README.md

In nyuglobalties/panelcleaner: An interactive interface to homogenize messy panel data into a long format

panelcleaner

Sometimes during data collection, survey structures may change over time due to a litany of potential issues. In order to combine these datasets together into a long format, panelcleaner attempts to identify as many issues as possible prior to binding datasets together by thoroughly documenting the state of each variable for each wave. Moreover, with the assistance of rcoder, categorical data can be easily recoded into a single, homogenized coding while not losing the labels or any associated metadata.

Usage

panelcleaner is built around a panel mapping file, which is a spreadsheet like this:

name\_1 description\_1 coding\_1 name\_2 description\_2 coding\_2 panel homogenized\_name homogenized\_coding id Participant ID id Partcipant ID test\_panel id question1 The first question q1 The first question test\_panel question\_1 question2 The second question coding(code(‘Yes’, 1), code(‘No’, 0)) q2 The second question coding(code(‘Yes’, ‘YES’), code(‘No’, ‘NO’)) test\_panel question\_2 coding(code(‘Yes’, 1), code(‘No’, 0))

The “name”, “description”, and “coding” columns refer to how the data was structured in waves 1 & 2. The “code” columns are specific to categorical variables, and the coding() objects (written out as strings to be interpreted later) represent the categorical level structure. See rcoder for more information.

Once the mapping is defined, the homogenization process can begin. The first step in the process is to gather waves of data into a panel object:

wave1 <- tibble::tibble(
  id = LETTERS[1:4],
  question1 = sample(1:100, 4, replace = TRUE),
  question2 = sample(0:1, 4, replace = TRUE)
)

wave2 <- tibble::tibble(
  id = LETTERS[1:4],
  q1 = sample(1:100, 4, replace = TRUE),
  q2 = sample(c("NO", "YES"), 4, replace = TRUE)
)

(test_panel <- enpanel("test_panel", wave1, wave2))
#> <Unhomogenized Panel: 'test_panel'>
#> Waves: ['1', '2']
#> ID column: 'id'
#> Wave column: 'wave'
#> No mapping attached
#> 
#> '1' content:
#> # A tibble: 4 x 3
#>   id    question1 question2
#>   <chr>     <int>     <int>
#> 1 A            47         1
#> 2 B            14         0
#> 3 C            85         0
#> 4 D            84         1
#> 
#> '2' content:
#> # A tibble: 4 x 3
#>   id       q1 q2   
#>   <chr> <int> <chr>
#> 1 A        22 YES  
#> 2 B        32 NO   
#> 3 C        12 NO   
#> 4 D        59 NO

The panel name is “test_panel”. Since multiple panels can be defined in the same mapping file, a panel name must be defined to distinguish which mapping belongs to which panel. Moreover, the panel is an unhomogenized panel, which means that waves cannot yet be bound together to form a single, long-format dataset.

The next step is to attach a panel mapping to the created panel:

panel_map <-
  panel_map_csv %>% 
  panel_mapping(waves = c(1, 2))

test_panel <-
  test_panel %>% 
  add_mapping(panel_map)

test_panel
#> <Unhomogenized Panel: 'test_panel'>
#> Waves: ['1', '2']
#> ID column: 'id'
#> Wave column: 'wave'
#> Attached mapping content:
#> # A tibble: 3 x 9
#>   name_1 description_1 coding_1 name_2 description_2 coding_2 panel
#> * <chr>  <chr>         <chr>    <chr>  <chr>         <chr>    <chr>
#> 1 id     Participant … <NA>     id     Partcipant ID <NA>     test…
#> 2 quest… The first qu… <NA>     q1     The first qu… <NA>     test…
#> 3 quest… The second q… coding(… q2     The second q… coding(… test…
#> # … with 2 more variables: homogenized_name <chr>, homogenized_coding <chr>
#> 
#> '1' content:
#> # A tibble: 4 x 3
#>   id    question1 question2
#>   <chr>     <int>     <int>
#> 1 A            47         1
#> 2 B            14         0
#> 3 C            85         0
#> 4 D            84         1
#> 
#> '2' content:
#> # A tibble: 4 x 3
#>   id       q1 q2   
#>   <chr> <int> <chr>
#> 1 A        22 YES  
#> 2 B        32 NO   
#> 3 C        12 NO   
#> 4 D        59 NO

panel_mapping() needs information about the specific waves mapped out, so we pass the loaded CSV data.frame through panel_mapping() to check that the CSV adheres to the panel mapping spec. Then we add the mapping to the panel so that the homogenization process can refer to it. Finally, we homogenize the panel.

