| as.data.frame {opm} | R Documentation |
These as.data.frame methods create a data frame
from aggregated and discretised values in a manner
distinct from extract. flatten
converts into a ‘flat’ data frame, including all
measurements in a single column (suitable, e.g., for
lattice).
## S4 method for signature 'MOPMX'
as.data.frame(x, row.names = NULL,
optional = FALSE, sep = "_", csv.data = TRUE, settings = TRUE,
include = FALSE, ..., stringsAsFactors = default.stringsAsFactors())
## S4 method for signature 'OPM'
as.data.frame(x, row.names = NULL,
optional = FALSE, sep = "_", csv.data = TRUE, settings = TRUE,
include = FALSE, ..., stringsAsFactors = default.stringsAsFactors())
## S4 method for signature 'OPMA'
as.data.frame(x, row.names = NULL,
optional = FALSE, sep = "_", csv.data = TRUE, settings = TRUE,
include = FALSE, ..., stringsAsFactors = default.stringsAsFactors())
## S4 method for signature 'OPMD'
as.data.frame(x, row.names = NULL,
optional = FALSE, sep = "_", csv.data = TRUE, settings = TRUE,
include = FALSE, ..., stringsAsFactors = default.stringsAsFactors())
## S4 method for signature 'OPMS'
as.data.frame(x, row.names = NULL,
optional = FALSE, sep = "_", csv.data = TRUE, settings = TRUE,
include = FALSE, ..., stringsAsFactors = default.stringsAsFactors())
## S4 method for signature 'kegg_compound'
as.data.frame(x, row.names = NULL,
optional = TRUE, ..., stringsAsFactors = FALSE)
## S4 method for signature 'kegg_compounds'
as.data.frame(x, row.names = NULL,
optional = TRUE, ..., stringsAsFactors = FALSE)
## S4 method for signature 'MOPMX'
flatten(object, include = NULL, fixed = list(),
factors = FALSE, ...)
## S4 method for signature 'OPM'
flatten(object, include = NULL, fixed = list(),
factors = TRUE, exact = TRUE, strict = TRUE, full = TRUE,
numbers = FALSE, ...)
## S4 method for signature 'OPMS'
flatten(object, include = NULL, fixed = list(), ...)
x |
Object of class There are |
row.names |
Optional vector for use as row names of the resulting data frame. Here, it is not recommended to try to set row names explicitly. |
optional |
Logical scalar passed to the list and
matrix methods of |
sep |
Character scalar used as word separator in
column names. Set this to |
csv.data |
Logical scalar indicating whether the
|
settings |
Logical scalar indicating whether the
|
stringsAsFactors |
Logical scalar passed to the list
and matrix methods of |
object |
|
include |
For For |
fixed |
|
factors |
Logical scalar. See the
|
exact |
Logical scalar. Passed to
|
strict |
Logical scalar. Passed to
|
full |
Logical scalar. Replace well coordinates by full names? |
numbers |
Logical scalar. Use numbers instead of well names? This is not recommended for must usages. |
... |
Optional other arguments passed to
|
The as.data.frame methods for OPMX
objects are mainly intended to produce objects that can
easily be written to CSV files, for instance
using write.table from the utils package.
There are no opm methods other than
batch_opm (which can write such files) that
make use of the created kind of objects. In particular,
they cannot be input again into opm.
The following entries are contained in the generated data frame:
Optionally the csv_data entries that
identify the plate.
The names of the wells. Always included.
For OPMA objects (and
OPMS objects that contain them as well as
MOPMX objects that contain such
OPMA or OPMS objects), always
the aggregated data (curve parameters), one column for
each point estimate, upper and lower confidence interval
of each parameter.
For OPMA objects (and
OPMS objects that contain them as well as
MOPMX objects that contain such
OPMA or OPMS objects),
optionally the used aggregation settings, one column per
entry, except for the ‘options’ entry (which is
not a scalar). The column names are prefixed with
"Aggr" followed by sep. If sep is
empty, opm_opt("comb.key.join") is used.
For OPMD objects (and
OPMS objects that contain them as well as
MOPMX objects that contain such
OPMD or OPMS objects), always
one column with the discretised data.
For OPMD objects (and
OPMS objects that contain them as well as
MOPMX objects that contain such
OPMD or OPMS objects),
optionally the used discretisation settings, one column
per entry, except for the ‘options’ entry (which
is not a scalar). The column names are prefixed with
"Disc" followed by sep. If sep is
empty, opm_opt("comb.key.join") is used.
The limits of using CSV as output format already show up in this list, and in general we recommend to generate YAML or JSON output instead.
For the as.data.frame methods of the other
classes, see substrate_info.
In the data frame returned by flatten, column
names are unchecked (not converted to variable names).
The three last columns are coding for time, well and
value, with the exact spelling of the column names given
by param_names.
The OPMS method yields an additional column
for the plate, the exact spelling of its name also being
available via param_names. This column
contains the position of each plate within object.
