Package 'presentresults' reference manual

Title:	Helpers to present results into table or graphs
Description:	The package provides wrappers to turn results from a long format into wide or graphics. For results in a long format, it will work with an analysis key which have the following format analysis_type @/@ dependent_variable %/% dependent_variable_value @/@ independent_variable %/% independent_variable_value.
Authors:	Mehedi Khan [aut] , Yann Say [aut, cre]
Maintainer:	Yann Say <[email protected]>
License:	MIT + file LICENSE
Version:	0.0.0.9002
Built:	2024-11-27 04:10:24 UTC
Source:	https://github.com/impact-initiatives/presentresults

Helper to add the label analysis key

Description

Helper to add the label analysis key

Usage

add_label_analysis_key(results)
add_label_analysis_key(results)

Arguments

results

results table with the label_* columns

Value

results with label analysis key

Add labels to the result table

Description

Add labels to the result table

Usage

add_label_columns_to_results_table(results_table, dictionary)
add_label_columns_to_results_table(results_table, dictionary)

Arguments

`results_table`	Result object with an analysis key.
`dictionary`	Dictionary created with create_label_dictionary

Value

results table with label columns

Examples

add_label_columns_to_results_table(
  results_table = presentresults_MSNA2024_results_table,
  dictionary = presentresults_MSNA2024_dictionary
)
add_label_columns_to_results_table(
  results_table = presentresults_MSNA2024_results_table,
  dictionary = presentresults_MSNA2024_dictionary
)

Create number of cluster and number of hh surveyed per group/strata

Description

Create number of cluster and number of hh surveyed per group/strata

Usage

create_group_clusters(
  results_table,
  analysis_key = "analysis_key",
  dataset,
  cluster_name = NULL
)
create_group_clusters(
  results_table,
  analysis_key = "analysis_key",
  dataset,
  cluster_name = NULL
)

Arguments

`results_table`	results table with analysis key
`analysis_key`	String with the name of the analysis key. Default is "analysis_key"
`dataset`	dataset
`cluster_name`	String with the name of the cluster id in the dataset.

Value

A dataframe with number of HH and cluster in each group

Examples

create_group_clusters(
  results_table = presentresults_resultstable,
  dataset = presentresults_MSNA_template_data,
  cluster_name = "cluster_id"
)

create_group_clusters(
  results_table = presentresults_resultstable,
  dataset = presentresults_MSNA_template_data,
  cluster_name = "cluster_id"
)

Creates a table for the IPC

Description

Create a table from a results table with a key analysis in a format to be shared with the IPC TWG.

