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237
vignettes/AMR.Rmd
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@ -1,11 +1,12 @@
---
title: "The AMR package - How to conduct AMR analysis"
title: "How to conduct AMR analysis"
author: "Matthijs S. Berends"
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 3
vignette: >
%\VignetteIndexEntry{The AMR package - How to conduct AMR analysis}
%\VignetteIndexEntry{How to conduct AMR analysis}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
@ -15,18 +16,18 @@ editor_options:
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#"
comment = "#",
fig.width = 7.5,
fig.height = 4.5
)
# set to original language (English)
Sys.setlocale(locale = "C")
```
**Note:** values on this page will be regenerated with every website update since it is written in [RMarkdown](https://rmarkdown.rstudio.com/), so actual results will change over time. However, the methodology remains unchanged. This page was generated on `r format(Sys.Date(), "%d %B %Y")`.
**Note:** values on this page will change with every website update since they are based on randomly created values and the page was written in [RMarkdown](https://rmarkdown.rstudio.com/). However, the methodology remains unchanged. This page was generated on `r format(Sys.Date(), "%d %B %Y")`.
# Introduction
(work in progress)
## Introduction
# Tutorial
For this tutorial, we will create fake demonstration data to work with.
You can skip to [Cleaning the data](#cleaning-the-data) if you already have your own data ready. If you start your analysis, try to make the structure of your data generally look like this:
@ -105,12 +106,12 @@ Using the `sample()` function, we can randomly select items from all objects we
```{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),
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(0.6, 0.05, 0.35)),
amcl = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.75, 0.1, 0.15)),
cipr = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.8, 0, 0.2)),
gent = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.92, 0, 0.07))
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))
)
```
@ -121,7 +122,7 @@ 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, echo = TRUE, results = 'hide'}
```{r preview data set 1, eval = FALSE}
head(data)
```
@ -134,7 +135,7 @@ Now, let's start the cleaning and the analysis!
## Cleaning the data
Use the frequency table function `freq()` to look specifically for unique values in any variable. For example, for the `gender` variable:
```{r freq gender 1, echo = TRUE, results = 'hide'}
```{r freq gender 1, eval = FALSE}
data %>% freq(gender) # this would be the same: freq(data$gender)
```
@ -144,7 +145,7 @@ 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:
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))
@ -154,7 +155,7 @@ We also want to transform the antibiotics, because in real life data we don't kn
```{r transform abx}
data <- data %>%
mutate_at(vars(amox:cipr), as.rsi)
mutate_at(vars(amox:gent), as.rsi)
```
Finally, we will apply [EUCAST rules](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the `eucast_rules()` function can also apply additional rules, like forcing <help title="ATC: J01CA01">ampicillin</help> = R when <help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.
@ -166,12 +167,13 @@ data <- eucast_rules(data, col_mo = "bacteria")
```
## Adding new variables
Now we have the microbial ID, we can add some taxonomic properties:
Now that we have the microbial ID, we can add some taxonomic properties:
```{r new taxo}
data <- data %>%
mutate(gramstain = mo_gramstain(bacteria),
family = mo_family(bacteria))
genus = mo_genus(bacteria),
species = mo_species(bacteria))
```
### First isolates
@ -182,7 +184,7 @@ To conduct an analysis of antimicrobial resistance, you must [only include the f
The Clinical and Laboratory Standards Institute (CLSI) appoints this as follows:
> *(...) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, **only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype)**. The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded.*
<br>Chapter 6.4, M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. https://clsi.org/standards/products/microbiology/documents/m39/
<br>[M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4](https://clsi.org/standards/products/microbiology/documents/m39/)
This `AMR` package includes this methodology with the `first_isolate()` function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:
```{r 1st isolate}
@ -198,13 +200,13 @@ data_1st <- data %>%
```
For future use, the above two syntaxes can be shortened with the `filter_first_isolate()` function:
```{r 1st isolate filter 2, results = 'hide', message = FALSE}
```{r 1st isolate filter 2, eval = FALSE}
data_1st <- data %>%
filter_first_isolate()
```
### First *weighted* isolates
We made a slight twist to the CLSI algorithm, to take into account antimicrobial results. Imagine this data, sorted on date:
We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Imagine this data, sorted on date:
```{r, echo = FALSE, message = FALSE, warning = FALSE, results = 'asis'}
weighted_df <- data %>%
@ -248,7 +250,7 @@ weighted_df2 %>%
knitr::kable(align = "c")
```
Instead of `r sum(weighted_df$first)`, now `r sum(weighted_df2$first_weighted)` isolates are flagged. In total, `r AMR:::percent(sum(data$first_weighted) / nrow(data))` of all isolates are marked 'first weighted' - `r AMR:::percent((sum(data$first_weighted) / nrow(data)) - (sum(data$first) / nrow(data)))` more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
Instead of `r sum(weighted_df$first)`, now `r sum(weighted_df2$first_weighted)` isolates are flagged. In total, `r AMR:::percent(sum(data$first_weighted) / nrow(data))` of all isolates are marked 'first weighted' - `r AMR:::percent((sum(data$first_weighted) / nrow(data)) -- (sum(data$first) / nrow(data)))` more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
As with `filter_first_isolate()`, there's a shortcut for this new algorithm too:
```{r 1st isolate filter 3, results = 'hide', message = FALSE, warning = FALSE}
@ -261,12 +263,12 @@ So we end up with `r format(nrow(data_1st), big.mark = ",")` isolates for analys
We can remove unneeded columns:
```{r}
data_1st <- data_1st %>%
select(-first, -keyab)
select(-c(first, keyab))
```
Now our data looks like:
```{r preview data set 3, echo = TRUE, results = 'hide'}
```{r preview data set 3, eval = FALSE}
head(data_1st)
```
@ -277,4 +279,189 @@ knitr::kable(head(data_1st), align = "c")
Time for the analysis!
