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vignettes/datatables.R
In Rmonize: Support Retrospective Harmonization of Data
library(dplyr)
library(tibble)
library(stringr)
library(DT)
library(stats)
#### glossary ####
# glossary <-
# tribble(
# ~`Object`,
# ~`Requirement`,
#
# # DataSchema
# '<h3>DataSchema</h3>
# A DataSchema defines the harmonized variables to be generated, and also
# represents metadata of an associated harmonized dossier. It must be a list
# of data frame or data frame extension (e.g. a tibble) objects with elements
# named "Variables" (required) and "Categories" (if any). The "Variables"
# element must contain at least the `name` column, and the "Categories" element
# must contain at least the `variable` and `name` columns to be usable in any
# function. To be considered as a minimum workable DataSchema, in "Variables"
# the `name` column must also have unique and non-null entries, and in
# "Categories" the combination of `variable` and `name` columns must also be
# unique.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/dataschema%20-%20template.xlsx"
# download>Download template</a>
# </button>
#
# ',
#
# '<p style="color:red"><b>Variables</b></p>
# <ul>
# <li style="color:red">name</li>
# <li>label:xx</li>
# <li>valueType</li>
# </ul>
# <p><b>Categories</b></p>
# <ul>
# <li style="color:red">variable</li>
# <li style="color:red">name</li>
# <li>label::xx</li>
# <li>missing</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = data_dict_mlstr</li>
# <li style="color:red">Rmonize::class = dataschema_mlstr</li>
# </ul>',
#
# # ss DataSchema
# '<h3>Harmonized data dictionary</h3>
#
# An harmonized data dictionary is the metadata for a harmonized dataset that has
# been harmonized from an input dataset.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/harmo_data_dict%20-%20template.xlsx"
# download>Download template</a>
# </button>
#
# ',
#
# '<p style="color:red"><b>Variables</b></p>
# <ul>
# <li style="color:red">name</li>
# <li>label:xx</li>
# <li>valueType</li>
# <li>Mlstr_harmo::rule_category</li>
# <li>Mlstr_harmo::algorithm</li>
# <li>Mlstr_harmo::status</li>
# <li>Mlstr_harmo::status_detail</li>
# <li style="color:red">attributes madshapR::class = data_dict_mlstr</li>
# <li style="color:red">attributes Rmonize::class = dataschema_mlstr</li>
# </ul>
#
# <p><b>Categories</b></p>
# <ul>
# <li style="color:red">variable</li>
# <li style="color:red">name</li>
# <li>label::xx</li>
# <li>missing</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = data_dict_mlstr</li>
# <li style="color:red">Rmonize::class = dataschema_mlstr</li>
# </ul>',
#
# # Data Processing Elements
# '<h3>Data Processing Elements</h3>
#
# The Data Processing Elements (DPE) is a table that gathers all the harmonization
# rules used to create each variable defined in the DataSchema. This table is
# typically an Excel spreadsheet where each row indicates how the
# input variables are processed to generate a harmonized variable.
# Once this table is filled, it is used in the harmo_process() function along with
# the other two parameters (data_schema and dossier) to create the
# harmonized_dossier. If harmonization cannot be performed correctly due to errors
# in the DPE, the harmonized_dossier generated can be sent to the
# show_harmo_error() function, allowing the user to correct the DPE and restart
# the process.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/data_processing_elements%20-%20template.csv"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Data Processing Elements name)</em></b></p>
