Crunching Function for XlsForm

library(kobocruncher)

Data examples to demo the package

Preparing objects

Data loading

datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )
# MainFrame
datalist[["main"]]
#> # A tibble: 5 × 26
#>   start               end                 location         profile.country
#>   <dttm>              <dttm>              <chr>            <chr>          
#> 1 2022-10-27 08:57:57 2022-11-10 08:49:09 private_facility VEN            
#> 2 2022-10-24 11:36:52 2022-10-24 15:46:00 home             HND            
#> 3 2022-10-26 16:19:35 2022-10-26 18:15:07 subcenter        SLV            
#> 4 2022-10-26 16:19:35 2022-10-26 17:51:14 subcenter        SLV            
#> 5 2022-10-26 14:02:06 2022-10-26 16:10:25 home             COL            
#> # ℹ 22 more variables: profile.occupation <chr>, profile.reason <chr>,
#> #   profile.reason.accomodation <dbl>, profile.reason.employment <dbl>,
#> #   profile.reason.education <dbl>, profile.reason.community <dbl>,
#> #   profile.reason.safety <dbl>, profile.reason.movement <dbl>,
#> #   profile.reason.reunification <dbl>, profile.reason.no_answer <dbl>,
#> #   profile.reason.other <dbl>, profile.HHSize <dbl>, `_id` <dbl>,
#> #   `_uuid` <chr>, `_submission_time` <dbl>, `_validation_status` <lgl>, …
# Second Frame - based on presence of repeat within the form, aka nested or
# hierarchical data structure, etc... 
datalist[["members"]]
#> # A tibble: 12 × 14
#>    members.age members.sex `_index` `_parent_table_name`      `_parent_index`
#>          <dbl> <chr>          <dbl> <chr>                               <dbl>
#>  1          56 male               1 Sample Dataset KoboloadeR               1
#>  2           2 male               2 Sample Dataset KoboloadeR               2
#>  3           3 male               3 Sample Dataset KoboloadeR               2
#>  4          10 female             4 Sample Dataset KoboloadeR               2
#>  5          45 male               5 Sample Dataset KoboloadeR               2
#>  6          35 female             6 Sample Dataset KoboloadeR               2
#>  7           4 male               7 Sample Dataset KoboloadeR               3
#>  8          34 female             8 Sample Dataset KoboloadeR               3
#>  9          34 male               9 Sample Dataset KoboloadeR               3
#> 10          51 female            10 Sample Dataset KoboloadeR               4
#> 11          21 male              11 Sample Dataset KoboloadeR               5
#> 12          25 female            12 Sample Dataset KoboloadeR               5
#> # ℹ 9 more variables: `_submission__id` <dbl>, `_submission__uuid` <chr>,
#> #   `_submission__submission_time` <dbl>,
#> #   `_submission__validation_status` <lgl>, `_submission__notes` <lgl>,
#> #   `_submission__status` <chr>, `_submission__submitted_by` <lgl>,
#> #   `_submission__tags` <lgl>, parent_index <dbl>

Extend the xlsform to add instructions for the analysis plan

Now we can extend the xlsform that was used to document key next steps in the data preparation.

kobo_prepare_form(xlsformpath = system.file("form.xlsx", package = "kobocruncher"),
                  xlsformpathout = NULL,
                  label_language = "")
#> [1] "There was an error in the xlsform preparation step!!! \n\n"
#> $message
#> [1] "`path` does not exist: ''"
#> 
#> $call
#> NULL
#> 
#> attr(,"class")
#> [1] "try-error"

