Frequency table

Create a frequency table of a vector with items or a data frame. Supports quasiquotation and markdown for reports. The best practice is: data %>% freq(var).
top_freq can be used to get the top/bottom n items of a frequency table, with counts as names.

frequency_tbl(x, ..., sort.count = TRUE,
  nmax = getOption("max.print.freq"), na.rm = TRUE, row.names = TRUE,
  markdown = !interactive(), digits = 2, quote = FALSE,
  header = !markdown, title = NULL, na = "<NA>", droplevels = TRUE,
  sep = " ", decimal.mark = getOption("OutDec"),
  big.mark = ifelse(decimal.mark != ",", ",", "."))

freq(x, ..., sort.count = TRUE, nmax = getOption("max.print.freq"),
  na.rm = TRUE, row.names = TRUE, markdown = !interactive(),
  digits = 2, quote = FALSE, header = !markdown, title = NULL,
  na = "<NA>", droplevels = TRUE, sep = " ",
  decimal.mark = getOption("OutDec"), big.mark = ifelse(decimal.mark !=
  ",", ",", "."))

top_freq(f, n)

header(f, property = NULL)

# S3 method for frequency_tbl
print(x, nmax = getOption("max.print.freq",
  default = 15), markdown = !interactive(), header = !markdown,
  decimal.mark = getOption("OutDec"), big.mark = ifelse(decimal.mark !=
  ",", ",", "."), ...)

Arguments

x	vector of any class or a data.frame, tibble (may contain a grouping variable) or table
...	up to nine different columns of x when x is a data.frame or tibble, to calculate frequencies from - see Examples
sort.count	sort on count, i.e. frequencies. This will be TRUE at default for everything except when using grouping variables.
nmax	number of row to print. The default, 15, uses getOption("max.print.freq"). Use nmax = 0, nmax = Inf, nmax = NULL or nmax = NA to print all rows.
na.rm	a logical value indicating whether NA values should be removed from the frequency table. The header (if set) will always print the amount of NAs.
row.names	a logical value indicating whether row indices should be printed as 1:nrow(x)
markdown	a logical value indicating whether the frequency table should be printed in markdown format. This will print all rows (except when nmax is defined) and is default behaviour in non-interactive R sessions (like when knitting RMarkdown files).
digits	how many significant digits are to be used for numeric values in the header (not for the items themselves, that depends on getOption("digits"))
quote	a logical value indicating whether or not strings should be printed with surrounding quotes
header	a logical value indicating whether an informative header should be printed
title	text to show above frequency table, at default to tries to coerce from the variables passed to x
na	a character string that should be used to show empty (NA) values (only useful when na.rm = FALSE)
droplevels	a logical value indicating whether in factors empty levels should be dropped
sep	a character string to separate the terms when selecting multiple columns
decimal.mark	used for prettying (longish) numerical and complex sequences. Passed to prettyNum: that help page explains the details.
big.mark	used for prettying (longish) numerical and complex sequences. Passed to prettyNum: that help page explains the details.
f	a frequency table
n	number of top n items to return, use -n for the bottom n items. It will include more than n rows if there are ties.
property	property in header to return this value directly

Value

A data.frame (with an additional class "frequency_tbl") with five columns: item, count, percent, cum_count and cum_percent.

Details

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. This package also has a vignette available to explain the use of this function further, run browseVignettes("AMR") to read it.

For numeric values of any class, these additional values will all be calculated with na.rm = TRUE and shown into the header:

• Mean, using mean

• Standard Deviation, using sd

• Coefficient of Variation (CV), the standard deviation divided by the mean

• Mean Absolute Deviation (MAD), using mad

• Tukey Five-Number Summaries (minimum, Q1, median, Q3, maximum), using fivenum

• Interquartile Range (IQR) calculated as Q3 - Q1 using the Tukey Five-Number Summaries, i.e. not using the quantile function

• Coefficient of Quartile Variation (CQV, sometimes called coefficient of dispersion), calculated as (Q3 - Q1) / (Q3 + Q1) using the Tukey Five-Number Summaries

• Outliers (total count and unique count), using boxplot.stats

For dates and times of any class, these additional values will be calculated with na.rm = TRUE and shown into the header:

• Oldest, using min

• Newest, using max, with difference between newest and oldest

• Median, using median, with percentage since oldest

In factors, all factor levels that are not existing in the input data will be dropped.

The function top_freq uses top_n internally and will include more than n rows if there are ties.

