presentresults

The goal of presentresults is to create outputs from a results table (long format with analysis key).

The analysis key is the unique identifier of the analysis. The format is the following:

• analysis type @/@ analysis variable %/% analysis variable value @/@ grouping variable %/% grouping variable value

• analysis type @/@ dependent variable %/% dependent variable value @/@ independent variable %/% independent variable value

If there are two or more grouping variables it would look like that

• analysis type @/@ analysis variable %/% analysis variable value @/@ grouping variable 1 %/% grouping variable value 1 -/- grouping variable 2 %/% grouping variable value 2

There are 3 types of separators:

• @/@ will separate the top level information: analysis type, the analysis (dependent) variable information and the grouping (independent) variable

• %/% will separate the analysis and grouping information: it will separate the variable name and the variable value

• -/- will separate 2 variables in case there are multiple variable in either the analysis or grouping sets.

The current analysis types available are :

• mean
• median
• prop_select_one: proportion for select one
• prop_select_multiple: proportion for select multiple
• ratio

Installation

You can install the development version of presentresults from GitHub with:

# install.packages("devtools")
devtools::install_github("impact-initiatives/presentresults")

library(presentresults)

Large table variables (lines) per groups (columns)

This is how to turn a results table into a wide table variable per group.

example_variable_x_group <- presentresults_resultstable %>%
  create_table_variable_x_group(analysis_key = "analysis_key")

example_variable_x_group[1:6, 1:9]
#> # A tibble: 6 × 9
#>   analysis_type   analysis_var analysis_var_value `stat_locationA %/% displaced`
#>   <chr>           <chr>        <chr>                                       <dbl>
#> 1 prop_select_one fcs_cat      low                                         0.258
#> 2 prop_select_one fcs_cat      medium                                      0.323
#> 3 prop_select_one fcs_cat      high                                        0.419
#> 4 prop_select_one rcsi_cat     low                                         0.290
#> 5 prop_select_one rcsi_cat     medium                                      0.258
#> 6 prop_select_one rcsi_cat     high                                        0.452
#> # ℹ 5 more variables: `stat_low_locationA %/% displaced` <dbl>,
#> #   `stat_upp_locationA %/% displaced` <dbl>,
#> #   `stat_locationA %/% non-displaced` <dbl>,
#> #   `stat_low_locationA %/% non-displaced` <dbl>,
#> #   `stat_upp_locationA %/% non-displaced` <dbl>

example_variable_x_group %>%
  create_xlsx_variable_x_group(file_path = "mytable.xlsx")

The table without the higher and lower confidence bound.

example_variable_x_group <- presentresults_resultstable %>%
  create_table_variable_x_group(value_columns = "stat")

example_variable_x_group[1:6, 1:9]
#> # A tibble: 6 × 9
#>   analysis_type   analysis_var analysis_var_value `locationA %/% displaced`
#>   <chr>           <chr>        <chr>                                  <dbl>
#> 1 prop_select_one fcs_cat      low                                    0.258
#> 2 prop_select_one fcs_cat      medium                                 0.323
#> 3 prop_select_one fcs_cat      high                                   0.419
#> 4 prop_select_one rcsi_cat     low                                    0.290
#> 5 prop_select_one rcsi_cat     medium                                 0.258
#> 6 prop_select_one rcsi_cat     high                                   0.452
#> # ℹ 5 more variables: `locationA %/% non-displaced` <dbl>,
#> #   `locationB %/% displaced` <dbl>, `locationB %/% non-displaced` <dbl>,
#> #   locationA <dbl>, locationB <dbl>

presentresults_resultstable %>%
  create_table_variable_x_group() %>%
  create_xlsx_variable_x_group(
    file_path = "mytable.xlsx",
    value_columns = "stat"
  )

Large table groups (lines) per variables (columns)

This is how to turn a results table into a wide group per variable. This format is made to be read in Excel.

example_group_x_variable <- create_table_group_x_variable(presentresults_resultstable, value_columns = "stat")

