Merging and interpolation of observations
Jens Daniel Müller
08 November, 2019
Last updated: 2019-11-08
Checks: 7 0
Knit directory: BloomSail/
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| File | Version | Author | Date | Message |
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| html | 72687ee | jens-daniel-mueller | 2019-11-08 | Build site. |
| html | 74212a6 | jens-daniel-mueller | 2019-11-08 | Build site. |
| html | 33e3659 | jens-daniel-mueller | 2019-10-22 | Build site. |
| Rmd | efcafd1 | jens-daniel-mueller | 2019-10-22 | Added data base, merging, and RT determination |
| html | 1595fe9 | jens-daniel-mueller | 2019-10-21 | Build site. |
| 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 and pCO2 data
Merging summarized data sets
# Load Sensor and HydroC data ---------------------------------------------
CTD <- read_csv(here::here("Data/_summarized_data_files",
"Tina_V_Sensor_Profiles_Transects.csv"),
col_types = list("pCO2" = col_double())) %>%
rename(pCO2_analog = pCO2)
HC <- read_csv(here::here("Data/_summarized_data_files", "Tina_V_HydroC.csv"))
# Time offset correction ----------------------------------------------
# Time offset was determined by comparing zeroing reads from Sensor and HC
# in the plots produced in the section Time stamp synchronzity below
# before applying this correction
CTD <- CTD %>%
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 --------------------------------------------
df <- full_join(CTD, HC) %>%
arrange(date_time)
rm(HC, CTD)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
df <-
df %>%
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, cast) %>%
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 -------------------------------------------------
#
# df <- df %>%
# mutate(day = yday(date_time))
#
# for (dayID in unique(df$day)) {
#
# df %>%
# filter(day == dayID) %>%
# ggplot()+
# geom_point(aes(date_time, pCO2, col="HC"))+
# geom_point(aes(date_time, dep, col="dep"))+
# geom_point(aes(date_time, pH, col="pH"))+
# geom_point(aes(date_time, pCO2_analog, col="Sensor_int"))
#
# ggsave(here::here("/Plots/TinaV/Sensor/HydroC_diagnostics/Timing/day",
# paste(dayID,"_day_HydroC_merged.jpg", sep="")),
# width = 10, height = 4)
# }
#
#
# for (depID in unique(df$deployment)) {
#
# df_dep <- df %>%
# filter(deployment == depID, Zero == 1)
#
# for (zerID in unique(df_dep$Zero_ID)) {
#
# df_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)
#
# }
# }# add counter for date_time observations
df <- df %>%
add_count(date_time)
# find triplicated time stamp and select only first observation, and merge
df_no_triple <- df %>%
filter(n <= 2)
df_triple_clean <- df %>%
filter(n > 2) %>%
slice(1)
df <- full_join(df_no_triple, df_triple_clean)
rm(list=setdiff(ls(), "df"))
# find duplicated time stamps and shift first by one second, and merge
df %>%
distinct(date_time)# A tibble: 603,770 x 1
date_time
<dttm>
1 2018-06-16 03:58:45
2 2018-06-16 03:59:00
3 2018-06-16 03:59:15
4 2018-06-16 03:59:30
5 2018-06-16 03:59:45
6 2018-06-16 04:00:00
7 2018-06-16 04:00:15
8 2018-06-16 04:00:30
9 2018-06-16 04:00:45
10 2018-06-16 04:01:00
# ... with 603,760 more rowsdf_no_duplicated <- df %>%
filter(n == 1)
df_duplicated <- df %>%
filter(n == 2)
df_duplicated_first <- df_duplicated %>%
group_by(date_time) %>%
slice(1) %>%
ungroup() %>%
mutate(date_time = date_time - 1)
df_duplicated_second <- df_duplicated %>%
group_by(date_time) %>%
slice(2) %>%
ungroup()
df_duplicated_clean <- full_join(df_duplicated_first, df_duplicated_second) %>%
arrange(date_time)
