Package {hoboR}

Title: Weather Station Data Summarization and Manipulation for HOBO Data Loggers
Version: 1.1.0
Author: Ricardo I. Alcala Briseno [aut, cre], Adam Carson [ctb], Yung-Hsiang Lan [ctb], Ebba Peterson [ctb], Niklaus J. Grunwald [ctb], Jared M. LeBoldus [ctb]
Maintainer: Ricardo I. Alcala Briseno <ria5282@psu.edu>
Description: Processing of CSV files generated by HOBO weather stations and data loggers. The package automatically imports multiple HOBO data records, removes duplicate records, identifies impossible values, subsets user-defined time ranges, and summarizes environmental data.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Depends: R (≥ 4.3.0)
Imports: dplyr, lubridate, reshape, reshape2, ggplot2, tidyr, scales
Suggests: testthat (≥ 3.0.0)
URL: https://www.r-project.org, https://leboldus-lab.github.io/hoboR/
BugReports: https://github.com/LeBoldus-Lab/hoboR/issues
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-05-31 04:57:29 UTC; ricar
Repository: CRAN
Date/Publication: 2026-06-04 11:40:03 UTC

Calibrator HOBO data in CSV format

Description

Calculates the difference between HOBO devices under controlled conditions. This additive function calculates the difference among hobo loggers using a base correction to HOBO loggers.

Usage

calibrator(list.data, columns = c(2, 7, 12), times, round = 7)

Arguments

list.data

A list containing the HOBO CSV files.

columns

The columns to be used for calibration.

times

The times in a vector of dates to be included in the calibration process.

round

The number of decimal places to round the results to.

Value

a data frame with the differences for data correction, to use with corrector

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata/calibration", package = "hoboR")
 
folder=paste0(rep("canopy", 5), 1:5)

pathtoread = data = list()

for (i in seq_along(folder)){
  pathtoread[[i]] <- paste0(path, "/",folder[i])
  # Loading all hobo files
  data[[i]] <- hobinder(as.character(pathtoread[i]), header = TRUE, skip = 0,
   channels = "ON" ) # channels is a new feature
}

# Make sure you enter the date & time format with zeros, 
# for example 08:00 instead of 8:00 for 8am.
times <- c("2022-03-22 01:00", "2022-03-22 02:00", "2022-03-22 03:00", 
           "2022-03-22 04:00", "2022-03-22 05:00", "2022-03-22 06:00", 
           "2022-03-22 07:00", "2022-03-22 08:00", "2022-03-22 09:00") 
           
variables <- c(3, 8, 13) # Select the weather variables 
           
calibrationmeans <- calibrator(data, columns= variables, times = times)

Correction test for HOBO data from calibrator

Description

Additive function to calculate the difference among hobo loggers to calibrate using a base correction to the data

Usage

correction(data, w.var = "FULL", calibrate = calibrate)

Arguments

data

a list of CVS data containing the hobo

w.var

a column to correct the weather variable e.g., Temperature, RH (relative humidity), or FULL, will use the output of calibrator

calibrate

a value to correct the weather variable, must be numeric or USEFILE, will use the output of calibrator

Value

a data frame with the differences for data correction, to use with corrector

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata/calibration", package = "hoboR")

folder=paste0(rep("canopy", 5), 1:5)

pathtoread = data = list()

for (i in seq_along(folder)){
  pathtoread[[i]] <- paste0(path, "/",folder[i])
  # Loading all hobo files
  data[[i]] <- hobinder(as.character(pathtoread[i]), header = TRUE, skip = 0,
   channels = "ON" ) # channels is a new feature
}
 
# Double-check you enter the same date format
times <- c("2022-03-22 01:00", "2022-03-22 02:00", "2022-03-22 03:00", 
            "2022-03-22 04:00","2022-03-22 05:00", "2022-03-22 06:00", 
            "2022-03-22 07:00", "2022-03-22 08:00","2022-03-22 09:00") 
            
variables <- c(3, 8, 13) # Select the weather variables 

meanvars <- calibrator(data, columns = variables, times = times)
                
calibratedfiles <- correction(data = data, 
                              w.var = "FULL", 
                              calibrate = meanvars)

Correction Test for HOBO data from calibrator

Description

This function calculates the difference among HOBO loggers, finding the variance and using it as a base correction. It's designed to adjust HOBO data based on calibration files and specified thresholds.