Prepare data dictionnary

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
# Survey
questions <- as.data.frame(dico[["variables"]])
knitr::kable(utils::head(questions, 10))
type list_name name label hint name_or repeatvar scope chapter subchapter disaggregation correlate appearance
today NA today NA NA today main NA NA NA NA
date NA date Interview date this is a hint date main NA NA NA NA
select_one location location Where is the interview taking place this is a hint location main NA NA NA NA
begin_group NA profile.profile Respondant profile NA profile main profile NA NA NA NA
select_one countries profile.country What is your country of Origin? this is a hint country main profile NA NA NA NA
text NA profile.occupation What’s you occupation? this is a hint occupation main profile NA NA NA NA
select_multiple reasons profile.reason Why did you left? this is a hint reason main profile NA NA NA NA
integer NA profile.HHSize What’s the size of your household? this is a hint HHSize main profile NA NA NA NA
end_group NA NA NA NA NA main NA NA NA NA
begin_repeat NA members.members Please enter information for each family member NA members members members NA NA NA NA 
# Choices
responses <- as.data.frame(dico[["modalities"]])
knitr::kable(utils::head(responses, 10))
list_name name label order
location home Home NA
location subcenter Community Center NA
location private_facility Private facility NA
sex male Male NA
sex female Female NA
countries COL Colombia NA
countries CUB Cuba NA
countries SLV El Salvador NA
countries GTM Guatemala NA
countries HND Honduras NA 
# Settings
metadata <- as.data.frame(dico[["settings"]])
knitr::kable(utils::head(metadata, 10))
form_title form_id
Sample Dataset KoboloadeR koboloadeR 
# Report ToC
toc <- as.data.frame(dico[["plan"]])
knitr::kable(utils::head(toc, 10))
type label name
today NA today
date Interview date date
select_one Where is the interview taking place location
begin_group Respondant profile profile.profile
select_one What is your country of Origin? profile.country
text What’s you occupation? profile.occupation
select_multiple Why did you left? profile.reason
integer What’s the size of your household? profile.HHSize
begin_repeat Please enter information for each family member members.members
integer Age members.age 
# Indicator
indicator <- as.data.frame(dico[["indicator"]])
knitr::kable(utils::head(indicator, 10))
type name label list_name hint repeatvar calculation chapter subchapter disaggregation correlate cluster predict score mappoint mappoly

Data Processing

Indicator Calculation

Indicator calculation

xlsformpath <- system.file("sample_xlsform.xlsx", package = "kobocruncher")
xlsformpathout <- paste0(tempdir(),"/", "sample_xlsform_withindic.xlsx")

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )

## Check if we add no indicator
expanded  <- kobo_indicator(datalist = datalist,
                            dico = dico,
                            indicatoradd = NULL ,
                            xlsformpath = xlsformpath,
                            xlsformpathout = xlsformpathout)
#> no calculated indicators has been defined...


## Example 1: Simple dummy filter
indicatoradd <- c(  name =  "inColombia",
                    type = "select_one",
                    label = "Is from Colombia",
                    repeatvar = "main",
  calculation = "dplyr::if_else(datalist[[\"main\"]]$profile.country == 
   \"COL\", \"yes\",\"no\")")

expanded  <- kobo_indicator(datalist = datalist,
                    dico = dico,
                 indicatoradd = indicatoradd ,
                 xlsformpath = xlsformpath,
                 xlsformpathout = xlsformpathout)
## Replace existing
dico <- expanded[["dico"]]
datalist <- expanded[["datalist"]]

## Check my new indicator
table(datalist[[1]]$inColombia, useNA = "ifany")
#> 
#>  no yes 
#>   4   1



## Example 2: calculation on nested elements and build an indicator list
indicatoradd2 <- c(  name =  "hasfemalemembers",
              type = "select_one",
              label = "HH has female members ",
              repeatvar = "main",
              calculation = "datalist[[\"members\"]] |>
                            dplyr::select( members.sex, parent_index) |>
                            tidyr::gather(  parent_index, members.sex) |>
                            dplyr::count(parent_index, members.sex) |>
                            tidyr::spread(members.sex, n, fill = 0)  |>
                           dplyr::select( female)")

indicatorall <- list(indicatoradd, indicatoradd2  ) 

expanded  <- kobo_indicator(datalist = datalist,
                    dico = dico,
                 indicatoradd = indicatorall ,
                 xlsformpath = xlsformpath,
                 xlsformpathout = xlsformpathout)
## Replace existing
dico <- expanded[["dico"]]
datalist <- expanded[["datalist"]]