Examples

library(dplyr)

# this all gives the same result:
freq(septic_patients$hospital_id)
#> 
#> 
#> **Frequency table**  
#> 
#> 
#> |   |Item | Count| Percent| Cum. Count| Cum. Percent|
#> |:--|:----|-----:|-------:|----------:|------------:|
#> |1  |D    |   762|   38.1%|        762|        38.1%|
#> |2  |B    |   663|   33.2%|      1,425|        71.3%|
#> |3  |A    |   321|   16.1%|      1,746|        87.3%|
#> |4  |C    |   254|   12.7%|      2,000|       100.0%|
#> 
#> 
freq(septic_patients[, "hospital_id"])
#> 
#> 
#> **Frequency table**  
#> 
#> 
#> |   |Item | Count| Percent| Cum. Count| Cum. Percent|
#> |:--|:----|-----:|-------:|----------:|------------:|
#> |1  |D    |   762|   38.1%|        762|        38.1%|
#> |2  |B    |   663|   33.2%|      1,425|        71.3%|
#> |3  |A    |   321|   16.1%|      1,746|        87.3%|
#> |4  |C    |   254|   12.7%|      2,000|       100.0%|
#> 
#> 
septic_patients$hospital_id %>% freq()
#> 
#> 
#> **Frequency table**  
#> 
#> 
#> |   |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%|
#> 
#> 
septic_patients[, "hospital_id"] %>% freq()
#> 
#> 
#> **Frequency table**  
#> 
#> 
#> |   |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%|
#> 
#> 
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%|
#> 
#> 
septic_patients %>% freq(hospital_id)  #<- easiest to remember (tidyverse)
#> 
#> 
#> **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%|
#> 
#> 


# you could also use `select` or `pull` to get your variables
septic_patients %>%
  filter(hospital_id == "A") %>%
  select(mo) %>%
  freq()
#> 
#> 
#> **Frequency table**  
#> 
#> 
#> |   |Item         | Count| Percent| Cum. Count| Cum. Percent|
#> |:--|:------------|-----:|-------:|----------:|------------:|
#> |1  |B_ESCHR_COL  |    62|   19.3%|         62|        19.3%|
#> |2  |B_STPHY_EPI  |    46|   14.3%|        108|        33.6%|
#> |3  |B_STPHY_CNS  |    38|   11.8%|        146|        45.5%|
#> |4  |B_STPHY_AUR  |    35|   10.9%|        181|        56.4%|
#> |5  |B_STPHY_HOM  |    25|    7.8%|        206|        64.2%|
#> |6  |B_STRPTC_PNE |    12|    3.7%|        218|        67.9%|
#> |7  |B_PROTS_MIR  |    11|    3.4%|        229|        71.3%|
#> |8  |B_ENTRC_FAE  |    10|    3.1%|        239|        74.5%|
#> |9  |B_KLBSL_PNE  |     8|    2.5%|        247|        76.9%|
#> |10 |B_STRPTC_PYO |     7|    2.2%|        254|        79.1%|
#> |11 |B_BCTRD_FRA  |     5|    1.6%|        259|        80.7%|
#> |12 |B_KLBSL_OXY  |     5|    1.6%|        264|        82.2%|
#> |13 |B_STRPTC     |     5|    1.6%|        269|        83.8%|
#> |14 |B_ENTRC_IUM  |     4|    1.2%|        273|        85.0%|
#> |15 |B_STRPTC_MIT |     4|    1.2%|        277|        86.3%|
#> |16 |B_CRYNB      |     3|    0.9%|        280|        87.2%|
#> |17 |B_PDMNS_AER  |     3|    0.9%|        283|        88.2%|
#> |18 |B_STPHY_CAP  |     3|    0.9%|        286|        89.1%|
#> |19 |B_STRPTC_DYS |     3|    0.9%|        289|        90.0%|
#> |20 |F_CANDD_GLB  |     3|    0.9%|        292|        91.0%|
#> |21 |B_ACNTB      |     2|    0.6%|        294|        91.6%|
#> |22 |B_ENTRB_CLO  |     2|    0.6%|        296|        92.2%|
#> |23 |B_HMPHL_INF  |     2|    0.6%|        298|        92.8%|
#> |24 |B_MCRCCC     |     2|    0.6%|        300|        93.5%|
#> |25 |B_PROTS_VUL  |     2|    0.6%|        302|        94.1%|
#> |26 |B_SERRT_MAR  |     2|    0.6%|        304|        94.7%|
#> |27 |B_STPHY_COH  |     2|    0.6%|        306|        95.3%|
#> |28 |B_STRPTC_BOV |     2|    0.6%|        308|        96.0%|
#> |29 |B_AMYCS_ODO  |     1|    0.3%|        309|        96.3%|
#> |30 |B_ARCCC_URI  |     1|    0.3%|        310|        96.6%|
#> |31 |B_CTRDM_PER  |     1|    0.3%|        311|        96.9%|
#> |32 |B_CTRDM_SEP  |     1|    0.3%|        312|        97.2%|
#> |33 |B_STPHY_SCH  |     1|    0.3%|        313|        97.5%|
#> |34 |B_STRPTC_AGA |     1|    0.3%|        314|        97.8%|
#> |35 |B_STRPTC_EQU |     1|    0.3%|        315|        98.1%|
#> |36 |B_STRPTC_GRA |     1|    0.3%|        316|        98.4%|
#> |37 |B_STRPTC_GRB |     1|    0.3%|        317|        98.8%|
#> |38 |B_STRPTC_SAN |     1|    0.3%|        318|        99.1%|
#> |39 |B_VLLNL_PAR  |     1|    0.3%|        319|        99.4%|
#> |40 |F_CANDD_ALB  |     1|    0.3%|        320|        99.7%|
#> |41 |F_CANDD_TRO  |     1|    0.3%|        321|       100.0%|
#> 
#> 