# <ul>
# <li style="color:red">dataschema_variable</li>
# <li>label::xx</li>
# <li>valueType</li>
# <li style="color:red">input_dataset</li>
# <li style="color:red">Mlstr_harmo::rule_category</li>
# <li style="color:red">Mlstr_harmo::algorithm</li>
# <li>Mlstr_harmo::status</li>
# <li>Mlstr_harmo::status_detail</li>
# <li>Mlstr_harmo::comment</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">Rmonize::class = data_proc_elem</li>
# </ul>',
#
# # data_dict
# '<h3>Data dictionary</h3>
# A data dictionary contains metadata about variables and can be associated
# with a dataset. It must be a list of data frame-like objects with elements
# named "Variables" (required) and "Categories" (if any). To be usable in any
# function, the "Variables" element must contain at least the `name` column,
# and the "Categories" element must contain at least the `variable` and `name`
# columns. To be considered as a minimum workable data dictionary, in
# "Variables" the `name` column must also have unique and non-null entries,
# and in "Categories" the combination of `variable` and `name` columns must
# also be unique.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/data_dictionary%20-%20template.xlsx"
# download>Download template</a>
# </button>
#
# ',
#
# '<p style="color:red"><b>Variables</b></p>
# <ul>
# <li style="color:red">name</li>
# <li>label:xx</li>
# <li>valueType</li>
# </ul>
# <p><b>Categories</b></p>
# <ul>
# <li style="color:red">variable</li>
# <li style="color:red">name</li>
# <li>label::xx</li>
# <li>missing</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = data_dict_mlstr</li>
# </ul>',
#
# # dataset
# '<h3>Dataset</h3>
# A dataset must be a data frame-like object and can be associated with a
# data dictionary. If no data dictionary is provided, a minimum workable
# data dictionary will be generated as needed by relevant functions.
# An identifier `id` column for sorting can be specified by the user. If
# specified, the `id` values must be non-missing and will be used in functions
# that require it. If no identifier column is specified, indexing is handled
# automatically by the function.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/dataset%20-%20template.csv"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Dataset name)</em></b></p>
# <ul>
# <li style="color:red">col_id</li>
# <li>col_2</li>
# <li>...</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = dataset</li>
# <li style="color:red">madshapR::col_id</li>
# </ul>',
#
# # dossier
# '<h3>Dossier</h3>
# A dossier must be a named list containing at least one data frame or
# data frame extension (e.g. a tibble), each of them being datasets.
# The name of each tibble will be use as the reference name of the dataset.
#
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/dossier%20-%20template.xlsx"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Dossier name)</em></b></p>
# <ul>
# <li style="color:red">dataset 1</li>
# <li>dataset 2</li>
# <li>...</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = dossier</li>
# </ul>',
#
# # harmo_dataset
# '<h3>Harmonized dataset</h3>
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/harmonized_dataset%20-%20template.csv"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Harmonized dataset name)</em></b></p>
# <ul>
# <li style="color:red">dataschema_variable_id</li>
# <li>dataschema_variable_2</li>
# <li>...</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = dataset</li>
# <li style="color:red">madshapR::col_id</li>
# </ul>',
#
# # pooled harmonized dataset
# '<h3>Harmonized dataset</h3>
# The first column refers to the name of each dataset which is the name of each
# tibble in the dossier. The second column refers to the column id in each
# harmonized dataset and which identifies unique combination (concatenated)
# observation/dataset. These two columns are added to ensure every information
# is safe during the process. The pooled_harmonized dataset comes with its
# data dictionary which is the harmonized dossier DataSchema, to which the
# two extra columns are added.
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/pooled_harmonized_dataset%20-%20template.csv"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Pooled harmonized dataset name)</em></b></p>
