Package 'addindicators'

Calculating FEWSNET Food Consumption-Livelihood Coping Matrix

Description

Calculating FEWSNET Food Consumption-Livelihood Coping Matrix

Usage

add_eg_fclcm_phase(
  dataset,
  fc_phase_var = "fc_phase",
  fc_phase_1 = "Phase 1 FC",
  fc_phase_2 = "Phase 2 FC",
  fc_phase_3 = "Phase 3 FC",
  fc_phase_4 = "Phase 4 FC",
  fc_phase_5 = "Phase 5 FC",
  lcs_cat_var = "lcsi_cat",
  lcs_cat_none = "None",
  lcs_cat_stress = "Stress",
  lcs_cat_crisis = "Crisis",
  lcs_cat_emergency = "Emergency",
  fclcm_phase_var = "fclcm_phase"
)

Arguments

dataset	Dataset
fc_phase_var	Column name containing food consumption phase.
fc_phase_1	The name of the value "Phase 1 FC" (by default) in the food consumption phase.
fc_phase_2	The name of the value "Phase 2 FC" (by default) in the food consumption phase.
fc_phase_3	The name of the value "Phase 3 FC" (by default) in the food consumption phase.
fc_phase_4	The name of the value "Phase 4 FC" (by default) in the food consumption phase.
fc_phase_5	The name of the value "Phase 5 FC" (by default) in the food consumption phase.
lcs_cat_var	Column name containing livelihood coping category.
lcs_cat_none	The name of the value "None" (by default) in the livelihood coping category.
lcs_cat_stress	The name of the value "Stress" (by default) in the livelihood coping category.
lcs_cat_crisis	The name of the value "Crisis" (by default) in the livelihood coping category.
lcs_cat_emergency	The name of the value "Emergency" (by default) in the livelihood coping category.
fclcm_phase_var	A character vector which will be the column name for FSLC phase.

Value

Returns a dataframe with a additional column for FCLC phase.

Examples

test_df <- data.frame(
  lcsi_cat = c("None", "Stress"),
  fc_phase = c("Phase 1 FC", "Phase 2 FC")
)
test_df |> add_eg_fclcm_phase()

---

Add the food consumption matrix to the dataset

Description

Add the food consumption matrix to the dataset

Usage

add_eg_fcm_phase(
  dataset,
  fcs_column_name = "fcs_cat",
  rcsi_column_name = "rcsi_cat",
  hhs_column_name = "hhs_cat",
  fcs_categories_acceptable = "Acceptable",
  fcs_categories_poor = "Poor",
  fcs_categories_borderline = "Borderline",
  rcsi_categories_low = "No to Low",
  rcsi_categories_medium = "Medium",
  rcsi_categories_high = "High",
  hhs_categories_none = "None",
  hhs_categories_little = "Little",
  hhs_categories_moderate = "Moderate",
  hhs_categories_severe = "Severe",
  hhs_categories_very_severe = "Very Severe"
)

Arguments

dataset	A dataframe
fcs_column_name	A string specifying the column name of the food consumption score in the dataset
rcsi_column_name	A string specifying the column name of the reduced coping strategy index in the dataset
hhs_column_name	A string specifying the column name of the household hunger scale in the dataset
fcs_categories_acceptable	The name of the value "Acceptable" (by default) in the fcs categories
fcs_categories_poor	The name of the value "Poor" (by default) in the fcs categories
fcs_categories_borderline	The name of the value "Borderline" (by default) in the fcs categories
rcsi_categories_low	The name of the value "No to Low" (by default) in the rcsi categories
rcsi_categories_medium	The name of the value "Medium" (by default) in the rcsi categories
rcsi_categories_high	The name of the value "High" (by default) in the rcsi categories
hhs_categories_none	The name of the value "None" (by default) in the hhs categories
hhs_categories_little	The name of the value "Little" (by default) in the hhs categories
hhs_categories_moderate	The name of the value "Moderate" (by default) in the hhs categories
hhs_categories_severe	The name of the value "Severe" (by default) in the hhs categories
hhs_categories_very_severe	The name of the value "Very Severe" (by default) in the hhs categories

