From 95c49fdf79ba4e947642ecf6937ed299ff90c0a0 Mon Sep 17 00:00:00 2001 From: "Matthijs S. Berends" Date: Wed, 27 Feb 2019 17:39:39 +0100 Subject: [PATCH] global var fix --- R/globals.R | 1 + docs/articles/AMR.html | 503 +++++++++--------- .../AMR_files/figure-html/plot 1-1.png | Bin 33622 -> 33801 bytes .../AMR_files/figure-html/plot 3-1.png | Bin 21129 -> 21133 bytes .../AMR_files/figure-html/plot 4-1.png | Bin 69623 -> 69616 bytes .../AMR_files/figure-html/plot 5-1.png | Bin 51371 -> 51367 bytes docs/articles/benchmarks.html | 72 +-- .../figure-html/unnamed-chunk-5-1.png | Bin 27642 -> 29588 bytes tests/testthat/test-eucast_rules.R | 20 +- tests/testthat/test-mo_property.R | 17 +- 10 files changed, 314 insertions(+), 299 deletions(-) diff --git a/R/globals.R b/R/globals.R index caf62176..56931c53 100755 --- a/R/globals.R +++ b/R/globals.R @@ -42,6 +42,7 @@ globalVariables(c(".", "first_isolate_row_index", "Freq", "fullname", + "fullname_lower", "genus", "gramstain", "index", diff --git a/docs/articles/AMR.html b/docs/articles/AMR.html index 3e234c50..28630d85 100644 --- a/docs/articles/AMR.html +++ b/docs/articles/AMR.html @@ -327,71 +327,71 @@ -2011-10-07 -O2 +2011-09-11 +K7 Hospital C -Streptococcus pneumoniae -R -S -S -S -F - - -2013-10-20 -C10 -Hospital A -Escherichia coli -S -R -S -S -M - - -2014-08-25 -O4 -Hospital A -Escherichia coli -S -S -S -S -F - - -2011-10-28 -E3 -Hospital B -Streptococcus pneumoniae -S -S -S -S -M - - -2010-08-31 -H1 -Hospital A -Klebsiella pneumoniae -R -S -R -S -M - - -2014-11-03 -U7 -Hospital A Staphylococcus aureus S S R S +M + + +2017-06-12 +M3 +Hospital C +Klebsiella pneumoniae +R +S +S +S +M + + +2015-03-15 +S9 +Hospital A +Streptococcus pneumoniae +S +S +S +S F + +2016-01-03 +I7 +Hospital C +Klebsiella pneumoniae +S +S +S +S +M + + +2017-11-25 +R3 +Hospital D +Staphylococcus aureus +R +S +S +S +F + + +2012-04-03 +B9 +Hospital B +Staphylococcus aureus +S +I +S +S +M +

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

@@ -411,8 +411,8 @@ #> #> Item Count Percent Cum. Count Cum. Percent #> --- ----- ------- -------- ----------- ------------- -#> 1 M 10,303 51.5% 10,303 51.5% -#> 2 F 9,697 48.5% 20,000 100.0% +#> 1 M 10,472 52.4% 10,472 52.4% +#> 2 F 9,528 47.6% 20,000 100.0%

So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values M and F. From a researcher 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 %>%
@@ -443,10 +443,10 @@
 #> Kingella kingae (no changes)
 #> 
 #> EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-#> Table 1:  Intrinsic resistance in Enterobacteriaceae (1256 changes)
+#> Table 1:  Intrinsic resistance in Enterobacteriaceae (1252 changes)
 #> Table 2:  Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)
 #> Table 3:  Intrinsic resistance in other Gram-negative bacteria (no changes)
-#> Table 4:  Intrinsic resistance in Gram-positive bacteria (2821 changes)
+#> Table 4:  Intrinsic resistance in Gram-positive bacteria (2741 changes)
 #> Table 8:  Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)
 #> Table 9:  Interpretive rules for B-lactam agents and Gram-negative rods (no changes)
 #> Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria (no changes)
@@ -462,9 +462,9 @@
 #> Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)
 #> Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)
 #> 
-#> => EUCAST rules affected 7,457 out of 20,000 rows
+#> => EUCAST rules affected 7,388 out of 20,000 rows
 #>    -> added 0 test results
-#>    -> changed 4,077 test results (0 to S; 0 to I; 4,077 to R)
+#> -> changed 3,993 test results (0 to S; 0 to I; 3,993 to R)

