BB;" > dplyr< / span > ::< span style = "color: #00BB00;" > lag()< / span > masks < span style = "color: #0000BB;" > stats< / span > ::lag()< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #BB0000;" > ✖< / span > < span style = "color: #0000BB;" > recipes< / span > ::< span style = "color: #00BB00;" > step()< / span > masks < span style = "color: #0000BB;" > stats< / span > ::step()< / span > < / span >
< span > < / span >
< span > < span class = "co" > # Your data could look like this:< / span > < / span >
< span > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://tidymodels.tidymodels.org" class = "external-link" > tidymodels< / a > < / span > < span class = "op" > )< / span > < span class = "co" > # For machine learning workflows, and data manipulation (dplyr, tidyr, ...)< / span > < / span > < / code > < / pre > < / div >
< p > Prepare the data: < / p >
< div class = "sourceCode" id = "cb2 "> < pre class = "downlit sourceCode r" >
< code class = "sourceCode R "> < span < span class = "co" > # Your data could look like this: < / span > < / span >
< span > < span class = "va" > example_isolates< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #949494;" > # A tibble: 2,000 × < / span > < / span > < / span >
< span > < span class = "co" > #> date patient age gender ward mo PEN OXA FLC AMX < / span > < / span >
@ -215,7 +202,7 @@ three steps: preprocessing, model specification, and fitting.</p>
< h4 id = "preprocessing-with-a-recipe" > 1. Preprocessing with a Recipe< a class = "anchor" aria-label = "anchor" href = "#preprocessing-with-a-recipe" > < / a >
< / h4 >
< p > We create a recipe to preprocess the data for modelling.< / p >
< div class = "sourceCode" id = "cb2 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb3 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Define the recipe for data preprocessing< / span > < / span >
< span > < span class = "va" > resistance_recipe< / span > < span class = "op" > < -< / span > < span class = "fu" > recipe< / span > < span class = "op" > (< / span > < span class = "va" > mo< / span > < span class = "op" > ~< / span > < span class = "va" > .< / span > , data < span class = "op" > =< / span > < span class = "va" > data< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < / span >
< span > < span class = "fu" > step_corr< / span > < span class = "op" > (< / span > < span class = "fu" > < a href = "https://rdrr.io/r/base/c.html" class = "external-link" > c< / a > < / span > < span class = "op" > (< / span > < span class = "fu" > < a href = "../reference/antimicrobial_selectors.html" > aminoglycosides< / a > < / span > < span class = "op" > (< / span > < span class = "op" > )< / span > , < span class = "fu" > < a href = "../reference/antimicrobial_selectors.html" > betalactams< / a > < / span > < span class = "op" > (< / span > < span class = "op" > )< / span > < span class = "op" > )< / span > , threshold < span class = "op" > =< / span > < span class = "fl" > 0.9< / span > < span class = "op" > )< / span > < / span >
@ -233,7 +220,7 @@ three steps: preprocessing, model specification, and fitting.</p>
< p > For a recipe that includes at least one preprocessing operation, like
we have with < code > step_corr()< / code > , the necessary parameters can be
estimated from a training set using < code > prep()< / code > :< / p >
< div class = "sourceCode" id = "cb3 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb4 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "fu" > prep< / span > < span class = "op" > (< / span > < span class = "va" > resistance_recipe< / span > < span class = "op" > )< / span > < / span >
< span > < span class = "co" > #> < span style = "color: #0000BB;" > ℹ < / span > < span style = "color: #0000BB; background-color: #EEEEEE;" > aminoglycosides()< / span > < span style = "color: #0000BB;" > using columns '< / span > < span style = "color: #0000BB; font-weight: bold;" > GEN< / span > < span style = "color: #0000BB;" > ' (gentamicin), '< / span > < span style = "color: #0000BB; font-weight: bold;" > TOB< / span > < span style = "color: #0000BB;" > '< / span > < / span > < / span >
< span > < span class = "co" > < span style = "color: #0000BB;" > #> (tobramycin), '< / span > < span style = "color: #0000BB; font-weight: bold;" > AMK< / span > < span style = "color: #0000BB;" > ' (amikacin), and '< / span > < span style = "color: #0000BB; font-weight: bold;" > KAN< / span > < span style = "color: #0000BB;" > ' (kanamycin)< / span > < / span > < / span >