Usage

correction.test(
  list.data,
  calibrationfile,
  w.var = c(3, 8, 13),
  times = times,
  threshold = c(1, 5, 10)
)

Arguments

list.data

A list of CSV data frames containing the HOBO data.

calibrationfile

A data frame representing the calibration file.

w.var

A vector of column indices to be used in the correction.

times

A vector of times for which the data is relevant.

threshold

A vector of threshold values for passing the correction test. The smaller the value the highest precision.

Value

A data frame with the differences for data correction, to be used with a corrector.

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata/calibration", package = "hoboR")

folder=paste0(rep("canopy", 5), 1:5)

pathtoread = data = list()

for (i in seq_along(folder)){
  pathtoread[[i]] <- paste0(path, "/",folder[i])
  # Loading all hobo files
  data[[i]] <- hobinder(as.character(pathtoread[i]), header = TRUE, skip = 0,
   channels = "ON" ) # channels is a new feature
}
 
# Double-check you enter the same date format
times <- c("2022-03-22 01:00", "2022-03-22 02:00", "2022-03-22 03:00", 
            "2022-03-22 04:00","2022-03-22 05:00", "2022-03-22 06:00", 
            "2022-03-22 07:00", "2022-03-22 08:00","2022-03-22 09:00") 
            
variables <- c(3, 8, 13) # Select the weather variables 

meanvars <- calibrator(data, columns = variables, times = times)
 
correction.test(list.data = data, calibrationfile =  meanvars, 
                w.var = variables, 
                times = times, 
                threshold = c(1, 5, 10))

HOBO count NA's This function counts the number of NA's in your data set

Description

HOBO software

Usage

count_NAs(data, w.var)

Arguments

data

dataframe with suspected NA's

w.var

weather variables to test

Value

Returns the numbers of NAs for the impossible values

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples

path <- system.file("extdata", package = "hoboR")

csvfiles <- hobinder(path, header = TRUE, skip = 1, channels = "OFF") 

cleancsv <- hobocleaner(csvfiles)

NAdata <- sensorfailures(cleancsv, condition = "<", threshold = c(0, 0), 
                            w.var = c("Rain", "Wetness"))
                            
count_NAs(NAdata, "Temp")

Reads HOBO data in CSV format

Description

Two functions that read the original data downloaded from HOBO software adding file names as metadata for each .csv file and cleans the data from duplicates creating a continuous file from all .csv's

Usage

hobinder(path, channels = "OFF", ...)

Arguments

path

select the path to the directory with the csv files

channels

turn on or off additional channels in HOBO data logger, default "OFF"

...

arguments to be passed to methods

Value

large csv file

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata", package = "hoboR")

csvfiles <- hobinder(path, header = TRUE, skip = 1, channels = "OFF") 

head(csvfiles)

Reads HOBO data in CSV format

Description

Functions that cleans the original data downloaded from the HOBO software, cleans the data and removes duplicates creating a continuous file from all .csv's

Usage

hobocleaner(file, format = "ymd", na.rm = TRUE)

Arguments

file

CSV from hobinder

format

Select the time format, month, day, and year (mdy), year, month, and day (ymd) or year two digits, month and day (yymd)

na.rm

TRUE or FALSE to remove NAs, TRUE is default

Value

formatted data frame and duplicate values removed

Author(s)

Ricardo I Alcala Briseno, ria5292@psu.edu

Examples


path <- system.file("extdata", package = "hoboR")

csvfiles <- hobinder(path, header = TRUE, skip = 1, channels = "OFF") 

cleancsv <- hobocleaner(csvfiles)

head(cleancsv)

Correlation plot for variables within a time range

Description

This function provides a time point for a specified number of days. HOBO software

