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reference/get_episode.md
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@@ -178,27 +178,29 @@ x
df <- example_isolates[sample(seq_len(2000), size = 100), ]
get_episode(df$date, episode_days = 60) # indices
#> [1] 21 7 4 46 15 6 11 27 44 25 20 8 30 25 43 33 13 46 2 44 40 15 33 46 2
#> [26] 32 4 19 22 45 23 45 9 44 39 16 7 46 10 34 37 45 4 20 11 14 5 19 7 40
#> [51] 6 47 31 4 31 42 7 13 29 38 31 18 28 9 43 17 23 41 12 8 35 13 2 45 4
#> [76] 3 34 36 15 23 24 15 9 41 12 1 44 17 18 37 32 23 35 38 44 37 38 10 24 26
#> [1] 20 43 19 12 43 41 12 28 30 15 41 18 34 6 15 9 29 8 45 16 43 10 14 9 42
#> [26] 48 8 28 24 4 31 45 20 43 47 4 30 2 15 22 15 19 32 27 4 11 12 3 2 28
#> [51] 35 2 42 27 7 3 17 45 46 11 13 37 25 36 37 17 47 1 32 21 20 35 41 31 32
#> [76] 24 21 33 48 25 40 42 39 6 23 5 11 35 13 33 16 38 18 49 44 2 48 26 9 3
is_new_episode(df$date, episode_days = 60) # TRUE/FALSE
#> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> [13] TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE
#> [25] FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
#> [37] FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
#> [49] FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
#> [61] FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE
#> [73] FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
#> [85] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [97] FALSE FALSE FALSE TRUE
#> [1] TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE
#> [13] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE
#> [25] TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
#> [37] FALSE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE TRUE
#> [49] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE
#> [61] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
#> [73] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE
#> [85] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
#> [97] FALSE TRUE FALSE FALSE
# filter on results from the third 60-day episode only, using base R
df[which(get_episode(df$date, 60) == 3), ]
#> # A tibble: 1 × 46
#> # A tibble: 3 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
#> 1 2003-01-06 894506 83 M ICU B_STRPT_PNMN S NA NA S
#> 1 2002-10-20 F35553 51 M ICU B_STPHY_AURS S NA S NA
#> 2 2002-09-24 CF9318 29 M ICU B_CMPYL_JEJN NA NA NA NA
#> 3 2002-08-31 149442 80 F ICU B_STPHY_AURS R NA S R
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
@@ -232,19 +234,19 @@ if (require("dplyr")) {
arrange(patient, condition, date)
}
#> # A tibble: 100 × 4
#> # Groups: patient, condition [94]
#> # Groups: patient, condition [97]
#> patient date condition new_episode
#> <chr> <date> <chr> <lgl>
#> 1 060287 2007-03-11 A TRUE
#> 2 0E2483 2007-11-10 A TRUE
#> 3 101305 2006-12-13 A TRUE
#> 4 141061 2014-10-22 A TRUE
#> 5 151041 2006-02-10 A TRUE
#> 6 15D386 2004-08-01 B TRUE
#> 7 187841 2008-04-22 A TRUE
#> 8 189363 2004-06-22 C TRUE
#> 9 208305 2015-09-14 A TRUE
#> 10 257844 2011-05-22 A TRUE
#> 1 018637 2005-09-28 C TRUE
#> 2 022060 2004-05-04 C TRUE
