vignettes/AMR.Rmd
AMR.Rmd
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 26 August 2022.
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 28 August 2022.
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:
LETTERS
A
Z
patients
A1
Z10
-patients_table <- data.frame(patient_id = patients, - gender = c(rep("M", 135), - rep("F", 125)))
patients_table <- data.frame(patient_id = patients, - gender = c(rep("M", 135), - rep("F", 125)))
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.
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")
bacteria <- c("Escherichia coli", "Staphylococcus aureus", - "Streptococcus pneumoniae", "Klebsiella pneumoniae")
bacteria <- c( + "Escherichia coli", "Staphylococcus aureus", + "Streptococcus pneumoniae", "Klebsiella pneumoniae" +)
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, using the random_rsi() function.
sample()
random_rsi()
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(c("Hospital A", - "Hospital B", - "Hospital C", - "Hospital D"), - 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 = random_rsi(sample_size, prob_RSI = c(0.35, 0.60, 0.05)), - AMC = random_rsi(sample_size, prob_RSI = c(0.15, 0.75, 0.10)), - CIP = random_rsi(sample_size, prob_RSI = c(0.20, 0.80, 0.00)), - GEN = random_rsi(sample_size, prob_RSI = c(0.08, 0.92, 0.00)))
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:
left_join()
dplyr
patients_table
data <- data %>% left_join(patients_table)
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))
data <- data %>% - mutate(gramstain = mo_gramstain(bacteria), - genus = mo_genus(bacteria), - species = mo_species(bacteria))
data <- data %>% + mutate( + gramstain = mo_gramstain(bacteria), + genus = mo_genus(bacteria), + species = mo_species(bacteria) + )
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.
AMR
first_isolate()
all_microbials()
The outcome of the function can easily be added to our data:
-data <- data %>% +data <- data %>% mutate(first = first_isolate(info = TRUE)) # Determining first isolates using an episode length of 365 days # ℹ Using column 'bacteria' as input for `col_mo`. @@ -472,20 +487,22 @@ 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 10,709 'phenotype-based' first isolates (53.5% of total where a +# Including isolates from ICU. +# => Found 10,729 'phenotype-based' first isolates (53.6% of total where a # microbial ID was available)
data <- data %>% +data <- data %>% mutate(first = first_isolate(info = TRUE)) # Determining first isolates using an episode length of 365 days # ℹ Using column 'bacteria' as input for `col_mo`. @@ -472,20 +487,22 @@ 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 10,709 'phenotype-based' first isolates (53.5% of total where a +# Including isolates from ICU. +# => Found 10,729 'phenotype-based' first isolates (53.6% of total where a # microbial ID was available)
data <- data %>% mutate(first = first_isolate(info = TRUE)) # Determining first isolates using an episode length of 365 days # ℹ Using column 'bacteria' as input for `col_mo`. @@ -472,20 +487,22 @@ 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 10,709 'phenotype-based' first isolates (53.5% of total where a +# Including isolates from ICU. +# => Found 10,729 'phenotype-based' first isolates (53.6% of total where a # microbial ID was available)
So only 53.5% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
So only 53.6% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
-data_1st <- data %>% +data_1st <- data %>% filter(first == TRUE)
data_1st <- data %>% +data_1st <- data %>% filter(first == TRUE)
data_1st <- data %>% filter(first == TRUE)
For future use, the above two syntaxes can be shortened:
-data_1st <- data %>% - filter_first_isolate()
data_1st <- data %>% - filter_first_isolate()
So we end up with 10,709 isolates for analysis. Now our data looks like:
data_1st <- data %>% + filter_first_isolate() +# Including isolates from ICU.
So we end up with 10,729 isolates for analysis. Now our data looks like:
head(data_1st)
Time for the analysis!
