impactR4PHU is designed for creating quality check reports, cleaning,
analysing and outputing results of core outcome indicators of Public
Health Unit. This package will target mainly Food Security and
Livelihoods, WASH, Nutrition and Health Sectors.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("impact-initiatives/impactR4PHU")library(impactR4PHU)
df <- impactR4PHU_data_templatedf_with_fcs <- df %>% add_fcs(
cutoffs = "normal",
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"
)
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 0cfd1539-4be… NA <NA> NA NA
## 2 0fc8a427-f30… NA <NA> NA NA
## 3 14c3baf8-d4b… 32.5 Borderline 6 6
## 4 1a8de690-60a… 23 Borderline 4 6
## 5 1c92baf4-107… 23.5 Borderline 4 3
## 6 1d7ca542-5eb… 62.5 Acceptable 4 9
## 7 1ecfd059-c21… 29.5 Borderline 14 3
## 8 205d37b1-5a6… 40 Acceptable 4 12
## 9 218f7539-061… 92.5 Acceptable 12 18
## 10 2d56cf0a-a45… NA <NA> NA NA
## 11 3186cfde-19a… 35 Borderline 14 6
## 12 31d0cfb8-21d… 43 Acceptable 12 3
## 13 328e7cd6-651… 12.5 Poor 4 6
## 14 36584aec-f27… 23 Borderline 6 6
## 15 37b5a861-0f2… 40 Acceptable 4 3
## 16 38b615cf-0fd… 77 Acceptable 4 21
## 17 3aef5849-5ca… 29.5 Borderline 14 3
## 18 3b6948fe-340… 53 Acceptable 14 12
## 19 3c1704f5-247… 33.5 Borderline 14 3
## 20 3e02914b-eb2… 29 Borderline 14 6
## # 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_hhs(
fsl_hhs_nofoodhh = "fsl_hhs_nofoodhh",
fsl_hhs_nofoodhh_freq = "fsl_hhs_nofoodhh_freq",
fsl_hhs_sleephungry = "fsl_hhs_sleephungry",
fsl_hhs_sleephungry_freq = "fsl_hhs_sleephungry_freq",
fsl_hhs_alldaynight = "fsl_hhs_alldaynight",
fsl_hhs_alldaynight_freq = "fsl_hhs_alldaynight_freq",
yes_answer = "yes",
no_answer = "no",
rarely_answer = "rarely",
sometimes_answer = "sometimes",
often_answer = "often"
)
df_with_hhs %>%
dplyr::select(
uuid, fsl_hhs_comp1, fsl_hhs_comp2, fsl_hhs_comp3,
fsl_hhs_score, fsl_hhs_cat_ipc, fsl_hhs_cat, num_hh
) %>%
head(20)## uuid fsl_hhs_comp1 fsl_hhs_comp2
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 NA NA
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc NA NA
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e 0 0
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 0 0
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c 0 0
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 0 0
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c 0 0
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 0 0
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 0 0
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 NA NA
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb 0 0
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 2 2
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 1 1
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 0 0
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b 0 0
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce 0 0
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 1 1
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 0 0
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 1 1
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 0 0
## fsl_hhs_comp3 fsl_hhs_score fsl_hhs_cat_ipc fsl_hhs_cat num_hh
## 1 NA NA <NA> <NA> <NA>
## 2 NA NA <NA> <NA> <NA>
## 3 0 0 None No or Little 3
## 4 0 0 None No or Little 3
## 5 0 0 None No or Little 4
## 6 0 0 None No or Little 4
## 7 0 0 None No or Little 4
## 8 0 0 None No or Little 4
## 9 1 1 No or Little No or Little 4
## 10 NA NA <NA> <NA> <NA>
## 11 0 0 None No or Little 4
## 12 2 6 Very Severe Severe 4
## 13 1 3 Moderate Moderate 4
## 14 0 0 None No or Little 4
## 15 0 0 None No or Little 3
## 16 0 0 None No or Little 4
## 17 0 2 Moderate Moderate 3
## 18 0 0 None No or Little 4
## 19 1 3 Moderate Moderate 4
## 20 0 0 None No or Little 3
df_with_lcsi <- df_with_hhs %>% add_lcsi(
fsl_lcsi_stress1 = "fsl_lcsi_stress1",
fsl_lcsi_stress2 = "fsl_lcsi_stress2",
fsl_lcsi_stress3 = "fsl_lcsi_stress3",
fsl_lcsi_stress4 = "fsl_lcsi_stress4",
fsl_lcsi_crisis1 = "fsl_lcsi_crisis1",
fsl_lcsi_crisis2 = "fsl_lcsi_crisis2",
fsl_lcsi_crisis3 = "fsl_lcsi_crisis3",
fsl_lcsi_emergency1 = "fsl_lcsi_emergency1",
fsl_lcsi_emergency2 = "fsl_lcsi_emergency2",
