library(knitr) # load knitr to enable options library(respR) # load respR opts_chunk$set(collapse = TRUE, comment = "#>", cache = FALSE, tidy = TRUE, highlight = TRUE)
respR
integrates nicely with the tidyverse, specifically with dplyr
functions e.g. select()
, filter()
and mutate()
, and magrittr
pipe operators ("%>%
") to clearly express workflows in an organised sequence.
It also works with the new native pipe operator (|>
) introduced in R v4.1, however the dpylr
pipes have some additional functionality. For more information, see the Pipes chapter in the online R for Data Science book.
Here we show how using %>%
pipes can make data analysis worklows simpler for the user.
Typical analysis using regular R
syntax:
# 1. check data for errors, select cols 1 and 15: urch <- inspect(urchins.rd, 1, 15) # 2. automatically determine linear segment: rate <- auto_rate(urch) # 3. convert units out <- convert_rate(rate, "mg/l", "s", "mg/h/kg", 0.6, 0.4)
Alternatively, use tidyverse
pipes:
urchins.rd %>% # using the urchins dataset, select(1, 15) %>% # select columns 1 and 15 inspect() %>% # inspect the data, then auto_rate() %>% # automatically determine most linear segment convert_rate("mg/l", "s", "mg/h/kg", 0.6, 0.4) # convert units
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