vignettes/AMR.Rmd
AMR.Rmd
Now, let’s start the cleaning and the analysis!
So only 61.5% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the @@ -644,11 +644,11 @@ on it with the data_1st <- data %>% filter_first_isolate() -So we end up with 12 291 isolates for analysis. Now our data looks +So we end up with 12 300 isolates for analysis. Now our data looks like: head(data_1st) - + @@ -661,7 +661,7 @@ like: - + @@ -683,48 +683,48 @@ like: -4 -2016-09-29 -O10 -Hospital B -B_ESCHR_COLI +2 +2017-01-11 +D6 +Hospital D +B_STPHY_AURS +R +R R -I S -I -F -Gram-negative -Escherichia -coli +M +Gram-positive +Staphylococcus +aureus TRUE -5 -2015-11-01 -M1 -Hospital B +10 +2011-03-19 +U9 +Hospital D B_ESCHR_COLI S S +R S -I -M +F Gram-negative Escherichia coli TRUE -9 -2011-03-28 -W6 +11 +2010-12-27 +A2 Hospital C B_ESCHR_COLI R R -R -R -F +S +S +M Gram-negative Escherichia coli @@ -732,14 +732,14 @@ like: 12 -2016-01-01 -J2 +2012-12-11 +I9 Hospital B B_KLBSL_PNMN R -I -S -I +R +R +R M Gram-negative Klebsiella @@ -747,35 +747,35 @@ like: TRUE -14 -2013-09-11 -C2 +13 +2015-07-02 +Y10 Hospital D -B_ESCHR_COLI +B_STPHY_AURS +R +R S -S -S -I -M -Gram-negative -Escherichia -coli -TRUE - - -16 -2017-06-17 -W2 -Hospital B -B_STRPT_PNMN -S -S -I R F Gram-positive -Streptococcus -pneumoniae +Staphylococcus +aureus +TRUE + + +15 +2015-08-12 +V4 +Hospital B +B_STPHY_AURS +R +I +R +I +F +Gram-positive +Staphylococcus +aureus TRUE @@ -811,8 +811,8 @@ readable: data_1st %>% freq(genus, species) Frequency table Class: character -Length: 12,291 -Available: 12,291 (100%, NA: 0 = 0%) +Length: 12,300 +Available: 12,300 (100%, NA: 0 = 0%) Unique: 4 Shortest: 16 Longest: 24 @@ -837,33 +837,33 @@ Longest: 24 1 Escherichia coli -5,932 -48.26% -5,932 -48.26% +5,826 +47.37% +5,826 +47.37% 2 Staphylococcus aureus -3,234 -26.31% -9,166 -74.57% +3,233 +26.28% +9,059 +73.65% 3 Streptococcus pneumoniae -1,831 -14.90% -10,997 -89.47% +1,875 +15.24% +10,934 +88.89% 4 Klebsiella pneumoniae -1,294 -10.53% -12,291 +1,366 +11.11% +12,300 100.00% @@ -884,14 +884,14 @@ antibiotic class they are in: - + - + @@ -912,86 +912,71 @@ antibiotic class they are in: -2011-03-28 -W6 -Hospital C -B_ESCHR_COLI -R -R -R -R -F -Gram-negative -Escherichia -coli -TRUE - - -2017-06-17 -W2 +2012-12-11 +I9 Hospital B -B_STRPT_PNMN -S -S -I -R -F -Gram-positive -Streptococcus -pneumoniae -TRUE - - -2015-08-02 -T7 -Hospital B -B_ESCHR_COLI -R -R -R -R -F -Gram-negative -Escherichia -coli -TRUE - - -2012-04-29 -Q6 -Hospital D -B_ESCHR_COLI -R -S -R -R -F -Gram-negative -Escherichia -coli -TRUE - - -2014-12-09 -E7 -Hospital B -B_STRPT_PNMN +B_KLBSL_PNMN R R R R M -Gram-positive -Streptococcus +Gram-negative +Klebsiella pneumoniae TRUE -2010-08-29 -N8 +2015-07-02 +Y10 +Hospital D +B_STPHY_AURS +R +R +S +R +F +Gram-positive +Staphylococcus +aureus +TRUE + + +2012-09-28 +N1 +Hospital D +B_ESCHR_COLI +R +I +I +R +M +Gram-negative +Escherichia +coli +TRUE + + +2011-07-31 +K9 Hospital B B_ESCHR_COLI +R +S I +R +M +Gram-negative +Escherichia +coli +TRUE + + +2012-06-02 +V9 +Hospital D +B_ESCHR_COLI +S S I R @@ -1001,6 +986,21 @@ antibiotic class they are in: coli TRUE + +2012-02-06 +M7 +Hospital B +B_STRPT_PNMN +R +R +I +R +M +Gram-positive +Streptococcus +pneumoniae +TRUE + If you want to get a quick glance of the number of isolates in @@ -1012,7 +1012,6 @@ different bug/drug combinations, you can use the head() # show first 6 rows - mo ab S @@ -1022,58 +1021,52 @@ different bug/drug combinations, you can use the -2 E. coli -AMC -2766 -1182 -1984 -5932 +S +1489 +1236 +3101 +5826 -1 E. coli -AMX -1503 -1271 -3158 -5932 +S1 +2720 +1160 +1946 +5826 -3 E. coli -CIP -2090 -1798 -2044 -5932 +S2 +1974 +1845 +2007 +5826 -4 E. coli -GEN -2053 -1791 -2088 -5932 +S3 +2027 +1830 +1969 +5826 -6 K. pneumoniae -AMC -590 -264 -440 -1294 +S +0 +0 +1366 +1366 -5 K. pneumoniae -AMX -0 -0 -1294 -1294 +S1 +570 +310 +486 +1366 @@ -1094,35 +1087,35 @@ different bug/drug combinations, you can use the E. coli -GEN -2053 -1791 -2088 -5932 +S +2027 +1830 +1969 +5826 K. pneumoniae -GEN -443 -418 -433 -1294 +S +501 +402 +463 +1366 S. aureus -GEN -1084 -1013 -1137 -3234 +S +1155 +992 +1086 +3233 S. pneumoniae -GEN +S 0 0 -1831 -1831 +1875 +1875 @@ -1154,7 +1147,7 @@ I (proportion_SI(), equa own:
filter()
data_1st <- data %>% filter_first_isolate() -So we end up with 12 291 isolates for analysis. Now our data looks +So we end up with 12 300 isolates for analysis. Now our data looks like: head(data_1st) - + @@ -661,7 +661,7 @@ like: - + @@ -683,48 +683,48 @@ like: -4 -2016-09-29 -O10 -Hospital B -B_ESCHR_COLI +2 +2017-01-11 +D6 +Hospital D +B_STPHY_AURS +R +R R -I S -I -F -Gram-negative -Escherichia -coli +M +Gram-positive +Staphylococcus +aureus TRUE -5 -2015-11-01 -M1 -Hospital B +10 +2011-03-19 +U9 +Hospital D B_ESCHR_COLI S S +R S -I -M +F Gram-negative Escherichia coli TRUE -9 -2011-03-28 -W6 +11 +2010-12-27 +A2 Hospital C B_ESCHR_COLI R R -R -R -F +S +S +M Gram-negative Escherichia coli @@ -732,14 +732,14 @@ like: 12 -2016-01-01 -J2 +2012-12-11 +I9 Hospital B B_KLBSL_PNMN R -I -S -I +R +R +R M Gram-negative Klebsiella @@ -747,35 +747,35 @@ like: TRUE -14 -2013-09-11 -C2 +13 +2015-07-02 +Y10 Hospital D -B_ESCHR_COLI +B_STPHY_AURS +R +R S -S -S -I -M -Gram-negative -Escherichia -coli -TRUE - - -16 -2017-06-17 -W2 -Hospital B -B_STRPT_PNMN -S -S -I R F Gram-positive -Streptococcus -pneumoniae +Staphylococcus +aureus +TRUE + + +15 +2015-08-12 +V4 +Hospital B +B_STPHY_AURS +R +I +R +I +F +Gram-positive +Staphylococcus +aureus TRUE @@ -811,8 +811,8 @@ readable: data_1st %>% freq(genus, species) Frequency table Class: character -Length: 12,291 -Available: 12,291 (100%, NA: 0 = 0%) +Length: 12,300 +Available: 12,300 (100%, NA: 0 = 0%) Unique: 4 Shortest: 16 Longest: 24 @@ -837,33 +837,33 @@ Longest: 24 1 Escherichia coli -5,932 -48.26% -5,932 -48.26% +5,826 +47.37% +5,826 +47.37% 2 Staphylococcus aureus -3,234 -26.31% -9,166 -74.57% +3,233 +26.28% +9,059 +73.65% 3 Streptococcus pneumoniae -1,831 -14.90% -10,997 -89.47% +1,875 +15.24% +10,934 +88.89% 4 Klebsiella pneumoniae -1,294 -10.53% -12,291 +1,366 +11.11% +12,300 100.00% @@ -884,14 +884,14 @@ antibiotic class they are in: - + - + @@ -912,86 +912,71 @@ antibiotic class they are in: -2011-03-28 -W6 -Hospital C -B_ESCHR_COLI -R -R -R -R -F -Gram-negative -Escherichia -coli -TRUE - - -2017-06-17 -W2 +2012-12-11 +I9 Hospital B -B_STRPT_PNMN -S -S -I -R -F -Gram-positive -Streptococcus -pneumoniae -TRUE - - -2015-08-02 -T7 -Hospital B -B_ESCHR_COLI -R -R -R -R -F -Gram-negative -Escherichia -coli -TRUE - - -2012-04-29 -Q6 -Hospital D -B_ESCHR_COLI -R -S -R -R -F -Gram-negative -Escherichia -coli -TRUE - - -2014-12-09 -E7 -Hospital B -B_STRPT_PNMN +B_KLBSL_PNMN R R R R M -Gram-positive -Streptococcus +Gram-negative +Klebsiella pneumoniae TRUE -2010-08-29 -N8 +2015-07-02 +Y10 +Hospital D +B_STPHY_AURS +R +R +S +R +F +Gram-positive +Staphylococcus +aureus +TRUE + + +2012-09-28 +N1 +Hospital D +B_ESCHR_COLI +R +I +I +R +M +Gram-negative +Escherichia +coli +TRUE + + +2011-07-31 +K9 Hospital B B_ESCHR_COLI +R +S I +R +M +Gram-negative +Escherichia +coli +TRUE + + +2012-06-02 +V9 +Hospital D +B_ESCHR_COLI +S S I R @@ -1001,6 +986,21 @@ antibiotic class they are in: coli TRUE + +2012-02-06 +M7 +Hospital B +B_STRPT_PNMN +R +R +I +R +M +Gram-positive +Streptococcus +pneumoniae +TRUE + If you want to get a quick glance of the number of isolates in @@ -1012,7 +1012,6 @@ different bug/drug combinations, you can use the head() # show first 6 rows - mo ab S @@ -1022,58 +1021,52 @@ different bug/drug combinations, you can use the -2 E. coli -AMC -2766 -1182 -1984 -5932 +S +1489 +1236 +3101 +5826 -1 E. coli -AMX -1503 -1271 -3158 -5932 +S1 +2720 +1160 +1946 +5826 -3 E. coli -CIP -2090 -1798 -2044 -5932 +S2 +1974 +1845 +2007 +5826 -4 E. coli -GEN -2053 -1791 -2088 -5932 +S3 +2027 +1830 +1969 +5826 -6 K. pneumoniae -AMC -590 -264 -440 -1294 +S +0 +0 +1366 +1366 -5 K. pneumoniae -AMX -0 -0 -1294 -1294 +S1 +570 +310 +486 +1366 @@ -1094,35 +1087,35 @@ different bug/drug combinations, you can use the E. coli -GEN -2053 -1791 -2088 -5932 +S +2027 +1830 +1969 +5826 K. pneumoniae -GEN -443 -418 -433 -1294 +S +501 +402 +463 +1366 S. aureus -GEN -1084 -1013 -1137 -3234 +S +1155 +992 +1086 +3233 S. pneumoniae -GEN +S 0 0 -1831 -1831 +1875 +1875 @@ -1154,7 +1147,7 @@ I (proportion_SI(), equa own:
data_1st <- data %>% filter_first_isolate()
So we end up with 12 291 isolates for analysis. Now our data looks +
So we end up with 12 300 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 12,291 -Available: 12,291 (100%, NA: 0 = 0%) +Length: 12,300 +Available: 12,300 (100%, NA: 0 = 0%) Unique: 4
Shortest: 16 Longest: 24
If you want to get a quick glance of the number of isolates in @@ -1012,7 +1012,6 @@ different bug/drug combinations, you can use the head() # show first 6 rows