fsl_lcsi_emergency3 = "fsl_lcsi_emergency3",
yes_val = "yes",
no_val = "no_had_no_need",
exhausted_val = "no_exhausted",
not_applicable_val = "not_applicable"
)
df_with_lcsi %>%
dplyr::select(uuid, fsl_lcsi_cat, fsl_lcsi_cat_exhaust, fsl_lcsi_cat_yes) %>%
head(20)## uuid fsl_lcsi_cat fsl_lcsi_cat_exhaust
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 <NA> <NA>
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc <NA> <NA>
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e Stress None
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 None None
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c Stress None
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 Emergency None
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c Stress Stress
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 Stress None
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 None None
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 <NA> <NA>
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb Emergency None
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 Emergency Crisis
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 None None
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 None None
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b Stress None
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce None None
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 Stress None
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 None None
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 Stress Stress
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 Stress Stress
## fsl_lcsi_cat_yes
## 1 <NA>
## 2 <NA>
## 3 Stress
## 4 None
## 5 Stress
## 6 Emergency
## 7 None
## 8 Stress
## 9 None
## 10 <NA>
## 11 Emergency
## 12 Emergency
## 13 None
## 14 None
## 15 Stress
## 16 None
## 17 Stress
## 18 None
## 19 Stress
## 20 None
df_with_rcsi <- df_with_lcsi %>% add_rcsi(
fsl_rcsi_lessquality = "fsl_rcsi_lessquality",
fsl_rcsi_borrow = "fsl_rcsi_borrow",
fsl_rcsi_mealsize = "fsl_rcsi_mealsize",
fsl_rcsi_mealadult = "fsl_rcsi_mealadult",
fsl_rcsi_mealnb = "fsl_rcsi_mealnb"
)
df_with_rcsi %>%
dplyr::select(uuid, fsl_rcsi_score, fsl_rcsi_cat) %>%
head(20)## uuid fsl_rcsi_score fsl_rcsi_cat
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 NA <NA>
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc NA <NA>
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e 9 Medium
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 NA <NA>
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c 7 Medium
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 7 Medium
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c 6 Medium
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 11 Medium
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 6 Medium
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 NA <NA>
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb 5 Medium
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 34 High
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 16 Medium
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 5 Medium
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b 13 Medium
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce NA <NA>
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 12 Medium
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 NA <NA>
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 8 Medium
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 5 Medium
df_with_hdds <- df_with_rcsi %>% add_hdds(
fsl_hdds_cereals = "fsl_hdds_cereals",
fsl_hdds_tubers = "fsl_hdds_tubers",
fsl_hdds_veg = "fsl_hdds_veg",
fsl_hdds_fruit = "fsl_hdds_fruit",
fsl_hdds_meat = "fsl_hdds_meat",
fsl_hdds_eggs = "fsl_hdds_eggs",
fsl_hdds_fish = "fsl_hdds_fish",
fsl_hdds_legumes = "fsl_hdds_legumes",
fsl_hdds_dairy = "fsl_hdds_dairy",
fsl_hdds_oil = "fsl_hdds_oil",
fsl_hdds_sugar = "fsl_hdds_sugar",
fsl_hdds_condiments = "fsl_hdds_condiments"
)
df_with_hdds %>%
dplyr::select(uuid, fsl_hdds_score, fsl_hdds_cat) %>%
head(20)## uuid fsl_hdds_score fsl_hdds_cat
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 NA <NA>
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc NA <NA>
