Overcome tibble error: Error: All columns in a tibble must be vectors. x Column `solar.time` is NULL.

[abbyR]

Hi there! unsure if this is a problem due to Tidyverse or my code, but I am receiving this error message while trying to run a bayesian model with my data. The data in question was first coded from excel time using chron and then to POSIXct solar time, which is necessary in order for the model to run. For some reason, the program wants to read the data as a vector and won't run otherwise. I am working on Windows with R-version 4.0.3, tidyverse version 1.3.0, and tibble version 3.0.4. Any help would be much appreciated!

kup_in <- read.csv("2.5k_metabolizer_input.csv") #download Input file
#Converts excel date/times to R date/times 
options(chron.origin = c(month=1, day=1, year=1900))
TC <- chron((kup_in$UStc-2), format = c(dates="Day/Month/Year", times = "h:m:s"))

#Chron datetimes, change format
chron.time <- chron::chron(TC)
time.format <- "%Y-%m-%d %H:%M:%S"
text.time <- format(chron.time, time.format) 
posix.time.localtz <- as.POSIXct(text.time, format=time.format, tz='America/Anchorage')

#Convert to POSIXct time and force timzone to America/Anchorage
solar.time <- streamMetabolizer::calc_solar_time(posix.time.localtz, longitude=-149.39)

#CREATE METABOLIZER DATA COMPONENTS
kup_in$SolarTime <-  streamMetabolizer::calc_solar_time(posix.time.localtz, longitude=-149.39)
new.solar.time <- kup_in$SolarTime
BP <- kup_in$BP
DO.obs <- kup_in$USDO
light <- kup_in$Light
Q <- kup_in$Q
depth <- kup_in$Depth
tempC <- kup_in$USt
kup_in$tempK <- paste((kup_in$USt)+273.15) #create col for temp in Kelvin
tempK <- kup_in$tempK #create vector for kelvin temp
tempK <- as.numeric(tempK) #change class to numeric
kup_in$DO.sat <- exp(-139.34411+
                       ((1.575701*10^5)/tempK)-
                       ((6.642308*10^7)/tempK^2)+
                       ((1.243800*10^10)/tempK^3)-
                       ((8.621949*10^11)/tempK^4)) #create col in data set for DOsat
DO.sat <- exp(-139.34411+
                ((1.575701*10^5)/tempK)-
                ((6.642308*10^7)/tempK^2)+
                ((1.243800*10^10)/tempK^3)-
                ((8.621949*10^11)/tempK^4)) #Use Benson&Krause eq. to calc satDO
est.k600 <- kup_in$k600

#GENERATE BAYESIAN INPUT DATA FILE 
dat_bayes <- NULL
dat_bayes <- data.frame(new.solar.time,DO.obs,DO.sat,depth,tempC,light,Q,est.k600)
colnames(dat_bayes)<- c("solar.time","DO.obs","DO.sat","depth","temp.water","light","discharge","k600")

#Fit priors...
#Run model
bayes_fit_kup_2 <- metab(bayes_specs_2, data=dat_bayes)

#ERROR MESSAGE:
Error: All columns in a tibble must be vectors.
x Column `solar.time` is NULL.
Run `rlang::last_error()` to see where the error occurred.

[nirgrahamuk]

Hi!

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue? Please have a look at this guide, to see how to create one:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[abbyR]

Sorry about that, please find here:

suppressPackageStartupMessages({
  library(tidyverse)
  library(streamMetabolizer)
  library(ggpubr)
  library(lubridate)
  library(rstan)
  library(StanHeaders)
  library(chron)
  library(Rcpp)
  library(RcppArmadillo)
  library(reprex)
  library(datapasta)
})
search()
packageVersion("tidyverse")
# SET DIRECTORY AND ADJUST DATE/TIME; ADDED UPDATED SOLAR TIME EDITS
setwd("C:\\Users\\Administrator\\Desktop")
getwd()
kup_in <- read.csv("reprex_data.csv") #download Input file
names(kup_in)[1:2] <- c("Date","Time") #Had to manually set colnames bc Windows messed up formatting

#Converts excel date/times to R date/times 
options(chron.origin = c(month=1, day=1, year=1900))
TC <- chron((kup_in$UStc-2), format = c(dates="Day/Month/Year", times = "h:m:s"))

