vignettes/updateOutputRange.md
In bgctw/equidIO: Read and Update Equidistant Data
Updating parts in an equidistant grouped data.frame
Example setup
In order to demonstrate updating, we setup some example data.
The timestamp is equidistant by 30 minutes within each group. There are
two groups: canopyPosition (10 records each) and treatment (only one
level). There are two fake data columns: resp and original row number.
require(equidIO, quietly = TRUE)
require(dplyr, quietly = TRUE)
dsTarget1 <- tibble(
date = seq(ISOdatetime(2010,1,1,0,0,30, tz = "UTC"), by = "30 min", length.out = 10)
, resp = as.numeric(1:10)
, origRow = 1:10
, canopyPosition = "openLand"
, treatment = "Nadd"
)
dsTarget2 <- dsTarget1 %>% mutate(
canopyPosition = "treeCovered"
, origRow = 1:10 + nrow(dsTarget1)
)
indexColumns <- c("canopyPosition","treatment")
dsTarget <- rbind( dsTarget1, dsTarget2) %>%
arrange(canopyPosition, date) %>%
mutate(canopyPosition = factor(canopyPosition)) %>%
group_by_at(vars(indexColumns))
dsTarget
## # A tibble: 20 x 5
## # Groups: canopyPosition, treatment [2]
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 00:00:30 1. 1 openLand Nadd
## 2 2010-01-01 00:30:30 2. 2 openLand Nadd
## 3 2010-01-01 01:00:30 3. 3 openLand Nadd
## 4 2010-01-01 01:30:30 4. 4 openLand Nadd
## 5 2010-01-01 02:00:30 5. 5 openLand Nadd
## 6 2010-01-01 02:30:30 6. 6 openLand Nadd
## 7 2010-01-01 03:00:30 7. 7 openLand Nadd
## 8 2010-01-01 03:30:30 8. 8 openLand Nadd
## 9 2010-01-01 04:00:30 9. 9 openLand Nadd
## 10 2010-01-01 04:30:30 10. 10 openLand Nadd
## 11 2010-01-01 00:00:30 1. 11 treeCovered Nadd
## 12 2010-01-01 00:30:30 2. 12 treeCovered Nadd
## 13 2010-01-01 01:00:30 3. 13 treeCovered Nadd
## 14 2010-01-01 01:30:30 4. 14 treeCovered Nadd
## 15 2010-01-01 02:00:30 5. 15 treeCovered Nadd
## 16 2010-01-01 02:30:30 6. 16 treeCovered Nadd
## 17 2010-01-01 03:00:30 7. 17 treeCovered Nadd
## 18 2010-01-01 03:30:30 8. 18 treeCovered Nadd
## 19 2010-01-01 04:00:30 9. 19 treeCovered Nadd
## 20 2010-01-01 04:30:30 10. 20 treeCovered Nadd
Updating target by new data
Now we have some new data dsNew and want to replace the corresponding
rows dsTarget. The indexColumns by default correspond to the grouping
variables in the new data.
dsNew <- dsNew0 <- ungroup(dsTarget) %>% #ungroup for slicing across groups
slice(3:8) %>%
mutate(resp = resp + 1000) %>%
group_by_at(vars(group_vars(dsTarget))) # restore grouping
dsNew
## # A tibble: 6 x 5
## # Groups: canopyPosition, treatment [1]
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 01:00:30 1003. 3 openLand Nadd
## 2 2010-01-01 01:30:30 1004. 4 openLand Nadd
## 3 2010-01-01 02:00:30 1005. 5 openLand Nadd
## 4 2010-01-01 02:30:30 1006. 6 openLand Nadd
## 5 2010-01-01 03:00:30 1007. 7 openLand Nadd
## 6 2010-01-01 03:30:30 1008. 8 openLand Nadd
dsUp <- updateOutputRange(dsTarget, dsNew) %>%
arrange(canopyPosition, date)
dsUp %>% ungroup() %>% slice(2:12)
## # A tibble: 11 x 5
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 00:30:30 2. 2 openLand Nadd
## 2 2010-01-01 01:00:30 1003. 3 openLand Nadd
## 3 2010-01-01 01:30:30 1004. 4 openLand Nadd
## 4 2010-01-01 02:00:30 1005. 5 openLand Nadd
## 5 2010-01-01 02:30:30 1006. 6 openLand Nadd
## 6 2010-01-01 03:00:30 1007. 7 openLand Nadd
## 7 2010-01-01 03:30:30 1008. 8 openLand Nadd
## 8 2010-01-01 04:00:30 9. 9 openLand Nadd
## 9 2010-01-01 04:30:30 10. 10 openLand Nadd
## 10 2010-01-01 00:00:30 1. 11 treeCovered Nadd
## 11 2010-01-01 00:30:30 2. 12 treeCovered Nadd
From the respiration column it can be seen that the corresponding rows
have been updated by an amount of +1000. The rows of the other groups
have not been modified.
