Creating Frequency Tables

Matthijs S. Berends

Introduction

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 gender variable of the septic_patients dataset:

septic_patients %>% freq(gender)

Frequency table of gender

	Item	Count	Percent	Cum. Count	Cum. Percent
1	M	1,031	51.6%	1,031	51.6%
2	F	969	48.5%	2,000	100.0%

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:

my_patients <- septic_patients %>% left_join_microorganisms()
Joining, by = "mo"

Now all variables of the microorganisms dataset have been joined to the septic_patients dataset. The microorganisms dataset consists of the following variables:

colnames(microorganisms)
 [1] "mo"         "tsn"        "genus"      "species"    "subspecies"
 [6] "fullname"   "family"     "order"      "class"      "phylum"    
[11] "subkingdom" "kingdom"    "gramstain"  "prevalence" "ref"       

If we compare the dimensions between the old and new dataset, we can see that these 14 variables were added:

dim(septic_patients)
[1] 2000   49
dim(my_patients)
[1] 2000   63

So now the genus and species variables are available. A frequency table of these combined variables can be created like this:

my_patients %>% freq(genus, species)

Frequency table of genus and species

	Item	Count	Percent	Cum. Count	Cum. Percent
1	Escherichia coli	467	23.4%	467	23.4%
2	Staphylococcus coagulase negative	313	15.7%	780	39.0%
3	Staphylococcus aureus	235	11.8%	1,015	50.7%
4	Staphylococcus epidermidis	174	8.7%	1,189	59.5%
5	Streptococcus pneumoniae	117	5.9%	1,306	65.3%
6	Staphylococcus hominis	81	4.1%	1,387	69.4%
7	Klebsiella pneumoniae	58	2.9%	1,445	72.3%
8	Enterococcus faecalis	39	2.0%	1,484	74.2%
9	Proteus mirabilis	36	1.8%	1,520	76.0%
10	Pseudomonas aeruginosa	30	1.5%	1,550	77.5%
11	Serratia marcescens	25	1.3%	1,575	78.8%
12	Enterobacter cloacae	23	1.2%	1,598	79.9%
13	Enterococcus faecium	21	1.1%	1,619	81.0%
14	Staphylococcus capitis	21	1.1%	1,640	82.0%
15	Bacteroides fragilis	20	1.0%	1,660	83.0%
16	Enterococcus species	20	1.0%	1,680	84.0%
17	Streptococcus group B	18	0.9%	1,698	84.9%
18	Klebsiella oxytoca	16	0.8%	1,714	85.7%
19	Streptococcus pyogenes	16	0.8%	1,730	86.5%
20	Streptococcus dysgalactiae	14	0.7%	1,744	87.2%
21	Streptococcus group A	13	0.7%	1,757	87.9%
22	Streptococcus mitis	13	0.7%	1,770	88.5%
23	Streptococcus salivarius	12	0.6%	1,782	89.1%
24	Streptococcus agalactiae	11	0.6%	1,793	89.7%
25	Streptococcus species	11	0.6%	1,804	90.2%
26	Corynebacterium species	10	0.5%	1,814	90.7%
27	Streptococcus bovis	10	0.5%	1,824	91.2%
28	Clostridium difficile	9	0.5%	1,833	91.7%
29	Haemophilus influenzae	8	0.4%	1,841	92.1%
30	Candida albicans	7	0.4%	1,848	92.4%
31	Staphylococcus haemolyticus	7	0.4%	1,855	92.8%
32	Streptococcus constellatus	7	0.4%	1,862	93.1%
33	Candida glabrata	6	0.3%	1,868	93.4%
34	Citrobacter freundii	6	0.3%	1,874	93.7%
35	Corynebacterium striatum	6	0.3%	1,880	94.0%
