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param rules for EUCAST
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148
README.md
148
README.md
@@ -280,37 +280,40 @@ plot(mic_data)
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### Overwrite/force resistance based on EUCAST rules
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This is also called *interpretive reading*.
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```r
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before <- data.frame(bact = c("STAAUR", # Staphylococcus aureus
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"ENCFAE", # Enterococcus faecalis
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"ESCCOL", # Escherichia coli
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"KLEPNE", # Klebsiella pneumoniae
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"PSEAER"), # Pseudomonas aeruginosa
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vanc = "-", # Vancomycin
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amox = "-", # Amoxicillin
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coli = "-", # Colistin
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cfta = "-", # Ceftazidime
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cfur = "-", # Cefuroxime
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stringsAsFactors = FALSE)
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before
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# bact vanc amox coli cfta cfur
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# 1 STAAUR - - - - -
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# 2 ENCFAE - - - - -
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# 3 ESCCOL - - - - -
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# 4 KLEPNE - - - - -
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# 5 PSEAER - - - - -
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a <- data.frame(mo = c("Staphylococcus aureus",
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"Enterococcus faecalis",
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"Escherichia coli",
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"Klebsiella pneumoniae",
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"Pseudomonas aeruginosa"),
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vanc = "-", # Vancomycin
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amox = "-", # Amoxicillin
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coli = "-", # Colistin
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cfta = "-", # Ceftazidime
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cfur = "-", # Cefuroxime
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peni = "S", # Benzylpenicillin
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cfox = "S", # Cefoxitin
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stringsAsFactors = FALSE)
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a
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# mo vanc amox coli cfta cfur peni cfox
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# 1 Staphylococcus aureus - - - - - S S
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# 2 Enterococcus faecalis - - - - - S S
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# 3 Escherichia coli - - - - - S S
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# 4 Klebsiella pneumoniae - - - - - S S
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# 5 Pseudomonas aeruginosa - - - - - S S
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# Now apply those rules; just need a column with bacteria IDs and antibiotic results:
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after <- EUCAST_rules(before, col_mo = "bact")
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after
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# bact vanc amox coli cfta cfur
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# 1 STAAUR - - R R -
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# 2 ENCFAE - - R R R
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# 3 ESCCOL R - - - -
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# 4 KLEPNE R R - - -
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# 5 PSEAER R R - - R
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b <- EUCAST_rules(a) # 18 results are forced as R or S
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b
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# mo vanc amox coli cfta cfur peni cfox
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# 1 Staphylococcus aureus - S R R S S S
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# 2 Enterococcus faecalis - - R R R S R
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# 3 Escherichia coli R - - - - R S
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# 4 Klebsiella pneumoniae R R - - - R S
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# 5 Pseudomonas aeruginosa R R - - R R R
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```
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Bacteria IDs can be retrieved with the `guess_mo` function. It uses any type of info about a microorganism as input. For example, all these will return value `STAAUR`, the ID of *S. aureus*:
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Bacteria IDs can be retrieved with the `guess_mo` function. It uses any type of info about a microorganism as input. For example, all these will return value `B_STPHY_AUR`, the ID of *S. aureus*:
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```r
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guess_mo("stau")
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guess_mo("STAU")
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@@ -319,6 +322,7 @@ guess_mo("S. aureus")
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guess_mo("S aureus")
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guess_mo("Staphylococcus aureus")
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guess_mo("MRSA") # Methicillin Resistant S. aureus
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guess_mo("MSSA") # Methicillin Susceptible S. aureus
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guess_mo("VISA") # Vancomycin Intermediate S. aureus
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guess_mo("VRSA") # Vancomycin Resistant S. aureus
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```
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@@ -359,32 +363,32 @@ Factors sort on item by default:
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```r
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septic_patients %>% freq(hospital_id)
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# Frequency table of `hospital_id`