# <ul>
# <li style="color:red">unique_col_id</li>
# <li style="color:red">unique_col_dataset</li>
# <li style="color:red">dataschema_variable_id</li>
# <li>dataschema_variable_2</li>
# <li>...</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = dataset</li>
# <li style="color:red">madshapR::col_id</li>
# <li style="color:red">Rmonize::class = pooled_harmonized_dataset</li>
# <li style="color:red">Rmonize::unique_col_id</li>
# <li style="color:red">Rmonize::unique_col_dataset</li>
# </ul>',
#
# # harmo_dossier
# '<h3>Harmonized dossier</h3>
# A harmonized dossier must be a named list containing at least one data frame
# or data frame extension (e.g. a tibble), each of them being
# harmonized dataset(s). It is generally the product of applying harmonization
# processing to a dossier object. The name of each tibble will be use as the
# reference name of the dataset. A harmonized dossier has four attributes :
# `Rmonize::class` which is "harmonized_dossier" ; `Rmonize::DataSchema`
# (provided by user) ; `Rmonize::Data Processing Elements` ;
# `Rmonize::harmonized_col_id` (provided by user) which refers to the column
# in each dataset which identifies unique combination observation/dataset.
# This id column name is the same across the dataset(s), the DataSchema and
# the Data Processing Elements (created by using `id_creation`) and is used to
# initiate the process of harmonization.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/harmonized_dossier%20-%20template.xlsx"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Harmonized dossier name)</em></b></p>
# <ul>
# <li style="color:red">dataset 1</li>
# <li>dataset 2</li>
# <li>...</li>
# <li style="color:red">attributes Rmonize::class = harmonized_dossier</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">Rmonize::class = harmonized_dossier</li>
# <li style="color:red">Rmonize::DataSchema</li>
# <li style="color:red">Rmonize::harmonized_col_id</li>
# </ul>'
#
# )
#
#
#
#
# # mlstr_status <-
# # tribble(
# # ~`Mlstr_harmo::status`,~`definition`,~`Mlstr_harmo::status_detail`,
# # '<code><b>complete</b></code>' ,
# # 'Input variable :
# # <ul>
# # <li>is the same</li>
# # <li>Needs transformation</li>
# # <li>unknown</li>
# # </ul>',
# #
# # '<br>
# # <ul>
# # <li><code>identical</code></li>
# # <li><code>compatible</code></li>
# # <li><code>unknown</code></li>
# # </ul>',
# #
# #
# #
# # '<code><b>partial*</b></code>',
# # 'With loss of information, input variable :
# # <ul>
# # <li>approximates results</li>
# # <li>needs confirmation</li>
# # <li>unknown</li>
# # </ul>',
# #
# # '<br>
# # <ul>
# # <li><code>proximate</code></li>
# # <li><code>tentative</code></li>
# # <li><code>unknown</code></li>
# # </ul>',
# #
# #
# # '<code><b>impossible</b></code>' ,
# # 'The DataSchema variable :
# # <ul>
# # <li>is not collected</li>
# # <li>cannot be used</li>
# # <li>unknown</li>
# # </ul>',
# #
# # '<br>
# # <ul>
# # <li><code>unavailable</code></li>
# # <li><code>incompatible</code></li>
# # <li><code>unknown</code></li>
# # </ul>',
# #
# # '<code><b>undetermined</b></code>' , 'Harmonization status not determined.','',
# # '<code><b>na</b></code>' , 'Harmonization status is not relevant.','',
# # '<code><b>error</b></code>' , 'When error, the error message provided <brr>
# # by R is stored here','')
#
# #### DT_glossary ####
# DT_glossary <-
# glossary %>%
# datatable(
# escape = FALSE,
# rownames = FALSE,
# class = 'cell-border stripe',
# options = list(
# pageLength = nrow(.),
# ordering = TRUE,
# autoWidth = TRUE,
# columnDefs = list(list(width = '50%', targets = c(1))))) %>%
# formatStyle(seq_len(ncol(.$x$data)),'vertical-align'='top') %>%
# formatStyle(seq_len(ncol(.$x$data)),'text-align' = 'left')
#
#
#### data_proc_elem_def ####
data_proc_elem_def <-
tribble(
~`column name`,~`definition`,
'<code><b>dataschema_variable</b></code>' , 'Variable defined in the DataSchema' ,
'<code><b>input_dataset</b></code>' , 'Input dataset where the <br>
input variable(s) is/are taken from' ,
'<code><b>input_variables</b></code>' , 'Input variable(s) (name(s) of the column in the dataset)',
'<code><b>Mlstr_harmo::rule_category</b></code>', 'Rule category' ,
'<code><b>Mlstr_harmo::algorithm</b></code>' , 'Algorithm associated to the rule category' ,
)
#### col_names_dpe ####
col_names_dpe <- c(
'<b>dataschema_variable</b>',
'<b>input_dataset</b>',
'<b>input_variables</b>',
'<b>Mlstr_harmo::rule_category</b>',
'<b>Mlstr_harmo::algorithm</b>')
#### id_creation ####
id_creation = paste(
'<code>',c(
'harmonized_id',
'input_dataset',
'input_id',
'<b>id_creation</b>',
'<b>id_creation</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### direct_mapping ####
