addindicators is designed for creating and checking (occasionally)
composite indicators such as Food Consumption Indicators and others.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("impact-initiatives/addindicators")library(addindicators)
df <- addindicators_MSNA_template_datadf_with_fcs <- df %>% add_eg_fcs(
cutoffs = "normal",
fsl_fcs_cereal = "fs_fcs_cereals_grains_roots_tubers",
fsl_fcs_legumes = "fs_fcs_beans_nuts",
fsl_fcs_veg = "fs_fcs_vegetables_leaves",
fsl_fcs_fruit = "fs_fcs_fruit",
fsl_fcs_meat = "fs_fcs_meat_fish_eggs",
fsl_fcs_dairy = "fs_fcs_dairy",
fsl_fcs_sugar = "fs_fcs_sugar",
fsl_fcs_oil = "fs_fcs_oil_fat_butter"
)
df_with_fcs %>%
dplyr::select(
uuid, fsl_fcs_score, fsl_fcs_cat, fcs_weight_cereal1, fcs_weight_legume2,
fcs_weight_dairy3, fcs_weight_meat4, fcs_weight_veg5,
fcs_weight_fruit6, fcs_weight_oil7, fcs_weight_sugar8
) %>%
head(20)## # A tibble: 20 × 11
## uuid fsl_fcs_score fsl_fcs_cat fcs_weight_cereal1 fcs_weight_legume2
## <chr> <dbl> <chr> <dbl> <dbl>
## 1 eaf540cd-32b… 48.5 Acceptable 0 0
## 2 89e706c3-53d… 50.5 Acceptable 4 6
## 3 afd921c6-e54… 46.5 Acceptable 8 0
## 4 d8b05f39-ba8… 42.5 Acceptable 14 3
## 5 d6b42f9e-c20… 55.5 Acceptable 6 6
## 6 f1b9ec67-20d… 37.5 Acceptable 0 6
## 7 95ea286d-ae8… 50.5 Acceptable 4 15
## 8 85b4a96f-cea… 31.5 Borderline 0 15
## 9 ef13a764-0af… 49.5 Acceptable 14 0
## 10 1a69e87b-ec6… 52.5 Acceptable 8 12
## 11 5613d0fe-34d… 69 Acceptable 12 3
## 12 091aef7d-2b3… 34 Borderline 4 0
## 13 e21a34f5-1a4… 36 Acceptable 0 6
## 14 42dc8573-e2d… 39 Acceptable 14 3
## 15 3a180db5-d12… 47 Acceptable 10 9
## 16 789a632b-53d… 46.5 Acceptable 4 9
## 17 cd41675b-eb4… 61.5 Acceptable 14 18
## 18 f741c29d-b7c… 77 Acceptable 10 12
## 19 2516eba7-789… 76.5 Acceptable 2 15
## 20 c7896215-b36… 20.5 Poor 2 3
## # ℹ 6 more variables: fcs_weight_dairy3 <dbl>, fcs_weight_meat4 <dbl>,
## # fcs_weight_veg5 <dbl>, fcs_weight_fruit6 <dbl>, fcs_weight_oil7 <dbl>,
## # fcs_weight_sugar8 <dbl>
df_with_hhs <- df_with_fcs %>% add_eg_hhs(
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"
)
df_with_hhs %>%
dplyr::select(
uuid, hhs_comp1, hhs_comp2, hhs_comp3,
hhs_score, hhs_cat_ipc, hhs_cat, hh_size
) %>%
head(20)## uuid hhs_comp1 hhs_comp2 hhs_comp3
## 1 eaf540cd-32bd-41474b-b4beb5-d62fc987e45a 0 0 0
## 2 89e706c3-53d8-4a4049-898586-4926085db71e 1 0 0
## 3 afd921c6-e54a-4c4740-919c93-87f59bd0e63a 0 2 0
## 4 d8b05f39-ba85-494c4d-808c84-9dc57823a4f1 0 0 0
## 5 d6b42f9e-c209-4c4541-808a81-86bea53df142 0 0 0
## 6 f1b9ec67-20db-47404d-a3ada0-1a37e5c49d02 1 0 2
## 7 95ea286d-ae86-47404a-828487-feba6d1503c9 0 0 0
## 8 85b4a96f-cea2-4f4b48-9d929f-5d76892f31b0 0 0 0
## 9 