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e 6 High
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 5 High
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c 7 High
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 6 High
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c 5 High
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 7 High
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 8 High
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 NA <NA>
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb 8 High
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 4 Medium
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 7 High
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 6 High
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b 7 High
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce 4 Medium
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 6 High
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 6 High
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 9 High
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 3 Medium
Notice that these functions are also pipable
df_with_fcm_1 <- df_with_hdds %>%
add_fcm_phase(
fcs_column_name = "fsl_fcs_cat",
rcsi_column_name = "fsl_rcsi_cat",
hhs_column_name = "fsl_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 = "No or Little",
hhs_categories_moderate = "Moderate",
hhs_categories_severe = "Severe",
hhs_categories_very_severe = "Very Severe"
)
df_with_fcm_1 %>%
dplyr::select(uuid, fc_cell, fc_phase) %>%
head(20)## uuid fc_cell fc_phase
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 NA <NA>
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc NA <NA>
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e 21 Phase 2 FC
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 NA <NA>
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c 21 Phase 2 FC
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 16 Phase 2 FC
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c 21 Phase 2 FC
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 16 Phase 2 FC
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 17 Phase 2 FC
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 NA <NA>
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb 21 Phase 2 FC
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 35 Phase 4 FC
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 28 Phase 3 FC
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 21 Phase 2 FC
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b 16 Phase 2 FC
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce NA <NA>
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 23 Phase 3 FC
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 NA <NA>
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 23 Phase 3 FC
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 21 Phase 2 FC
Notice that these functions are also pipable
df_with_fcm_2 <- df_with_hdds %>%
add_fcm_phase(
hdds_column_name = "fsl_hdds_cat",
rcsi_column_name = "fsl_rcsi_cat",
hhs_column_name = "fsl_hhs_cat_ipc",
hdds_categories_low = "Low",
hdds_categories_medium = "Medium",
hdds_categories_high = "High",
rcsi_categories_low = "No to Low",
rcsi_categories_medium = "Medium",
rcsi_categories_high = "High",
hhs_categories_none = "None",
hhs_categories_little = "No or Little",
hhs_categories_moderate = "Moderate",
hhs_categories_severe = "Severe",
hhs_categories_very_severe = "Very Severe"
)
df_with_fcm_2 %>%
dplyr::select(uuid, fc_cell, fc_phase) %>%
head(20)## uuid fc_cell fc_phase
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 NA <NA>
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc NA <NA>
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e 21 Phase 2 FC
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 NA <NA>
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c 21 Phase 2 FC
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 16 Phase 2 FC
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c 21 Phase 2 FC
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 16 Phase 2 FC
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 17 Phase 2 FC