Usage

create_ipc_table(
  results_table,
  analysis_key = "analysis_key",
  dataset,
  cluster_name = NULL,
  fcs_cat_var = "fsl_fcs_cat",
  fcs_cat_values = c("Poor", "Borderline", "Acceptable"),
  fcs_set = c("fsl_fcs_cereal", "fsl_fcs_legumes", "fsl_fcs_veg", "fsl_fcs_fruit",
    "fsl_fcs_meat", "fsl_fcs_dairy", "fsl_fcs_sugar", "fsl_fcs_oil"),
  hhs_cat_var = "fsl_hhs_cat_ipc",
  hhs_cat_values = c("None", "No or Little", "Moderate", "Severe", "Very Severe"),
  hhs_cat_yesno_set = c("fsl_hhs_nofoodhh", "fsl_hhs_sleephungry", "fsl_hhs_alldaynight"),
  hhs_value_yesno_set = c("yes", "no"),
  hhs_cat_freq_set = c("fsl_hhs_nofoodhh_freq", "fsl_hhs_sleephungry_freq",
    "fsl_hhs_alldaynight_freq"),
  hhs_value_freq_set = c("rarely", "sometimes", "often"),
  rcsi_cat_var = "fsl_rcsi_cat",
  rcsi_cat_values = c("No to Low", "Medium", "High"),
  rcsi_set = c("fsl_rcsi_lessquality", "fsl_rcsi_borrow", "fsl_rcsi_mealsize",
    "fsl_rcsi_mealadult", "fsl_rcsi_mealnb"),
  lcsi_cat_var = "fsl_lcsi_cat",
  lcsi_cat_values = c("None", "Stress", "Crisis", "Emergency"),
  lcsi_set = c("fsl_lcsi_stress1", "fsl_lcsi_stress2", "fsl_lcsi_stress3",
    "fsl_lcsi_stress4", "fsl_lcsi_crisis1", "fsl_lcsi_crisis2", "fsl_lcsi_crisis3",
    "fsl_lcsi_emergency1", "fsl_lcsi_emergency2", "fsl_lcsi_emergency3"),
  lcsi_value_set = c("yes", "no_had_no_need", "no_exhausted", "not_applicable"),
  with_hdds = TRUE,
  hdds_cat = "fsl_hdds_cat",
  hdds_cat_values = c("Low", "Medium", "High"),
  hdds_set = c("fsl_hdds_cereals", "fsl_hdds_tubers", "fsl_hdds_veg", "fsl_hdds_fruit",
    "fsl_hdds_meat", "fsl_hdds_eggs", "fsl_hdds_fish", "fsl_hdds_legumes",
    "fsl_hdds_dairy", "fsl_hdds_oil", "fsl_hdds_sugar", "fsl_hdds_condiments"),
  hdds_value_set = c("yes", "no"),
  with_fclc = FALSE,
  fclc_matrix_var = "fclcm_phase",
  fclc_matrix_values = c("Phase 1 FCLC", "Phase 2 FCLC", "Phase 3 FCLC", "Phase 4 FCLC",
    "Phase 5 FCLC"),
  fc_matrix_var = "fsl_fc_phase",
  fc_matrix_values = c("Phase 1 FC", "Phase 2 FC", "Phase 3 FC", "Phase 4 FC",
    "Phase 5 FC"),
  other_variables = NULL,
  stat_col = "stat",
  proportion_name = "prop_select_one",
  mean_name = "mean"
)
create_ipc_table(
  results_table,
  analysis_key = "analysis_key",
  dataset,
  cluster_name = NULL,
  fcs_cat_var = "fsl_fcs_cat",
  fcs_cat_values = c("Poor", "Borderline", "Acceptable"),
  fcs_set = c("fsl_fcs_cereal", "fsl_fcs_legumes", "fsl_fcs_veg", "fsl_fcs_fruit",
    "fsl_fcs_meat", "fsl_fcs_dairy", "fsl_fcs_sugar", "fsl_fcs_oil"),
  hhs_cat_var = "fsl_hhs_cat_ipc",
  hhs_cat_values = c("None", "No or Little", "Moderate", "Severe", "Very Severe"),
  hhs_cat_yesno_set = c("fsl_hhs_nofoodhh", "fsl_hhs_sleephungry", "fsl_hhs_alldaynight"),
  hhs_value_yesno_set = c("yes", "no"),
  hhs_cat_freq_set = c("fsl_hhs_nofoodhh_freq", "fsl_hhs_sleephungry_freq",
    "fsl_hhs_alldaynight_freq"),
  hhs_value_freq_set = c("rarely", "sometimes", "often"),
  rcsi_cat_var = "fsl_rcsi_cat",
  rcsi_cat_values = c("No to Low", "Medium", "High"),
  rcsi_set = c("fsl_rcsi_lessquality", "fsl_rcsi_borrow", "fsl_rcsi_mealsize",
    "fsl_rcsi_mealadult", "fsl_rcsi_mealnb"),
  lcsi_cat_var = "fsl_lcsi_cat",
  lcsi_cat_values = c("None", "Stress", "Crisis", "Emergency"),
  lcsi_set = c("fsl_lcsi_stress1", "fsl_lcsi_stress2", "fsl_lcsi_stress3",
    "fsl_lcsi_stress4", "fsl_lcsi_crisis1", "fsl_lcsi_crisis2", "fsl_lcsi_crisis3",
    "fsl_lcsi_emergency1", "fsl_lcsi_emergency2", "fsl_lcsi_emergency3"),
  lcsi_value_set = c("yes", "no_had_no_need", "no_exhausted", "not_applicable"),
  with_hdds = TRUE,
  hdds_cat = "fsl_hdds_cat",
  hdds_cat_values = c("Low", "Medium", "High"),
  hdds_set = c("fsl_hdds_cereals", "fsl_hdds_tubers", "fsl_hdds_veg", "fsl_hdds_fruit",
    "fsl_hdds_meat", "fsl_hdds_eggs", "fsl_hdds_fish", "fsl_hdds_legumes",
    "fsl_hdds_dairy", "fsl_hdds_oil", "fsl_hdds_sugar", "fsl_hdds_condiments"),
  hdds_value_set = c("yes", "no"),
  with_fclc = FALSE,
  fclc_matrix_var = "fclcm_phase",
  fclc_matrix_values = c("Phase 1 FCLC", "Phase 2 FCLC", "Phase 3 FCLC", "Phase 4 FCLC",
    "Phase 5 FCLC"),
  fc_matrix_var = "fsl_fc_phase",
  fc_matrix_values = c("Phase 1 FC", "Phase 2 FC", "Phase 3 FC", "Phase 4 FC",
    "Phase 5 FC"),
  other_variables = NULL,
  stat_col = "stat",
  proportion_name = "prop_select_one",
  mean_name = "mean"
)

