R/neuronlist.R
neuronlist-dataframe-methods.Rd[.neuronlist and [<-.neuronlist behave like the
corresponding base methods ([.data.frame, [<-.data.frame)
allowing extraction or replacement of parts of the data.frame attached to
the neuronlist.
droplevels Remove redundant factor levels in dataframe
attached to neuronlist
with Evaluate expression in the context of dataframe
attached to a neuronlist
head Return the first part of data.frame attached to
neuronlist
tail Return the last part of data.frame attached to
neuronlist
# S3 method for neuronlist
[(x, i, j, drop)
# S3 method for neuronlist
[(x, i, j) <- value
# S3 method for neuronlist
droplevels(x, except = NULL, ...)
# S3 method for neuronlist
with(data, expr, ...)
# S3 method for neuronlist
head(x, ...)
# S3 method for neuronlist
tail(x, ...)A neuronlist object
elements to extract or replace. Numeric or character or, for [
only, empty. Numeric values are coerced to integer as if by
as.integer. See [.data.frame for details.
logical. If TRUE the result is coerced to the
lowest possible dimension. The default is to drop if only one
column is left, but not to drop if only one row is left.
A suitable replacement value: it will be repeated a whole
number of times if necessary and it may be coerced: see the
Coercion section. If NULL, deletes the column if a single
column is selected.
indices of columns from which not to drop levels
Further arguments passed to default methods (and usually ignored)
A neuronlist object
The expression to evaluate
the attached dataframe with levels dropped (NB not the neuronlist)
[.data.frame, @seealso [<-.data.frame
droplevels
with
head
tail
Other neuronlist:
*.neuronlist(),
is.neuronlist(),
neuronlistfh(),
neuronlistz(),
neuronlist(),
nlapply(),
read.neurons(),
write.neurons()
## treat kcs20 as data.frame
kcs20[1, ]
#> gene_name Name idid soma_side
#> FruMARCM-M001205_seg002 FruMARCM-M001205_seg002 fru-M-500112 1024 L
#> flipped Driver Gender X Y Z
#> FruMARCM-M001205_seg002 FALSE fru-Gal4 M 361.4849 95.0448 84.10259
#> exemplar cluster idx type
#> FruMARCM-M001205_seg002 FruMARCM-M001205_seg002 9 156 gamma
kcs20[1:3, ]
#> gene_name Name idid soma_side
#> FruMARCM-M001205_seg002 FruMARCM-M001205_seg002 fru-M-500112 1024 L
#> GadMARCM-F000122_seg001 GadMARCM-F000122_seg001 Gad1-F-900005 10616 L
#> GadMARCM-F000050_seg001 GadMARCM-F000050_seg001 Gad1-F-100010 8399 R
#> flipped Driver Gender X Y Z
#> FruMARCM-M001205_seg002 FALSE fru-Gal4 M 361.4849 95.04480 84.10259
#> GadMARCM-F000122_seg001 FALSE Gad1-Gal4 F 367.8332 105.86755 94.73446
#> GadMARCM-F000050_seg001 TRUE Gad1-Gal4 F 382.8279 61.73213 97.28057
#> exemplar cluster idx type
