resistance predict update

vignettes/AMR.Rmd

@@ -17,9 +17,9 @@ editor_options:
 ```{r setup, include = FALSE, results = 'markup'}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#",
+  comment = "#>",
   fig.width = 7.5,
-  fig.height = 4.5
+  fig.height = 5
 )
 ```
 
@@ -106,14 +106,21 @@ ab_interpretations <- c("S", "I", "R")
 Using the `sample()` function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the `prob` parameter.
 
 ```{r merge data}
-data <- data.frame(date = sample(dates, 5000, replace = TRUE),
-                   patient_id = sample(patients, 5000, replace = TRUE),
-                   hospital = sample(hospitals, 5000, replace = TRUE, prob = c(0.30, 0.35, 0.15, 0.20)),
-                   bacteria = sample(bacteria, 5000, replace = TRUE, prob = c(0.50, 0.25, 0.15, 0.10)),
-                   amox = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.60, 0.05, 0.35)),
-                   amcl = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.75, 0.10, 0.15)),
-                   cipr = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.80, 0.00, 0.20)),
-                   gent = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.92, 0.00, 0.08))
+sample_size <- 20000
+data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
+                   patient_id = sample(patients, size = sample_size, replace = TRUE),
+                   hospital = sample(hospitals, size = sample_size, replace = TRUE,
+                                     prob = c(0.30, 0.35, 0.15, 0.20)),
+                   bacteria = sample(bacteria, size = sample_size, replace = TRUE,
+                                     prob = c(0.50, 0.25, 0.15, 0.10)),
+                   amox = sample(ab_interpretations, size = sample_size, replace = TRUE,
+                                 prob = c(0.60, 0.05, 0.35)),
+                   amcl = sample(ab_interpretations, size = sample_size, replace = TRUE,
+                                 prob = c(0.75, 0.10, 0.15)),
+                   cipr = sample(ab_interpretations, size = sample_size, replace = TRUE,
+                                 prob = c(0.80, 0.00, 0.20)),
+                   gent = sample(ab_interpretations, size = sample_size, replace = TRUE,
+                                 prob = c(0.92, 0.00, 0.08))
                    )
 ```
 
@@ -124,6 +131,7 @@ data <- data %>% left_join(patients_table)
 ```
 
 The resulting data set contains 5,000 blood culture isolates. With the `head()` function we can preview the first 6 values of this data set:
+
 ```{r preview data set 1, eval = FALSE}
 head(data)
 ```
@@ -148,6 +156,7 @@ data %>% freq(gender, markdown = FALSE, header = TRUE)
 So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values `M` and `F`. From a researcher perspective: there are slightly more men. Nothing we didn't already know.
 
 The data is already quite clean, but we still need to transform some variables. The `bacteria` column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The `mutate()` function of the `dplyr` package makes this really easy:
+
 ```{r transform mo 1}
 data <- data %>%
   mutate(bacteria = as.mo(bacteria))
@@ -202,6 +211,7 @@ data_1st <- data %>%
 ```
 
 For future use, the above two syntaxes can be shortened with the `filter_first_isolate()` function:
+
 ```{r 1st isolate filter 2, eval = FALSE}
 data_1st <- data %>% 
   filter_first_isolate()
@@ -263,6 +273,7 @@ data_1st <- data %>%
 So we end up with `r format(nrow(data_1st), big.mark = ",")` isolates for analysis. 
 
 We can remove unneeded columns:
+
 ```{r}
 data_1st <- data_1st %>% 
   select(-c(first, keyab))
@@ -359,6 +370,7 @@ data_1st %>%
 ```
 
 To make a transition to the next part, let's see how this difference could be plotted:
+
 ```{r plot 1}
 data_1st %>% 
   group_by(genus) %>% 
@@ -391,6 +403,7 @@ ggplot(a_data_set,
 ```
 
 The `AMR` package contains functions to extend this `ggplot2` package, for example `geom_rsi()`. It automatically transforms data with `count_df()` or `portion_df()` and show results in stacked bars. Its simplest and shortest example:
+
 ```{r plot 3}
 ggplot(data_1st) +
   geom_rsi(translate_ab = FALSE)
@@ -424,6 +437,7 @@ ggplot(data_1st %>% group_by(genus)) +
 ```
 
