R/neuronlist.R
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
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 |
value | The new data.frame to be attached to |
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.
data.frame
, neuronlist
#> 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