# multiple selected variables will be pasted together
septic_patients %>%
  left_join_microorganisms %>%
  filter(hospital_id == "A") %>%
  freq(genus, species)
#> Joining, by = "mo"
#> 
#> 
#> **Frequency table of `genus` and `species`**  
#> 
#> 
#> |   |Item                              | Count| Percent| Cum. Count| Cum. Percent|
#> |:--|:---------------------------------|-----:|-------:|----------:|------------:|
#> |1  |Escherichia coli                  |    62|   19.3%|         62|        19.3%|
#> |2  |Staphylococcus epidermidis        |    46|   14.3%|        108|        33.6%|
#> |3  |Staphylococcus coagulase negative |    38|   11.8%|        146|        45.5%|
#> |4  |Staphylococcus aureus             |    35|   10.9%|        181|        56.4%|
#> |5  |Staphylococcus hominis            |    25|    7.8%|        206|        64.2%|
#> |6  |Streptococcus pneumoniae          |    12|    3.7%|        218|        67.9%|
#> |7  |Proteus mirabilis                 |    11|    3.4%|        229|        71.3%|
#> |8  |Enterococcus faecalis             |    10|    3.1%|        239|        74.5%|
#> |9  |Klebsiella pneumoniae             |     8|    2.5%|        247|        76.9%|
#> |10 |Streptococcus pyogenes            |     7|    2.2%|        254|        79.1%|
#> |11 |Bacteroides fragilis              |     5|    1.6%|        259|        80.7%|
#> |12 |Klebsiella oxytoca                |     5|    1.6%|        264|        82.2%|
#> |13 |Streptococcus species             |     5|    1.6%|        269|        83.8%|
#> |14 |Enterococcus faecium              |     4|    1.2%|        273|        85.0%|
#> |15 |Streptococcus mitis               |     4|    1.2%|        277|        86.3%|
#> |16 |Candida glabrata                  |     3|    0.9%|        280|        87.2%|
#> |17 |Corynebacterium species           |     3|    0.9%|        283|        88.2%|
#> |18 |Pseudomonas aeruginosa            |     3|    0.9%|        286|        89.1%|
#> |19 |Staphylococcus capitis            |     3|    0.9%|        289|        90.0%|
#> |20 |Streptococcus dysgalactiae        |     3|    0.9%|        292|        91.0%|
#> |21 |Acinetobacter species             |     2|    0.6%|        294|        91.6%|
#> |22 |Enterobacter cloacae              |     2|    0.6%|        296|        92.2%|
#> |23 |Haemophilus influenzae            |     2|    0.6%|        298|        92.8%|
#> |24 |Micrococcus species               |     2|    0.6%|        300|        93.5%|
#> |25 |Proteus vulgaris                  |     2|    0.6%|        302|        94.1%|
#> |26 |Serratia marcescens               |     2|    0.6%|        304|        94.7%|
#> |27 |Staphylococcus cohnii             |     2|    0.6%|        306|        95.3%|
#> |28 |Streptococcus bovis               |     2|    0.6%|        308|        96.0%|
#> |29 |Actinomyces odontolyticus         |     1|    0.3%|        309|        96.3%|
#> |30 |Aerococcus urinae                 |     1|    0.3%|        310|        96.6%|
#> |31 |Candida albicans                  |     1|    0.3%|        311|        96.9%|
#> |32 |Candida tropicalis                |     1|    0.3%|        312|        97.2%|
#> |33 |Clostridium perfringens           |     1|    0.3%|        313|        97.5%|
#> |34 |Clostridium septicum              |     1|    0.3%|        314|        97.8%|
#> |35 |Staphylococcus schleiferi         |     1|    0.3%|        315|        98.1%|
#> |36 |Streptococcus agalactiae          |     1|    0.3%|        316|        98.4%|
#> |37 |Streptococcus equi                |     1|    0.3%|        317|        98.8%|
#> |38 |Streptococcus group A             |     1|    0.3%|        318|        99.1%|
#> |39 |Streptococcus group B             |     1|    0.3%|        319|        99.4%|
#> |40 |Streptococcus sanguinis           |     1|    0.3%|        320|        99.7%|
#> |41 |Veillonella parvula               |     1|    0.3%|        321|       100.0%|
#> 
#> 


