Usage of the package
This chapter shows the usage of the package and the underlying
functions. As factorial experiments are omnipresent in all science and
technology fields, a factorial ab-design will be used as an example.
Although some parameters are worth for agriculture only, most other are
useful for every user.
Load the package
use the following command to load the package after installation.
library("ggplot2")
library("agricolae")
library("agricolaeplotr")
library("raster")
Example: factorial ab design with complete randomization
First we need to create an design using agricolae package. All
examples used in the package are directly taken from agricolae.
After creation of the object, everything is ready to plot a basic
plot. It is also assumed, that the height and the width of each plot is
set to 1. In agricultural designs, it is recommended to input the
measures from a plot to have an idea about the dimensions needed to
establish such a experiment in the field. Knowing the needed amount in
meters or other units is important for machinery and management of the
experiment.
Complete randomized designs do not have a factor like blocks, so the
user is required to input a suitable number for columns and rows. The
product of the number of rows and columns must be greater than the size
of the experiment, to give the program the chance to place all
plots.
Following figure shows the output of a basic design. The output is a
standard ggplot2 design. This implies that the user can do all
operations, that ggplot2 and other packages that uses ggplot2 functions,
can be applied without having layer restrictions or too specialized
layers that prevent other transformations. The user also might use
plotly to create interactive visualizations of the designs. Useful for
field demonstrations for all project stakeholders i.e. (scientists,
farmers, funding agencies).
library(agricolae) # origin of the needed design object
trt<-c(3,2) # factorial 3x2
outdesign <- design.ab(trt, r=3, serie=2,design = 'crd')
head(outdesign$book,10)
## plots r A B
## 1 101 1 1 2
## 2 102 1 2 1
## 3 103 1 1 1
## 4 104 2 1 2
## 5 105 2 1 1
## 6 106 1 3 1
## 7 107 1 2 2
## 8 108 2 2 2
## 9 109 2 2 1
## 10 110 3 2 1
plot_design.factorial_crd(outdesign,ncols=7,nrows=3, width = 1, height = 1)
Change Axis and orientation of the design
The user can change the order of both axis. Although it is a standard
to enumerate plots from left to right, increasing from bottom, there is
no restriction to enumerate in other ways. The following change reverts
the y axis. Reverting y axis causes to have same orientation as
‘agricolae’ packages shows in some sketches. If the user wants to have
same orientation, one must set reverse_y=TRUE.
revert the y axis
plot_design.factorial_crd(outdesign,ncols=6,nrows=3, width = 1, height = 1, reverse_y = TRUE)
revert the x axis
plot_design.factorial_crd(outdesign,ncols=6,nrows=3, width = 1, height = 1, reverse_x = TRUE)
revert both axis
plot_design.factorial_crd(outdesign,ncols=6,nrows=3, width = 1, height = 1, reverse_x = TRUE,reverse_y = TRUE)
Changing width and height
As described before, the user can decide the dimensions of each plot.
The function assumes that each plot has same dimensions as the others.
The dimensions are meant for a single plot, not for the
entire field dimensions.
plot_design.factorial_crd(outdesign,ncols=6,nrows=3, width = 1, height = 2.5 , reverse_x = FALSE,reverse_y = TRUE)
Changing space_width and space_height
These parameters revert to the space of each plot. If both set to 1,
there is no space between each plot. Zero indicates a 100 percent space
between each plot, so no rectangle will be shown. Setting values greater
one causes in overlaying plots, which are not possible in agricultural
experimental design. Therefore the range of this parameter is between
>0 and 1.
plot_design.factorial_crd(outdesign,ncols=6,nrows=3, width = 1, height = 2.5 , reverse_x = FALSE,reverse_y = TRUE,space_width = 1,space_height = 1)
This plot will show more space between each plot. If calculating the
net area, the space parameter needs to be multiplied with the width and
height respectively.
plot_design.factorial_crd(outdesign,ncols=6,nrows=3, width = 1, height = 2.5 , reverse_x = FALSE,reverse_y = TRUE,space_width = 0.7,space_height = 0.8)
Change of label column
The user can input a column name as a string, that must be in the
columns of the design$book table. The input will be checked if it does
so.
