GerminaR is a platform base in open source package to calculate and graphic the main germination indices in R. GerminaR include a web application called “GerminQuant for R” for interactive analysis.
Analysis for the germination experiment can follow a routine. The functions will de explain according to the data set included in the GerminaR package: “prosopis”.
# Install packages and dependencies
library(GerminaR)
library(dplyr)
# load data
fb <- prosopis %>%
mutate(across(c(nacl, temp, rep), as.factor))
# Prosopis data set
fb %>%
head(10) %>%
kable(caption = "Prosopis dataset")
rep | nacl | temp | seeds | D0 | D1 | D2 | D3 | D4 | D5 | D6 | D7 | D8 | D9 | D10 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | 25 | 50 | 0 | 39 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 0 | 25 | 50 | 0 | 40 | 9 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 0 | 25 | 50 | 0 | 34 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 0 | 25 | 50 | 0 | 43 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 0 | 30 | 50 | 0 | 48 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 0 | 30 | 50 | 0 | 47 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 0 | 30 | 50 | 0 | 50 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 0 | 30 | 50 | 0 | 49 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 0.5 | 25 | 50 | 0 | 10 | 37 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 0.5 | 25 | 50 | 0 | 18 | 30 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
# germination analysis (ten variables)
gsm <- ger_summary(SeedN = "seeds"
, evalName = "D"
, data = fb
)
# Prosopis data set processed
gsm %>%
head(10) %>%
mutate(across(where(is.numeric), ~round(., 2))) %>%
kable(caption = "Function ger_summary performe ten germination indices")
rep | nacl | temp | seeds | grs | grp | mgt | mgr | gsp | unc | syn | vgt | sdg | cvg |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | 25 | 50 | 50 | 100 | 1.28 | 0.78 | 78.12 | 0.95 | 0.63 | 0.33 | 0.57 | 44.75 |
2 | 0 | 25 | 50 | 50 | 100 | 1.22 | 0.82 | 81.97 | 0.82 | 0.67 | 0.22 | 0.46 | 38.09 |
3 | 0 | 25 | 50 | 50 | 100 | 1.32 | 0.76 | 75.76 | 0.90 | 0.56 | 0.22 | 0.47 | 35.70 |
4 | 0 | 25 | 50 | 50 | 100 | 1.14 | 0.88 | 87.72 | 0.58 | 0.75 | 0.12 | 0.35 | 30.75 |
1 | 0 | 30 | 50 | 50 | 100 | 1.04 | 0.96 | 96.15 | 0.24 | 0.92 | 0.04 | 0.20 | 19.03 |
2 | 0 | 30 | 50 | 50 | 100 | 1.06 | 0.94 | 94.34 | 0.33 | 0.88 | 0.06 | 0.24 | 22.63 |
3 | 0 | 30 | 50 | 50 | 100 | 1.00 | 1.00 | 100.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.00 |
4 | 0 | 30 | 50 | 50 | 100 | 1.02 | 0.98 | 98.04 | 0.14 | 0.96 | 0.02 | 0.14 | 13.86 |
1 | 0.5 | 25 | 50 | 50 | 100 | 1.90 | 0.53 | 52.63 | 1.08 | 0.58 | 0.38 | 0.61 | 32.34 |
2 | 0.5 | 25 | 50 | 50 | 100 | 1.70 | 0.59 | 58.82 | 1.20 | 0.48 | 0.38 | 0.61 | 36.14 |
## Germination Percentage (GRP)
# analysis of variance
av <- aov(grp ~ nacl*temp + rep, data = gsm)
# mean comparison test
mc_grp <- ger_testcomp(aov = av
, comp = c("temp", "nacl")
, type = "snk"
)
# data result
mc_grp$table %>%
kable(caption = "Germination percentage mean comparision")
temp | nacl | grp | std | r | ste | se | min | max | sig |
---|---|---|---|---|---|---|---|---|---|
25 | 0 | 100.0 | 0.000000 | 4 | 0.0000000 | 0.8413648 | 100 | 100 | a |
25 | 0.5 | 100.0 | 0.000000 | 4 | 0.0000000 | 0.8413648 | 100 | 100 | a |
25 | 1 | 96.0 | 1.632993 | 4 | 0.8164966 | 0.8413648 | 94 | 98 | abc |
25 | 1.5 | 96.0 | 1.632993 | 4 | 0.8164966 | 0.8413648 | 94 | 98 | abc |