example_group_x_variable[1:6, 1:10]
#>                                       group_var_value
#> header_analysis_var                   group_var_value
#> header_analysis_var_value             group_var_value
#> header_analysis_type                  group_var_value
#> 1                             locationA %/% displaced
#> 2                         locationA %/% non-displaced
#> 3                             locationB %/% displaced
#>                           fcs_cat %/% low %/% prop_select_one
#> header_analysis_var                                   fcs_cat
#> header_analysis_var_value                                 low
#> header_analysis_type                          prop_select_one
#> 1                                           0.258064516129032
#> 2                                                        0.25
#> 3                                            0.37037037037037
#>                           fcs_cat %/% medium %/% prop_select_one
#> header_analysis_var                                      fcs_cat
#> header_analysis_var_value                                 medium
#> header_analysis_type                             prop_select_one
#> 1                                               0.32258064516129
#> 2                                                          0.375
#> 3                                              0.407407407407407
#>                           fcs_cat %/% high %/% prop_select_one
#> header_analysis_var                                    fcs_cat
#> header_analysis_var_value                                 high
#> header_analysis_type                           prop_select_one
#> 1                                            0.419354838709677
#> 2                                                        0.375
#> 3                                            0.222222222222222
#>                           rcsi_cat %/% low %/% prop_select_one
#> header_analysis_var                                   rcsi_cat
#> header_analysis_var_value                                  low
#> header_analysis_type                           prop_select_one
#> 1                                            0.290322580645161
#> 2                                                        0.375
#> 3                                            0.259259259259259
#>                           rcsi_cat %/% medium %/% prop_select_one
#> header_analysis_var                                      rcsi_cat
#> header_analysis_var_value                                  medium
#> header_analysis_type                              prop_select_one
#> 1                                               0.258064516129032
#> 2                                               0.458333333333333
#> 3                                               0.518518518518518
#>                           rcsi_cat %/% high %/% prop_select_one
#> header_analysis_var                                    rcsi_cat
#> header_analysis_var_value                                  high
#> header_analysis_type                            prop_select_one
#> 1                                             0.451612903225806
#> 2                                             0.166666666666667
#> 3                                             0.222222222222222
#>                           lcs_cat %/% none %/% prop_select_one
#> header_analysis_var                                    lcs_cat
#> header_analysis_var_value                                 none
#> header_analysis_type                           prop_select_one
#> 1                                            0.193548387096774
#> 2                                           0.0416666666666667
#> 3                                            0.296296296296296
#>                           lcs_cat %/% stress %/% prop_select_one
#> header_analysis_var                                      lcs_cat
#> header_analysis_var_value                                 stress
#> header_analysis_type                             prop_select_one
#> 1                                              0.129032258064516
#> 2                                              0.458333333333333
#> 3                                              0.259259259259259
#>                           lcs_cat %/% emergency %/% prop_select_one
#> header_analysis_var                                         lcs_cat
#> header_analysis_var_value                                 emergency
#> header_analysis_type                                prop_select_one
#> 1                                                  0.32258064516129
#> 2                                                 0.291666666666667
#> 3                                                 0.185185185185185

Export a table group per variable in Excel

presentresults_resultstable %>%
  create_table_group_x_variable() %>%
  create_xlsx_group_x_variable(file_path = "mytable.xlsx")

Adding labels to results table

You can add labels to the results table. See the vignette for more information.

label_results <- add_label_columns_to_results_table(
  results_table = presentresults_MSNA2024_results_table,
  dictionary = presentresults_MSNA2024_dictionary
)
#> Joining with `by = join_by(analysis_type)`
#> Joining with `by = join_by(analysis_key)`

Work in progress, but the idea will be to export it after.

label_results <- label_results %>%
  dplyr::filter(group_var != "hoh_gender")
example_variable_x_group <- label_results %>%
  create_table_group_x_variable(analysis_key = "label_analysis_key", value_columns = "stat")

Example for the IPC table

no_nas_presentresults_resultstable <- presentresults_resultstable %>%
  dplyr::filter(!(analysis_type == "prop_select_one" & is.na(analysis_var_value)))

example_ipc <- create_ipc_table(
 results_table = no_nas_presentresults_resultstable,
 dataset = presentresults_MSNA_template_data,
 cluster_name = "cluster_id",
 fcs_cat_var = "fcs_cat",
 fcs_cat_values = c("low", "medium", "high"),
 fcs_set = c(
   "fs_fcs_cereals_grains_roots_tubers",
   "fs_fcs_beans_nuts",
   "fs_fcs_dairy",
   "fs_fcs_meat_fish_eggs",
   "fs_fcs_vegetables_leaves",
   "fs_fcs_fruit",
   "fs_fcs_oil_fat_butter",
   "fs_fcs_sugar",
   "fs_fcs_condiment"
 ),
 hhs_cat_var = "hhs_cat",
 hhs_cat_values = c("none", "slight", "moderate", "severe", "very_severe"),
 hhs_cat_yesno_set = c("fs_hhs_nofood_yn", "fs_hhs_sleephungry_yn", "fs_hhs_daynoteating_yn"),
 hhs_cat_freq_set = c("fs_hhs_nofood_freq", "fs_hhs_sleephungry_freq", "fs_hhs_daynoteating_freq"),
 hhs_value_freq_set = c("rarely_1_2", "sometimes_3_10", "often_10_times"),
 rcsi_cat_var = "rcsi_cat",
 rcsi_cat_values = c("low", "medium", "high"),
 rcsi_set = c("rCSILessQlty", "rCSIBorrow", "rCSIMealSize", "rCSIMealAdult", "rCSIMealNb"),
 lcsi_cat_var = "lcs_cat",
 lcsi_cat_values = c("none", "stress", "emergency", "crisis"),
 lcsi_set = c(
   "liv_stress_lcsi_1",
   "liv_stress_lcsi_2",
   "liv_stress_lcsi_3",
   "liv_stress_lcsi_4",
   "liv_crisis_lcsi_1",
   "liv_crisis_lcsi_2",
   "liv_crisis_lcsi_3",
   "liv_emerg_lcsi_1",
   "liv_emerg_lcsi_2",
   "liv_emerg_lcsi_3"
 ),
 with_hdds = FALSE
)
#> Joining with `by = join_by(analysis_key)`
#> Joining with `by = join_by(group_var_value)`