@ -277,7 +264,7 @@ much with existing, other variables.</p>
< / h4 >
< p > We define a logistic regression model since resistance prediction is
a binary classification task.< / p >
< div class = "sourceCode" id = "cb4 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb5 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Specify a logistic regression model< / span > < / span >
< span > < span class = "va" > logistic_model< / span > < span class = "op" > < -< / span > < span class = "fu" > logistic_reg< / span > < span class = "op" > (< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < / span >
< span > < span class = "fu" > set_engine< / span > < span class = "op" > (< / span > < span class = "st" > "glm"< / span > < span class = "op" > )< / span > < span class = "co" > # Use the Generalised Linear Model engine< / span > < / span >
@ -300,7 +287,7 @@ engine.</li>
< / h4 >
< p > We bundle the recipe and model together into a < code > workflow< / code > ,
which organises the entire modeling process.< / p >
< div class = "sourceCode" id = "cb5 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb6 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Combine the recipe and model into a workflow< / span > < / span >
< span > < span class = "va" > resistance_workflow< / span > < span class = "op" > < -< / span > < span class = "fu" > workflow< / span > < span class = "op" > (< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < / span >
< span > < span class = "fu" > add_recipe< / span > < span class = "op" > (< / span > < span class = "va" > resistance_recipe< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < span class = "co" > # Add the preprocessing recipe< / span > < / span >
@ -328,7 +315,7 @@ which organises the entire modeling process.</p>
< p > To train the model, we split the data into training and testing sets.
Then, we fit the workflow on the training set and evaluate its
performance.< / p >
< div class = "sourceCode" id = "cb6 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb7 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Split data into training and testing sets< / span > < / span >
< span > < span class = "fu" > < a href = "https://rdrr.io/r/base/Random.html" class = "external-link" > set.seed< / a > < / span > < span class = "op" > (< / span > < span class = "fl" > 123< / span > < span class = "op" > )< / span > < span class = "co" > # For reproducibility< / span > < / span >
< span > < span class = "va" > data_split< / span > < span class = "op" > < -< / span > < span class = "fu" > initial_split< / span > < span class = "op" > (< / span > < span class = "va" > data< / span > , prop < span class = "op" > =< / span > < span class = "fl" > 0.8< / span > < span class = "op" > )< / span > < span class = "co" > # 80% training, 20% testing< / span > < / span >
@ -350,7 +337,7 @@ testing sets.</li>
functions are internally called again. For training, these functions are
called since they are stored in the recipe.< / p >
< p > Next, we evaluate the model on the testing data.< / p >
< div class = "sourceCode" id = "cb7 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb8 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Make predictions on the testing set< / span > < / span >
< span > < span class = "va" > predictions< / span > < span class = "op" > < -< / span > < span class = "va" > fitted_workflow< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < / span >
< span > < span class = "fu" > < a href = "https://rdrr.io/r/stats/predict.html" class = "external-link" > predict< / a > < / span > < span class = "op" > (< / span > < span class = "va" > testing_data< / span > < span class = "op" > )< / span > < span class = "co" > # Generate predictions< / span > < / span >
@ -417,7 +404,7 @@ kappa.</li>
< p > It appears we can predict the Gram stain with a 99.5% accuracy based
on AMR results of only aminoglycosides and beta-lactam antibiotics. The
ROC curve looks like this:< / p >
< div class = "sourceCode" id = "cb8 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb9 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "va" > predictions< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < / span >
