Merging and interpolation of observations
Jens Daniel Müller
29 April, 2020
Last updated: 2020-04-29
Checks: 7 0
Knit directory: BloomSail/
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| Rmd | 4131b9c | jens-daniel-mueller | 2019-10-21 | finisehd read CTD and HydroC, created merging Rmd |
library(tidyverse)
library(lubridate)
library(zoo)
# library(dygraphs)
# library(xts)CTD (ts) + HydroC CO2 data (th)
Merging summarized data sets
# Load Sensor and HydroC data ---------------------------------------------
ts <- read_csv(here::here("Data/_summarized_data_files",
"ts.csv"),
col_types = list("pCO2_analog" = col_double()))
th <- read_csv(here::here("Data/_summarized_data_files",
"th.csv"))
# th_full <- read_csv(here::here("Data/_summarized_data_files", "Tina_V_HydroC_full.csv"))
# Time offset correction ----------------------------------------------
# Time offset was determined by comparing zeroing reads from Sensor and th
# in the plots produced in the section Time stamp synchronzity below
# before applying this correction
ts <- ts %>%
mutate(day = yday(date_time),
date_time = if_else(day >= 206 & day <= 220,
date_time - 80, date_time - 10)) %>%
select(-day)
# Merge Sensor and HydroC data --------------------------------------------
tm <- full_join(ts, th) %>%
arrange(date_time)
# tm_full <- full_join(ts, th_full) %>%
# arrange(date_time)
rm(th, ts)Interpolation to common time stamp
CTD and auxillary recordings (15 sec measurment interval) are interpolated to HydroC time stamps (first 10 sec, than 1 sec measurement interval) when gaps between observations are not larger than 20. Thereafter, HydroC readings not falling in regular transects/profilings are removed, by removing rows with NA depth values. Furthermore, CTD readings without corresponding HydroC reading are removed, except during periods when HydroC was not operating.
# Interpolate Sensor data to HydroC timestamp
tm <- tm %>%
mutate(dep_maxgap = na.approx(dep, na.rm = FALSE, maxgap = 20),
dep = approxfun(date_time, dep)(date_time),
sal = approxfun(date_time, sal)(date_time),
tem = approxfun(date_time, tem)(date_time),
pCO2_analog = approxfun(date_time, pCO2_analog)(date_time)) %>%
filter(!is.na(dep)) %>% #remove HC readings not falling in regular transects/profilings
select(- dep_maxgap) %>%
fill(ID, type, station) %>%
filter(!is.na(deployment) | is.na(pCO2_analog)) # removes CTD readings without corresponding HydroC reading, except during periods when HydroC was not operating
# tm_full <-
# tm_full %>%
# mutate(dep = na.approx(dep, na.rm = FALSE, maxgap = 20),
# sal = na.approx(sal, na.rm = FALSE, maxgap = 20),
# tem = na.approx(tem, na.rm = FALSE, maxgap = 20),
# pCO2_analog = na.approx(pCO2_analog, na.rm = FALSE, maxgap = 20),
# pH = na.approx(pH, na.rm = FALSE, maxgap = 20),
# V_pH = na.approx(V_pH, na.rm = FALSE, maxgap = 20),
# O2 = na.approx(O2, na.rm = FALSE, maxgap = 20),
# Chl = na.approx(Chl, na.rm = FALSE, maxgap = 20)) %>%
# filter(!is.na(dep)) %>% #remove HC readings not falling in regular transects/profilings
# fill(ID, type, station) %>%
# filter(!is.na(deployment) | is.na(pCO2_analog)) # removes CTD readings without corresponding HydroC reading, except during periods when HydroC was not operating
# Time stamp synchronzity -------------------------------------------------
#
# tm <- tm %>%
# mutate(day = yday(date_time))
#
# for (dayID in unique(tm$day)) {
#
# tm %>%
# filter(day == dayID) %>%
# ggplot()+
# geom_point(aes(date_time, pCO2, col="HC"))+
# geom_point(aes(date_time, dep, col="dep"))+
# geom_point(aes(date_time, pH, col="pH"))+
# geom_point(aes(date_time, pCO2_analog, col="Sensor_int"))
#
# ggsave(here::here("/Plots/TinaV/Sensor/HydroC_diagnostics/Timing/day",
# paste(dayID,"_day_HydroC_merged.jpg", sep="")),
# width = 10, height = 4)
# }
#
#
# for (depID in unique(tm$deployment)) {
#
# tm_dep <- tm %>%
# filter(deployment == depID, Zero == 1)
#
# for (zerID in unique(tm_dep$Zero_ID)) {
#
# tm_dep %>%
# filter(Zero_ID == zerID) %>%
# ggplot()+
# geom_point(aes(date_time, pCO2, col="HC"))+
# geom_point(aes(date_time, pCO2_analog, col="Sensor_int"))
#
# ggsave(here::here("/Plots/TinaV/Sensor/HydroC_diagnostics/Timing/Zeroing",
# paste(depID,"_deployment_",zerID,"_Zero_ID_HydroC.jpg", sep="")),
# width = 10, height = 4)
#
# }
# }Write merged file
tm %>%
write_csv(here::here("Data/_merged_data_files", "tm.csv"))
#write_csv(tm_full, here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_full.csv"))
rm(tm)Time series pCO2
tm <- read_csv(here::here("data/_merged_data_files",
"tm.csv"),
col_types = cols(ID = col_character(),
pCO2_analog = col_double(),
pCO2_corr = col_double(),
Zero = col_factor(),
Flush = col_factor(),
Zero_ID = col_integer(),
deployment = col_integer(),
duration = col_double(),
mixing = col_character()))
tm %>%
filter(!is.na(pCO2_corr)) %>%
ggplot()+
geom_path(aes(date_time, pCO2_corr, col = "HydroC, drift corrected"))+
geom_path(aes(date_time, pCO2_analog, col = "analog CTD"))+
scale_color_brewer(palette = "Set1", name = "pCO2 record")+
labs(y=expression(pCO[2]~(µatm)), x="")+
facet_wrap(~ID, scales = "free_x", ncol = 1)
pCO2 record after interpolation to HydroC timestamp (analog output from HydroC and drift corrected data provided by Contos). ID refers to the starting date of each cruise. Please note that pCO2_analog measurement range is technically restricted to 100-500 µatm. Zeroing periods are included.