#> 3 032343 2003-06-09 B TRUE
#> 4 080086 2010-08-08 C TRUE
#> 5 126334 2009-11-26 A TRUE
#> 6 149442 2002-08-31 C TRUE
#> 7 14D826 2006-09-14 A TRUE
#> 8 16F0F7 2010-01-17 C TRUE
#> 9 179451 2007-09-15 B TRUE
#> 10 189795 2008-10-19 B TRUE
#> # ℹ 90 more rows
if (require("dplyr")) {
@@ -258,19 +260,19 @@ if (require("dplyr")) {
arrange(patient, ward, date)
}
#> # A tibble: 100 × 5
#> # Groups: ward, patient [89]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <int> <lgl>
#> 1 Clinical 2007-03-11 060287 1 TRUE
#> 2 Clinical 2007-11-10 0E2483 1 TRUE
#> 3 Clinical 2006-12-13 101305 1 TRUE
#> 4 Clinical 2014-10-22 141061 1 TRUE
#> 5 Clinical 2006-02-10 151041 1 TRUE
#> 6 ICU 2004-08-01 15D386 1 TRUE
#> 7 Clinical 2008-04-22 187841 1 TRUE
#> 8 Clinical 2004-06-22 189363 1 TRUE
#> 9 Clinical 2015-09-14 208305 1 TRUE
#> 10 Clinical 2011-05-22 257844 1 TRUE
#> # Groups: ward, patient [94]
#> ward date patient new_index new_logical
#> <chr> <date> <chr> <int> <lgl>
#> 1 Clinical 2005-09-28 018637 1 TRUE
#> 2 ICU 2004-05-04 022060 1 TRUE
#> 3 Clinical 2003-06-09 032343 1 TRUE
#> 4 Clinical 2010-08-08 080086 1 TRUE
#> 5 Outpatient 2009-11-26 126334 1 TRUE
#> 6 ICU 2002-08-31 149442 1 TRUE
#> 7 Clinical 2006-09-14 14D826 1 TRUE
#> 8 Clinical 2010-01-17 16F0F7 1 TRUE
#> 9 ICU 2007-09-15 179451 1 TRUE
#> 10 Clinical 2008-10-19 189795 1 TRUE
#> # ℹ 90 more rows
if (require("dplyr")) {
@@ -286,9 +288,9 @@ if (require("dplyr")) {
#> # A tibble: 3 × 5
#> ward n_patients n_episodes_365 n_episodes_60 n_episodes_30
#> <chr> <int> <int> <int> <int>
#> 1 Clinical 55 12 34 39
#> 2 ICU 28 10 25 26
#> 3 Outpatient 6 5 6 6
#> 1 Clinical 55 14 33 43
#> 2 ICU 33 11 23 27
#> 3 Outpatient 6 5 5 5
# grouping on patients and microorganisms leads to the same
# results as first_isolate() when using 'episode-based':
@@ -306,7 +308,7 @@ if (require("dplyr")) {
identical(x, y)
}
#> [1] FALSE
#> [1] TRUE
# but is_new_episode() has a lot more flexibility than first_isolate(),
# since you can now group on anything that seems relevant:
@@ -317,19 +319,19 @@ if (require("dplyr")) {
select(group_vars(.), flag_episode)
}
#> # A tibble: 100 × 4
#> # Groups: patient, mo, ward [93]
#> patient mo ward flag_episode
#> <chr> <mo> <chr> <lgl>
#> 1 0E2483 B_ESCHR_COLI Clinical TRUE
#> 2 F24801 B_STRPT_AGLC ICU TRUE
#> 3 A26548 B_STPHY_CONS ICU TRUE
#> 4 422833 B_ENTRC_FCLS Clinical TRUE
#> 5 AFD3B1 UNKNOWN Clinical TRUE
#> 6 F35553 B_ENTRBC_CLOC ICU TRUE
#> 7 316893 B_STPHY_CONS Clinical TRUE
#> 8 B54813 B_ENTRC ICU TRUE
#> 9 E06844 B_STPHY_AURS Outpatient TRUE
#> 10 B65162 B_STRPT_PNMN Clinical TRUE
#> # Groups: patient, mo, ward [97]
#> patient mo ward flag_episode
#> <chr> <mo> <chr> <lgl>
#> 1 189795 B_AERCC_URNQ Clinical TRUE
#> 2 5C1947 B_ESCHR_COLI Clinical TRUE
#> 3 D22254 B_ESCHR_COLI Clinical TRUE
#> 4 018637 B_ESCHR_COLI Clinical TRUE
#> 5 483195 B_ESCHR_COLI ICU TRUE
#> 6 324415 B_PROTS_MRBL ICU TRUE
#> 7 671180 B_ESCHR_COLI Clinical TRUE
#> 8 59B5BD B_ESCHR_COLI Clinical TRUE
#> 9 443847 B_STPHY_CONS Clinical TRUE
#> 10 690B42 B_ESCHR_COLI ICU TRUE
#> # ℹ 90 more rows
# }
```