< span > < span class = "fu" > roc_curve< / span > < span class = "op" > (< / span > < span class = "va" > mo< / span > , < span class = "va" > `.pred_Gram-negative`< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < / span >
< span > < span class = "fu" > < a href = "https://ggplot2.tidyverse.org/reference/autoplot.html" class = "external-link" > autoplot< / a > < / span > < span class = "op" > (< / span > < span class = "op" > )< / span > < / span > < / code > < / pre > < / div >
@ -466,7 +453,7 @@ model.</li>
< / h3 >
< p > We start by transforming the < code > example_isolates< / code > dataset
into a structured time-series format.< / p >
< div class = "sourceCode" id = "cb9 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb10 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Load required libraries< / span > < / span >
< span > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://msberends.github.io/AMR/" > AMR< / a > < / span > < span class = "op" > )< / span > < / span >
< span > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://tidymodels.tidymodels.org" class = "external-link" > tidymodels< / a > < / span > < span class = "op" > )< / span > < / span >
@ -499,11 +486,18 @@ into a structured time-series format.</p>
< span > < span class = "co" > #> < span style = "color: #BCBCBC;" > 9< / span > < span style = "text-decoration: underline;" > 2< / span > 006 Gram-negative 0.379 0 0.1 < / span > < / span >
< span > < span class = "co" > #> < span style = "color: #BCBCBC;" > 10< / span > < span style = "text-decoration: underline;" > 2< / span > 006 Gram-positive 0.778 0.333 0.353 < / span > < / span >
< span > < span class = "co" > #> < span style = "color: #949494;" > # ℹ < / span > < / span > < / span > < / code > < / pre > < / div >
< p > < strong > Explanation:< / strong > - < code > mo_name(mo)< / code > : Converts
microbial codes into proper species names. - < code > < a href = "../reference/proportion.html" > resistance()< / a > < / code > :
Converts AMR results into numeric values (proportion of resistant
isolates). - < code > group_by(year, ward, species)< / code > : Aggregates
resistance rates by year and ward .< / p >
< p > < strong > Explanation:< / strong > < / p >
< ul >
< li >
< code > mo_name(mo)< / code > : Converts microbial codes into proper
species names .< / li >
< li >
< code > < a href = "../reference/proportion.html" > resistance()< / a > < / code > : Converts AMR results into numeric values
(proportion of resistant isolates).< / li >
< li >
< code > group_by(year, ward, species)< / code > : Aggregates resistance
rates by year and ward.< / li >
< / ul >
< / div >
< div class = "section level3" >
< h3 id = "defining-the-workflow-1" >
@ -514,7 +508,7 @@ preprocessing step, a model specification, and the fitting process.</p>
< div class = "section level4" >
< h4 id = "preprocessing-with-a-recipe-1" > 1. Preprocessing with a Recipe< a class = "anchor" aria-label = "anchor" href = "#preprocessing-with-a-recipe-1" > < / a >
< / h4 >
< div class = "sourceCode" id = "cb10 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb11 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Define the recipe< / span > < / span >
< span > < span class = "va" > resistance_recipe_time< / span > < span class = "op" > < -< / span > < span class = "fu" > recipe< / span > < span class = "op" > (< / span > < span class = "va" > res_AMX< / span > < span class = "op" > ~< / span > < span class = "va" > year< / span > < span class = "op" > +< / span > < span class = "va" > gramstain< / span > , data < span class = "op" > =< / span > < span class = "va" > data_time< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < / span >
< span > < span class = "fu" > step_dummy< / span > < span class = "op" > (< / span > < span class = "va" > gramstain< / span > , one_hot < span class = "op" > =< / span > < span class = "cn" > TRUE< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < span class = "co" > # Convert categorical to numerical< / span > < / span >
@ -534,17 +528,23 @@ preprocessing step, a model specification, and the fitting process.</p>
< span > < span class = "co" > #> < span style = "color: #00BBBB;" > •< / span > Dummy variables from: < span style = "color: #0000BB;" > gramstain< / span > < / span > < / span >
< span > < span class = "co" > #> < span style = "color: #00BBBB;" > •< / span > Centering and scaling for: < span style = "color: #0000BB;" > year< / span > < / span > < / span >