tm %>%
filter(!is.na(pCO2_corr)) %>%
ggplot()+
geom_path(aes(date_time, pCO2_corr - pCO2_analog))+
ylim(-50, 50)+
labs(y=expression(pCO[2]~(µatm)), x="")+
facet_wrap(~ID, scales = "free_x", ncol = 1)
pCO2 difference betweeb HydroC and drift corrected data provided by Contos. Please note that pCO2 range is restricted to +/- 50 µatm.
Sensor and track data
Sensor <- read_csv(here::here("data/_merged_data_files",
"BloomSail_CTD_HydroC.csv"),
col_types = cols(ID = col_character(),
pCO2_analog = col_double(),
pCO2 = col_double(),
Zero = col_factor(),
Flush = col_factor(),
Zero_ID = col_integer(),
deployment = col_integer(),
duration = col_double(),
mixing = col_character()))
track <-
read_csv(here::here("Data/_summarized_data_files",
"TinaV_Track.csv"))
df <- full_join(Sensor, track) %>%
arrange(date_time)
# interpolate track data and than remove columns that originate from track time stamp
df <-
df %>%
mutate(lat = na.approx(lat, na.rm = FALSE, maxgap = 20),
lon = na.approx(lon, na.rm = FALSE, maxgap = 20)) %>%
filter(!is.na(dep))
df %>%
write_csv(here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC_track.csv"))
rm(df)
# Tasks / open questions
- Check interpolation to CTD Sensor timestamp as alternative option
<br>
<p>
<button type="button" class="btn btn-default btn-workflowr btn-workflowr-sessioninfo"
data-toggle="collapse" data-target="#workflowr-sessioninfo"
style = "display: block;">
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Session information
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</p>
<div id="workflowr-sessioninfo" class="collapse">
```r
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_Germany.1252 LC_CTYPE=English_Germany.1252
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=English_Germany.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] zoo_1.8-7 lubridate_1.7.4 forcats_0.5.0 stringr_1.4.0
[5] dplyr_0.8.5 purrr_0.3.3 readr_1.3.1 tidyr_1.0.2
[9] tibble_3.0.0 ggplot2_3.3.0 tidyverse_1.3.0 workflowr_1.6.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-2 jsonlite_1.6.1 rstudioapi_0.11 generics_0.0.2
[5] magrittr_1.5 farver_2.0.3 gtable_0.3.0 rmarkdown_2.1
[9] vctrs_0.2.4 fs_1.4.0 hms_0.5.3 xml2_1.3.0
[13] pillar_1.4.3 htmltools_0.4.0 haven_2.2.0 later_1.0.0
[17] broom_0.5.5 cellranger_1.1.0 lattice_0.20-41 tidyselect_1.0.0
[21] knitr_1.28 git2r_0.26.1 whisker_0.4 lifecycle_0.2.0
[25] pkgconfig_2.0.3 R6_2.4.1 digest_0.6.25 xfun_0.12
[29] colorspace_1.4-1 rprojroot_1.3-2 stringi_1.4.6 yaml_2.2.1
[33] evaluate_0.14 labeling_0.3 fansi_0.4.1 httr_1.4.1
[37] compiler_3.6.3 here_0.1 cli_2.0.2 withr_2.1.2
[41] backports_1.1.5 munsell_0.5.0 DBI_1.1.0 modelr_0.1.6
[45] Rcpp_1.0.4 readxl_1.3.1 highr_0.8 dbplyr_1.4.2
[49] ellipsis_0.3.0 assertthat_0.2.1 tools_3.6.3 reprex_0.3.0
[53] httpuv_1.5.2 scales_1.1.0 crayon_1.3.4 glue_1.3.2
[57] rlang_0.4.5 nlme_3.1-145 rvest_0.3.5 promises_1.1.0
[61] grid_3.6.3