Arguments

data

A data frame with the HOBO data and a Date column

summariseby

Provide the interval date to present (e.g., "month")

by

Summary function for aggregation (e.g., "mean")

na.rm

Logical, whether to remove NAs from the result

Value

A ggplot object representing the correlation heatmap

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata", package = "hoboR")

csvfiles <- hobinder(path, header = TRUE, skip = 1, channels = "OFF") 

cleaned <- hobocleaner(csvfiles, format = "ymd")

hobocorrelations(cleaned, summariseby = "month", by = "mean", na.rm = FALSE)

Calculate Date Range Temperature Using HOBO Data

Description

This function calculates the means for weather data collected by HOBO loggers over a specified date range. It's designed for use with data exported from HOBO software.

Usage

hoborange(
  data,
  start = "2022-08-04 09:05",
  end = "2022-10-04 09:05",
  na.rm = TRUE
)

Arguments

data

A data frame containing the HOBO data, including a Date column in POSIXct format.

start

The start of the date range in "yyyy-mm-dd HH:MM" format.

end

The end of the date range in "yyyy-mm-dd HH:MM" format.

na.rm

A logical value indicating whether NA values should be removed before calculation.

Value

A subset of the original data frame limited to the specified date range.

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata", package = "hoboR")

csvfiles <- hobinder(path, header = TRUE, skip = 1, channels = "OFF") 

cleancsv <- hobocleaner(csvfiles)

site.ranges <- hoborange(cleancsv, start = "2022-08-04 09:05",  
                          end = "2022-10-04 09:05")

Summarise HOBO data by time intervals

Description

This function calculates hobo weather by minutes HOBO software

Usage

hobotime(data, summariseby = "5 mins", na.rm = TRUE, na.action = na.omit)

Arguments

data

a data frame with the hobo data and a Date column

summariseby

a time interval in minmutes

na.rm

logical vector TRUE or FALSE

na.action

na.omit remove rows with NA's, na.pass keeps NA's

Value

a data frame summarized by minutes

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata", package = "hoboR")

data <- hobinder(path, header = TRUE, skip = 1, channels = "OFF") 

cleandata <- hobocleaner(data, format = "ymd")

# this function needs clean data 
subset <- hobotime(cleandata,  summariseby = "5 mins", na.rm = TRUE)

head(subset)

HOBO impossible values

Description

Functions that gets the mean by date of the cleaned data downloaded from the HOBO software

Usage

impossiblevalues(data, showrows = 10, ...)

Arguments

data

Cleaned hobo data frame from ⁠original csv⁠ or hobocleaner and hobotime

showrows

Number of rows to show for maximum values, default is 10

...

arguments to be passed to methods

Value

Gives the rows with impossible values

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata", package = "hoboR")

csvfiles <- hobinder(path, header = TRUE, skip = 1, channels = "OFF") 

cleancsv <- hobocleaner(csvfiles)

impossiblevalues(cleancsv, showrows = 10)

mean HOBO data in CSV format

Description

Functions that gets the summary statistics by summarizing by date the cleaned data downloaded from the HOBO software

Arguments

data

cleaned hobo data frame from hobocleaneri

summarisedby

select a time interval 60 min, 24 hours, 1 day

na.rm

TRUE or FALSE to remove NAs, TRUE is default

minmax

TRUE or FALSE to retain min and max temperatures

Value

smaller data frame with means and standard deviation

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata", package = "hoboR")

data <- hobinder(path, header = TRUE, skip = 1, channels = "OFF") 

cleandata <- hobocleaner(data, format = "ymd")
 
# this function needs clean data#needs clean data 
hobodata <- meanhobo(cleandata, summariseby = "5 mins", 
                                    na.rm = TRUE, minmax = TRUE)

Calculates sampling rates from a CSV format

Description

This function calculates incidence and rates for baiting Phytophthora collected on dates for baited and removed leaves HOBO software

Usage

sampling.rates(samples, n, round)

Arguments

samples

a csv with the format

n

Mandatory. Specifies the number of replicates of the experiment

round

Optional. Specifies the number of decimal places for rounding the output incidence calculated from a csv table designed for baiting Phytophthora

