# How to conduct AMR analysis • AMR (for R)

Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 10 July 2019.

# Introduction

For this tutorial, we will create fake demonstration data to work with.

You can skip to Cleaning the data if you already have your own data ready. If you start your analysis, try to make the structure of your data generally look like this:

date | patient_id | mo | AMX | CIP
--- | --- | --- | --- | ---
2019-07-10 | abcd | Escherichia coli | S | S
2019-07-10 | abcd | Escherichia coli | S | R
2019-07-10 | efgh | Escherichia coli | R | S

## Needed R packages

As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the tidyverse packages `dplyr` and `ggplot2` by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.

Our `AMR` package depends on these packages and even extends their use and functions.

    library(dplyr)
    library(ggplot2)
    library(AMR)

    # (if not yet installed, install with:)
    # install.packages(c("tidyverse", "AMR"))

# Creation of data

We will create some fake example data to use for analysis. For antimicrobial resistance analysis, we need at least: a patient ID, name or code of a microorganism, a date and antimicrobial results (an antibiogram). It could also include a specimen type (e.g. to filter on blood or urine), the ward type (e.g. to filter on ICUs).

With additional columns (like a hospital name, the patients gender of even [well-defined] clinical properties) you can do a comparative analysis, as this tutorial will demonstrate too.

## Patients

To start with patients, we need a unique list of patients.

    patients <- unlist(lapply(LETTERS, paste0, 1:10))

The `LETTERS` object is available in R - it’s a vector with 26 characters: `A` to `Z`. The `patients` object we just created is now a vector of length 260, with values (patient IDs) varying from `A1` to `Z10`. Now we we also set the gender of our patients, by putting the ID and the gender in a table:

    patients_table <- data.frame(patient_id = patients,
                                 gender = c(rep("M", 135),
                                            rep("F", 125)))

The first 135 patient IDs are now male, the other 125 are female.

## Dates

Let’s pretend that our data consists of blood cultures isolates from between 1 January 2010 and 1 January 2018.

    dates <- seq(as.Date("2010-01-01"), as.Date("2018-01-01"), by = "day")

This `dates` object now contains all days in our date range.

#### Microorganisms

For this tutorial, we will uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae:

    bacteria <- c("Escherichia coli", "Staphylococcus aureus",
                  "Streptococcus pneumoniae", "Klebsiella pneumoniae")

## Other variables

For completeness, we can also add the hospital where the patients was admitted and we need to define valid antibmicrobial results for our randomisation:

    hospitals <- c("Hospital A", "Hospital B", "Hospital C", "Hospital D")
    ab_interpretations <- c("S", "I", "R")

## Put everything together

Using the `sample()` function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the `prob` parameter.

    sample_size <- 20000
    data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
                       patient_id = sample(patients, size = sample_size, replace = TRUE),
                       hospital = sample(hospitals, size = sample_size, replace = TRUE,
                                         prob = c(0.30, 0.35, 0.15, 0.20)),
                       bacteria = sample(bacteria, size = sample_size, replace = TRUE,
                                         prob = c(0.50, 0.25, 0.15, 0.10)),
                       AMX = sample(ab_interpretations, size = sample_size, replace = TRUE,
                                     prob = c(0.60, 0.05, 0.35)),
                       AMC = sample(ab_interpretations, size = sample_size, replace = TRUE,
                                     prob = c(0.75, 0.10, 0.15)),
                       CIP = sample(ab_interpretations, size = sample_size, replace = TRUE,
                                     prob = c(0.80, 0.00, 0.20)),
                       GEN = sample(ab_interpretations, size = sample_size, replace = TRUE,
                                     prob = c(0.92, 0.00, 0.08))
                       )

Using the `left_join()` function from the `dplyr` package, we can ‘map’ the gender to the patient ID using the `patients_table` object we created earlier:

    data <- data %>% left_join(patients_table)

The resulting data set contains 20,000 blood culture isolates. With the `head()` function we can preview the first 6 values of this data set:

    head(data)

date | patient_id | hospital | bacteria | AMX | AMC | CIP | GEN | gender
--- | --- | --- | --- | --- | --- | --- | --- | ---
2011-07-20 | X1 | Hospital C | Staphylococcus aureus | R | I | S | S | F
2017-12-18 | Q8 | Hospital B | Streptococcus pneumoniae | S | S | S | S | F
2015-12-08 | X5 | Hospital B | Staphylococcus aureus | S | S | R | R | F
2011-12-08 | X3 | Hospital B | Klebsiella pneumoniae | S | S | S | S | F
2011-01-02 | E1 | Hospital A | Streptococcus pneumoniae | R | S | S | S | M
2012-10-22 | K1 | Hospital C | Streptococcus pneumoniae | S | S | S | S | M

Now, let’s start the cleaning and the analysis!