Value

this function returns a dataframe with a column called fc_cell that includes values from 1 to 45 representing the Food Consumption Score Matrix and the fc_phase column that includes the different 5 phases of food consumption

Examples

test_data <- data.frame(
  fcs_cat = c("Acceptable", "Poor", "Borderline", "Acceptable"),
  rcsi_cat = c("No to Low", "Medium", "No to Low", "High"),
  hhs_cat = c("None", "Little", "Severe", "Very Severe")
)
add_eg_fcm_phase(test_data,
  fcs_column_name = "fcs_cat",
  rcsi_column_name = "rcsi_cat",
  hhs_column_name = "hhs_cat",
  fcs_categories_acceptable = "Acceptable",
  fcs_categories_poor = "Poor",
  fcs_categories_borderline = "Borderline",
  rcsi_categories_low = "No to Low",
  rcsi_categories_medium = "Medium",
  rcsi_categories_high = "High",
  hhs_categories_none = "None",
  hhs_categories_little = "Little",
  hhs_categories_moderate = "Moderate",
  hhs_categories_severe = "Severe",
  hhs_categories_very_severe = "Very Severe"
)

---

add_eg_fcs

Description

add_eg_fcs

Usage

add_eg_fcs(
  .dataset,
  cutoffs = c("normal", "alternative"),
  fsl_fcs_cereal = "fsl_fcs_cereal",
  fsl_fcs_legumes = "fsl_fcs_legumes",
  fsl_fcs_veg = "fsl_fcs_veg",
  fsl_fcs_fruit = "fsl_fcs_fruit",
  fsl_fcs_meat = "fsl_fcs_meat",
  fsl_fcs_dairy = "fsl_fcs_dairy",
  fsl_fcs_sugar = "fsl_fcs_sugar",
  fsl_fcs_oil = "fsl_fcs_oil"
)

Arguments

.dataset	the clean dataset
cutoffs	either "normal", or "alternative". The default is set to normal
fsl_fcs_cereal	the name of the variable that indicates the number of days cereals were consumed
fsl_fcs_legumes	the name of the variable that indicates the number of days legumes were consumed
fsl_fcs_veg	the name of the variable that indicates the number of days vegetables were consumed
fsl_fcs_fruit	the name of the variable that indicates the number of days fruits were consumed
fsl_fcs_meat	the name of the variable that indicates the number of days meat/fish were consumed
fsl_fcs_dairy	the name of the variable that indicates the number of days dairy were consumed
fsl_fcs_sugar	the name of the variable that indicates the number of days cereals was consumed
fsl_fcs_oil	the name of the variable that indicates the number of days oild were consumed

Value

the dataset with fsl_fcs_score and fsl_fcs_cat computed, as well as the 8 weighted food groups

Examples

df1 <- data.frame(
  fsl_fcs_cereal = c(1, 2, 3, 2, 5, 6, 7),
  fsl_fcs_legumes = c(3, 4, 5, 6, 1, 6, 5),
  fsl_fcs_veg = c(3, 2, 1, 6, 5, 4, 3),
  fsl_fcs_fruit = c(1, 4, 6, 2, 2, 2, 4),
  fsl_fcs_meat = c(5, 4, 3, 2, 7, 4, 5),
  fsl_fcs_dairy = c(1, 2, 6, 7, 3, 4, 2),
  fsl_fcs_sugar = c(1, 7, 6, 5, 2, 3, 4),
  fsl_fcs_oil = c(2, 3, 6, 5, 1, 7, 4)
)
add_eg_fcs(.dataset = df1,
  cutoffs = "normal"
)

---

Add the household hunger scale to the dataset

Description

Add the household hunger scale to the dataset

Usage

add_eg_hhs(
  .dataset,
  hhs_nofoodhh_1 = "fs_hhs_nofood_yn",
  hhs_nofoodhh_1a = "fs_hhs_nofood_freq",
  hhs_sleephungry_2 = "fs_hhs_sleephungry_yn",
  hhs_sleephungry_2a = "fs_hhs_sleephungry_freq",
  hhs_alldaynight_3 = "fs_hhs_daynoteating_yn",
  hhs_alldaynight_3a = "fs_hhs_daynoteating_freq",
  yes_answer = "yes",
  no_answer = "no",
  rarely_answer = "rarely_1_2",
  sometimes_answer = "sometimes_3_10",
  often_answer = "often_10_times"
)