# group a variable and analyse another
septic_patients %>%
  group_by(hospital_id) %>%
  freq(gender)
#> 
#> 
#> **Frequency table of `gender` (grouped by `hospital_id`)**  
#> 
#> 
#> |   |Group |Item | Count| Percent| Cum. Count| Cum. Percent|
#> |:--|:-----|:----|-----:|-------:|----------:|------------:|
#> |1  |1     |F    |   148|    7.4%|        148|         7.4%|
#> |2  |      |M    |   173|    8.7%|        321|        16.1%|
#> |3  |2     |F    |   332|   16.6%|        332|        16.6%|
#> |4  |      |M    |   331|   16.6%|        663|        33.2%|
#> |5  |3     |F    |   121|    6.1%|        121|         6.1%|
#> |6  |      |M    |   133|    6.7%|        254|        12.7%|
#> |7  |4     |F    |   368|   18.4%|        368|        18.4%|
#> |8  |      |M    |   394|   19.7%|        762|        38.1%|
#> 
#> 


# get top 10 bugs of hospital A as a vector
septic_patients %>%
  filter(hospital_id == "A") %>%
  freq(mo) %>%
  top_freq(10)
#>             62             46             38             35             25 
#>  "B_ESCHR_COL"  "B_STPHY_EPI"  "B_STPHY_CNS"  "B_STPHY_AUR"  "B_STPHY_HOM" 
#>             12             11             10              8              7 
#> "B_STRPTC_PNE"  "B_PROTS_MIR"  "B_ENTRC_FAE"  "B_KLBSL_PNE" "B_STRPTC_PYO" 


# save frequency table to an object
years <- septic_patients %>%
  mutate(year = format(date, "%Y")) %>%
  freq(year)


# show only the top 5
years %>% print(nmax = 5)
#> 
#> 
#> **Frequency table of `year`**  
#> 
#> 
#> |   |Item | Count| Percent| Cum. Count| Cum. Percent|
#> |:--|:----|-----:|-------:|----------:|------------:|
#> |1  |2017 |   168|    8.4%|        168|         8.4%|
#> |2  |2004 |   167|    8.4%|        335|        16.8%|
#> |3  |2016 |   143|    7.2%|        478|        23.9%|
#> |4  |2002 |   136|    6.8%|        614|        30.7%|
#> |5  |2003 |   135|    6.8%|        749|        37.5%|
#> 
#> (omitted 11 entries, n = 1,251 [62.6%])
#> 
#> 


# save to an object with formatted percentages
years <- format(years)


# print a histogram of numeric values
septic_patients %>%
  freq(age) %>%
  hist()


# or print all points to a regular plot
septic_patients %>%
  freq(age) %>%
  plot()


# transform to a data.frame or tibble
septic_patients %>%
  freq(age) %>%
  as.data.frame()
#>    item count percent cum_count cum_percent
#> 1    83   102  0.0510       102      0.0510
#> 2    80    75  0.0375       177      0.0885
#> 3    75    72  0.0360       249      0.1245
#> 4    79    72  0.0360       321      0.1605
#> 5    78    70  0.0350       391      0.1955
#> 6    76    65  0.0325       456      0.2280
#> 7    82    62  0.0310       518      0.2590
#> 8    86    61  0.0305       579      0.2895
#> 9    81    58  0.0290       637      0.3185
#> 10   87    57  0.0285       694      0.3470
#> 11   74    54  0.0270       748      0.3740
#> 12   73    53  0.0265       801      0.4005
#> 13   77    52  0.0260       853      0.4265
#> 14   67    51  0.0255       904      0.4520
#> 15   88    51  0.0255       955      0.4775
#> 16   70    50  0.0250      1005      0.5025
#> 17   69    49  0.0245      1054      0.5270
#> 18   71    47  0.0235      1101      0.5505
#> 19   72    45  0.0225      1146      0.5730
#> 20   65    43  0.0215      1189      0.5945
#> 21   66    42  0.0210      1231      0.6155
#> 22   85    42  0.0210      1273      0.6365
#> 23   68    41  0.0205      1314      0.6570
#> 24   47    39  0.0195      1353      0.6765
#> 25   90    35  0.0175      1388      0.6940
#> 26   89    34  0.0170      1422      0.7110
#> 27   84    33  0.0165      1455      0.7275
#> 28   62    32  0.0160      1487      0.7435
#> 29   59    31  0.0155      1518      0.7590
#> 30   64    31  0.0155      1549      0.7745
#> 31   57    29  0.0145      1578      0.7890
#> 32   63    28  0.0140      1606      0.8030
#> 33   51    27  0.0135      1633      0.8165
#> 34   52    25  0.0125      1658      0.8290
#> 35   60    25  0.0125      1683      0.8415
#> 36   53    23  0.0115      1706      0.8530
#> 37   58    23  0.0115      1729      0.8645
#> 38   50    22  0.0110      1751      0.8755
#> 39   56    21  0.0105      1772      0.8860
#> 40   45    20  0.0100      1792      0.8960
#> 41   55    17  0.0085      1809      0.9045
#> 42   61    17  0.0085      1826      0.9130
#> 43   93    16  0.0080      1842      0.9210
#> 44   43    15  0.0075      1857      0.9285
#> 45   44    13  0.0065      1870      0.9350
#> 46   46    13  0.0065      1883      0.9415
#> 47   41    10  0.0050      1893      0.9465
#> 48   48    10  0.0050      1903      0.9515
#> 49   30     9  0.0045      1912      0.9560
#> 50   54     9  0.0045      1921      0.9605
#> 51   92     9  0.0045      1930      0.9650
#> 52   91     8  0.0040      1938      0.9690
#> 53   42     7  0.0035      1945      0.9725
#> 54   38     6  0.0030      1951      0.9755
#> 55   94     6  0.0030      1957      0.9785
#> 56   20     4  0.0020      1961      0.9805
#> 57   39     4  0.0020      1965      0.9825
#> 58   49     4  0.0020      1969      0.9845
#> 59   19     3  0.0015      1972      0.9860
#> 60   29     3  0.0015      1975      0.9875
#> 61   33     3  0.0015      1978      0.9890
#> 62   37     3  0.0015      1981      0.9905
#> 63   40     3  0.0015      1984      0.9920
#> 64   18     2  0.0010      1986      0.9930
#> 65   24     2  0.0010      1988      0.9940
#> 66   31     2  0.0010      1990      0.9950
#> 67   36     2  0.0010      1992      0.9960
#> 68   97     2  0.0010      1994      0.9970
#> 69   14     1  0.0005      1995      0.9975
#> 70   22     1  0.0005      1996      0.9980
#> 71   32     1  0.0005      1997      0.9985
#> 72   34     1  0.0005      1998      0.9990
#> 73   35     1  0.0005      1999      0.9995
#> 74   95     1  0.0005      2000      1.0000