direct_mapping = paste(
'<code>',c(
'output_var',
'input_dataset',
'input_var',
'<b>direct_mapping</b>',
'<b>direct_mapping</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### recode ####
recode = paste(
'<code>',c(
'output_var',
'input_dataset',
'input_var',
'<b>recode</b>',
'<b>recode(</b>
"YES" = 1 ;
"NO" = 0 ;
ELSE = NA<b>) </b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### case_when ####
case_when = paste(
'<code>',c(
'output_var',
'input_dataset',
'input_var_x ; input_var_y',
'<b>case_when</b>' ,
'<b>case_when(</b>
var_x == 1 ~ var_z ;
ELSE ~ NA<b>)</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### operation ####
operation = paste(
'<code>',c(
'output_var',
'input_dataset',
'input_var',
'<b>operation</b>' ,
'<em># simple R code here such as :</em></b>
(<b>mean(</b>input_var<b>)</b> - input_var<b>)</b> / <b>stats::sd(</b>input_var<b>)</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### paste ####
paste = paste(
'<code>',c(
'output_var',
'input_dataset',
'<b>__BLANK__</b>' ,
'<b>paste</b>' ,
'"Hello word"'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### other ####
other = paste(
'<code>',c(
'output_var',
'input_dataset',
'<b>__BLANK__</b>' ,
'<b>other</b>' ,
'# place complex R code here...'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### impossible ####
impossible = paste(
'<code>',c(
'output_var',
'input_dataset',
'<b>impossible</b>' ,
'<b>impossible</b>' ,
'<b>impossible</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### undetermined ####
undetermined = paste(
'<code>',c(
'output_var',
'input_dataset',
'<b>undetermined</b>',
'<b>undetermined</b>',
'<b>undetermined</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### rule_categories ####
rule_categories <-
bind_rows(
id_creation,
direct_mapping,
recode,
case_when,
operation,
paste,
other,
impossible,
undetermined)
#### data_proc_elem_expl ####
data_proc_elem_expl <-
rule_categories %>% slice(1:3) %>%
mutate(`<b>input_dataset</b>` = str_replace(
`<b>input_dataset</b>`,'DATASET','dataset_MELBOURNE')) %>%
bind_rows(
rule_categories %>% slice(1,5,8) %>%
mutate(`<b>input_dataset</b>` = str_replace(
`<b>input_dataset</b>`,'DATASET','dataset_PARIS')) %>%
mutate(`<b>input_variables</b>` = str_replace(
`<b>input_variables</b>`,'col_id','ID_part')) %>%
mutate(`<b>dataschema_variable</b>` = str_replace(
`<b>dataschema_variable</b>`,'variable_D','variable_A')) %>%
mutate(`<b>dataschema_variable</b>` = str_replace(
`<b>dataschema_variable</b>`,'variable_G','variable_B'))
) %>%
mutate(`<b>dataschema_variable</b>` = str_replace(
`<b>dataschema_variable</b>`,'variable_ID','<b>variable_ID</b>')) %>%
mutate(`<b>Mlstr_harmo::rule_category</b>` = ifelse(
str_detect(`<b>dataschema_variable</b>`, 'variable_ID'),
'<code><b>id_creation</b></code>',`<b>Mlstr_harmo::rule_category</b>`)) %>%
add_column(`<b>index</b>` = c(
'<code>1</code>','<code>2</code>','<code>3</code>',
'<code>1</code>','<code>2</code>','<code>3</code>'),.before = TRUE)
#### DT_rule_categories ####
DT_rule_categories <-
rule_categories %>%
datatable(
extensions = "Buttons",
escape = FALSE,
rownames = FALSE,
class = 'cell-border stripe',
options = list(
searching = FALSE,
columnDefs = list(list(className = 'dt-head-left', targets = "_all")),
headerCallback = DT::JS("function(thead) { $(thead).css('font-size', '0.7em');}"),
paging = FALSE,
info = FALSE,
scrollX = TRUE,
ordering = FALSE,
pageLength = nrow(.),
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel')
))
# #### DT_data_proc_elem_expl ####
# # DT_data_proc_elem_expl <-
# # data_proc_elem_expl %>%
# # select(-last_col()) %>%
# # datatable(
# # escape = FALSE,
# # rownames = FALSE,
# # class = 'cell-border stripe',
# # options = list(
# # pageLength = nrow(.),
# # columnDefs = list(list(className = 'dt-head-left', targets = "_all")),
# # headerCallback = DT::JS("function(thead) { $(thead).css('font-size', '0.7em');}"),
# # ordering = TRUE,
# # scrollX = TRUE),
# # autoHideNavigation = TRUE)
#
#### DT_impundebla ####
DT_impundebla <-
rule_categories %>%
dplyr::filter(str_detect(`<b>input_variables</b>`, 'BLANK|impossible|undetermined')) %>%
datatable(
escape = FALSE,
rownames = FALSE,
class = 'cell-border stripe',
options = list(
pageLength = nrow(.),
columnDefs = list(list(className = 'dt-head-left', targets = "_all")),
headerCallback = DT::JS("function(thead) { $(thead).css('font-size', '0.7em');}"),
ordering = FALSE,
scrollX = TRUE),
autoHideNavigation = TRUE)
#### DT_id_creation ####
DT_id_creation <-
t(id_creation %>%