@@ -489,8 +489,8 @@ #> 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 5,704 first isolates (28.5% of total)

-

So only 28.5% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:

+#> => Found 5,651 first isolates (28.3% of total) +

So only 28.3% 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 with the filter_first_isolate() function:

@@ -516,10 +516,10 @@ 1 -2010-01-18 -I8 +2010-02-18 +U6 B_ESCHR_COL -I +S S S S @@ -527,41 +527,41 @@ 2 -2010-03-30 -I8 +2010-04-16 +U6 B_ESCHR_COL S -I +R S S FALSE 3 -2010-10-05 -I8 +2010-05-10 +U6 B_ESCHR_COL -R -R +S +S S S FALSE 4 -2011-01-26 -I8 +2010-07-20 +U6 B_ESCHR_COL S -R -R S -TRUE +S +S +FALSE 5 -2011-02-01 -I8 +2010-07-24 +U6 B_ESCHR_COL S S @@ -571,62 +571,62 @@ 6 -2011-03-08 -I8 +2010-12-11 +U6 B_ESCHR_COL -R S R S +R FALSE 7 -2011-04-11 -I8 +2010-12-14 +U6 B_ESCHR_COL S -R S +R S FALSE 8 -2011-04-23 -I8 +2010-12-31 +U6 B_ESCHR_COL S S -R +S S FALSE 9 -2011-06-21 -I8 +2011-02-02 +U6 B_ESCHR_COL +S +R R -S -S S FALSE 10 -2011-07-06 -I8 +2011-02-16 +U6 B_ESCHR_COL +R S -S -S +R S FALSE -

Only 2 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.

+

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(.)) %>% 
@@ -637,7 +637,7 @@
 #> 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 15,909 first weighted isolates (79.5% of total)
+#> => Found 15,906 first weighted isolates (79.5% of total) @@ -654,10 +654,10 @@ - - + + - + @@ -666,23 +666,23 @@ - - + + - + - + - - + + - - + + @@ -690,95 +690,95 @@ - - - - - - - - - - - - - - + + - + + + + + + + + + + + + + - - + + - + - - + + - + - - + + - + - - + + + + - - - - + + + - - +
isolate
12010-01-18I82010-02-18U6 B_ESCHR_COLIS S S S
22010-03-30I82010-04-16U6 B_ESCHR_COL SIR S S FALSEFALSETRUE
32010-10-05I82010-05-10U6 B_ESCHR_COLRRSS S S FALSE
42011-01-26I8B_ESCHR_COLSRRSTRUETRUE
52011-02-01I82010-07-20U6 B_ESCHR_COL S S S S FALSETRUEFALSE
52010-07-24U6B_ESCHR_COLSSSSFALSEFALSE
62011-03-08I82010-12-11U6 B_ESCHR_COLR S R SR FALSE TRUE
72011-04-11I82010-12-14U6 B_ESCHR_COL SR SR S FALSE TRUE
82011-04-23I82010-12-31U6 B_ESCHR_COL S SRS S FALSE TRUE
92011-06-21I82011-02-02U6 B_ESCHR_COLSR RSS S FALSE TRUE
102011-07-06I82011-02-16U6 B_ESCHR_COLR SSSR S FALSE TRUE
-

Instead of 2, now 9 isolates are flagged. In total, 79.5% of all isolates are marked ‘first weighted’ - 51% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.

+

Instead of 1, now 8 isolates are flagged. In total, 79.5% of all isolates are marked ‘first weighted’ - 51.3% 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 15,909 isolates for analysis.

+

So we end up with 15,906 isolates for analysis.