# Cleaning the data

Use the frequency table function `freq()` to look specifically for unique values in any variable. For example, for the `gender` variable:

    data %>% freq(gender) # this would be the same: freq(data$gender)

    # Frequency table of `gender` from `data` (20,000 x 9)
    #
    # Class:   factor (numeric)
    # Length:  20,000 (of which NA: 0 = 0.00%)
    # Levels:  2: F, M
    # Unique:  2
    #
    #      Item     Count   Percent   Cum. Count   Cum. Percent
    # ---  -----  -------  --------  -----------  -------------
    # 1    M       10,454     52.3%       10,454          52.3%
    # 2    F        9,546     47.7%       20,000         100.0%

So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values `M` and `F`. From a researchers perspective: there are slightly more men. Nothing we didn’t already know.

The data is already quite clean, but we still need to transform some variables. The `bacteria` column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The `mutate()` function of the `dplyr` package makes this really easy:

    data <- data %>%
      mutate(bacteria = as.mo(bacteria))

We also want to transform the antibiotics, because in real life data we don’t know if they are really clean. The `as.rsi()` function ensures reliability and reproducibility in these kind of variables. The `mutate_at()` will run the `as.rsi()` function on defined variables:

    data <- data %>%
      mutate_at(vars(AMX:GEN), as.rsi)

Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the `eucast_rules()` function can also apply additional rules, like forcing ampicillin = R when amoxicillin/clavulanic acid = R.

Because the amoxicillin (column `AMX`) and amoxicillin/clavulanic acid (column `AMC`) in our data were generated randomly, some rows will undoubtedly contain AMX = S and AMC = R, which is technically impossible. The `eucast_rules()` fixes this:

    data <- eucast_rules(data, col_mo = "bacteria")
    #
    # Rules by the European Committee on Antimicrobial Susceptibility Testing (EUCAST)
    # http://eucast.org/
    #
    # EUCAST Clinical Breakpoints (v9.0, 2019)
    # Aerococcus sanguinicola (no new changes)
    # Aerococcus urinae (no new changes)
    # Anaerobic Gram-negatives (no new changes)
    # Anaerobic Gram-positives (no new changes)
    # Campylobacter coli (no new changes)
    # Campylobacter jejuni (no new changes)
    # Enterobacteriales (Order) (no new changes)
    # Enterococcus (no new changes)
    # Haemophilus influenzae (no new changes)
    # Kingella kingae (no new changes)
    # Moraxella catarrhalis (no new changes)
    # Pasteurella multocida (no new changes)
    # Staphylococcus (no new changes)
    # Streptococcus groups A, B, C, G (no new changes)
    # Streptococcus pneumoniae (1,481 new changes)
    # Viridans group streptococci (no new changes)
    #
    # EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
    # Table 01: Intrinsic resistance in Enterobacteriaceae (1,328 new changes)
    # Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria (no new changes)
    # Table 03: Intrinsic resistance in other Gram-negative bacteria (no new changes)
    # Table 04: Intrinsic resistance in Gram-positive bacteria (2,778 new changes)
    # Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci (no new changes)
    # Table 09: Interpretive rules for B-lactam agents and Gram-negative rods (no new changes)
    # Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no new changes)
    # Table 12: Interpretive rules for aminoglycosides (no new changes)
    # Table 13: Interpretive rules for quinolones (no new changes)
    #
    # Other rules
    # Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,245 new changes)
    # Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (118 new changes)
    # Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no new changes)
    # Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no new changes)
    # Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no new changes)
    # Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no new changes)
    #
    # --------------------------------------------------------------------------
    # EUCAST rules affected 6,581 out of 20,000 rows, making a total of 7,950 edits
    # => added 0 test results
    #
    # => changed 7,950 test results
    #    - 120 test results changed from S to I
    #    - 4,812 test results changed from S to R
    #    - 1,089 test results changed from I to S
    #    - 317 test results changed from I to R
    #    - 1,588 test results changed from R to S
    #    - 24 test results changed from R to I
    # --------------------------------------------------------------------------
    #
    # Use verbose = TRUE (on your original data) to get a data.frame with all specified edits instead.

