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Example vis-NIRS and reference dataset

Source: R/data.R
ikeogu.2017.Rd

The `ikeogu.2017` data set contains raw vis-NIRS scans, total carotenoid content, and cassava root dry matter content (using the oven method) from the 2017 PLOS One paper by Ikeogu et al. This dataset contains a subset of the original scans and reference values from the supplementary files of the paper. `ikeogu.2017` is a `data.frame` that contains the following columns:

  • study.name = Name of the study as described in Ikeogu et al. (2017).

  • sample.id = Unique identifier for each individual root sample

  • DMC.oven = Cassava root dry matter content, the percentage of dry weight relative to fresh weight of a sample after oven drying.

  • TCC = Total carotenoid content (\(\mu g/g\), unknown whether on a fresh or dry weight basis) as measured by high performance liquid chromatography

  • X350:X2500 = spectral reflectance measured with the QualitySpec Trek: S-10016 vis-NIR spectrometer. Each cell represents the mean of 150 scans on a single root at a single wavelength.

Usage

ikeogu.2017

Format

An object of class tbl_df (inherits from tbl, data.frame) with 175 rows and 2155 columns.

References

Ikeogu, U.N., F. Davrieux, D. Dufour, H. Ceballos, C.N. Egesi, et al. 2017. Rapid analyses of dry matter content and carotenoids in fresh cassava roots using a portable visible and near infrared spectrometer (Vis/NIRS). PLOS One 12(12): 1–17. doi: 10.1371/journal.pone.0188918.

Author

Original authors: Ikeogu, U.N., F. Davrieux, D. Dufour, H. Ceballos, C.N. Egesi, and J. Jannink. Reformatted by Jenna Hershberger.

Examples

library(magrittr)
library(ggplot2)
data(ikeogu.2017)
ikeogu.2017[1:10, 1:10]
#> # A tibble: 10 × 10
#>    study.name sample.id  DMC.oven   TCC  X350  X351  X352  X353  X354  X355
#>    <chr>      <chr>         <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 C16Mcal    C16Mcal_1      39.6  1.00 0.488 0.495 0.506 0.494 0.500 0.496
#>  2 C16Mcal    C16Mcal_2      35.5 17.0  0.573 0.568 0.599 0.593 0.581 0.597
#>  3 C16Mcal    C16Mcal_3      42.0 21.6  0.599 0.627 0.624 0.606 0.607 0.624
#>  4 C16Mcal    C16Mcal_4      39.0  2.43 0.517 0.516 0.514 0.536 0.542 0.536
#>  5 C16Mcal    C16Mcal_5      33.4 24.0  0.519 0.548 0.554 0.549 0.549 0.567
#>  6 C16Mcal    C16Mcal_6      32.1 19.0  0.576 0.566 0.589 0.591 0.613 0.628
#>  7 C16Mcal    C16Mcal_7      35.8  6.61 0.530 0.536 0.525 0.539 0.537 0.529
#>  8 C16Mcal    C16Mcal_8      26.3 14.1  0.596 0.596 0.602 0.608 0.604 0.610
#>  9 C16Mcal    C16Mcal_9      38.1 28.9  0.675 0.662 0.688 0.694 0.697 0.695
#> 10 C16Mcal    C16Mcal_10     31.8 18.4  0.510 0.527 0.535 0.538 0.542 0.551
ikeogu.2017 %>%
  dplyr::select(-starts_with("X")) %>%
  dplyr::group_by(study.name) %>%
  tidyr::pivot_longer(cols = c(DMC.oven:TCC), names_to = "trait", values_to = "value") %>%
  tidyr::drop_na(value) %>%
  ggplot2::ggplot(aes(x = study.name, y = value, fill = study.name)) +
  facet_wrap(~trait, scales = "free_y", nrow = 2) +
  geom_boxplot()