We can remove unneeded columns:

data_1st <- data_1st %>% 
   select(-c(first, keyab))
@@ -786,6 +786,7 @@
head(data_1st)
+ @@ -802,73 +803,30 @@ - - + + + - - + - + + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + - + @@ -876,21 +834,70 @@ - - - + + + + - + + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
date patient_id hospital
2011-10-07O212011-09-11K7 Hospital CB_STRPT_PNERB_STPHY_AUR S S RFSM Gram positiveStreptococcuspneumoniaeStaphylococcusaureus TRUE
2013-10-20C10Hospital AB_ESCHR_COLSRSSMGram negativeEscherichiacoliTRUE
2014-08-25O4Hospital AB_ESCHR_COLSSSSFGram negativeEscherichiacoliTRUE
2011-10-28E3Hospital BB_STRPT_PNESSSRMGram positiveStreptococcuspneumoniaeTRUE
2010-08-31H1Hospital A22017-06-12M3Hospital C B_KLBSL_PNE R SRS S M Gram negativepneumoniae TRUE
2014-11-03U7
32015-03-15S9 Hospital AB_STPHY_AURB_STRPT_PNES S S RS F Gram positiveStreptococcuspneumoniaeTRUE
42016-01-03I7Hospital CB_KLBSL_PNERSSSMGram negativeKlebsiellapneumoniaeTRUE
62012-04-03B9Hospital BB_STPHY_AURSISSMGram positive Staphylococcus aureus TRUE
82012-12-23D1Hospital CB_ESCHR_COLRSRSMGram negativeEscherichiacoliTRUE

Time for the analysis!

@@ -908,9 +915,9 @@
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 a data.frame (15,909 x 13)

+

Frequency table of genus and species from a data.frame (15,906 x 13)

Columns: 2
-Length: 15,909 (of which NA: 0 = 0.00%)
+Length: 15,906 (of which NA: 0 = 0.00%)
Unique: 4

Shortest: 16
Longest: 24

@@ -927,33 +934,33 @@ Longest: 24

1 Escherichia coli -7,837 -49.3% -7,837 -49.3% +7,989 +50.2% +7,989 +50.2% 2 Staphylococcus aureus -3,940 -24.8% -11,777 -74.0% +3,924 +24.7% +11,913 +74.9% 3 Streptococcus pneumoniae -2,554 -16.1% -14,331 -90.1% +2,476 +15.6% +14,389 +90.5% 4 Klebsiella pneumoniae -1,578 -9.9% -15,909 +1,517 +9.5% +15,906 100.0% @@ -964,7 +971,7 @@ Longest: 24

Resistance percentages

The functions portion_R, portion_RI, portion_I, portion_IS and portion_S can be used to determine the portion of a specific antimicrobial outcome. They can be used on their own:

data_1st %>% portion_IR(amox)
-#> [1] 0.4787856
+#> [1] 0.4769269

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

data_1st %>% 
   group_by(hospital) %>% 
@@ -977,19 +984,19 @@ Longest: 24

Hospital A -0.4717819 +0.4828164 Hospital B -0.4802632 +0.4792111 Hospital C -0.4870184 +0.4738384 Hospital D -0.4804169 +0.4661534 @@ -1007,23 +1014,23 @@ Longest: 24

Hospital A -0.4717819 -4731 +0.4828164 +4772 Hospital B -0.4802632 -5624 +0.4792111 +5628 Hospital C -0.4870184 -2388 +0.4738384 +2389 Hospital D -0.4804169 -3166 +0.4661534 +3117 @@ -1043,27 +1050,27 @@ Longest: 24

