-
Notifications
You must be signed in to change notification settings - Fork 12
/
vctrs.R
executable file
·202 lines (192 loc) · 6.32 KB
/
vctrs.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
# ==================================================================== #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
# Center Groningen in The Netherlands, in collaboration with many #
# colleagues from around the world, see our website. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
# These are all S3 implementations for the vctrs package,
# that is used internally by tidyverse packages such as dplyr.
# They are to convert AMR-specific classes to bare characters and integers.
# All of them will be exported using s3_register() in R/zzz.R when loading the package.
# see https://github.com/tidyverse/dplyr/issues/5955 why this is required
# S3: ab_selector ----
# this does not need a .default method since it's used internally only
vec_ptype2.character.ab_selector <- function(x, y, ...) {
x
}
vec_ptype2.ab_selector.character <- function(x, y, ...) {
y
}
vec_cast.character.ab_selector <- function(x, to, ...) {
unclass(x)
}
# S3: ab_selector_any_all ----
# this does not need a .default method since it's used internally only
vec_ptype2.logical.ab_selector_any_all <- function(x, y, ...) {
x
}
vec_ptype2.ab_selector_any_all.logical <- function(x, y, ...) {
y
}
vec_cast.logical.ab_selector_any_all <- function(x, to, ...) {
unclass(x)
}
# S3: ab ----
vec_ptype2.ab.default <- function (x, y, ..., x_arg = "", y_arg = "") {
x
}
vec_ptype2.ab.ab <- function(x, y, ...) {
x
}
vec_cast.character.ab <- function(x, to, ...) {
as.character(x)
}
vec_cast.ab.character <- function(x, to, ...) {
return_after_integrity_check(x, "antimicrobial drug code", as.character(AMR_env$AB_lookup$ab))
}
# S3: av ----
vec_ptype2.av.default <- function (x, y, ..., x_arg = "", y_arg = "") {
x
}
vec_ptype2.av.av <- function(x, y, ...) {
x
}
vec_cast.character.av <- function(x, to, ...) {
as.character(x)
}
vec_cast.av.character <- function(x, to, ...) {
return_after_integrity_check(x, "antiviral drug code", as.character(AMR_env$AV_lookup$av))
}
# S3: mo ----
vec_ptype2.mo.default <- function (x, y, ..., x_arg = "", y_arg = "") {
x
}
vec_ptype2.mo.mo <- function(x, y, ...) {
x
}
vec_cast.character.mo <- function(x, to, ...) {
as.character(x)
}
vec_cast.mo.character <- function(x, to, ...) {
add_MO_lookup_to_AMR_env()
return_after_integrity_check(x, "microorganism code", as.character(AMR_env$MO_lookup$mo))
}
# S3: disk ----
vec_ptype_full.disk <- function(x, ...) {
"disk"
}
vec_ptype_abbr.disk <- function(x, ...) {
"dsk"
}
vec_ptype2.disk.default <- function (x, y, ..., x_arg = "", y_arg = "") {
NA_disk_[0]
}
vec_ptype2.disk.disk <- function(x, y, ...) {
NA_disk_[0]
}
vec_cast.disk.disk <- function(x, to, ...) {
as.disk(x)
}
vec_cast.integer.disk <- function(x, to, ...) {
unclass(x)
}
vec_cast.disk.integer <- function(x, to, ...) {
as.disk(x)
}
vec_cast.double.disk <- function(x, to, ...) {
unclass(x)
}
vec_cast.disk.double <- function(x, to, ...) {
as.disk(x)
}
vec_cast.character.disk <- function(x, to, ...) {
unclass(x)
}
vec_cast.disk.character <- function(x, to, ...) {
as.disk(x)
}
# S3: mic ----
vec_ptype2.mic.default <- function (x, y, ..., x_arg = "", y_arg = "") {
# this will make sure that currently implemented MIC levels are returned
NA_mic_[0]
}
vec_ptype2.mic.mic <- function(x, y, ...) {
# this will make sure that currently implemented MIC levels are returned
NA_mic_[0]
}
vec_cast.mic.mic <- function(x, to, ...) {
# this will make sure that currently implemented MIC levels are returned
as.mic(x)
}
vec_cast.character.mic <- function(x, to, ...) {
as.character(x)
}
vec_cast.double.mic <- function(x, to, ...) {
as.double(x)
}
vec_cast.integer.mic <- function(x, to, ...) {
as.integer(x)
}
vec_cast.factor.mic <- function(x, to, ...) {
factor(as.character(x))
}
vec_cast.mic.double <- function(x, to, ...) {
as.mic(x)
}
vec_cast.mic.character <- function(x, to, ...) {
as.mic(x)
}
vec_cast.mic.integer <- function(x, to, ...) {
as.mic(x)
}
vec_cast.mic.factor <- function(x, to, ...) {
as.mic(x)
}
vec_math.mic <- function(.fn, x, ...) {
.fn(as.double(x), ...)
}
vec_arith.mic <- function(op, x, y, ...) {
vctrs::vec_arith(op, as.double(x), as.double(y))
}
# S3: sir ----
vec_ptype2.sir.default <- function (x, y, ..., x_arg = "", y_arg = "") {
NA_sir_[0]
}
vec_ptype2.sir.sir <- function(x, y, ...) {
NA_sir_[0]
}
vec_ptype2.character.sir <- function(x, y, ...) {
NA_sir_[0]
}
vec_cast.sir.sir <- function(x, to, ...) {
# this makes sure that old SIR objects (with S/I/R) are converted to the current structure (S/SDD/I/R/NI)
as.sir(x)
}
vec_cast.character.sir <- function(x, to, ...) {
as.character(x)
}
vec_cast.sir.character <- function(x, to, ...) {
as.sir(x)
}