In priviere/PPBstats: An R package to perform analysis found within PPB programmes

Unipart network for seed lots analysis

This section deals with unipart network that represents the relationships between seed lots.

Steps with PPBstats

• Format the data with format_data_PPBstats()
• get descriptive plot with plot()

Format the data

The format required is a data frame with the following compulsory columns as factor:

• "seed_lot_parent" : name of the parent seed lot in the relationship
• "seed_lot_child" ; name of the child seed lots in the relationship
• "relation_type" : the type of relationship between the seed lots
• "relation_year_start" : the year when the relationship starts
• "relation_year_end" : the year when the relationship stops
• "germplasm_parent" : the germplasm associated to the parent seed lot
• "location_parent" : the location associated to the parent seed lot
• "year_parent" : the year of the last relationship of the parent seed lot
• "germplasm_child" : the germplasm associated to the child seed lot
• "location_child" : the location associated to the child seed lot
• "year_child" : represents the year of the last relation event of the child seed lot

Possible options are : "long_parent", "lat_parent", "long_child", "lat_child" to get map representation, supplementary variables with tags: "_parent", "_child" or "_relation".

The format of the data are checked by the function format_data_PPBstats() with the following arguments :

• type : "data_network"
• network_part : "unipart"
• vertex_type : "seed_lots"

The function returns list of igraph object^[http://igraph.org/r/] coming from igraph::graph_from_data_frame().

data(data_network_unipart_sl)
head(data_network_unipart_sl)

net_unipart_sl = format_data_PPBstats(
  type = "data_network",
  data = data_network_unipart_sl, 
  network_part = "unipart", 
  vertex_type =  "seed_lots")
length(net_unipart_sl)
head(net_unipart_sl)

Describe the data

The different representations are done with the plot() function. The name of the list is all_data as representation takes all data (and not only a given year or location as unipart network for location or bipart network).

For network representation, set plot_type = "network" diffusion event are displayed with a curve. in_col can be settled to customize color of vertex.

p_net = plot(net_unipart_sl, plot_type = "network", in_col = "location")
p_net

In order to get the network organized in a chronologiical order and by location, set organize_sl = TRUE. This representation is possible if the seed lots are under the following format : GERMPLASM_LOCATION_YEAR_DIGIT.

p_net_org = plot(net_unipart_sl, plot_type = "network", organize_sl = TRUE)
p_net_org

To have information on the seed lots that are represented, plot_type = "barplot" can be used. Choose what to represent on the x axis and in color as well as the number of parameter per plot.

p_bar = plot(net_unipart_sl, plot_type = "barplot", in_col = "location", 
                          x_axis = "germplasm", nb_parameters_per_plot_x_axis = 5, 
                          nb_parameters_per_plot_in_col = 5)
p_bar$all_data$barplot$`germplasm-1|location-1` # first element of the plot

Barplot can also be use to study the relation within the network. The name of the relation must be put in the argument vec_variables. The results is a list of two elements for each variable:

• nb_received: number of seed lots that end the relation
• nb_given: number of seed lots that start the relation

p_bar = plot(net_unipart_sl, plot_type = "barplot", vec_variables = "diffusion",
                  nb_parameters_per_plot_x_axis = 100, x_axis = "location", in_col = "year")
p_bar

Location present on the network can be displayed on a map with plot_type = "map". When using map, do not forget to use credit : Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.

p_map = plot(net_unipart_sl, plot_type = "map", labels_on = "location")
p_map

It can be interesting to plot information regarding a variable on map with a pie with plot_type = "map" and by setting arguments data_to_pie and variable:

nb_values = 30
data_to_pie = data.frame(
  seed_lot = rep(c("germ-4_loc-4_2009_0001", "germ-9_loc-4_2009_0001", "germ-10_loc-3_2009_0001", "germ-12_loc-3_2007_0001", "germ-11_loc-2_2009_0001", "germ-10_loc-2_2009_0001"), each = nb_values),
  location = rep(c("loc-1", "loc-1", "loc-3", "loc-3", "loc-2", "loc-2"), each = nb_values),
  year = rep(c("2009", "2008", "2007", "2007", "2009", "2009"), each = nb_values),
  germplasm = rep(c("germ-7", "germ-2", "germ-6", "germ-4", "germ-5", "germ-13"), each = nb_values),
  block = 1,
  X = 1,
  Y = 1,
  y1 = rnorm(nb_values*6, 10, 2), # quanti
  y2 = rep(c("cat1", "cat1", "cat2", "cat3", "cat3", "cat4"), each = nb_values)  # quali
)

data_to_pie$seed_lot = as.factor(as.character(data_to_pie$seed_lot))
data_to_pie$location = as.factor(as.character(data_to_pie$location))
data_to_pie$year = as.factor(as.character(data_to_pie$year))
data_to_pie$germplasm = as.factor(as.character(data_to_pie$germplasm))
data_to_pie$block = as.factor(as.character(data_to_pie$block))
data_to_pie$X = as.factor(as.character(data_to_pie$X))
data_to_pie$Y = as.factor(as.character(data_to_pie$Y))

data_to_pie = format_data_PPBstats(data_to_pie, type = "data_agro")

# y1 is a quantitative variable
p_map_pies_y1 = plot(net_unipart_sl, data_to_pie, plot_type = "map", vec_variables = "y1")
p_map_pies_y1

# y2 is a qualitative variable
p_map_pies_y2 = plot(net_unipart_sl, data_to_pie, plot_type = "map", vec_variables = "y2")
p_map_pies_y2

or on the network with a pie with plot_type = "network" and by setting arguments data_to_pie and vec_variables:

# y1 is a quantitative variable
p_net_pies_y1 = plot(net_unipart_sl, data_to_pie, plot_type = "network", vec_variables = "y1")
p_net_pies_y1

# y2 is a qualitative variable
p_net_pies_y2 = plot(net_unipart_sl, data_to_pie, plot_type = "network", vec_variables = "y2")
p_net_pies_y2

The same can be done regarding relation type of the network. This can be displayed on a map but not on a network.

p_map_pies_diff = plot(net_unipart_sl, plot_type = "map", vec_variables = "diffusion")
p_map_pies_diff

Here the pies represent the repartition of the number of seed lots.

priviere/PPBstats documentation built on May 6, 2021, 1:20 a.m.