mirror of https://github.com/msberends/AMR.git
189 lines
6.2 KiB
Plaintext
Executable File
189 lines
6.2 KiB
Plaintext
Executable File
---
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title: "Creating Frequency Tables"
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author: "Matthijs S. Berends"
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output:
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rmarkdown::html_vignette:
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toc: true
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vignette: >
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%\VignetteIndexEntry{Creating Frequency Tables}
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%\VignetteEngine{knitr::rmarkdown}
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%\VignetteEncoding{UTF-8}
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---
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```{r setup, include = FALSE, results = 'markup'}
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#"
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)
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library(dplyr)
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library(AMR)
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```
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## Introduction
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Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the `freq` function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. We take the `septic_patients` dataset (included in this AMR package) as example.
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## Frequencies of one variable
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To only show and quickly review the content of one variable, you can just select this variable in various ways. Let's say we want to get the frequencies of the `sex` variable of the `septic_patients` dataset:
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```{r, echo = TRUE, results = 'hide'}
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# just using base R
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freq(septic_patients$sex)
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# using base R to select the variable and pass it on with a pipe from the dplyr package
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septic_patients$sex %>% freq()
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# do it all with pipes, using the `select` function from the dplyr package
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septic_patients %>%
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select(sex) %>%
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freq()
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# or the preferred way: using a pipe to pass the variable on to the freq function
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septic_patients %>% freq(sex) # this also shows 'age' in the title
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```
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This will all lead to the following table:
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```{r, echo = TRUE}
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freq(septic_patients$sex)
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```
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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.
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## Frequencies of more than one variable
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Multiple variables will be pasted into one variable to review individual cases, keeping a univariate frequency table.
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For illustration, we could add some more variables to the `septic_patients` dataset to learn about bacterial properties:
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```{r, echo = TRUE, results = 'hide'}
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my_patients <- septic_patients %>% left_join_microorganisms()
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```
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Now all variables of the `microorganisms` dataset have been joined to the `septic_patients` dataset. The `microorganisms` dataset consists of the following variables:
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```{r, echo = TRUE}
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colnames(microorganisms)
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```
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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:
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```{r, echo = TRUE}
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dim(septic_patients)
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dim(my_patients)
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```
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So now the `genus` and `species` variables are available. A frequency table of these combined variables can be created like this:
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```{r, echo = TRUE}
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my_patients %>% freq(genus, species)
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```
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## Frequencies of numeric values
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Frequency tables can be created of any input.
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In case of numeric values (like integers, doubles, etc.) additional descriptive statistics will be calculated and shown into the header:
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```{r, echo = TRUE}
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# # get age distribution of unique patients
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septic_patients %>%
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distinct(patient_id, .keep_all = TRUE) %>%
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freq(age, nmax = 5)
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```
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So the following properties are determined, where `NA` values are always ignored:
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* **Mean**
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* **Standard deviation**
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* **Coefficient of variation** (CV), the standard deviation divided by the mean
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* **Five numbers of Tukey** (min, Q1, median, Q3, max)
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* **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
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* **Outliers** (total count and unique count)
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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)`.
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## Frequencies of factors
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Frequencies of factors will be sorted on factor level instead of item count by default. This can be changed with the `sort.count` parameter. Frequency tables of factors always show the factor level as an additional last column.
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`sort.count` is `TRUE` by default, except for factors. Compare this default behaviour...
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```{r, echo = TRUE}
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septic_patients %>%
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freq(hospital_id)
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```
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... with this, where items are now sorted on count:
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```{r, echo = TRUE}
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septic_patients %>%
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freq(hospital_id, sort.count = TRUE)
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```
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All classes will be printed into the header. Variables with the new `rsi` class of this AMR package are actually ordered factors and have three classes (look at `Class` in the header):
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```{r, echo = TRUE}
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septic_patients %>%
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select(amox) %>%
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freq()
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```
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## Frequencies of dates
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Frequencies of dates will show the oldest and newest date in the data, and the amount of days between them:
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```{r, echo = TRUE}
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septic_patients %>%
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select(date) %>%
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freq(nmax = 5)
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```
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## Assigning a frequency table to an object
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A frequency table is actaually a regular `data.frame`, with the exception that it contains an additional class.
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```{r, echo = TRUE}
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my_df <- septic_patients %>% freq(age)
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class(my_df)
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```
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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:
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```{r, echo = TRUE}
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dim(my_df)
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```
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## Additional parameters
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### Parameter `na.rm`
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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:
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```{r, echo = TRUE}
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septic_patients %>%
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freq(amox, na.rm = FALSE)
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```
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### Parameter `row.names`
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The default frequency tables shows row indices. To remove them, use `row.names = FALSE`:
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```{r, echo = TRUE}
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septic_patients %>%
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freq(hospital_id, row.names = FALSE)
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```
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### Parameter `markdown`
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The `markdown` parameter can be used in reports created with R Markdown. This will always print all rows:
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```{r, echo = TRUE}
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septic_patients %>%
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freq(hospital_id, markdown = TRUE)
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```
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----
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```{r, echo = FALSE}
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# this will print "2018" in 2018, and "2018-yyyy" after 2018.
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yrs <- paste(unique(c(2018, format(Sys.Date(), "%Y"))), collapse = "-")
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```
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AMR, (c) `r yrs`, `r packageDescription("AMR")$URL`
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Licensed under the [GNU General Public License v2.0](https://github.com/msberends/AMR/blob/master/LICENSE).
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