# Adding new variables

Now that we have the microbial ID, we can add some taxonomic properties:

    data <- data %>%
      mutate(gramstain = mo_gramstain(bacteria),
             genus = mo_genus(bacteria),
             species = mo_species(bacteria))

## First isolates

We also need to know which isolates we can actually use for analysis.

To conduct an analysis of antimicrobial resistance, you must only include the first isolate of every patient per episode (Hindler et al., Clin Infect Dis. 2007). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be selection bias.

The Clinical and Laboratory Standards Institute (CLSI) appoints this as follows:

> (…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype). The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded.
> M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4

This `AMR` package includes this methodology with the `first_isolate()` function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:

    data <- data %>%
      mutate(first = first_isolate(.))
    # NOTE: Using column `bacteria` as input for `col_mo`.
    # NOTE: Using column `date` as input for `col_date`.
    # NOTE: Using column `patient_id` as input for `col_patient_id`.
    # => Found 5,679 first isolates (28.4% of total)

So only 28.4% is suitable for resistance analysis! We can now filter on it with the `filter()` function, also from the `dplyr` package:

    data_1st <- data %>%
      filter(first == TRUE)

For future use, the above two syntaxes can be shortened with the `filter_first_isolate()` function:

    data_1st <- data %>%
      filter_first_isolate()

## First weighted isolates

We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient B6, sorted on date:

isolate | date | patient_id | bacteria | AMX | AMC | CIP | GEN | first
--- | --- | --- | --- | --- | --- | --- | --- | ---
1 | 2010-02-26 | B6 | B_ESCHR_COL | R | S | R | S | TRUE
2 | 2010-05-20 | B6 | B_ESCHR_COL | R | S | S | S | FALSE
3 | 2010-05-28 | B6 | B_ESCHR_COL | R | S | S | S | FALSE
4 | 2010-06-27 | B6 | B_ESCHR_COL | R | S | S | S | FALSE
5 | 2010-09-06 | B6 | B_ESCHR_COL | R | S | S | S | FALSE
6 | 2010-10-16 | B6 | B_ESCHR_COL | R | S | S | S | FALSE
7 | 2010-10-20 | B6 | B_ESCHR_COL | S | S | S | S | FALSE
8 | 2010-11-01 | B6 | B_ESCHR_COL | S | S | S | R | FALSE
9 | 2010-11-14 | B6 | B_ESCHR_COL | S | S | S | S | FALSE
10 | 2011-01-25 | B6 | B_ESCHR_COL | R | S | S | S | FALSE

Only 1 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The `key_antibiotics()` function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.

If a column exists with a name like ‘key(…)ab’ the `first_isolate()` function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:

    data <- data %>%
      mutate(keyab = key_antibiotics(.)) %>%
      mutate(first_weighted = first_isolate(.))
    # NOTE: Using column `bacteria` as input for `col_mo`.
    # NOTE: Using column `bacteria` as input for `col_mo`.
    # NOTE: Using column `date` as input for `col_date`.
    # NOTE: Using column `patient_id` as input for `col_patient_id`.
    # NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.
    # [Criterion] Inclusion based on key antibiotics, ignoring I.
    # => Found 15,003 first weighted isolates (75.0% of total)

isolate | date | patient_id | bacteria | AMX | AMC | CIP | GEN | first | first_weighted
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
1 | 2010-02-26 | B6 | B_ESCHR_COL | R | S | R | S | TRUE | TRUE
2 | 2010-05-20 | B6 | B_ESCHR_COL | R | S | S | S | FALSE | TRUE
3 | 2010-05-28 | B6 | B_ESCHR_COL | R | S | S | S | FALSE | FALSE
4 | 2010-06-27 | B6 | B_ESCHR_COL | R | S | S | S | FALSE | FALSE
5 | 2010-09-06 | B6 | B_ESCHR_COL | R | S | S | S | FALSE | FALSE
6 | 2010-10-16 | B6 | B_ESCHR_COL | R | S | S | S | FALSE | FALSE
7 | 2010-10-20 | B6 | B_ESCHR_COL | S | S | S | S | FALSE | TRUE
8 | 2010-11-01 | B6 | B_ESCHR_COL | S | S | S | R | FALSE | TRUE
9 | 2010-11-14 | B6 | B_ESCHR_COL | S | S | S | S | FALSE | TRUE
10 | 2011-01-25 | B6 | B_ESCHR_COL | R | S | S | S | FALSE | TRUE

Instead of 1, now 6 isolates are flagged. In total, 75% of all isolates are marked ‘first weighted’ - 46.6% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.

