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 `sex` variable of the `septic_patients` dataset:
```{r, echo = TRUE, results = 'hide'}
# # just using base R
freq(septic_patients$sex)
# # using base R to select the variable and pass it on with a pipe
septic_patients$sex %>% freq()
# # do it all with pipes, using the `select` function of the dplyr package
septic_patients %>%
select(sex) %>%
freq()
```
This will all lead to the following table:
```{r, echo = TRUE}
freq(septic_patients$sex)
```
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}
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}
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 %>%
select(genus, species) %>%
freq()
```
## 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) %>%
select(age) %>%
freq(nmax = 5)
```
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
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.
`sort.count` is `TRUE` by default, except for factors. Compare this default behaviour:
```{r, echo = TRUE}
septic_patients %>%
select(hospital_id) %>%
freq()
```
To this, where items are now sorted on item count:
```{r, echo = TRUE}
septic_patients %>%
select(hospital_id) %>%
freq(sort.count = TRUE)
```
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):
```{r, echo = TRUE}
septic_patients %>%
select(amox) %>%
freq()
```
## 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 %>%
select(date) %>%
freq(nmax = 5)
```
## 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 %>%
select(amox) %>%
freq(na.rm = FALSE)
```
### Parameter `markdown`
The `markdown` parameter can be used in reports created with R Markdown. This will always print all rows:
```{r, echo = TRUE}
septic_patients %>%
select(hospital_id) %>%
freq(markdown = TRUE)
```
### Parameter `as.data.frame`
With the `as.data.frame` parameter you can assign the frequency table to an object, or just print it as a `data.frame` to the console:
```{r, echo = TRUE}
my_df <- septic_patients %>%
select(hospital_id) %>%
freq(as.data.frame = TRUE)
my_df
class(my_df)
```
----
```{r, echo = FALSE}
# this will print "2018" in 2018, and "2018-yyyy" after 2018.