# or transform (back) to a vector
septic_patients %>%
  freq(age) %>%
  as.vector()
#>    [1] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
#>   [25] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
#>   [49] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
#>   [73] 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
#>   [97] 83 83 83 83 83 83 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80
#>  [121] 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80
#>  [145] 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80
#>  [169] 80 80 80 80 80 80 80 80 80 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75
#>  [193] 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75
#>  [217] 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 75
#>  [241] 75 75 75 75 75 75 75 75 75 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79
#>  [265] 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79
#>  [289] 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79
#>  [313] 79 79 79 79 79 79 79 79 79 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78
#>  [337] 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78
#>  [361] 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78 78
#>  [385] 78 78 78 78 78 78 78 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76
#>  [409] 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76
#>  [433] 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76
#>  [457] 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82
#>  [481] 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82
#>  [505] 82 82 82 82 82 82 82 82 82 82 82 82 82 82 86 86 86 86 86 86 86 86 86 86
#>  [529] 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86
#>  [553] 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86
#>  [577] 86 86 86 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
#>  [601] 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81
#>  [625] 81 81 81 81 81 81 81 81 81 81 81 81 81 87 87 87 87 87 87 87 87 87 87 87
#>  [649] 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87
#>  [673] 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 74 74
#>  [697] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
#>  [721] 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74
#>  [745] 74 74 74 74 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73
#>  [769] 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73 73
#>  [793] 73 73 73 73 73 73 73 73 73 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77
#>  [817] 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77 77
#>  [841] 77 77 77 77 77 77 77 77 77 77 77 77 77 67 67 67 67 67 67 67 67 67 67 67
#>  [865] 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67
#>  [889] 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 88 88 88 88 88 88 88 88
#>  [913] 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88
#>  [937] 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 70 70 70 70 70
#>  [961] 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70
#>  [985] 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70 69 69 69
#> [1009] 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69
#> [1033] 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 69 71 71
#> [1057] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71
#> [1081] 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 71 72 72 72
#> [1105] 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72
#> [1129] 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 72 65 65 65 65 65 65
#> [1153] 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65 65
#> [1177] 65 65 65 65 65 65 65 65 65 65 65 65 65 66 66 66 66 66 66 66 66 66 66 66
#> [1201] 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66 66
#> [1225] 66 66 66 66 66 66 66 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85
#> [1249] 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85
#> [1273] 85 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68
#> [1297] 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 47 47 47 47 47 47
#> [1321] 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47 47
#> [1345] 47 47 47 47 47 47 47 47 47 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90
#> [1369] 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 89 89 89 89
#> [1393] 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89
#> [1417] 89 89 89 89 89 89 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84
#> [1441] 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 62 62 62 62 62 62 62 62 62
#> [1465] 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 62 59
#> [1489] 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59 59
#> [1513] 59 59 59 59 59 59 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64
#> [1537] 64 64 64 64 64 64 64 64 64 64 64 64 64 57 57 57 57 57 57 57 57 57 57 57
#> [1561] 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 57 63 63 63 63 63 63
#> [1585] 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 63 51 51
#> [1609] 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51
#> [1633] 51 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52
#> [1657] 52 52 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60
#> [1681] 60 60 60 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53 53
#> [1705] 53 53 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58 58
#> [1729] 58 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 56
#> [1753] 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 56 45 45 45 45
#> [1777] 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 55 55 55 55 55 55 55 55
#> [1801] 55 55 55 55 55 55 55 55 55 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61
#> [1825] 61 61 93 93 93 93 93 93 93 93 93 93 93 93 93 93 93 93 43 43 43 43 43 43
#> [1849] 43 43 43 43 43 43 43 43 43 44 44 44 44 44 44 44 44 44 44 44 44 44 46 46
#> [1873] 46 46 46 46 46 46 46 46 46 46 46 41 41 41 41 41 41 41 41 41 41 48 48 48
#> [1897] 48 48 48 48 48 48 48 30 30 30 30 30 30 30 30 30 54 54 54 54 54 54 54 54
#> [1921] 54 92 92 92 92 92 92 92 92 92 91 91 91 91 91 91 91 91 42 42 42 42 42 42
#> [1945] 42 38 38 38 38 38 38 94 94 94 94 94 94 20 20 20 20 39 39 39 39 49 49 49
#> [1969] 49 19 19 19 29 29 29 33 33 33 37 37 37 40 40 40 18 18 24 24 31 31 36 36
#> [1993] 97 97 14 22 32 34 35 95