df <- full_join(df_no_duplicated, df_duplicated_clean)
df %>%
distinct(date_time)# A tibble: 607,747 x 1
date_time
<dttm>
1 2018-06-16 03:58:45
2 2018-06-16 03:59:00
3 2018-06-16 03:59:15
4 2018-06-16 03:59:30
5 2018-06-16 03:59:45
6 2018-06-16 04:00:00
7 2018-06-16 04:00:15
8 2018-06-16 04:00:30
9 2018-06-16 04:00:45
10 2018-06-16 04:01:00
# ... with 607,737 more rowsrm(list=setdiff(ls(), "df"))
# remaining duplicates are observations where other observations with a +/- 1 sec timestamp exist
# for those cases, only the first duplicated observation is selected (similar to triplicate treatment)
df_still_no_duplicated <- df %>%
select(-n) %>%
add_count(date_time) %>%
filter(n == 1)
df_still_duplicated_first <- df %>%
select(-n) %>%
add_count(date_time) %>%
filter(n == 2) %>%
group_by(date_time) %>%
slice(1)
df <- full_join(df_still_no_duplicated, df_still_duplicated_first)
df %>%
distinct(date_time)# A tibble: 607,747 x 1
date_time
<dttm>
1 2018-06-16 03:58:45
2 2018-06-16 03:59:00
3 2018-06-16 03:59:15
4 2018-06-16 03:59:30
5 2018-06-16 03:59:45
6 2018-06-16 04:00:00
7 2018-06-16 04:00:15
8 2018-06-16 04:00:30
9 2018-06-16 04:00:45
10 2018-06-16 04:01:00
# ... with 607,737 more rowsrm(list=setdiff(ls(), "df"))
df <- df %>%
select(-n)write_csv(df, here::here("Data/_merged_data_files", "BloomSail_CTD_HydroC.csv"))HC <-
read_csv2(here::here("Data/TinaV/Sensor/HydroC-pCO2/corrected_Contros",
"parameter&pCO2s(method 43).txt"),
col_names = c("date_time", "Zero", "Flush", "p_NDIR",
"p_in", "T_control", "T_gas", "%rH_gas",
"Signal_raw", "Signal_ref", "T_sensor",
"pCO2", "Runtime", "nr.ave")) %>%
mutate(Flush = as.factor(as.character(Flush)),
Zero = as.factor(as.character(Zero)))df %>%
filter(!is.na(pCO2)) %>%
ggplot()+
geom_path(aes(date_time, pCO2, 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 measurement range is restricted to 100-500 µatm here due to the settings of the analog voltage output of the sensor. Zeroing periods are included.
# ts <- xts(cbind(df$pCO2, df$dep), order.by = df$date_time)
# names(ts) <- c("pCO2", "Depth")
#
# ts %>%
# dygraph() %>%
# dyRangeSelector() %>% #dateWindow = c("2012-01-01", "2016-12-31")
# dySeries("pCO2", label = "pCO2") %>%
# dySeries("Depth", axis = 'y2', label = "Depth") %>%
# dyAxis("y", label = "pCO2 [µatm]") %>%
# dyAxis("y2", label = "Depth [m]") %>%
# dyOptions(drawPoints = TRUE, pointSize = 1)Tasks / open questions
- Check interpolation to CTD Sensor timestamp as alternative option
sessionInfo()R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] zoo_1.8-6 lubridate_1.7.4 forcats_0.4.0 stringr_1.4.0
[5] dplyr_0.8.3 purrr_0.3.3 readr_1.3.1 tidyr_1.0.0
[9] tibble_2.1.3 ggplot2_3.2.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.10 haven_2.1.1
[4] lattice_0.20-35 colorspace_1.4-1 vctrs_0.2.0
[7] generics_0.0.2 htmltools_0.4.0 yaml_2.2.0
[10] utf8_1.1.4 rlang_0.4.1 pillar_1.4.2
[13] glue_1.3.1 withr_2.1.2 RColorBrewer_1.1-2
[16] modelr_0.1.5 readxl_1.3.1 lifecycle_0.1.0
[19] munsell_0.5.0 gtable_0.3.0 workflowr_1.4.0
[22] cellranger_1.1.0 rvest_0.3.4 evaluate_0.14
[25] labeling_0.3 knitr_1.25 fansi_0.4.0
[28] highr_0.8 broom_0.5.2 Rcpp_1.0.2
[31] scales_1.0.0 backports_1.1.5 jsonlite_1.6
[34] fs_1.3.1 hms_0.5.1 digest_0.6.22
[37] stringi_1.4.3 grid_3.5.0 rprojroot_1.3-2
[40] here_0.1 cli_1.1.0 tools_3.5.0
[43] magrittr_1.5 lazyeval_0.2.2 crayon_1.3.4
[46] whisker_0.4 pkgconfig_2.0.3 zeallot_0.1.0
[49] ellipsis_0.3.0 xml2_1.2.2 assertthat_0.2.1
[52] rmarkdown_1.16 httr_1.4.1 rstudioapi_0.10
[55] R6_2.4.0 nlme_3.1-137 git2r_0.26.1
[58] compiler_3.5.0