#> FruMARCM-M001205_seg002 FruMARCM-M001205_seg002 9 156 gamma
#> GadMARCM-F000122_seg001 GadMARCM-F000122_seg001 70 1519 gamma
#> GadMARCM-F000050_seg001 GadMARCM-F000050_seg001 57 1132 ab
kcs20[, 1:4]
#> gene_name Name idid soma_side
#> FruMARCM-M001205_seg002 FruMARCM-M001205_seg002 fru-M-500112 1024 L
#> GadMARCM-F000122_seg001 GadMARCM-F000122_seg001 Gad1-F-900005 10616 L
#> GadMARCM-F000050_seg001 GadMARCM-F000050_seg001 Gad1-F-100010 8399 R
#> GadMARCM-F000142_seg002 GadMARCM-F000142_seg002 Gad1-F-300043 10647 L
#> FruMARCM-F000270_seg001 FruMARCM-F000270_seg001 fru-F-400045 9758 L
#> FruMARCM-F001115_seg002 FruMARCM-F001115_seg002 fru-F-300059 6182 R
#> FruMARCM-M001051_seg002 FruMARCM-M001051_seg002 fru-M-100078 1500 R
#> GadMARCM-F000423_seg001 GadMARCM-F000423_seg001 Gad1-F-300107 9541 R
#> ChaMARCM-F000586_seg002 ChaMARCM-F000586_seg002 Cha-F-300150 7113 R
#> FruMARCM-M001339_seg001 FruMARCM-M001339_seg001 fru-M-300145 1145 R
#> GadMARCM-F000476_seg001 GadMARCM-F000476_seg001 Gad1-F-400089 9612 R
#> FruMARCM-F000085_seg001 FruMARCM-F000085_seg001 fru-F-400017 11472 R
#> FruMARCM-F000706_seg001 FruMARCM-F000706_seg001 fru-F-000031 7810 R
#> FruMARCM-M000842_seg002 FruMARCM-M000842_seg002 fru-M-400058 1689 L
#> FruMARCM-F001494_seg002 FruMARCM-F001494_seg002 fru-F-200098 6341 R
#> FruMARCM-F000188_seg001 FruMARCM-F000188_seg001 fru-F-200021 6405 R
#> GadMARCM-F000071_seg001 GadMARCM-F000071_seg001 Gad1-F-300023 10541 R
#> FruMARCM-M000115_seg001 FruMARCM-M000115_seg001 fru-M-100014 2389 L
#> GadMARCM-F000442_seg002 GadMARCM-F000442_seg002 Gad1-F-700033 9569 R
#> FruMARCM-F001929_seg001 FruMARCM-F001929_seg001 fru-F-400181 4694 L
kcs20[, 'soma_side']
#> [1] L L R L L R R R R R R R R L R R R L R L
#> Levels: L M R
# alternative to as.data.frame(kcs20)
kcs20[, ]
#> gene_name Name idid soma_side
#> FruMARCM-M001205_seg002 FruMARCM-M001205_seg002 fru-M-500112 1024 L
#> GadMARCM-F000122_seg001 GadMARCM-F000122_seg001 Gad1-F-900005 10616 L
#> GadMARCM-F000050_seg001 GadMARCM-F000050_seg001 Gad1-F-100010 8399 R
#> GadMARCM-F000142_seg002 GadMARCM-F000142_seg002 Gad1-F-300043 10647 L
#> FruMARCM-F000270_seg001 FruMARCM-F000270_seg001 fru-F-400045 9758 L
#> FruMARCM-F001115_seg002 FruMARCM-F001115_seg002 fru-F-300059 6182 R
#> FruMARCM-M001051_seg002 FruMARCM-M001051_seg002 fru-M-100078 1500 R
#> GadMARCM-F000423_seg001 GadMARCM-F000423_seg001 Gad1-F-300107 9541 R
#> ChaMARCM-F000586_seg002 ChaMARCM-F000586_seg002 Cha-F-300150 7113 R
#> FruMARCM-M001339_seg001 FruMARCM-M001339_seg001 fru-M-300145 1145 R
#> GadMARCM-F000476_seg001 GadMARCM-F000476_seg001 Gad1-F-400089 9612 R
#> FruMARCM-F000085_seg001 FruMARCM-F000085_seg001 fru-F-400017 11472 R