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# Class: factor
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# Length: 2000 (of which NA: 0 = 0.0%)
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# Class: factor (numeric)
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# Length: 2000 (of which NA: 0 = 0.00%)
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# Unique: 4
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#
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# Item Count Percent Cum. Count Cum. Percent (Factor Level)
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# --- ----- ------ -------- ----------- ------------- ---------------
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# 1 A 319 16.0% 319 16.0% 1
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# 2 B 661 33.1% 980 49.0% 2
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# 3 C 256 12.8% 1236 61.8% 3
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# 4 D 764 38.2% 2000 100.0% 4
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# 1 A 321 16.1% 321 16.1% 1
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# 2 B 663 33.1% 984 49.2% 2
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# 3 C 254 12.7% 1238 61.9% 3
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# 4 D 762 38.1% 2000 100.0% 4
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```
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This can be changed with the `sort.count` parameter:
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```r
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septic_patients %>% freq(hospital_id, sort.count = TRUE)
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# Frequency table of `hospital_id`
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# Class: factor
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# Length: 2000 (of which NA: 0 = 0.0%)
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# Class: factor (numeric)
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# Length: 2000 (of which NA: 0 = 0.00%)
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# Unique: 4
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#
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# Item Count Percent Cum. Count Cum. Percent (Factor Level)
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# --- ----- ------ -------- ----------- ------------- ---------------
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# 1 D 764 38.2% 764 38.2% 4
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# 2 B 661 33.1% 1425 71.2% 2
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# 3 A 319 16.0% 1744 87.2% 1
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# 4 C 256 12.8% 2000 100.0% 3
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# 1 D 762 38.1% 762 38.1% 4
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# 2 B 663 33.1% 1425 71.2% 2
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# 3 A 321 16.1% 1746 87.3% 1
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# 4 C 254 12.7% 2000 100.0% 3
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```
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All other types, like numbers, characters and dates, sort on count by default:
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@@ -397,7 +401,7 @@ septic_patients %>% freq(date)
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#
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# Oldest: 2 January 2002
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# Newest: 28 December 2017 (+5839)
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# Median: 7 Augustus 2009 (~48%)
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# Median: 31 July 2009 (~47%)
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#
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# Item Count Percent Cum. Count Cum. Percent
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# --- ----------- ------ -------- ----------- -------------
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@@ -408,14 +412,14 @@ septic_patients %>% freq(date)
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# 5 2015-11-19 7 0.4% 41 2.1%
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# 6 2005-12-22 6 0.3% 47 2.4%
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# 7 2015-10-12 6 0.3% 53 2.6%
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# 8 2002-05-16 5 0.2% 58 2.9%
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# 9 2004-02-02 5 0.2% 63 3.1%
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# 10 2004-02-18 5 0.2% 68 3.4%
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# 11 2005-08-16 5 0.2% 73 3.6%
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# 12 2005-09-01 5 0.2% 78 3.9%
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# 13 2006-06-29 5 0.2% 83 4.2%
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# 14 2007-08-10 5 0.2% 88 4.4%
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# 15 2008-08-29 5 0.2% 93 4.7%
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# 8 2002-02-27 5 0.2% 58 2.9%
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# 9 2003-10-20 5 0.2% 63 3.1%
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# 10 2004-02-02 5 0.2% 68 3.4%
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# 11 2004-02-18 5 0.2% 73 3.6%
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# 12 2004-06-22 5 0.2% 78 3.9%
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# 13 2004-12-01 5 0.2% 83 4.2%
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# 14 2005-08-16 5 0.2% 88 4.4%
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# 15 2005-09-01 5 0.2% 93 4.7%
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# [ reached getOption("max.print.freq") -- omitted 1136 entries, n = 1907 (95.3%) ]
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```
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For numeric values, some extra descriptive statistics will be calculated:
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@@ -423,26 +427,26 @@ For numeric values, some extra descriptive statistics will be calculated:
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freq(runif(n = 10, min = 1, max = 5))
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# Frequency table
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# Class: numeric
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# Length: 10 (of which NA: 0 = 0.0%)
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# Length: 10 (of which NA: 0 = 0.00%)
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# Unique: 10
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#
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# Mean: 3.4