mutate(`<b>Mlstr_harmo::rule_category</b>` = '<code><b>id_creation</b></code>')) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_direct_mapping <-
t(direct_mapping) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_recode <-
t(recode) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_case_when <-
t(case_when) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_paste <-
t(paste) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_operation <-
t(operation) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_other <-
t(other) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_impossible <-
t(impossible) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_undetermined <-
t(undetermined) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
#### DT_data_proc_elem_def ####
DT_data_proc_elem_def <-
data_proc_elem_def %>%
datatable(
escape = FALSE,
rownames = FALSE,
class = 'cell-border stripe',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
# #### DT_mlstr_status ####
# DT_mlstr_status <-
# mlstr_status %>%
# datatable(
# escape = FALSE,
# rownames = FALSE,
# class = 'cell-border stripe',
# options = list(pageLength = nrow(.), ordering = FALSE),
# autoHideNavigation = TRUE)
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library(dplyr)
library(tibble)
library(stringr)
library(DT)
library(stats)
#### glossary ####
# glossary <-
# tribble(
# ~`Object`,
# ~`Requirement`,
#
# # DataSchema
# '<h3>DataSchema</h3>
# A DataSchema defines the harmonized variables to be generated, and also
# represents metadata of an associated harmonized dossier. It must be a list
# of data frame or data frame extension (e.g. a tibble) objects with elements
# named "Variables" (required) and "Categories" (if any). The "Variables"
# element must contain at least the `name` column, and the "Categories" element
# must contain at least the `variable` and `name` columns to be usable in any
# function. To be considered as a minimum workable DataSchema, in "Variables"
# the `name` column must also have unique and non-null entries, and in
# "Categories" the combination of `variable` and `name` columns must also be
# unique.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/dataschema%20-%20template.xlsx"
# download>Download template</a>
# </button>
#
# ',
#
# '<p style="color:red"><b>Variables</b></p>
# <ul>
# <li style="color:red">name</li>
# <li>label:xx</li>
# <li>valueType</li>
# </ul>
# <p><b>Categories</b></p>
# <ul>
# <li style="color:red">variable</li>
# <li style="color:red">name</li>
# <li>label::xx</li>
# <li>missing</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = data_dict_mlstr</li>
# <li style="color:red">Rmonize::class = dataschema_mlstr</li>
# </ul>',
#
# # ss DataSchema
# '<h3>Harmonized data dictionary</h3>
#
# An harmonized data dictionary is the metadata for a harmonized dataset that has
# been harmonized from an input dataset.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/harmo_data_dict%20-%20template.xlsx"
# download>Download template</a>
# </button>
#
# ',
#
# '<p style="color:red"><b>Variables</b></p>
# <ul>
# <li style="color:red">name</li>
# <li>label:xx</li>
# <li>valueType</li>
# <li>Mlstr_harmo::rule_category</li>
# <li>Mlstr_harmo::algorithm</li>
# <li>Mlstr_harmo::status</li>
# <li>Mlstr_harmo::status_detail</li>
# <li style="color:red">attributes madshapR::class = data_dict_mlstr</li>
# <li style="color:red">attributes Rmonize::class = dataschema_mlstr</li>
# </ul>
#
# <p><b>Categories</b></p>
# <ul>
# <li style="color:red">variable</li>
# <li style="color:red">name</li>
# <li>label::xx</li>
# <li>missing</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = data_dict_mlstr</li>
# <li style="color:red">Rmonize::class = dataschema_mlstr</li>
# </ul>',
#
# # Data Processing Elements
# '<h3>Data Processing Elements</h3>
#
# The Data Processing Elements (DPE) is a table that gathers all the harmonization
# rules used to create each variable defined in the DataSchema. This table is
# typically an Excel spreadsheet where each row indicates how the
# input variables are processed to generate a harmonized variable.
# Once this table is filled, it is used in the harmo_process() function along with
# the other two parameters (data_schema and dossier) to create the
# harmonized_dossier. If harmonization cannot be performed correctly due to errors
# in the DPE, the harmonized_dossier generated can be sent to the
# show_harmo_error() function, allowing the user to correct the DPE and restart
# the process.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/data_processing_elements%20-%20template.csv"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Data Processing Elements name)</em></b></p>