identical(septic_patients %>%
            freq(age) %>%
            as.vector() %>%
            sort(),
          sort(septic_patients$age)) # TRUE
#> [1] TRUE


# it also supports `table` objects
table(septic_patients$gender,
      septic_patients$age) %>%
  freq(sep = " **sep** ")
#> 
#> 
#> **Frequency table of a `table` object**  
#> 
#> 
#> |    |Item         | Count| Percent| Cum. Count| Cum. Percent|
#> |:---|:------------|-----:|-------:|----------:|------------:|
#> |1   |F **sep** 83 |    55|    2.8%|         55|         2.8%|
#> |2   |M **sep** 78 |    51|    2.6%|        106|         5.3%|
#> |3   |M **sep** 83 |    47|    2.4%|        153|         7.7%|
#> |4   |M **sep** 82 |    43|    2.2%|        196|         9.8%|
#> |5   |M **sep** 79 |    42|    2.1%|        238|        11.9%|
#> |6   |F **sep** 80 |    41|    2.1%|        279|        14.0%|
#> |7   |F **sep** 76 |    39|    2.0%|        318|        15.9%|
#> |8   |F **sep** 75 |    38|    1.9%|        356|        17.8%|
#> |9   |F **sep** 86 |    38|    1.9%|        394|        19.7%|
#> |10  |M **sep** 71 |    35|    1.8%|        429|        21.5%|
#> |11  |M **sep** 75 |    34|    1.7%|        463|        23.2%|
#> |12  |M **sep** 77 |    34|    1.7%|        497|        24.9%|
#> |13  |M **sep** 80 |    34|    1.7%|        531|        26.6%|
#> |14  |F **sep** 81 |    33|    1.7%|        564|        28.2%|
#> |15  |M **sep** 88 |    31|    1.6%|        595|        29.8%|
#> |16  |F **sep** 79 |    30|    1.5%|        625|        31.3%|
#> |17  |M **sep** 74 |    30|    1.5%|        655|        32.8%|
#> |18  |M **sep** 87 |    30|    1.5%|        685|        34.3%|
#> |19  |F **sep** 65 |    29|    1.5%|        714|        35.7%|
#> |20  |M **sep** 73 |    29|    1.5%|        743|        37.2%|
#> |21  |M **sep** 69 |    28|    1.4%|        771|        38.6%|
#> |22  |M **sep** 72 |    28|    1.4%|        799|        40.0%|
#> |23  |F **sep** 67 |    27|    1.4%|        826|        41.3%|
#> |24  |F **sep** 87 |    27|    1.4%|        853|        42.7%|
#> |25  |M **sep** 76 |    26|    1.3%|        879|        44.0%|
#> |26  |F **sep** 66 |    25|    1.3%|        904|        45.2%|
#> |27  |F **sep** 70 |    25|    1.3%|        929|        46.5%|
#> |28  |M **sep** 70 |    25|    1.3%|        954|        47.7%|
#> |29  |M **sep** 81 |    25|    1.3%|        979|        49.0%|
#> |30  |F **sep** 47 |    24|    1.2%|      1,003|        50.1%|
#> |31  |F **sep** 73 |    24|    1.2%|      1,027|        51.4%|
#> |32  |F **sep** 74 |    24|    1.2%|      1,051|        52.6%|
#> |33  |M **sep** 67 |    24|    1.2%|      1,075|        53.8%|
#> |34  |M **sep** 86 |    23|    1.2%|      1,098|        54.9%|
#> |35  |F **sep** 89 |    22|    1.1%|      1,120|        56.0%|
#> |36  |M **sep** 68 |    22|    1.1%|      1,142|        57.1%|
#> |37  |M **sep** 85 |    22|    1.1%|      1,164|        58.2%|
#> |38  |F **sep** 69 |    21|    1.1%|      1,185|        59.3%|
#> |39  |M **sep** 62 |    21|    1.1%|      1,206|        60.3%|
#> |40  |F **sep** 85 |    20|    1.0%|      1,226|        61.3%|
#> |41  |F **sep** 88 |    20|    1.0%|      1,246|        62.3%|
#> |42  |F **sep** 90 |    20|    1.0%|      1,266|        63.3%|
#> |43  |M **sep** 64 |    20|    1.0%|      1,286|        64.3%|
#> |44  |F **sep** 68 |    19|    1.0%|      1,305|        65.3%|
#> |45  |F **sep** 78 |    19|    1.0%|      1,324|        66.2%|
#> |46  |F **sep** 82 |    19|    1.0%|      1,343|        67.2%|
#> |47  |F **sep** 45 |    18|    0.9%|      1,361|        68.1%|
#> |48  |F **sep** 60 |    18|    0.9%|      1,379|        69.0%|
#> |49  |F **sep** 77 |    18|    0.9%|      1,397|        69.9%|
#> |50  |F **sep** 84 |    18|    0.9%|      1,415|        70.8%|
#> |51  |M **sep** 51 |    18|    0.9%|      1,433|        71.7%|
#> |52  |M **sep** 52 |    18|    0.9%|      1,451|        72.6%|
#> |53  |F **sep** 57 |    17|    0.9%|      1,468|        73.4%|
#> |54  |F **sep** 72 |    17|    0.9%|      1,485|        74.3%|
#> |55  |M **sep** 66 |    17|    0.9%|      1,502|        75.1%|
#> |56  |F **sep** 58 |    16|    0.8%|      1,518|        75.9%|
#> |57  |F **sep** 59 |    16|    0.8%|      1,534|        76.7%|
#> |58  |M **sep** 56 |    16|    0.8%|      1,550|        77.5%|
#> |59  |M **sep** 47 |    15|    0.8%|      1,565|        78.3%|
#> |60  |M **sep** 59 |    15|    0.8%|      1,580|        79.0%|
#> |61  |M **sep** 84 |    15|    0.8%|      1,595|        79.8%|
#> |62  |M **sep** 90 |    15|    0.8%|      1,610|        80.5%|
#> |63  |F **sep** 63 |    14|    0.7%|      1,624|        81.2%|
