1 Background

On July 10th I calibrated the HOBO logs to a Kessler NIST K750 RTD Thermometer and an apogee instruments underwater quantum flux PAR meter.

Each was a 2 point calibration matching time-stamp to reference instrument read-out.

NIST temp & time 27.03 , 07/10/2024 10:40:00 27.26, 07/10/2024 11:00:00

Apogee par & time 1600, 07/10/2024 11:10:00 760, 07/10/2024 11:40:00

1.1 Inputs

1.2 Outputs

2 Setup

2.1 Install packages

if ("tidyverse" %in% rownames(installed.packages()) == 'FALSE') install.packages('tidyverse')
if ("broom" %in% rownames(installed.packages()) == 'FALSE') install.packages('broom')

2.2 Load libraries

library(tidyverse)
library(broom) # for tidy()

2.3 Load HOBO logs

amb1_blue <- read.csv("Amb1_blue 2024-07-10 13_10_13 HST (Data HST).csv")
amb1_bluegreen <- read.csv("Amb1_bluegreen 2024-07-10 13_10_51 HST (Data HST).csv")
amb2_orangepink <- read.csv("Amb2_orangepink 2024-07-10 11_47_18 HST (Data HST).csv")
amb2_pink <- read.csv("Amb2_pink 2024-07-10 11_48_03 HST (Data HST).csv")
hot1_black <- read.csv("Hot1_black 2024-07-10 13_09_24 HST (Data HST).csv")
hot1_green <- read.csv("Hot1_green 2024-07-10 13_08_26 HST (Data HST).csv")
hot2_orange <- read.csv("Hot2_orange 2024-07-10 11_44_18 HST (Data HST).csv")
hot2_yellow <- read.csv("Hot2_yellow 2024-07-10 11_46_08 HST (Data HST).csv")

3 References

# Example inputs for temperature calibration
ref_temp <- c(27.03, 27.26)  # NIST temperatures
ref_time_temp <- as.POSIXct(c("07/10/2024 10:40:00", "07/10/2024 11:00:00"),
                            format="%m/%d/%Y %H:%M:%S")

# For PAR calibration
ref_par <- c(1600, 760)
ref_time_par <- as.POSIXct(c("07/10/2024 11:10:00", "07/10/2024 11:40:00"),
                           format="%m/%d/%Y %H:%M:%S")

cal_table <- tibble::tibble(
  datetime = as.POSIXct(c("2024-07-10 10:40:00", "2024-07-10 11:00:00",
                          "2024-07-10 11:10:00", "2024-07-10 11:40:00")),
  ref_temp = c(27.03, 27.26, NA, NA),  # NIST
  ref_par  = c(NA, NA, 1600, 760)      # Apogee PAR
)

4 Format logs

amb1_blue

amb1_blue <- amb1_blue %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>%
  mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% 
  mutate(pendent = "amb1_blue") %>% 
  select(datetime, temp, lux, pendent) %>% 
  filter(datetime %in% c(ref_time_temp, ref_time_par))

amb1_bluegreen

amb1_bluegreen <- amb1_bluegreen %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>%
  mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% 
  mutate(pendent = "amb1_bluegreen") %>% 
  select(datetime, temp, lux, pendent) %>% 
  filter(datetime %in% c(ref_time_temp, ref_time_par))

amb2_orangepink

amb2_orangepink <- amb2_orangepink %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>%
  mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% 
  mutate(pendent = "amb2_orangepink") %>% 
  select(datetime, temp, lux, pendent) %>% 
  filter(datetime %in% c(ref_time_temp, ref_time_par))

amb2_pink

amb2_pink <- amb2_pink %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>%
  mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% 
  mutate(pendent = "amb2_pink") %>% 
  select(datetime, temp, lux, pendent) %>% 
  filter(datetime %in% c(ref_time_temp, ref_time_par))

hot1_black

hot1_black <- hot1_black %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>%
  mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% 
  mutate(pendent = "hot1_black") %>% 
  select(datetime, temp, lux, pendent) %>% 
  filter(datetime %in% c(ref_time_temp, ref_time_par))

hot1_green

hot1_green <- hot1_green %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>%
  mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% 
  mutate(pendent = "hot1_green") %>% 
  select(datetime, temp, lux, pendent) %>% 
  filter(datetime %in% c(ref_time_temp, ref_time_par))

hot2_orange

hot2_orange <- hot2_orange %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>%
  mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% 
  mutate(pendent = "hot2_orange") %>% 
  select(datetime, temp, lux, pendent)%>% 
  filter(datetime %in% c(ref_time_temp, ref_time_par))

hot2_yellow

hot2_yellow <- hot2_yellow %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>%
  mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% 
  mutate(pendent = "hot2_yellow") %>% 
  select(datetime, temp, lux, pendent) %>% 
  filter(datetime %in% c(ref_time_temp, ref_time_par))

