-| 2014-12-01 |
-V4 |
+2011-02-12 |
+O4 |
Hospital A |
-Escherichia coli |
-R |
-I |
-I |
-R |
-F |
-
-
-| 2010-08-12 |
-I7 |
-Hospital C |
-Staphylococcus aureus |
-R |
-S |
-S |
-S |
-M |
-
-
-| 2012-07-28 |
-V1 |
-Hospital B |
Streptococcus pneumoniae |
-S |
-S |
-R |
+I |
+I |
+I |
S |
F |
-| 2012-09-30 |
-D9 |
-Hospital D |
+2011-03-08 |
+W5 |
+Hospital C |
Escherichia coli |
-S |
+I |
+I |
+I |
+I |
+F |
+
+
+| 2015-09-22 |
+R6 |
+Hospital B |
+Escherichia coli |
+I |
+R |
+R |
+I |
+F |
+
+
+| 2013-03-03 |
+E2 |
+Hospital B |
+Staphylococcus aureus |
+I |
+R |
+R |
R |
-S |
-S |
M |
-| 2011-08-02 |
-M4 |
-Hospital D |
-Escherichia coli |
-S |
+2014-09-08 |
+I5 |
+Hospital B |
+Klebsiella pneumoniae |
+I |
+I |
R |
-S |
-S |
+I |
M |
-| 2015-05-14 |
-N5 |
-Hospital C |
-Staphylococcus aureus |
-I |
+2017-10-13 |
+F8 |
+Hospital D |
+Escherichia coli |
I |
+S |
I |
S |
M |
@@ -507,16 +507,16 @@ Longest: 1
| 1 |
M |
-10,361 |
-51.81% |
-10,361 |
-51.81% |
+10,425 |
+52.13% |
+10,425 |
+52.13% |
| 2 |
F |
-9,639 |
-48.20% |
+9,575 |
+47.88% |
20,000 |
100.00% |
@@ -629,9 +629,9 @@ takes into account the antimicrobial susceptibility test results using
#> Basing inclusion on all antimicrobial results, using a points threshold of
#> 2
#> Including isolates from ICU.
-#> => Found 12,431 'phenotype-based' first isolates (62.2% of total where a
+#> => Found 12,293 'phenotype-based' first isolates (61.5% of total where a
#> microbial ID was available)
-So only 62.2% is suitable for resistance analysis! We can now filter
+
So only 61.5% is suitable for resistance analysis! We can now filter
on it with the filter() function, also from the
dplyr package:
-So we end up with 12 431 isolates for analysis. Now our data looks
+
So we end up with 12 293 isolates for analysis. Now our data looks
like:
-
+
@@ -658,7 +658,7 @@ like:
-
+
@@ -681,45 +681,13 @@ like:
| 1 |
-2014-12-01 |
-V4 |
+2011-02-12 |
+O4 |
Hospital A |
-B_ESCHR_COLI |
-R |
-I |
-I |
-R |
-F |
-Gram-negative |
-Escherichia |
-coli |
-TRUE |
-
-
-| 2 |
-2010-08-12 |
-I7 |
-Hospital C |
-B_STPHY_AURS |
-R |
-S |
-S |
-S |
-M |
-Gram-positive |
-Staphylococcus |
-aureus |
-TRUE |
-
-
-| 3 |
-2012-07-28 |
-V1 |
-Hospital B |
B_STRPT_PNMN |
-S |
-S |
-R |
+I |
+I |
+I |
R |
F |
Gram-positive |
@@ -728,30 +696,30 @@ like:
TRUE |
-| 6 |
-2015-05-14 |
-N5 |
+2 |
+2011-03-08 |
+W5 |
Hospital C |
-B_STPHY_AURS |
-I |
-I |
-I |
-S |
-M |
-Gram-positive |
-Staphylococcus |
-aureus |
-TRUE |
-
-
-| 7 |
-2011-03-07 |
-X4 |
-Hospital D |
B_ESCHR_COLI |
I |
I |
-S |
+I |
+I |
+F |
+Gram-negative |
+Escherichia |
+coli |
+TRUE |
+
+
+| 3 |
+2015-09-22 |
+R6 |
+Hospital B |
+B_ESCHR_COLI |
+R |
+R |
+R |
I |
F |
Gram-negative |
@@ -760,19 +728,51 @@ like:
TRUE |
-| 8 |
-2016-11-12 |
-T7 |
-Hospital A |
-B_KLBSL_PNMN |
-R |
+6 |
+2017-10-13 |
+F8 |
+Hospital D |
+B_ESCHR_COLI |
+I |
S |
I |
S |
+M |
+Gram-negative |
+Escherichia |
+coli |
+TRUE |
+
+
+| 7 |
+2013-03-22 |
+H3 |
+Hospital D |
+B_STRPT_PNMN |
+R |
+R |
+I |
+R |
+M |