Arguments

.dataset	Dataset
hhs_nofoodhh_1	The name of the column "In the past 4 weeks (30 days), was there ever no food to eat of any kind in your house because of lack of resources to get food?". It has to be a string.
hhs_nofoodhh_1a	The name of the column "How often did this happen in the past (4 weeks/30 days)?". It has to be a string.
hhs_sleephungry_2	The name of the column "In the past 4 weeks (30 days), did you or any household member go to sleep at night hungry because there was not enough food?". It has to be a string.
hhs_sleephungry_2a	The name of the column "How often did this happen in the past (4 weeks/30 days)?". It has to be a string.
hhs_alldaynight_3	The name of the column "In the past 4 weeks (30 days), did you or any household member go a whole day and night without eating anything at all because there was not enough food?". It has to be a string.
hhs_alldaynight_3a	The name of the column "How often did this happen in the past (4 weeks/30 days)?". It has to be a string.
yes_answer	Value used for "Yes"
no_answer	Value used for the "No"
rarely_answer	Value used for "Rarely (1-2)"
sometimes_answer	Value used for "Sometimes (3-10)"
often_answer	Value used for "Often (10+ times)

Value

It returns the dataframe with 12 extras columns: recoded hhs questions, score for the 3 sets of questions (from 0 to 2), the HHS score (from 0 to 6), the HHS category and the HHS IPC category

Examples

{
  input_data <- data.frame(
    fs_hhs_nofood_yn = c("no", "yes", "no", "no", "no"),
    fs_hhs_nofood_freq = c(NA_character_, "rarely_1_2",
                           NA_character_, NA_character_, NA_character_),
    fs_hhs_sleephungry_yn = c("no", "no", "yes", "no", "no"),
    fs_hhs_sleephungry_freq = c(NA_character_, NA_character_,
                                "often_10_times", NA_character_, NA_character_),
    fs_hhs_daynoteating_yn = c("no", "no", "yes", "yes", "yes"),
    fs_hhs_daynoteating_freq = c(NA_character_, NA_character_, "often_10_times",
                                 "rarely_1_2", "sometimes_3_10")
  )

  add_eg_hhs(
    .dataset = input_data,
    hhs_nofoodhh_1 = "fs_hhs_nofood_yn",
    hhs_nofoodhh_1a = "fs_hhs_nofood_freq",
    hhs_sleephungry_2 = "fs_hhs_sleephungry_yn",
    hhs_sleephungry_2a = "fs_hhs_sleephungry_freq",
    hhs_alldaynight_3 = "fs_hhs_daynoteating_yn",
    hhs_alldaynight_3a = "fs_hhs_daynoteating_freq",
    yes_answer = "yes",
    no_answer = "no",
    rarely_answer = "rarely_1_2",
    sometimes_answer = "sometimes_3_10",
    often_answer = "often_10_times"
  )
}

---

Add LCSI

Description

Function to calculate Livelihood Coping Strategy Index (LCSI)

Usage

add_eg_lcsi(
  .dataset,
  lcsi_stress_vars,
  lcsi_crisis_vars,
  lcsi_emergency_vars,
  yes_val = NULL,
  no_val = NULL,
  exhausted_val = NULL,
  not_applicable_val = NULL,
  ignore_NA = FALSE
)

Arguments

.dataset	A dataframe with the ten LCSI variables needed for analysis.
lcsi_stress_vars	A vector of character values that are the column names for the four stress LCSI variables.
lcsi_crisis_vars	A vector of character values that are the column names for the three crisis LCSI variables.
lcsi_emergency_vars	A vector of character values that are the column names for the thre emergency LCSI variables.
yes_val	A character value in the dataset associated with "Yes, used this coping strategy in the last 30 days."
no_val	A character value in the dataset associated with "No, have not used this coping strategy in the last 30 days."
exhausted_val	A character value in the dataset associated with "No, haven't used in the last 30 days because I've exhausted this coping strategy in the last 6 or 12 months."
not_applicable_val	A character value in the dataset associated with "This coping strategy is not applicable for the household.
ignore_NA	Default is FALSE. If set to TRUE, the missing values will be ignored.