#Chron datetimes, change format
chron.time <- chron::chron(TC)
time.format <- "%Y-%m-%d %H:%M:%S"
text.time <- format(chron.time, time.format) #as.POSIXct works poorly with chron
posix.time.localtz <- as.POSIXct(text.time, format=time.format, tz='America/Anchorage')
#Convert to POSIXct time and force timzone to America/Anchorage
solar.time <- streamMetabolizer::calc_solar_time(posix.time.localtz, longitude=-149.39)

#CREATE METABOLIZER DATA COMPONENTS
kup_in$SolarTime <-  streamMetabolizer::calc_solar_time(posix.time.localtz, longitude=-149.39)
new.solar.time <- kup_in$SolarTime
BP <- kup_in$BP
DO.obs <- kup_in$USDO
light <- kup_in$Light
Q <- kup_in$Q
depth <- kup_in$Depth
tempC <- kup_in$USt
kup_in$tempK <- paste((kup_in$USt)+273.15) #create col for temp in Kelvin
tempK <- kup_in$tempK #create vector for kelvin temp
tempK <- as.numeric(tempK) #change class to numeric
kup_in$DO.sat <- exp(-139.34411+
                       ((1.575701*10^5)/tempK)-
                       ((6.642308*10^7)/tempK^2)+
                       ((1.243800*10^10)/tempK^3)-
                       ((8.621949*10^11)/tempK^4)) #create col in data set for DOsat
DO.sat <- exp(-139.34411+
                ((1.575701*10^5)/tempK)-
                ((6.642308*10^7)/tempK^2)+
                ((1.243800*10^10)/tempK^3)-
                ((8.621949*10^11)/tempK^4)) #Use Benson&Krause eq. to calc satDO
est.k600 <- kup_in$k600


#GENERATE BAYESIAN INPUT DATA FILE 
dat_bayes <- NULL
dat_bayes <- data.frame(new.solar.time,DO.obs,DO.sat,depth,tempC,light,Q,est.k600)
colnames(dat_bayes)<- c("solar.time","DO.obs","DO.sat","depth","temp.water","light","discharge","k600")
str(dat_bayes)

# Set the model type.
bayes_name <- mm_name(type='bayes', pool_K600='none',
                      err_obs_iid=TRUE, err_proc_acor=FALSE, err_proc_iid=TRUE, 
                      ode_method = 'trapezoid', deficit_src='DO_mod', engine='stan')

#### Set the model specs. For final run pump up the burnin steps to several thousand
bayes_specs_2 <- revise(specs(bayes_name), 
                        burnin_steps=1000, saved_steps=500,
                        day_start=3, day_end=27,
                        GPP_daily_mu = 2, GPP_daily_lower = 0,GPP_daily_sigma = 2,
                        ER_daily_mu = -4, ER_daily_upper = 0, ER_daily_sigma = 3,
                        K600_daily_meanlog = log(12),
                        K600_daily_sdlog = 0.01,
                        chains=1) 
bayes_specs_2
#remove k600 and discharge from dat_bayes
dat_bayes  <- dat_bayes[,c(-7:-8)]

#run the model with the specs
bayes_fit_kup_2 <- metab(bayes_specs_2, data=dat_bayes)

