The output is a data frame with 5 new columns. First the MSNI-related variables: msni_score, msni_in_need, and msni_in_acute_need. The two latter are used for metrics 1 and 2. Second, the number of sectoral needs sector_in_need_n and the sectoral needs profile sector_needs_profile. These two are used for metric 3 and 4. sector needs profile is NA if no sectoral need is identified.
Usage
add_msni(
df,
comp_foodsec_score = "comp_foodsec_score",
comp_snfi_score = "comp_snfi_score",
comp_wash_score = "comp_wash_score",
comp_prot_score = "comp_prot_score",
comp_health_score = "comp_health_score",
comp_edu_score = "comp_edu_score",
comp_foodsec_in_need = "comp_foodsec_in_need",
comp_snfi_in_need = "comp_snfi_in_need",
comp_wash_in_need = "comp_wash_in_need",
comp_prot_in_need = "comp_prot_in_need",
comp_health_in_need = "comp_health_in_need",
comp_edu_in_need = "comp_edu_in_need"
)Arguments
- df
A data frame.
- comp_foodsec_score
Column name for the food security composite score.
- comp_snfi_score
Column name for the SNFI composite score.
- comp_wash_score
Column name for the WASH composite score.
- comp_prot_score
Column name for the protection composite score.
- comp_health_score
Column name for the health composite score.
- comp_edu_score
Column name for the education composite score.
- comp_foodsec_in_need
Column name for food security in need.
- comp_snfi_in_need
Column name for SNFI in need.
- comp_wash_in_need
Column name for WASH in need.
- comp_prot_in_need
Column name for protection in need.
- comp_health_in_need
Column name for health in need.
- comp_edu_in_need
Column name for education in need.