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(v0.7.1.9015) Remove freq()
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@ -144,7 +144,14 @@ knitr::kable(head(data), align = "c")
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Now, let's start the cleaning and the analysis!
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# Cleaning the data
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Use the frequency table function `freq()` to look specifically for unique values in any variable. For example, for the `gender` variable:
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We also created a package dedicated to data cleaning and checking, called the `clean` package. It gets automatically installed with the `AMR` package, so we only have to load it:
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```{r lib clean, message = FALSE}
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library(clean)
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```
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Use the frequency table function `freq()` from this `clean` package to look specifically for unique values in any variable. For example, for the `gender` variable:
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```{r freq gender 1, eval = FALSE}
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data %>% freq(gender) # this would be the same: freq(data$gender)
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@ -72,7 +72,13 @@ We can now add the interpretation of MDR-TB to our data set:
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my_TB_data$mdr <- mdr_tb(my_TB_data)
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```
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And review the result with a frequency table:
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We also created a package dedicated to data cleaning and checking, called the `clean` package. It gets automatically installed with the `AMR` package, so we only have to load it:
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```{r lib clean, message = FALSE}
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library(clean)
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```
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It contains the `freq()` function, to create a frequency table:
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```{r, results = 'asis'}
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freq(my_TB_data$mdr)
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@ -60,7 +60,17 @@ data <- WHONET %>%
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mutate_at(vars(AMP_ND10:CIP_EE), as.rsi)
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```
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No errors or warnings, so all values are transformed succesfully. Let's check it though, with a couple of frequency tables:
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No errors or warnings, so all values are transformed succesfully.
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We created a package dedicated to data cleaning and checking, called the `clean` package. It gets automatically installed with the `AMR` package, so we only have to load it:
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```{r lib clean, message = FALSE}
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library(clean)
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```
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It contains the `freq()` function, to create frequency tables.
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So let's check our data, with a couple of frequency tables:
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```{r, results = 'asis'}
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# our newly created `mo` variable
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@ -1,182 +0,0 @@
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---
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title: "How to create frequency tables"
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author: "Matthijs S. Berends"
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date: '`r format(Sys.Date(), "%d %B %Y")`'
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output:
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rmarkdown::html_vignette:
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toc: true
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toc_depth: 3
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vignette: >
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%\VignetteIndexEntry{How to create frequency tables}
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%\VignetteEncoding{UTF-8}
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%\VignetteEngine{knitr::rmarkdown}
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editor_options:
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chunk_output_type: console
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---
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```{r setup, include = FALSE, results = 'asis'}
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#",
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results = 'asis',
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fig.width = 7.5,
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fig.height = 4.5
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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 `gender` variable of the `septic_patients` dataset:
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```{r, echo = TRUE}
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# Any of these will work:
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# freq(septic_patients$gender)
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# freq(septic_patients[, "gender"])
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# Using tidyverse:
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# septic_patients$gender %>% freq()
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# septic_patients[, "gender"] %>% freq()
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# septic_patients %>% freq("gender")
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# Probably the fastest and easiest:
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septic_patients %>% freq(gender)
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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, results = 'markup'}
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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, results = 'markup'}
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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 %>%
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freq(genus, species, nmax = 15)
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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, header = TRUE)
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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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* **Mean absolute deviation** (MAD), the median of the absolute deviations from the median - a more robust statistic than the standard deviation
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* **Five numbers of Tukey**, namely: the minimum, Q1, median, Q3 and maximum
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* **Interquartile range** (IQR), the distance between Q1 and Q3
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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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To sort frequencies of factors on their levels instead of item count, use the `sort.count` parameter.
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`sort.count` is `TRUE` by default. 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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... to this, where items are now sorted on factor levels:
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```{r, echo = TRUE}
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septic_patients %>%
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freq(hospital_id, sort.count = FALSE)
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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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freq(AMX, header = TRUE)
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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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freq(date, nmax = 5, header = TRUE)
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```
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## Assigning a frequency table to an object
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A frequency table is actually 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 you can remove `NA` values from the frequency table (defaults to `TRUE`, but the number of `NA` values will always be shown into the header):
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```{r, echo = TRUE}
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septic_patients %>%
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freq(AMX, na.rm = FALSE)
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```
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### Parameter `row.names`
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A frequency table 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 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. Without markdown (like in regular R), a frequency table would print like:
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```{r, echo = TRUE, results = 'markup'}
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septic_patients %>%
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freq(hospital_id, markdown = FALSE)
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```
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