bor

Overview

bor is an R package designed to transform focal observations’ data, referring to the occurrence of social interactions, into asymmetric data matrices. Each matrix cell provides counts on the number of times a specific type of social interaction was initiated by the row subject and directed to the column subject.

Currently, the package includes three objects:

Read bor help file (?bor) to learn more about this package.

Installation

# to install R packages from github you need the devtools package
# to install devtools from CRAN type:
install.packages("devtools")

# to install bor from github type:
devtools::install_github("davidnsousa/bor")

Details

ex_field_data details the required structure of raw focal observations’ data that can be passed to the dtable() function. Below we present the first lines of this data frame:

head(ex_field_data)
##   id1       act obs
## 1  s2         x   1
## 2  s3         0   1
## 3  s5         x   1
## 4  s1      +.s4   1
## 5  s1         x   1
## 6  s5 s4.-;s2.+   1

See ?ex_field_data for further details. dtable() function does not require that the input data frame has matching column names to that of ex_field_data, but input data frame should include three columns, with the type of data as described above and in the same column order.

Example

The following uses dtable() function to convert ex_field_data data frame (see details above) in a new data frame (e.g., data) that can be passed to countb() function. dtable() function requires that recorded social interactions’ codes are provided (bset argument). In ex_field_data these codes are + and -. Below we leave bsep, asep, missing and noc dtable()’s arguments at their default values (“.”, “;”, “x”, “0” respectively). See ?dtable for further details.

 b <- c("+","-")
 data <- dtable(ex_field_data, bset = b)
 head(data)
##   id1  id2 sender_id1 behavior no_occurrence missing observer
## 1  s2 <NA>         NA     <NA>            NA       1        1
## 2  s3 <NA>         NA     <NA>             1      NA        1
## 3  s5 <NA>         NA     <NA>            NA       1        1
## 4  s1   s4          1        +            NA      NA        1
## 5  s1 <NA>         NA     <NA>            NA       1        1
## 6  s5   s4          0        -            NA      NA        1

data object has 7 columns: * id1- focal subject’s identification code. * id2- identification code of the social interactions partner. * sender_id1 - indicates whether the focal subject was the initiator/sender (coded 1) or the target of the social interaction (coded 0). * behavior - indicates the code of the social interaction recorded. * no_occurrence - indicates whether no social interaction were recorded (coded 1; NA otherwise). * missing - indicates whether the focal subject was unavailable for observation (coded 1; NA otherwise). * observer - observer’s identification code.

countb() function can now be used on data to compute asymmetric data matrices, containing the number of times a specific type of social interaction was initiated by the row subject and directed to the column subject (target), separately for each social interaction and for each observer. Data matrices are stored inside a list (e.g., observations).

  data2 <- countb(data)
  data2
## $`1`
## $`1`$`-`
##    s1 s2 s3 s4 s5
## s1  0  1  0  0  2
## s2  0  0  0  0  1
## s3  0  0  0  1  0
## s4  0  0  1  0  1
## s5  0  1  1  1  0
## 
## $`1`$`+`
##    s1 s2 s3 s4 s5
## s1  0  1  1  1  0
## s2  0  0  2  0  2
## s3  2  0  0  0  0
## s4  0  1  0  0  0
## s5  0  0  0  1  0
## 
## 
## $`2`
## $`2`$`-`
##    s1 s2 s3 s4 s5
## s1  0  0  1  2  0
## s2  0  0  0  3  0
## s3  0  1  0  1  0
## s4  0  2  1  0  1
## s5  1  1  1  0  0
## 
## $`2`$`+`
##    s1 s2 s3 s4 s5
## s1  0  0  1  0  0
## s2  0  0  0  2  0
## s3  0  1  0  2  1
## s4  0  0  1  0  0
## s5  1  0  1  0  0

data2 is a list of lists: one for each observer (in this example data2$`1` and data2$`2`). Inside each list there is one asymmetric interaction matrix per social interaction recorded (e.g., data2$`1`$`-`). Cells in these matrices provide counts on the number of times a specific type of social interaction was initiated by the row subject and directed to the column subject.

See ?countb for further details on the countb() function.