## Check my new indicator
table(datalist[[1]]$hasfemalemembers, useNA = "ifany")
#> female
#> 0 1 2 
#> 1 3 1

#   Example of calculations:
#   
#   1. Create a filters on specific criteria
#   'dplyr::if_else(datalist[["main"]]$variable =="criteria", "yes","no")'
#   
#   
#   2. Ratio between 2 numeric variable
#   'datalist[["main"]]$varnum1 / datalist[["main"]]$varnum2'
#   
#   
#   3. Calculation on date - month between data and now calculated in months
#   'lubridate::interval( datalist[["main"]]$datetocheck, 
#                         lubridate::today()) %/%  months(1)'
#   
#   4. Discretization of numeric variable according to quintile
#   'Hmisc::cut2(datalist[["main"]]$varnum, g =5)'
#   
#   5. Discretization of numeric variable according to fixed break - 
#   for instance case size from integer to categoric
#   'cut(datalist[["main"]]$casesize, breaks = c(0, 1, 2, 3,5,30), 
#   labels = c("Case.size.1", "Case.size.2", "Case.size.3", 
#   "Case.size.4.5", "Case.size.6.or.more" ), include.lowest=TRUE)'
#   
#   6. Aggregate variable from nested frame (aka within repeat) to parent table
#   'datalist[["members"]] |>
#       dplyr::select( members.sex, parent_index) |>
#       tidyr::gather(  parent_index, members.sex) |>
#       dplyr::count(parent_index, members.sex) |>
#       tidyr::spread(members.sex, n, fill = 0)  |>
#       dplyr::select( female)'

Weight the dataset

#kobo_weight()

Assess Disclosure Risk

to do….

# dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
# datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )
# 
# kobo_anonymise(datalist = datalist,
#                    dico = dico,
#                 indicatoradd = indicatoradd   )

Labeling functions

Get the correct frame for a specific variable

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )

data <- kobo_frame(datalist = datalist,
                   dico = dico,
                   var = "members.sex"   )
knitr::kable(utils::head(data,5))
members.age members.sex _index _parent_table_name _parent_index _submission__id _submission__uuid _submission__submission_time _submission__validation_status _submission__notes _submission__status _submission__submitted_by _submission__tags parent_index X_id
56 male 1 Sample Dataset KoboloadeR 1 20759478 48cc75b7-3d86-4c3e-a99b-24b4032b7b9c 44685.89 NA NA submitted_via_web NA NA 1 1
2 male 2 Sample Dataset KoboloadeR 2 20756978 f1c3d36c-3c25-4581-9f35-9c8ec405d744 44685.84 NA NA submitted_via_web NA NA 2 2
3 male 3 Sample Dataset KoboloadeR 2 20756978 f1c3d36c-3c25-4581-9f35-9c8ec405d744 44685.84 NA NA submitted_via_web NA NA 2 3
10 female 4 Sample Dataset KoboloadeR 2 20756978 f1c3d36c-3c25-4581-9f35-9c8ec405d744 44685.84 NA NA submitted_via_web NA NA 2 4
45 male 5 Sample Dataset KoboloadeR 2 20756978 f1c3d36c-3c25-4581-9f35-9c8ec405d744 44685.84 NA NA submitted_via_web NA NA 2 5

Get the label for a specific variable

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )

label_varname(dico = dico, 
              x ="profile.country")
#> [1] "What is your country of Origin?"

Get interpretation hint for a specific variable

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )

label_varhint(dico = dico, 
              x ="profile.country")
#> [1] "this is a hint"

Get all the choices labels options for a specific variable

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )

data <- kobo_frame(datalist = datalist,
                   dico = dico,
                   var = "profile.country"   )

label_choiceset(dico = dico, 
                x="profile.country")(data$profile.country)
#>           VEN           HND           SLV           SLV           COL 
#>   "Venezuela"    "Honduras" "El Salvador" "El Salvador"    "Colombia"

## Test when there's no dictionnary
data$profile.occupation
#> [1] "Consultant" "farmer"     "vendor"     "teacher"    "farmer"
label_choiceset(dico = dico, 
                x="profile.occupation")(data$profile.occupation)
#>         <NA>         <NA>         <NA>         <NA>         <NA> 
#> "Consultant"     "farmer"     "vendor"    "teacher"     "farmer"


label_choiceset(dico = dico, 
                x="profile.occupation")(data$profile.occupation)
#>         <NA>         <NA>         <NA>         <NA>         <NA> 
#> "Consultant"     "farmer"     "vendor"    "teacher"     "farmer"

Plotting Functions

Univariate

Plotting Select one variable

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )

plot_select_one(datalist = datalist,
              dico = dico, 
              var = "profile.country",
              showcode = TRUE)
#> 
#> What is your country of Origin?
#> `plot_select_one(datalist, dico, "profile.country", datasource = params$datasource, n = 4)` 
#> 
#> 