Ensuring equidistant records
If there are additional rows, they are appended.
Moreover, if there are missing records between the times of the new data
and the target, those rows are added with missing values.
In the following example, rows 17:20 of the original target are added to
a data.frame that only consists of rows 1:14. The updated data has
additional rows 15 and 16 with missing values.
iRowsT <- 1:14
iRowsN <- 17:20
dsTarget1 <- slice(ungroup(dsTarget), iRowsT)
dsNew <- slice(ungroup(dsTarget), iRowsN) %>% mutate(resp = resp + 1000)
dsUp <- updateOutputRange(dsTarget1, dsNew, indexColumns = indexColumns) %>%
arrange(canopyPosition, date)
dsUp %>% ungroup() %>% slice(14:18)
## # A tibble: 5 x 5
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 01:30:30 4. 14 treeCovered Nadd
## 2 2010-01-01 02:00:30 NA NA treeCovered Nadd
## 3 2010-01-01 02:30:30 NA NA treeCovered Nadd
## 4 2010-01-01 03:00:30 1007. 17 treeCovered Nadd
## 5 2010-01-01 03:30:30 1008. 18 treeCovered Nadd
Adding missing factor levels
If the new data.frame contains factors, with new levels. The target
factor is re-leveled with a warning.
If the new data contains new levels of the index group, then records are
added.
iRowsN <- c(8:20)
# drop canopyPosition level "treeCovered"
dsTarget1 <- slice(ungroup(dsTarget), 1:10) %>% droplevels()
dsNew <- slice(ungroup(dsTarget), iRowsN) %>%
mutate(resp = resp + 1000) %>%
droplevels()
dsUp <- updateOutputRange(dsTarget1, dsNew, indexColumns = indexColumns) %>%
arrange(canopyPosition, date)
## Warning in expandAllInconsistentFactorLevels(list(dsTarget, dsNew),
## .noWarningCols = ".group"): releveling factors canopyPosition
dsUp %>% ungroup() %>% slice(6:14)
## # A tibble: 9 x 5
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 02:30:30 6. 6 openLand Nadd
## 2 2010-01-01 03:00:30 7. 7 openLand Nadd
## 3 2010-01-01 03:30:30 1008. 8 openLand Nadd
## 4 2010-01-01 04:00:30 1009. 9 openLand Nadd
## 5 2010-01-01 04:30:30 1010. 10 openLand Nadd
## 6 2010-01-01 00:00:30 1001. 11 treeCovered Nadd
## 7 2010-01-01 00:30:30 1002. 12 treeCovered Nadd
## 8 2010-01-01 01:00:30 1003. 13 treeCovered Nadd
## 9 2010-01-01 01:30:30 1004. 14 treeCovered Nadd
Updating data in a file
Convenience, function updateRData executes updateOutputRange to a
data.frame stored in .RData format, as written by save.
fileName <- file.path(tempdir(),"updateRDataExample.RData")
save(dsTarget, file = fileName)
updateRData(dsNew0, fileName)
## updated updateRDataExample in file /tmp/Rtmp1c8Wlr/updateRDataExample.RData
load(fileName)
updateRDataExample %>%
arrange(canopyPosition, date) %>% ungroup() %>% slice(2:12)
## # A tibble: 11 x 5
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 00:30:30 2. 2 openLand Nadd
## 2 2010-01-01 01:00:30 1003. 3 openLand Nadd
## 3 2010-01-01 01:30:30 1004. 4 openLand Nadd
## 4 2010-01-01 02:00:30 1005. 5 openLand Nadd
## 5 2010-01-01 02:30:30 1006. 6 openLand Nadd
## 6 2010-01-01 03:00:30 1007. 7 openLand Nadd
## 7 2010-01-01 03:30:30 1008. 8 openLand Nadd
## 8 2010-01-01 04:00:30 9. 9 openLand Nadd
## 9 2010-01-01 04:30:30 10. 10 openLand Nadd
## 10 2010-01-01 00:00:30 1. 11 treeCovered Nadd
## 11 2010-01-01 00:30:30 2. 12 treeCovered Nadd
bgctw/equidIO documentation built on Nov. 17, 2020, 5:05 a.m.