< span > < span class = "co" > #> < span style = "color: #00BBBB;" > •< / span > Sparse, unbalanced variable filter on: < span style = "color: #0000BB;" > all_predictors()< / span > < / span > < / span > < / code > < / pre > < / div >
< p > < strong > Explanation:< / strong > - < code > step_dummy()< / code > : Encodes
categorical variables (< code > ward< / code > , < code > species< / code > ) as
numerical indicators. - < code > step_normalize()< / code > : Normalises the
< code > year< / code > variable. - < code > step_nzv ()< / code > : Removes near-zero
variance pre dictors.< / p >
< p > < strong > Explanation:< / strong > < / p >
< ul >
< li >
< code > step_dummy ()< / code > : Encodes categorical variables
(< code > ward< / code > , < code > species< / code > ) as numerical in dica tors.< / li >
< li >
< code > step_normalize()< / code > : Normalises the < code > year< / code >
variable.< / li >
< li >
< code > step_nzv()< / code > : Removes near-zero variance predictors.< / li >
< / ul >
< / div >
< div class = "section level4" >
< h4 id = "specifying-the-model-1" > 2. Specifying the Model< a class = "anchor" aria-label = "anchor" href = "#specifying-the-model-1" > < / a >
< / h4 >
< p > We use a linear regression model to predict resistance trends.< / p >
< div class = "sourceCode" id = "cb11 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb12 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Define the linear regression model< / span > < / span >
< span > < span class = "va" > lm_model< / span > < span class = "op" > < -< / span > < span class = "fu" > linear_reg< / span > < span class = "op" > (< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < / span >
< span > < span class = "fu" > set_engine< / span > < span class = "op" > (< / span > < span class = "st" > "lm"< / span > < span class = "op" > )< / span > < span class = "co" > # Use linear regression< / span > < / span >
@ -553,15 +553,20 @@ variance predictors.</p>
< span > < span class = "co" > #> Linear Regression Model Specification (regression)< / span > < / span >
< span > < span class = "co" > #> < / span > < / span >
< span > < span class = "co" > #> Computational engine: lm< / span > < / span > < / code > < / pre > < / div >
< p > < strong > Explanation:< / strong > - < code > linear_reg()< / code > : Defines a
linear regression model. - < code > set_engine("lm")< / code > : Uses R’
built-in linear regression engine.< / p >
< p > < strong > Explanation:< / strong > < / p >
< ul >
< li >
< code > linear_reg()< / code > : Defines a linear regression model.< / li >
< li >
< code > set_engine("lm")< / code > : Uses R’
engine.< / li >
< / ul >
< / div >
< div class = "section level4" >
< h4 id = "building-the-workflow-1" > 3. Building the Workflow< a class = "anchor" aria-label = "anchor" href = "#building-the-workflow-1" > < / a >
< / h4 >
< p > We combine the preprocessing recipe and model into a workflow.< / p >
< div class = "sourceCode" id = "cb12 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb13 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Create workflow< / span > < / span >
< span > < span class = "va" > resistance_workflow_time< / span > < span class = "op" > < -< / span > < span class = "fu" > workflow< / span > < span class = "op" > (< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < / span >
< span > < span class = "fu" > add_recipe< / span > < span class = "op" > (< / span > < span class = "va" > resistance_recipe_time< / span > < span class = "op" > )< / span > < span class = "op" > < a href = "https://magrittr.tidyverse.org/reference/pipe.html" class = "external-link" > %> %< / a > < / span > < / span >
@ -591,7 +596,7 @@ built-in linear regression engine.</p>
< / h3 >
< p > We split the data into training and testing sets, fit the model, and
evaluate performance.< / p >
< div class = "sourceCode" id = "cb13 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb14 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "co" > # Split the data< / span > < / span >
< span > < span class = "fu" > < a href = "https://rdrr.io/r/base/Random.html" class = "external-link" > set.seed< / a > < / span > < span class = "op" > (< / span > < span class = "fl" > 123< / span > < span class = "op" > )< / span > < / span >