#> FruMARCM-F000706_seg001 FruMARCM-F000706_seg001 fru-F-000031 7810 R
#> FruMARCM-M000842_seg002 FruMARCM-M000842_seg002 fru-M-400058 1689 L
#> FruMARCM-F001494_seg002 FruMARCM-F001494_seg002 fru-F-200098 6341 R
#> FruMARCM-F000188_seg001 FruMARCM-F000188_seg001 fru-F-200021 6405 R
#> GadMARCM-F000071_seg001 GadMARCM-F000071_seg001 Gad1-F-300023 10541 R
#> FruMARCM-M000115_seg001 FruMARCM-M000115_seg001 fru-M-100014 2389 L
#> GadMARCM-F000442_seg002 GadMARCM-F000442_seg002 Gad1-F-700033 9569 R
#> FruMARCM-F001929_seg001 FruMARCM-F001929_seg001 fru-F-400181 4694 L
#> flipped Driver Gender X Y Z
#> FruMARCM-M001205_seg002 FALSE fru-Gal4 M 361.4849 95.04480 84.10259
#> GadMARCM-F000122_seg001 FALSE Gad1-Gal4 F 367.8332 105.86755 94.73446
#> GadMARCM-F000050_seg001 TRUE Gad1-Gal4 F 382.8279 61.73213 97.28057
#> GadMARCM-F000142_seg002 FALSE Gad1-Gal4 F 349.5917 78.18986 96.69280
#> FruMARCM-F000270_seg001 FALSE fru-Gal4 F 387.5236 114.80344 87.84156
#> FruMARCM-F001115_seg002 TRUE fru-Gal4 F 352.0216 121.72034 100.52308
#> FruMARCM-M001051_seg002 TRUE fru-Gal4 M 338.7782 118.68985 95.47755
#> GadMARCM-F000423_seg001 TRUE Gad1-Gal4 F 401.4795 76.32671 97.92564
#> ChaMARCM-F000586_seg002 TRUE Cha-Gal4 F 340.1020 79.79322 92.21622
#> FruMARCM-M001339_seg001 TRUE fru-Gal4 M 393.1358 102.42494 92.82986
#> GadMARCM-F000476_seg001 TRUE Gad1-Gal4 F 339.5274 60.45391 94.78038
#> FruMARCM-F000085_seg001 TRUE fru-Gal4 F 344.0347 78.32091 100.64333
#> FruMARCM-F000706_seg001 TRUE fru-Gal4 F 406.5796 80.13186 94.32588
#> FruMARCM-M000842_seg002 FALSE fru-Gal4 M 403.8388 62.06659 89.97595
#> FruMARCM-F001494_seg002 TRUE fru-Gal4 F 348.3770 115.99559 95.77683
#> FruMARCM-F000188_seg001 TRUE fru-Gal4 F 329.2778 78.68618 92.02949
#> GadMARCM-F000071_seg001 TRUE Gad1-Gal4 F 341.3460 77.38931 91.88780
#> FruMARCM-M000115_seg001 FALSE fru-Gal4 M 388.2444 65.12638 91.88102
#> GadMARCM-F000442_seg002 TRUE Gad1-Gal4 F 348.7375 78.76781 89.38335
#> FruMARCM-F001929_seg001 FALSE fru-Gal4 F 372.0023 115.12707 91.70584
#> exemplar cluster idx type
#> FruMARCM-M001205_seg002 FruMARCM-M001205_seg002 9 156 gamma
#> GadMARCM-F000122_seg001 GadMARCM-F000122_seg001 70 1519 gamma
#> GadMARCM-F000050_seg001 GadMARCM-F000050_seg001 57 1132 ab
#> GadMARCM-F000142_seg002 GadMARCM-F000142_seg002 71 1535 apbp
#> FruMARCM-F000270_seg001 FruMARCM-F000270_seg001 64 1331 ab
#> FruMARCM-F001115_seg002 FruMARCM-F001115_seg002 44 795 ab
#> FruMARCM-M001051_seg002 FruMARCM-M001051_seg002 16 268 ab
#> GadMARCM-F000423_seg001 GadMARCM-F000423_seg001 61 1265 apbp
#> ChaMARCM-F000586_seg002 ChaMARCM-F000586_seg002 52 898 apbp