Bind logs

cal_logs <- rbind(amb1_blue,
                  amb1_bluegreen,
                  amb2_orangepink,
                  amb2_pink,
                  hot1_black,
                  hot1_green,
                  hot2_orange,
                  hot2_yellow)

5 Create calibrations

5.1 Temp

For each sensor, calculate slope and intercept using the two point calibration

# Define your calibration temperature reference times
cal_temp_times <- as.POSIXct(c("2024-07-10 10:40:00", "2024-07-10 11:00:00"))

# Sample dataframe 'cal_logs' containing datetime, temp, lux, pendent

# Step 1: Filter calibration temp points only
temp_cal_points <- cal_logs %>%
  filter(datetime %in% cal_temp_times) %>%
  select(pendent, datetime, temp) %>%
  mutate(ref_temp = ifelse(datetime == cal_temp_times[1], 27.03, 27.26))

# Step 2: Group by sensor and fit linear model temp ~ ref_temp
temp_fit_results <- temp_cal_points %>%
  group_by(pendent) %>%
  summarise(
    model = list(lm(ref_temp ~ temp, data = cur_data()))
  ) %>%
  mutate(
    coef = map(model, tidy)
  ) %>%
  unnest(coef) %>%
  select(pendent, term, estimate) %>%
  pivot_wider(names_from = term, values_from = estimate) %>%
  rename(intercept = `(Intercept)`, slope = temp)

# fit_results now contains one row per sensor with slope and intercept ready to use
print(temp_fit_results)

# A tibble: 8 × 3
  pendent         intercept slope
  <chr>               <dbl> <dbl>
1 amb1_blue            19.8 0.256
2 amb1_bluegreen       19.1 0.284
3 amb2_orangepink      18.2 0.315
4 amb2_pink            19.1 0.284
5 hot1_black           19.5 0.271
6 hot1_green           18.3 0.315
7 hot2_orange          19.5 0.267
8 hot2_yellow          17.6 0.338

Average temp calibration

avg_temp <- temp_fit_results %>%
  summarize(
    avg_temp_intercept = mean(intercept),
    avg_temp_slope = mean(slope))

avg_temp

avg_temp_intercept	avg_temp_slope
18.89328	0.2912324

5.2 PAR

# Define your calibration PAR reference times
cal_par_times <- as.POSIXct(c("2024-07-10 11:10:00", "2024-07-10 11:40:00"))

# Sample dataframe 'cal_logs' containing datetime, temp, lux, pendent

# Step 1: Filter calibration par points only
par_cal_points <- cal_logs %>%
  filter(datetime %in% cal_par_times) %>%
  select(pendent, datetime, lux) %>%
  mutate(ref_par = ifelse(datetime == cal_par_times[1], 1600, 760))

# Step 2: Group by sensor and fit linear model lux ~ ref_par
par_fit_results <- par_cal_points %>%
  group_by(pendent) %>%
  summarise(
    model = list(lm(ref_par ~ lux, data = cur_data()))
  ) %>%
  mutate(
    coef = map(model, tidy)
  ) %>%
  unnest(coef) %>%
  select(pendent, term, estimate) %>%
  pivot_wider(names_from = term, values_from = estimate) %>%
  rename(intercept = `(Intercept)`, slope = lux)

# fit_results now contains one row per sensor with slope and intercept ready to use
print(par_fit_results)

# A tibble: 8 × 3
  pendent         intercept  slope
  <chr>               <dbl>  <dbl>
1 amb1_blue          -1400. 0.0626
2 amb1_bluegreen     -1428. 0.0727
3 amb2_orangepink     -776. 0.0573
4 amb2_pink          -4905. 0.158 
5 hot1_black         -1427. 0.0697
6 hot1_green         -1797. 0.0868
7 hot2_orange        -1968. 0.0805
8 hot2_yellow        -1998. 0.0957

Average PAR calibration

avg_par <- par_fit_results %>%
  summarize(
    avg_par_intercept = mean(intercept),
    avg_par_slope = mean(slope))

avg_par

avg_par_intercept	avg_par_slope
-1962.403	0.0853845

6 Apply calibrations

To apply the intercept and slope from your linear regression calibration to raw data and generate calibrated values, you use the formula:

\[ \text{Calibrated value} = (\text{slope} \times \text{raw value}) + \text{intercept} \]