ef13a764-0af7-4f494c-838b88-6cb31a50842e 1 0 1
## 10 1a69e87b-ec61-4e4a40-8f868a-fe24c6a705bd 1 1 1
## 11 5613d0fe-34dc-474c43-b4b0bd-36a4c8edf902 0 0 0
## 12 091aef7d-2b31-4f4741-a5a8af-36e8f1bd075a 0 2 0
## 13 e21a34f5-1a46-42404b-b7b6be-7bc9286d0f13 1 0 0
## 14 42dc8573-e2d0-43484b-aaada2-c37ef865d041 0 0 0
## 15 3a180db5-d126-4d4b49-808d88-b3e5c71d908f 1 1 1
## 16 789a632b-53da-4c4f40-a0a1ad-f53ca2e9074b 0 0 0
## 17 cd41675b-eb48-444e4f-b8b7b3-1e493cb02f5a 0 0 0
## 18 f741c29d-b7c5-424a4d-94999c-6018bac9274e 0 0 0
## 19 2516eba7-789c-4c4b41-afa0ad-f0a7365bd81c 2 0 0
## 20 c7896215-b36f-40444c-aaa2af-fa4d37c6502e 1 1 2
## hhs_score hhs_cat_ipc hhs_cat hh_size
## 1 0 None No or Little 19
## 2 1 Little No or Little 21
## 3 2 Moderate Moderate 21
## 4 0 None No or Little 23
## 5 0 None No or Little 22
## 6 3 Moderate Moderate 19
## 7 0 None No or Little 22
## 8 0 None No or Little 17
## 9 2 Moderate Moderate 21
## 10 3 Moderate Moderate 19
## 11 0 None No or Little 16
## 12 2 Moderate Moderate 17
## 13 1 Little No or Little 19
## 14 0 None No or Little 20
## 15 3 Moderate Moderate 19
## 16 0 None No or Little 19
## 17 0 None No or Little 17
## 18 0 None No or Little 22
## 19 2 Moderate Moderate 23
## 20 4 Severe Severe 21
df_with_lcsi <- df_with_hhs %>% add_eg_lcsi(
lcsi_stress_vars = c("liv_stress_lcsi_1", "liv_stress_lcsi_2", "liv_stress_lcsi_3", "liv_stress_lcsi_4"),
lcsi_crisis_vars = c("liv_crisis_lcsi_1", "liv_crisis_lcsi_2", "liv_crisis_lcsi_3"),
lcsi_emergency_vars = c("liv_emerg_lcsi_1", "liv_emerg_lcsi_2", "liv_emerg_lcsi_3"),
yes_val = "yes",
no_val = "no_had_no_need",
exhausted_val = "no_exhausted",
not_applicable_val = "not_applicable"
)
df_with_lcsi %>%
dplyr::select(uuid, lcsi_cat, lcsi_cat_exhaust, lcsi_cat_yes) %>%
head(20)## uuid lcsi_cat lcsi_cat_exhaust
## 1 eaf540cd-32bd-41474b-b4beb5-d62fc987e45a Emergency Emergency
## 2 89e706c3-53d8-4a4049-898586-4926085db71e Emergency Emergency
## 3 afd921c6-e54a-4c4740-919c93-87f59bd0e63a Emergency Emergency
## 4 d8b05f39-ba85-494c4d-808c84-9dc57823a4f1 Emergency Emergency
## 5 d6b42f9e-c209-4c4541-808a81-86bea53df142 Emergency Emergency
## 6 f1b9ec67-20db-47404d-a3ada0-1a37e5c49d02 Emergency Emergency
## 7 95ea286d-ae86-47404a-828487-feba6d1503c9 Emergency Emergency
## 8 85b4a96f-cea2-4f4b48-9d929f-5d76892f31b0 Emergency Crisis
## 9 ef13a764-0af7-4f494c-838b88-6cb31a50842e Emergency Emergency
## 10 1a69e87b-ec61-4e4a40-8f868a-fe24c6a705bd Emergency Emergency
## 11 5613d0fe-34dc-474c43-b4b0bd-36a4c8edf902 Emergency None
## 12 091aef7d-2b31-4f4741-a5a8af-36e8f1bd075a Crisis Crisis
## 13 e21a34f5-1a46-42404b-b7b6be-7bc9286d0f13 Crisis Crisis
## 14 