Arguments

`results_table`	results table with analysis key
`analysis_key`	String with the name of the analysis key. Default is "analysis_key"
`dataset`	dataset used to create the analysis results to calculate the number of clusters or number of survey.
`cluster_name`	string with the name of column of the cluster in the dataset. Default is NULL, it will calculate the number of interviews only.
`fcs_cat_var`	The string with the name of the Food Consumption Score. Default is "fsl_fcs_cat"
`fcs_cat_values`	String with the options of the Food Consumption Score. Default is c("Poor", "Borderline", "Acceptable")
`fcs_set`	String for the Food Consumption Score questions set. Default is c("fcs_cereal", "fcs_pulses", "fcs_milk", "fcs_meat", "fcs_veg", "fcs_fruit", "fcs_oil", "fcs_sugar", "fcs_spices")
`hhs_cat_var`	The string with the name of the Household Hunger Scale. Default is "fsl_hhs_cat_ipc"
`hhs_cat_values`	String with the options of the Household Hunger Scale. Default is c("None", "No or Little", "Moderate", "Severe", "Very Severe")
`hhs_cat_yesno_set`	String for the Household Hunger Scale yes-no questions set. Default is c("fsl_hhs_nofoodhh", "fsl_hhs_sleephungry", "fsl_hhs_alldaynight")
`hhs_value_yesno_set`	String for the values of the Household Hunger Scale yes-no questions set. Default is c("yes", "no")
`hhs_cat_freq_set`	String for the Household Hunger Scale frequency questions set. Default is c("fsl_hhs_nofoodhh_freq", "fsl_hhs_sleephungry_freq", "fsl_hhs_alldaynight_freq")
`hhs_value_freq_set`	String for the values of the Household Hunger Scale frequency questions set. Default is c("rarely", "sometimes", "often")
`rcsi_cat_var`	The string with the name of the reduced Coping Strategy Index. Default is "fsl_rcsi_cat"
`rcsi_cat_values`	String with the options of the reduced Coping Strategy Index. Default is c("No to Low", "Medium", "High")
`rcsi_set`	String for the reduced Coping Strategy Index questions set. Default is c("fsl_rcsi_lessquality", "fsl_rcsi_borrow", "fsl_rcsi_mealsize", "fsl_rcsi_mealadult", "fsl_rcsi_mealnb")
`lcsi_cat_var`	The string with the name of the Livelihood Coping Strategy Index. Default is "fsl_lcsi_cat"
`lcsi_cat_values`	String with the options of the Livelihood Coping Strategy Index. Default is c("None", "Stress", "Crisis", "Emergency")
`lcsi_set`	String for the Livelihood Coping Strategy Index questions set. Default is c("fsl_lcsi_stress1", "fsl_lcsi_stress2", "fsl_lcsi_stress3", "fsl_lcsi_stress4", "fsl_lcsi_crisis1", "fsl_lcsi_crisis2", "fsl_lcsi_crisis3", "fsl_lcsi_emergency1", "fsl_lcsi_emergency2", "fsl_lcsi_emergency3")
`lcsi_value_set`	String for the values of the Livelihood Coping Strategy Index questions set. Default is c("yes", "no_had_no_need", "no_exhausted", "not_applicable")
`with_hdds`	TRUE or FALSE, whether to include the FCLC and FC values.
`hdds_cat`	String with the name of the Household Dietary Diversity Score. Default is "fsl_hdds_cat"
`hdds_cat_values`	String with the options of the Household Dietary Diversity Score. Default is c("Low", "Medium", "High")
`hdds_set`	String for the Household Dietary Diversity Score. Default is c("fsl_hdds_cereals", "fsl_hdds_tubers", "fsl_hdds_veg", "fsl_hdds_fruit", "fsl_hdds_meat", "fsl_hdds_eggs", "fsl_hdds_fish", "fsl_hdds_legumes", "fsl_hdds_dairy", "fsl_hdds_oil", "fsl_hdds_sugar", "fsl_hdds_condiments")
`hdds_value_set`	String for the values of the Household Dietary Diversity Score questions set. Default is c("yes", "no")
`with_fclc`	TRUE or FALSE, whether to include the FCLC and FC values. Default is set to FALSE.
`fclc_matrix_var`	String with the name of the food consumption livelihood matrix from FEWSNET. Default is "fclcm_phase"
`fclc_matrix_values`	String with the options of the food consumption livelihood matrix Default is c("Phase 1 FCLC", "Phase 2 FCLC", "Phase 3 FCLC", "Phase 4 FCLC", "Phase 5 FCLC")
`fc_matrix_var`	String with the name of the food consumption matrix from FEWSNET. Default is "fsl_fc_phase"
`fc_matrix_values`	String with the options of the food consumption matrix. Default is c("Phase 1 FC", "Phase 2 FC", "Phase 3 FC", "Phase 4 FC", "Phase 5 FC")
`other_variables`	String for the names of other variables to include. Default is NULL
`stat_col`	String for the name of the column with the mean or the proportion. Default is "stat"
`proportion_name`	String how a proportion is called in the analysis key. Default is "prop_select_one"
`mean_name`	String how a mean is called in the analysis key. Default is "mean"