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Updating parts in an equidistant grouped data.frame
Example setup
In order to demonstrate updating, we setup some example data.
The timestamp is equidistant by 30 minutes within each group. There are two groups: canopyPosition (10 records each) and treatment (only one level). There are two fake data columns: resp and original row number.
require(equidIO, quietly = TRUE)
require(dplyr, quietly = TRUE)
dsTarget1 <- tibble(
date = seq(ISOdatetime(2010,1,1,0,0,30, tz = "UTC"), by = "30 min", length.out = 10)
, resp = as.numeric(1:10)
, origRow = 1:10
, canopyPosition = "openLand"
, treatment = "Nadd"
)
dsTarget2 <- dsTarget1 %>% mutate(
canopyPosition = "treeCovered"
, origRow = 1:10 + nrow(dsTarget1)
)
indexColumns <- c("canopyPosition","treatment")
dsTarget <- rbind( dsTarget1, dsTarget2) %>%
arrange(canopyPosition, date) %>%
mutate(canopyPosition = factor(canopyPosition)) %>%
group_by_at(vars(indexColumns))
dsTarget
## # A tibble: 20 x 5
## # Groups: canopyPosition, treatment [2]
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 00:00:30 1. 1 openLand Nadd
## 2 2010-01-01 00:30:30 2. 2 openLand Nadd
## 3 2010-01-01 01:00:30 3. 3 openLand Nadd
## 4 2010-01-01 01:30:30 4. 4 openLand Nadd
## 5 2010-01-01 02:00:30 5. 5 openLand Nadd
## 6 2010-01-01 02:30:30 6. 6 openLand Nadd
## 7 2010-01-01 03:00:30 7. 7 openLand Nadd
## 8 2010-01-01 03:30:30 8. 8 openLand Nadd
## 9 2010-01-01 04:00:30 9. 9 openLand Nadd
## 10 2010-01-01 04:30:30 10. 10 openLand Nadd
## 11 2010-01-01 00:00:30 1. 11 treeCovered Nadd
## 12 2010-01-01 00:30:30 2. 12 treeCovered Nadd
## 13 2010-01-01 01:00:30 3. 13 treeCovered Nadd
## 14 2010-01-01 01:30:30 4. 14 treeCovered Nadd
## 15 2010-01-01 02:00:30 5. 15 treeCovered Nadd
## 16 2010-01-01 02:30:30 6. 16 treeCovered Nadd
## 17 2010-01-01 03:00:30 7. 17 treeCovered Nadd
## 18 2010-01-01 03:30:30 8. 18 treeCovered Nadd
## 19 2010-01-01 04:00:30 9. 19 treeCovered Nadd
## 20 2010-01-01 04:30:30 10. 20 treeCovered Nadd
Updating target by new data
Now we have some new data dsNew and want to replace the corresponding
rows dsTarget. The indexColumns by default correspond to the grouping
variables in the new data.
dsNew <- dsNew0 <- ungroup(dsTarget) %>% #ungroup for slicing across groups
slice(3:8) %>%
mutate(resp = resp + 1000) %>%
group_by_at(vars(group_vars(dsTarget))) # restore grouping
dsNew
## # A tibble: 6 x 5
## # Groups: canopyPosition, treatment [1]
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 01:00:30 1003. 3 openLand Nadd
## 2 2010-01-01 01:30:30 1004. 4 openLand Nadd
## 3 2010-01-01 02:00:30 1005. 5 openLand Nadd
## 4 2010-01-01 02:30:30 1006. 6 openLand Nadd
## 5 2010-01-01 03:00:30 1007. 7 openLand Nadd
## 6 2010-01-01 03:30:30 1008. 8 openLand Nadd
dsUp <- updateOutputRange(dsTarget, dsNew) %>%
arrange(canopyPosition, date)
dsUp %>% ungroup() %>% slice(2:12)
## # A tibble: 11 x 5
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 00:30:30 2. 2 openLand Nadd
## 2 2010-01-01 01:00:30 1003. 3 openLand Nadd
## 3 2010-01-01 01:30:30 1004. 4 openLand Nadd
## 4 2010-01-01 02:00:30 1005. 5 openLand Nadd
## 5 2010-01-01 02:30:30 1006. 6 openLand Nadd
## 6 2010-01-01 03:00:30 1007. 7 openLand Nadd
## 7 2010-01-01 03:30:30 1008. 8 openLand Nadd
## 8 2010-01-01 04:00:30 9. 9 openLand Nadd
## 9 2010-01-01 04:30:30 10. 10 openLand Nadd
## 10 2010-01-01 00:00:30 1. 11 treeCovered Nadd
## 11 2010-01-01 00:30:30 2. 12 treeCovered Nadd
From the respiration column it can be seen that the corresponding rows have been updated by an amount of +1000. The rows of the other groups have not been modified.
