1 title: “Visualizing Household Survey Data: Income by Gender in El Salvador”

author: “Jess”

date: “2024-10-22”

output:

html_document: theme: flatly
toc: true toc_float: true toc_depth: 2
code_folding: hide
number_sections: true fig_width: 7 fig_height: 5 highlight: tango
df_print: paged

2 Maps of El Salvador

Maps could make a compelling visualisation for a PPT with donors, in project proposals or donor reports to illustrate the context where we work, or the impact of our interventions.Possible uses include:

Visualization of Geographic Impact
Data-Driven Insights
Monitoring and Reporting Progress
Highlighting Vulnerable Areas
Showcasing Results
Support for Strategic Decision-Making

3 Here are Maps of El Salvador at 3 administrative levels: National, Departments and Districts

Right now its based on the old administrative boundries i.e 264 municipalities not the 44 that came into affect may 2024. But when we get the updated polygons, we can make upto-date maps.

## Total number of NAME_1 : 14

## Total number of NAME_2 : 266

4 We can show where this most recent survey took place

We can see here the 7 districts where the HH was conducted highlited and labelled

## Reading layer `gadm41_SLV_2' from data source 
##   `C:\Users\jlloydevans\OneDrive - International Organization for Migration - IOM\Documentos\RStudio\Nueva carpeta2\gadm41_SLV_2.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 266 features and 13 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -90.12486 ymin: 13.15264 xmax: -87.68375 ymax: 14.45055
## Geodetic CRS:  WGS 84

## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data

## Warning: Removed 259 rows containing missing values or values outside the scale range
## (`geom_text()`).

## [1] "Unique department names from the survey data for District 1:"
## [1] "San Salvador" "Santa Ana"    "La Libertad"  "Chalatenango" "La Unión"    
## [6] "Usulután"    
## Reading layer `gadm41_SLV_1' from data source 
##   `C:\Users\jlloydevans\OneDrive - International Organization for Migration - IOM\Documentos\RStudio\Nueva carpeta2\gadm41_SLV_1.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 14 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -90.12486 ymin: 13.15264 xmax: -87.68375 ymax: 14.45055
## Geodetic CRS:  WGS 84
## [1] "Unique department names from the shapefile:"
##  [1] "Ahuachapán"   "Cabañas"      "Chalatenango" "Cuscatlán"    "La Libertad" 
##  [6] "La Paz"       "La Unión"     "Morazán"      "San Miguel"   "San Salvador"
## [11] "San Vicente"  "Santa Ana"    "Sonsonate"    "Usulután"    
## 
##  No Yes 
##   8   6

## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data

## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_text()`).

5 Heatmap showing mean income by department for the entire population surveyed, overlaying with data from the HH survey (Using Heatmaps)

We can also overlay this with variables in the HH dataset to create maps that explain charictarists of the surveyed population.

In this map, we overlay it hosuehold data which shows the mean income of all respondents who took the survey in these departments.

The darker colours represent a higher mean income

## Reading layer `gadm41_SLV_1' from data source 
##   `C:\Users\jlloydevans\OneDrive - International Organization for Migration - IOM\Documentos\RStudio\Nueva carpeta2\gadm41_SLV_1.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 14 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -90.12486 ymin: 13.15264 xmax: -87.68375 ymax: 14.45055
## Geodetic CRS:  WGS 84

6 Overlaying with data from the HH survey, showing differences between gender (Using Heatmaps)

Highlights gender income disparities at a departmental level
Identifies regions where women are particularly disadvantaged economically
Pinpointing areas for targeted interventions
Can aligns with donor priorities on gender equity and economic empowerment