+Gram-positive |
+Streptococcus |
+pneumoniae |
+TRUE |
+
+
+| 8 |
+2014-11-05 |
+Y10 |
+Hospital D |
+B_ESCHR_COLI |
+R |
+R |
+S |
+R |
F |
Gram-negative |
-Klebsiella |
-pneumoniae |
+Escherichia |
+coli |
TRUE |
@@ -808,8 +808,8 @@ readable:
data_1st %>% freq(genus, species)
Frequency table
Class: character
-Length: 12,431
-Available: 12,431 (100%, NA: 0 = 0%)
+Length: 12,293
+Available: 12,293 (100%, NA: 0 = 0%)
Unique: 4
Shortest: 16
Longest: 24
@@ -834,33 +834,33 @@ Longest: 24
| 1 |
Escherichia coli |
-5,970 |
-48.03% |
-5,970 |
-48.03% |
+5,873 |
+47.78% |
+5,873 |
+47.78% |
| 2 |
Staphylococcus aureus |
-3,335 |
-26.83% |
-9,305 |
-74.85% |
+3,225 |
+26.23% |
+9,098 |
+74.01% |
| 3 |
Streptococcus pneumoniae |
-1,817 |
-14.62% |
-11,122 |
-89.47% |
+1,844 |
+15.00% |
+10,942 |
+89.01% |
| 4 |
Klebsiella pneumoniae |
-1,309 |
-10.53% |
-12,431 |
+1,351 |
+10.99% |
+12,293 |
100.00% |
@@ -909,13 +909,43 @@ antibiotic class they are in:
-| 2014-12-01 |
-V4 |
+2011-02-12 |
+O4 |
Hospital A |
-B_ESCHR_COLI |
+B_STRPT_PNMN |
+I |
+I |
+I |
+R |
+F |
+Gram-positive |
+Streptococcus |
+pneumoniae |
+TRUE |
+
+
+| 2013-03-22 |
+H3 |
+Hospital D |
+B_STRPT_PNMN |
+R |
R |
I |
-I |
+R |
+M |
+Gram-positive |
+Streptococcus |
+pneumoniae |
+TRUE |
+
+
+| 2014-11-05 |
+Y10 |
+Hospital D |
+B_ESCHR_COLI |
+R |
+R |
+S |
R |
F |
Gram-negative |
@@ -924,71 +954,41 @@ antibiotic class they are in:
TRUE |
-| 2012-07-28 |
-V1 |
+2013-09-13 |
+A8 |
Hospital B |
B_STRPT_PNMN |
S |
S |
R |
R |
-F |
+M |
Gram-positive |
Streptococcus |
pneumoniae |
TRUE |
-| 2016-08-30 |
-Q3 |
+2016-08-04 |
+S3 |
Hospital D |
-B_STRPT_PNMN |
-R |
-R |
+B_STPHY_AURS |
R |
+I |
+I |
R |
F |
Gram-positive |
-Streptococcus |
-pneumoniae |
-TRUE |
-
-
-| 2013-03-03 |
-F7 |
-Hospital B |
-B_STPHY_AURS |
-I |
-S |
-I |
-R |
-M |
-Gram-positive |
Staphylococcus |
aureus |
TRUE |
-
-| 2010-10-30 |
-H10 |
-Hospital A |
-B_STRPT_PNMN |
-I |
-I |
-S |
-R |
-M |
-Gram-positive |
-Streptococcus |
-pneumoniae |
-TRUE |
-
-| 2011-04-07 |
-D8 |
+2013-05-16 |
+K6 |
Hospital C |
B_ESCHR_COLI |
-S |
+I |
S |
R |
R |
@@ -1020,50 +1020,50 @@ different bug/drug combinations, you can use the
| E. coli |
AMC |
-2855 |
-1161 |
-1954 |
-5970 |
+2744 |
+1216 |
+1913 |
+5873 |
| E. coli |
AMX |
-1518 |
-1282 |
-3170 |
-5970 |
+1504 |
+1272 |
+3097 |
+5873 |
| E. coli |
CIP |
-2087 |
-1796 |
-2087 |
-5970 |
+2018 |
+1818 |
+2037 |
+5873 |
| E. coli |
GEN |
-2119 |
-1854 |
-1997 |
-5970 |
+2067 |
+1812 |
+1994 |
+5873 |
| K. pneumoniae |
AMC |
-615 |
-256 |
-438 |
-1309 |
+599 |
+275 |
+477 |
+1351 |
| K. pneumoniae |
AMX |
0 |
0 |
-1309 |
-1309 |
+1351 |
+1351 |
@@ -1085,34 +1085,34 @@ different bug/drug combinations, you can use the
| E. coli |
GEN |
-2119 |
-1854 |
-1997 |
-5970 |
+2067 |
+1812 |
+1994 |
+5873 |
| K. pneumoniae |
GEN |
-456 |
-409 |
-444 |
-1309 |
+450 |
+437 |
+464 |
+1351 |
| S. aureus |
GEN |
-1122 |
-1075 |
-1138 |
-3335 |
+1128 |
+980 |
+1117 |
+3225 |
| S. pneumoniae |
GEN |
0 |
0 |
-1817 |
-1817 |