Details

For arguments that are *_values or *_set, and other_variables, the order of appearance will be the order in the table.

Value

a list with:

a wide table with groups of interest in the rows, and the variables in the columns in a format that can be shared to the IPC TWG. This table should be pass into create_xlsx_group_x_variable
the dataset that was provided.

Examples

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

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
)

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

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
)

Create a dictionary for the labeling results

Description

Create a dictionary for the labeling results

Usage

create_label_dictionary(
  kobo_survey_sheet,
  kobo_choices_sheet,
  label_column = "label::english",
  analysis_type_dictionary = NULL,
  results_table = NULL
)
create_label_dictionary(
  kobo_survey_sheet,
  kobo_choices_sheet,
  label_column = "label::english",
  analysis_type_dictionary = NULL,
  results_table = NULL
)

Arguments

`kobo_survey_sheet`	KOBO survey sheet to be used.
`kobo_choices_sheet`	KOBO choices sheet to be used.
`label_column`	label column from the KOBO tools to be used.
`analysis_type_dictionary`	Analysis type dictionary, a data frame with analysis type and label_analysis_type to be used. By default parameters is set to NULL. It will use a default dataframe. See section analysis_type_dictionary for more details.
`results_table`	result object with an analysis key. Default is NULL, it will be used with review_kobo_labels.

Value

A list with 3 dataframes: dictionary_survey, dictionary_choices, analysis_type_dictionary

analysis_type_dictionary

The default analysis dictionary is created like that.

data.frame(analysis_type = c("prop_select_one",
                             "prop_select_multiple",
                             "mean",
                             "ratio",
                             "median"),
           label_analysis_type = c("Proportion (single choice)",
                                   "Proportion (multiple choice)",
                                   "Mean",
                                   "Ratio",
                                   "Median")

Examples


create_label_dictionary(
  kobo_survey_sheet = presentresults_MSNA2024_kobotool_fixed$kobo_survey,
  kobo_choices_sheet = presentresults_MSNA2024_kobotool_fixed$kobo_choices
)


french_dictionary <- data.frame(
  analysis_type = c(
    "prop_select_one",
    "prop_select_multiple",
    "mean",
    "ratio",
    "median"
  ),
  label_analysis_type = c(
    "Proportion (Choix unique)",
    "Proportion (Choix multiple)",
    "Moyenne",
    "Ratio",
    "Médiane"
  )
)

create_label_dictionary(
  kobo_survey_sheet = presentresults_MSNA2024_kobotool_fixed$kobo_survey,
  kobo_choices_sheet = presentresults_MSNA2024_kobotool_fixed$kobo_choices,
  label_column = "label::french",
  analysis_type_dictionary = french_dictionary
)

create_label_dictionary(
  kobo_survey_sheet = presentresults_MSNA2024_kobotool_fixed$kobo_survey,
  kobo_choices_sheet = presentresults_MSNA2024_kobotool_fixed$kobo_choices
)


french_dictionary <- data.frame(
  analysis_type = c(
    "prop_select_one",
    "prop_select_multiple",
    "mean",
    "ratio",
    "median"
  ),
  label_analysis_type = c(
    "Proportion (Choix unique)",
    "Proportion (Choix multiple)",
    "Moyenne",
    "Ratio",
    "Médiane"
  )
)

create_label_dictionary(
  kobo_survey_sheet = presentresults_MSNA2024_kobotool_fixed$kobo_survey,
  kobo_choices_sheet = presentresults_MSNA2024_kobotool_fixed$kobo_choices,
  label_column = "label::french",
  analysis_type_dictionary = french_dictionary
)