Value

Returns a dataframe with added columns for LCSI indicators. - lcsi_x_yes : 1 means one of the of the x strategies was used (*yes_val*) - lcsi_x_exhaust: 1 means one of the x strategies was exhausted and could not be used (*exhausted_val*) - lcsi_x: 1 means one of the x strategies was if either used (*yes_val*) or exhausted (*exhausted_val*) Where x is stress, crisis or emergency - lcsi_cat_yes : the highest category between the lcsi_x_yes - lcsi_cat_exhast: the highest category between the lcsi_x_exhaust - lcsi_cat: the highest category between the lcsi_x

Examples

{
  input_data1 <- data.frame(
stress1 =             c("No", "No", "Exhausted", "Not Applicable", "No"),
stress2 =             c("No", "Yes", "Not Applicable", "No", "No"),
stress3 =             c("Not Applicable", "Not Applicable", "Yes", "No", "No"),
stress4 =             c("Not Applicable", "No", "Yes", "Yes", "No"),
crisis1 =             c("No", "Not Applicable", "Yes", "Exhausted", "No"),
crisis2 =             c("No", "No", "No", "No", "No"),
crisis3 =             c("No", "No", "Yes", "Not Applicable", "No"),
emergency1 =          c("No", "Not Applicable", "Not Applicable", "No", "No"),
emergency2 =          c("No", "Not Applicable", "Yes", "Not Applicable", "No"),
emergency3 =          c("Not Applicable", "No", "Not Applicable", "No", "Exhausted"))

add_eg_lcsi(.dataset = input_data1,
lcsi_stress_vars = c("stress1", "stress2", "stress3", "stress4"),
lcsi_crisis_vars = c("crisis1", "crisis2", "crisis3"),
lcsi_emergency_vars = c("emergency1", "emergency2", "emergency3"),
yes_val = "Yes",
no_val = "No",
exhausted_val = "Exhausted",
not_applicable_val = "Not Applicable")

}

---

Add indicator for reduced Household CSI Score(rcsi)

Description

Add indicator for reduced Household CSI Score(rcsi)

Usage

add_eg_rcsi(
  data,
  rCSILessQlty = "rCSILessQlty",
  rCSIBorrow = "rCSIBorrow",
  rCSIMealSize = "rCSIMealSize",
  rCSIMealAdult = "rCSIMealAdult",
  rCSIMealNb = "rCSIMealNb",
  new_colname = "rcsi"
)

Arguments

data	dataset
rCSILessQlty	Column representing question- During the last 7 days, were there days (and, if so, how many) when your household had to rely on less preferred and less expensive food to cope with a lack of food or money to buy it?
rCSIBorrow	Column representing question- During the last 7 days, were there days (and, if so, how many) when your household had to borrow food or rely on help from a relative or friend to cope with a lack of food or money to buy it?
rCSIMealSize	Column representing question- During the last 7 days, were there days (and, if so, how many) when your household had to limit portion size of meals at meal times to cope with a lack of food or money to buy it?
rCSIMealAdult	Column representing question- During the last 7 days, were there days (and, if so, how many) when your household had to restrict consumption by adults in order for small children to eat to cope with a lack of food or money to buy it?
rCSIMealNb	Column representing question - During the last 7 days, were there days (and, if so, how many) when your household had to reduce number of meals eaten in a day to cope with a lack of food or money to buy it?
new_colname	The prefix for the new columns. It has to be a string.

Value

A dataset with one additional column.

Examples

test_data <- data.frame(
  rCSILessQlty = c(1, 2, 3, 1),
  rCSIBorrow = c(0, 0, 3, 0),
  rCSIMealSize = c(4, 2, 6, 1),
  rCSIMealAdult = c(4, 3, 5, 0),
  rCSIMealNb = c(2, 5, NA_integer_, 1)
)
add_eg_rcsi(test_data)

---

Analysis by population group

Description

Survey data

Usage

addindicators_analysis_by_group

Details

These data sets include HH data (raw and clean data) and analysis along with cleaning log.