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 NA <NA>
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb 21 Phase 2 FC
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 35 Phase 4 FC
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 28 Phase 3 FC
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 21 Phase 2 FC
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b 16 Phase 2 FC
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce NA <NA>
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 23 Phase 3 FC
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 NA <NA>
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 23 Phase 3 FC
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 21 Phase 2 FC
Notice that these functions are also pipable
df_with_fcm_3 <- df_with_hdds %>%
add_fcm_phase(
fcs_column_name = "fsl_fcs_cat",
hhs_column_name = "fsl_hhs_cat_ipc",
fcs_categories_acceptable = "Acceptable",
fcs_categories_poor = "Poor",
fcs_categories_borderline = "Borderline",
hhs_categories_none = "None",
hhs_categories_little = "No or Little",
hhs_categories_moderate = "Moderate",
hhs_categories_severe = "Severe",
hhs_categories_very_severe = "Very Severe"
)
df_with_fcm_3 %>%
dplyr::select(uuid, fc_cell, fc_phase) %>%
head(20)## uuid fc_cell fc_phase
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 NA <NA>
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc NA <NA>
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e 21 Phase 2 FC
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 NA <NA>
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c 21 Phase 2 FC
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 16 Phase 2 FC
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c 21 Phase 2 FC
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 16 Phase 2 FC
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 17 Phase 2 FC
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 NA <NA>
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb 21 Phase 2 FC
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 35 Phase 4 FC
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 28 Phase 3 FC
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 21 Phase 2 FC
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b 16 Phase 2 FC
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce NA <NA>
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 23 Phase 3 FC
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 NA <NA>
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 23 Phase 3 FC
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 21 Phase 2 FC
Notice that these functions are also pipable
df_with_fcm_4 <- df_with_hdds %>%
add_fcm_phase(
hdds_column_name = "fsl_hdds_cat",
hhs_column_name = "fsl_hhs_cat_ipc",
hdds_categories_low = "Low",
hdds_categories_medium = "Medium",
hdds_categories_high = "High",
hhs_categories_none = "None",
hhs_categories_little = "No or Little",
hhs_categories_moderate = "Moderate",
hhs_categories_severe = "Severe",
hhs_categories_very_severe = "Very Severe"
)
df_with_fcm_4 %>%
dplyr::select(uuid, fc_cell, fc_phase) %>%
head(20)## uuid fc_cell fc_phase
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 NA <NA>
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc NA <NA>
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e 21 Phase 2 FC
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 NA <NA>
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c 21 Phase 2 FC
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 16 Phase 2 FC
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c 21 Phase 2 FC
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 16 Phase 2 FC
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 17 Phase 2 FC
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 NA <NA>
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb 21 Phase 2 FC
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 35 Phase 4 FC
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 28 Phase 3 FC
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 21 Phase 2 FC
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b 16 Phase 2 FC
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce NA <NA>