< span > < span class = "va" > data_split_time< / span > < span class = "op" > < -< / span > < span class = "fu" > initial_split< / span > < span class = "op" > (< / span > < span class = "va" > data_time< / span > , prop < span class = "op" > =< / span > < span class = "fl" > 0.8< / span > < span class = "op" > )< / span > < / span >
@ -618,17 +623,25 @@ evaluate performance.</p>
< span > < span class = "co" > #> < span style = "color: #BCBCBC;" > 1< / span > rmse standard 0.077< span style = "text-decoration: underline;" > 4< / span > < / span > < / span >
< span > < span class = "co" > #> < span style = "color: #BCBCBC;" > 2< / span > rsq standard 0.711 < / span > < / span >
< span > < span class = "co" > #> < span style = "color: #BCBCBC;" > 3< / span > mae standard 0.070< span style = "text-decoration: underline;" > 4< / span > < / span > < / span > < / code > < / pre > < / div >
< p > < strong > Explanation:< / strong > - < code > initial_split()< / code > : Splits
data into training and testing sets. - < code > fit()< / code > : Trains the
workflow. - < code > < a href = "https://rdrr.io/r/stats/predict.html" class = "external-link" > predict()< / a > < / code > : Generates resistance predictions. -
< code > metrics ()< / code > : Evaluates model performance.< / p >
< p > < strong > Explanation:< / strong > < / p >
< ul >
< li >
< code > initial_split ()< / code > : Splits data into training and testing
sets.< / li >
< li >
< code > fit()< / code > : Trains the workflow.< / li >
< li >
< code > < a href = "https://rdrr.io/r/stats/predict.html" class = "external-link" > predict()< / a > < / code > : Generates resistance predictions.< / li >
< li >
< code > metrics()< / code > : Evaluates model performance.< / li >
< / ul >
< / div >
< div class = "section level3" >
< h3 id = "visualising-predictions" >
< strong > Visualising Predictions< / strong > < a class = "anchor" aria-label = "anchor" href = "#visualising-predictions" > < / a >
< / h3 >
< p > We plot resistance trends over time for amoxicillin.< / p >
< div class = "sourceCode" id = "cb14 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb15 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "kw" > < a href = "https://rdrr.io/r/base/library.html" class = "external-link" > library< / a > < / span > < span class = "op" > (< / span > < span class = "va" > < a href = "https://ggplot2.tidyverse.org" class = "external-link" > ggplot2< / a > < / span > < span class = "op" > )< / span > < / span >
< span > < / span >
< span > < span class = "co" > # Plot actual vs predicted resistance over time< / span > < / span >
@ -642,7 +655,7 @@ workflow. - <code><a href="https://rdrr.io/r/stats/predict.html" class="external
< p > < img src = "AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png" width = "720" > < / p >
< p > Additionally, we can visualise resistance trends in
< code > ggplot2< / code > and directly add linear models there:< / p >
< div class = "sourceCode" id = "cb15 " > < pre class = "downlit sourceCode r" >
< div class = "sourceCode" id = "cb16 " > < pre class = "downlit sourceCode r" >
< code class = "sourceCode R" > < span > < span class = "fu" > < a href = "https://ggplot2.tidyverse.org/reference/ggplot.html" class = "external-link" > ggplot< / a > < / span > < span class = "op" > (< / span > < span class = "va" > data_time< / span > , < span class = "fu" > < a href = "https://ggplot2.tidyverse.org/reference/aes.html" class = "external-link" > aes< / a > < / span > < span class = "op" > (< / span > x < span class = "op" > =< / span > < span class = "va" > year< / span > , y < span class = "op" > =< / span > < span class = "va" > res_AMX< / span > , color < span class = "op" > =< / span > < span class = "va" > gramstain< / span > < span class = "op" > )< / span > < span class = "op" > )< / span > < span class = "op" > +< / span > < / span >
< span > < span class = "fu" > < a href = "https://ggplot2.tidyverse.org/reference/geom_path.html" class = "external-link" > geom_line< / a > < / span > < span class = "op" > (< / span > < span class = "op" > )< / span > < span class = "op" > +< / span > < / span >
< span > < span class = "fu" > < a href = "https://ggplot2.tidyverse.org/reference/labs.html" class = "external-link" > labs< / a > < / span > < span class = "op" > (< / span > title < span class = "op" > =< / span > < span class = "st" > "AMX Resistance Trends"< / span > ,< / span >
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