From c506d2893b1283857d5f77e8732f343b3eb095b6 Mon Sep 17 00:00:00 2001
From: "Matthijs S. Berends" So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values The data is already quite clean, but we still need to transform some variables. The So only 28.9% is suitable for resistance analysis! We can now filter on it with the So only 28.4% is suitable for resistance analysis! We can now filter on it with the For future use, the above two syntaxes can be shortened with the Instead of 2, now 8 isolates are flagged. In total, 79.8% of all isolates are marked ‘first weighted’ - 50.9% 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 2, now 7 isolates are flagged. In total, 79.7% 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 So we end up with 15,963 isolates for analysis. So we end up with 15,935 isolates for analysis. We can remove unneeded columns: Time for the analysis! Or can be used like the Frequency table of Frequency table of Columns: 2 Shortest: 16
-
+2016-10-19
-U5
+2010-07-18
+Z5
Hospital B
+Streptococcus pneumoniae
+S
+S
+S
+S
+F
+
+
-2017-01-13
+P8
+Hospital C
Escherichia coli
-S
-S
R
S
-F
-
-
-2016-05-27
-C10
-Hospital A
-Staphylococcus aureus
-R
-S
-S
-S
-M
-
-
-2014-11-05
-A2
-Hospital A
-Staphylococcus aureus
-S
-S
-R
-S
-M
-
-
2017-03-21
-N6
-Hospital B
-Staphylococcus aureus
-S
-I
S
S
F
-
2010-03-02
-X6
+2014-07-31
+K5
Hospital B
Escherichia coli
S
S
S
S
-F
+M
-
+2015-08-17
-B8
-Hospital D
-Staphylococcus aureus
+2017-07-12
+U8
+Hospital B
+Klebsiella pneumoniae
+S
+S
+S
+S
+F
+
+
+2013-09-10
+Y1
+Hospital B
+Klebsiella pneumoniae
+R
R
S
S
+F
+
+
@@ -411,8 +411,8 @@
#>
#> Item Count Percent Cum. Count Cum. Percent
#> --- ----- ------- -------- ----------- -------------
-#> 1 M 10,390 52.0% 10,390 52.0%
-#> 2 F 9,610 48.1% 20,000 100.0%
+#> 1 M 10,383 51.9% 10,383 51.9%
+#> 2 F 9,617 48.1% 20,000 100.0%
2017-07-30
+X10
+Hospital C
+Streptococcus pneumoniae
S
-M
+S
+S
+S
+F
M
and F
. From a researcher perspective: there are slightly more men. Nothing we didn’t already know.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 (1369 changes)
+#> Table 1: Intrinsic resistance in Enterobacteriaceae (1340 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 (2815 changes)
+#> Table 4: Intrinsic resistance in Gram-positive bacteria (2699 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 7,563 out of 20,000 rows
+#> => EUCAST rules affected 7,406 out of 20,000 rows
#> -> added 0 test results
-#> -> changed 4,184 test results (0 to S; 0 to I; 4,184 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 5,780 first isolates (28.9% of total)
filter()
function, also from the dplyr
package:filter()
function, also from the dplyr
package:filter_first_isolate()
function:
1
-2010-01-05
-G4
+2010-03-12
+I7
B_ESCHR_COL
S
S
@@ -527,19 +527,19 @@
2
-2010-03-10
-G4
+2010-04-30
+I7
B_ESCHR_COL
+S
+S
R
S
-S
-S
FALSE
3
-2010-07-15
-G4
+2010-06-28
+I7
B_ESCHR_COL
R
S
@@ -549,8 +549,8 @@
4
-2010-09-07
-G4
+2010-07-08
+I7
B_ESCHR_COL
S
S
@@ -560,67 +560,67 @@
5
-2010-11-10
-G4
+2010-08-22
+I7
B_ESCHR_COL
R
-S
+I
S
S
FALSE
6
-2011-01-23
-G4
+2010-09-04
+I7
B_ESCHR_COL
-I
R
+I
S
S
-TRUE
+FALSE
7
-2011-02-21
-G4
+2010-11-16
+I7
B_ESCHR_COL
+R
S
-I
S
S
FALSE
8
-2011-02-25
-G4
+2011-03-31
+I7
B_ESCHR_COL
+S
+S
R
S
-S
-S
-FALSE
+TRUE
9
-2011-02-28
-G4
+2011-05-25
+I7
B_ESCHR_COL
S
S
-S
+R
S
FALSE
@@ -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 15,963 first weighted isolates (79.8% of total)
+#> => Found 15,935 first weighted isolates (79.7% of total)
10
-2011-04-03
-G4
+2011-08-25
+I7
B_ESCHR_COL
R
S
-S
+R
S
FALSE
-
isolate
@@ -654,8 +654,8 @@
1
-2010-01-05
-G4
