Extract or replace parts of a SIM data frame

Extract or replace subsets of SIM data frames. The behaviour of the functions is very close to the one of the corresponding data.frame functions, see [.data.frame. However, modifications of the SIM columns or suppression of core columns will turn the object into a standard data.frame to avoid issues in e.g. graphical representations, see below for details.

Usage

# S3 method for class 'sim_df'
x$name <- value

# S3 method for class 'sim_df'
x[[i, j]] <- value

# S3 method for class 'sim_df'
x[i, j, ..., drop]

# S3 method for class 'sim_df'
x[i, j] <- value

Arguments

x: a data frame of spatial interaction models, an object of class sim_df
name: a literal character string
value: a suitable replacement value: it will be repeated a whole number of times if necessary and it may be coerced: see the Coercion section in [.data.frame. If NULL, deletes the column if a single column is selected.
i, j, ...: elements to extract or replace. For [ and [[, these are numeric or character or, for [ only, empty or logical. Numeric values are coerced to integer as if by as.integer(). For replacement by [, a logical matrix is allowed.
drop: If TRUE the result is coerced to the lowest possible dimension. The default is to drop if only one column is left, but not to drop if only one row is left.

Value

For [ a sim_df, a data.frame or a single column depending on the values of i and j.

For [[ a column of the sim_df (or NULL) or an element of a column when two indices are used.

For $ a column of the sim_df (or NULL).

For [<-, [[<-, and $<- a sim_df or a data frame (see details).

Details

In a sim_df, the core columns are derived from the sim_list column. Replacement functions maintain this property by updating the columns after any modification of the sim_list column. Modifications of the core columns are rejected (removing a core column is accepted but this turns the sim_df into a standard data.frame).

In addition, the sim_list column obeys the restrictions on sim_list modifications (i.e., a sim_list contains a homogeneous collection of spatial interaction models).

Extraction functions keep the sim_df class only if the result is a data frame with a sim_list column, the core columns and potentially additional columns.

Examples

distances <- french_cities_distances[1:10, 1:10] / 1000 ## convert to km
production <- rep(1, 10)
attractiveness <- log(french_cities$area[1:10])
all_flows <- grid_blvim(distances, production, seq(1.05, 1.45, by = 0.2),
  seq(1, 3, by = 0.5) / 400,
  attractiveness,
  bipartite = FALSE,
  epsilon = 0.1, iter_max = 1000
)
all_flows_df <- sim_df(all_flows)
## the models as a sim_list
all_flows_df[, "sim"]
#> Collection of 15 spatial interaction models with 10 origin locations and 10
#> destination locations computed on the following grid:
#> • alpha: 1.05, 1.25, and 1.45
#> • beta: 0.0025, 0.00375, 0.005, 0.00625, and 0.0075
## subset of the rows, still a sim_df
all_flows_df[1:5, ]
#>   alpha    beta diversity  potential iterations converged          sim
#> 1  1.05 0.00250  1.967999  2.6558277       1000      TRUE c(0.8172....
#> 2  1.25 0.00250  1.999659  2.8865532        800      TRUE c(0.8687....
#> 3  1.45 0.00250  1.990039  3.3304103       1001     FALSE c(0.8383....
#> 4  1.05 0.00375  2.672869 -1.2996429       1001     FALSE c(0.8971....
#> 5  1.25 0.00375  1.999562 -0.5530286        500      TRUE c(0.9454....
## subset of the columns, not a sim_df (alpha is missing)
all_flows_df[6:10, 2:7]
#>       beta diversity potential iterations converged          sim
#> 6  0.00375  1.999209  0.364261        500      TRUE c(0.9438....
#> 7  0.00500  6.385427 -4.260122       1001     FALSE c(0.7890....
#> 8  0.00500  3.195527 -3.593181       1000      TRUE c(0.9488....
#> 9  0.00500  1.999114 -2.356379        400      TRUE c(0.9778....
#> 10 0.00625  9.691171 -6.043453       1001     FALSE c(0.7243....
all_flows_2 <- grid_blvim(distances, log(french_cities$population[1:10]),
  seq(1.05, 1.45, by = 0.2),
  seq(1, 3, by = 0.5) / 400,
  attractiveness,
  bipartite = FALSE,
  epsilon = 0.1, iter_max = 1000
)
## replace the sim_list column by the new models
## before
all_flows_df$diversity
#>  [1] 1.967999 1.999659 1.990039 2.672869 1.999562 1.999209 6.385427 3.195527
#>  [9] 1.999114 9.691171 3.984711 3.355835 9.938952 6.741682 4.725279
all_flows_df$sim <- all_flows_2
## after (all core columns have been updated)
all_flows_df$diversity
#>  [1] 1.971704 1.999842 1.996445 2.484213 1.999639 1.999358 6.191392 2.684352
#>  [9] 1.999157 9.400831 3.889657 3.254730 9.842626 6.659397 3.964254