Ensuring equidistant records
If there are additional rows, they are appended.
Moreover, if there are missing records between the times of the new data and the target, those rows are added with missing values.
In the following example, rows 17:20 of the original target are added to a data.frame that only consists of rows 1:14. The updated data has additional rows 15 and 16 with missing values.
iRowsT <- 1:14
iRowsN <- 17:20
dsTarget1 <- slice(ungroup(dsTarget), iRowsT)
dsNew <- slice(ungroup(dsTarget), iRowsN) %>% mutate(resp = resp + 1000)
dsUp <- updateOutputRange(dsTarget1, dsNew, indexColumns = indexColumns) %>%
arrange(canopyPosition, date)
dsUp %>% ungroup() %>% slice(14:18)
## # A tibble: 5 x 5
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 01:30:30 4. 14 treeCovered Nadd
## 2 2010-01-01 02:00:30 NA NA treeCovered Nadd
## 3 2010-01-01 02:30:30 NA NA treeCovered Nadd
## 4 2010-01-01 03:00:30 1007. 17 treeCovered Nadd
## 5 2010-01-01 03:30:30 1008. 18 treeCovered Nadd
Adding missing factor levels
If the new data.frame contains factors, with new levels. The target factor is re-leveled with a warning.
If the new data contains new levels of the index group, then records are added.
iRowsN <- c(8:20)
# drop canopyPosition level "treeCovered"
dsTarget1 <- slice(ungroup(dsTarget), 1:10) %>% droplevels()
dsNew <- slice(ungroup(dsTarget), iRowsN) %>%
mutate(resp = resp + 1000) %>%
droplevels()
dsUp <- updateOutputRange(dsTarget1, dsNew, indexColumns = indexColumns) %>%
arrange(canopyPosition, date)
## Warning in expandAllInconsistentFactorLevels(list(dsTarget, dsNew),
## .noWarningCols = ".group"): releveling factors canopyPosition
dsUp %>% ungroup() %>% slice(6:14)
## # A tibble: 9 x 5
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 02:30:30 6. 6 openLand Nadd
## 2 2010-01-01 03:00:30 7. 7 openLand Nadd
## 3 2010-01-01 03:30:30 1008. 8 openLand Nadd
## 4 2010-01-01 04:00:30 1009. 9 openLand Nadd
## 5 2010-01-01 04:30:30 1010. 10 openLand Nadd
## 6 2010-01-01 00:00:30 1001. 11 treeCovered Nadd
## 7 2010-01-01 00:30:30 1002. 12 treeCovered Nadd
## 8 2010-01-01 01:00:30 1003. 13 treeCovered Nadd
## 9 2010-01-01 01:30:30 1004. 14 treeCovered Nadd
Updating data in a file
Convenience, function updateRData executes updateOutputRange to a
data.frame stored in .RData format, as written by save.
fileName <- file.path(tempdir(),"updateRDataExample.RData")
save(dsTarget, file = fileName)
updateRData(dsNew0, fileName)
## updated updateRDataExample in file /tmp/Rtmp1c8Wlr/updateRDataExample.RData
load(fileName)
updateRDataExample %>%
arrange(canopyPosition, date) %>% ungroup() %>% slice(2:12)
## # A tibble: 11 x 5
## date resp origRow canopyPosition treatment
## <dttm> <dbl> <int> <fct> <chr>
## 1 2010-01-01 00:30:30 2. 2 openLand Nadd
## 2 2010-01-01 01:00:30 1003. 3 openLand Nadd
## 3 2010-01-01 01:30:30 1004. 4 openLand Nadd
## 4 2010-01-01 02:00:30 1005. 5 openLand Nadd
## 5 2010-01-01 02:30:30 1006. 6 openLand Nadd
## 6 2010-01-01 03:00:30 1007. 7 openLand Nadd
## 7 2010-01-01 03:30:30 1008. 8 openLand Nadd
## 8 2010-01-01 04:00:30 9. 9 openLand Nadd
## 9 2010-01-01 04:30:30 10. 10 openLand Nadd
## 10 2010-01-01 00:00:30 1. 11 treeCovered Nadd
## 11 2010-01-01 00:30:30 2. 12 treeCovered Nadd
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