I have collected data from sensors and need to split the data into 1 day data frames automatically.
This is how my data looks like:
2 2021-10-20 20:17:14 151 -135.9 8.304 -339.5 8.175 23.13232 78.95514 97.10153
3 2021-10-20 20:27:15 152 -136.9 8.302 -340.6 8.175 23.89337 86.71063 98.07861
4 2021-10-20 20:37:15 153 -138.2 8.302 -340.5 8.177 23.00682 80.71004 96.15726
5 2021-10-20 20:47:16 154 -138.8 8.302 -341.0 8.176 23.76786 83.38557 98.30032
I used tibbletime and dplyr to get a tbl_time object.
So far I was able to create a Subset for day1 and manually it would be easy to do it for day 2 and 3 etc. In the end I will have like a year of data, so this will be a pain in the a to do manually.
Here's the line of code I used:
day1<- filter_time(bio2_table, time_formula = '2021-10-20' ~ '2021-10-20')
I'm a r noob but I still want to believe there is a way that r does all the work itself.
Thanks!
CodePudding user response：
What data type is your date object?
The first step is to get the date format right, then you can group_split() to get a list of data frames, each of which represents a specific date.
If interested in this approach, you could look at the setNames() function to name each data frame.
data %>%
mutate(date = as.Date(format(date, "%y-%m-%d")))%>%
group_split(date)
CodePudding user response：
This returns a data frame with vectors as your grouped data:
t(aggregate( dat[,-2], by=list(dat$V2), list ))
[,1] [,2] [,3]
Group.1 "2021-10-20" "2021-10-21" "2021-10-22"
V1 Integer,4 6 7
V3 Character,4 "20:47:16" "20:47:16"
V4 Integer,4 155 156
V5 Numeric,4 -135.8 -131.8
V6 Numeric,4 8.306 10.302
V7 Numeric,4 -339 -347
V8 Numeric,4 8.166 8.187
V9 Numeric,4 25.76789 15.76786
V10 Numeric,4 91.38557 87.38557
V11 Numeric,4 102.3003 111.3003
# OR
aggregate( dat[,-2], by=list(dat$V2), list )
Group.1 V1 V3
1 2021-10-20 2, 3, 4, 5 20:17:14, 20:27:15, 20:37:15, 20:47:16
2 2021-10-21 6 20:47:16
3 2021-10-22 7 20:47:16
V4 V5 V6
1 151, 152, 153, 154 -135.9, -136.9, -138.2, -138.8 8.304, 8.302, 8.302, 8.302
2 155 -135.8 8.306
3 156 -131.8 10.302
V7 V8
1 -339.5, -340.6, -340.5, -341.0 8.175, 8.175, 8.177, 8.176
2 -339 8.166
3 -347 8.187
V9 V10
1 23.13232, 23.89337, 23.00682, 23.76786 78.95514, 86.71063, 80.71004, 83.38557
2 25.76789 91.38557
3 15.76786 87.38557
V11
1 97.10153, 98.07861, 96.15726, 98.30032
2 102.3003
3 111.3003
Data
dat <- structure(list(V1 = 2:7, V2 = c("2021-10-20", "2021-10-20", "2021-10-20",
"2021-10-20", "2021-10-21", "2021-10-22"), V3 = c("20:17:14",
"20:27:15", "20:37:15", "20:47:16", "20:47:16", "20:47:16"),
V4 = 151:156, V5 = c(-135.9, -136.9, -138.2, -138.8, -135.8,
-131.8), V6 = c(8.304, 8.302, 8.302, 8.302, 8.306, 10.302
), V7 = c(-339.5, -340.6, -340.5, -341, -339, -347), V8 = c(8.175,
8.175, 8.177, 8.176, 8.166, 8.187), V9 = c(23.13232, 23.89337,
23.00682, 23.76786, 25.76789, 15.76786), V10 = c(78.95514,
86.71063, 80.71004, 83.38557, 91.38557, 87.38557), V11 = c(97.10153,
98.07861, 96.15726, 98.30032, 102.30032, 111.30032)), class = "data.frame", row.names = c(NA,
-6L))
dat
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
1 2 2021-10-20 20:17:14 151 -135.9 8.304 -339.5 8.175 23.13232 78.95514
2 3 2021-10-20 20:27:15 152 -136.9 8.302 -340.6 8.175 23.89337 86.71063
3 4 2021-10-20 20:37:15 153 -138.2 8.302 -340.5 8.177 23.00682 80.71004
4 5 2021-10-20 20:47:16 154 -138.8 8.302 -341.0 8.176 23.76786 83.38557
5 6 2021-10-21 20:47:16 155 -135.8 8.306 -339.0 8.166 25.76789 91.38557
6 7 2021-10-22 20:47:16 156 -131.8 10.302 -347.0 8.187 15.76786 87.38557
V11
1 97.10153
2 98.07861
3 96.15726
4 98.30032
5 102.30032
6 111.30032