data.frame(
  stringsAsFactors = FALSE,
              Date = c("6/28/2018","6/28/2018",
                       "6/28/2018","6/28/2018","6/28/2018"),
              Time = c("12:00:00 AM","12:05:00 AM",
                       "12:10:00 AM","12:15:00 AM","12:20:00 AM"),
              UStc = c(43279,43279.00347,43279.00694,
                       43279.01042,43279.01389),
               USt = c(9.13, 9.1, 9.07, 9.03, 9),
             Light = c(55.37360911,55.93355151,
                       53.89058287,56.48904704,58.9929764),
             Depth = c(0.37036501,0.370455948,
                       0.370455948,0.370000078,0.370000078)
suppressPackageStartupMessages({
  library(tidyverse)
  library(streamMetabolizer)
  library(ggpubr)
  library(lubridate)
  library(rstan)
  library(StanHeaders)
  library(chron)
  library(Rcpp)
  library(RcppArmadillo)
  library(reprex)
})
search()
#>  [1] ".GlobalEnv"                "package:reprex"           
#>  [3] "package:RcppArmadillo"     "package:Rcpp"             
#>  [5] "package:chron"             "package:rstan"            
#>  [7] "package:StanHeaders"       "package:lubridate"        
#>  [9] "package:ggpubr"            "package:streamMetabolizer"
#> [11] "package:forcats"           "package:stringr"          
#> [13] "package:dplyr"             "package:purrr"            
#> [15] "package:readr"             "package:tidyr"            
#> [17] "package:tibble"            "package:ggplot2"          
#> [19] "package:tidyverse"         "package:stats"            
#> [21] "package:graphics"          "package:grDevices"        
#> [23] "package:utils"             "package:datasets"         
#> [25] "package:methods"           "Autoloads"                
#> [27] "tools:callr"               "package:base"
packageVersion("tidyverse")
#> [1] '1.3.0'
# SET DIRECTORY AND ADJUST DATE/TIME; ADDED UPDATED SOLAR TIME EDITS
setwd("C:\\Users\\Administrator\\Desktop")
getwd()
#> [1] "C:/Users/Administrator/Desktop"
kup_in <- read.csv("reprex_data.csv") #download Input file
names(kup_in)[1:2] <- c("Date","Time") #Had to manually set colnames bc Windows messed up formatting

#Converts excel date/times to R date/times 
options(chron.origin = c(month=1, day=1, year=1900))
TC <- chron((kup_in$UStc-2), format = c(dates="Day/Month/Year", times = "h:m:s"))

#Chron datetimes, change format
chron.time <- chron::chron(TC)
time.format <- "%Y-%m-%d %H:%M:%S"
text.time <- format(chron.time, time.format) #as.POSIXct works poorly with chron
posix.time.localtz <- as.POSIXct(text.time, format=time.format, tz='America/Anchorage')
#Convert to POSIXct time and force timzone to America/Anchorage
solar.time <- streamMetabolizer::calc_solar_time(posix.time.localtz, longitude=-149.39)

#CREATE METABOLIZER DATA COMPONENTS
kup_in$SolarTime <-  streamMetabolizer::calc_solar_time(posix.time.localtz, longitude=-149.39)
new.solar.time <- kup_in$SolarTime
BP <- kup_in$BP
DO.obs <- kup_in$USDO
light <- kup_in$Light
Q <- kup_in$Q
depth <- kup_in$Depth
tempC <- kup_in$USt
kup_in$tempK <- paste((kup_in$USt)+273.15) #create col for temp in Kelvin
tempK <- kup_in$tempK #create vector for kelvin temp
tempK <- as.numeric(tempK) #change class to numeric
kup_in$DO.sat <- exp(-139.34411+
                       ((1.575701*10^5)/tempK)-
                       ((6.642308*10^7)/tempK^2)+
                       ((1.243800*10^10)/tempK^3)-
                       ((8.621949*10^11)/tempK^4)) #create col in data set for DOsat
DO.sat <- exp(-139.34411+
                ((1.575701*10^5)/tempK)-
                ((6.642308*10^7)/tempK^2)+
                ((1.243800*10^10)/tempK^3)-
                ((8.621949*10^11)/tempK^4)) #Use Benson&Krause eq. to calc satDO
est.k600 <- kup_in$k600


#GENERATE BAYESIAN INPUT DATA FILE 
dat_bayes <- NULL
dat_bayes <- data.frame(new.solar.time,DO.obs,DO.sat,depth,tempC,light,Q,est.k600)
colnames(dat_bayes)<- c("solar.time","DO.obs","DO.sat","depth","temp.water","light","discharge","k600")
str(dat_bayes)
#> 'data.frame':    287 obs. of  8 variables:
#>  $ solar.time: POSIXct, format: "2018-06-27 22:04:04" "2018-06-27 22:09:03" ...
#>  $ DO.obs    : num  10.6 10.6 10.6 10.6 10.6 ...
#>  $ DO.sat    : num  11.5 11.5 11.5 11.6 11.6 ...
#>  $ depth     : num  0.37 0.37 0.37 0.37 0.37 ...
#>  $ temp.water: num  9.13 9.1 9.07 9.03 9 8.97 8.94 8.91 8.87 8.84 ...
#>  $ light     : num  55.4 55.9 53.9 56.5 59 ...
#>  $ discharge : num  4.86 4.87 4.87 4.84 4.84 ...
#>  $ k600      : num  0.726 0.726 0.726 0.725 0.727 ...