## Unique bingen Values:
## [1]  1  0 NA
## Counts of Each bingen Value:
## 
##     0     1 
## 11926 10224 
## Male dataset (first few rows):
## # A tibble: 6 × 299
##   i6_07 i6_05 h5       h3    g1      hogar factor encuesta departamento distrito
##   <dbl> <dbl> <chr>    <chr> <chr>   <dbl>  <dbl>    <dbl> <chr>        <chr>   
## 1     0     0 "02. No" 60    "02. N…  1000   28.1   118038 San Salvador Ilopango
## 2     0     0 "02. No" 60    ""       1000   28.1   118038 San Salvador Ilopango
## 3     0     0 "02. No" 60    ""       1000   28.1   118038 San Salvador Ilopango
## 4     0     0 ""       <NA>  "02. N… 10003   66.3   404149 Santa Ana    Santa A…
## 5     0     0 ""       <NA>  "02. N… 10005   66.3   404150 Santa Ana    Santa A…
## 6     0     0 ""       <NA>  "02. N… 10007   66.3   404151 Santa Ana    Santa A…
## # ℹ 289 more variables: cod_distrito <dbl>, area <dbl+lbl>, canton <chr>,
## #   fecha <date>, hora <dttm>, resultado_visita <chr>, consentimiento <chr>,
## #   b1 <dbl+lbl>, b2 <chr>, b2_esp <chr>, b3 <chr>, b3_esp <chr>, b4 <chr>,
## #   b4_esp <chr>, b5 <chr>, b6 <dbl+lbl>, b7 <dbl>, b8 <dbl+lbl>, c1 <chr>,
## #   c1_01 <dbl>, c1_02 <dbl>, c1_03 <dbl>, c1_04 <dbl>, c1_05 <dbl>,
## #   c1_06 <dbl>, c1_07 <dbl>, c1_08 <dbl>, c1_09 <dbl>, c1_10 <dbl>,
## #   c1_11 <dbl>, c1_otro <dbl>, c1_99 <dbl>, c1_esp <chr>, c2 <chr>, …
## Female dataset (first few rows):
## # A tibble: 6 × 299
##   i6_07 i6_05 h5       h3    g1      hogar factor encuesta departamento distrito
##   <dbl> <dbl> <chr>    <chr> <chr>   <dbl>  <dbl>    <dbl> <chr>        <chr>   
## 1     0     0 "02. No" 60    02. No   1000   28.1   118038 San Salvador Ilopango
## 2     0     0 ""       <NA>  03. NS… 10003   66.3   404149 Santa Ana    Santa A…
## 3     0     0 ""       <NA>  03. NS… 10007   66.3   404151 Santa Ana    Santa A…
## 4     0     0 ""       <NA>  01. Sí  10007   66.3   404151 Santa Ana    Santa A…
## 5     0     0 "01. Sí" 50    02. No   1001   28.1   118039 San Salvador Ilopango
## 6     0     0 ""       <NA>  01. Sí  10011   66.3   404153 Santa Ana    Santa A…
## # ℹ 289 more variables: cod_distrito <dbl>, area <dbl+lbl>, canton <chr>,
## #   fecha <date>, hora <dttm>, resultado_visita <chr>, consentimiento <chr>,
## #   b1 <dbl+lbl>, b2 <chr>, b2_esp <chr>, b3 <chr>, b3_esp <chr>, b4 <chr>,
## #   b4_esp <chr>, b5 <chr>, b6 <dbl+lbl>, b7 <dbl>, b8 <dbl+lbl>, c1 <chr>,
## #   c1_01 <dbl>, c1_02 <dbl>, c1_03 <dbl>, c1_04 <dbl>, c1_05 <dbl>,
## #   c1_06 <dbl>, c1_07 <dbl>, c1_08 <dbl>, c1_09 <dbl>, c1_10 <dbl>,
## #   c1_11 <dbl>, c1_otro <dbl>, c1_99 <dbl>, c1_esp <chr>, c2 <chr>, …
## Number of males: 10224 
## Number of females: 11926 
## Number of NA values in bingen column: 13

## Reading layer `gadm41_SLV_1' from data source 
##   `C:\Users\jlloydevans\OneDrive - International Organization for Migration - IOM\Documentos\RStudio\Nueva carpeta2\gadm41_SLV_1.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 14 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -90.12486 ymin: 13.15264 xmax: -87.68375 ymax: 14.45055
## Geodetic CRS:  WGS 84