Examples

addindicators_analysis_by_group
addindicators_raw_data
addindicators_cleaning_log
addindicators_clean_data
addindicators_survey
addindicators_choices
addindicators_overall_analysis

---

Choices tab of kobo tool

Description

Choices tab of kobo tool

Usage

addindicators_choices

---

Clean data

Description

Clean data

Usage

addindicators_clean_data

---

Cleaning log

Description

Cleaning log

Usage

addindicators_cleaning_log

---

Dataset with food consumption, household hunger Score component

Description

Dataset with food consumption, household hunger Score component

Usage

addindicators_food_consumption_df

---

MSNA template dataset (example)

Description

MSNA template dataset (example)

Usage

addindicators_MSNA_template_data

---

Nation/all population level analysis.

Description

Nation/all population level analysis.

Usage

addindicators_overall_analysis

---

Raw data

Description

Raw data

Usage

addindicators_raw_data

---

Survey tab of kobo tool

Description

Survey tab of kobo tool

Usage

addindicators_survey

---

Review 1 column comparing it to another one and spots differences

Description

Review 1 column comparing it to another one and spots differences

Usage

review_one_variable(
  dataset,
  column_to_review,
  column_to_compare_with,
  uuid_column = "uuid",
  prefix = "review",
  return_dataset = FALSE
)

Arguments

dataset	A dataset to be check.
column_to_review	Name of the column to review.
column_to_compare_with	Name of the column to compare with.
uuid_column	uuid column in the dataset. Default is uuid.
prefix	Prefix to be used for the review and comment column. Default is "review".
return_dataset	Logical, if the result table should be returned. Default is "FALSE".

Value

The review table, or the review table added to the results.

Examples

test_numeric <- data.frame(
  test = c(
    "test equality",
    "test difference",
    "test Missing in y",
    "test Missing in x",
    "test equality rounding in x",
    "test equality rounding in y",
    "test difference rounding in x",
    "test difference rounding in y"
  ),
  var_x = c(0, 1, 2, NA, 0.00019, 0.0002, 0.00035, 0.0003),
  var_y = c(0, 2, NA, 3, 0.0002, 0.00019, 0.0003, 0.00035),
  uuid = letters[1:8]
)
review_one_variable(test_numeric,
  column_to_review = "var_x",
  column_to_compare_with = "var_y"
)

---

Review columns comparing it to another set of columns and spots differences

Description

review_variables is a wrapper around review_one_variable

Usage

review_variables(
  dataset,
  columns_to_review,
  columns_to_compare_with,
  uuid_column = "uuid",
  prefix = "review"
)

Arguments

dataset	A dataset to be check.
columns_to_review	Vectors of columns to review (should be paired with columns_to_compare_with).
columns_to_compare_with	Vectors of columns to compare with (should be paired with columns_to_review).
uuid_column	uuid column in the dataset. Default is uuid.
prefix	Prefix to be used for the review and comment column. Default is "review"

Value

A list with two objects: - the result table the review and comment columns - the review table

Examples

test_numeric_2_var <- data.frame(
  test = c(
    "test equality",
    "test difference",
    "test Missing in y",
    "test Missing in x",
    "test equality rounding in x",
    "test equality rounding in y",
    "test difference rounding in x",
    "test difference rounding in y"
  ),
  stat_col_one.x = c(0, 1, 2, NA, 0.00019, 0.0002, 0.00035, 0.0003),
  stat_col_two.x = c(0, 1, 2, NA, 0.00019, 0.0002, 0.00035, 0.0003),
  stat_col_one.y = c(0, 2, NA, 3, 0.0002, 0.00019, 0.0003, 0.00035),
  stat_col_two.y = c(0, 2, NA, 3, 0.0002, 0.00019, 0.0003, 0.00035),
  uuid = letters[1:8]
)

actual_results <- review_variables(test_numeric_2_var,
  columns_to_review = c("stat_col_one.x", "stat_col_two.x"),
  columns_to_compare_with = c("stat_col_one.y", "stat_col_two.y")
)

---