plot_design.factorial_crd(outdesign,ncols=6,nrows=3, width = 1, height = 2.5 , reverse_x = FALSE,reverse_y = TRUE, factor_name = "B")
Change of factor name column
The user can input a factor_name as a string, that must be in the
columns of the design$book table. The input will be checked if it does
so. This example shows how the selection of a different factor than the
first one (default) will be performed for factorial experiments in
complete randomized designs.
set.seed(129984)
trt<-c(3,2) # factorial 3x2
outdesign <- design.ab(trt, r=3, serie=2,design = 'crd')
plot_design.factorial_crd(outdesign,ncols=3,nrows=6, width = 1, height = 2.5 , reverse_x = FALSE,reverse_y = TRUE, factor_name = "B")
poster_theme
This function adds changes to a ggplot, which are good for poster
presentations.
set.seed(129866478)
trt<-c(3,2) # factorial 3x2
outdesign <- design.ab(trt, r=3, serie=2,design = 'crd')
plot_design.factorial_crd(outdesign,ncols=3,nrows=6, width = 1, height = 2.5 , reverse_x = FALSE,reverse_y = TRUE, factor_name = "B") + theme_poster()
plotting continuous outcomes
Plotting design maps like a heat map has some advantages. First, it
shows some generals eyeballing trends in the data. Second, missing data
are shown as a plot, therefore a technician can go to the experiment and
retake samples, if needed. NA are plotted in gray.
pres_theme
This function adds changes to a ggplot, which are good for
presentations in slideshows.
set.seed(12986)
trt<-c(3,2) # factorial 3x2
outdesign <- design.ab(trt, r=3, serie=2,design = 'crd')
plot_design.factorial_crd(outdesign,ncols=3,nrows=6, width = 1, height = 2.5 , reverse_x = FALSE,reverse_y = TRUE, factor_name = "B") + theme_pres()
re-scaling colors
The user can use a scale for factors. Sometimes useful if factors are
in a gradient manner, for example nitrogen fertilizer application or
amount of irrigation in Liter.
trt<-c(3,2) # factorial 3x2
outdesign <- design.ab(trt, r=3, serie=2,design = 'crd')
plot_design.factorial_crd(outdesign,ncols=3,nrows=6, width = 1, height = 2.5 , reverse_x = FALSE,reverse_y = TRUE, factor_name = "B") + theme_pres() + scale_fill_viridis_d()
Plotting of exogenous variables
In experiment analysis, a visualization of the results prior to
analysis is an important step in assuring a high quality. identifying
plots with missing data enables a re-sampling of the missing plots, so
less numeric data imputation will be necessary.
The following example shows, how to plot an exogenous variable. The
variable is here the factor_name. We assume that we want to plot yield
of a crop experiment, and use in addition a scaling parameter to recolor
the fill. The code is the following:
set.seed(23488833)
trt <-c(3,2)
outdesign<-design.ab(trt, serie=2, design="lsd",seed = 454555)
length_table <- dim(outdesign$book)[1] # length of the table
outdesign$book$yield <- sample(c(5:12,c(NA,NA,NA)), size = length_table, replace = TRUE)
plot_design.factorial_lsd(outdesign,factor_name = "yield") + scale_fill_viridis_c()
The missing yields are easily to be seen.
Plotting of exogenous variables from foreign tables
Sometimes, experiment data are in data frames, that must be merged
with the outdesign object. Therefore, a second data frame will be merged
with the outdesign and the merged data frame will be stored in the
outdesign$book.
set.seed(23488833)
trt <-c(3,2)
outdesign<-design.ab(trt, serie=2, design="lsd",seed = 454555)
length_table <- dim(outdesign$book)[1] # length of the table
yield <- sample(c(5:20,c(NA,NA,NA)), size = length_table, replace = TRUE)
df <- cbind(plots=outdesign$book$plots,yield)
head(df,10)
## plots yield
## [1,] 101 NA
## [2,] 102 NA
## [3,] 103 9
## [4,] 104 13
## [5,] 105 19
## [6,] 106 19
## [7,] 201 NA
## [8,] 202 9
## [9,] 203 6
## [10,] 204 11
outdesign$book <- merge(outdesign$book,df, by.x = "plots", by.y = "plots")
plot_design.factorial_lsd(outdesign,factor_name = "yield") + scale_fill_viridis_c()
use your own design tables
It is also possible to use own generated experiments designs. However
this comes with some caveats. As the functions are quite strict in
parameter naming, the column names should match with those from
agricolae. The design type and the applied design, sometimes referred as
design type, needs to be set in a list in advance.