## Exmaple with lumping
plot_select_one(datalist = datalist,
              dico = dico, 
              var = "profile.country",
              n = 1,
              showcode = TRUE)
#> 
#> What is your country of Origin?
#> `plot_select_one(datalist, dico, "profile.country", datasource = params$datasource, n = 1)` 
#> 
#> 


# plot_select_one(datalist = datalist,
#               dico = dico, 
#               var = "profile.countryerror",
#               showcode = TRUE)

Plotting Select multiple variable

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )


plot_select_multiple(datalist = datalist,
              dico = dico, 
              var = "profile.reason",
              datasource = NULL,
              showcode = TRUE
            )
#> Why did you left?
#> `plot_select_multiple(datalist, dico, "profile.reason", datasource=params$datasource, n=7)` 
#> 
#> 


## Displaying the usage of the lumping option..
plot_select_multiple(datalist = datalist,
              dico = dico, 
              var = "profile.reason",
              n = 5,
             datasource = NULL,
              showcode = TRUE
            )
#> Why did you left?
#> `plot_select_multiple(datalist, dico, "profile.reason", datasource=params$datasource, n=5)` 
#> 
#> 


# plot_select_multiple(datalist = datalist,
#               dico = dico, 
#               var = "profile.reason1",
#               showcode = TRUE
#             )

Plotting Numeric variable

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )

plot_integer(datalist = datalist,
              dico = dico, 
              var = "members.age",
              showcode = TRUE)
#> Age
#> `plot_integer(datalist, dico, "members.age", datasource=params$datasource)` 
#> 
#> 
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Plotting Open Text variable

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )

plot_text(datalist = datalist,
              dico = dico, 
              var = "profile.occupation",
              showcode = TRUE)
#> Warning in tm_map.SimpleCorpus(., toSpace, "/"): transformation drops documents
#> Warning in tm_map.SimpleCorpus(., toSpace, "@"): transformation drops documents
#> Warning in tm_map.SimpleCorpus(., toSpace, "\\|"): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(., tm::content_transformer(tolower)):
#> transformation drops documents
#> Warning in tm_map.SimpleCorpus(., tm::removeNumbers): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(., tm::removePunctuation): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(., tm::stripWhitespace): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(., tm::stemDocument): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(., tm::removeWords, tm::stopwords("english")):
#> transformation drops documents
#> Warning in tm_map.SimpleCorpus(., tm::removeWords, c("blabla1", "blabla2")):
#> transformation drops documents
#> 
#> What's you occupation?
#>  `plot_text(datalist, dico, "profile.occupation", datasource=params$datasource)` 
#> 
#>

Bivariate

Plotting Select one variable with cross tabulation

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )

plot_select_one_cross(datalist = datalist,
              dico = dico, 
              var = "profile.country",
              by_var = "profile.occupation",
              showcode = TRUE
              )
#> What is your country of Origin?
#> `plot_select_one_cross(datalist, dico, var="profile.country", by_var="profile.occupation",datasource=params$datasource, n=4,n_by=4 )` 
#> 
#> 

## test if variable are not in the same frame...
plot_select_one_cross(datalist = datalist,
              dico = dico, 
              var = "profile.country",
              by_var = "members.sex",
              n = 5,
              n_by = 5,
              showcode = TRUE
              )

Plotting Select multiple variable with cross-tabulation

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )
plot_select_multiple_cross(datalist = datalist,
              dico = dico, 
              var = "profile.reason",
              by_var = "location",
              showcode = TRUE)
#> Why did you left?
#> `plot_select_multiple_cross(datalist, dico, var="profile.reason", by_var="location", datasource=params$datasource, n=7, n_by=3 )` 
#> 
#> 


## test lumping
plot_select_multiple_cross(datalist = datalist,
              dico = dico, 
              var = "profile.reason",
              by_var = "location",
              n = 4, 
              showcode = TRUE)
#> Why did you left?
#> `plot_select_multiple_cross(datalist, dico, var="profile.reason", by_var="location", datasource=params$datasource, n=4, n_by=3 )` 
#> 
#>

Plotting Numeric variable with cross-tabulation

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )

plot_integer_cross(datalist = datalist,
              dico = dico, 
              var = "members.age",
              by_var = "members.sex",
              showcode = TRUE)

Plotting Correlation

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )

plot_correlation(datalist = datalist,
              dico = dico, 
              var = "profile.occupation",
              by_var = "profile.country",
              datasource = NULL)
#> Warning in stats::chisq.test(formula$target, formula$tested): Chi-squared
#> approximation may be incorrect
#> 
#>  No significant association found between profile.occupation &  profile.country (p.value :0.3505).

