## ----setup, include = FALSE--------------------------------------------------- # rmd style knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE, message = FALSE, fig.align = 'center', fig.width = 6 ) options(tibble.print_min = 5, tibble.print_max = 5) # load packages library(hatchR) library(lubridate) library(readr) library(dplyr) library(nycflights13) library(tibble) library(ggplot2) ## ----echo = FALSE, out.width = '50%'------------------------------------------ knitr::include_graphics("img/workflow.png") ## ----eval = FALSE------------------------------------------------------------- # library(lubridate) ## ----------------------------------------------------------------------------- today() now() ## ----eval=FALSE--------------------------------------------------------------- # library(readr) ## ----------------------------------------------------------------------------- csv <- " date,datetime 2022-01-02,2022-01-02 05:12 " read_csv(csv) ## ----------------------------------------------------------------------------- ymd("2017-01-31") mdy("January 31st, 2017") mdy_hm("01/31/2017 08:01") ymd_hms("2017-01-31 20:11:59") ## ----eval=FALSE--------------------------------------------------------------- # library(nycflights13) # library(dplyr) ## ----------------------------------------------------------------------------- flights |> select(year, month, day) |> mutate(date = make_date(year, month, day)) flights |> select(year, month, day, hour, minute) |> mutate(departure = make_datetime(year, month, day, hour, minute)) ## ----------------------------------------------------------------------------- Sys.timezone() ## ----------------------------------------------------------------------------- x1 <- ymd_hms("2024-06-01 12:00:00", tz = "America/New_York") x1 ## ----eval=FALSE--------------------------------------------------------------- # library(readr) ## ----------------------------------------------------------------------------- path_cr <- system.file("extdata/crooked_river.csv", package = "hatchR") path_wi <- system.file("extdata/woody_island.csv", package = "hatchR") ## ----------------------------------------------------------------------------- crooked_river <- read_csv(path_cr) woody_island <- read_csv(path_wi) ## ----eval=FALSE--------------------------------------------------------------- # library(tibble) ## ----------------------------------------------------------------------------- glimpse(crooked_river) glimpse(woody_island) ## ----eval=FALSE--------------------------------------------------------------- # library(readr) # library(tibble) # your_data <- read_csv("data/your_data.csv") # glimpse(your_data) ## ----------------------------------------------------------------------------- crooked_river <- read.csv(path_cr) woody_island <- read.csv(path_wi) glimpse(crooked_river) # note date column imported as a character () glimpse(woody_island) # note date column imported as a character () ## ----------------------------------------------------------------------------- # if your date is in the form "2000-09-01 12:00:00" crooked_river$date <- ymd_hms(crooked_river$date) # if your date is in the form "2000-09-01" woody_island$date <- mdy(woody_island$date) glimpse(crooked_river) glimpse(woody_island) ## ----eval = FALSE------------------------------------------------------------- # library(hatchR) ## ----------------------------------------------------------------------------- plot_check_temp(data = crooked_river, dates = date, temperature = temp_c, temp_min = 0, temp_max = 12) ## ----------------------------------------------------------------------------- # set seed for reproducibility set.seed(123) # create vector of date-times for a year at 30 minute intervals dates <- seq( from = ymd_hms("2000-01-01 00:00:00"), to = ymd_hms("2000-12-31 23:59:59"), by = "30 min" ) # simulate temperature data fake_data <- tibble( date = dates, temp = rnorm(n = length(dates), mean = 10, sd = 3) |> abs() ) # check it glimpse(fake_data) ## ----------------------------------------------------------------------------- fake_data_sum <- summarize_temp(data = fake_data, temperature = temp, dates = date) nrow(fake_data) #17568 records nrow(fake_data_sum) #366 records; 2000 was a leap year :) ## ----------------------------------------------------------------------------- # note we use fake_data_sum instead of fake_data plot_check_temp(data = fake_data_sum, dates = date, temperature = daily_temp, temp_min = 5, temp_max = 15) ## ----warning=TRUE, message=TRUE----------------------------------------------- check_continuous(data = crooked_river, dates = date) check_continuous(data = woody_island, dates = date) ## ----warning=TRUE, message=TRUE----------------------------------------------- check_continuous(data = crooked_river[-5,], dates = date)