homogenized_panel <-
  test_panel %>% 
  homogenize_panel() %>% 
  bind_waves()

homogenized_panel
#> <Homogenized Panel: 'test_panel'>
#> Waves: ['1', '2']
#> ID column: 'id'
#> Wave column: 'wave'
#> Attached mapping content:
#> # A tibble: 3 x 9
#>   name_1 description_1 coding_1 name_2 description_2 coding_2 panel
#> * <chr>  <chr>         <chr>    <chr>  <chr>         <chr>    <chr>
#> 1 id     Participant … <NA>     id     Partcipant ID <NA>     test…
#> 2 quest… The first qu… <NA>     q1     The first qu… <NA>     test…
#> 3 quest… The second q… coding(… q2     The second q… coding(… test…
#> # … with 2 more variables: homogenized_name <chr>, homogenized_coding <chr>
#> 
#> Content:
#> # A tibble: 8 x 4
#>   id    question_1 question_2 wave 
#>   <chr>      <int>      <dbl> <chr>
#> 1 A             47          1 1    
#> 2 B             14          0 1    
#> 3 C             85          0 1    
#> 4 D             84          1 1    
#> 5 A             22          1 2    
#> 6 B             32          0 2    
#> 7 C             12          0 2    
#> 8 D             59          0 2

To extract the underlying data, use as.data.frame() to convert the homogenized panel to a mapped_df, a data.frame-like object that carries the panel mapping as an attribute.

str(as.data.frame(homogenized_panel))
#> tibble [8 × 4] (S3: mapped_df/tbl_df/tbl/data.frame)
#>  $ id        : chr [1:8] "A" "B" "C" "D" ...
#>  $ question_1: int [1:8] 47 14 85 84 22 32 12 59
#>  $ question_2: num [1:8] 1 0 0 1 1 0 0 0
#>  $ wave      : chr [1:8] "1" "1" "1" "1" ...
#>  - attr(*, "mapping")= tibble [3 × 9] (S3: panel_mapping/tbl_df/tbl/data.frame)
#>   ..$ name_1            : chr [1:3] "id" "question1" "question2"
#>   ..$ description_1     : chr [1:3] "Participant ID" "The first question" "The second question"
#>   ..$ coding_1          : chr [1:3] NA NA "coding(code('Yes', 1), code('No', 0))"
#>   ..$ name_2            : chr [1:3] "id" "q1" "q2"
#>   ..$ description_2     : chr [1:3] "Partcipant ID" "The first question" "The second question"
#>   ..$ coding_2          : chr [1:3] NA NA "coding(code('Yes', 'YES'), code('No', 'NO'))"
#>   ..$ panel             : chr [1:3] "test_panel" "test_panel" "test_panel"
#>   ..$ homogenized_name  : chr [1:3] "id" "question_1" "question_2"
#>   ..$ homogenized_coding: chr [1:3] NA NA "coding(code('Yes', 1), code('No', 0))"
#>   ..- attr(*, "schema")=List of 5
#>   .. ..$ wave_name         : chr "name"
#>   .. ..$ wave_coding       : chr "coding"
#>   .. ..$ panel             : chr "panel"
#>   .. ..$ homogenized_name  : chr "homogenized_name"
#>   .. ..$ homogenized_coding: chr "homogenized_coding"
#>  - attr(*, "id_col")= chr "id"
#>  - attr(*, "waves_col")= chr "wave"

Installation

As panelcleaner does not exist on CRAN yet, you can install the latest development version of this package via:

# install.packages("remotes")
remotes::install_github("Global-TIES-for-Children/panelcleaner")

Contributing

Please note that the panelcleaner project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

nyuglobalties/panelcleaner documentation built on March 30, 2023, 11:01 a.m.