7 Summary

--- title: "HOBO logger calibrations" subtitle: "Two loggers per temperature bath (2 ambient, 2 hot)" date: 07/10/2024 categories: [protocols, records] --- # Background On July 10th I calibrated the HOBO pendent (temp + lux) loggers to a Kessler NIST K750 RTD Thermometer and an apogee instruments underwater quantum flux PAR meter. Typical examples of lux levels: - Full daylight: about 10,000 to 25,000 lux - Overcast daylight: about 1,000 lux - Typical office lighting: about 300 to 500 lux - Moonlight: about 1 lux Each was a 2 point calibration matching time-stamp to reference instrument read-out. NIST temp & time: - 27.03 , 07/10/2024 10:40:00 - 27.26, 07/10/2024 11:00:00 Apogee par & time: - 1600, 07/10/2024 11:10:00 - 760, 07/10/2024 11:40:00 ## Inputs ## Outputs # Setup ```{r setup, include=FALSE} knitr::opts_chunk$set( fig.width = 12, fig.height = 8, fig.align = "center", dpi = 600, echo = TRUE, # Display code chunks eval = TRUE, # Evaluate code chunks warning = FALSE, # Hide warnings message = FALSE, # Hide messages comment = "" # Prevents appending '##' to beginning of lines in code output ) ``` ## Install packages ```{r, eval=FALSE} if ("tidyverse" %in% rownames(installed.packages()) == 'FALSE') install.packages('tidyverse') if ("broom" %in% rownames(installed.packages()) == 'FALSE') install.packages('broom') ``` ## Load libraries ```{r} library(tidyverse) library(broom) # for tidy() ``` ## Load HOBO calibration logs ```{r} cal_amb1_blue <- read.csv("calibrations/Amb1_blue 2024-07-10 13_10_13 HST (Data HST).csv") cal_amb1_bluegreen <- read.csv("calibrations/Amb1_bluegreen 2024-07-10 13_10_51 HST (Data HST).csv") cal_amb2_orangepink <- read.csv("calibrations/Amb2_orangepink 2024-07-10 11_47_18 HST (Data HST).csv") cal_amb2_pink <- read.csv("calibrations/Amb2_pink 2024-07-10 11_48_03 HST (Data HST).csv") cal_hot1_black <- read.csv("calibrations/Hot1_black 2024-07-10 13_09_24 HST (Data HST).csv") cal_hot1_green <- read.csv("calibrations/Hot1_green 2024-07-10 13_08_26 HST (Data HST).csv") cal_hot2_orange <- read.csv("calibrations/Hot2_orange 2024-07-10 11_44_18 HST (Data HST).csv") cal_hot2_yellow <- read.csv("calibrations/Hot2_yellow 2024-07-10 11_46_08 HST (Data HST).csv") ``` ## Load HOBO experiment logs ```{r} exp_amb1_blue <- read.csv("experiment/Amb1_blue 2024-07-18 10_39_10 HST (Data HST).csv") exp_amb1_bluegreen <- read.csv("experiment/Amb1_bluegreen 2024-07-18 10_40_19 HST (Data HST).csv") exp_amb2_orangepink <- read.csv("experiment/Amb2_orangepink 2024-07-18 10_36_23 HST (Data HST).csv") exp_amb2_pink <- read.csv("experiment/Amb2_pink 2024-07-18 10_35_38 HST (Data HST).csv") exp_hot1_black <- read.csv("experiment/Hot1_black 2024-07-18 10_38_17 HST (Data HST).csv") exp_hot1_green <- read.csv("experiment/Hot1_green 2024-07-18 10_37_19 HST (Data HST).csv") exp_hot2_orange <- read.csv("experiment/Hot2_orange 2024-07-18 10_33_21 HST (Data HST).csv") exp_hot2_yellow <- read.csv("experiment/Hot2_yellow 2024-07-18 10_34_56 HST (Data HST).csv") ``` # Calibration references ```{r} # Example inputs for temperature calibration ref_temp <- c(27.03, 27.26) # NIST temperatures ref_time_temp <- as.POSIXct(c("07/10/2024 10:40:00", "07/10/2024 11:00:00"), format="%m/%d/%Y %H:%M:%S") # For PAR calibration ref_par <- c(1600, 760) ref_time_par <- as.POSIXct(c("07/10/2024 11:10:00", "07/10/2024 11:40:00"), format="%m/%d/%Y %H:%M:%S") cal_table <- tibble::tibble( datetime = as.POSIXct(c("2024-07-10 10:40:00", "2024-07-10 11:00:00", "2024-07-10 11:10:00", "2024-07-10 11:40:00")), ref_temp = c(27.03, 27.26, NA, NA), # NIST ref_par = c(NA, NA, 1600, 760) # Apogee PAR ) ``` # Format calibration logs 1. amb1_blue ```{r} cal_amb1_blue <- cal_amb1_blue %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "amb1_blue") %>% select(datetime, temp, lux, pendent) %>% filter(datetime %in% c(ref_time_temp, ref_time_par)) ``` 