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 23 Phase 3 FC
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 NA <NA>
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 23 Phase 3 FC
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 21 Phase 2 FC
Notice that these functions are also pipable
df_with_fclcm <- df_with_fcm_1 %>% ## Taken from previous Example
add_fclcm_phase()
df_with_fclcm %>%
dplyr::select(uuid, fclcm_phase) %>%
head(20)## uuid fclcm_phase
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 <NA>
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc <NA>
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e Phase 2 FCLC
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 <NA>
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c Phase 2 FCLC
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 Phase 3 FCLC
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c Phase 2 FCLC
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 Phase 2 FCLC
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 Phase 2 FCLC
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 <NA>
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb Phase 3 FCLC
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 Phase 4 FCLC
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 Phase 3 FCLC
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 Phase 2 FCLC
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b Phase 2 FCLC
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce <NA>
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 Phase 3 FCLC
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 <NA>
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 Phase 3 FCLC
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 Phase 2 FCLC
df_nut <- impactR4PHU_data_nut_templatedf_with_muac <- df_nut %>%
add_muac()
df_with_muac %>%
dplyr::select(
uuid, sam_muac, mam_muac, gam_muac) %>%
head(20)## # A tibble: 20 × 4
## uuid sam_muac mam_muac gam_muac
## <chr> <dbl> <dbl> <dbl>
## 1 7b4261fa-61a5-4a4948-999093-13bc7e9f0658 0 1 1
## 2 83c0a56b-15fd-4f4349-b2bcbd-806912fb3c5d 0 0 0
## 3 6401c279-8a6f-464b4d-919598-da125739e64c 0 0 0
## 4 1ecfd059-c215-4d4746-94999b-87920feb4a6c 0 0 0
## 5 4b038c2e-25a6-484641-aca6a7-cf387e4b29d1 0 0 0
## 6 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 0 0 0
## 7 512bce03-78ea-404742-8e8d83-e53a8296c0d4 0 0 0
## 8 1a8de690-60af-45494a-8b8487-78f45ec16b39 0 0 0
## 9 53a2e761-34cb-434c46-b3bdbc-b0fc1295673d 0 0 0
## 10 4d5b1089-1aec-424f49-aba5a9-b3ade80461fc 0 0 0
## 11 ef2963c7-ef67-4e4446-bab5b7-7e9d0431fa8c 0 0 0
## 12 98fdb3a2-2c1a-4f424b-8d8782-b21d683ea94f 0 0 0
## 13 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 0 0 0
## 14 a725301d-21b7-444c42-919f95-2f769503b184 0 0 0
## 15 4b038c2e-25a6-484641-aca6a7-cf387e4b29d1 0 0 0
## 16 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 0 0 0
## 17 3c1704f5-2473-474e4f-808982-f9830c51d7b2 NA NA NA
## 18 ef0d36a5-493b-444048-bbbab9-bf719e4850a6 0 0 0
## 19 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 NA NA NA
## 20 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 NA NA NA
df_with_mfaz <- df_with_muac %>%
add_mfaz()## ================================================================================
df_with_mfaz %>%
dplyr::select(
uuid, mfaz, severe_mfaz, moderate_mfaz, global_mfaz) %>%
head(20)## # A tibble: 20 × 5
## uuid mfaz severe_mfaz moderate_mfaz global_mfaz
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 7b4261fa-61a5-4a4948-999093-13bc… -3.4 1 0 1
## 2 83c0a56b-15fd-4f4349-b2bcbd-8069… -1.48 0 0 0
## 3 6401c279-8a6f-464b4d-919598-da12… -2.33 0 1 1
## 4 1ecfd059-c215-4d4746-94999b-8792… -2.38 0 1 1
## 5 4b038c2e-25a6-484641-aca6a7-cf38… -1.3 0 0 0
## 6 3b6948fe-3409-4f4143-b3bab2-8630… -0.62 0 0 0
## 7 512bce03-78ea-404742-8e8d83-e53a… -0.8 0 0 0
## 8 1a8de690-60af-45494a-8b8487-78f4… -1.88 0 0 0
## 9 53a2e761-34cb-434c46-b3bdbc-b0fc… -0.43 0 0 0
## 10 4d5b1089-1aec-424f49-aba5a9-b3ad… 0.42 0 0 0
## 11 ef2963c7-ef67-4e4446-bab5b7-7e9d… -0.57 0 0 0
## 12 98fdb3a2-2c1a-4f424b-8d8782-b21d… 0.51 0 0 0
## 13 1d7ca542-5ebf-434e44-949e9a-d368… -0.73 0 0 0
## 14 a725301d-21b7-444c42-919f95-2f76… 0.4 0 0 0
## 15 4b038c2e-25a6-484641-aca6a7-cf38… 1.05 0 0 0
## 16 1d7ca542-5ebf-434e44-949e9a-d368… 1.63 0 0 0
## 17 3c1704f5-2473-474e4f-808982-f983… NA NA NA NA
## 18 ef0d36a5-493b-444048-bbbab9-bf71… 1.04 0 0 0
## 19 31d0cfb8-21d7-414b4f-94999f-04a1… NA NA NA NA
## 20 31d0cfb8-21d7-414b4f-94999f-04a1… NA NA NA NA
df_iycf <- impactR4PHU_iycf_template_data