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,709 -Available: 10,709 (100.0%, NA: 0 = 0.0%) +Length: 10,729 +Available: 10,729 (100.0%, NA: 0 = 0.0%) Unique: 4
Shortest: 16 Longest: 24
Using tidyverse selections, you can also select or filter columns based on the antibiotic class they are in:
-data_1st %>% +data_1st %>% filter(any(aminoglycosides() == "R"))
data_1st %>% +data_1st %>% filter(any(aminoglycosides() == "R"))
data_1st %>% filter(any(aminoglycosides() == "R"))
# ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the bug_drug_combinations() function:
bug_drug_combinations()
-data_1st %>% - bug_drug_combinations() %>% +data_1st %>% + bug_drug_combinations() %>% head() # show first 6 rows
data_1st %>% - bug_drug_combinations() %>% +data_1st %>% + bug_drug_combinations() %>% head() # show first 6 rows
data_1st %>% + bug_drug_combinations() %>% head() # show first 6 rows
# ℹ Using column 'bacteria' as input for `col_mo`.
-data_1st %>% - select(bacteria, aminoglycosides()) %>% +data_1st %>% + select(bacteria, aminoglycosides()) %>% bug_drug_combinations()
data_1st %>% - select(bacteria, aminoglycosides()) %>% +data_1st %>% + select(bacteria, aminoglycosides()) %>% bug_drug_combinations()
data_1st %>% + select(bacteria, aminoglycosides()) %>% bug_drug_combinations()
# ℹ For `aminoglycosides()` using column 'GEN' (gentamicin) # ℹ Using column 'bacteria' as input for `col_mo`.
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:
proportion_R()
resistance()
proportion_SI()
susceptibility()
data_1st %>% resistance(AMX) -# [1] 0.5464563
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
-data_1st %>% - group_by(hospital) %>% +data_1st %>% + group_by(hospital) %>% summarise(amoxicillin = resistance(AMX))
data_1st %>% - group_by(hospital) %>% +data_1st %>% + group_by(hospital) %>% summarise(amoxicillin = resistance(AMX))
data_1st %>% + group_by(hospital) %>% summarise(amoxicillin = resistance(AMX))
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):
n_rsi()
n_distinct()
-data_1st %>% - group_by(hospital) %>% - summarise(amoxicillin = resistance(AMX), - available = n_rsi(AMX))
data_1st %>% - group_by(hospital) %>% - summarise(amoxicillin = resistance(AMX), - available = n_rsi(AMX))
data_1st %>% + group_by(hospital) %>% + summarise( + amoxicillin = resistance(AMX), + available = n_rsi(AMX) + )
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), - gentamicin = susceptibility(GEN), - amoxiclav_genta = susceptibility(AMC, GEN))
data_1st %>% - group_by(genus) %>% - summarise(amoxiclav = susceptibility(AMC), - gentamicin = susceptibility(GEN), - amoxiclav_genta = susceptibility(AMC, GEN))
data_1st %>% + group_by(genus) %>% + summarise( + amoxiclav = susceptibility(AMC), + gentamicin = susceptibility(GEN), + amoxiclav_genta = susceptibility(AMC, GEN) + )
Or if you are curious for the resistance within certain antibiotic classes, use a antibiotic class selector such as penicillins(), which automatically will include the columns AMX and AMC of our data:
penicillins()
AMX
AMC
-data_1st %>% +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(set_ab_names, penicillins())
data_1st %>% +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(set_ab_names, penicillins())
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(set_ab_names, penicillins())
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), - "2. Gentamicin" = susceptibility(GEN), - "3. Amoxi/clav + genta" = susceptibility(AMC, GEN)) %>% +data_1st %>% + 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, - y = value, - fill = antibiotic)) + + tidyr::pivot_longer(-genus, names_to = "antibiotic") %>% + ggplot(aes( + x = genus, + y = value, + fill = antibiotic + )) + geom_col(position = "dodge2")