## Analysing the data
(work in progress)
You might want to start by getting an idea of how the data is distributed. It's an important start, because it also decides how you will continue your analysis.
## Dispersion of species
To just get an idea how the species are distributed, create a frequency table with our `freq()` function. We created the `genus` and `species` column earlier based on the microbial ID. With `paste()`, we can concatenate them together.
The `freq()` function can be used like the base R language was intended:
```{r freq 1, eval = FALSE}
freq(paste(data_1st$genus, data_1st$species))
```
Or can be used like the `dplyr` way, which is easier readable:
```{r freq 2a, eval = FALSE}
data_1st %>% freq(genus, species)
```
```{r freq 2b, results = 'asis', echo = FALSE}
data_1st %>%
freq(genus, species, header = TRUE)
```
### Resistance percentages
The functions `portion_R`, `portion_RI`, `portion_I`, `portion_IS` and `portion_S` can be used to determine the portion of a specific antimicrobial outcome. They can be used on their own:
```{r}
data_1st %>% portion_IR(amox)
```
Or can be used in conjuction with `group_by()` and `summarise()`, both from the `dplyr` package:
```{r, eval = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = portion_IR(amox))
```
```{r, echo = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = portion_IR(amox)) %>%
knitr::kable(align = "c", big.mark = ",")
```
Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the `n_rsi()` can be used, which works exactly like `n_distinct()` from the `dplyr` package. It counts all isolates available for every group (i.e. values S, I or R):
```{r, eval = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = portion_IR(amox),
available = n_rsi(amox))
```
```{r, echo = FALSE}
data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = portion_IR(amox),
available = n_rsi(amox)) %>%
knitr::kable(align = "c", big.mark = ",")
```
These functions can also be used to get the portion of multiple antibiotics, to calculate co-resistance very easily:
```{r, eval = FALSE}
data_1st %>%
group_by(genus) %>%
summarise(amoxicillin = portion_S(amcl),
gentamicin = portion_S(gent),
"amox + gent" = portion_S(amcl, gent))
```
```{r, echo = FALSE}
data_1st %>%
group_by(genus) %>%
summarise(amoxicillin = portion_S(amcl),
gentamicin = portion_S(gent),
"amox + gent" = portion_S(amcl, gent)) %>%
knitr::kable(align = "c", big.mark = ",")
```
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) %>%
summarise("1. Amoxicillin" = portion_S(amcl),
"2. Gentamicin" = portion_S(gent),
"3. Amox + gent" = portion_S(amcl, gent)) %>%
tidyr::gather("Antibiotic", "S", -genus) %>%
ggplot(aes(x = genus,
y = S,
fill = Antibiotic)) +
geom_col(position = "dodge2")
```
### Plots
To show results in plots, most R users would nowadays use the `ggplot2` package. This package lets you create plots in layers. You can read more about it [on their website](https://ggplot2.tidyverse.org/). A quick example would look like these syntaxes:
```{r plot 2, eval = FALSE}
ggplot(data = a_data_set,
mapping = aes(x = year,
y = value)) +
geom_col() +
labs(title = "A title",
subtitle = "A subtitle",
x = "My X axis",
y = "My Y axis")
ggplot(a_data_set,
aes(year, value) +
geom_bar()
```
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)
```
Omit the `translate_ab = FALSE` to have the antibiotic codes (amox, amcl, cipr, gent) translated to official WHO names (amoxicillin, amoxicillin and betalactamase inhibitor, ciprofloxacin, gentamicin).
