Data formats used in the orgsurveyr package

The orgsurveyr package works with the following data frame formats:

indiv_df: Data frame with one row per individual in the organisation and variables as multiple columns (wide)
indiv_tall_df: Data frame with multiple rows per individual in the organisation but a fixed number of columns (tall)
org_tall_df: Data frame with multiple rows per unit in the organisation and a fixed number of columns (tall)

The get_df_format and check_df_format functions can be used to determine and check the data frame format.

Examples


# examine the tbl_graph object the data relates to
data(tg_org)
tidygraph::as_tbl_graph(tg_org)
#> # A tbl_graph: 38 nodes and 37 edges
#> #
#> # A rooted tree
#> #
#> # Node Data: 38 x 4 (active)
#>   unit_id org_depth is_leaf unit_size
#>   <chr>       <int> <lgl>       <dbl>
#> 1 1               0 FALSE           3
#> 2 3               1 FALSE           2
#> 3 4               1 FALSE           3
#> 4 5               1 FALSE           3
#> 5 10              2 FALSE           3
#> 6 11              2 FALSE           2
#> # ... with 32 more rows
#> #
#> # Edge Data: 37 x 2
#>    from    to
#>   <int> <int>
#> 1     1     2
#> 2     1     3
#> 3     1     4
#> # ... with 34 more rows

# indiv_df example
data(tg_org_indiv_df)
dplyr::tbl_df(tg_org_indiv_df)
#> # A tibble: 146 x 5
#>    individual_id individual_name unit_id test_var test_var2
#>    <chr>         <chr>           <chr>      <dbl>     <dbl>
#>  1 1             1_1             1           7.27      14.5
#>  2 2             1_2             1           8.53      20.4
#>  3 3             1_3             1           9.21      15.8
#>  4 4             3_1             3          12.9       23.5
#>  5 5             3_2             3           5.02      14.6
#>  6 6             4_1             4          11.0       20.0
#>  7 7             4_2             4           6.33      14.2
#>  8 8             4_3             4          13.8       20.2
#>  9 9             5_1             5           7.48      17.0
#> 10 10            5_2             5          11.7       18.3
#> # ... with 136 more rows

# minimal indiv_df example
data(tg_org_indiv_minimal_df)
dplyr::tbl_df(tg_org_indiv_minimal_df)
#> # A tibble: 146 x 2
#>    individual_id unit_id
#>    <chr>         <chr>  
#>  1 1             1      
#>  2 2             1      
#>  3 3             1      
#>  4 4             3      
#>  5 5             3      
#>  6 6             4      
#>  7 7             4      
#>  8 8             4      
#>  9 9             5      
#> 10 10            5      
#> # ... with 136 more rows

# indiv_tall_df example
data(tg_org_indiv_tall_df)
dplyr::tbl_df(tg_org_indiv_tall_df)
#> # A tibble: 292 x 3
#>    individual_id metric_id value
#>    <chr>         <chr>     <dbl>
#>  1 1             test_var   7.27
#>  2 2             test_var   8.53
#>  3 3             test_var   9.21
#>  4 4             test_var  12.9 
#>  5 5             test_var   5.02
#>  6 6             test_var  11.0 
#>  7 7             test_var   6.33
#>  8 8             test_var  13.8 
#>  9 9             test_var   7.48
#> 10 10            test_var  11.7 
#> # ... with 282 more rows

# org_tall_df example
data(tg_org_summarised_df)
dplyr::tbl_df(tg_org_summarised_df)
#> # A tibble: 76 x 3
#>    unit_id metric_id value
#>    <chr>   <chr>     <dbl>
#>  1 1       test_var   10.2
#>  2 1       test_var2  20.2
#>  3 10      test_var   10.9
#>  4 10      test_var2  20.2
#>  5 11      test_var   10.4
#>  6 11      test_var2  20.8
#>  7 13      test_var   11.0
#>  8 13      test_var2  19.6
#>  9 14      test_var   10.2
#> 10 14      test_var2  21.1
#> # ... with 66 more rows

Examples

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