42dc8573-e2d0-43484b-aaada2-c37ef865d041 Emergency Emergency
## 15 3a180db5-d126-4d4b49-808d88-b3e5c71d908f Emergency None
## 16 789a632b-53da-4c4f40-a0a1ad-f53ca2e9074b Emergency Stress
## 17 cd41675b-eb48-444e4f-b8b7b3-1e493cb02f5a Crisis Crisis
## 18 f741c29d-b7c5-424a4d-94999c-6018bac9274e Emergency Emergency
## 19 2516eba7-789c-4c4b41-afa0ad-f0a7365bd81c Emergency Emergency
## 20 c7896215-b36f-40444c-aaa2af-fa4d37c6502e Emergency Crisis
## lcsi_cat_yes
## 1 Emergency
## 2 Emergency
## 3 Crisis
## 4 Crisis
## 5 Emergency
## 6 Stress
## 7 Crisis
## 8 Emergency
## 9 Emergency
## 10 Emergency
## 11 Emergency
## 12 Crisis
## 13 Stress
## 14 Stress
## 15 Emergency
## 16 Emergency
## 17 Crisis
## 18 Crisis
## 19 Emergency
## 20 Emergency
df_with_rcsi <- df_with_lcsi %>% add_eg_rcsi(
rCSILessQlty = "rCSILessQlty",
rCSIBorrow = "rCSIBorrow",
rCSIMealSize = "rCSIMealSize",
rCSIMealAdult = "rCSIMealAdult",
rCSIMealNb = "rCSIMealNb",
new_colname = "rcsi"
)## Variable name for rcsi score is rcsi_score
## Variable name for rcsi category is rcsi_cat
df_with_rcsi %>%
dplyr::select(uuid, rcsi_score, rcsi_cat) %>%
head(20)## uuid rcsi_score rcsi_cat
## 1 eaf540cd-32bd-41474b-b4beb5-d62fc987e45a 34 High
## 2 89e706c3-53d8-4a4049-898586-4926085db71e 37 High
## 3 afd921c6-e54a-4c4740-919c93-87f59bd0e63a 30 High
## 4 d8b05f39-ba85-494c4d-808c84-9dc57823a4f1 29 High
## 5 d6b42f9e-c209-4c4541-808a81-86bea53df142 14 Medium
## 6 f1b9ec67-20db-47404d-a3ada0-1a37e5c49d02 37 High
## 7 95ea286d-ae86-47404a-828487-feba6d1503c9 20 High
## 8 85b4a96f-cea2-4f4b48-9d929f-5d76892f31b0 31 High
## 9 ef13a764-0af7-4f494c-838b88-6cb31a50842e 32 High
## 10 1a69e87b-ec61-4e4a40-8f868a-fe24c6a705bd 17 Medium
## 11 5613d0fe-34dc-474c43-b4b0bd-36a4c8edf902 9 Medium
## 12 091aef7d-2b31-4f4741-a5a8af-36e8f1bd075a 8 Medium
## 13 e21a34f5-1a46-42404b-b7b6be-7bc9286d0f13 35 High
## 14 42dc8573-e2d0-43484b-aaada2-c37ef865d041 17 Medium
## 15 3a180db5-d126-4d4b49-808d88-b3e5c71d908f 40 High
## 16 789a632b-53da-4c4f40-a0a1ad-f53ca2e9074b 31 High
## 17 cd41675b-eb48-444e4f-b8b7b3-1e493cb02f5a 24 High
## 18 f741c29d-b7c5-424a4d-94999c-6018bac9274e 15 Medium
## 19 2516eba7-789c-4c4b41-afa0ad-f0a7365bd81c 34 High
## 20 c7896215-b36f-40444c-aaa2af-fa4d37c6502e 25 High
Notice that these functions are also pipable
df_with_fcm <- df_with_rcsi %>%
add_eg_fcm_phase(
fcs_column_name = "fsl_fcs_cat",
rcsi_column_name = "rcsi_cat",
hhs_column_name = "hhs_cat_ipc",
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"
)
df_with_fcm %>%
dplyr::select(uuid, fc_cell, fc_phase) %>%
head(20)## uuid fc_cell fc_phase