Multivariate

Plotting Likert

dicolikert <- kobo_dico( xlsformpath = system.file("form_likert.xlsx", package = "kobocruncher") )
datalistlikert <- kobo_data(datapath = system.file("data_likert.xlsx", package = "kobocruncher") )
#> Warning: Unknown or uninitialised column: `_index`.

plot_likert(datalist = datalistlikert,
            dico = dicolikert,
            datasource = NULL,
            scopei =  "group_ei8jz33",
            repeatvari =   "main",
            ## getting the list_name and corresponding label
            list_namei = "yk0td68" 
          )
#> Warning: `funs()` was deprecated in dplyr 0.8.0.
#> ℹ Please use a list of either functions or lambdas:
#> 
#> # Simple named list: list(mean = mean, median = median)
#> 
#> # Auto named with `tibble::lst()`: tibble::lst(mean, median)
#> 
#> # Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
#> ℹ The deprecated feature was likely used in the kobocruncher package.
#>   Please report the issue at
#>   <https://github.com/Edouard-Legoupil/kobocruncher/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Warning in get_plot_component(plot, "guide-box"): Multiple components found;
#> returning the first one. To return all, use `return_all = TRUE`.

Plotting clusters

to do….

Plotting prediction

to do….

Plotting scores

to do….

Plotting Header variable

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )

plot_header( dico = dico, 
              var = "profile.profile")
#> ------
#> 
#> 
#> 
#> ## Respondant profile

# class(plot_header( dico = dico, 
#               var = "profile.profile"))
# 
dput(plot_header( dico = dico,
              var = "profile.profile"))
#> ------
#> 
#> 
#> 
#> ## Respondant profile
#> 
#> NULL
# 
message(plot_header( dico = dico, 
               var = "profile.profile"))
#> ------
#> 
#> 
#> 
#> ## Respondant profile
#> 

cat(plot_header( dico = dico, 
               var = "profile.profile"))
#> ------
#> 
#> 
#> 
#> ## Respondant profile

print(plot_header( dico = dico, 
              var = "profile.profile"),
      useSource = FALSE)
#> ------
#> 
#> 
#> 
#> ## Respondant profile
#> 
#> NULL

Report generation

Crunching Variables based on a plan

dico <- kobo_dico( xlsformpath = system.file("sample_xlsform.xlsx", package = "kobocruncher") )
datalist <- kobo_data(datapath = system.file("data.xlsx", package = "kobocruncher") )

kobo_cruncher(datalist = datalist,
              dico = dico,
              datasource = "a great survey!")
#> 
#> Where is the interview taking place
#> `plot_select_one(datalist, dico, "location", datasource = params$datasource, n = 5)` 
#> 
#> 

#> ------
#> 
#> 
#> 
#> ## Respondant profile
#> 
#> 
#> What is your country of Origin?
#> `plot_select_one(datalist, dico, "profile.country", datasource = params$datasource, n = 5)` 
#> 
#> 

#> Warning in tm_map.SimpleCorpus(., toSpace, "/"): transformation drops documents
#> Warning in tm_map.SimpleCorpus(., toSpace, "@"): transformation drops documents
#> Warning in tm_map.SimpleCorpus(., toSpace, "\\|"): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(., tm::content_transformer(tolower)):
#> transformation drops documents
#> Warning in tm_map.SimpleCorpus(., tm::removeNumbers): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(., tm::removePunctuation): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(., tm::stripWhitespace): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(., tm::stemDocument): transformation drops
#> documents
#> Warning in tm_map.SimpleCorpus(., tm::removeWords, tm::stopwords("english")):
#> transformation drops documents
#> Warning in tm_map.SimpleCorpus(., tm::removeWords, c("blabla1", "blabla2")):
#> transformation drops documents
#> 
#> What's you occupation?
#>  `plot_text(datalist, dico, "profile.occupation", datasource=params$datasource)` 
#> 
#> 

#> Why did you left?
#> `plot_select_multiple(datalist, dico, "profile.reason", datasource=params$datasource, n=5)` 
#> 
#> 