The MOPMX method yields a another
additional column for the plate type. There is currently
no safeguard against having several OPMX
objects of the same plate type within a
MOPMX object.
The as.data.frame methods create a data frame with
one row for each combination of well and plate.
The flatten methods create a data frame with one
row for each combination of time point, well and plate.
utils::write.table stats::reshape pkgutils::flatten
Other conversion-functions: extract,
extract_columns, merge,
oapply, opmx,
plates, rep,
rev, sort,
split, to_yaml,
unique
## OPMD method of as.data.frame()
summary(x <- as.data.frame(vaas_1))
## File
## ./E. coli DSM 30083T_vim10_7B__1_28_PMX_0_8#30#2010_F_ 7B_5.csv:96
##
##
##
##
##
##
## Plate_Type Position Setup_Time Well
## Gen III:96 7-B:96 8/30/2010 1:53:08 PM:96 A01 : 1
## A02 : 1
## A03 : 1
## A04 : 1
## A05 : 1
## A06 : 1
## (Other):90
## mu lambda A AUC
## Min. : 1.287 Min. :-18.834 Min. : 50.72 Min. : 3923
## 1st Qu.: 5.912 1st Qu.: 2.005 1st Qu.:186.12 1st Qu.:12229
## Median : 15.978 Median : 4.810 Median :277.15 Median :20969
## Mean : 20.263 Mean : 5.935 Mean :236.63 Mean :18203
## 3rd Qu.: 21.442 3rd Qu.: 7.547 3rd Qu.:293.50 3rd Qu.:23268
## Max. :153.069 Max. : 58.959 Max. :356.72 Max. :32335
##
## mu_CI95_low lambda_CI95_low A_CI95_low AUC_CI95_low
## Min. :-43.14 Min. :-40.1574 Min. : 51.42 Min. : 3886
## 1st Qu.: 2.94 1st Qu.: -8.7412 1st Qu.:185.53 1st Qu.:12113
## Median : 8.88 Median : -2.6709 Median :276.99 Median :20828
## Mean : 10.92 Mean : -6.7823 Mean :236.41 Mean :18061
## 3rd Qu.: 16.27 3rd Qu.: -0.4249 3rd Qu.:293.14 3rd Qu.:23091
## Max. : 93.21 Max. : 23.7262 Max. :355.86 Max. :32125
##
## mu_CI95_high lambda_CI95_high A_CI95_high AUC_CI95_high
## Min. : 9.637 Min. : 2.674 Min. : 54.37 Min. : 3941
## 1st Qu.: 14.818 1st Qu.: 8.675 1st Qu.:188.50 1st Qu.:12290
## Median : 22.493 Median : 15.366 Median :278.62 Median :21028
## Mean : 36.812 Mean : 32.963 Mean :238.40 Mean :18273
## 3rd Qu.: 47.074 3rd Qu.: 51.628 3rd Qu.:294.75 3rd Qu.:23347
## Max. :245.128 Max. :119.078 Max. :357.18 Max. :32436
##
## Aggr_software Aggr_version Aggr_method Discretized Disc_software
## opm:96 0.1-0:96 grofit:96 Mode :logical opm:96
## FALSE:16
## TRUE :62
## NA's :18
##
##
##
## Disc_version Disc_method
## 0.7-0:96 kmeans:96
##
##
##
##
##
##
stopifnot(is.data.frame(x), nrow(x) == 96)
## OPMS method of as.data.frame()
summary(x <- as.data.frame(vaas_4[, , 1:10]))
## File
## ./E. coli DSM 18039_vim10_12B__1_28_PMX_0_8#30#2010_E_12B_5.csv :10
## ./E. coli DSM 30083T_vim10_7B__1_28_PMX_0_8#30#2010_F_ 7B_5.csv :10
## ./P. aeruginosa DSM 1707_vim10_17B__1_28_PMX_0_8#30#2010_D_17B_5.csv:10
## ./P. aeruginosa St. 429_vim10_22B__1_28_PMX_0_8#30#2010_C_22B_5.csv :10
##
##
##
## Plate_Type Position Setup_Time Well
## Gen III:40 12-B:10 8/30/2010 1:19:11 PM :10 A01 : 4
## 7-B:10 8/30/2010 1:53:08 PM :10 A02 : 4
## 17-B:10 8/30/2010 12:31:46 PM:10 A03 : 4
## 22-B:10 8/30/2010 11:28:54 AM:10 A04 : 4
## A05 : 4
## A06 : 4
## (Other):16
## mu lambda A AUC
## Min. : 0.1307 Min. :-107.9099 Min. : 22.37 Min. : 1888
## 1st Qu.: 1.3441 1st Qu.: -12.2332 1st Qu.: 32.88 1st Qu.: 2351
## Median : 3.6118 Median : -0.9945 Median : 55.80 Median : 4721
## Mean : 7.3197 Mean : -11.0518 Mean :110.73 Mean : 8396