25 | 2 | 94.5 | 2.516611 | 4 | 1.2583057 | 0.8413648 | 92 | 98 | bc |
30 | 0 | 100.0 | 0.000000 | 4 | 0.0000000 | 0.8413648 | 100 | 100 | a |
30 | 0.5 | 100.0 | 0.000000 | 4 | 0.0000000 | 0.8413648 | 100 | 100 | a |
30 | 1 | 98.5 | 1.914854 | 4 | 0.9574271 | 0.8413648 | 96 | 100 | a |
30 | 1.5 | 98.5 | 3.000000 | 4 | 1.5000000 | 0.8413648 | 94 | 100 | a |
30 | 2 | 94.0 | 1.632993 | 4 | 0.8164966 | 0.8413648 | 92 | 96 | c |
35 | 0 | 100.0 | 0.000000 | 4 | 0.0000000 | 0.8413648 | 100 | 100 | a |
35 | 0.5 | 98.0 | 2.309401 | 4 | 1.1547005 | 0.8413648 | 96 | 100 | ab |
35 | 1 | 96.0 | 2.828427 | 4 | 1.4142136 | 0.8413648 | 92 | 98 | abc |
35 | 1.5 | 98.5 | 1.914854 | 4 | 0.9574271 | 0.8413648 | 96 | 100 | a |
35 | 2 | 20.0 | 1.632993 | 4 | 0.8164966 | 0.8413648 | 18 | 22 | d |
40 | 0 | 100.0 | 0.000000 | 4 | 0.0000000 | 0.8413648 | 100 | 100 | a |
40 | 0.5 | 96.0 | 1.632993 | 4 | 0.8164966 | 0.8413648 | 94 | 98 | abc |
40 | 1 | 98.5 | 1.914854 | 4 | 0.9574271 | 0.8413648 | 96 | 100 | a |
40 | 1.5 | 10.5 | 1.914854 | 4 | 0.9574271 | 0.8413648 | 8 | 12 | e |
40 | 2 | 0.0 | 0.000000 | 4 | 0.0000000 | 0.8413648 | 0 | 0 | f |
# bar graphics for germination percentage
grp <- mc_grp$table %>%
fplot(data = .
, type = "bar"
, x = "temp"
, y = "grp"
, group = "nacl"
, ylimits = c(0, 120, 30)
, ylab = "Germination ('%')"
, xlab = "Temperature"
, glab = "NaCl (MPa)"
, error = "ste"
, sig = "sig"
, color = F
)
grp
## Mean Germination Time (MGT)
# analysis of variance
av <- aov(mgt ~ nacl*temp + rep, data = gsm)
# mean comparison test
mc_mgt <- ger_testcomp(aov = av
, comp = c("temp", "nacl")
, type = "snk")
# data result
mc_mgt$table %>%
kable(caption = "Mean germination time comparison")
temp | nacl | mgt | std | r | ste | se | min | max | sig |
---|---|---|---|---|---|---|---|---|---|
25 | 0 | 1.240000 | 0.0783156 | 4 | 0.0391578 | 0.073785 | 1.140000 | 1.320000 | j |
25 | 0.5 | 1.830000 | 0.0901850 | 4 | 0.0450925 | 0.073785 | 1.700000 | 1.900000 | i |
25 | 1 | 2.701218 | 0.1512339 | 4 | 0.0756169 | 0.073785 | 2.531915 | 2.897959 | g |
25 | 1.5 | 5.442365 | 0.0415525 | 4 | 0.0207763 | 0.073785 | 5.382979 | 5.479167 | c |
25 | 2 | 6.523349 | 0.3068542 | 4 | 0.1534271 | 0.073785 | 6.063830 | 6.695652 | b |
30 | 0 | 1.030000 | 0.0258199 | 4 | 0.0129099 | 0.073785 | 1.000000 | 1.060000 | j |
30 | 0.5 | 1.100000 | 0.0432049 | 4 | 0.0216025 | 0.073785 | 1.060000 | 1.160000 | j |
30 | 1 | 1.898129 | 0.0609184 | 4 | 0.0304592 | 0.073785 | 1.833333 | 1.959184 | i |
30 | 1.5 | 2.994362 | 0.1138473 | 4 | 0.0569236 | 0.073785 | 2.900000 | 3.160000 | f |
30 | 2 | 4.388259 | 0.0676715 | 4 | 0.0338357 | 0.073785 | 4.326087 | 4.446809 | d |
35 | 0 | 1.015000 | 0.0191485 | 4 | 0.0095743 | 0.073785 | 1.000000 | 1.040000 | j |
35 | 0.5 | 1.076250 | 0.0291905 | 4 | 0.0145952 | 0.073785 | 1.060000 | 1.120000 | j |
35 | 1 | 1.817607 | 0.2398098 | 4 | 0.1199049 | 0.073785 | 1.653061 | 2.173913 | i |
35 | 1.5 | 3.370480 | 0.0159689 | 4 | 0.0079844 | 0.073785 | 3.354167 | 3.387755 | e |
35 | 2 | 6.984343 | 0.3784214 | 4 | 0.1892107 | 0.073785 | 6.555556 | 7.400000 | a |
40 | 0 | 1.035000 | 0.0191485 | 4 | 0.0095743 | 0.073785 | 1.020000 | 1.060000 | j |
40 | 0.5 | 2.327648 | 0.0512449 | 4 | 0.0256225 | 0.073785 | 2.255319 | 2.375000 | h |
40 | 1 | 2.728780 | 0.1714562 | 4 | 0.0857281 | 0.073785 | 2.520833 | 2.940000 | g |
40 | 1.5 | 3.287500 | 0.1012651 | 4 | 0.0506326 | 0.073785 | 3.166667 | 3.400000 | e |
# bar graphics for mean germination time
mgt <- mc_mgt$table %>%
fplot(data = .