The following example provides a mock setup for an experiment. The
second chunk of code shows how the list is set up and how the parameters
should be named for a randomized complete block design in a factorial
way. It is possible to plot measured outcomes from plots, as ggplot
supports plotting continuous values.
set.seed(1298664)
plots <- as.factor(1:(8*6))
block <- as.factor((rep(1:6,each=8)))
A <- as.vector(replicate(8,sample(rep(1:2,times=3),6,replace=FALSE)))
outcome <- runif(48,20,100)
experiment <- cbind(plots,block,A,outcome)
experiment <- as.data.frame(experiment)
head(experiment)
## plots block A outcome
## 1 1 1 1 37.89540
## 2 2 1 2 61.40711
## 3 3 1 2 63.09927
## 4 4 1 1 98.15421
## 5 5 1 2 31.26676
## 6 6 1 1 90.85659
experiment_design <- list()
experiment_design$parameters$design<- "factorial"
experiment_design$parameters$applied <- "rcbd"
experiment_design$book <- experiment
head(experiment_design)
## $parameters
## $parameters$design
## [1] "factorial"
##
## $parameters$applied
## [1] "rcbd"
##
##
## $book
## plots block A outcome
## 1 1 1 1 37.89540
## 2 2 1 2 61.40711
## 3 3 1 2 63.09927
## 4 4 1 1 98.15421
## 5 5 1 2 31.26676
## 6 6 1 1 90.85659
## 7 7 1 1 59.96641
## 8 8 1 2 55.55669
## 9 9 2 1 93.40734
## 10 10 2 1 81.13919
## 11 11 2 2 59.49315
## 12 12 2 2 66.98163
## 13 13 2 2 36.60583
## 14 14 2 1 65.60282
## 15 15 2 1 59.15911
## 16 16 2 1 99.62496
## 17 17 3 2 81.11360
## 18 18 3 2 45.91223
## 19 19 3 2 43.13358
## 20 20 3 1 54.39296
## 21 21 3 1 99.81043
## 22 22 3 2 64.92204
## 23 23 3 2 57.84001
## 24 24 3 1 68.44613
## 25 25 4 1 32.92130
## 26 26 4 1 51.88154
## 27 27 4 1 43.57497
## 28 28 4 2 42.05324
## 29 29 4 2 41.76551
## 30 30 4 2 28.24783
## 31 31 4 2 86.46124
## 32 32 4 1 90.25177
## 33 33 5 1 50.94946
## 34 34 5 2 23.45559
## 35 35 5 1 61.84104
## 36 36 5 2 60.42443
## 37 37 5 2 38.63727
## 38 38 5 2 74.36960
## 39 39 5 1 56.44705
## 40 40 5 1 91.73986
## 41 41 6 2 22.08535
## 42 42 6 1 35.80243
## 43 43 6 2 90.73155
## 44 44 6 2 43.10991
## 45 45 6 1 90.60870
## 46 46 6 1 89.13865
## 47 47 6 2 76.97044
## 48 48 6 1 62.76907
plot_design.factorial_rcbd(experiment_design,factor_name = "A")
plot_design.factorial_rcbd(experiment_design,factor_name = "outcome")
plot split plot designs
Split plots designs are designs that divide a main plot into two or
more smaller, equal sized subplots. This leads to the definition of the
dimensions. Te dimensions are meant for a single main plot, the super
set of all belonging subplots. The three functions of this family are
returning two plots. One for the main factor, a second one for the
subplots. The dimension for the subplots are those from main plot,
divided by the number of subplots, in width. To ensure only one plot is
produced at the time, the user is required to choose a subplot design or
an main plot design. To have both plots, the user has currently to run
the function twice.