df_with_iycf <- df_iycf %>%
add_iycf(uuid = "_submission__uuid",
age_months = "child_age_months_2")
df_with_iycf %>%
dplyr::select(
uuid, age_months, starts_with("iycf_")) %>%
head(20)## # A tibble: 20 × 67
## uuid age_months iycf_1 iycf_2 iycf_3 iycf_4 iycf_5 iycf_6a iycf_6b iycf_6c
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 0f1346… 11 yes first… no yes yes 1 0 1
## 2 0f1346… 11 yes first… no no no 1 0 0
## 3 bae8a3… 17 yes first… no yes no 1 0 0
## 4 8561a2… 9 yes first… no yes no 1 0 1
## 5 8561a2… 9 yes first… no yes no 1 0 1
## 6 b69d05… 12 no <NA> <NA> <NA> yes 1 0 1
## 7 1e0496… 19 no <NA> <NA> <NA> no 1 0 1
## 8 373977… 14 yes immed… no yes no 1 0 1
## 9 373977… 16 yes immed… no yes no 1 0 1
## 10 4b806c… 20 no <NA> <NA> <NA> no 1 0 1
## 11 4b806c… 12 yes immed… no yes no 1 0 1
## 12 5cce47… 12 yes immed… no yes yes 1 1 1
## 13 78cde5… 20 yes immed… yes yes no 1 1 1
## 14 d929f0… 11 yes immed… no no no 1 1 1
## 15 987389… 14 yes immed… yes yes no 1 1 0
## 16 0fa5d9… 11 yes immed… yes yes no 0 0 0
## 17 18ef33… 10 yes immed… yes yes yes 1 1 1
## 18 5a91d4… 7 yes immed… yes yes yes 1 0 0
## 19 e4a46d… 17 no <NA> <NA> <NA> no 1 0 1
## 20 28b24b… 21 no <NA> <NA> <NA> no 1 0 1
## # ℹ 57 more variables: iycf_6d <dbl>, iycf_6e <dbl>, iycf_6f <dbl>,
## # iycf_6g <dbl>, iycf_6h <dbl>, iycf_6i <dbl>, iycf_6j <dbl>,
## # iycf_6j_swt <lgl>, iycf_6c_swt <chr>, iycf_6d_swt <chr>, iycf_6h_swt <chr>,
## # iycf_7a <dbl>, iycf_7b <dbl>, iycf_7c <dbl>, iycf_7d <dbl>, iycf_7e <dbl>,
## # iycf_7f <dbl>, iycf_7g <dbl>, iycf_7h <dbl>, iycf_7i <dbl>, iycf_7j <dbl>,
## # iycf_7k <dbl>, iycf_7l <dbl>, iycf_7m <dbl>, iycf_7n <dbl>, iycf_7o <dbl>,
## # iycf_7p <dbl>, iycf_7q <dbl>, iycf_7r <dbl>, iycf_cf_check <lgl>, …
tool <- impactR4PHU_survey_templatefsl_flags <- df_with_fclcm %>%
check_fsl_flags(tool.survey = tool,
grouping = "enumerator")
fsl_flags %>%
dplyr::select(uuid, group,starts_with("flag_")) %>%
head(20)## uuid group flag_meat_cereal_ratio
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 1 NA
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc 5 NA
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e 2 0
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 2 0
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c 2 0
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 5 1
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c 2 0
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 2 0
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 2 1
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 3 NA
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb 4 0
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 4 0
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 3 0
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 3 0
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b 4 1
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce 2 1
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 4 0
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 5 0
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 2 0
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 5 0
## flag_low_cereal flag_low_oil flag_low_fcs flag_high_fcs flag_sd_foodgroup
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 1 1 0 0 1
## 4 1 1 0 0 1
## 5 1 1 0 0 1
## 6 1 0 0 1 NA
## 7 0 0 0 0 NA
## 8 1 1 0 0 NA
## 9 0 1 0 1 NA
## 10 NA NA NA NA NA
## 11 0 0 0 0 NA
## 12 0 1 0 0 NA
## 13 1 1 0 0 NA
## 14 1 0 0 0 NA
## 15 1 0 0 0 NA
## 16 1 1 0 1 NA
## 17 0 0 0 0 NA
## 18 0 0 0 0 NA
## 19 0 0 0 0 NA
## 20 0 0 0 0 NA
## flag_protein_rcsi flag_fcs_rcsi flag_high_rcsi flag_rcsi_children
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 0 0 0 NA