Create a table for MSNA Indicator Maps 1.2 tool

Description

Create a table for MSNA Indicator Maps 1.2 tool

Usage

create_table_for_map(
  results_table,
  group_var_value_column = "group_var_value",
  analysis_var_column = "analysis_var",
  stat_column = "stat",
  number_classes = 5
)
create_table_for_map(
  results_table,
  group_var_value_column = "group_var_value",
  analysis_var_column = "analysis_var",
  stat_column = "stat",
  number_classes = 5
)

Arguments

`results_table`	Results table from analysistools filtered for only value per admin.
`group_var_value_column`	Name of the column with the group/dependent variable values. Default is "group_var_value".
`analysis_var_column`	Name of the column with the analysis/independent variable names. Default is "analysis_var".
`stat_column`	Name of the column with the stat. Default is "stat".
`number_classes`	Number of classes for the map. It will convert percentages to classes. Default value is 5.

Details

There can be 5 or 6 classes as follow:

5 classes:

1 : 0
2 : <= 25 %
3 : <= 50 %
4 : <= 75 %
5 : <= 100 %
NA: Anything else

6 classes:

1 : 0
2 : <= 20 %
3 : <= 40 %
4 : <= 60 %
5 : <= 80 %
6 : <= 100 %
NA: Anything else

Value

A wide table with group variables in rows and the indicators coded in their classes in the columns.

Examples

presentresults::presentresults_MSNA2024_results_table |>
  dplyr::filter(
    analysis_var == "wash_drinking_water_source_cat",
    analysis_var_value == "surface_water",
    group_var == "admin1"
  ) |>
  create_table_for_map()

presentresults::presentresults_MSNA2024_results_table |>
  dplyr::filter(
    analysis_var == "wash_drinking_water_source_cat",
    analysis_var_value == "surface_water",
    group_var == "admin1"
  ) |>
  create_table_for_map()

Create a wide table with indicators in the columns

Description

Create a wide table with indicators in the columns

Usage

create_table_group_x_variable(
  results_table,
  analysis_key = "analysis_key",
  value_columns = c("stat", "stat_low", "stat_upp")
)
create_table_group_x_variable(
  results_table,
  analysis_key = "analysis_key",
  value_columns = c("stat", "stat_low", "stat_upp")
)

Arguments

`results_table`	result object with an analysis key
`analysis_key`	name of the columns of the analysis key, as character vector. Default is "analysis_key"
`value_columns`	names of the columns with the stats of interest, as character vector. Default is c("stat", "stat_low", "stat_upp")

Value

a wide table with grouping variables as rows and analysed variables as columns

Examples

create_table_group_x_variable(presentresults_resultstable, value_columns = "stat")

create_table_group_x_variable(presentresults_resultstable, value_columns = "stat")

Turns a long format table into a wide format

Description

Turns a long format table into a wide format

Usage

create_table_variable_x_group(
  results_table,
  analysis_key = "analysis_key",
  value_columns = c("stat", "stat_low", "stat_upp"),
  list_for_excel = FALSE
)
create_table_variable_x_group(
  results_table,
  analysis_key = "analysis_key",
  value_columns = c("stat", "stat_low", "stat_upp"),
  list_for_excel = FALSE
)

Arguments

`results_table`	results table with analysis key
`analysis_key`	analysis key following this description "analysis_type @/@ dependent_variable %/% dependent_variable_value @/@ independent_variable %/% independent_variable_value "
`value_columns`	string containing the names of the columns with the stats to export
`list_for_excel`	Default is FALSE, the function will return a dataframe. If set to TRUE, it will return a list of dataframe with the grouping variable as name. This format makes it easier to write excel files with different tab.

Value

a data frame in a wide format with the analysis type, analysis variable, analysis variable value and the group variable value as columns. If list_for_excel is set to TRUE, it will return a list per grouping variable.