## 3rd Qu.: 7.3213 3rd Qu.: 1.9040 3rd Qu.:164.78 3rd Qu.:11873
## Max. :52.2623 Max. : 27.0147 Max. :331.92 Max. :28651
##
## mu_CI95_low lambda_CI95_low A_CI95_low AUC_CI95_low
## Min. :-57.6867 Min. :-35.770 Min. : 22.78 Min. : 1852
## 1st Qu.: -1.2904 1st Qu.:-22.161 1st Qu.: 29.67 1st Qu.: 2314
## Median : 0.7860 Median : -8.521 Median : 55.31 Median : 4678
## Mean : -0.9155 Mean :-10.686 Mean :110.40 Mean : 8324
## 3rd Qu.: 4.9110 3rd Qu.: -1.257 3rd Qu.:165.34 3rd Qu.:11763
## Max. : 40.1572 Max. : 18.215 Max. :331.78 Max. :28387
##
## mu_CI95_high lambda_CI95_high A_CI95_high AUC_CI95_high
## Min. : 9.628 Min. : 2.674 Min. : 25.86 Min. : 1910
## 1st Qu.: 11.560 1st Qu.: 35.142 1st Qu.: 39.40 1st Qu.: 2378
## Median : 15.892 Median : 75.167 Median : 59.26 Median : 4743
## Mean : 29.953 Mean : 65.580 Mean :114.14 Mean : 8432
## 3rd Qu.: 34.949 3rd Qu.: 90.713 3rd Qu.:167.48 3rd Qu.:11928
## Max. :127.385 Max. :132.751 Max. :332.76 Max. :28793
##
## Aggr_software Aggr_version Aggr_method Discretized Disc_software
## opm:40 0.1-0:40 grofit:40 Mode :logical opm:40
## FALSE:26
## TRUE :8
## NA's :6
##
##
##
## Disc_version Disc_method
## 0.7-0:40 kmeans:40
##
##
##
##
##
##
stopifnot(is.data.frame(x), nrow(x) == 10 * 4)
## OPM method of flatten()
# distinct numbers of columns due to distinct selection settings
head(x <- flatten(vaas_1))
## Time Well Value
## 1 0.00 A01 (Negative Control) 40
## 2 0.25 A01 (Negative Control) 40
## 3 0.50 A01 (Negative Control) 32
## 4 0.75 A01 (Negative Control) 34
## 5 1.00 A01 (Negative Control) 35
## 6 1.25 A01 (Negative Control) 35
stopifnot(is.data.frame(x), identical(dim(x), c(36864L, 3L)))
head(x <- flatten(vaas_1, fixed = "TEST", include = "Strain"))
## metadata(object, include, exact = exact, strict = strict) "TEST" Time
## 1 DSM30083T TEST 0.00
## 2 DSM30083T TEST 0.25
## 3 DSM30083T TEST 0.50
## 4 DSM30083T TEST 0.75
## 5 DSM30083T TEST 1.00
## 6 DSM30083T TEST 1.25
## Well Value
## 1 A01 (Negative Control) 40
## 2 A01 (Negative Control) 40
## 3 A01 (Negative Control) 32
## 4 A01 (Negative Control) 34
## 5 A01 (Negative Control) 35
## 6 A01 (Negative Control) 35
stopifnot(is.data.frame(x), identical(dim(x), c(36864L, 5L)))
## OPMS method of flatten()
# distinct numbers of columns due to distinct selection settings
head(x <- flatten(vaas_4[, , 1:10]))
## Plate Time Well Value
## 1 Plate 1 0.00 A01 (Negative Control) 35
## 2 Plate 1 0.25 A01 (Negative Control) 32
## 3 Plate 1 0.50 A01 (Negative Control) 30
## 4 Plate 1 0.75 A01 (Negative Control) 35
## 5 Plate 1 1.00 A01 (Negative Control) 33
## 6 Plate 1 1.25 A01 (Negative Control) 32
stopifnot(is.data.frame(x), identical(dim(x), c(15360L, 4L)))
head(x <- flatten(vaas_4[, , 1:10], fixed = "TEST", include = ~ Strain))
## metadata(object, include, exact = exact, strict = strict) Plate "TEST"
## 1 DSM18039 Plate 1 TEST
## 2 DSM18039 Plate 1 TEST
## 3 DSM18039 Plate 1 TEST
## 4 DSM18039 Plate 1 TEST
## 5 DSM18039 Plate 1 TEST
## 6 DSM18039 Plate 1 TEST
## Time Well Value
## 1 0.00 A01 (Negative Control) 35
## 2 0.25 A01 (Negative Control) 32
## 3 0.50 A01 (Negative Control) 30
## 4 0.75 A01 (Negative Control) 35
## 5 1.00 A01 (Negative Control) 33
## 6 1.25 A01 (Negative Control) 32
stopifnot(is.data.frame(x), identical(dim(x), c(15360L, 6L)))