# <ul>
# <li style="color:red">dataschema_variable</li>
# <li>label::xx</li>
# <li>valueType</li>
# <li style="color:red">input_dataset</li>
# <li style="color:red">Mlstr_harmo::rule_category</li>
# <li style="color:red">Mlstr_harmo::algorithm</li>
# <li>Mlstr_harmo::status</li>
# <li>Mlstr_harmo::status_detail</li>
# <li>Mlstr_harmo::comment</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">Rmonize::class = data_proc_elem</li>
# </ul>',
#
# # data_dict
# '<h3>Data dictionary</h3>
# A data dictionary contains metadata about variables and can be associated
# with a dataset. It must be a list of data frame-like objects with elements
# named "Variables" (required) and "Categories" (if any). To be usable in any
# function, the "Variables" element must contain at least the `name` column,
# and the "Categories" element must contain at least the `variable` and `name`
# columns. To be considered as a minimum workable data dictionary, in
# "Variables" the `name` column must also have unique and non-null entries,
# and in "Categories" the combination of `variable` and `name` columns must
# also be unique.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/data_dictionary%20-%20template.xlsx"
# download>Download template</a>
# </button>
#
# ',
#
# '<p style="color:red"><b>Variables</b></p>
# <ul>
# <li style="color:red">name</li>
# <li>label:xx</li>
# <li>valueType</li>
# </ul>
# <p><b>Categories</b></p>
# <ul>
# <li style="color:red">variable</li>
# <li style="color:red">name</li>
# <li>label::xx</li>
# <li>missing</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = data_dict_mlstr</li>
# </ul>',
#
# # dataset
# '<h3>Dataset</h3>
# A dataset must be a data frame-like object and can be associated with a
# data dictionary. If no data dictionary is provided, a minimum workable
# data dictionary will be generated as needed by relevant functions.
# An identifier `id` column for sorting can be specified by the user. If
# specified, the `id` values must be non-missing and will be used in functions
# that require it. If no identifier column is specified, indexing is handled
# automatically by the function.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/dataset%20-%20template.csv"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Dataset name)</em></b></p>
# <ul>
# <li style="color:red">col_id</li>
# <li>col_2</li>
# <li>...</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = dataset</li>
# <li style="color:red">madshapR::col_id</li>
# </ul>',
#
# # dossier
# '<h3>Dossier</h3>
# A dossier must be a named list containing at least one data frame or
# data frame extension (e.g. a tibble), each of them being datasets.
# The name of each tibble will be use as the reference name of the dataset.
#
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/dossier%20-%20template.xlsx"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Dossier name)</em></b></p>
# <ul>
# <li style="color:red">dataset 1</li>
# <li>dataset 2</li>
# <li>...</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = dossier</li>
# </ul>',
#
# # harmo_dataset
# '<h3>Harmonized dataset</h3>
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/harmonized_dataset%20-%20template.csv"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Harmonized dataset name)</em></b></p>
# <ul>
# <li style="color:red">dataschema_variable_id</li>
# <li>dataschema_variable_2</li>
# <li>...</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = dataset</li>
# <li style="color:red">madshapR::col_id</li>
# </ul>',
#
# # pooled harmonized dataset
# '<h3>Harmonized dataset</h3>
# The first column refers to the name of each dataset which is the name of each
# tibble in the dossier. The second column refers to the column id in each
# harmonized dataset and which identifies unique combination (concatenated)
# observation/dataset. These two columns are added to ensure every information
# is safe during the process. The pooled_harmonized dataset comes with its
# data dictionary which is the harmonized dossier DataSchema, to which the
# two extra columns are added.
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/pooled_harmonized_dataset%20-%20template.csv"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Pooled harmonized dataset name)</em></b></p>
