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freq: support for table

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dr. M.S. (Matthijs) Berends
2018-07-09 14:02:58 +02:00
parent 18c91786bf
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9 changed files with 199 additions and 93 deletions
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@ -47,9 +47,12 @@ With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you c
This package is available on CRAN and also here on GitHub.
### From CRAN (recommended)
Latest released version on CRAN:
[![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR)
Downloads via RStudio CRAN server (downloads by all other CRAN mirrors not measured):
Downloads via RStudio CRAN server (downloads by all other CRAN mirrors **not** measured, including the official https://cran.r-project.org):
[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/AMR)](https://cranlogs.r-pkg.org/downloads/daily/last-month/AMR)
@ -122,80 +125,91 @@ after
```
### Frequency tables
Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types: `freq` (or `frequency_tbl`).
Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types: `freq` (or `frequency_tbl`). It can be used in two ways:
```r
## Factors sort on item by default:
# Like base R:
freq(mydata$myvariable)
freq(septic_patients$hospital_id)
# And like tidyverse:
mydata %>% freq(myvariable)
```
Factors sort on item by default:
```r
septic_patients %>% freq(hospital_id)
# Frequency table of `hospital_id`
# Class: factor
# Length: 2000 (of which NA: 0 = 0.0%)
# Unique: 5
#
# Item Count Percent Cum. Count Cum. Percent (Factor Level)
# ----- ------ -------- ----------- ------------- ---------------
# A 233 11.7% 233 11.7% 1
# B 583 29.1% 816 40.8% 2
# C 221 11.1% 1037 51.8% 3
# D 650 32.5% 1687 84.4% 4
# E 313 15.7% 2000 100.0% 5
# Item Count Percent Cum. Count Cum. Percent (Factor Level)
# --- ----- ------ -------- ----------- ------------- ---------------
# 1 A 233 11.7% 233 11.7% 1
# 2 B 583 29.1% 816 40.8% 2
# 3 C 221 11.1% 1037 51.8% 3
# 4 D 650 32.5% 1687 84.4% 4
# 5 E 313 15.7% 2000 100.0% 5
```
## This can be changed with the `sort.count` parameter:
freq(septic_patients$hospital_id, sort.count = TRUE)
This can be changed with the `sort.count` parameter:
```r
septic_patients %>% freq(hospital_id, sort.count = TRUE)
# Frequency table of `hospital_id`
# Class: factor
# Length: 2000 (of which NA: 0 = 0.0%)
# Unique: 5
#
# Item Count Percent Cum. Count Cum. Percent (Factor Level)
# ----- ------ -------- ----------- ------------- ---------------
# D 650 32.5% 650 32.5% 4
# B 583 29.1% 1233 61.7% 2
# E 313 15.7% 1546 77.3% 5
# A 233 11.7% 1779 88.9% 1
# C 221 11.1% 2000 100.0% 3
# Item Count Percent Cum. Count Cum. Percent (Factor Level)
# --- ----- ------ -------- ----------- ------------- ---------------
# 1 D 650 32.5% 650 32.5% 4
# 2 B 583 29.1% 1233 61.7% 2
# 3 E 313 15.7% 1546 77.3% 5
# 4 A 233 11.7% 1779 88.9% 1
# 5 C 221 11.1% 2000 100.0% 3
```
## Other types, like numbers or dates, sort on count by default:
> freq(septic_patients$date)
All other types, like numbers, characters and dates, sort on count by default:
```r
septic_patients %>% freq(date)
# Frequency table of `date`
# Class: Date
# Length: 2000 (of which NA: 0 = 0.0%)
# Unique: 1662
#
# Oldest: 2 January 2001
# Newest: 18 October 2017 (+6133)
# Median: 6 December 2009 (~53%)
#
# Item Count Percent Cum. Count Cum. Percent
# ----------- ------ -------- ----------- -------------
# 2008-12-24 5 0.2% 5 0.2%
# 2010-12-10 4 0.2% 9 0.4%
# 2011-03-03 4 0.2% 13 0.6%
# 2013-06-24 4 0.2% 17 0.8%
# 2017-09-01 4 0.2% 21 1.1%
# 2002-09-02 3 0.2% 24 1.2%
# 2003-10-14 3 0.2% 27 1.4%
# 2004-06-25 3 0.2% 30 1.5%
# 2004-06-27 3 0.2% 33 1.7%
# 2004-10-29 3 0.2% 36 1.8%
# 2005-09-27 3 0.2% 39 2.0%
# 2006-08-01 3 0.2% 42 2.1%
# 2006-10-10 3 0.2% 45 2.2%
# 2007-11-16 3 0.2% 48 2.4%
# 2008-03-09 3 0.2% 51 2.5%
# ... and 1647 more (n = 1949; 97.5%). Use `nmax` to show more rows.
## For numeric values, some extra descriptive statistics will be calculated:
> freq(runif(n = 10, min = 1, max = 5))
# Item Count Percent Cum. Count Cum. Percent
# --- ----------- ------ -------- ----------- -------------
# 1 2008-12-24 5 0.2% 5 0.2%
# 2 2010-12-10 4 0.2% 9 0.4%
# 3 2011-03-03 4 0.2% 13 0.6%
# 4 2013-06-24 4 0.2% 17 0.8%
# 5 2017-09-01 4 0.2% 21 1.1%
# 6 2002-09-02 3 0.2% 24 1.2%
# 7 2003-10-14 3 0.2% 27 1.4%
# 8 2004-06-25 3 0.2% 30 1.5%
# 9 2004-06-27 3 0.2% 33 1.7%
# 10 2004-10-29 3 0.2% 36 1.8%
# 11 2005-09-27 3 0.2% 39 2.0%
# 12 2006-08-01 3 0.2% 42 2.1%
# 13 2006-10-10 3 0.2% 45 2.2%
# 14 2007-11-16 3 0.2% 48 2.4%
# 15 2008-03-09 3 0.2% 51 2.5%
# [ reached getOption("max.print.freq") -- omitted 1647 entries, n = 1949 (97.5%) ]
```
For numeric values, some extra descriptive statistics will be calculated:
```r
freq(runif(n = 10, min = 1, max = 5))
# Frequency table
# Class: numeric
# Length: 10 (of which NA: 0 = 0.0%)
# Unique: 10
#
# Mean: 3
# Std. dev.: 0.93 (CV: 0.31)
# Five-Num: 1.1 | 2.3 | 3.1 | 3.8 | 4.0 (CQV: 0.25)
# Mean: 2.9
# Std. dev.: 1.3 (CV: 0.43, MAD: 1.5)
# Five-Num: 1.5 | 1.7 | 2.6 | 4.0 | 4.7 (IQR: 2.3, CQV: 0.4)
# Outliers: 0
#
# Item Count Percent Cum. Count Cum. Percent