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new g.test() and edited freq()

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dr. M.S. (Matthijs) Berends
2018-07-01 21:40:37 +02:00
parent f7af8a81da
commit 3527894b49
18 changed files with 893 additions and 296 deletions
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61
vignettes/freq.R
View File

@ -10,20 +10,23 @@ library(AMR)
# just using base R
freq(septic_patients$sex)
# using base R to select the variable and pass it on with a pipe
# using base R to select the variable and pass it on with a pipe from the dplyr package
septic_patients$sex %>% freq()
# do it all with pipes, using the `select` function of the dplyr package
# do it all with pipes, using the `select` function from the dplyr package
septic_patients %>%
select(sex) %>%
freq()
# or the preferred way: using a pipe to pass the variable on to the freq function
septic_patients %>% freq(sex) # this also shows 'age' in the title
## ---- echo = TRUE--------------------------------------------------------
freq(septic_patients$sex)
## ---- echo = TRUE, results = 'hide'--------------------------------------
my_patients <- septic_patients %>%
left_join_microorganisms()
my_patients <- septic_patients %>% left_join_microorganisms()
## ---- echo = TRUE--------------------------------------------------------
colnames(microorganisms)
@ -33,26 +36,21 @@ dim(septic_patients)
dim(my_patients)
## ---- echo = TRUE--------------------------------------------------------
my_patients %>%
select(genus, species) %>%
freq()
my_patients %>% freq(genus, species)
## ---- echo = TRUE--------------------------------------------------------
# # get age distribution of unique patients
septic_patients %>%
distinct(patient_id, .keep_all = TRUE) %>%
select(age) %>%
freq(nmax = 5)
freq(age, nmax = 5)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
select(hospital_id) %>%
freq()
freq(hospital_id)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
select(hospital_id) %>%
freq(sort.count = TRUE)
freq(hospital_id, sort.count = TRUE)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
@ -65,29 +63,24 @@ septic_patients %>%
freq(nmax = 5)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
select(amox) %>%
freq(na.rm = FALSE)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
select(hospital_id) %>%
freq(row.names = FALSE)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
select(hospital_id) %>%
freq(markdown = TRUE)
## ---- echo = TRUE--------------------------------------------------------
my_df <- septic_patients %>%
select(hospital_id) %>%
freq(as.data.frame = TRUE)
my_df
my_df <- septic_patients %>% freq(age)
class(my_df)
## ---- echo = TRUE--------------------------------------------------------
dim(my_df)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
freq(amox, na.rm = FALSE)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
freq(hospital_id, row.names = FALSE)
## ---- echo = TRUE--------------------------------------------------------
septic_patients %>%
freq(hospital_id, markdown = TRUE)
## ---- echo = FALSE-------------------------------------------------------
# this will print "2018" in 2018, and "2018-yyyy" after 2018.
yrs <- c(2018:format(Sys.Date(), "%Y"))

61
vignettes/freq.Rmd
View File

@ -30,13 +30,17 @@ To only show and quickly review the content of one variable, you can just select
# just using base R
freq(septic_patients$sex)
# using base R to select the variable and pass it on with a pipe
# using base R to select the variable and pass it on with a pipe from the dplyr package
septic_patients$sex %>% freq()
# do it all with pipes, using the `select` function of the dplyr package
# do it all with pipes, using the `select` function from the dplyr package
septic_patients %>%
select(sex) %>%
freq()
# or the preferred way: using a pipe to pass the variable on to the freq function
septic_patients %>% freq(sex) # this also shows 'age' in the title
```
This will all lead to the following table:
```{r, echo = TRUE}
@ -50,8 +54,7 @@ Multiple variables will be pasted into one variable to review individual cases,
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()
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}
@ -66,9 +69,7 @@ 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()
my_patients %>% freq(genus, species)
```
## Frequencies of numeric values
@ -81,8 +82,7 @@ In case of numeric values (like integers, doubles, etc.) additional descriptive
# # get age distribution of unique patients
septic_patients %>%
distinct(patient_id, .keep_all = TRUE) %>%
select(age) %>%
freq(nmax = 5)
freq(age, nmax = 5)
```
So the following properties are determined, where `NA` values are always ignored:
@ -109,16 +109,14 @@ Frequencies of factors will be sorted on factor level instead of item count by d
```{r, echo = TRUE}
septic_patients %>%
select(hospital_id) %>%
freq()
freq(hospital_id)
```
... with this, where items are now sorted on count:
```{r, echo = TRUE}
septic_patients %>%
select(hospital_id) %>%
freq(sort.count = TRUE)
freq(hospital_id, 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):
@ -139,6 +137,21 @@ septic_patients %>%
freq(nmax = 5)
```
## 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`
@ -146,8 +159,7 @@ With the `na.rm` parameter (defaults to `TRUE`, but they will always be shown in
```{r, echo = TRUE}
septic_patients %>%
select(amox) %>%
freq(na.rm = FALSE)
freq(amox, na.rm = FALSE)
```
### Parameter `row.names`
@ -155,8 +167,7 @@ The default frequency tables shows row indices. To remove them, use `row.names =
```{r, echo = TRUE}
septic_patients %>%
select(hospital_id) %>%
freq(row.names = FALSE)
freq(hospital_id, row.names = FALSE)
```
### Parameter `markdown`
@ -164,21 +175,7 @@ The `markdown` parameter can be used in reports created with R Markdown. This wi
```{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)
freq(hospital_id, markdown = TRUE)
```
----