# Set the model type.
bayes_name <- mm_name(type='bayes', pool_K600='none',
                      err_obs_iid=TRUE, err_proc_acor=FALSE, err_proc_iid=TRUE, 
                      ode_method = 'trapezoid', deficit_src='DO_mod', engine='stan')

#### Set the model specs. For final run pump up the burnin steps to several thousand
bayes_specs_2 <- revise(specs(bayes_name), 
                        burnin_steps=1000, saved_steps=500,
                        day_start=3, day_end=27,
                        GPP_daily_mu = 2, GPP_daily_lower = 0,GPP_daily_sigma = 2,
                        ER_daily_mu = -4, ER_daily_upper = 0, ER_daily_sigma = 3,
                        K600_daily_meanlog = log(12),
                        K600_daily_sdlog = 0.01,
                        chains=1) 
bayes_specs_2
#> Model specifications:
#>   model_name               b_np_oipi_tr_plrckm.stan                             
#>   engine                   stan                                                 
#>   split_dates              FALSE                                                
#>   keep_mcmcs               TRUE                                                 
#>   keep_mcmc_data           TRUE                                                 
#>   day_start                3                                                    
#>   day_end                  27                                                   
#>   day_tests                full_day, even_timesteps, complete_data, pos_disch...
#>   required_timestep        NA                                                   
#>   GPP_daily_mu             2                                                    
#>   GPP_daily_lower          0                                                    
#>   GPP_daily_sigma          2                                                    
#>   ER_daily_mu              -4                                                   
#>   ER_daily_upper           0                                                    
#>   ER_daily_sigma           3                                                    
#>   K600_daily_meanlog       2.484906649788                                       
#>   K600_daily_sdlog         0.01                                                 
#>   err_obs_iid_sigma_scale  0.03                                                 
#>   err_proc_iid_sigma_scale 5                                                    
#>   params_in                GPP_daily_mu, GPP_daily_lower, GPP_daily_sigma, ER...
#>   params_out               GPP, ER, DO_R2, GPP_daily, ER_daily, K600_daily, e...
#>   n_chains                 4                                                    
#>   n_cores                  4                                                    
#>   burnin_steps             1000                                                 
#>   saved_steps              500                                                  
#>   thin_steps               1                                                    
#>   verbose                  FALSE                                                
#>   chains                   1
#remove k600 and discharge from dat_bayes
dat_bayes  <- dat_bayes[,c(-7:-8)]

#run the model with the specs
bayes_fit_kup_2 <- metab(bayes_specs_2, data=dat_bayes)
#> Warning: `select_()` is deprecated as of dplyr 0.7.0.
#> Please use `select()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Warning: `select_vars()` is deprecated as of dplyr 0.8.4.
#> Please use `tidyselect::vars_select()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Warning: `mutate_()` is deprecated as of dplyr 0.7.0.
#> Please use `mutate()` instead.
#> See vignette('programming') for more help
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Warning: `data_frame()` is deprecated as of tibble 1.1.0.
#> Please use `tibble()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Error: All columns in a tibble must be vectors.
#> x Column `solar.time` is NULL.
#> Timing stopped at: 1.44 0 1.43

[nirgrahamuk]

Im sorry, but please consult the guide a second time.
The purpose of a reprex is for us to be able to run the code to achieve the problematic state. This is not possible if data is not provided.

[abbyR]

Apologies, hopefully this works better?

