For as.data.frame, when there is no attached data.frame the result will be a data.frame with 0 columns but an appropriate number of rows, named by the objects in the neuronlist.

data.frame<- methods set the data frame attached to an object. At present this is only used for neuronlist objects.

# S3 method for neuronlist
as.data.frame(x, row.names = names(x),
  optional = FALSE, ...)

data.frame(x) <- value

# S3 method for neuronlist
data.frame(x) <- value

Arguments

x

neuronlist to convert

row.names

row names (defaults to names of objects in neuronlist, which is nearly always what you want.)

optional

ignored in this method

...

additional arguments passed to data.frame (see examples)

value

The new data.frame to be attached to x

Value

for as.data.frame.neuronlist, a data.frame with length(x) rows, named according to names(x) and containing the columns from the attached data.frame, when present.

for data.frame<-.neuronlist, a neuronlist with the attached data.frame.

See also

Examples

#> 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 #> 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 #> 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
# add additional variables str(as.data.frame(kcs20, i=seq(kcs20), abc=LETTERS[seq(kcs20)]))
#> 'data.frame': 20 obs. of 16 variables: #> $ gene_name: chr "FruMARCM-M001205_seg002" "GadMARCM-F000122_seg001" "GadMARCM-F000050_seg001" "GadMARCM-F000142_seg002" ... #> $ Name : chr "fru-M-500112" "Gad1-F-900005" "Gad1-F-100010" "Gad1-F-300043" ... #> $ idid : num 1024 10616 8399 10647 9758 ... #> $ soma_side: Factor w/ 3 levels "L","M","R": 1 1 3 1 1 3 3 3 3 3 ... #> $ flipped : logi FALSE FALSE TRUE FALSE FALSE TRUE ... #> $ Driver : chr "fru-Gal4" "Gad1-Gal4" "Gad1-Gal4" "Gad1-Gal4" ... #> $ Gender : chr "M" "F" "F" "F" ... #> $ X : num 361 368 383 350 388 ... #> $ Y : num 95 105.9 61.7 78.2 114.8 ... #> $ Z : num 84.1 94.7 97.3 96.7 87.8 ... #> $ exemplar : Factor w/ 96 levels "5HT1bMARCM-M000076_seg001",..: 60 78 76 79 41 45 59 82 6 63 ... #> $ cluster : int 9 70 57 71 64 44 16 61 52 12 ... #> $ idx : int 156 1519 1132 1535 1331 795 268 1265 898 190 ... #> $ type : Factor w/ 3 levels "ab","apbp","gamma": 3 3 1 2 1 1 1 2 2 1 ... #> $ i : int 1 2 3 4 5 6 7 8 9 10 ... #> $ abc : Factor w/ 20 levels "A","B","C","D",..: 1 2 3 4 5 6 7 8 9 10 ...
# stop character columns being turned into factors newdf <- as.data.frame(kcs20, i=seq(kcs20), abc=LETTERS[seq(kcs20)], stringsAsFactors=FALSE) str(newdf)
#> 'data.frame': 20 obs. of 16 variables: #> $ gene_name: chr "FruMARCM-M001205_seg002" "GadMARCM-F000122_seg001" "GadMARCM-F000050_seg001" "GadMARCM-F000142_seg002" ... #> $ Name : chr "fru-M-500112" "Gad1-F-900005" "Gad1-F-100010" "Gad1-F-300043" ... #> $ idid : num 1024 10616 8399 10647 9758 ... #> $ soma_side: Factor w/ 3 levels "L","M","R": 1 1 3 1 1 3 3 3 3 3 ... #> $ flipped : logi FALSE FALSE TRUE FALSE FALSE TRUE ... #> $ Driver : chr "fru-Gal4" "Gad1-Gal4" "Gad1-Gal4" "Gad1-Gal4" ... #> $ Gender : chr "M" "F" "F" "F" ... #> $ X : num 361 368 383 350 388 ... #> $ Y : num 95 105.9 61.7 78.2 114.8 ... #> $ Z : num 84.1 94.7 97.3 96.7 87.8 ... #> $ exemplar : Factor w/ 96 levels "5HT1bMARCM-M000076_seg001",..: 60 78 76 79 41 45 59 82 6 63 ... #> $ cluster : int 9 70 57 71 64 44 16 61 52 12 ... #> $ idx : int 156 1519 1132 1535 1331 795 268 1265 898 190 ... #> $ type : Factor w/ 3 levels "ab","apbp","gamma": 3 3 1 2 1 1 1 2 2 1 ... #> $ i : int 1 2 3 4 5 6 7 8 9 10 ... #> $ abc : chr "A" "B" "C" "D" ...
data.frame(kcs20)=newdf