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@@ -240,31 +199,31 @@ Content not found. Please use links in the navbar.The resulting data set contains 20,000 blood culture isolates. With the head() function we can preview the first 6 rows of this data set:
-head(data)head(data)| date | @@ -344,70 +346,70 @@|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-02-04 | -V9 | -Hospital B | +2014-03-31 | +U1 | +Hospital C | Escherichia coli | -S | +I | S | S | S | F | |||
| 2011-09-24 | -D2 | -Hospital A | -Streptococcus pneumoniae | -R | +2015-07-17 | +M4 | +Hospital B | +Staphylococcus aureus | +S | S | S | S | M | ||
| 2014-06-20 | -Z9 | +2013-03-06 | +B9 | Hospital C | -Staphylococcus aureus | +Streptococcus pneumoniae | S | S | S | S | -F | +M | |||
| 2018-01-01 | -P4 | -Hospital C | +2016-10-06 | +D8 | +Hospital B | Escherichia coli | -R | S | S | S | -F | +S | +M | ||
| 2014-12-14 | -W2 | +2013-05-12 | +F10 | Hospital A | Escherichia coli | R | S | +R | S | -S | -F | +M | |||
| 2016-07-27 | -R6 | -Hospital B | -Staphylococcus aureus | -R | +2016-07-15 | +H7 | +Hospital A | +Escherichia coli | S | -R | S | -F | +S | +S | +M |
-Cleaning the data
-We also created a package dedicated to data cleaning and checking, called the cleaner package. It freq() function can be used to create frequency tables.
We also created a package dedicated to data cleaning and checking, called the cleaner package. It freq() function can be used to create frequency tables.
For example, for the gender variable:
-data %>% freq(gender)data %>% freq(gender)Frequency table
Class: character
Length: 20,000
@@ -441,63 +443,63 @@ Longest: 1
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:
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:
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 is.rsi.eligible() can check which columns are probably columns with R/SI test results. Using mutate() and across(), we can apply the transformation to the formal <rsi> class:
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 is.rsi.eligible() can check which columns are probably columns with R/SI test results. Using mutate() and across(), we can apply the transformation to the formal <rsi> class:
is.rsi.eligible(data)
# [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE
-colnames(data)[is.rsi.eligible(data)]
+colnames(data)[is.rsi.eligible(data)]
# [1] "AMX" "AMC" "CIP" "GEN"
data <- data %>%
- mutate(across(where(is.rsi.eligible), 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
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
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 = "all")-Adding new variables
+Adding new variablesNow that we have the microbial ID, we can add some taxonomic properties:
data <- data %>%
- mutate(gramstain = mo_gramstain(bacteria),
+ mutate(gramstain = mo_gramstain(bacteria),
genus = mo_genus(bacteria),
species = mo_species(bacteria))-First isolates
+First isolatesWe 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.
+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(…) 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 and is able to apply the four different methods as defined by Hindler et al. in 2007: phenotype-based, episode-based, patient-based, isolate-based. The right method depends on your goals and analysis, but the default phenotype-based method is in any case the method to properly correct for most duplicate isolates. This method also takes into account the antimicrobial susceptibility test results using all_microbials(). Read more about the methods on the first_isolate() page.
This AMR package includes this methodology with the first_isolate() function and is able to apply the four different methods as defined by Hindler et al. in 2007: phenotype-based, episode-based, patient-based, isolate-based. The right method depends on your goals and analysis, but the default phenotype-based method is in any case the method to properly correct for most duplicate isolates. This method also takes into account the antimicrobial susceptibility test results using all_microbials(). Read more about the methods on the first_isolate() page.