## 4 NA NA NA NA
## 5 0 0 0 NA
## 6 0 0 0 NA
## 7 0 0 0 NA
## 8 0 0 0 NA
## 9 0 0 0 NA
## 10 NA NA NA NA
## 11 0 0 0 NA
## 12 0 0 0 NA
## 13 0 0 0 NA
## 14 0 0 0 NA
## 15 0 0 0 NA
## 16 NA NA NA NA
## 17 0 0 0 NA
## 18 NA NA NA NA
## 19 0 0 0 NA
## 20 0 0 0 NA
## flag_fcsrcsi_box flag_sd_rcsicoping flag_severe_hhs flag_lcsi_coherence
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA 0 0
## 4 NA NA 0 0
## 5 NA NA 0 0
## 6 NA NA 0 0
## 7 NA NA 0 0
## 8 NA NA 0 0
## 9 NA NA 0 0
## 10 NA NA NA NA
## 11 NA NA 0 1
## 12 NA NA 1 0
## 13 NA NA 0 0
## 14 NA NA 0 0
## 15 NA NA 0 0
## 16 NA NA 0 0
## 17 NA NA 0 0
## 18 NA NA 0 0
## 19 NA NA 0 0
## 20 NA NA 0 0
## flag_lcsi_severity flag_lcsi_na flag_lcsi_liv_livestock
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 1 NA NA
## 7 NA NA NA
## 8 NA NA 1
## 9 NA NA NA
## 10 NA NA NA
## 11 1 NA NA
## 12 1 NA NA
## 13 NA NA NA
## 14 NA NA NA
## 15 NA NA NA
## 16 NA NA NA
## 17 NA NA NA
## 18 NA NA NA
## 19 NA NA NA
## 20 NA NA NA
## flag_lcsi_liv_agriculture flag_lcsi_displ flag_fc_cell
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA 0
## 4 NA NA NA
## 5 NA NA 0
## 6 NA NA 0
## 7 NA NA 0
## 8 NA NA 0
## 9 NA NA 0
## 10 NA NA NA
## 11 1 NA 0
## 12 NA NA 0
## 13 NA NA 0
## 14 NA NA 0
## 15 NA NA 0
## 16 NA NA NA
## 17 NA NA 0
## 18 NA NA NA
## 19 NA NA 0
## 20 NA NA 0
## flag_low_sugar_cond_hdds
## 1 NA
## 2 NA
## 3 0
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 NA
## 11 0
## 12 0
## 13 0
## 14 0
## 15 0
## 16 0
## 17 0
## 18 0
## 19 0
## 20 0
anthro_flags <- df_with_mfaz %>%
check_anthro_flags(loop_index = "loop_index")
anthro_flags %>%
dplyr::select(uuid, group,starts_with("flag_"), ends_with("noflag")) %>%
head(20)## uuid group flag_sd_mfaz
## 1 7b4261fa-61a5-4a4948-999093-13bc7e9f0658 All 0
## 2 83c0a56b-15fd-4f4349-b2bcbd-806912fb3c5d All 0
## 3 6401c279-8a6f-464b4d-919598-da125739e64c All 0
## 4 1ecfd059-c215-4d4746-94999b-87920feb4a6c All 0
## 5 4b038c2e-25a6-484641-aca6a7-cf387e4b29d1 All 0
## 6 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 All 0
## 7 512bce03-78ea-404742-8e8d83-e53a8296c0d4 All 0
## 8 1a8de690-60af-45494a-8b8487-78f45ec16b39 All 0
## 9 53a2e761-34cb-434c46-b3bdbc-b0fc1295673d All 0
## 10 4d5b1089-1aec-424f49-aba5a9-b3ade80461fc All 0
## 11 ef2963c7-ef67-4e4446-bab5b7-7e9d0431fa8c All 0
## 12 98fdb3a2-2c1a-4f424b-8d8782-b21d683ea94f All 0
## 13 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 All 0
## 14 a725301d-21b7-444c42-919f95-2f769503b184 All 0
## 15 4b038c2e-25a6-484641-aca6a7-cf387e4b29d1 All 0
## 16 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 All 0
## 17 3c1704f5-2473-474e4f-808982-f9830c51d7b2 All NA
## 18 ef0d36a5-493b-444048-bbbab9-bf719e4850a6 All 0
## 19 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 All NA
## 20 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 All NA
## flag_extreme_muac flag_edema_pitting mfaz_noflag mean_mfaz_noflag
## 1 0 NA -3.40 -0.639
## 2 0 NA -1.48 -0.639
## 3 0 0 -2.33 -0.639
## 4 0 NA -2.38 -0.639
## 5 0 0 -1.30 -0.639
## 6 0 NA -0.62 -0.639
## 7 0 NA -0.80 -0.639
## 8 0 NA -1.88 -0.639
## 9 0 0 -0.43 -0.639
## 10 0 0 0.42 -0.639
## 11 0 NA -0.57 -0.639
## 12 0 0 0.51 -0.639
## 13 0 0 -0.73 -0.639
## 14 0 0 0.40 -0.639
## 15 0 NA 1.05 -0.639
## 16 0 0 1.63 -0.639
## 17 NA 0 NA -0.639
## 18 0 0 1.04 -0.639
## 19 NA NA NA -0.639
## 20 NA NA NA -0.639
## sd_mfaz_noflag global_mfaz_noflag moderate_mfaz_noflag severe_mfaz_noflag
## 1 1.38 1 0 1
## 2 1.38 0 0 0
## 3 1.38 1 1 0
## 4 1.38 1 1 0
## 5 1.38 0 0 0
## 6 1.38 0 0 0
## 7 1.38 0 0 0
## 8 1.38 0 0 0
## 9 1.38 0 0 0
## 10 1.38 0 0 0
## 11 1.38 0 0 0
## 12 1.38 0 0 0
## 13 1.38 0 0 0
## 14 1.38 0 0 0
## 15 1.38 0 0 0
## 16 1.38 0 0 0
## 17 1.38 NA NA NA
## 18 1.38 0 0 0
## 19 1.38 NA NA NA
## 20 1.38 NA NA NA
## gam_muac_noflag mam_muac_noflag sam_muac_noflag muac_noflag
## 1 1 1 0 12.1
## 2 0 0 0 12.5
## 3 0 0 0 12.8
## 4 0 0 0 13.2
## 5 0 0 0 13.7
## 6 0 0 0 13.7
## 7 0 0 0 13.8
## 8 0 0 0 14.1
## 9 0 0 0 14.4
## 10 0 0 0 14.7
## 11 0 0 0 14.7
## 12 0 0 0 14.8
## 13 0 0 0 15.0
## 14 0 0 0 15.4
## 15 0 0 0 15.4
## 16 0 0 0 15.8
## 17 NA NA NA NA
## 18 0 0 0 17.8
## 19 NA NA NA NA
## 20 NA NA NA NA
container_df <- impactR4PHU_data_wash_template
wash_flags <- df %>%
check_wash_flags(data_container_loop = container_df,