Escherichia -0.7256603 -0.8983029 -0.9729488 +0.7335086 +0.9034923 +0.9759670 Klebsiella -0.7338403 -0.8935361 -0.9689480 +0.7303889 +0.8998022 +0.9709954 Staphylococcus -0.7309645 -0.9233503 -0.9794416 +0.7303772 +0.9161570 +0.9775739 Streptococcus -0.7372749 +0.7302100 0.0000000 -0.7372749 +0.7302100 diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png index 90345fcf600ea35bd681e23923a7b69d8f44a7bd..54f17c54ff6821a322bc342984498c319361f248 100644 GIT binary patch literal 33801 zcmeFa2UwNqmL-g3si(AnV*nGOP!T1X5R{~#A|N?RG$9HKh?1j&5En?)&%j-0r#a%s=zA&%O85wK!jR!`^GJwf2jrijpkj%1tX7 z7#JAkjvrBDU|1Hxz_9ccoB7c7*%xx(?{uE!b0v}ZSUzqUm z$H=d2_&C(@nER-0^)Ytw(5aWfN} z=*R4fNR8-4u!{qZtR(<8jv%W;VwF9&u@4Rsfnh3Y5;8OqHS%z`JI@$7id z+f-HoU5$)#{M4cR>W51ojQ7@db#;}VNgHU&4LC@9ChJ9&R#H-;{d^^P879$!skXaR z=?Z^=&BCS{JsPtU{W26ImuX`>3d>(VmEU~sMUc%v<3{Os@80cj~q)xK#yvHu*$GZo^gOuwdP8-rgoGoRRFo@bTlm4q1&gu9w!W 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print(S.aureus, unit = "ms", signif = 3) #> Unit: milliseconds -#> expr min lq mean median uq max neval -#> as.mo("sau") 16.30 16.40 20.9 16.60 18.70 55.6 10 -#> as.mo("stau") 33.70 33.70 37.9 33.80 33.90 74.4 10 -#> as.mo("staaur") 16.30 16.30 16.6 16.40 16.70 17.8 10 -#> as.mo("STAAUR") 16.30 16.30 25.6 16.40 16.50 63.9 10 -#> as.mo("S. aureus") 24.20 24.30 27.2 24.60 26.70 45.2 10 -#> as.mo("S. aureus") 24.20 24.20 39.1 24.90 64.00 85.6 10 -#> as.mo("Staphylococcus aureus") 7.28 7.33 11.1 7.36 7.44 40.6 10

+#> expr min lq mean median uq max neval +#> as.mo("sau") 16.30 16.30 21.40 16.6 17.80 59.0 10 +#> as.mo("stau") 33.20 33.20 37.10 33.3 33.40 71.7 10 +#> as.mo("staaur") 16.40 16.60 29.50 17.5 50.10 69.3 10 +#> as.mo("STAAUR") 16.30 16.40 19.70 16.5 16.70 49.0 10 +#> as.mo("S. aureus") 24.30 24.40 33.00 25.0 26.50 65.5 10 +#> as.mo("S. aureus") 24.30 24.30 35.60 24.8 28.50 91.0 10 +#> as.mo("Staphylococcus aureus") 7.31 7.38 9.12 7.4 7.93 21.3 10

In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.

To achieve this speed, the as.mo function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of Thermus islandicus (B_THERMS_ISL), a bug probably never found before in humans:

-

That takes 7.2 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like Thermus islandicus) are almost fast - these are the most probable input from most data sets.

+#> as.mo("theisl") 288.0 289.0 317.0 328.0 329.0 335 10 +#> as.mo("THEISL") 288.0 288.0 307.0 290.0 334.0 338 10 +#> as.mo("T. islandicus") 73.0 73.3 82.6 73.4 73.9 126 10 +#> as.mo("T. islandicus") 72.8 72.9 81.9 73.6 74.8 118 10 +#> as.mo("Thermus islandicus") 65.2 65.7 83.1 65.9 109.0 114 10 +

That takes 6.6 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like Thermus islandicus) are almost fast - these are the most probable input from most data sets.

In the figure below, we compare Escherichia coli (which is very common) with Prevotella brevis (which is moderately common) and with Thermus islandicus (which is very uncommon):

par(mar = c(5, 16, 4, 2)) # set more space for left margin text (16)
 
@@ -287,8 +287,8 @@
 print(run_it, unit = "ms", signif = 3)
 #> Unit: milliseconds
 #>            expr min  lq mean median  uq max neval
-#>  mo_fullname(x) 716 738  778    763 778 899    10
-

So transforming 500,000 values (!!) of 50 unique values only takes 0.76 seconds (762 ms). You only lose time on your unique input values.

+#> mo_fullname(x) 722 753 797 798 804 918 10 +

So transforming 500,000 values (!!) of 50 unique values only takes 0.8 seconds (798 ms). You only lose time on your unique input values.