As with `filter_first_isolate()`, there’s a shortcut for this new algorithm too:

    data_1st <- data %>%
      filter_first_weighted_isolate()

So we end up with 15,003 isolates for analysis.

We can remove unneeded columns:

    data_1st <- data_1st %>%
      select(-c(first, keyab))

Now our data looks like:

    head(data_1st)

| date | patient_id | hospital | bacteria | AMX | AMC | CIP | GEN | gender | gramstain | genus | species | first_weighted
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
2 | 2017-12-18 | Q8 | Hospital B | B_STRPT_PNE | S | S | S | R | F | Gram-positive | Streptococcus | pneumoniae | TRUE
3 | 2015-12-08 | X5 | Hospital B | B_STPHY_AUR | S | S | R | R | F | Gram-positive | Staphylococcus | aureus | TRUE
4 | 2011-12-08 | X3 | Hospital B | B_KLBSL_PNE | R | S | S | S | F | Gram-negative | Klebsiella | pneumoniae | TRUE
5 | 2011-01-02 | E1 | Hospital A | B_STRPT_PNE | R | R | S | R | M | Gram-positive | Streptococcus | pneumoniae | TRUE
6 | 2012-10-22 | K1 | Hospital C | B_STRPT_PNE | S | S | S | R | M | Gram-positive | Streptococcus | pneumoniae | TRUE
7 | 2016-07-14 | C5 | Hospital D | B_STRPT_PNE | R | R | S | R | M | Gram-positive | Streptococcus | pneumoniae | TRUE

Time for the analysis!

# Analysing the data

You might want to start by getting an idea of how the data is distributed. It’s an important start, because it also decides how you will continue your analysis. Although this package contains a convenient function to make frequency tables, exploratory data analysis (EDA) is not the primary scope of this package. Use a package like `DataExplorer` for that, or read the free online book Exploratory Data Analysis with R by Roger D. Peng.

## Dispersion of species

To just get an idea how the species are distributed, create a frequency table with our `freq()` function. We created the `genus` and `species` column earlier based on the microbial ID. With `paste()`, we can concatenate them together.

The `freq()` function can be used like the base R language was intended:

    freq(paste(data_1st$genus, data_1st$species))

Or can be used like the `dplyr` way, which is easier readable:

    data_1st %>% freq(genus, species)

Frequency table of `genus` and `species` from `data_1st` (15,003 x 13)

Columns: 2
Length: 15,003 (of which NA: 0 = 0.00%)
Unique: 4

Shortest: 16
Longest: 24

| Item | Count | Percent | Cum. Count | Cum. Percent
--- | --- | --- | --- | --- | ---
1 | Escherichia coli | 7,251 | 48.3% | 7,251 | 48.3%
2 | Staphylococcus aureus | 3,834 | 25.6% | 11,085 | 73.9%
3 | Streptococcus pneumoniae | 2,328 | 15.5% | 13,413 | 89.4%
4 | Klebsiella pneumoniae | 1,590 | 10.6% | 15,003 | 100.0%

## Resistance percentages

The functions `portion_S()`, `portion_SI()`, `portion_I()`, `portion_IR()` and `portion_R()` can be used to determine the portion of a specific antimicrobial outcome. As per the EUCAST guideline of 2019, we calculate resistance as the portion of R (`portion_R()`) and susceptibility as the portion of S and I (`portion_SI()`). These functions can be used on their own:

    data_1st %>% portion_R(AMX)
    # [1] 0.4710391

Or can be used in conjuction with `group_by()` and `summarise()`, both from the `dplyr` package:

    data_1st %>%
      group_by(hospital) %>%
      summarise(amoxicillin = portion_R(AMX))

hospital | amoxicillin
--- | ---
Hospital A | 0.4698715
Hospital B | 0.4707917
Hospital C | 0.4548753
Hospital D | 0.4847403

Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the `n_rsi()` can be used, which works exactly like `n_distinct()` from the `dplyr` package. It counts all isolates available for every group (i.e. values S, I or R):

    data_1st %>%
      group_by(hospital) %>%
      summarise(amoxicillin = portion_R(AMX),
                available = n_rsi(AMX))

hospital | amoxicillin | available
--- | --- | ---
Hospital A | 0.4698715 | 4514
Hospital B | 0.4707917 | 5204
Hospital C | 0.4548753 | 2205
Hospital D | 0.4847403 | 3080