+1844 |
+1844 |
@@ -1144,7 +1144,7 @@ I (proportion_SI(), equa
own:
+#> [1] 0.5805743
Or can be used in conjunction with group_by() and
summarise(), both from the dplyr package:
@@ -1159,19 +1159,19 @@ own:
| Hospital A |
-0.5871733 |
+0.5836502 |
| Hospital B |
-0.5822430 |
+0.5788257 |
| Hospital C |
-0.5788635 |
+0.5675105 |
| Hospital D |
-0.5717638 |
+0.5893679 |
@@ -1196,23 +1196,23 @@ all isolates available for every group (i.e. values S, I or R):
| Hospital A |
-0.5871733 |
-3711 |
+0.5836502 |
+3682 |
| Hospital B |
-0.5822430 |
-4280 |
+0.5788257 |
+4326 |
| Hospital C |
-0.5788635 |
-1883 |
+0.5675105 |
+1896 |
| Hospital D |
-0.5717638 |
-2557 |
+0.5893679 |
+2389 |
@@ -1237,27 +1237,27 @@ therapies very easily:
| Escherichia |
-0.6726968 |
-0.6654941 |
-0.8934673 |
+0.6742721 |
+0.6604802 |
+0.8923889 |
| Klebsiella |
-0.6653934 |
-0.6608098 |
-0.8800611 |
+0.6469282 |
+0.6565507 |
+0.8793486 |
| Staphylococcus |
-0.6773613 |
-0.6587706 |
-0.8917541 |
+0.6731783 |
+0.6536434 |
+0.8862016 |
| Streptococcus |
-0.4777105 |
+0.4766811 |
0.0000000 |
-0.4777105 |
+0.4766811 |
@@ -1285,23 +1285,23 @@ classes, use a antibiotic class selector such as
| Hospital A |
-58.7% |
-36.2% |
+58.4% |
+36.1% |
| Hospital B |
-58.2% |
-35.9% |
+57.9% |
+36.2% |
| Hospital C |
-57.9% |
-34.9% |
+56.8% |
+34.6% |
| Hospital D |
-57.2% |
-34.5% |
+58.9% |
+35.8% |
@@ -1417,16 +1417,18 @@ classes) <mic> and <disk>:
mic_values <- random_mic(size = 100)
mic_values
#> Class 'mic'
-#> [1] 0.0625 0.125 16 0.025 0.005 128 8 4 0.002 32
-#> [11] 64 0.5 0.025 0.01 8 0.002 0.5 1 0.125 0.001
-#> [21] 0.005 0.025 0.005 0.125 0.001 128 0.01 2 64 128
-#> [31] 8 1 1 128 8 4 16 0.5 0.25 0.25
-#> [41] 16 256 0.002 8 0.025 0.5 128 8 256 64
-#> [51] 0.025 128 0.025 0.001 1 0.002 0.125 0.001 256 0.01
-#> [61] 64 0.0625 0.025 16 0.5 0.125 256 1 1 128
-#> [71] 32 0.01 0.01 0.0625 128 4 16 0.005 256 8
-#> [81] 8 0.005 0.01 0.025 32 64 1 0.005 128 0.25
-#> [91] 16 0.001 0.5 0.001 0.25 0.0625 2 2 0.001 0.125
# base R:
plot(mic_values)
<mic> and <disk>:
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")
-#> [1] "here"
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
@@ -1461,10 +1462,10 @@ plotting:
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro")
disk_values
#> Class 'disk'
-#> [1] 27 29 20 31 27 23 30 27 19 24 31 20 19 28 23 25 17 22 20 24 24 28 17 29 26
-#> [26] 29 31 23 23 30 18 29 30 23 21 19 25 24 22 18 28 25 24 18 25 25 27 24 24 27
-#> [51] 27 18 18 25 22 27 30 24 31 26 19 30 21 22 23 19 23 23 22 26 22 29 29 29 29
-#> [76] 26 26 24 30 17 30 17 25 27 17 27 20 23 17 21 17 27 18 23 30 19 22 20 30 17
+#> [1] 23 21 21 24 27 18 21 29 31 18 17 26 21 21 27 25 28 31 30 17 29 21 18 30 25
+#> [26] 28 31 17 25 23 22 17 27 24 30 31 24 24 25 27 18 31 20 21 29 30 17 21 20 19
+#> [51] 25 21 17 17 26 25 30 26 19 18 21 28 27 21 30 29 21 17 30 28 26 27 23 31 27
+#> [76] 31 31 26 24 30 31 22 20 17 17 23 31 27 17 28 27 18 27 29 24 24 28 20 19 26
# base R:
plot(disk_values, mo = "E. coli", ab = "cipro")