Alice Hobbs
Reputation: 1215
dplyr package in R: subset() function to subset the dataframe by month; Error -<0 rows> (or 0-length row.names)
Problem Statement
I have a data frame called 'New_Blue' (see below) and I want to subset the data frame by month using the 'subset()' function in the 'dplyr' package.
I understand this question has been asked zillion times but after consulting Stack Overflow and other forums, I still cannot resolve my problem being an inexperienced R user.
Throughout the analysis, I successfully subsetted the data frame for all months except 'September' and 'October'.
When I run the code, the results for 'September' and 'October' display this error below:
<0 rows> (or 0-length row.names)
<0 rows> (or 0-length row.names). As checked this happens either if there is some space at the end of the variable name, or there are no values. However, no columns have missing values and the data frame has 536 observations.
I have rechecked the data frame for inconsistent errors but I cannot find any, I filled all empty cells with zero's, and I wondered if the problem is a row logicals vs row numbers vs row names issue.
After extensive research for many hours, I still cannot solve this problem!
Could anyone please help?
Many thanks in advance, I would be deeply appreciative!
R Code:
##Set the seed
set.seed(45L)
##Change the format of the columns
New_Blue$Year<-as.numeric(New_Blue$Year)
New_Blue$Month<-as.factor(New_Blue$Month)
New_Blue$Latitude<-as.numeric(New_Blue$Latitude)
New_Blue$Longitude<-as.numeric(New_Blue$Longitude)
New_Blue$Best<-as.numeric(New_Blue$Best)
##Structure of the data
str(New_Blue)
##Results
New_Blue$Year<-as.numeric(New_Blue$Year)
New_Blue$Month<-as.factor(New_Blue$Month)
New_Blue$Latitude<-as.numeric(New_Blue$Latitude)
New_Blue$Longitude<-as.numeric(New_Blue$Longitude)
New_Blue$Best<-as.numeric(New_Blue$Best)
Attempts to solve the problem
##Is the problem the row logicals vs row numbers vs row names?
New_Blue_Data <- New_Blue[1:536, ]
New_Blue_Data[rep(TRUE, 536), ]
#Convert NA's to zero's
New_Blue[is.na(New_Blue)] <- 0
Subsetting the Data by Month:
package(dplyr)
##Subset the data frame by the column 'Month'
January_Whales<-subset(Blue, Month == "January")
February_Whales<-subset(Blue, Month == "February")
March_Whales<-subset(Blue, Month == "March")
April_Whales<-subset(Blue, Month == "April")
May_Whales<-subset(Blue, Month == "May")
August_Whales<-subset(Blue, Month == "August")
September_Whales<-subset(Blue, Month == "September")
October_Whales<-subset(Blue, Month == "October")
November_Whales<-subset(Blue, Month == "November")
December_Whales<-subset(Blue, Month == "December")
##Error message
September_Whales
[1] Year Month Latitude Longitude Best
<0 rows> (or 0-length row.names)
October_Whales
[1] Year Month Latitude Longitude Best
<0 rows> (or 0-length row.names)
Data Frame:
structure(list(Year = c(2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L), Month = structure(c(6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 9L, 9L, 9L, 9L, 9L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L), .Label = c("April", "August", "August ", "December",
"February", "January", "July ", "March", "May", "November", "November ",
"October ", "September "), class = "factor"), Latitude = c(5.832,
