freq.RmdFrequency 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.
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:
# Any of these will work:
# freq(septic_patients$gender)
# freq(septic_patients[, "gender"])
# Using tidyverse:
# septic_patients$gender %>% freq()
# septic_patients[, "gender"] %>% freq()
# septic_patients %>% freq("gender")
# Probably the fastest and easiest:
septic_patients %>% freq(gender) Frequency table of gender from a data.frame (2,000 x 49)
Class: character
Length: 2,000 (of which NA: 0 = 0.00%)
Unique: 2
Shortest: 1
Longest: 1
| Item | Count | Percent | Cum. Count | Cum. Percent | |
|---|---|---|---|---|---|
| 1 | M | 1,031 | 51.6% | 1,031 | 51.6% |
| 2 | F | 969 | 48.4% | 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.
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:
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" "col_id" "fullname" "kingdom" "phylum"
# [6] "class" "order" "family" "genus" "species"
# [11] "subspecies" "rank" "ref" "species_id" "source"
# [16] "prevalence"If we compare the dimensions between the old and new dataset, we can see that these 15 variables were added:
So now the genus and species variables are available. A frequency table of these combined variables can be created like this:
Frequency table of genus and species from a data.frame (2,000 x 64)
Columns: 2
Length: 2,000 (of which NA: 0 = 0.00%)
Unique: 95
Shortest: 8
Longest: 34
| Item | Count | Percent | Cum. Count | Cum. Percent | |
|---|---|---|---|---|---|
| 1 | Escherichia coli | 467 | 23.4% | 467 | 23.4% |
| 2 | Staphylococcus coagulase-negative | 313 | 15.6% | 780 | 39.0% |
| 3 | Staphylococcus aureus | 235 | 11.7% | 1,015 | 50.7% |
| 4 | Staphylococcus epidermidis | 174 | 8.7% | 1,189 | 59.4% |
| 5 | Streptococcus pneumoniae | 117 | 5.8% | 1,306 | 65.3% |
| 6 | Staphylococcus hominis | 81 | 4.0% | 1,387 | 69.4% |
| 7 | Klebsiella pneumoniae | 58 | 2.9% | 1,445 | 72.2% |
| 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 | 1.2% | 1,575 | 78.8% |
| 12 | Enterobacter cloacae | 23 | 1.2% | 1,598 | 79.9% |
| 13 | Enterococcus faecium | 21 | 1.0% | 1,619 | 81.0% |
| 14 | Staphylococcus capitis | 21 | 1.0% | 1,640 | 82.0% |
| 15 | Bacteroides fragilis | 20 | 1.0% | 1,660 | 83.0% |
(omitted 80 entries, n = 340 [17.0%])
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:
# # get age distribution of unique patients
septic_patients %>%
distinct(patient_id, .keep_all = TRUE) %>%
freq(age, nmax = 5, header = TRUE)Frequency table of age from a data.frame (981 x 49)
Class: numeric
Length: 981 (of which NA: 0 = 0.00%)
Unique: 73
Mean: 71.08
SD: 14.05 (CV: 0.20, MAD: 13.34)
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
Mean absolute deviation (MAD), the median of the absolute deviations from the median - a more robust statistic than the standard deviation
Five numbers of Tukey, namely: the minimum, Q1, median, Q3 and maximum
Interquartile range (IQR), the distance between Q1 and Q3
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.
To sort frequencies of factors on their levels instead of item count, use the sort.count parameter.