data.frame(
  stringsAsFactors = FALSE,
              Date = c("6/28/2018","6/28/2018",
                       "6/28/2018","6/28/2018","6/28/2018"),
              Time = c("12:00:00 AM","12:05:00 AM",
                       "12:10:00 AM","12:15:00 AM","12:20:00 AM"),
              UStc = c(43279,43279.00347,43279.00694,
                       43279.01042,43279.01389),
               USt = c(9.13, 9.1, 9.07, 9.03, 9),
              USDO = c(10.60079025,10.61051408,
                       10.61051408,10.62138836,10.6313428),
             Light = c(55.37360911,55.93355151,
                       53.89058287,56.48904704,58.9929764),
             Depth = c(0.37036501,0.370455948,
                       0.370455948,0.370000078,0.370000078)
)
#>        Date        Time     UStc  USt     USDO    Light     Depth
#> 1 6/28/2018 12:00:00 AM 43279.00 9.13 10.60079 55.37361 0.3703650
#> 2 6/28/2018 12:05:00 AM 43279.00 9.10 10.61051 55.93355 0.3704559
#> 3 6/28/2018 12:10:00 AM 43279.01 9.07 10.61051 53.89058 0.3704559
#> 4 6/28/2018 12:15:00 AM 43279.01 9.03 10.62139 56.48905 0.3700001
#> 5 6/28/2018 12:20:00 AM 43279.01 9.00 10.63134 58.99298 0.3700001
suppressPackageStartupMessages({
  library(tidyverse)
  library(streamMetabolizer)
  library(ggpubr)
  library(lubridate)
  library(rstan)
  library(StanHeaders)
  library(chron)
  library(Rcpp)
  library(RcppArmadillo)
  library(reprex)
  library(datapasta)
})
search()
#>  [1] ".GlobalEnv"                "package:datapasta"        
#>  [3] "package:reprex"            "package:RcppArmadillo"    
#>  [5] "package:Rcpp"              "package:chron"            
#>  [7] "package:rstan"             "package:StanHeaders"      
#>  [9] "package:lubridate"         "package:ggpubr"           
#> [11] "package:streamMetabolizer" "package:forcats"          
#> [13] "package:stringr"           "package:dplyr"            
#> [15] "package:purrr"             "package:readr"            
#> [17] "package:tidyr"             "package:tibble"           
#> [19] "package:ggplot2"           "package:tidyverse"        
#> [21] "package:stats"             "package:graphics"         
#> [23] "package:grDevices"         "package:utils"            
#> [25] "package:datasets"          "package:methods"          
#> [27] "Autoloads"                 "tools:callr"              
#> [29] "package:base"
packageVersion("tidyverse")
#> [1] '1.3.0'
# SET DIRECTORY AND ADJUST DATE/TIME; ADDED UPDATED SOLAR TIME EDITS
setwd("C:\\Users\\Administrator\\Desktop")
getwd()
#> [1] "C:/Users/Administrator/Desktop"
kup_in <- read.csv("reprex_data.csv") #download Input file
names(kup_in)[1:2] <- c("Date","Time") #Had to manually set colnames bc Windows messed up formatting

#Converts excel date/times to R date/times 
options(chron.origin = c(month=1, day=1, year=1900))
TC <- chron((kup_in$UStc-2), format = c(dates="Day/Month/Year", times = "h:m:s"))

#Chron datetimes, change format
chron.time <- chron::chron(TC)
time.format <- "%Y-%m-%d %H:%M:%S"
text.time <- format(chron.time, time.format) #as.POSIXct works poorly with chron
posix.time.localtz <- as.POSIXct(text.time, format=time.format, tz='America/Anchorage')
#Convert to POSIXct time and force timzone to America/Anchorage
solar.time <- streamMetabolizer::calc_solar_time(posix.time.localtz, longitude=-149.39)

#CREATE METABOLIZER DATA COMPONENTS
kup_in$SolarTime <-  streamMetabolizer::calc_solar_time(posix.time.localtz, longitude=-149.39)
new.solar.time <- kup_in$SolarTime
BP <- kup_in$BP
DO.obs <- kup_in$USDO
light <- kup_in$Light
Q <- kup_in$Q
depth <- kup_in$Depth
tempC <- kup_in$USt
kup_in$tempK <- paste((kup_in$USt)+273.15) #create col for temp in Kelvin
tempK <- kup_in$tempK #create vector for kelvin temp
tempK <- as.numeric(tempK) #change class to numeric
kup_in$DO.sat <- exp(-139.34411+
                       ((1.575701*10^5)/tempK)-
                       ((6.642308*10^7)/tempK^2)+
                       ((1.243800*10^10)/tempK^3)-
                       ((8.621949*10^11)/tempK^4)) #create col in data set for DOsat
DO.sat <- exp(-139.34411+
                ((1.575701*10^5)/tempK)-
                ((6.642308*10^7)/tempK^2)+
                ((1.243800*10^10)/tempK^3)-
                ((8.621949*10^11)/tempK^4)) #Use Benson&Krause eq. to calc satDO
est.k600 <- kup_in$k600


