GerminaR 1.6

Flavio Lozano-Isla, Omar Benites-Alfaro, Denise Garcia de Santana, Marli A. Ranal, Marcelo Francisco Pompelli

2020-09-01

The package GerminaR has been developed to calculate different germination indices and graphical functions to analyze punctual and accumulative germination. For calculating the indices is necessary acumulative germination data. For more details, you can read the description of each index, the seed germination dataset and analysis in the germinar’s user manual. (GerminaQuant)

First we load the GerminaR package. It provides the prosopis dataset set that we will work throughout all the examples.

Data: GerminaR

The prosopis dataset contains information from an experiment containing information from germination experiment with Prosopis juliflor under different osmotic potentials and temperatures evaluated during 10 days.

library(GerminaR)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(GerminaR)
dt <- prosopis %>% mutate(across(c(nacl, temp, rep), as.factor))

List of the principal functions

Interactive shiny application

The functionGerminaQuant() activates an interactive application with friendly interface for performing the different germination, statistical and graphic analysis. For activation of some function could be necessary internet connection

GerminaQuant()

Summary of Germination Variables

The function ger_summary(), according to the accumulative germination data, calculates several germination indices maintaining the values of each experimental unit and experiments factor for statistical analysis.

smr <- ger_summary(SeedN = "seeds"
                   , evalName = "D"
                   , data = dt
                   )

knitr::kable(head(smr, 10),align = "c")
rep nacl temp seeds grs grp mgt mgr gsp unc syn vgt sdg cvg
1 0 25 50 50 100 1.28 0.7812500 78.12500 0.9461447 0.6302041 0.3281633 0.5728554 44.75433
2 0 25 50 50 100 1.22 0.8196721 81.96721 0.8157272 0.6661224 0.2159184 0.4646702 38.08772
3 0 25 50 50 100 1.32 0.7575758 75.75758 0.9043815 0.5559184 0.2220408 0.4712121 35.69788
4 0 25 50 50 100 1.14 0.8771930 87.71930 0.5842388 0.7542857 0.1228571 0.3505098 30.74648
1 0 30 50 50 100 1.04 0.9615385 96.15385 0.2422922 0.9216327 0.0391837 0.1979487 19.03353
2 0 30 50 50 100 1.06 0.9433962 94.33962 0.3274449 0.8848980 0.0575510 0.2398979 22.63188
3 0 30 50 50 100 1.00 1.0000000 100.00000 0.0000000 1.0000000 0.0000000 0.0000000 0.00000
4 0 30 50 50 100 1.02 0.9803922 98.03922 0.1414405 0.9600000 0.0200000 0.1414214 13.86484
1 0.5 25 50 50 100 1.90 0.5263158 52.63158 1.0844751 0.5812245 0.3775510 0.6144518 32.33957
2 0.5 25 50 50 100 1.70 0.5882353 58.82353 1.1985488 0.4800000 0.3775510 0.6144518 36.14422

On the other hand, you can analyze each variable independently using the following germination indexes.

Plot function

fplot() is generic plot function optimized for publication graphs and you can add modification using ggplot2 package.

grt <- ger_intime(Factor = "nacl"
                  , SeedN = "seeds"
                  , evalName = "D"
                  , method = "percentage"
                  , data = dt)

fplot(data = grt
      , type = "line"
      , x = "evaluation"
      , y = "mean"
      , groups = "nacl"
      , ylab = "Germination ('%')"
      , xlab = "days"
      , glab  = "NaCl (mM)"
      , legend = "top"
      , sig = NULL
      , color = T
      )