Value

smaller data frame with incidence and rates

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata/sampling", package = "hoboR")
#' 
samples <- read.csv(paste0(path, "/", "sampling.csv"))
 
samp.rates <- sampling.rates(samples, n = 9, round = 2)

HOBO Remove Values This function is designed to identify and address values that are likely to be out of range, such as temperatures exceeding the maximum recorded in your study area or relative humidity (RH) values exceeding 100%, which are not feasible. The sensorfailure() function allows you to detect and flag these anomalous readings, facilitating the cleansing of your dataset by replacing all out-of-range values with NA's, maintaining the integrity and accuracy of of your data analysis.

Description

HOBO software

Usage

sensorfailures(
  data,
  condition = ">",
  threshold = c(34, 8),
  w.var = c("Temp", "Rain")
)

Arguments

data

Convert to NA the impossible values from HOBO data frame from ⁠original csv⁠ OR hobocleaner OR hobotime

condition

The condition for removal, one of (">", "<", "==", ">=", "<=")

threshold

Numeric vector specifying the threshold values for removal

w.var

weather variables, can be a single or multiple variables

Value

Returns the data with NAs for the impossible values

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata", package = "hoboR")

csvfiles <- hobinder(path, header = TRUE, skip = 1, channels = "OFF") 

data <- hobocleaner(csvfiles)

sensorfailures(data, condition = ">", 
                threshold = c(50, 3000, 101), 
                w.var = c("Temp", "Rain", "Wetness"))

NAdata <- sensorfailures(data, condition = "<", 
                threshold = c(0, 0), 
                w.var = c("Rain", "Wetness"))

Test HOBO data for calibration

Description

Check if the times date are present in the list of HOBO files

Usage

testhobolist(data, times)

Arguments

data

a list of CVS data containing hobo data

times

a series of times <- c("2022-03-22 01:00", "2022-03-22 02:00", "2022-03-22 03:00")

Value

a data frame with the total entries and the count of entries present in each data set

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata/calibration", package = "hoboR")
 
folder=paste0(rep("canopy", 5), 1:5)

pathtoread = data = list()

for (i in seq_along(folder)){
  pathtoread[[i]] <- paste0(path, "/",folder[i])
  # Loading all hobo files
  data[[i]] <- hobinder(as.character(pathtoread[i]), header = TRUE, skip = 0,
   channels = "ON" ) # channels is a new feature
}

# Make sure you enter the date & time format with zeros, 
# for example 08:00 instead of 8:00 for 8am.
times <- c("2022-03-22 01:00", "2022-03-22 02:00", "2022-03-22 03:00", 
           "2022-03-22 04:00", "2022-03-22 05:00", "2022-03-22 06:00", 
           "2022-03-22 07:00", "2022-03-22 08:00", "2022-03-22 09:00") 

data <- testhobolist(data, times)

Timestamp for Specific Intervals

Description

This function provides a time point for a specified number of days. HOBO software

Usage

timestamp(
  data,
  stamp = "yyyy/mm/dd: ss",
  by = "24 hours",
  days = 100,
  na.rm = TRUE,
  plot = TRUE,
  var = "Temp"
)

Arguments

data

A data frame with the HOBO data and a Date column

stamp

Provide a date

by

Provide the interval date to present (e.g., "24 hours")

days

Number of days for the interval

na.rm

Logical, whether to remove NAs from the result

plot

Logical, whether to generate a plot

var

Variable to plot (default is "Temp")

Value

A data frame summarized by minutes

Author(s)

Ricardo I Alcala Briseno, ria5282@psu.edu

Examples


path <- system.file("extdata", package = "hoboR")

data <- hobinder(path, header = TRUE, skip = 1, channels = "OFF")

cleandata <- hobocleaner(data, format = "ymd")

datastamp <- timestamp(cleandata, stamp ="2022-08-04 12:00", 
                        by = "1 hour", days = 60, na.rm = TRUE, 
                        plot = TRUE, var = "Temp")