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# Std. dev.: 1.3 (CV: 0.38, MAD: 1.3)
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# Five-Num: 1.6 | 2.0 | 3.9 | 4.7 | 4.8 (IQR: 2.7, CQV: 0.4)
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# Mean: 3.1
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# Std. dev.: 1.3 (CV: 0.43, MAD: 1.8)
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# Five-Num: 1.3 | 1.7 | 3.2 | 4.3 | 5.0 (IQR: 2.6, CQV: 0.43)
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# Outliers: 0
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#
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# Item Count Percent Cum. Count Cum. Percent
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# --- --------- ------ -------- ----------- -------------
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# 1 1.568997 1 10.0% 1 10.0%
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# 2 1.993575 1 10.0% 2 20.0%
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# 3 2.022348 1 10.0% 3 30.0%
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# 4 2.236038 1 10.0% 4 40.0%
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# 5 3.579828 1 10.0% 5 50.0%
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# 6 4.178081 1 10.0% 6 60.0%
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# 7 4.394818 1 10.0% 7 70.0%
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# 8 4.689871 1 10.0% 8 80.0%
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# 9 4.698626 1 10.0% 9 90.0%
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# 10 4.751488 1 10.0% 10 100.0%
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# 1 1.271079 1 10.0% 1 10.0%
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# 2 1.333975 1 10.0% 2 20.0%
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# 3 1.714946 1 10.0% 3 30.0%
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# 4 2.751871 1 10.0% 4 40.0%
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# 5 3.090140 1 10.0% 5 50.0%
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# 6 3.260850 1 10.0% 6 60.0%
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# 7 3.824105 1 10.0% 7 70.0%
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# 8 4.278028 1 10.0% 8 80.0%
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# 9 4.436265 1 10.0% 9 90.0%
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# 10 4.996694 1 10.0% 10 100.0%
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#
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# Warning message:
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# All observations are unique.
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@@ -457,15 +461,17 @@ Data sets to work with antibiotics and bacteria properties.
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```r
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# Data set with complete taxonomic trees from ITIS, containing of
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# the three kingdoms Bacteria, Fungi and Protozoa
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microorganisms # A tibble: 18,831 x 15
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# Data set with 2000 random blood culture isolates from anonymised
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# septic patients between 2001 and 2017 in 5 Dutch hospitals
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septic_patients # A tibble: 2,000 x 49
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microorganisms # data.frame: 18,833 x 15
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microorganisms.old # data.frame: 2,383 x 4
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# Data set with ATC antibiotics codes, official names, trade names
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# and DDDs (oral and parenteral)
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antibiotics # A tibble: 423 x 18
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antibiotics # data.frame: 423 x 18
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# Data set with 2000 random blood culture isolates from anonymised
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# septic patients between 2001 and 2017 in 5 Dutch hospitals
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septic_patients # data.frame: 2,000 x 49
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```
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## Benchmarks
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@@ -501,7 +507,7 @@ microbenchmark(A = as.mo("stau"),
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# F 10.69445 10.73852 10.80334 10.79596 10.86856 10.97465 10
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```
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The more an input value resembles a full name, the faster the result will be found. In the table above, all measurements are in milliseconds, tested on a quite regular Linux server from 2007 with 2 GB RAM. A value of 10.8 milliseconds means it can roughly determine 93 different input values per second. It case of 36.2 milliseconds, this is only 28 input values per second.
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The more an input value resembles a full name, the faster the result will be found. In the table above, all measurements are in milliseconds, tested on a quite regular Linux server from 2007 with 2 GB RAM. A value of 10.8 milliseconds means it will roughly determine 93 different (unique) input values per second. It case of 36.2 milliseconds, this is only 28 input values per second.
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To improve 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 far less faster. See this example for the ID of *Burkholderia nodosa* (`B_BRKHL_NOD`):
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@@ -523,7 +529,7 @@ microbenchmark(B = as.mo("burnod"),
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```
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(Note: `A` is missing here, because `as.mo("buno")` returns `F_BUELL_NOT`: the ID of the fungus *Buellia notabilis*)
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That takes up to 12 times as much time! A value of 190.4 milliseconds means it can only determine 5 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.
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That takes up to 12 times as much time! A value of 190.4 milliseconds means it can only determine ~5 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.
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To relieve this pitfall and further improve performance, two important calculations take almost no time at all: **repetive results** and **already precalculated results**.
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