Examples

presentresults_resultstable %>% create_table_variable_x_group("analysis_key", "stat")
presentresults_resultstable %>% create_table_variable_x_group("analysis_key", "stat")

Write a table group by variable into Excel

Description

Write a table group by variable into Excel

Usage

create_xlsx_group_x_variable(
  table_group_x_variable,
  table_name = "group_x_table",
  dataset_name = "dataset",
  file_path,
  table_sheet = "table_group_x_variable",
  dataset_sheet = "dataset",
  write_file = TRUE,
  overwrite = FALSE
)
create_xlsx_group_x_variable(
  table_group_x_variable,
  table_name = "group_x_table",
  dataset_name = "dataset",
  file_path,
  table_sheet = "table_group_x_variable",
  dataset_sheet = "dataset",
  write_file = TRUE,
  overwrite = FALSE
)

Arguments

`table_group_x_variable`	a table create by create_table_group_x_variable
`table_name`	string with the name of table to write. It will only be used if it is part of a list. Default is "group_x_table".
`dataset_name`	string with the name of dataset to write. It will only be used if it is part of a list. Default is "dataset".
`file_path`	File path, it should contains the file name
`table_sheet`	string with the name of the sheet to write the table, default is "table_group_x_variable"
`dataset_sheet`	string with the name of the sheet to write the dataset, default is "dataset"
`write_file`	Default is TRUE, it will write the file. If set to FALSE, it will return a workbook object from openxlsx
`overwrite`	Default is FALSE, it will overwrite the file if set to TRUE.

Value

An excel file formatted.

Examples

## Not run: 
presentresults_resultstable %>%
  create_table_group_x_variable() %>%
  create_xlsx_group_x_variable(file_path = "mytable.xlsx")

## End(Not run)
## Not run: 
presentresults_resultstable %>%
  create_table_group_x_variable() %>%
  create_xlsx_group_x_variable(file_path = "mytable.xlsx")

## End(Not run)

Write a table variable by group into Excel

Description

Write a table variable by group into Excel

Usage

create_xlsx_variable_x_group(
  table_group_x_variable,
  file_path = NULL,
  table_name = "variable_x_group_table",
  value_columns = c("stat", "stat_low", "stat_upp"),
  total_columns = NULL,
  readme_sheet_name = "readme",
  table_sheet_name = "variable_x_group_table",
  overwrite = FALSE
)
create_xlsx_variable_x_group(
  table_group_x_variable,
  file_path = NULL,
  table_name = "variable_x_group_table",
  value_columns = c("stat", "stat_low", "stat_upp"),
  total_columns = NULL,
  readme_sheet_name = "readme",
  table_sheet_name = "variable_x_group_table",
  overwrite = FALSE
)

Arguments

`table_group_x_variable`	a table create by create_table_variable_x_group
`file_path`	File names and path. Default is NULL which will return a workbook instead of an excel file.
`table_name`	string with the name of table to write. It will only be used if it is part of a list. Default is "variable_x_group_table".
`value_columns`	string containing the names of the columns with the stats to export
`total_columns`	string containing the names of the columns with the totals (n, n_total, n_weighted, n_total_weighted, etc.) to export
`readme_sheet_name`	string with the name of the sheet to write the read me page, default is "readme"
`table_sheet_name`	string with the name of the sheet to write the table, default is "variable_x_group_table"
`overwrite`	Default is FALSE, it will overwrite the file if set to TRUE.

Value

An excel file formatted.

Examples

## Not run: 
presentresults_resultstable %>%
  create_table_variable_x_group() %>%
  create_xlsx_variable_x_group(file_path = "mytable.xlsx")

## End(Not run)
## Not run: 
presentresults_resultstable %>%
  create_table_variable_x_group() %>%
  create_xlsx_variable_x_group(file_path = "mytable.xlsx")

## End(Not run)

IMPACT colors hex code

Description

IMPACT colors hex code

Usage

impact_colors
impact_colors

Details

IMPACT colors hex code

IMPACT palettes

Description

IMPACT palettes

Usage

impact_palettes
impact_palettes

Details

IMPACT palettes hex code

MSNA dataset generated by xslxform

Description

Datasets for example

Usage

presentresults_MSNA_template_data
presentresults_MSNA_template_data

Examples

presentresults_MSNA_template_data
presentresults_resultstable
presentresults_MSNA_template_data
presentresults_resultstable

MSNA2024 dictionary

Description

Dictionary to be used to label results

Usage

presentresults_MSNA2024_dictionary
presentresults_MSNA2024_dictionary

Details

An example of dictionary to be used in add_label_columns_to_results_table() create_label_dictionary(kobo_survey_sheet = presentresults_MSNA2024_kobotool_fixed$kobo_survey, kobo_choices_sheet = presentresults_MSNA2024_kobotool_fixed$kobo_choices, results_table = presentresults_MSNA2024_results_table)

results table example

Description

KOBO Template 2024 V10 without duplicated labels and names.