# <ul>
# <li style="color:red">unique_col_id</li>
# <li style="color:red">unique_col_dataset</li>
# <li style="color:red">dataschema_variable_id</li>
# <li>dataschema_variable_2</li>
# <li>...</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">madshapR::class = dataset</li>
# <li style="color:red">madshapR::col_id</li>
# <li style="color:red">Rmonize::class = pooled_harmonized_dataset</li>
# <li style="color:red">Rmonize::unique_col_id</li>
# <li style="color:red">Rmonize::unique_col_dataset</li>
# </ul>',
#
# # harmo_dossier
# '<h3>Harmonized dossier</h3>
# A harmonized dossier must be a named list containing at least one data frame
# or data frame extension (e.g. a tibble), each of them being
# harmonized dataset(s). It is generally the product of applying harmonization
# processing to a dossier object. The name of each tibble will be use as the
# reference name of the dataset. A harmonized dossier has four attributes :
# `Rmonize::class` which is "harmonized_dossier" ; `Rmonize::DataSchema`
# (provided by user) ; `Rmonize::Data Processing Elements` ;
# `Rmonize::harmonized_col_id` (provided by user) which refers to the column
# in each dataset which identifies unique combination observation/dataset.
# This id column name is the same across the dataset(s), the DataSchema and
# the Data Processing Elements (created by using `id_creation`) and is used to
# initiate the process of harmonization.
#
# <br><br>
#
# <button><a
# href="https://maelstrom-research.github.io/Rmonize-documentation/templates/harmonized_dossier%20-%20template.xlsx"
# download>Download template</a>
# </button>
#
# ',
#
# '<p><b><em>(Harmonized dossier name)</em></b></p>
# <ul>
# <li style="color:red">dataset 1</li>
# <li>dataset 2</li>
# <li>...</li>
# <li style="color:red">attributes Rmonize::class = harmonized_dossier</li>
# </ul>
#
# <p><b>R attributes</b></p>
# <ul>
# <li style="color:red">Rmonize::class = harmonized_dossier</li>
# <li style="color:red">Rmonize::DataSchema</li>
# <li style="color:red">Rmonize::harmonized_col_id</li>
# </ul>'
#
# )
#
#
#
#
# # mlstr_status <-
# # tribble(
# # ~`Mlstr_harmo::status`,~`definition`,~`Mlstr_harmo::status_detail`,
# # '<code><b>complete</b></code>' ,
# # 'Input variable :
# # <ul>
# # <li>is the same</li>
# # <li>Needs transformation</li>
# # <li>unknown</li>
# # </ul>',
# #
# # '<br>
# # <ul>
# # <li><code>identical</code></li>
# # <li><code>compatible</code></li>
# # <li><code>unknown</code></li>
# # </ul>',
# #
# #
# #
# # '<code><b>partial*</b></code>',
# # 'With loss of information, input variable :
# # <ul>
# # <li>approximates results</li>
# # <li>needs confirmation</li>
# # <li>unknown</li>
# # </ul>',
# #
# # '<br>
# # <ul>
# # <li><code>proximate</code></li>
# # <li><code>tentative</code></li>
# # <li><code>unknown</code></li>
# # </ul>',
# #
# #
# # '<code><b>impossible</b></code>' ,
# # 'The DataSchema variable :
# # <ul>
# # <li>is not collected</li>
# # <li>cannot be used</li>
# # <li>unknown</li>
# # </ul>',
# #
# # '<br>
# # <ul>
# # <li><code>unavailable</code></li>
# # <li><code>incompatible</code></li>
# # <li><code>unknown</code></li>
# # </ul>',
# #
# # '<code><b>undetermined</b></code>' , 'Harmonization status not determined.','',
# # '<code><b>na</b></code>' , 'Harmonization status is not relevant.','',
# # '<code><b>error</b></code>' , 'When error, the error message provided <brr>
# # by R is stored here','')
#
# #### DT_glossary ####
# DT_glossary <-
# glossary %>%
# datatable(
# escape = FALSE,
# rownames = FALSE,
# class = 'cell-border stripe',
# options = list(
# pageLength = nrow(.),
# ordering = TRUE,
# autoWidth = TRUE,
# columnDefs = list(list(width = '50%', targets = c(1))))) %>%
# formatStyle(seq_len(ncol(.$x$data)),'vertical-align'='top') %>%
# formatStyle(seq_len(ncol(.$x$data)),'text-align' = 'left')
#
#
#### data_proc_elem_def ####
data_proc_elem_def <-
tribble(
~`column name`,~`definition`,
'<code><b>dataschema_variable</b></code>' , 'Variable defined in the DataSchema' ,
'<code><b>input_dataset</b></code>' , 'Input dataset where the <br>
input variable(s) is/are taken from' ,
'<code><b>input_variables</b></code>' , 'Input variable(s) (name(s) of the column in the dataset)',
'<code><b>Mlstr_harmo::rule_category</b></code>', 'Rule category' ,
'<code><b>Mlstr_harmo::algorithm</b></code>' , 'Algorithm associated to the rule category' ,
)
#### col_names_dpe ####
col_names_dpe <- c(
'<b>dataschema_variable</b>',
'<b>input_dataset</b>',
'<b>input_variables</b>',
'<b>Mlstr_harmo::rule_category</b>',
'<b>Mlstr_harmo::algorithm</b>')
#### id_creation ####
id_creation = paste(
'<code>',c(
'harmonized_id',
'input_dataset',
'input_id',
'<b>id_creation</b>',
'<b>id_creation</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### direct_mapping ####
direct_mapping = paste(
'<code>',c(
'output_var',
'input_dataset',
'input_var',
'<b>direct_mapping</b>',
'<b>direct_mapping</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### recode ####
recode = paste(
'<code>',c(
'output_var',
'input_dataset',
'input_var',
'<b>recode</b>',
'<b>recode(</b>
"YES" = 1 ;
"NO" = 0 ;
ELSE = NA<b>) </b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### case_when ####
case_when = paste(
'<code>',c(
'output_var',
'input_dataset',
'input_var_x ; input_var_y',
'<b>case_when</b>' ,
'<b>case_when(</b>
var_x == 1 ~ var_z ;