#> What's the size of your household?
#> `plot_integer(datalist, dico, "profile.HHSize", datasource=params$datasource)` 
#> 
#> 
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#> Age
#> `plot_integer(datalist, dico, "members.age", datasource=params$datasource)` 
#> 
#> 
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#> 
#> Sex
#> `plot_select_one(datalist, dico, "members.sex", datasource = params$datasource, n = 5)` 
#> 
#>

Crunching Likert componnents

dicolikert <- kobo_dico( xlsformpath = system.file("form_likert.xlsx", package = "kobocruncher") )
datalistlikert <- kobo_data(datapath = system.file("data_likert.xlsx", package = "kobocruncher") )
#> Warning: Unknown or uninitialised column: `_index`.

kobo_likert(datalist = datalistlikert,
              dico = dicolikert,
              datasource = "a great survey!")
#> 3 groups of likert questions in the form`plot_likert(datalist, dico, scopei="group_ei8jz33", list_namei="yk0td68", repeatvari="main",datasource=params$datasource)` 
#> 
#> 
#> Warning in get_plot_component(plot, "guide-box"): Multiple components found;
#> returning the first one. To return all, use `return_all = TRUE`.

#> `plot_likert(datalist, dico, scopei="group_pm0cj55", list_namei="yk0td68", repeatvari="main",datasource=params$datasource)` 
#> 
#> 
#> Warning in get_plot_component(plot, "guide-box"): Multiple components found;
#> returning the first one. To return all, use `return_all = TRUE`.

#> `plot_likert(datalist, dico, scopei="group_wc0ig44", list_namei="yk0td68", repeatvari="main",datasource=params$datasource)` 
#> 
#> 
#> Warning in get_plot_component(plot, "guide-box"): Multiple components found;
#> returning the first one. To return all, use `return_all = TRUE`.

Archive files in RIDL


### Example used for each template 
## Time to archive your work once done!!
# namethisfile = basename(rstudioapi::getSourceEditorContext()$path )  
# if( params$publish == "yes"){
#   kobo_ridl(ridl = params$ridl,
#             datafolder = params$datafolder,
#             form = params$form,
#             namethisfile =  namethisfile ,
#             visibility =  params$visibility,
#             stage = params$stage) }

Report Template A for Automatic Data Exploration

# template_1_exploration(datafolder= "data-raw",
#                                    ridl =  "ridlproject",
#                                     data = "data.xlsx" ,
#                                     form =  "form.xlsx",
#                                     datasource = "Study name reference",
#                                     publish =  "no", 
#                                     republish = "no",
#                                     visibility = "public",
#                                     stage = "exploration_initial",
#                                    language = "",
#                                    folder = "Report")

Report Template B for Joint Data Interpretation Session

The second template is used following the systematic data exploration. It will generate a PowerPoint presentation

See a more detailed presentation of that step here: https://www.youtube.com/watch?v=0jE-Y7g88K4&feature=youtu.be&t=2305

#' Second Template to prepare a presentation for the Joint Data Interpretation Session
#' 
# usethis::use_rmarkdown_template(
#   template_name = "template_2_interpretation",
#   template_dir = NULL,
#   template_description = "Joint Data Interpretation",
#   template_create_dir = TRUE
# )

Report Template C for Note taking

The third template can be used in a similar way than the presentation template. It will generate a word document in order to take note.

An automatic table of content is generated but might required to be refreshed after the word document creation

#' Report Template 3 for Dissemination and Data Story Telling Template
#' The last template can be used to take note of the data interpretation session. 
#' It will generate a PDF or an paginated HTML page
# usethis::use_rmarkdown_template(
#   template_name = "template_C_notes",
#   template_dir = NULL,
#   template_description = "Note taking",
#   template_create_dir = TRUE
# )

Report Template D for Dissemination and Data Story Telling Template

The last template can be used to build the final report. It includes some instructions and guidance on how to organize the content to increase your audience

It will generate a PDF or an paginated HTML page

#' Report Template 3 for Dissemination and Data Story Telling Template
#' The last template can be used to take note of the data interpretation session. 
#' It will generate a PDF or an paginated HTML page
# usethis::use_rmarkdown_template(
#   template_name = "template_D_dissemination",
#   template_dir = NULL,
#   template_description = "Data brief and Story Telling",
#   template_create_dir = TRUE
# )

run_app

# run_app()