+2010-03-12
+I7
B_ESCHR_COL
S
S
@@ -666,8 +666,20 @@
+2
-2010-03-10
-G4
+2010-04-30
+I7
+B_ESCHR_COL
+S
+S
+R
+S
+FALSE
+TRUE
+
+
-3
+2010-06-28
+I7
B_ESCHR_COL
R
S
@@ -676,22 +688,10 @@
FALSE
TRUE
-
3
-2010-07-15
-G4
-B_ESCHR_COL
-R
-S
-S
-S
-FALSE
-FALSE
-
4
-2010-09-07
-G4
+2010-07-08
+I7
B_ESCHR_COL
S
S
@@ -702,11 +702,11 @@
5
-2010-11-10
-G4
+2010-08-22
+I7
B_ESCHR_COL
R
-S
+I
S
S
FALSE
@@ -714,23 +714,23 @@
6
-2011-01-23
-G4
+2010-09-04
+I7
B_ESCHR_COL
-I
R
+I
S
S
-TRUE
-TRUE
+FALSE
+FALSE
7
-2011-02-21
-G4
+2010-11-16
+I7
B_ESCHR_COL
+R
S
-I
S
S
FALSE
@@ -738,47 +738,47 @@
8
-2011-02-25
-G4
+2011-03-31
+I7
B_ESCHR_COL
+S
+S
R
S
-S
-S
-FALSE
+TRUE
TRUE
9
-2011-02-28
-G4
+2011-05-25
+I7
B_ESCHR_COL
S
S
-S
+R
S
FALSE
-TRUE
+FALSE
10
-2011-04-03
-G4
+2011-08-25
+I7
B_ESCHR_COL
R
S
-S
+R
S
FALSE
TRUE
filter_first_isolate()
, there’s a shortcut for this new algorithm too:
-
date
patient_id
hospital
@@ -803,101 +802,95 @@
-
1
-2016-10-19
-U5
+2010-07-18
+Z5
Hospital B
-B_ESCHR_COL
+B_STRPT_PNE
+S
S
S
R
-S
F
-Gram negative
-Escherichia
-coli
+Gram positive
+Streptococcus
+pneumoniae
TRUE
-
-3
-2014-11-05
-A2
-Hospital A
-B_STPHY_AUR
-S
-S
-R
-S
-M
-Gram positive
-Staphylococcus
-aureus
-TRUE
-
-
-5
-2010-03-02
-X6
-Hospital B
-B_ESCHR_COL
-S
-S
-S
-S
-F
-Gram negative
-Escherichia
-coli
-TRUE
-
-
-6
-2015-08-17
-B8
-Hospital D
-B_STPHY_AUR
-R
-S
-S
-S
-M
-Gram positive
-Staphylococcus
-aureus
-TRUE
-
-
-7
-2013-01-25
-M3
-Hospital A
-B_STPHY_AUR
-R
-R
-S
-S
-M
-Gram positive
-Staphylococcus
-aureus
-TRUE
-
-
+8
-2013-07-27
-E2
+2017-01-13
+P8
Hospital C
+B_ESCHR_COL
+R
+S
+S
+S
+F
+Gram negative
+Escherichia
+coli
+TRUE
+
+
+2014-07-31
+K5
+Hospital B
+B_ESCHR_COL
+S
+S
+S
+S
+M
+Gram negative
+Escherichia
+coli
+TRUE
+
+
+2017-07-12
+U8
+Hospital B
B_KLBSL_PNE
R
S
S
S
-M
+F
Gram negative
Klebsiella
pneumoniae
TRUE
+
+2013-09-10
+Y1
+Hospital B
+B_KLBSL_PNE
+R
+R
+S
+S
+F
+Gram negative
+Klebsiella
+pneumoniae
+TRUE
+
+
2017-07-30
+X10
+Hospital C
+B_STRPT_PNE
+S
+S
+S
+R
+F
+Gram positive
+Streptococcus
+pneumoniae
+TRUE
+dplyr
way, which is easier readable:genus
and species
from a data.frame
(15,963 x 13)genus
and species
from a data.frame
(15,935 x 13)
-Length: 15,963 (of which NA: 0 = 0.00%)
+Length: 15,935 (of which NA: 0 = 0.00%)
Unique: 4
Longest: 24
1
Escherichia coli
-7,910
+7,898
49.6%
-7,910
+7,898
49.6%
2
Staphylococcus aureus
-3,889
-24.4%
-11,799
-73.9%
+4,017
+25.2%
+11,915
+74.8%
3
Streptococcus pneumoniae
-2,480
-15.5%
-14,279
-89.5%
+2,449
+15.4%
+14,364
+90.1%
@@ -971,7 +964,7 @@ Longest: 244
Klebsiella pneumoniae
-1,684
-10.5%
-15,963
+1,571
+9.9%
+15,935
100.0%
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:
Or can be used in conjuction with group_by()
and summarise()
, both from the dplyr
package:
data_1st %>%
group_by(hospital) %>%
@@ -984,19 +977,19 @@ Longest: 24
Hospital A
-0.4765396
+0.4740616
Hospital B
-0.4750632
+0.4748265
Hospital C
-0.4830405
+0.4910305
Hospital D
-0.4657107
+0.4777953
@@ -1014,23 +1007,23 @@ Longest: 24
Hospital A
-0.4765396
-4774
+0.4740616
+4742
Hospital B
-0.4750632
-5534
+0.4748265
+5621
Hospital C
-0.4830405
-2447
+0.4910305
+2397
Hospital D
-0.4657107
-3208
+0.4777953
+3175
@@ -1050,27 +1043,27 @@ Longest: 24
Escherichia
-0.7353982
-0.8972187
-0.9734513
+0.7295518
+0.9013674
+0.9772094
Klebsiella
-0.7369359
-0.9014252
-0.9786223
+0.7517505
+0.9000637
+0.9789943
Staphylococcus
-0.7413217
-0.9161738
-0.9763435
+0.7353747
+0.9168534
+0.9788399
Streptococcus
-0.7181452
+0.7390772
0.0000000
-0.7181452
+0.7390772
diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png
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