If we group on e.g. the `genus` column and add some additional functions from our package, we can create this:
```{r plot 4}
# group the data on `genus`
ggplot(data_1st %>% group_by(genus)) +
# create bars with genus on x axis
# it looks for variables with class `rsi`,
# of which we have 4 (earlier created with `as.rsi`)
geom_rsi(x = "genus") +
# split plots on antibiotic
facet_rsi(facet = "Antibiotic") +
# make R red, I yellow and S green
scale_rsi_colours() +
# show percentages on y axis
scale_y_percent(breaks = 0:4 * 25) +
# turn 90 degrees, make it bars instead of columns
coord_flip() +
# add labels
labs(title = "Resistance per genus and antibiotic",
subtitle = "(this is fake data)") +
# and print genus in italic to follow our convention
# (is now y axis because we turned the plot)
theme(axis.text.y = element_text(face = "italic"))
```
To simplify this, we also created the `ggplot_rsi()` function, which combines almost all above functions:
```{r plot 5}
data_1st %>%
group_by(genus) %>%
ggplot_rsi(x = "genus",
facet = "Antibiotic",
breaks = 0:4 * 25,
datalabels = FALSE) +
coord_flip()
```
### Using an independence test to compare resistance
The next example uses the included `septic_patients`, which is an anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This `data.frame` can be used to practice AMR analysis.
We will compare the resistance to fosfomycin (column `fosf`) in hospital A and D. The input for the final `fisher.test()` will be this:
```{r, echo = FALSE, results = 'asis'}
septic_patients %>%
filter(hospital_id %in% c("A", "D")) %>%
select(hospital_id, fosf) %>%
group_by(hospital_id) %>%
count_df(combine_IR = TRUE) %>%
tidyr::spread(hospital_id, Value) %>%
select(A, D) %>%
bind_cols(tibble(" " = c("IR", "S")), .) %>%
as.matrix() %>%
knitr::kable()
```
We can transform the data and apply the test in only a couple of lines:
```{r}
septic_patients %>%
filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and D
select(hospital_id, fosf) %>% # select the hospitals and fosfomycin
group_by(hospital_id) %>% # group on the hospitals
count_df(combine_IR = TRUE) %>% # count all isolates per group (hospital_id)
tidyr::spread(hospital_id, Value) %>% # transform output so A and D are columns
select(A, D) %>% # and select these only
as.matrix() %>% # transform to good old matrix for fisher.test()
fisher.test() # do Fisher's Exact Test
```
As can be seen, the p value is 0.03, which means that the fosfomycin resistances found in hospital A and D are really different.

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---
title: "How to apply EUCAST rules"
author: "Matthijs S. Berends"
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 3
vignette: >
%\VignetteIndexEntry{How to apply EUCAST rules}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#",
fig.width = 7.5,
fig.height = 4.5
)
# set to original language (English)
Sys.setlocale(locale = "C")
```
## Introduction
What are EUCAST rules? The European Committee on Antimicrobial Susceptibility Testing (EUCAST) states [on their website](http://www.eucast.org/expert_rules_and_intrinsic_resistance/):
> *EUCAST expert rules are a tabulated collection of expert knowledge on intrinsic resistances, exceptional resistance phenotypes and interpretive rules that may be applied to antimicrobial susceptibility testing in order to reduce errors and make appropriate recommendations for reporting particular resistances.*
In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the `eucast_rules()` function we use for this purpose can also apply additional rules, like forcing <help title="ATC: J01CA01">ampicillin</help> = R in isolates when <help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.
*(more will be available soon)*
### Benefit for empiric therapy success estimation
*(will be available soon)*
## Examples
*(will be available soon)*

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---
title: "How to use the *G*-test"
author: "Matthijs S. Berends"
output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{How to use the G-test}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#"
)
# set to original language (English)
Sys.setlocale(locale = "C")
```
*(will be available soon - in the meanwhile, please read about [this *G*-test in the manual](./reference/g.test.html))*

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---
title: "How to predict antimicrobial resistance"
author: "Matthijs S. Berends"
output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{How to predict antimicrobial resistance}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#"
)
# set to original language (English)
Sys.setlocale(locale = "C")
```
*(will be available soon)*

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@ -0,0 +1,24 @@
---
title: "How to get properties of an antibiotic"
author: "Matthijs S. Berends"
output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{How to get properties of an antibiotic}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#"
)
# set to original language (English)
Sys.setlocale(locale = "C")
```
*(will be available soon)*

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---
title: "How to create frequency tables"
author: "Matthijs S. Berends"
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 3
vignette: >
%\VignetteIndexEntry{How to create frequency tables}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE, results = 'asis'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#",
results = 'asis',
fig.width = 7.5,
fig.height = 4.5
)
# set to original language (English)
Sys.setlocale(locale = "C")
library(dplyr)
library(AMR)
```
## Introduction
Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the `freq` function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. We take the `septic_patients` dataset (included in this AMR package) as example.