sort.count is TRUE by default. Compare this default behaviour…
Frequency table of hospital_id from a data.frame (2,000 x 49)
Class: factor (numeric)
Length: 2,000 (of which NA: 0 = 0.00%)
Levels: 4: A, B, C, D
Unique: 4
| Item | Count | Percent | Cum. Count | Cum. Percent | |
|---|---|---|---|---|---|
| 1 | D | 762 | 38.1% | 762 | 38.1% |
| 2 | B | 663 | 33.2% | 1,425 | 71.2% |
| 3 | A | 321 | 16.0% | 1,746 | 87.3% |
| 4 | C | 254 | 12.7% | 2,000 | 100.0% |
… to this, where items are now sorted on factor levels:
Frequency table of hospital_id from a data.frame (2,000 x 49)
Class: factor (numeric)
Length: 2,000 (of which NA: 0 = 0.00%)
Levels: 4: A, B, C, D
Unique: 4
| Item | Count | Percent | Cum. Count | Cum. Percent | |
|---|---|---|---|---|---|
| 1 | A | 321 | 16.0% | 321 | 16.0% |
| 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):
Frequency table of AMX from a data.frame (2,000 x 49)
Class: factor > ordered > rsi (numeric)
Length: 2,000 (of which NA: 771 = 38.55%)
Levels: 3: S < I < R
Unique: 3
Drug: Amoxicillin (AMX, J01CA04)
Group: Beta-lactams/penicillins
%SI: 44.43%
| Item | Count | Percent | Cum. Count | Cum. Percent | |
|---|---|---|---|---|---|
| 1 | R | 683 | 55.6% | 683 | 55.6% |
| 2 | S | 543 | 44.2% | 1,226 | 99.8% |
| 3 | I | 3 | 0.2% | 1,229 | 100.0% |
Frequencies of dates will show the oldest and newest date in the data, and the amount of days between them:
Frequency table of date from a data.frame (2,000 x 49)
Class: Date (numeric)
Length: 2,000 (of which NA: 0 = 0.00%)
Unique: 1,140
Oldest: 2 January 2002
Newest: 28 December 2017 (+5,839)
Median: 31 July 2009 (47.39%)
| 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 | 2.0% |
(omitted 1,135 entries, n = 1,959 [98.0%])
A frequency table is actually a regular data.frame, with the exception that it contains an additional class.
[1] “freq” “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:
[1] 74 5
na.rm
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):
Frequency table of AMX from a data.frame (2,000 x 49)
Class: factor > ordered > rsi (numeric)
Length: 2,000 (of which NA: 771 = 38.55%)
Levels: 3: S < I < R
Unique: 4
Drug: Amoxicillin (AMX, J01CA04)
Group: Beta-lactams/penicillins
%SI: 44.43%
| Item | Count | Percent | Cum. Count | Cum. Percent | |
|---|---|---|---|---|---|
| 1 | (NA) | 771 | 38.6% | 771 | 38.6% |
| 2 | R | 683 | 34.2% | 1,454 | 72.7% |
| 3 | S | 543 | 27.2% | 1,997 | 99.8% |
| 4 | I | 3 | 0.2% | 2,000 | 100.0% |
row.names
A frequency table shows row indices. To remove them, use row.names = FALSE:
Frequency table of hospital_id from a data.frame (2,000 x 49)
Class: factor (numeric)
Length: 2,000 (of which NA: 0 = 0.00%)
Levels: 4: A, B, C, D
Unique: 4
| Item | Count | Percent | Cum. Count | Cum. Percent |
|---|---|---|---|---|
| D | 762 | 38.1% | 762 | 38.1% |
| B | 663 | 33.2% | 1,425 | 71.2% |
| A | 321 | 16.0% | 1,746 | 87.3% |
| C | 254 | 12.7% | 2,000 | 100.0% |
markdown
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:
septic_patients %>%
freq(hospital_id, markdown = FALSE)
# Frequency table of `hospital_id` from a data.frame (2,000 x 49)
#
# Class: factor (numeric)
# Length: 2,000 (of which NA: 0 = 0.00%)
# Levels: 4: A, B, C, D
# Unique: 4
#
# Item Count Percent Cum. Count Cum. Percent
# --- ----- ------ -------- ----------- -------------
# 1 D 762 38.1% 762 38.1%
# 2 B 663 33.2% 1,425 71.2%
# 3 A 321 16.0% 1,746 87.3%
# 4 C 254 12.7% 2,000 100.0%