## 1 eaf540cd-32bd-41474b-b4beb5-d62fc987e45a 31 Phase 2 FC
## 2 89e706c3-53d8-4a4049-898586-4926085db71e 32 Phase 2 FC
## 3 afd921c6-e54a-4c4740-919c93-87f59bd0e63a 33 Phase 3 FC
## 4 d8b05f39-ba85-494c4d-808c84-9dc57823a4f1 31 Phase 2 FC
## 5 d6b42f9e-c209-4c4541-808a81-86bea53df142 16 Phase 2 FC
## 6 f1b9ec67-20db-47404d-a3ada0-1a37e5c49d02 33 Phase 3 FC
## 7 95ea286d-ae86-47404a-828487-feba6d1503c9 31 Phase 2 FC
## 8 85b4a96f-cea2-4f4b48-9d929f-5d76892f31b0 36 Phase 2 FC
## 9 ef13a764-0af7-4f494c-838b88-6cb31a50842e 33 Phase 3 FC
## 10 1a69e87b-ec61-4e4a40-8f868a-fe24c6a705bd 18 Phase 2 FC
## 11 5613d0fe-34dc-474c43-b4b0bd-36a4c8edf902 16 Phase 2 FC
## 12 091aef7d-2b31-4f4741-a5a8af-36e8f1bd075a 23 Phase 3 FC
## 13 e21a34f5-1a46-42404b-b7b6be-7bc9286d0f13 32 Phase 2 FC
## 14 42dc8573-e2d0-43484b-aaada2-c37ef865d041 16 Phase 2 FC
## 15 3a180db5-d126-4d4b49-808d88-b3e5c71d908f 33 Phase 3 FC
## 16 789a632b-53da-4c4f40-a0a1ad-f53ca2e9074b 31 Phase 2 FC
## 17 cd41675b-eb48-444e4f-b8b7b3-1e493cb02f5a 31 Phase 2 FC
## 18 f741c29d-b7c5-424a4d-94999c-6018bac9274e 16 Phase 2 FC
## 19 2516eba7-789c-4c4b41-afa0ad-f0a7365bd81c 33 Phase 3 FC
## 20 c7896215-b36f-40444c-aaa2af-fa4d37c6502e 44 Phase 4 FC
Notice that these functions are also pipable
df_with_fclcm <- df_with_fcm %>% ## Taken from previous Example
add_eg_fclcm_phase()
df_with_fclcm %>%
dplyr::select(uuid, fclcm_phase) %>%
head(20)## uuid fclcm_phase
## 1 eaf540cd-32bd-41474b-b4beb5-d62fc987e45a Phase 3 FCLC
## 2 89e706c3-53d8-4a4049-898586-4926085db71e Phase 3 FCLC
## 3 afd921c6-e54a-4c4740-919c93-87f59bd0e63a Phase 4 FCLC
## 4 d8b05f39-ba85-494c4d-808c84-9dc57823a4f1 Phase 3 FCLC
## 5 d6b42f9e-c209-4c4541-808a81-86bea53df142 Phase 3 FCLC
## 6 f1b9ec67-20db-47404d-a3ada0-1a37e5c49d02 Phase 4 FCLC
## 7 95ea286d-ae86-47404a-828487-feba6d1503c9 Phase 3 FCLC
## 8 85b4a96f-cea2-4f4b48-9d929f-5d76892f31b0 Phase 3 FCLC
## 9 ef13a764-0af7-4f494c-838b88-6cb31a50842e Phase 4 FCLC
## 10 1a69e87b-ec61-4e4a40-8f868a-fe24c6a705bd Phase 3 FCLC
## 11 5613d0fe-34dc-474c43-b4b0bd-36a4c8edf902 Phase 3 FCLC
## 12 091aef7d-2b31-4f4741-a5a8af-36e8f1bd075a Phase 3 FCLC
## 13 e21a34f5-1a46-42404b-b7b6be-7bc9286d0f13 Phase 3 FCLC
## 14 42dc8573-e2d0-43484b-aaada2-c37ef865d041 Phase 3 FCLC
## 15 3a180db5-d126-4d4b49-808d88-b3e5c71d908f Phase 4 FCLC
## 16 789a632b-53da-4c4f40-a0a1ad-f53ca2e9074b Phase 3 FCLC
## 17 cd41675b-eb48-444e4f-b8b7b3-1e493cb02f5a Phase 3 FCLC
## 18 f741c29d-b7c5-424a4d-94999c-6018bac9274e Phase 3 FCLC
## 19 2516eba7-789c-4c4b41-afa0ad-f0a7365bd81c Phase 4 FCLC
## 20 c7896215-b36f-40444c-aaa2af-fa4d37c6502e Phase 4 FCLC
The logic behind review_variables is to compare the results from 2 codes to create the composite variable.