set.seed(1298664)
t1<-c('a','b','c','d','e',"f","g","h")
t2<-c("u",'v','w','x','y',"z")
outdesign2 <- design.split(trt1=t1, trt2=t2, r=r,serie = 2,
seed = 0, kinds = 'Super-Duper',
randomization=TRUE,first=TRUE,design = 'lsd')
plot_split_lsd(outdesign2,factor_name_1 = "t1",factor_name_2 = "t2",width = 2,height = 2, subplots = FALSE,labels = "plots")
plot_split_lsd(outdesign2,width = 2,height = 2, subplots = TRUE, labels = "splots", factor_name_1 = "t1", factor_name_2 = "t2")
set.seed(1298664)
t1<-c('a','b','c','d','e','f','g')
t2<-c('v','w','x','y','z')
r <- 4
outdesign2 <- design.split(trt1=t1, trt2=t2, r=r,
serie = 2, seed = 0, kinds = 'Super-Duper',
randomization=TRUE,first=TRUE,design = 'crd')
plot_split_crd(outdesign2,ncols = 5,nrows=6, subplots = FALSE,
factor_name_1 = "t1",factor_name_2 = "t2")
plot_split_crd(outdesign2,ncols = 5,nrows=6, subplots = TRUE, labels="splots",factor_name_1 = "t1",factor_name_2 = "t2")
set.seed(1298664)
T1<-c('a','b','c','d','e',"f","g")
T2<-c("we",'v','w','x','y','z',"d")
r = 3
outdesign2 <- design.split(trt1=T1, trt2=T2, r=r,serie = 2,
seed = 0, kinds = 'Super-Duper',randomization=TRUE,
first=TRUE,design = 'rcbd')
plot_split_rcbd(outdesign2,width = 5,height = 5,subplots = FALSE,
factor_name_1 = "T1",factor_name_2 = "T2")
plot_split_rcbd(outdesign2,width = 5,height = 5,labels = "splots",
factor_name_1 = "T1",factor_name_2 = "T2")
Referring to other packages
If the user can provide a table containing all necessary information
about the coordinates of each plot, the user can choose the ‘desplot’
package.
There are two functions namely ‘ggdesplot’ and ‘ggdesplot’ from the
‘desplot’ package, that has some other features. However, the user
cannot swap the x and y coordinates that easy as here and there is no
way to adjust for height and length of a plot (preferable in meter). The
user needs to provide x and y coordinates to be able to use ‘desplot’
and ‘ggdesplot’.
Planed features for future versions
- match size of subplot to those to mainplots for all split plot
designs
- create own function for user specific experiment designs with same
API
- add field designs for replicated experiments made in ‘FielDHub’
trt<-c(3,2) # factorial 3x2
outdesign <- design.ab(trt, r=3, serie=2,design = 'crd')
plt <- plot_design.factorial_crd(outdesign,ncols=3,nrows=6, width = 5, height = 7.5 , reverse_x = FALSE,reverse_y = TRUE, factor_name = "B") + theme_pres() + scale_fill_viridis_d()
spat_df <- make_polygons(plt,east = 3454206.89,
north = 5939183.21 ,
projection_output = '+init=EPSG:4326')
plot(spat_df["fill"],col=spat_df$fill)
# this part does not work well in a vignette
library(leaflet)
spat_df <- sf:::as_Spatial(spat_df)
spat_df <- sp::elide(spat_df,rotate = -90)
leaflet(spat_df) %>% addPolygons(
fillColor = spat_df$fill,
opacity=1,
color="black",
fillOpacity = 1) %>% addProviderTiles(provider = "OpenStreetMap.DE")
Output of central properties of a design of experiment
The user can choose some outputs to have deeper insights of the
experiment.
The net plot is more or less the actually used plot, without the
borders on a plot level.
The gross plot is the sum of the net plot and the border of a
plot.
The sum of all gross plots with the respective space in between
is named the field.
The experiment shows some properties that are unrelated to the
plots on a field.
The user also can choose the combined output listed from the
elements by typing part = ‘all’.
The DOE_obj function uses the ggplot object and extract relevant
field information. This is required, as the summary cannot use the
ggplot object. The user also my use a different unit than meter
(default).
The user may extract the actual values, as they are separated from
the descriptors using normal data.frame related command and
operations.