Usage

presentresults_MSNA2024_kobotool_fixed
presentresults_MSNA2024_kobotool_fixed

Details

KOBO template 2024 with composite indicators information without duplicated labels and names.

KOBO template for MSNA 2024 with composite indicators information

Description

KOBO Template 2024 V10

Usage

presentresults_MSNA2024_kobotool_template
presentresults_MSNA2024_kobotool_template

Details

KOBO template 2024 with composite indicators information

MSNA 2024 results table with labels.

Description

MSNA 2024 labelled results table

Usage

presentresults_MSNA2024_labelled_results_table
presentresults_MSNA2024_labelled_results_table

results table example for MSNA 2024 tools

Description

Results table from MSNA2024 template

Usage

presentresults_MSNA2024_results_table
presentresults_MSNA2024_results_table

results table example

Description

results table example

Usage

presentresults_resultstable
presentresults_resultstable

Review if there are duplication of kobo name and labels in the kobo tools

Description

Review if there are duplication of kobo name and labels in the kobo tools

Usage

review_kobo_labels(
  kobo_survey_sheet,
  kobo_choices_sheet,
  label_column = "label::english",
  exclude_type = c("begin_group", "end_group", "beging_repeat", "end_repeat", "note"),
  results_table = NULL
)
review_kobo_labels(
  kobo_survey_sheet,
  kobo_choices_sheet,
  label_column = "label::english",
  exclude_type = c("begin_group", "end_group", "beging_repeat", "end_repeat", "note"),
  results_table = NULL
)

Arguments

`kobo_survey_sheet`	kobo survey sheet. It must contain type and name.
`kobo_choices_sheet`	kobo choices sheet. It must contain list_name and name.
`label_column`	Column name with the label to be used, default is "label::english"
`exclude_type`	Types to exclude in the review, default is c("begin_group", "end_group", "beging_repeat", "end_repeat", "note")
`results_table`	Results table with group_var and analysis_var columns (names of the different variables). The table will be used to review only the names and label that will appear.

Value

A data frame with the duplicated cases and the reasons. Empty if there is no duplication.

Examples

review_kobo_labels(
  kobo_survey_sheet = presentresults_MSNA2024_kobotool_template$kobo_survey,
  kobo_choices_sheet = presentresults_MSNA2024_kobotool_template$kobo_choices,
)
review_kobo_labels(
  kobo_survey_sheet = presentresults_MSNA2024_kobotool_template$kobo_survey,
  kobo_choices_sheet = presentresults_MSNA2024_kobotool_template$kobo_choices,
  results_table = presentresults_MSNA2024_results_table
)
review_kobo_labels(
  kobo_survey_sheet = presentresults_MSNA2024_kobotool_template$kobo_survey,
  kobo_choices_sheet = presentresults_MSNA2024_kobotool_template$kobo_choices,
)
review_kobo_labels(
  kobo_survey_sheet = presentresults_MSNA2024_kobotool_template$kobo_survey,
  kobo_choices_sheet = presentresults_MSNA2024_kobotool_template$kobo_choices,
  results_table = presentresults_MSNA2024_results_table
)

Barplot theme

Description

theme_barplot will fill the colors with the palette, put the y-axis to 0 to 100 %

Usage

theme_barplot(palette = impact_palettes$reach_palette)
theme_barplot(palette = impact_palettes$reach_palette)

Arguments

palette

color palette to be used in scale_fill_manual

Value

ggplot2 plot with filled colors with the palette, put the y-axis to 0 to 100 %

Examples

data_to_plot <- presentresults::presentresults_MSNA2024_labelled_results_table |>
  dplyr::filter(
    analysis_var == "wash_drinking_water_source_cat",
    group_var == "hoh_gender"
  )

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)) +
    theme_barplot()

data_to_plot <- presentresults::presentresults_MSNA2024_labelled_results_table |>
  dplyr::filter(
    analysis_var == "wash_drinking_water_source_cat",
    group_var == "hoh_gender"
  )

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)) +
    theme_barplot()

Theme for IMPACT Initiative

Description

It will set:

theme_minimal,
color of the text in REACH gray (see impact_colors$reach_gray),
Title in bold and with the color of the initiative (reach: impact_colors$red, impact: impact_colors$blue, and agora: impact_colors$bordeaux)

Usage

theme_impact(initiative = "reach")
theme_impact(initiative = "reach")

Arguments

initiative

Name of the initiative, should be "reach", "impact" or "agora".