The outcome of the function can easily be added to our data:
data <- data %>%
- mutate(first = first_isolate(info = TRUE))
+ mutate(first = first_isolate(info = TRUE))
# Determining first isolates using the 'phenotype-based' method and an
# episode length of 365 days
# ℹ Using column 'bacteria' as input for `col_mo`.
@@ -505,20 +507,20 @@ Longest: 1
# ℹ Using column 'patient_id' as input for `col_patient_id`.
# Basing inclusion on all antimicrobial results, using a points threshold of
# 2
-# => Found 10,656 first weighted isolates (phenotype-based, 53.3% of total
+# => Found 10,659 first weighted isolates (phenotype-based, 53.3% of total
# where a microbial ID was available)So only 53.3% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 53.3% 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:
data_1st <- data %>%
filter_first_isolate()So we end up with 10,656 isolates for analysis. Now our data looks like:
+So we end up with 10,659 isolates for analysis. Now our data looks like:
-head(data_1st)| 2 | -2011-09-24 | -D2 | -Hospital A | -B_STRPT_PNMN | -R | -R | +2015-07-17 | +M4 | +Hospital B | +B_STPHY_AURS | +S | +S | +S | S | -R | M | Gram-positive | -Streptococcus | -pneumoniae | +Staphylococcus | +aureus | TRUE |
| 3 | -2014-06-20 | -Z9 | +2013-03-06 | +B9 | Hospital C | -B_STPHY_AURS | +B_STRPT_PNMN | S | S | S | -S | -F | +R | +M | Gram-positive | -Staphylococcus | -aureus | +Streptococcus | +pneumoniae | TRUE | ||
| 6 | -2016-07-27 | -R6 | -Hospital B | -B_STPHY_AURS | +5 | +2013-05-12 | +F10 | +Hospital A | +B_ESCHR_COLI | R | S | R | S | -F | -Gram-positive | -Staphylococcus | -aureus | +M | +Gram-negative | +Escherichia | +coli | TRUE |
| 7 | -2011-07-24 | -W2 | +2016-07-27 | +E3 | Hospital A | -B_ESCHR_COLI | +B_STRPT_PNMN | R | R | -S | R | -F | -Gram-negative | -Escherichia | -coli | +R | +M | +Gram-positive | +Streptococcus | +pneumoniae | TRUE | |
| 9 | -2012-01-12 | -K9 | -Hospital C | -B_ESCHR_COLI | +8 | +2015-02-09 | +T6 | +Hospital A | +B_STPHY_AURS | S | S | S | S | -M | -Gram-negative | -Escherichia | -coli | +F | +Gram-positive | +Staphylococcus | +aureus | TRUE |
| 11 | -2013-03-11 | -B6 | -Hospital D | -B_KLBSL_PNMN | +9 | +2017-08-22 | +F8 | +Hospital B | +B_ESCHR_COLI | +R | R | S | -R | S | M | Gram-negative | -Klebsiella | -pneumoniae | +Escherichia | +coli | TRUE | |
| 1 | Escherichia coli | -4,664 | -43.77% | -4,664 | -43.77% | +4,644 | +43.57% | +4,644 | +43.57% | |||||||||||||
| 2 | Staphylococcus aureus | -2,730 | +2,731 | 25.62% | -7,394 | -69.39% | +7,375 | +69.19% | ||||||||||||||
| 3 | Streptococcus pneumoniae | -2,093 | -19.64% | -9,487 | -89.03% | +2,125 | +19.94% | +9,500 | +89.13% | |||||||||||||
| 4 | Klebsiella pneumoniae | -1,169 | -10.97% | -10,656 | +1,159 | +10.87% | +10,659 | 100.00% |
| 2011-09-24 | -D2 | -Hospital A | +2013-03-06 | +B9 | +Hospital C | B_STRPT_PNMN | -R | -R | +S | +S | S | R | M | Gram-positive | -Streptococcus | +Streptococcus | pneumoniae | TRUE | ||
| 2011-07-24 | -W2 | +2016-07-27 | +E3 | Hospital A | -B_ESCHR_COLI | +B_STRPT_PNMN | R | R | -S | R | -F | -Gram-negative | -Escherichia | -coli | +R | +M | +Gram-positive | +Streptococcus | +pneumoniae | TRUE |
| 2011-03-17 | -D5 | -Hospital A | -B_STRPT_PNMN | -S | -S | -S | -R | -M | -Gram-positive | -Streptococcus | -pneumoniae | -TRUE | -||||||||