#> |64  |M **sep** 53 |    14|    0.7%|      1,638|        81.9%|
#> |65  |M **sep** 63 |    14|    0.7%|      1,652|        82.6%|
#> |66  |M **sep** 65 |    14|    0.7%|      1,666|        83.3%|
#> |67  |M **sep** 61 |    13|    0.7%|      1,679|        84.0%|
#> |68  |F **sep** 50 |    12|    0.6%|      1,691|        84.6%|
#> |69  |F **sep** 71 |    12|    0.6%|      1,703|        85.2%|
#> |70  |M **sep** 57 |    12|    0.6%|      1,715|        85.8%|
#> |71  |M **sep** 89 |    12|    0.6%|      1,727|        86.4%|
#> |72  |M **sep** 93 |    12|    0.6%|      1,739|        87.0%|
#> |73  |F **sep** 62 |    11|    0.6%|      1,750|        87.5%|
#> |74  |F **sep** 64 |    11|    0.6%|      1,761|        88.1%|
#> |75  |M **sep** 50 |    10|    0.5%|      1,771|        88.6%|
#> |76  |F **sep** 43 |     9|    0.5%|      1,780|        89.0%|
#> |77  |F **sep** 46 |     9|    0.5%|      1,789|        89.5%|
#> |78  |F **sep** 51 |     9|    0.5%|      1,798|        89.9%|
#> |79  |F **sep** 53 |     9|    0.5%|      1,807|        90.4%|
#> |80  |F **sep** 55 |     9|    0.5%|      1,816|        90.8%|
#> |81  |M **sep** 30 |     9|    0.5%|      1,825|        91.3%|
#> |82  |F **sep** 44 |     8|    0.4%|      1,833|        91.7%|
#> |83  |M **sep** 55 |     8|    0.4%|      1,841|        92.1%|
#> |84  |F **sep** 41 |     7|    0.4%|      1,848|        92.4%|
#> |85  |F **sep** 48 |     7|    0.4%|      1,855|        92.8%|
#> |86  |F **sep** 52 |     7|    0.4%|      1,862|        93.1%|
#> |87  |M **sep** 58 |     7|    0.4%|      1,869|        93.5%|
#> |88  |M **sep** 60 |     7|    0.4%|      1,876|        93.8%|
#> |89  |F **sep** 92 |     6|    0.3%|      1,882|        94.1%|
#> |90  |M **sep** 43 |     6|    0.3%|      1,888|        94.4%|
#> |91  |F **sep** 38 |     5|    0.3%|      1,893|        94.7%|
#> |92  |F **sep** 42 |     5|    0.3%|      1,898|        94.9%|
#> |93  |F **sep** 56 |     5|    0.3%|      1,903|        95.2%|
#> |94  |M **sep** 44 |     5|    0.3%|      1,908|        95.4%|
#> |95  |M **sep** 54 |     5|    0.3%|      1,913|        95.7%|
#> |96  |F **sep** 20 |     4|    0.2%|      1,917|        95.9%|
#> |97  |F **sep** 54 |     4|    0.2%|      1,921|        96.1%|
#> |98  |F **sep** 61 |     4|    0.2%|      1,925|        96.3%|
#> |99  |F **sep** 91 |     4|    0.2%|      1,929|        96.5%|
#> |100 |F **sep** 93 |     4|    0.2%|      1,933|        96.7%|
#> |101 |F **sep** 94 |     4|    0.2%|      1,937|        96.9%|
#> |102 |M **sep** 46 |     4|    0.2%|      1,941|        97.1%|
#> |103 |M **sep** 91 |     4|    0.2%|      1,945|        97.3%|
#> |104 |F **sep** 39 |     3|    0.2%|      1,948|        97.4%|
#> |105 |M **sep** 19 |     3|    0.2%|      1,951|        97.6%|
#> |106 |M **sep** 33 |     3|    0.2%|      1,954|        97.7%|
#> |107 |M **sep** 41 |     3|    0.2%|      1,957|        97.9%|
#> |108 |M **sep** 48 |     3|    0.2%|      1,960|        98.0%|
#> |109 |M **sep** 92 |     3|    0.2%|      1,963|        98.2%|
#> |110 |F **sep** 31 |     2|    0.1%|      1,965|        98.3%|
#> |111 |F **sep** 49 |     2|    0.1%|      1,967|        98.4%|
#> |112 |M **sep** 18 |     2|    0.1%|      1,969|        98.5%|
#> |113 |M **sep** 24 |     2|    0.1%|      1,971|        98.6%|
#> |114 |M **sep** 29 |     2|    0.1%|      1,973|        98.7%|
#> |115 |M **sep** 36 |     2|    0.1%|      1,975|        98.8%|
#> |116 |M **sep** 37 |     2|    0.1%|      1,977|        98.9%|
#> |117 |M **sep** 40 |     2|    0.1%|      1,979|        99.0%|
#> |118 |M **sep** 42 |     2|    0.1%|      1,981|        99.1%|
#> |119 |M **sep** 45 |     2|    0.1%|      1,983|        99.2%|
#> |120 |M **sep** 49 |     2|    0.1%|      1,985|        99.3%|
#> |121 |M **sep** 94 |     2|    0.1%|      1,987|        99.4%|
#> |122 |M **sep** 97 |     2|    0.1%|      1,989|        99.5%|
#> |123 |F **sep** 22 |     1|    0.1%|      1,990|        99.5%|
#> |124 |F **sep** 29 |     1|    0.1%|      1,991|        99.6%|
#> |125 |F **sep** 34 |     1|    0.1%|      1,992|        99.6%|
#> |126 |F **sep** 35 |     1|    0.1%|      1,993|        99.7%|
#> |127 |F **sep** 37 |     1|    0.1%|      1,994|        99.7%|
#> |128 |F **sep** 40 |     1|    0.1%|      1,995|        99.8%|
#> |129 |M **sep** 14 |     1|    0.1%|      1,996|        99.8%|
#> |130 |M **sep** 32 |     1|    0.1%|      1,997|        99.9%|
#> |131 |M **sep** 38 |     1|    0.1%|      1,998|        99.9%|
#> |132 |M **sep** 39 |     1|    0.1%|      1,999|       100.0%|
#> |133 |M **sep** 95 |     1|    0.1%|      2,000|       100.0%|
#> 
#> 