36	Morganella morganii	6	0.3%	1,886	94.3%
37	Streptococcus anginosus	6	0.3%	1,892	94.6%
38	Streptococcus oralis	6	0.3%	1,898	94.9%
39	Acinetobacter baumannii	3	0.2%	1,901	95.1%
40	Acinetobacter species	3	0.2%	1,904	95.2%
41	Citrobacter koseri	3	0.2%	1,907	95.4%
42	Clostridium perfringens	3	0.2%	1,910	95.5%
43	Clostridium septicum	3	0.2%	1,913	95.7%
44	Enterobacter aerogenes	3	0.2%	1,916	95.8%
45	Gemella haemolysans	3	0.2%	1,919	96.0%
46	Micrococcus luteus	3	0.2%	1,922	96.1%
47	Micrococcus species	3	0.2%	1,925	96.3%
48	Salmonella enterica	3	0.2%	1,928	96.4%
49	Staphylococcus warneri	3	0.2%	1,931	96.6%
50	Streptococcus equi	3	0.2%	1,934	96.7%
51	Streptococcus group C	3	0.2%	1,937	96.9%
52	Streptococcus group G	3	0.2%	1,940	97.0%
53	Streptococcus intermedius	3	0.2%	1,943	97.2%
54	Streptococcus parasanguinis	3	0.2%	1,946	97.3%
55	Aerococcus urinae	2	0.1%	1,948	97.4%
56	Candida tropicalis	2	0.1%	1,950	97.5%
57	Citrobacter species	2	0.1%	1,952	97.6%
58	Enterococcus avium	2	0.1%	1,954	97.7%
59	Pantoea agglomerans	2	0.1%	1,956	97.8%
60	Pantoea species	2	0.1%	1,958	97.9%
61	Proteus vulgaris	2	0.1%	1,960	98.0%
62	Staphylococcus cohnii	2	0.1%	1,962	98.1%
63	Staphylococcus lugdunensis	2	0.1%	1,964	98.2%
64	Staphylococcus schleiferi	2	0.1%	1,966	98.3%
65	Stenotrophomonas maltophilia	2	0.1%	1,968	98.4%
66	Streptococcus mutans	2	0.1%	1,970	98.5%
67	Actinomyces odontolyticus	1	0.1%	1,971	98.6%
68	Campylobacter jejuni	1	0.1%	1,972	98.6%
69	Candida lusitaniae	1	0.1%	1,973	98.7%
70	Clostridium novyi	1	0.1%	1,974	98.7%
71	Corynebacterium tuberculostearicum	1	0.1%	1,975	98.8%
72	Dermabacter hominis	1	0.1%	1,976	98.8%
73	Eikenella corrodens	1	0.1%	1,977	98.9%
74	Enterococcus casseliflavus	1	0.1%	1,978	98.9%
75	Escherichia vulneris	1	0.1%	1,979	99.0%
76	Fusobacterium species	1	0.1%	1,980	99.0%
77	Globicatella sanguinis	1	0.1%	1,981	99.1%
78	Granulicatella adiacens	1	0.1%	1,982	99.1%
79	Haemophilus parainfluenzae	1	0.1%	1,983	99.2%
80	Hafnia alvei	1	0.1%	1,984	99.2%
81	Lactobacillus delbrueckii	1	0.1%	1,985	99.3%
82	Leuconostoc species	1	0.1%	1,986	99.3%
83	Listeria monocytogenes	1	0.1%	1,987	99.4%
84	Neisseria meningitidis	1	0.1%	1,988	99.4%
85	Neisseria sicca	1	0.1%	1,989	99.5%
86	Paenibacillus durus	1	0.1%	1,990	99.5%
87	Propionibacterium acnes	1	0.1%	1,991	99.6%
88	Proteus penneri	1	0.1%	1,992	99.6%
89	Rothia mucilaginosa	1	0.1%	1,993	99.7%
90	Sphingobacterium spiritivorum	1	0.1%	1,994	99.7%
91	Sphingomonas paucimobilis	1	0.1%	1,995	99.8%
92	Streptococcus equinus	1	0.1%	1,996	99.8%
93	Streptococcus gordonii	1	0.1%	1,997	99.9%
94	Streptococcus infantarius	1	0.1%	1,998	99.9%
95	Streptococcus sanguinis	1	0.1%	1,999	100.0%
96	Veillonella parvula	1	0.1%	2,000	100.0%

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. When creating frequency tables automatically (like here in markdown), add header = TRUE to also show the header in markdown reports:

# get age distribution of unique patients
septic_patients %>% 
  distinct(patient_id, .keep_all = TRUE) %>% 
  freq(age, nmax = 5, header = TRUE)

Frequency table of age

Class: numeric

Length: 981 (of which NA: 0 = 0.00%)

Unique: 73

Mean: 71

Std. dev.: 14 (CV: 0.2, MAD: 13)

Five-Num: 14 | 63 | 74 | 82 | 97 (IQR: 19, CQV: 0.13)

Outliers: 15 (unique count: 12)

	Item	Count	Percent	Cum. Count	Cum. Percent
1	83	44	4.5%	44	4.5%
2	76	43	4.4%	87	8.9%
3	75	37	3.8%	124	12.6%
4	82	33	3.4%	157	16.0%
5	78	32	3.3%	189	19.3%

(omitted 68 entries, n = 792 [80.7%])

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 74.

Frequencies of factors

Frequencies of factors can be sorted on factor level instead of item count with the sort.count parameter.

Default behaviour:

septic_patients %>%
  freq(hospital_id)

Frequency table of hospital_id

	Item	Count	Percent	Cum. Count	Cum. Percent
1	D	762	38.1%	762	38.1%
2	B	663	33.2%	1,425	71.3%
3	A	321	16.1%	1,746	87.3%
4	C	254	12.7%	2,000	100.0%

Sorting on item instead of count:

septic_patients %>%
  freq(hospital_id, sort.count = FALSE)

Frequency table of hospital_id

	Item	Count	Percent	Cum. Count	Cum. Percent
1	A	321	16.1%	321	16.1%
2	B	663	33.2%	984	49.2%
3	C	254	12.7%	1,238	61.9%
4	D	762	38.1%	2,000	100.0%

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):

septic_patients %>%
  freq(amox, header = TRUE)

Frequency table of amox

Class: factor > ordered > rsi (numeric)

Levels: S < I < R

Length: 2,000 (of which NA: 828 = 41.40%)

Unique: 3

	Item	Count	Percent	Cum. Count	Cum. Percent
1	R	683	58.3%	683	58.3%
2	S	486	41.5%	1,169	99.7%
3	I	3	0.3%	1,172	100.0%

Frequencies of dates

Frequencies of dates will show the oldest and newest date in the data, and the amount of days between them:

septic_patients %>%
  freq(date, nmax = 5, header = TRUE)

Frequency table of date

Class: Date (numeric)

Length: 2,000 (of which NA: 0 = 0.00%)

Unique: 1,140

Oldest: 2 januari 2002

Newest: 28 december 2017 (+5,839)

Median: 31 juli 2009 (~47%)

	Item	Count	Percent	Cum. Count	Cum. Percent
1	2016-05-21	10	0.5%	10	0.5%
2	2004-11-15	8	0.4%	18	0.9%
3	2013-07-29	8	0.4%	26	1.3%
4	2017-06-12	8	0.4%	34	1.7%
5	2015-11-19	7	0.4%	41	2.1%

(omitted 1,135 entries, n = 1,959 [98.0%])

Assigning a frequency table to an object

A frequency table is actually a regular data.frame, with the exception that it contains an additional class.

my_df <- septic_patients %>% freq(age)

class(my_df)
[1] "frequency_tbl" "data.frame"   

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:

dim(my_df)
[1] 74  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:

septic_patients %>%
  freq(amox, na.rm = FALSE)
Warning: Factor `item` contains implicit NA, consider using
`forcats::fct_explicit_na`

Frequency table of amox

	Item	Count	Percent	Cum. Count	Cum. Percent
1		828	41.4%	828	41.4%
2	R	683	34.2%	1,511	75.6%
3	S	486	24.3%	1,997	99.9%
4	I	3	0.2%	2,000	100.0%

Parameter row.names

The default frequency tables shows row indices. To remove them, use row.names = FALSE:

septic_patients %>%
  freq(hospital_id, row.names = FALSE)

Frequency table of hospital_id

Item	Count	Percent	Cum. Count	Cum. Percent
D	762	38.1%	762	38.1%
B	663	33.2%	1,425	71.3%
A	321	16.1%	1,746	87.3%
C	254	12.7%	2,000	100.0%

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