5.836, 5.83, 5.823, 5.823, 5.83, 5.803, 5.803, 5.886, 5.858,
5.931, 5.931, 5.865, 5.761, 5.859, 5.911, 5.919, 5.82, 5.841,
5.799, 5.776, 5.83, 5.797, 5.787, 5.795, 5.795, 5.807, 5.808,
5.807, 5.811, 5.762, 5.74, 5.773, 5.846, 5.78, 5.816, 5.831,
5.813, 5.785, 5.81, 5.819, 5.795, 5.801, 5.77, 5.853, 5.834,
5.814, 5.799, 5.826, 5.826, 5.829, 5.834, 5.878, 5.862, 5.925,
5.88, 5.867, 5.833, 5.8, 5.803, 5.809, 5.817, 5.847, 5.878, 5.879,
5.842, 5.83, 5.834, 5.873, 5.786, 5.833, 5.821, 5.821, 5.836,
5.836, 5.836, 5.828, 5.838, 5.828, 5.82, 5.8, 5.86, 5.841, 5.864,
5.862, 5.901, 5.841, 5.844, 5.844, 5.843, 5.836, 5.838, 5.757,
5.836, 5.869, 5.667, 5.818, 5.802, 5.8, 5.809, 5.725, 5.715,
5.722, 5.674, 5.706, 5.679, 5.814, 5.85, 5.839, 5.839, 5.823,
5.836, 5.832, 5.88, 5.781, 5.83, 5.875, 5.875, 5.782, 5.825,
5.856, 5.849, 5.797, 5.767, 5.78, 5.812, 5.806, 5.827, 5.847,
5.819, 5.804, 5.795, 5.867, 5.859, 5.811, 5.804, 5.865, 5.781,
5.816, 5.82, 5.83, 5.815, 5.822, 5.808, 5.789, 5.822, 5.789,
5.822, 5.87, 5.828, 5.824, 5.852, 5.864, 5.86, 5.845, 5.866,
5.758, 5.805, 5.842, 5.838, 5.838, 5.783, 5.853, 5.868, 5.883,
5.88, 5.803, 5.787, 5.811, 5.748, 5.826, 5.818, 5.783, 5.794,
5.779, 5.818, 5.754, 5.811, 5.754, 5.846, 5.846, 5.834, 5.79,
5.794, 5.885, 5.823, 5.787, 5.869, 5.76, 5.841, 5.783, 5.887,
5.847, 5.82, 5.812, 5.789, 5.805, 5.797, 5.904, 5.806, 5.843,
5.829, 5.841, 5.829, 5.839, 5.821, 5.806, 5.825, 5.836, 5.829,
5.812, 5.825, 5.816, 5.821, 5.857, 5.799, 5.839, 5.841, 5.837,
5.806, 5.855, 5.857, 5.849, 5.817, 5.805, 5.822, 5.853, 5.852,
5.838, 5.873, 5.803, 5.817, 5.848, 5.826, 5.789, 5.796, 5.837,
5.827, 5.83, 5.827, 5.823, 5.816, 5.822, 5.87, 5.863, 5.847,
5.839, 5.774, 5.8, 5.834, 5.838, 5.826, 5.826, 5.835, 5.841,
5.858, 5.363, 5.857, 5.856, 5.853, 5.857, 5.863, 5.835, 5.834,
5.842, 5.846, 5.849, 5.838, 5.833, 5.828, 5.837, 5.838, 5.811,
5.833, 5.869, 5.91, 5.863, 5.833, 5.831, 5.805, 5.826, 5.85,
5.821, 5.81, 5.805, 5.824, 5.821, 5.846, 5.845, 5.806, 5.817,
5.805, 5.819, 5.814, 5.819, 5.815, 5.812, 5.804, 5.814, 5.826,
5.825, 5.807, 5.82, 5.812, 5.824, 5.798, 5.831, 5.81, 5.792,
5.816, 5.802, 5.767, 5.761, 5.727, 5.76, 5.86, 5.789, 5.894,
5.798, 5.848, 5.821, 5.829, 5.794, 0, 5.805, 5.734, 5.776, 5.749,
5.83, 5.775, 5.824, 5.833, 5.82, 5.863, 5.835, 5.775, 5.761,
5.753, 5.849, 5.839, 5.836, 5.801, 5.837, 5.84, 5.862, 5.843,
5.859, 5.849, 5.863, 5.842, 5.838, 5.862, 5.846, 5.873, 5.844,
5.84, 5.876, 5.844, 5.872, 5.84, 5.836, 5.84, 5.855, 5.829, 5.807,
5.827, 5.837, 5.831, 5.875, 5.849, 5.841, 5.837, 5.803, 5.895,
5.769, 5.825, 5.829, 5.827, 5.81, 5.865, 5.787, 5.807, 5.821,
5.823, 5.848, 5.847, 5.902, 5.868, 5.868, 5.825, 5.837, 5.817,
5.817, 5.813, 5.839, 5.839, 5.843, 5.825, 5.973, 5.856, 5.856,
5.829, 5.844, 5.859, 5.839, 5.821, 5.832, 5.833, 5.842, 5.842,
5.842, 5.842, 5.842, 5.842, 5.842, 5.842, 5.85, 5.807, 5.847,
5.845, 5.842, 5.804, 5.793, 5.842, 5.822, 5.842, 5.844, 5.83,
5.824, 5.822, 5.806, 5.789, 5.816, 5.804, 5.888, 5.831, 5.828,
5.812, 5.822, 5.835, 5.835, 5.859, 5.811, 5.764, 5.782, 5.797,
5.825, 5.824, 5.841, 5.871, 5.823, 5.841, 5.787, 5.812, 5.802,
5.807, 5.758, 5.792, 5.789, 5.791, 5.826, 5.774, 5.809, 5.821,
5.76, 5.747, 5.846, 5.827, 5.813, 5.815, 5.864, 5.823, 5.779,