#> FruMARCM-M001339_seg001 FruMARCM-M001339_seg001 12 190 ab
#> GadMARCM-F000476_seg001 GadMARCM-F000476_seg001 63 1295 gamma
#> FruMARCM-F000085_seg001 FruMARCM-F000085_seg001 76 1718 gamma
#> FruMARCM-F000706_seg001 FruMARCM-F000706_seg001 53 1007 ab
#> FruMARCM-M000842_seg002 FruMARCM-M000842_seg002 18 318 ab
#> FruMARCM-F001494_seg002 FruMARCM-F001494_seg002 48 827 ab
#> FruMARCM-F000188_seg001 FruMARCM-F000188_seg001 49 842 ab
#> GadMARCM-F000071_seg001 GadMARCM-F000071_seg001 69 1484 gamma
#> FruMARCM-M000115_seg001 FruMARCM-M000115_seg001 22 406 gamma
#> GadMARCM-F000442_seg002 GadMARCM-F000442_seg002 62 1277 gamma
#> FruMARCM-F001929_seg001 FruMARCM-F001929_seg001 36 610 ab
## can also set columns
kcs13=kcs20[1:3]
kcs13[,'side']=as.character(kcs13[,'soma_side'])
head(kcs13)
#> gene_name Name idid soma_side
#> FruMARCM-M001205_seg002 FruMARCM-M001205_seg002 fru-M-500112 1024 L
#> GadMARCM-F000122_seg001 GadMARCM-F000122_seg001 Gad1-F-900005 10616 L
#> GadMARCM-F000050_seg001 GadMARCM-F000050_seg001 Gad1-F-100010 8399 R
#> flipped Driver Gender X Y Z
#> FruMARCM-M001205_seg002 FALSE fru-Gal4 M 361.4849 95.04480 84.10259
#> GadMARCM-F000122_seg001 FALSE Gad1-Gal4 F 367.8332 105.86755 94.73446
#> GadMARCM-F000050_seg001 TRUE Gad1-Gal4 F 382.8279 61.73213 97.28057
#> exemplar cluster idx type side
#> FruMARCM-M001205_seg002 FruMARCM-M001205_seg002 9 156 gamma L
#> GadMARCM-F000122_seg001 GadMARCM-F000122_seg001 70 1519 gamma L
#> GadMARCM-F000050_seg001 GadMARCM-F000050_seg001 57 1132 ab R
# or parts of columns
kcs13[1,'soma_side']='R'
kcs13['FruMARCM-M001205_seg002','soma_side']='L'
# remove a column
kcs13[,'side']=NULL
all.equal(kcs13, kcs20[1:3])
#> [1] TRUE
# can even replace the whole data.frame like this
kcs13[,]=kcs13[,]
all.equal(kcs13, kcs20[1:3])
#> [1] TRUE
## get row/column names of attached data.frame
# (unfortunately implementing ncol/nrow is challenging)
rownames(kcs20)
#> [1] "FruMARCM-M001205_seg002" "GadMARCM-F000122_seg001"
#> [3] "GadMARCM-F000050_seg001" "GadMARCM-F000142_seg002"
#> [5] "FruMARCM-F000270_seg001" "FruMARCM-F001115_seg002"
#> [7] "FruMARCM-M001051_seg002" "GadMARCM-F000423_seg001"
#> [9] "ChaMARCM-F000586_seg002" "FruMARCM-M001339_seg001"
#> [11] "GadMARCM-F000476_seg001" "FruMARCM-F000085_seg001"
#> [13] "FruMARCM-F000706_seg001" "FruMARCM-M000842_seg002"
#> [15] "FruMARCM-F001494_seg002" "FruMARCM-F000188_seg001"
#> [17] "GadMARCM-F000071_seg001" "FruMARCM-M000115_seg001"
#> [19] "GadMARCM-F000442_seg002" "FruMARCM-F001929_seg001"
colnames(kcs20)
#> [1] "gene_name" "Name" "idid" "soma_side" "flipped" "Driver"
#> [7] "Gender" "X" "Y" "Z" "exemplar" "cluster"
#> [13] "idx" "type"