example_ipc[["ipc_table"]][1:6, 1:10]
#>                                       group_var_value number_of_cluster
#> header_analysis_var                   group_var_value number_of_cluster
#> header_analysis_var_value             group_var_value              <NA>
#> header_analysis_type                  group_var_value              <NA>
#> 1                             locationA %/% displaced                 2
#> 2                         locationA %/% non-displaced                 2
#> 3                             locationB %/% displaced                 2
#>                           number_of_hh fcs_cat %/% low %/% prop_select_one
#> header_analysis_var       number_of_hh                             fcs_cat
#> header_analysis_var_value         <NA>                                 low
#> header_analysis_type              <NA>                     prop_select_one
#> 1                                   31                   0.258064516129032
#> 2                                   24                                0.25
#> 3                                   27                    0.37037037037037
#>                           fcs_cat %/% medium %/% prop_select_one
#> header_analysis_var                                      fcs_cat
#> header_analysis_var_value                                 medium
#> header_analysis_type                             prop_select_one
#> 1                                               0.32258064516129
#> 2                                                          0.375
#> 3                                              0.407407407407407
#>                           fcs_cat %/% high %/% prop_select_one
#> header_analysis_var                                    fcs_cat
#> header_analysis_var_value                                 high
#> header_analysis_type                           prop_select_one
#> 1                                            0.419354838709677
#> 2                                                        0.375
#> 3                                            0.222222222222222
#>                           hhs_cat %/% none %/% prop_select_one
#> header_analysis_var                                    hhs_cat
#> header_analysis_var_value                                 none
#> header_analysis_type                           prop_select_one
#> 1                                            0.225806451612903
#> 2                                            0.208333333333333
#> 3                                            0.222222222222222
#>                           hhs_cat %/% slight %/% prop_select_one
#> header_analysis_var                                      hhs_cat
#> header_analysis_var_value                                 slight
#> header_analysis_type                             prop_select_one
#> 1                                              0.258064516129032
#> 2                                              0.291666666666667
#> 3                                              0.222222222222222
#>                           hhs_cat %/% moderate %/% prop_select_one
#> header_analysis_var                                        hhs_cat
#> header_analysis_var_value                                 moderate
#> header_analysis_type                               prop_select_one
#> 1                                                0.225806451612903
#> 2                                               0.0833333333333333
#> 3                                                0.222222222222222
#>                           hhs_cat %/% severe %/% prop_select_one
#> header_analysis_var                                      hhs_cat
#> header_analysis_var_value                                 severe
#> header_analysis_type                             prop_select_one
#> 1                                             0.0967741935483871
#> 2                                                           0.25
#> 3                                              0.222222222222222

example_ipc %>%
  create_xlsx_group_x_variable(example_ipc, table_name = "ipc_table", file_path = "ipc_table.xlsx")

ggplot2 theme

There are some theme and palettes available to customise the graphs.

data_to_plot <- presentresults::presentresults_MSNA2024_labelled_results_table |>
  dplyr::filter(
    analysis_var == "wash_drinking_water_source_cat",
    group_var == "hoh_gender", 
    group_var_value %in% c("male", "female")
  ) |> 
  dplyr::mutate(label_analysis_var_value = factor(label_analysis_var_value,
                                                  levels = c("Improved",
                                                             "Unimproved",
                                                             "Surface water",
                                                             "Undefined")))

initialplot <- data_to_plot %>%
  ggplot2::ggplot() +
  ggplot2::geom_col(
    ggplot2::aes(
      x = label_analysis_var_value,
      y = stat,
      fill = label_group_var_value
    ),
    position = "dodge"
  ) +
  ggplot2::labs(
    title = stringr::str_wrap(unique(data_to_plot$indicator), 50),
    x = stringr::str_wrap(unique(data_to_plot$label_analysis_var), 50),
    fill = stringr::str_wrap(unique(data_to_plot$label_group_var), 20)
  )

initialplot + 
  theme_barplot() +
  theme_impact("reach")

Code of Conduct

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