| 2011-06-13 | -X10 | +2017-10-14 | +X8 | Hospital D | B_STRPT_PNMN | S | @@ -815,37 +802,52 @@ Longest: 24R | F | Gram-positive | -Streptococcus | -pneumoniae | -TRUE | -||||||||
| 2010-04-12 | -Z4 | -Hospital B | -B_STRPT_PNMN | -R | -R | -S | -R | -F | -Gram-positive | -Streptococcus | +Streptococcus | pneumoniae | TRUE | |||||||
| 2017-02-25 | -B5 | -Hospital B | +2011-06-11 | +V8 | +Hospital C | B_STRPT_PNMN | -I | -I | +R | +R | +R | +R | +F | +Gram-positive | +Streptococcus | +pneumoniae | +TRUE | +|||
| 2013-09-03 | +S3 | +Hospital D | +B_STRPT_PNMN | +R | +R | +R | +R | +F | +Gram-positive | +Streptococcus | +pneumoniae | +TRUE | +||||||||
| 2016-05-16 | +X5 | +Hospital C | +B_STRPT_PNMN | +S | +S | S | R | -M | +F | Gram-positive | -Streptococcus | +Streptococcus | pneumoniae | TRUE |
| E. coli | +E. coli | AMX | -2180 | -145 | -2339 | -4664 | +2213 | +141 | +2290 | +4644 |
| E. coli | +E. coli | AMC | -3360 | -176 | -1128 | -4664 | +3408 | +175 | +1061 | +4644 |
| E. coli | +E. coli | CIP | -3405 | +3393 | 0 | -1259 | -4664 | +1251 | +4644 | |
| E. coli | +E. coli | GEN | -4078 | +4060 | 0 | -586 | -4664 | +584 | +4644 | |
| K. pneumoniae | +K. pneumoniae | AMX | 0 | 0 | -1169 | -1169 | +1159 | +1159 | ||
| K. pneumoniae | +K. pneumoniae | AMC | -924 | -49 | -196 | -1169 | +905 | +39 | +215 | +1159 |
data_1st %>%
- select(bacteria, aminoglycosides()) %>%
+ select(bacteria, aminoglycosides()) %>%
bug_drug_combinations()# ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
# ℹ Using column 'bacteria' as input for `col_mo`.-Resistance percentages
+Resistance percentagesThe functions resistance() and susceptibility() can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() and proportion_R() can be used to determine the proportion of a specific antimicrobial outcome.
All these functions contain a minimum argument, denoting the minimum required number of test results for returning a value. These functions will otherwise return NA. The default is minimum = 30, following the CLSI M39-A4 guideline for applying microbial epidemiology.
All these functions contain a minimum argument, denoting the minimum required number of test results for returning a value. These functions will otherwise return NA. The default is minimum = 30, following the CLSI M39-A4 guideline for applying microbial epidemiology.
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
data_1st %>% resistance(AMX)
-# [1] 0.5442005Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
data_1st %>%
- group_by(hospital) %>%
- summarise(amoxicillin = resistance(AMX))| hospital | @@ -991,27 +993,27 @@ Longest: 24||
|---|---|---|
| Hospital A | -0.5512301 | +0.5379610 |
| Hospital B | -0.5383792 | +0.5353400 |
| Hospital C | -0.5587121 | +0.5317705 |
| Hospital D | -0.5329877 | +0.5521531 |
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):
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 = resistance(AMX),
+ group_by(hospital) %>%
+ summarise(amoxicillin = resistance(AMX),
available = n_rsi(AMX))| Hospital A | -0.5512301 | -3211 | +0.5379610 | +3227 |
| Hospital B | -0.5383792 | -3739 | +0.5353400 | +3721 |
| Hospital C | -0.5587121 | -1584 | +0.5317705 | +1621 |
| Hospital D | -0.5329877 | -2122 | +0.5521531 | +2090 |
These functions can also be used to get the proportion of multiple antibiotics, to calculate empiric susceptibility of combination therapies very easily:
data_1st %>%
- group_by(genus) %>%
- summarise(amoxiclav = susceptibility(AMC),