5.77, 5.815, 5.79, 5.811, 5.823, 5.789, 5.828, 5.842, 5.787,
5.81, 5.794, 5.811, 5.737, 5.75, 5.737, 5.729, 5.775, 5.772,
5.832, 5.832, 5.847, 5.748, 5.754, 5.824, 5.795, 5.769, 5.77,
5.815, 5.738, 5.837, 5.822, 5.829, 5.812, 5.845, 5.82, 5.827,
5.802, 5.821, 5.859, 5.88, 5.853, 5.854, 5.84, 5.805, 5.85, 5.848,
5.844, 5.823, 5.877, 5.853, 5.78, 5.805, 5.844, 5.808, 5.84,
5.842, 5.822, 5.819, 5.787, 5.788, 5.797, 5.869, 5.822), Longitude = c(80.35,
80.326, 80.275, 80.281, 80.216, 80.112, 80.287, 80.17, 80.441,
80.453, 80.431, 80.431, 80.469, 80.452, 80.502, 80.453, 80.441,
80.28, 80.181, 80.177, 80.13, 80.533, 80.436, 80.442, 80.629,
80.64, 80.712, 80.668, 80.611, 80.438, 80.493, 80.53, 80.501,
80.519, 80.519, 80.619, 80.572, 80.581, 80.581, 80.507, 80.574,
80.526, 80.52, 80.509, 80.468, 80.42, 80.369, 80.344, 80.39,
80.447, 80.491, 80.403, 80.444, 80.47, 80.439, 80.422, 80.411,
80.483, 80.433, 80.419, 80.503, 80.53, 80.503, 80.489, 80.558,
80.621, 80.712, 80.746, 80.556, 80.675, 80.462, 80.496, 80.497,
80.533, 80.45, 80.508, 80.53, 80.497, 80.503, 80.52, 80.463,
80.491, 80.603, 80.424, 80.46, 80.445, 80.472, 80.503, 80.393,
80.403, 80.421, 80.471, 80.418, 80.391, 80.607, 80.603, 80.449,
80.435, 80.436, 80.428, 80.41, 80.374, 80.381, 80.554, 80.469,
80.567, 80.634, 80.483, 80.548, 80.548, 80.605, 80.617, 80.554,
80.474, 80.459, 80.486, 80.486, 80.486, 80.483, 80.378, 80.376,
80.441, 80.405, 80.393, 80.423, 80.373, 80.492, 80.727, 80.611,
80.39, 80.286, 80.451, 80.594, 80.435, 80.471, 80.448, 80.462,
80.522, 80.471, 80.541, 80.526, 80.471, 80.455, 80.419, 80.411,
80.448, 80.411, 80.448, 80.436, 80.383, 80.361, 80.42, 80.45,
80.477, 80.488, 80.476, 80.492, 80.54, 80.602, 80.601, 80.617,
80.572, 80.511, 80.475, 80.423, 80.366, 80.404, 80.432, 80.531,
80.539, 80.456, 80.616, 80.667, 80.68, 80.662, 80.431, 80.544,
80.381, 80.334, 80.559, 80.579, 80.59, 80.45, 80.654, 80.5, 80.671,
80.553, 80.397, 80.604, 80.689, 80.65, 80.62, 80.818, 80.676,
80.737, 80.609, 80.672, 80.71, 80.537, 80.611, 80.468, 80.434,
80.459, 80.451, 80.443, 80.351, 80.369, 80.583, 80.491, 80.697,
80.675, 80.768, 80.707, 80.671, 80.622, 80.705, 80.436, 80.424,
80.441, 80.437, 80.544, 80.555, 80.535, 80.564, 80.543, 80.424,
80.475, 80.434, 80.449, 80.443, 80.518, 80.498, 80.515, 80.43,
80.436, 80.378, 80.404, 80.373, 80.391, 80.354, 80.286, 80.246,
80.398, 80.26, 80.391, 80.364, 80.335, 80.492, 80.51, 80.442,
80.432, 80.46, 80.46, 80.455, 80.425, 80.422, 80.475, 80.449,
80.481, 80.442, 80.47, 80.458, 80.508, 80.507, 80.501, 80.528,
80.528, 80.52, 80.493, 80.489, 80.5, 80.5, 80.557, 80.454, 80.464,
80.448, 80.45, 80.477, 80.487, 80.509, 80.5, 80.463, 80.453,
80.436, 80.483, 80.458, 80.461, 80.444, 80.459, 80.246, 80.176,
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))
Upvotes: 0
Views: 314
Answers (1)
hello_friend
Reputation: 5788
Base R one liner (separate into 12 dataframes - bring into global environment):
list2env(split(df, df$Month), .GlobalEnv)
Base R one liner (store 12 dataframes in a list):
# Split the dataframe up into a list of dataframes by the month:
month_list <- split(df, df$Month)
# Name the dataframes appropriately:
names(month_list) <- paste0(trimws(unique(df$Month), "both"), "_Whales")
Upvotes: 1
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