Value

ggplot2 plot with theme_minimal, bold title and color of the initiative.

Examples


data_to_plot <- presentresults::presentresults_MSNA2024_labelled_results_table |>
  dplyr::filter(
    analysis_var == "wash_drinking_water_source_cat",
    group_var == "hoh_gender"
  )

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)) +
    theme_impact("reach")
data_to_plot <- presentresults::presentresults_MSNA2024_labelled_results_table |>
  dplyr::filter(
    analysis_var == "wash_drinking_water_source_cat",
    group_var == "hoh_gender"
  )

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)) +
    theme_impact("reach")

Unite labels columns

Description

Unite labels columns

Usage

unite_labels(key_table)
unite_labels(key_table)

Arguments

key_table

a key table built with create_analysis_key_table

Value

a table with label_analysis_var, label_analysis_var_value, label_group_var, and label_group_var_value united and with a %/% as separator

Verify that which value of a vector is present in another vector

Description

Verify that which value of a vector is present in another vector

Usage

verify_grep_AinB(.A, .B)
verify_grep_AinB(.A, .B)

Arguments

`.A`	String of values to check
`.B`	Vector of string to check

Value

a vector of the length of values_to_check with TRUE or FALSE if the value appears at least once in .B

Examples


verify_grep_AinB(c("hhs_cat", "fsc_cat"), presentresults_resultstable$analysis_key)

verify_grep_AinB(c("hhs_cat", "fsc_cat"), presentresults_resultstable$analysis_key)

Verify that a given variable set as the expected number of values.

Description

Verify that a given variable set as the expected number of values.

Usage

verify_numbers_values(var_name, values_set, expected_number)
verify_numbers_values(var_name, values_set, expected_number)

Arguments

`var_name`	The name of the variable as string.
`values_set`	Vector with a the set of values.
`expected_number`	Expected numbers of unique value (excluding NA)

Value

If the number of unique value is different than the expected, it will show a warning.

Examples

verify_numbers_values("my_var", c("low", "borderline", "acceptable"), 3)
verify_numbers_values("my_var", c("low", "borderline", "acceptable", NA), 3)
verify_numbers_values("my_var", c("low", "acceptable", NA), 3)
verify_numbers_values("my_var", c("none", "low", "borderline", "acceptable"), 3)
verify_numbers_values("my_var", c("low", "borderline", "acceptable"), 3)
verify_numbers_values("my_var", c("low", "borderline", "acceptable", NA), 3)
verify_numbers_values("my_var", c("low", "acceptable", NA), 3)
verify_numbers_values("my_var", c("none", "low", "borderline", "acceptable"), 3)

`results_table`	Result object with an analysis key.
`columns_var_to_convert`	set of columns to add labels.
`dictionary_survey`	Dictionary with name and label to use. Should be created with create_label_dictionary.
`dictionary_survey_name_column`	name column in the dictionary.
`dictionary_choices`	Dictionary with list_name, name and label to use. Should be created with create_label_dictionary.
`dictionary_choices_survey_column`	name column for the survey variable column in the dictionary.
`dictionary_choices_choices_column`	name column for the choices value column in the dictionary.

Package 'presentresults'

Help Index

Helper to add the label analysis key

Description

Usage

Arguments

Value

Helper to add label columns

Description

Usage

Arguments

Value

Add labels to the result table

Description

Usage

Arguments

Value

Examples

Create number of cluster and number of hh surveyed per group/strata

Description

Usage

Arguments

Value

Examples

Creates a table for the IPC

Description

Usage

Arguments

Details

Value

Examples

Create a dictionary for the labeling results

Description

Usage

Arguments

Value

analysis_type_dictionary

Examples

Create a table for MSNA Indicator Maps 1.2 tool

Description

Usage

Arguments

Details

Value

Examples

Create a wide table with indicators in the columns

Description

Usage

Arguments

Value

Examples

Turns a long format table into a wide format

Description

Usage

Arguments

Value

Examples

Write a table group by variable into Excel

Description

Usage

Arguments

Value

Examples

Write a table variable by group into Excel

Description

Usage

Arguments

Value

Examples

IMPACT colors hex code

Description

Usage

Details

IMPACT palettes

Description

Usage

Details

MSNA dataset generated by xslxform

Description

Usage