These functions can also be used to get the portion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:

    data_1st %>%
      group_by(genus) %>%
      summarise(amoxiclav = portion_SI(AMC),
                gentamicin = portion_SI(GEN),
                amoxiclav_genta = portion_SI(AMC, GEN))

genus | amoxiclav | gentamicin | amoxiclav_genta
--- | --- | --- | ---
Escherichia | 0.9233209 | 0.8939457 | 0.9942077
Klebsiella | 0.8314465 | 0.9150943 | 0.9874214
Staphylococcus | 0.9238393 | 0.9160146 | 0.9921753
Streptococcus | 0.6121134 | 0.0000000 | 0.6121134

To make a transition to the next part, let’s see how this difference could be plotted:

    data_1st %>%
      group_by(genus) %>%
      summarise("1. Amoxi/clav" = portion_SI(AMC),
                "2. Gentamicin" = portion_SI(GEN),
                "3. Amoxi/clav + genta" = portion_SI(AMC, GEN)) %>%
      tidyr::gather("antibiotic", "S", -genus) %>%
      ggplot(aes(x = genus,
                 y = S,
                 fill = antibiotic)) +
      geom_col(position = "dodge2")

## Plots

To show results in plots, most R users would nowadays use the `ggplot2` package. This package lets you create plots in layers. You can read more about it on their website. A quick example would look like these syntaxes:

    ggplot(data = a_data_set,
           mapping = aes(x = year,
                         y = value)) +
      geom_col() +
      labs(title = "A title",
           subtitle = "A subtitle",
           x = "My X axis",
           y = "My Y axis")

    # or as short as:
    ggplot(a_data_set) +
      geom_bar(aes(year))

The `AMR` package contains functions to extend this `ggplot2` package, for example `geom_rsi()`. It automatically transforms data with `count_df()` or `portion_df()` and show results in stacked bars. Its simplest and shortest example:

    ggplot(data_1st) +
      geom_rsi(translate_ab = FALSE)

Omit the `translate_ab = FALSE` to have the antibiotic codes (AMX, AMC, CIP, GEN) translated to official WHO names (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, gentamicin).

If we group on e.g. the `genus` column and add some additional functions from our package, we can create this:

    # group the data on `genus`
    ggplot(data_1st %>% group_by(genus)) +
      # create bars with genus on x axis
      # it looks for variables with class `rsi`,
      # of which we have 4 (earlier created with `as.rsi`)
      geom_rsi(x = "genus") +
      # split plots on antibiotic
      facet_rsi(facet = "antibiotic") +
      # make R red, I yellow and S green
      scale_rsi_colours() +
      # show percentages on y axis
      scale_y_percent(breaks = 0:4 * 25) +
      # turn 90 degrees, to make it bars instead of columns
      coord_flip() +
      # add labels
      labs(title = "Resistance per genus and antibiotic",
           subtitle = "(this is fake data)") +
      # and print genus in italic to follow our convention
      # (is now y axis because we turned the plot)
      theme(axis.text.y = element_text(face = "italic"))

To simplify this, we also created the `ggplot_rsi()` function, which combines almost all above functions:

    data_1st %>%
      group_by(genus) %>%
      ggplot_rsi(x = "genus",
                 facet = "antibiotic",
                 breaks = 0:4 * 25,
                 datalabels = FALSE) +
      coord_flip()

## Independence test

The next example uses the included `septic_patients`, which is an anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This `data.frame` can be used to practice AMR analysis.

We will compare the resistance to fosfomycin (column `FOS`) in hospital A and D. The input for the `fisher.test()` can be retrieved with a transformation like this:

    check_FOS <- septic_patients %>%
      filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and D
      select(hospital_id, FOS) %>%             # select the hospitals and fosfomycin
      group_by(hospital_id) %>%                # group on the hospitals
      count_df(combine_SI = TRUE) %>%          # count all isolates per group (hospital_id)
      tidyr::spread(hospital_id, value) %>%    # transform output so A and D are columns
      select(A, D) %>%                         # and select these only
      as.matrix()                              # transform to good old matrix for fisher.test()

    check_FOS
    #       A  D
    # [1,] 25 77
    # [2,] 24 33

We can apply the test now with:

    # do Fisher's Exact Test
    fisher.test(check_FOS)
    #
    #   Fisher's Exact Test for Count Data
    #
    # data:  check_FOS
    # p-value = 0.03104
    # alternative hypothesis: true odds ratio is not equal to 1
    # 95 percent confidence interval:
    #  0.2111489 0.9485124
    # sample estimates:
    # odds ratio
    #  0.4488318

As can be seen, the p value is 0.031, which means that the fosfomycin resistances found in hospital A and D are really different.