+ group_by(genus) %>%
+ summarise(amoxiclav = susceptibility(AMC),
gentamicin = susceptibility(GEN),
amoxiclav_genta = susceptibility(AMC, GEN))| Escherichia | -0.7581475 | -0.8743568 | -0.9762007 | +Escherichia | +0.7715332 | +0.8742463 | +0.9773902 |
| Klebsiella | -0.8323353 | -0.8913601 | -0.9760479 | +Klebsiella | +0.8144953 | +0.9076790 | +0.9861950 |
| Staphylococcus | -0.7945055 | -0.8937729 | -0.9831502 | +Staphylococcus | +0.8000732 | +0.8883193 | +0.9791285 |
| Streptococcus | -0.5351171 | +Streptococcus | +0.5331765 | 0.0000000 | -0.5351171 | +0.5331765 |
data_1st %>%
# group by hospital
- group_by(hospital) %>%
+ group_by(hospital) %>%
# / -> select all penicillins in the data for calculation
# | / -> use resistance() for all peni's per hospital
# | | / -> print as percentages
- summarise(across(penicillins(), resistance, as_percent = TRUE)) %>%
+ summarise(across(penicillins(), resistance, as_percent = TRUE)) %>%
# format the antibiotic column names, using so-called snake case,
# so 'Amoxicillin/clavulanic acid' becomes 'amoxicillin_clavulanic_acid'
- rename_with(.fn = ab_name, .cols = penicillins(), snake_case = TRUE)| hospital | @@ -1104,68 +1106,68 @@ Longest: 24||||
|---|---|---|---|---|
| Hospital A | -55.1% | -27.0% | +53.8% | +26.0% |
| Hospital B | -53.8% | -26.2% | +53.5% | +26.9% |
| Hospital C | -55.9% | -27.7% | +53.2% | +26.3% |
| Hospital D | -53.3% | -27.0% | +55.2% | +26.2% |
To make a transition to the next part, let’s see how differences in the previously calculated combination therapies could be plotted:
data_1st %>%
- group_by(genus) %>%
- summarise("1. Amoxi/clav" = susceptibility(AMC),
+ group_by(genus) %>%
+ summarise("1. Amoxi/clav" = susceptibility(AMC),
"2. Gentamicin" = susceptibility(GEN),
"3. Amoxi/clav + genta" = susceptibility(AMC, GEN)) %>%
# pivot_longer() from the tidyr package "lengthens" data:
- tidyr::pivot_longer(-genus, names_to = "antibiotic") %>%
- ggplot(aes(x = genus,
+ tidyr::pivot_longer(-genus, names_to = "antibiotic") %>%
+ ggplot(aes(x = genus,
y = value,
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:
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,
+ggplot(data = a_data_set,
+ mapping = aes(x = year,
y = value)) +
- geom_col() +
- labs(title = "A title",
+ 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 proportion_df() and show results in stacked bars. Its simplest and shortest example:
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)) +
+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`)
@@ -1177,55 +1179,55 @@ Longest: 24
# show percentages on y axis
scale_y_percent(breaks = 0:4 * 25) +
# turn 90 degrees, to make it bars instead of columns
- coord_flip() +
+ coord_flip() +
# add labels
- labs(title = "Resistance per genus and antibiotic",
+ 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) %>%
+ group_by(genus) %>%
ggplot_rsi(x = "genus",
facet = "antibiotic",
breaks = 0:4 * 25,
datalabels = FALSE) +
- coord_flip()
-Plotting MIC and disk diffusion values
-The AMR package also extends the plot() and ggplot2::autoplot() functions for plotting minimum inhibitory concentrations (MIC, created with as.mic()) and disk diffusion diameters (created with as.disk()).
The AMR package also extends the plot() and ggplot2::autoplot() functions for plotting minimum inhibitory concentrations (MIC, created with as.mic()) and disk diffusion diameters (created with as.disk()).