2. amb1_bluegreen ```{r} cal_amb1_bluegreen <- cal_amb1_bluegreen %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "amb1_bluegreen") %>% select(datetime, temp, lux, pendent) %>% filter(datetime %in% c(ref_time_temp, ref_time_par)) ``` 3. amb2_orangepink ```{r} cal_amb2_orangepink <- cal_amb2_orangepink %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "amb2_orangepink") %>% select(datetime, temp, lux, pendent) %>% filter(datetime %in% c(ref_time_temp, ref_time_par)) ``` 4. amb2_pink ```{r} cal_amb2_pink <- cal_amb2_pink %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "amb2_pink") %>% select(datetime, temp, lux, pendent) %>% filter(datetime %in% c(ref_time_temp, ref_time_par)) ``` 5. hot1_black ```{r} cal_hot1_black <- cal_hot1_black %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "hot1_black") %>% select(datetime, temp, lux, pendent) %>% filter(datetime %in% c(ref_time_temp, ref_time_par)) ``` 6. hot1_green ```{r} cal_hot1_green <- cal_hot1_green %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "hot1_green") %>% select(datetime, temp, lux, pendent) %>% filter(datetime %in% c(ref_time_temp, ref_time_par)) ``` 7. hot2_orange ```{r} cal_hot2_orange <- cal_hot2_orange %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "hot2_orange") %>% select(datetime, temp, lux, pendent)%>% filter(datetime %in% c(ref_time_temp, ref_time_par)) ``` 8. hot2_yellow ```{r} cal_hot2_yellow <- cal_hot2_yellow %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "hot2_yellow") %>% select(datetime, temp, lux, pendent) %>% filter(datetime %in% c(ref_time_temp, ref_time_par)) ``` Bind logs ```{r} cal_logs <- rbind(cal_amb1_blue, cal_amb1_bluegreen, cal_amb2_orangepink, cal_amb2_pink, cal_hot1_black, cal_hot1_green, cal_hot2_orange, cal_hot2_yellow) ``` # Create calibrations ## Temp For each sensor, calculate slope and intercept using the two point calibration ```{r} # Define your calibration temperature reference times cal_temp_times <- as.POSIXct(c("2024-07-10 10:40:00", "2024-07-10 11:00:00")) # Sample dataframe 'cal_logs' containing datetime, temp, lux, pendent # Step 1: Filter calibration temp points only temp_cal_points <- cal_logs %>% filter(datetime %in% cal_temp_times) %>% select(pendent, datetime, temp) %>% mutate(ref_temp = ifelse(datetime == cal_temp_times[1], 27.03, 27.26)) # Step 2: Group by sensor and fit linear model temp ~ ref_temp temp_fit_results <- temp_cal_points %>% group_by(pendent) %>% summarise( model = list(lm(ref_temp ~ temp, data = cur_data())) ) %>% mutate( coef = map(model, tidy) ) %>% unnest(coef) %>% select(pendent, term, estimate) %>% pivot_wider(names_from = term, values_from = estimate) %>% rename(temp_intercept = `(Intercept)`, temp_slope = temp) # fit_results now contains one row per sensor with slope and intercept ready to use print(temp_fit_results) ``` Average temp calibration ```{r} avg_temp <- temp_fit_results %>% summarize( avg_temp_intercept = mean(temp_intercept), avg_temp_slope = mean(temp_slope)) avg_temp write.csv(avg_temp, "output/avg_temp.csv", row.names=FALSE) ``` ## PAR ```{r} # Define your calibration PAR reference times cal_par_times <- as.POSIXct(c("2024-07-10 11:10:00", "2024-07-10 11:40:00")) # Sample dataframe 'cal_logs' containing datetime, temp, lux, pendent # Step 1: Filter calibration par points only par_cal_points <- cal_logs %>% filter(datetime %in% cal_par_times) %>% select(pendent, datetime, lux) %>% mutate(ref_par = ifelse(datetime == cal_par_times[1], 1600, 760)) # Step 2: Group by sensor and fit linear model lux ~ ref_par par_fit_results <- par_cal_points %>% group_by(pendent) %>% summarise( model = list(lm(ref_par ~ lux, data = cur_data())) ) %>% mutate( coef = map(model, tidy) ) %>% unnest(coef) %>% select(pendent, term, estimate) %>% pivot_wider(names_from = term, values_from = estimate) %>% rename(lux_intercept = `(Intercept)`, lux_slope = lux) # fit_results