grouping = "enumerator")## Joining with `by = join_by(uuid)`
wash_flags %>%
dplyr::select(uuid, group,starts_with("flag_")) %>%
head(20)## uuid group flag_sd_litre flag_low_litre
## 1 0cfd1539-4be3-4c444a-8a8d8e-0d2a6bf74895 1 NA NA
## 2 0fc8a427-f30e-4a4341-b3b5b4-08a6392ef4dc 5 NA NA
## 3 14c3baf8-d4b0-43484c-8d8e8f-a5fd7134982e 2 NA NA
## 4 1a8de690-60af-45494a-8b8487-78f45ec16b39 2 NA NA
## 5 1c92baf4-107e-474c46-a3a8a5-6b2e815ad30c 2 0 0
## 6 1d7ca542-5ebf-434e44-949e9a-d3687ef9c145 5 NA NA
## 7 1ecfd059-c215-4d4746-94999b-87920feb4a6c 2 NA NA
## 8 205d37b1-5a6f-44484d-b3b1ba-4eafbdc50873 2 NA NA
## 9 218f7539-061b-404f44-96989f-b345c89a6e21 2 NA NA
## 10 2d56cf0a-a45c-444148-898e84-ab7f4de18259 3 NA NA
## 11 3186cfde-19a7-434748-bbb7b1-e369754821cb 4 NA NA
## 12 31d0cfb8-21d7-414b4f-94999f-04a15ce39d78 4 NA NA
## 13 328e7cd6-6517-4f4044-8f8c86-c710a84e5639 3 NA NA
## 14 36584aec-f271-47484b-999391-417e2a3d6b59 3 NA NA
## 15 37b5a861-0f21-4e4942-909295-34826ecd950b 4 NA NA
## 16 38b615cf-0fd3-4f4d4e-bfbab1-a07658b413ce 2 NA NA
## 17 3aef5849-5ca7-4c4841-8a8584-e64b1a8d0c92 4 NA NA
## 18 3b6948fe-3409-4f4143-b3bab2-86301b529fc7 5 NA NA
## 19 3c1704f5-2473-474e4f-808982-f9830c51d7b2 2 NA NA
## 20 3e02914b-eb25-484243-909498-dcfa793514b2 5 NA NA
## flag_high_litre flag_high_container flag_no_container
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA 0
## 4 NA NA 0
## 5 0 0 0
## 6 NA 0 0
## 7 NA NA 0
## 8 NA 0 0
## 9 NA 0 0
## 10 NA NA NA
## 11 NA NA 0
## 12 NA 0 0
## 13 NA 0 0
## 14 NA 0 0
## 15 NA NA 0
## 16 NA NA 0
## 17 NA NA 0
## 18 NA NA 0
## 19 NA NA 0
## 20 NA 0 0
msna_data <- impactR4PHU_MSNA_template_data
health_flags <- check_health_flags(
.dataset = msna_data
)
health_flags %>%
dplyr::select(uuid, group, starts_with("flag_")) %>%
head(20)## uuid group flag_severe_health_exp
## 1 eaf540cd-32bd-41474b-b4beb5-d62fc987e45a All 0
## 2 89e706c3-53d8-4a4049-898586-4926085db71e All 0
## 3 afd921c6-e54a-4c4740-919c93-87f59bd0e63a All 0
## 4 d8b05f39-ba85-494c4d-808c84-9dc57823a4f1 All 0
## 5 d6b42f9e-c209-4c4541-808a81-86bea53df142 All 0
## 6 f1b9ec67-20db-47404d-a3ada0-1a37e5c49d02 All 0
## 7 95ea286d-ae86-47404a-828487-feba6d1503c9 All 0
## 8 85b4a96f-cea2-4f4b48-9d929f-5d76892f31b0 All 0
## 9 ef13a764-0af7-4f494c-838b88-6cb31a50842e All 0
## 10 1a69e87b-ec61-4e4a40-8f868a-fe24c6a705bd All 0
## 11 5613d0fe-34dc-474c43-b4b0bd-36a4c8edf902 All 0
## 12 091aef7d-2b31-4f4741-a5a8af-36e8f1bd075a All 0
## 13 e21a34f5-1a46-42404b-b7b6be-7bc9286d0f13 All 0
## 14 42dc8573-e2d0-43484b-aaada2-c37ef865d041 All 0
## 15 3a180db5-d126-4d4b49-808d88-b3e5c71d908f All 0
## 16 789a632b-53da-4c4f40-a0a1ad-f53ca2e9074b All 0
## 17 cd41675b-eb48-444e4f-b8b7b3-1e493cb02f5a All 0
## 18 f741c29d-b7c5-424a4d-94999c-6018bac9274e All 0
## 19 2516eba7-789c-4c4b41-afa0ad-f0a7365bd81c All 0
## 20 c7896215-b36f-40444c-aaa2af-fa4d37c6502e All 0
## flag_catastrophic_health_exp
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 0
## 13 0
## 14 0
## 15 0
## 16 0
## 17 0
## 18 0
## 19 0
## 20 0
iycf_flags <- check_iycf_flags(
.dataset = df_with_iycf
)
iycf_flags %>%
dplyr::select(uuid, age_months, group, starts_with("flag_")) %>%
head(20)## # A tibble: 20 × 12
## uuid age_months group flag_yes_foods flag_yes_liquids flag_no_anything
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 0f1346c8-6… 11 All 0 0 0
## 2 0f1346c8-6… 11 All 0 0 0
## 3 bae8a3a7-d… 17 All 0 0 0
## 4 8561a27d-e… 9 All 0 0 0
## 5 8561a27d-e… 9 All 0 0 0
## 6 b69d0571-d… 12 All 0 0 NA
## 7 1e049682-6… 19 All 0 0 NA
## 8 3739779b-f… 14 All 0 0 0
## 9 3739779b-f… 16 All 0 0 0
## 10 4b806c8c-4… 20 All 0 0 NA
## 11 4b806c8c-4… 12 All 0 0 0
## 12 5cce4725-4… 12 All 0 0 0
## 13 78cde5ca-7… 20 All 0 0 0
## 14 d929f004-a… 11 All 0 0 0
## 15 98738911-e… 14 All 0 0 0
## 16 0fa5d936-e… 11 All 0 0 0
## 17 18ef3371-6… 10 All 0 0 0
## 18 5a91d4d2-d… 7 All 0 0 0
## 19 e4a46dc1-d… 17 All 0 0 NA
## 20 28b24ba6-0… 21 All 0 0 NA
## # ℹ 6 more variables: flag_no_foods <dbl>, flag_all_foods_no_meal <dbl>,
## # flag_some_foods_no_meal <dbl>, flag_high_mdd_low_mmf <dbl>,
## # flag_under6_nobf_nomilk <dbl>, flag_meats_nostaples <dbl>
Please note that the impactR4PHU project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.