data_1st %>% - group_by(genus) %>% - summarise("1. Amoxi/clav" = susceptibility(AMC), - "2. Gentamicin" = susceptibility(GEN), - "3. Amoxi/clav + genta" = susceptibility(AMC, GEN)) %>% +data_1st %>% + 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, - y = value, - fill = antibiotic)) + + tidyr::pivot_longer(-genus, names_to = "antibiotic") %>% + ggplot(aes( + x = genus, + y = value, + fill = antibiotic + )) + geom_col(position = "dodge2")
data_1st %>% + 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, - y = value, - fill = antibiotic)) + + tidyr::pivot_longer(-genus, names_to = "antibiotic") %>% + ggplot(aes( + x = genus, + y = value, + fill = antibiotic + )) + geom_col(position = "dodge2")
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:
ggplot2
-ggplot(data = a_data_set, - mapping = aes(x = year, - y = value)) + +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") + labs( + title = "A title", + subtitle = "A subtitle", + x = "My X axis", + y = "My Y axis" + ) # or as short as: ggplot(a_data_set) + @@ -1132,11 +1163,11 @@ Longest: 24 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`) - geom_rsi(x = "genus") + + geom_rsi(x = "genus") + # split plots on antibiotic facet_rsi(facet = "antibiotic") + # set colours to the R/SI interpretations (colour-blind friendly) @@ -1146,20 +1177,24 @@ Longest: 24 # 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)") + + 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 %>% +data_1st %>% group_by(genus) %>% - ggplot_rsi(x = "genus", - facet = "antibiotic", - breaks = 0:4 * 25, - datalabels = FALSE) + + ggplot_rsi( + x = "genus", + facet = "antibiotic", + breaks = 0:4 * 25, + datalabels = FALSE + ) + coord_flip() @@ -1171,16 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.5 0.025 0.025 2 0.005 64 0.001 0.025 >=256 0.025 -# [11] 4 1 32 0.002 0.01 64 16 0.001 4 0.005 -# [21] 0.125 2 >=256 8 >=256 64 0.5 64 0.0625 2 -# [31] 128 1 4 8 >=256 4 0.5 0.025 0.0625 >=256 -# [41] 0.125 64 1 >=256 1 0.001 0.01 2 0.005 0.025 -# [51] 16 0.001 8 32 0.025 1 0.025 0.25 64 0.125 -# [61] 0.0625 0.01 128 0.5 0.025 0.001 32 0.001 0.01 0.0625 -# [71] 8 2 0.25 0.002 2 0.01 0.025 0.025 128 128 -# [81] 0.002 1 0.25 >=256 128 >=256 0.5 0.0625 4 0.01 -# [91] 0.002 0.002 0.0625 2 0.01 8 0.0625 0.01 0.025 0.25 +# [1] 2 64 0.25 >=256 4 0.025 128 0.125 2 +# [10] 0.25 32 32 2 0.25 32 0.125 16 32 +# [19] 0.025 1 16 0.01 64 8 32 128 2 +# [28] 1 2 0.002 0.01 2 0.25 0.002 0.01 1 +# [37] 4 8 0.005 0.002 0.005 0.025 8 0.5 8 +# [46] 128 0.25 0.025 0.01 0.01 128 32 1 <=0.001 +# [55] 16 0.002 8 0.0625 0.125 64 0.125 0.0625 0.5 +# [64] 0.025 <=0.001 128 0.01 0.01 1 0.002 >=256 128 +# [73] 0.005 32 0.25 0.002 0.25 0.5 0.5 16 16 +# [82] 2 64 0.005 2 0.025 2 32 0.005 0.0625 +# [91] <=0.001 0.0625 64 0.002 0.025 0.005 64 0.002 1 +# [100] 32
ggplot(data = a_data_set, - mapping = aes(x = year, - y = value)) + +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") + labs( + title = "A title", + subtitle = "A subtitle", + x = "My X axis", + y = "My Y axis" + ) # or as short as: ggplot(a_data_set) + @@ -1132,11 +1163,11 @@ Longest: 24 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`) - geom_rsi(x = "genus") + + geom_rsi(x = "genus") + # split plots on antibiotic facet_rsi(facet = "antibiotic") + # set colours to the R/SI interpretations (colour-blind friendly) @@ -1146,20 +1177,24 @@ Longest: 24 # 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)") + + 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 %>% +data_1st %>% group_by(genus) %>% - ggplot_rsi(x = "genus", - facet = "antibiotic", - breaks = 0:4 * 25, - datalabels = FALSE) + + ggplot_rsi( + x = "genus", + facet = "antibiotic", + breaks = 0:4 * 25, + datalabels = FALSE + ) + coord_flip() @@ -1171,16 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.5 0.025 0.025 2 0.005 64 0.001 0.025 >=256 0.025 -# [11] 4 1 32 0.002 0.01 64 16 0.001 4 0.005 -# [21] 0.125 2 >=256 8 >=256 64 0.5 64 0.0625 2 -# [31] 128 1 4 8 >=256 4 0.5 0.025 0.0625 >=256 -# [41] 0.125 64 1 >=256 1 0.001 0.01 2 0.005 0.025 -# [51] 16 0.001 8 32 0.025 1 0.025 0.25 64 0.125 -# [61] 0.0625 0.01 128 0.5 0.025 0.001 32 0.001 0.01 0.0625 -# [71] 8 2 0.25 0.002 2 0.01 0.025 0.025 128 128 -# [81] 0.002 1 0.25 >=256 128 >=256 0.5 0.0625 4 0.01 -# [91] 0.002 0.002 0.0625 2 0.01 8 0.0625 0.01 0.025 0.25 +# [1] 2 64 0.25 >=256 4 0.025 128 0.125 2 +# [10] 0.25 32 32 2 0.25 32 0.125 16 32 +# [19] 0.025 1 16 0.01 64 8 32 128 2 +# [28] 1 2 0.002 0.01 2 0.25 0.002 0.01 1 +# [37] 4 8 0.005 0.002 0.005 0.025 8 0.5 8 +# [46] 128 0.25 0.025 0.01 0.01 128 32 1 <=0.001 +# [55] 16 0.002 8 0.0625 0.125 64 0.125 0.0625 0.5 +# [64] 0.025 <=0.001 128 0.01 0.01 1 0.002 >=256 128 +# [73] 0.005 32 0.25 0.002 0.25 0.5 0.5 16 16 +# [82] 2 64 0.005 2 0.025 2 32 0.005 0.0625 +# [91] <=0.001 0.0625 64 0.002 0.025 0.005 64 0.002 1 +# [100] 32
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") + labs( + title = "A title", + subtitle = "A subtitle", + x = "My X axis", + y = "My Y axis" + ) # or as short as: ggplot(a_data_set) + @@ -1132,11 +1163,11 @@ Longest: 24 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`) - geom_rsi(x = "genus") + + geom_rsi(x = "genus") + # split plots on antibiotic facet_rsi(facet = "antibiotic") + # set colours to the R/SI interpretations (colour-blind friendly) @@ -1146,20 +1177,24 @@ Longest: 24 # 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)") + + 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 %>% +data_1st %>% group_by(genus) %>% - ggplot_rsi(x = "genus", - facet = "antibiotic", - breaks = 0:4 * 25, - datalabels = FALSE) + + ggplot_rsi( + x = "genus", + facet = "antibiotic", + breaks = 0:4 * 25, + datalabels = FALSE + ) + coord_flip() @@ -1171,16 +1206,18 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.5 0.025 0.025 2 0.005 64 0.001 0.025 >=256 0.025 -# [11] 4 1 32 0.002 0.01 64 16 0.001 4 0.005 -# [21] 0.125 2 >=256 8 >=256 64 0.5 64 0.0625 2 -# [31] 128 1 4 8 >=256 4 0.5 0.025 0.0625 >=256 -# [41] 0.125 64 1 >=256 1 0.001 0.01 2 0.005 0.025 -# [51] 16 0.001 8 32 0.025 1 0.025 0.25 64 0.125 -# [61] 0.0625 0.01 128 0.5 0.025 0.001 32 0.001 0.01 0.0625 -# [71] 8 2 0.25 0.002 2 0.01 0.025 0.025 128 128 -# [81] 0.002 1 0.25 >=256 128 >=256 0.5 0.0625 4 0.01 -# [91] 0.002 0.002 0.0625 2 0.01 8 0.0625 0.01 0.025 0.25 +# [1] 2 64 0.25 >=256 4 0.025 128 0.125 2 +# [10] 0.25 32 32 2 0.25 32 0.125 16 32 +# [19] 0.025 1 16 0.01 64 8 32 128 2 +# [28] 1 2 0.002 0.01 2 0.25 0.002 0.01 1 +# [37] 4 8 0.005 0.002 0.005 0.025 8 0.5 8 +# [46] 128 0.25 0.025 0.01 0.01 128 32 1 <=0.001 +# [55] 16 0.002 8 0.0625 0.125 64 0.125 0.0625 0.5 +# [64] 0.025 <=0.001 128 0.01 0.01 1 0.002 >=256 128 +# [73] 0.005 32 0.25 0.002 0.25 0.5 0.5 16 16 +# [82] 2 64 0.005 2 0.025 2 32 0.005 0.0625 +# [91] <=0.001 0.0625 64 0.002 0.025 0.005 64 0.002 1 +# [100] 32
If we group on e.g. the genus column and add some additional functions from our package, we can create this:
genus
# 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`) - geom_rsi(x = "genus") + + geom_rsi(x = "genus") + # split plots on antibiotic facet_rsi(facet = "antibiotic") + # set colours to the R/SI interpretations (colour-blind friendly) @@ -1146,20 +1177,24 @@ Longest: 24 # 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)") + + 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:
ggplot_rsi()