With the random_mic() and random_disk() functions, we can generate sampled values for the new data types (S3 classes) <mic> and <disk>:
mic_values <- random_mic(size = 100)
mic_values
# Class <mic>
-# [1] 1 16 8 0.5 64 0.125 4 2 0.25 16
-# [11] 8 64 0.25 64 0.25 0.25 0.125 1 0.5 4
-# [21] 64 0.0625 >=128 0.5 0.5 >=128 0.25 0.125 16 32
-# [31] 16 0.0625 4 8 4 >=128 4 0.25 0.0625 0.5
-# [41] 2 0.125 0.25 1 16 2 0.125 1 2 2
-# [51] 0.25 0.5 8 4 8 1 4 64 2 32
-# [61] 0.5 2 1 8 8 32 64 4 2 32
-# [71] >=128 1 64 4 64 8 8 64 0.125 0.0625
-# [81] 8 >=128 0.0625 0.0625 0.25 8 8 0.0625 32 0.25
-# [91] 32 0.0625 0.125 1 >=128 16 0.0625 8 0.0625 2
# base R:
plot(mic_values)
# ggplot2:
-autoplot(mic_values)
But we could also be more specific, by generating MICs that are likely to be found in E. coli for ciprofloxacin:
mic_values <- random_mic(size = 100, mo = "E. coli", ab = "cipro")For the plot() and autoplot() function, we can define the microorganism and an antimicrobial agent the same way. This will add the interpretation of those values according to a chosen guidelines (defaults to the latest EUCAST guideline).
For the plot() and autoplot() function, we can define the microorganism and an antimicrobial agent the same way. This will add the interpretation of those values according to a chosen guidelines (defaults to the latest EUCAST guideline).
Default colours are colour-blind friendly, while maintaining the convention that e.g. ‘susceptible’ should be green and ‘resistant’ should be red:
# base R:
@@ -1233,7 +1235,7 @@ Longest: 24
# ggplot2:
-autoplot(mic_values, mo = "E. coli", ab = "cipro")
+autoplot(mic_values, mo = "E. coli", ab = "cipro")
For disk diffusion values, there is not much of a difference in plotting:
@@ -1242,17 +1244,17 @@ Longest: 24 # to review it. disk_values # Class <disk> -# [1] 25 19 26 17 25 27 22 17 29 29 27 28 21 18 26 29 21 27 24 17 18 19 23 19 21 -# [26] 18 23 21 27 29 18 25 24 17 31 18 22 25 30 19 29 26 27 22 22 28 21 23 22 20 -# [51] 30 19 23 21 30 17 19 22 18 25 29 28 25 31 20 23 20 22 27 24 21 21 24 19 31 -# [76] 19 18 27 20 27 21 25 26 29 27 29 21 27 27 25 20 22 25 26 29 19 19 23 19 20
# base R:
plot(disk_values, mo = "E. coli", ab = "cipro")
And when using the ggplot2 package, but now choosing the latest implemented CLSI guideline (notice that the EUCAST-specific term “Incr. exposure” has changed to “Intermediate”):
And when using the ggplot2 package, but now choosing the latest implemented CLSI guideline (notice that the EUCAST-specific term “Susceptible, incr. exp.” has changed to “Intermediate”):
-Independence test
+Independence testThe next example uses the example_isolates data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR data 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:
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:
# use package 'tidyr' to pivot data:
-library(tidyr)
+library(tidyr)
check_FOS <- example_isolates %>%
- 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
+ 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)
- pivot_wider(names_from = hospital_id, # transform output so A and D are columns
+ pivot_wider(names_from = hospital_id, # transform output so A and D are columns
values_from = value) %>%
- select(A, D) %>% # and only select these columns
- as.matrix() # transform to a good old matrix for fisher.test()
+ select(A, D) %>% # and only select these columns
+ as.matrix() # transform to a good old matrix for fisher.test()
check_FOS
# A D
@@ -1285,7 +1287,7 @@ Longest: 24
We can apply the test now with:
# do Fisher's Exact Test
-fisher.test(check_FOS)
+fisher.test(check_FOS)
#
# Fisher's Exact Test for Count Data
#
@@ -1313,11 +1315,13 @@ Longest: 24
@@ -1326,5 +1330,7 @@ Longest: 24
+
+