varieties<-c('perricholi','yungay','maria bonita','tomasa')
outdesign <-design.youden(varieties,r=2,serie=2,seed=23)
p <- plot_youden(outdesign, labels = 'varieties', width=4, height=3)
stats <- DOE_obj(p)
r <- to_table(stats,part = "net_plot", digits = 2)
r
## names vals
## 1 net plot height: m 2.60
## 2 net plot width: m 3.80
## 3 net plot diagonal: m 4.60
## 4 net plot area: m^2 9.70
## 5 share used plot area: 0.81
## 6 share space between plots: 0.19
## 7 space_width: m 0.45
## 8 space_height: m 0.20
r <- to_table(stats,part = "gross_plot", digits = 2)
r
## names vals
## 1 gross plot height: m 3.00
## 2 gross plot width: m 4.00
## 3 gross plot diagonal: m 5.00
## 4 gross plot area: m^2 12.00
## 5 gross space area: m^2 18.00
## 6 share used plot area: %/100 0.81
## 7 share space between plots: %/100 0.19
## 8 space_width: m 0.45
## 9 space_height: m 0.20
r <- to_table(stats,part = "field", digits = 2)
r
## names vals
## 1 relative design height: 0.85
## 2 relative design width: 0.95
## 3 net experiment diagonal: m 11.00
## 4 net experiment width: m 7.80
## 5 net experiment height: m 12.00
## 6 used plot area: m^2 78.00
## 7 used area DOE: m^2 90.00
## 8 used outer area: m^2 96.00
## 9 outer field diagonal: m 14.00
r <- to_table(stats,part = "experiment", digits = 2)
r
## names vals
## 1 xmin: 2.1
## 2 xmax: 9.9
## 3 ymin: 1.7
## 4 ymax: 13.0
## 5 number columns: 2.0
## 6 number rows: 4.0
## 7 number of plots: 8.0
## 8 number of factors: 4.0
r <- to_table(stats,part = "all", digits = 2)
r
## names vals
## 1 net plot height: m 2.60
## 2 net plot width: m 3.80
## 3 net plot diagonal: m 4.60
## 4 net plot area: m^2 9.70
## 5 share used plot area: 0.81
## 6 share space between plots: 0.19
## 7 space_width: m 0.45
## 8 space_height: m 0.20
## 9 gross plot height: m 3.00
## 10 gross plot width: m 4.00
## 11 gross plot diagonal: m 5.00
## 12 gross plot area: m^2 12.00
## 13 gross space area: m^2 18.00
## 14 relative design height: 0.85
## 15 relative design width: 0.95
## 16 net experiment diagonal: m 11.00
## 17 net experiment width: m 7.80
## 18 net experiment height: m 12.00
## 19 used plot area: m^2 78.00
## 20 used area DOE: m^2 90.00
## 21 used outer area: m^2 96.00
## 22 outer field diagonal: m 14.00
## 23 xmin: 2.10
## 24 xmax: 9.90
## 25 ymin: 1.70
## 26 ymax: 13.00
## 27 number columns: 2.00
## 28 number rows: 4.00
## 29 number of plots: 8.00
## 30 number of factors: 4.00
full command of a plot
Sometime it it useful to use same plotting function with a known
behaviour for plotting purposes. the user may choose the origin (0|0)
being in the lower left corner of the net plot. The field design needs
to be a data.frame, and not a list like usually used in ‘agricolae’.
varieties<-c('perricholi','yungay','maria bonita','tomasa')
outdesign <-design.youden(varieties,r=2,serie=2,seed=23)
design <- outdesign$book
design
## plots row col varieties
## 1 101 1 1 maria bonita
## 2 102 1 2 tomasa
## 3 201 2 1 perricholi
## 4 202 2 2 yungay
## 5 301 3 1 yungay
## 6 302 3 2 maria bonita
## 7 401 4 1 tomasa
## 8 402 4 2 perricholi
p <- full_control_positions(design,"col","row","varieties","plots",
width=3,height=4.5,
space_width=1,space_height=1,
shift_x=-0.5*3,shift_y=-0.5*4.5)
## plots row col varieties
## 1 101 1 1 maria bonita
## 2 102 1 2 tomasa
## 3 201 2 1 perricholi
## 4 202 2 2 yungay
## 5 301 3 1 yungay
## 6 302 3 2 maria bonita
## 7 401 4 1 tomasa
## 8 402 4 2 perricholi
p <- full_control_positions(design,"col","row","varieties","plots",
width=3,height=4.5,
space_width=0.93,space_height=0.945,
start_origin = TRUE)
## plots row col varieties
## 1 101 1 1 maria bonita
## 2 102 1 2 tomasa
## 3 201 2 1 perricholi
## 4 202 2 2 yungay
## 5 301 3 1 yungay
## 6 302 3 2 maria bonita
## 7 401 4 1 tomasa
## 8 402 4 2 perricholi