-data_1st %>% +data_1st %>% group_by(genus) %>% - ggplot_rsi(x = "genus", - facet = "antibiotic", - breaks = 0:4 * 25, - datalabels = FALSE) + + ggplot_rsi( + x = "genus", + facet = "antibiotic", + breaks = 0:4 * 25, + datalabels = FALSE + ) + coord_flip()
data_1st %>% +data_1st %>% group_by(genus) %>% - ggplot_rsi(x = "genus", - facet = "antibiotic", - breaks = 0:4 * 25, - datalabels = FALSE) + + ggplot_rsi( + x = "genus", + facet = "antibiotic", + breaks = 0:4 * 25, + datalabels = FALSE + ) + coord_flip()
data_1st %>% group_by(genus) %>% - ggplot_rsi(x = "genus", - facet = "antibiotic", - breaks = 0:4 * 25, - datalabels = FALSE) + + ggplot_rsi( + x = "genus", + facet = "antibiotic", + breaks = 0:4 * 25, + datalabels = FALSE + ) + coord_flip()
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 0.5 0.025 0.025 2 0.005 64 0.001 0.025 >=256 0.025 -# [11] 4 1 32 0.002 0.01 64 16 0.001 4 0.005 -# [21] 0.125 2 >=256 8 >=256 64 0.5 64 0.0625 2 -# [31] 128 1 4 8 >=256 4 0.5 0.025 0.0625 >=256 -# [41] 0.125 64 1 >=256 1 0.001 0.01 2 0.005 0.025 -# [51] 16 0.001 8 32 0.025 1 0.025 0.25 64 0.125 -# [61] 0.0625 0.01 128 0.5 0.025 0.001 32 0.001 0.01 0.0625 -# [71] 8 2 0.25 0.002 2 0.01 0.025 0.025 128 128 -# [81] 0.002 1 0.25 >=256 128 >=256 0.5 0.0625 4 0.01 -# [91] 0.002 0.002 0.0625 2 0.01 8 0.0625 0.01 0.025 0.25
# base R: plot(mic_values)
# 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 “Susceptible, incr. exp.” has changed to “Intermediate”):
-autoplot(disk_values, - mo = "E. coli", - ab = "cipro", - guideline = "CLSI")
autoplot(disk_values, - mo = "E. coli", - ab = "cipro", - guideline = "CLSI")
autoplot( + disk_values, + mo = "E. coli", + ab = "cipro", + guideline = "CLSI" +)
The 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.
example_isolates
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:
FOS
fisher.test()
The 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 practise AMR data analysis.
We will compare the resistance to amoxicillin/clavulanic acid (column FOS) between an ICU and other clinical wards. The input for the fisher.test() can be retrieved with a transformation like this:
# use package 'tidyr' to pivot data: 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 - 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 - values_from = value) %>% - select(A, D) %>% # and only select these columns - as.matrix() # transform to a good old matrix for fisher.test() + filter(ward %in% c("ICU", "Clinical")) %>% # filter on only these wards + select(ward, AMC) %>% # select the wards and amoxi/clav + group_by(ward) %>% # group on the wards + count_df(combine_SI = TRUE) %>% # count all isolates per group (ward) + pivot_wider( + names_from = ward, # transform output so "ICU" and "Clinical" are columns + values_from = value + ) %>% + select(ICU, Clinical) %>% # and only select these columns + as.matrix() # transform to a 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.test(check_FOS) # # Fisher's Exact Test for Count Data # # data: check_FOS -# p-value = 0.03104 +# p-value = 2.263e-07 # alternative hypothesis: true odds ratio is not equal to 1 # 95 percent confidence interval: -# 0.2111489 0.9485124 +# 0.435261 0.691614 # sample estimates: # odds ratio -# 0.4488318
As can be seen, the p value is 0.031, which means that the fosfomycin resistance found in isolates from patients in hospital A and D are really different.
As can be seen, the p value is practically zero (0.0000002263247), which means that the amoxicillin/clavulanic acid resistance found in isolates between patients in ICUs and other clinical wards are really different.