ELSE ~ NA<b>)</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### operation ####
operation = paste(
'<code>',c(
'output_var',
'input_dataset',
'input_var',
'<b>operation</b>' ,
'<em># simple R code here such as :</em></b>
(<b>mean(</b>input_var<b>)</b> - input_var<b>)</b> / <b>stats::sd(</b>input_var<b>)</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### paste ####
paste = paste(
'<code>',c(
'output_var',
'input_dataset',
'<b>__BLANK__</b>' ,
'<b>paste</b>' ,
'"Hello word"'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### other ####
other = paste(
'<code>',c(
'output_var',
'input_dataset',
'<b>__BLANK__</b>' ,
'<b>other</b>' ,
'# place complex R code here...'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### impossible ####
impossible = paste(
'<code>',c(
'output_var',
'input_dataset',
'<b>impossible</b>' ,
'<b>impossible</b>' ,
'<b>impossible</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### undetermined ####
undetermined = paste(
'<code>',c(
'output_var',
'input_dataset',
'<b>undetermined</b>',
'<b>undetermined</b>',
'<b>undetermined</b>'),
'</code>') %>% setNames(col_names_dpe) %>% as_tibble_row()
#### rule_categories ####
rule_categories <-
bind_rows(
id_creation,
direct_mapping,
recode,
case_when,
operation,
paste,
other,
impossible,
undetermined)
#### data_proc_elem_expl ####
data_proc_elem_expl <-
rule_categories %>% slice(1:3) %>%
mutate(`<b>input_dataset</b>` = str_replace(
`<b>input_dataset</b>`,'DATASET','dataset_MELBOURNE')) %>%
bind_rows(
rule_categories %>% slice(1,5,8) %>%
mutate(`<b>input_dataset</b>` = str_replace(
`<b>input_dataset</b>`,'DATASET','dataset_PARIS')) %>%
mutate(`<b>input_variables</b>` = str_replace(
`<b>input_variables</b>`,'col_id','ID_part')) %>%
mutate(`<b>dataschema_variable</b>` = str_replace(
`<b>dataschema_variable</b>`,'variable_D','variable_A')) %>%
mutate(`<b>dataschema_variable</b>` = str_replace(
`<b>dataschema_variable</b>`,'variable_G','variable_B'))
) %>%
mutate(`<b>dataschema_variable</b>` = str_replace(
`<b>dataschema_variable</b>`,'variable_ID','<b>variable_ID</b>')) %>%
mutate(`<b>Mlstr_harmo::rule_category</b>` = ifelse(
str_detect(`<b>dataschema_variable</b>`, 'variable_ID'),
'<code><b>id_creation</b></code>',`<b>Mlstr_harmo::rule_category</b>`)) %>%
add_column(`<b>index</b>` = c(
'<code>1</code>','<code>2</code>','<code>3</code>',
'<code>1</code>','<code>2</code>','<code>3</code>'),.before = TRUE)
#### DT_rule_categories ####
DT_rule_categories <-
rule_categories %>%
datatable(
extensions = "Buttons",
escape = FALSE,
rownames = FALSE,
class = 'cell-border stripe',
options = list(
searching = FALSE,
columnDefs = list(list(className = 'dt-head-left', targets = "_all")),
headerCallback = DT::JS("function(thead) { $(thead).css('font-size', '0.7em');}"),
paging = FALSE,
info = FALSE,
scrollX = TRUE,
ordering = FALSE,
pageLength = nrow(.),
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel')
))
# #### DT_data_proc_elem_expl ####
# # DT_data_proc_elem_expl <-
# # data_proc_elem_expl %>%
# # select(-last_col()) %>%
# # datatable(
# # escape = FALSE,
# # rownames = FALSE,
# # class = 'cell-border stripe',
# # options = list(
# # pageLength = nrow(.),
# # columnDefs = list(list(className = 'dt-head-left', targets = "_all")),
# # headerCallback = DT::JS("function(thead) { $(thead).css('font-size', '0.7em');}"),
# # ordering = TRUE,
# # scrollX = TRUE),
# # autoHideNavigation = TRUE)
#
#### DT_impundebla ####
DT_impundebla <-
rule_categories %>%
dplyr::filter(str_detect(`<b>input_variables</b>`, 'BLANK|impossible|undetermined')) %>%
datatable(
escape = FALSE,
rownames = FALSE,
class = 'cell-border stripe',
options = list(
pageLength = nrow(.),
columnDefs = list(list(className = 'dt-head-left', targets = "_all")),
headerCallback = DT::JS("function(thead) { $(thead).css('font-size', '0.7em');}"),
ordering = FALSE,
scrollX = TRUE),
autoHideNavigation = TRUE)
#### DT_id_creation ####
DT_id_creation <-
t(id_creation %>%
mutate(`<b>Mlstr_harmo::rule_category</b>` = '<code><b>id_creation</b></code>')) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_direct_mapping <-
t(direct_mapping) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_recode <-
t(recode) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_case_when <-
t(case_when) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_paste <-
t(paste) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_operation <-
t(operation) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_other <-
t(other) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_impossible <-
t(impossible) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
DT_undetermined <-
t(undetermined) %>%
datatable(
escape = FALSE,
colnames = rep('',ncol(.)),
class = 'cell-border stripe',
filter = 'none',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
#### DT_data_proc_elem_def ####
DT_data_proc_elem_def <-
data_proc_elem_def %>%
datatable(
escape = FALSE,
rownames = FALSE,
class = 'cell-border stripe',
options = list(pageLength = nrow(.), ordering = FALSE),
autoHideNavigation = TRUE)
# #### DT_mlstr_status ####
# DT_mlstr_status <-
# mlstr_status %>%
# datatable(
# escape = FALSE,
# rownames = FALSE,
# class = 'cell-border stripe',
# options = list(pageLength = nrow(.), ordering = FALSE),
# autoHideNavigation = TRUE)
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