 To simplify this, we also created the `ggplot_rsi()` function, which combines almost all above functions:
+
 ```{r plot 5}
 data_1st %>% 
   group_by(genus) %>%

vignettes/EUCAST.Rmd

@@ -17,7 +17,7 @@ editor_options:
 ```{r setup, include = FALSE, results = 'markup'}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#",
+  comment = "#>",
   fig.width = 7.5,
   fig.height = 4.5
 )

vignettes/atc_property.Rmd

@@ -16,7 +16,7 @@ editor_options:
 ```{r setup, include = FALSE, results = 'markup'}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#"
+  comment = "#>"
 )
 # set to original language (English)
 Sys.setlocale(locale = "C")

vignettes/mo_property.Rmd

@@ -16,8 +16,10 @@ editor_options:
 ```{r setup, include = FALSE, results = 'markup'}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#"
+  comment = "#>"
 )
+# set to original language (English)
+Sys.setlocale(locale = "C")
 ```
 
 *(will be available soon)*

vignettes/resistance_predict.Rmd

@@ -16,9 +16,9 @@ editor_options:
 ```{r setup, include = FALSE, results = 'markup'}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#",
+  comment = "#>",
   fig.width = 7.5,
-  fig.height = 4.5
+  fig.height = 4.75
 )
 ```
 
@@ -37,7 +37,7 @@ library(AMR)
 ```
 
 ## Prediction analysis
-Our package contains a function `resistance_predict()`, which takes the same input as functions for [other AMR analysis](./articles/AMR.html). Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.
+Our package contains a function `resistance_predict()`, which takes the same input as functions for [other AMR analysis](./AMR.html). Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.
 
 It is basically as easy as:
 ```{r, eval = FALSE}
@@ -53,27 +53,36 @@ predict_pita <- septic_patients %>%
   resistance_predict(col_ab = "pita")
 ```
 
+The function will look for a date column itself if `col_date` is not set.
+
+When running any of these commands, a summary of the regression model will be printed unless using `resistance_predict(..., info = FALSE)`.
+
 ```{r, echo = FALSE}
 predict_pita <- septic_patients %>% 
   resistance_predict(col_ab = "pita")
 ```
 
-The function will look for a data column itself if `col_date` is not set. The result is nothing more than a `data.frame`, containing the years, number of observations, actual observed resistance, the estimated resistance and the standard error below and above the estimation:
+This text is only a printed summary - the actual result (output) of the function is a `data.frame` containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:
+
 ```{r}
 predict_pita
 ```
 
 The function `plot` is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:
 
-```{r}
+```{r, fig.height = 5.5}
 plot(predict_pita)
 ```
 
-We also support the `ggplot2` package with the function `ggplot_rsi_predict()`:
+This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.
+
+We also support the `ggplot2` package with our custom function `ggplot_rsi_predict()` to create more appealing plots:
 
 ```{r}
-library(ggplot2)
 ggplot_rsi_predict(predict_pita)
+
+# choose for error bars instead of a ribbon
+ggplot_rsi_predict(predict_pita, ribbon = FALSE)
 ```
 
 ### Choosing the right model
@@ -84,7 +93,7 @@ Resistance is not easily predicted; if we look at vancomycin resistance in Gram
 septic_patients %>%
   filter(mo_gramstain(mo) == "Gram positive") %>%
   resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE) %>% 
-  plot()
+  ggplot_rsi_predict()
 ```
 
 Vancomycin resistance could be 100% in ten years, but might also stay around 0%. 
@@ -99,13 +108,22 @@ Valid values are:
 | `"loglin"` or `"poisson"`              | `glm(..., family = poisson)`  | Generalised linear model with poisson distribution  |
 | `"lin"` or `"linear"`                  | `lm()`                        | Linear model                                        |
 
-For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on observed years:
+For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on the observed years:
 
 ```{r}
 septic_patients %>%
   filter(mo_gramstain(mo) == "Gram positive") %>%
   resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE, model = "linear") %>% 
-  plot()
+  ggplot_rsi_predict()
 ```
 
 This seems more likely, doesn't it?
+
+The model itself is also available from the object, as an `attribute`:
+```{r}
+model <- attributes(predict_pita)$model
+
+summary(model)$family
+
+summary(model)$coefficients
+```