## Frequencies of one variable
To only show and quickly review the content of one variable, you can just select this variable in various ways. Let's say we want to get the frequencies of the `gender` variable of the `septic_patients` dataset:
```{r, echo = TRUE}
septic_patients %>% freq(gender)
```
This immediately shows the class of the variable, its length and availability (i.e. the amount of `NA`), the amount of unique values and (most importantly) that among septic patients men are more prevalent than women.
## Frequencies of more than one variable
Multiple variables will be pasted into one variable to review individual cases, keeping a univariate frequency table.
For illustration, we could add some more variables to the `septic_patients` dataset to learn about bacterial properties:
```{r, echo = TRUE, results = 'hide'}
my_patients <- septic_patients %>% left_join_microorganisms()
```
Now all variables of the `microorganisms` dataset have been joined to the `septic_patients` dataset. The `microorganisms` dataset consists of the following variables:
```{r, echo = TRUE, results = 'markup'}
colnames(microorganisms)
```
If we compare the dimensions between the old and new dataset, we can see that these `r ncol(my_patients) - ncol(septic_patients)` variables were added:
```{r, echo = TRUE, results = 'markup'}
dim(septic_patients)
dim(my_patients)
```
So now the `genus` and `species` variables are available. A frequency table of these combined variables can be created like this:
```{r, echo = TRUE}
my_patients %>%
freq(genus, species, nmax = 15)
```
## Frequencies of numeric values
Frequency tables can be created of any input.
In case of numeric values (like integers, doubles, etc.) additional descriptive statistics will be calculated and shown into the header:
```{r, echo = TRUE}
# # get age distribution of unique patients
septic_patients %>%
distinct(patient_id, .keep_all = TRUE) %>%
freq(age, nmax = 5, header = TRUE)
```
So the following properties are determined, where `NA` values are always ignored:
* **Mean**
* **Standard deviation**
* **Coefficient of variation** (CV), the standard deviation divided by the mean
* **Five numbers of Tukey** (min, Q1, median, Q3, max)
* **Coefficient of quartile variation** (CQV, sometimes called coefficient of dispersion), calculated as (Q3 - Q1) / (Q3 + Q1) using quantile with `type = 6` as quantile algorithm to comply with SPSS standards
* **Outliers** (total count and unique count)
So for example, the above frequency table quickly shows the median age of patients being `r my_patients %>% distinct(patient_id, .keep_all = TRUE) %>% pull(age) %>% median(na.rm = TRUE)`.
## Frequencies of factors
To sort frequencies of factors on factor level instead of item count, use the `sort.count` parameter.
`sort.count` is `TRUE` by default. Compare this default behaviour...
```{r, echo = TRUE}
septic_patients %>%
freq(hospital_id)
```
... with this, where items are now sorted on count:
```{r, echo = TRUE}
septic_patients %>%
freq(hospital_id, sort.count = FALSE)
```
All classes will be printed into the header (default is `FALSE` when using markdown like this document). Variables with the new `rsi` class of this AMR package are actually ordered factors and have three classes (look at `Class` in the header):
```{r, echo = TRUE}
septic_patients %>%
freq(amox, header = TRUE)
```
## Frequencies of dates
Frequencies of dates will show the oldest and newest date in the data, and the amount of days between them:
```{r, echo = TRUE}
septic_patients %>%
freq(date, nmax = 5, header = TRUE)
```
## Assigning a frequency table to an object
A frequency table is actaually a regular `data.frame`, with the exception that it contains an additional class.
```{r, echo = TRUE}
my_df <- septic_patients %>% freq(age)
class(my_df)
```
Because of this additional class, a frequency table prints like the examples above. But the object itself contains the complete table without a row limitation:
```{r, echo = TRUE}
dim(my_df)
```
## Additional parameters
### Parameter `na.rm`
With the `na.rm` parameter (defaults to `TRUE`, but they will always be shown into the header), you can include `NA` values in the frequency table:
```{r, echo = TRUE}
septic_patients %>%
freq(amox, na.rm = FALSE)
```
### Parameter `row.names`
The default frequency tables shows row indices. To remove them, use `row.names = FALSE`:
```{r, echo = TRUE}
septic_patients %>%
freq(hospital_id, row.names = FALSE)
```
### Parameter `markdown`
The `markdown` parameter is `TRUE` at default in non-interactive sessions, like in reports created with R Markdown. This will always print all rows, unless `nmax` is set.
```{r, echo = TRUE}
septic_patients %>%
freq(hospital_id, markdown = TRUE)
```

24
vignettes/mo_property.Rmd Executable file
View File

@ -0,0 +1,24 @@
---
title: "How to get properties of a microorganism"
author: "Matthijs S. Berends"
output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{How to get properties of a microorganism}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#"
)
# set to original language (English)
Sys.setlocale(locale = "C")
```
*(will be available soon)*