now contains one row per sensor with slope and intercept ready to use print(par_fit_results) ``` :::callout-caution amb2_pink sensor seems way off compared to the rest ::: Remove amb2_pink ```{r} par_fit_avg <- par_fit_results %>% filter(pendent != c("amb2_pink", "amb2_orangepink")) #select all rows except amb2 from pendent column ``` Average PAR calibration ```{r} avg_par <- par_fit_avg %>% summarize( avg_par_intercept = mean(lux_intercept), avg_par_slope = mean(lux_slope)) avg_par write.csv(avg_par, "output/avg_par.csv", row.names = FALSE) ``` # Format experiment logs 1. amb1_blue ```{r} exp_amb1_blue <- exp_amb1_blue %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "amb1_blue") %>% select(datetime, temp, lux, pendent) ``` 2. amb1_bluegreen ```{r} exp_amb1_bluegreen <- exp_amb1_bluegreen %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "amb1_bluegreen") %>% select(datetime, temp, lux, pendent) ``` 3. amb2_orangepink ```{r} exp_amb2_orangepink <- exp_amb2_orangepink %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "amb2_orangepink") %>% select(datetime, temp, lux, pendent) ``` 4. amb2_pink ```{r} exp_amb2_pink <- exp_amb2_pink %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "amb2_pink") %>% select(datetime, temp, lux, pendent) ``` 5. hot1_black ```{r} exp_hot1_black <- exp_hot1_black %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "hot1_black") %>% select(datetime, temp, lux, pendent) ``` 6. hot1_green ```{r} exp_hot1_green <- exp_hot1_green %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "hot1_green") %>% select(datetime, temp, lux, pendent) ``` 7. hot2_orange ```{r} exp_hot2_orange <- exp_hot2_orange %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "hot2_orange") %>% select(datetime, temp, lux, pendent) ``` 8. hot2_yellow ```{r} exp_hot2_yellow <- exp_hot2_yellow %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")) %>% mutate(pendent = "hot2_yellow") %>% select(datetime, temp, lux, pendent) ``` Bind experiment logs together ```{r} exp_logs <- rbind(exp_amb1_blue, exp_amb1_bluegreen, exp_amb2_orangepink, exp_amb2_pink, exp_hot1_black, exp_hot1_green, exp_hot2_orange, exp_hot2_yellow) ``` Join to calibrations (& remove NAs) ```{r} exp_logs <- exp_logs %>% left_join(temp_fit_results) %>% left_join(par_fit_results) %>% drop_na(temp, lux) ``` # Apply calibrations To apply the intercept and slope from a linear regression calibration to raw data and generate calibrated values, we can use the formula: $$ \text{Calibrated value} = (\text{slope} \times \text{raw value}) + \text{intercept} $$ ```{r} exp_logs_postcal <- exp_logs %>% rename(raw_temp = temp) %>% mutate( cal_temp = (temp_slope * raw_temp) + temp_intercept, par0 = (lux_slope * lux) + lux_intercept, par = pmax(par0, 0) ) # PAR cannot be negative ``` # Visualize thermal environment ```{r} ggplot(exp_logs_postcal, aes(x = datetime, y = cal_temp, color = pendent, alpha = 0.4))+ geom_point()+ geom_smooth()+ ggtitle("Calibrated temperatures across hot and ambient water baths")+ labs(x = "Date-time", y = "Temperature (°C)", color = "HOBO Sensor") ``` # Visualize PAR environment ```{r} ggplot(exp_logs_postcal, aes(x = datetime, y = par, color = pendent, alpha = 0.4))+ geom_point()+ geom_jitter()+ #geom_smooth()+ ylim(0, 500)+ ggtitle("Calibrated PAR across hot and ambient water baths")+ labs(x = "Date-time", y = "PAR (umol/m2)", color = "HOBO Sensor") ``` ::: callout-caution amb2_pink sensor may have had a bad calibration (it was way off from the others) ::: # Summary In summary, our temp baths hot and ambient separated consistently and our PAR conditions were roughly the same; which was the intent of the experimental setup. amb2_pink PAR calibration may be off, however amb2_orangepink sensor was also in the ambient 2 bath, and shows expected PAR values (the reason we had redundancy in our sensors was in case one went off)