, type = "bar"
, x = "temp"
, y = "mgt"
, group = "nacl"
, ylimits = c(0,10, 1)
, ylab = "Mean germination time (days)"
, xlab = "Temperature"
, glab = "NaCl (MPa)"
, sig = "sig"
, error = "ste"
, color = T
)
mgt
You can add at each plot different arguments as the standard error, significance of the mean test, color, labels and limits. The resulted graphics are performed for publications and allows to insert math expression in the titles.
The cumulative analysis of the germination allows to observe the evolution of the germination process, being able to be expressed as the percentage of germination or with the relative germination.
# data frame with percentage or relative germination in time by NaCl
git <- ger_intime(Factor = "nacl"
, SeedN = "seeds"
, evalName = "D"
, method = "percentage"
, data = fb
)
# data result
git %>%
head(10) %>%
kable(caption = "Cumulative germination by nacl factor")
nacl | evaluation | mean | r | std | min | max | ste |
---|---|---|---|---|---|---|---|
0 | 0 | 0.000 | 16 | 0.0000000 | 0 | 0 | 0.0000000 |
0.5 | 0 | 0.000 | 16 | 0.0000000 | 0 | 0 | 0.0000000 |
1 | 0 | 0.000 | 16 | 0.0000000 | 0 | 0 | 0.0000000 |
1.5 | 0 | 0.000 | 16 | 0.0000000 | 0 | 0 | 0.0000000 |
2 | 0 | 0.000 | 16 | 0.0000000 | 0 | 0 | 0.0000000 |
0 | 1 | 92.500 | 16 | 9.4516313 | 68 | 100 | 2.3629078 |
0.5 | 1 | 57.250 | 16 | 35.5818306 | 12 | 96 | 8.8954576 |
1 | 1 | 14.500 | 16 | 12.9099445 | 0 | 40 | 3.2274861 |
1.5 | 1 | 0.375 | 16 | 0.8062258 | 0 | 2 | 0.2015564 |
2 | 1 | 0.000 | 16 | 0.0000000 | 0 | 0 | 0.0000000 |
# graphic germination in time by NaCl
nacl <- git %>%
fplot(data = .
, type = "line"
, x = "evaluation"
, y = "mean"
, group = "nacl"
, ylimits = c(0, 110, 10)
, ylab = "Germination ('%')"
, xlab = "Day"
, glab = "NaCl (MPa)"
, color = T
, error = "ste"
)
nacl
# data frame with percentage or relative germination in time by temperature
git <- ger_intime(Factor = "temp"
, SeedN = "seeds"
, evalName = "D"
, method = "percentage"
, data = fb)
# data result
git %>%
head(10) %>%
kable(caption = "Cumulative germination by temperature factor")
temp | evaluation | mean | r | std | min | max | ste |
---|---|---|---|---|---|---|---|
25 | 0 | 0.0 | 20 | 0.00000 | 0 | 0 | 0.000000 |
30 | 0 | 0.0 | 20 | 0.00000 | 0 | 0 | 0.000000 |
35 | 0 | 0.0 | 20 | 0.00000 | 0 | 0 | 0.000000 |
40 | 0 | 0.0 | 20 | 0.00000 | 0 | 0 | 0.000000 |
25 | 1 | 20.1 | 20 | 31.25094 | 0 | 86 | 6.987922 |
30 | 1 | 40.1 | 20 | 45.10327 | 0 | 100 | 10.085399 |
35 | 1 | 45.2 | 20 | 44.09750 | 0 | 100 | 9.860501 |
40 | 1 | 26.3 | 20 | 37.31671 | 0 | 98 | 8.344270 |
25 | 2 | 48.6 | 20 | 45.07818 | 0 | 100 | 10.079787 |
30 | 2 | 62.4 | 20 | 45.70662 | 0 | 100 | 10.220310 |
# graphic germination in time by temperature
temp <- git %>%
fplot(data = .
, type = "line"
, x = "evaluation"
, y = "mean"
, group = "temp"
, ylimits = c(0, 110, 10)
, ylab = "Germination ('%')"
, xlab = "Day"
, glab = "Temperature"
, color = F
)
temp
As the function fplot()
is build using ggplot2 (Wickham et al.,
2020). You can add more arguments for modify the graphics
adding +
.
library(ggplot2)
git <- ger_intime(Factor = "temp"
, SeedN = "seeds"
, evalName = "D"
, method = "percentage"
, data = fb
)
ggplot <- git %>%
fplot(data = .
, type = "line"
, x = "evaluation"
, y = "mean"
, group = "temp"
, ylimits = c(0, 110, 10)
, ylab = "Germination ('%')"
, xlab = "Day"
, glab = "Temperature"
, color = T
) +
scale_x_continuous(n.breaks = 10, limits = c(0, 11))
ggplot