#GENERATE BAYESIAN INPUT DATA FILE 
dat_bayes <- NULL
dat_bayes <- data.frame(new.solar.time,DO.obs,DO.sat,depth,tempC,light,Q,est.k600)
colnames(dat_bayes)<- c("solar.time","DO.obs","DO.sat","depth","temp.water","light","discharge","k600")
str(dat_bayes)
#> 'data.frame':    287 obs. of  8 variables:
#>  $ solar.time: POSIXct, format: "2018-06-27 22:04:04" "2018-06-27 22:09:03" ...
#>  $ DO.obs    : num  10.6 10.6 10.6 10.6 10.6 ...
#>  $ DO.sat    : num  11.5 11.5 11.5 11.6 11.6 ...
#>  $ depth     : num  0.37 0.37 0.37 0.37 0.37 ...
#>  $ temp.water: num  9.13 9.1 9.07 9.03 9 8.97 8.94 8.91 8.87 8.84 ...
#>  $ light     : num  55.4 55.9 53.9 56.5 59 ...
#>  $ discharge : num  4.86 4.87 4.87 4.84 4.84 ...
#>  $ k600      : num  0.726 0.726 0.726 0.725 0.727 ...

# Set the model type.
bayes_name <- mm_name(type='bayes', pool_K600='none',
                      err_obs_iid=TRUE, err_proc_acor=FALSE, err_proc_iid=TRUE, 
                      ode_method = 'trapezoid', deficit_src='DO_mod', engine='stan')

#### Set the model specs. For final run pump up the burnin steps to several thousand
bayes_specs_2 <- revise(specs(bayes_name), 
                        burnin_steps=1000, saved_steps=500,
                        day_start=3, day_end=27,
                        GPP_daily_mu = 2, GPP_daily_lower = 0,GPP_daily_sigma = 2,
                        ER_daily_mu = -4, ER_daily_upper = 0, ER_daily_sigma = 3,
                        K600_daily_meanlog = log(12),
                        K600_daily_sdlog = 0.01,
                        chains=1) 
bayes_specs_2
#> Model specifications:
#>   model_name               b_np_oipi_tr_plrckm.stan                             
#>   engine                   stan                                                 
#>   split_dates              FALSE                                                
#>   keep_mcmcs               TRUE                                                 
#>   keep_mcmc_data           TRUE                                                 
#>   day_start                3                                                    
#>   day_end                  27                                                   
#>   day_tests                full_day, even_timesteps, complete_data, pos_disch...
#>   required_timestep        NA                                                   
#>   GPP_daily_mu             2                                                    
#>   GPP_daily_lower          0                                                    
#>   GPP_daily_sigma          2                                                    
#>   ER_daily_mu              -4                                                   
#>   ER_daily_upper           0                                                    
#>   ER_daily_sigma           3                                                    
#>   K600_daily_meanlog       2.484906649788                                       
#>   K600_daily_sdlog         0.01                                                 
#>   err_obs_iid_sigma_scale  0.03                                                 
#>   err_proc_iid_sigma_scale 5                                                    
#>   params_in                GPP_daily_mu, GPP_daily_lower, GPP_daily_sigma, ER...
#>   params_out               GPP, ER, DO_R2, GPP_daily, ER_daily, K600_daily, e...
#>   n_chains                 4                                                    
#>   n_cores                  4                                                    
#>   burnin_steps             1000                                                 
#>   saved_steps              500                                                  
#>   thin_steps               1                                                    
#>   verbose                  FALSE                                                
#>   chains                   1
#remove k600 and discharge from dat_bayes
dat_bayes  <- dat_bayes[,c(-7:-8)]

#run the model with the specs
bayes_fit_kup_2 <- metab(bayes_specs_2, data=dat_bayes)
#> Warning: `select_()` is deprecated as of dplyr 0.7.0.
#> Please use `select()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Warning: `select_vars()` is deprecated as of dplyr 0.8.4.
#> Please use `tidyselect::vars_select()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Warning: `mutate_()` is deprecated as of dplyr 0.7.0.
#> Please use `mutate()` instead.
#> See vignette('programming') for more help
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Warning: `data_frame()` is deprecated as of tibble 1.1.0.
#> Please use `tibble()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Error: All columns in a tibble must be vectors.
#> x Column `solar.time` is NULL.
#> Timing stopped at: 1.41 0.01 1.42