In this example, the Food Consumption Score from the first example will be compared.
review_df <- addindicators_MSNA_template_data %>% add_eg_fcs(
cutoffs = "normal",
fsl_fcs_cereal = "fs_fcs_cereals_grains_roots_tubers",
fsl_fcs_legumes = "fs_fcs_beans_nuts",
fsl_fcs_veg = "fs_fcs_vegetables_leaves",
fsl_fcs_fruit = "fs_fcs_fruit",
fsl_fcs_meat = "fs_fcs_meat_fish_eggs",
fsl_fcs_dairy = "fs_fcs_dairy",
fsl_fcs_sugar = "fs_fcs_sugar",
fsl_fcs_oil = "fs_fcs_oil_fat_butter"
)The new results and the results to be reviewed are bound together by the uuid.
binded_df <- df_with_fcs %>%
dplyr::full_join(review_df, by = "uuid")There are 2 functions to review: - review_one_variable, to review only one variable - review_variables, a wrapper around review_one_variable to be able to review several variables.
review_one_variable <- review_one_variable(binded_df,
column_to_review = "fsl_fcs_cat.x",
column_to_compare_with = "fsl_fcs_cat.y"
)
review_one_variable$review_check_fsl_fcs_cat.x %>% mean()## [1] 1
review_one_variable$review_comment_fsl_fcs_cat.x %>% table(useNA = "ifany")## .
## Same results
## 100
review_results <- review_variables(binded_df,
columns_to_review = c("fsl_fcs_score.x", "fsl_fcs_cat.x"),
columns_to_compare_with = c("fsl_fcs_score.y", "fsl_fcs_cat.y")
)
review_results$review_table %>%
dplyr::group_by(variable) %>%
dplyr::summarise(prop_correction = mean(review_check))## # A tibble: 2 × 2
## variable prop_correction
## <chr> <dbl>
## 1 fsl_fcs_cat.x 1
## 2 fsl_fcs_score.x 1
review_results$review_table %>%
dplyr::group_by(variable, review_comment) %>%
dplyr::tally(sort = T)## # A tibble: 2 × 3
## # Groups: variable [2]
## variable review_comment n
## <chr> <glue> <int>
## 1 fsl_fcs_cat.x Same results 100
## 2 fsl_fcs_score.x Same results 100
test_categorical <- data.frame(
test = c(
"test equality",
"test difference",
"test Missing in y",
"test Missing in x",
"test equality missing in both"
),
var_x = c("A", "B", "C", NA, NA),
var_y = c("A", "A", NA, "D", NA),
uuid = letters[1:5]
)
review_one_variable(test_categorical,
column_to_review = "var_x",
column_to_compare_with = "var_y"
)## uuid review_check_var_x review_comment_var_x
## 1 a TRUE Same results
## 2 b FALSE Different results
## 3 c FALSE Missing in var_y
## 4 d FALSE Missing in var_x
## 5 e TRUE Same results
Please note that the addindicators project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.