# only get selected columns
septic_patients %>%
  freq(hospital_id) %>%
  select(item, percent)
#> 
#> 
#> **Frequency table of `hospital_id`**  
#> 
#> 
#> |   |Item | Percent|
#> |:--|:----|-------:|
#> |1  |D    |   38.1%|
#> |2  |B    |   33.2%|
#> |3  |A    |   16.1%|
#> |4  |C    |   12.7%|
#> 
#> 

septic_patients %>%
  freq(hospital_id) %>%
  select(-count, -cum_count)
#> 
#> 
#> **Frequency table of `hospital_id`**  
#> 
#> 
#> |   |Item | Percent| Cum. Percent|
#> |:--|:----|-------:|------------:|
#> |1  |D    |   38.1%|        38.1%|
#> |2  |B    |   33.2%|        71.3%|
#> |3  |A    |   16.1%|        87.3%|
#> |4  |C    |   12.7%|       100.0%|
#> 
#> 


# check differences between frequency tables
diff(freq(septic_patients$trim),
     freq(septic_patients$trsu))
#> Differences between frequency tables
#> 
#> |Item | Count #1| Count #2| Difference| Diff. percent|
#> |:----|--------:|--------:|----------:|-------------:|
#> |S    |      918|     1392|       +474|        +51.6%|
#> |R    |      571|      361|       -210|        -36.8%|
#> |I    |       10|        6|         -4|        -40.0%|