proportion_SI()
data_1st %>% resistance(AMX) -# [1] 0.5826214
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
dplyr
@@ -1169,19 +1162,19 @@ own: Hospital A -0.5758659 +0.5931973 Hospital B -0.5828610 +0.5891020 Hospital C -0.5882029 +0.5969416 Hospital D -0.5881384 +0.5818110 @@ -1206,23 +1199,23 @@ all isolates available for every group (i.e. values S, I or R): Hospital A -0.5758659 -3638 +0.5931973 +3675 Hospital B -0.5828610 -4411 +0.5891020 +4276 Hospital C -0.5882029 -1814 +0.5969416 +1831 Hospital D -0.5881384 -2428 +0.5818110 +2518 @@ -1247,27 +1240,27 @@ therapies very easily: Escherichia -0.6655428 -0.6480108 -0.8833446 +0.6659801 +0.6620323 +0.8884312 Klebsiella -0.6599691 -0.6653787 -0.8802164 +0.6442167 +0.6610542 +0.8755490 Staphylococcus -0.6719233 -0.6484230 -0.8862090 +0.6523353 +0.6640891 +0.8892669 Streptococcus -0.4729656 +0.4650667 0.0000000 -0.4729656 +0.4650667 @@ -1295,23 +1288,23 @@ classes, use a antibiotic class selector such as Hospital A -57.6% -36.5% +59.3% +37.6% Hospital B -58.3% -36.2% +58.9% +37.3% Hospital C -58.8% -36.3% +59.7% +36.4% Hospital D -58.8% -35.7% +58.2% +36.3% @@ -1427,18 +1420,16 @@ classes) <mic> and <disk>: mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.005 2 2 128 8 0.01 0.005 0.25 2 -# [10] 8 256 0.125 256 0.002 0.0625 0.025 8 16 -# [19] 0.002 0.5 0.25 256 16 2 0.002 0.01 0.01 -# [28] 1 256 2 64 0.002 128 8 2 128 -# [37] 0.002 4 0.025 32 8 0.125 32 0.5 8 -# [46] 0.125 0.5 128 0.005 0.005 0.5 2 <=0.001 0.025 -# [55] 0.002 0.0625 1 32 0.125 64 0.002 0.025 0.01 -# [64] 32 8 0.01 4 0.125 0.125 64 0.5 0.0625 -# [73] 1 256 1 32 16 0.0625 1 16 64 -# [82] 0.125 0.002 16 0.0625 128 32 64 0.0625 64 -# [91] 2 2 0.005 256 0.25 0.0625 0.01 4 <=0.001 -# [100] 1
<mic>
<disk>
mic_values <- random_mic(size = 100) mic_values # Class 'mic' -# [1] 0.005 2 2 128 8 0.01 0.005 0.25 2 -# [10] 8 256 0.125 256 0.002 0.0625 0.025 8 16 -# [19] 0.002 0.5 0.25 256 16 2 0.002 0.01 0.01 -# [28] 1 256 2 64 0.002 128 8 2 128 -# [37] 0.002 4 0.025 32 8 0.125 32 0.5 8 -# [46] 0.125 0.5 128 0.005 0.005 0.5 2 <=0.001 0.025 -# [55] 0.002 0.0625 1 32 0.125 64 0.002 0.025 0.01 -# [64] 32 8 0.01 4 0.125 0.125 64 0.5 0.0625 -# [73] 1 256 1 32 16 0.0625 1 16 64 -# [82] 0.125 0.002 16 0.0625 128 32 64 0.0625 64 -# [91] 2 2 0.005 256 0.25 0.0625 0.01 4 <=0.001 -# [100] 1
# base R: plot(mic_values)
disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class 'disk' -# [1] 17 30 22 31 20 25 23 17 24 17 30 26 21 20 28 19 28 29 24 17 29 30 31 27 25 -# [26] 29 21 21 18 25 19 25 22 26 28 18 25 19 24 30 23 22 24 22 22 22 18 24 19 19 -# [51] 17 17 28 29 28 24 20 23 25 29 31 21 31 18 18 18 28 22 18 25 29 24 27 29 23 -# [76] 21 31 17 29 30 24 29 21 29 24 17 28 18 19 31 27 25 26 18 27 27 29 28 25 26
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
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