-

So going from mo_fullname("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0006 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:

+#> expr min lq mean median uq max neval +#> A 11.20 11.300 15.800 11.500 11.500 51.000 10 +#> B 22.10 22.200 22.400 22.500 22.600 22.800 10 +#> C 0.31 0.315 0.461 0.548 0.567 0.567 10 +

So going from mo_fullname("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0005 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:

+#> A 0.335 0.360 0.393 0.372 0.442 0.461 10 +#> B 0.370 0.394 0.431 0.427 0.455 0.562 10 +#> C 0.353 0.401 0.498 0.494 0.581 0.653 10 +#> D 0.278 0.290 0.334 0.328 0.376 0.415 10 +#> E 0.214 0.262 0.303 0.314 0.352 0.364 10 +#> F 0.240 0.273 0.307 0.312 0.345 0.366 10 +#> G 0.246 0.270 0.318 0.299 0.357 0.474 10 +#> H 0.245 0.267 0.301 0.291 0.341 0.367 10

Of course, when running mo_phylum("Firmicutes") the function has zero knowledge about the actual microorganism, namely S. aureus. But since the result would be "Firmicutes" too, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.

+#> en 14.63 14.95 15.07 15.18 15.20 15.26 10 +#> de 23.66 23.71 23.92 23.75 23.98 24.84 10 +#> nl 23.29 23.66 27.61 23.75 23.83 63.02 10 +#> es 23.41 23.63 27.79 23.77 23.81 64.45 10 +#> it 23.33 23.58 23.92 23.88 23.94 24.74 10 +#> fr 23.25 23.40 31.86 23.64 28.15 63.10 10 +#> pt 23.31 23.77 28.00 24.01 24.74 63.00 10

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pneumoniae", + "Pseudomonas aeruginosa", + "Enterobacter aerogenes"), amox = "-", # Amoxicillin stringsAsFactors = FALSE) - b <- data.frame(mo = c("KLEPNE", # Klebsiella pneumoniae - "PSEAER", # Pseudomonas aeruginosa - "ENTAER"), # Enterobacter aerogenes + b <- data.frame(mo = c("Klebsiella pneumoniae", + "Pseudomonas aeruginosa", + "Enterobacter aerogenes"), amox = "R", # Amoxicillin stringsAsFactors = FALSE) expect_identical(suppressWarnings(eucast_rules(a, "mo", info = FALSE)), b) expect_identical(suppressWarnings(eucast_rules(a, "mo", info = TRUE)), b) expect_identical(suppressWarnings(interpretive_reading(a, "mo", info = TRUE)), b) - a <- data.frame(mo = c("STAAUR", # Staphylococcus aureus - "STCGRA"), # Streptococcus pyognenes (Lancefield Group A) + a <- data.frame(mo = c("Staphylococcus aureus", + "Streptococcus group A"), coli = "-", # Colistin stringsAsFactors = FALSE) - b <- data.frame(mo = c("STAAUR", # Staphylococcus aureus - "STCGRA"), # Streptococcus pyognenes (Lancefield Group A) + b <- data.frame(mo = c("Staphylococcus aureus", + "Streptococcus group A"), coli = "R", # Colistin stringsAsFactors = FALSE) expect_equal(suppressWarnings(eucast_rules(a, "mo", info = FALSE)), b) diff --git a/tests/testthat/test-mo_property.R b/tests/testthat/test-mo_property.R index dc420dd8..ba63aedc 100644 --- a/tests/testthat/test-mo_property.R +++ b/tests/testthat/test-mo_property.R @@ -78,10 +78,17 @@ test_that("mo_property works", { expect_identical(suppressWarnings(mo_ref("Chlamydia psittaci")), "Page, 1968") expect_identical(mo_ref("Chlamydophila psittaci"), "Everett et al., 1999") - # check vector with random values - #library(dplyr) - #df_sample <- AMR::microorganisms %>% sample_n(100) - #expect_identical(df_sample %>% pull(mo) %>% mo_fullname(language = "en"), - # df_sample %>% pull(fullname)) + + # outcome of mo_fullname must always return the fullname from the data set + library(dplyr) + a <- microorganisms %>% + transmute(mo, + # fullname from the original data: + f1 = fullname, + # newly created fullname based on MO code: + f2 = mo_fullname(mo, language = "en")) %>% + filter(f1 != f2) + + expect_equal(nrow(a), 0) })