HBA2025_results
Paloma
2025-01-27
Last updated: 2025-03-03
Checks: 5 2
Knit directory: QUAIL-Mex/
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| Rmd | 1b8f52e | Paloma | 2025-03-03 | cleaning |
Introduction
Here you will find the code used to obtain results shown in the annual meeting of the HBA, 2025.
Abstract:
Coping with water insecurity: women’s strategies and emotional responses in Iztapalapa, Mexico City
Water insecurity in urban areas presents distinctive challenges, particularly in marginalized communities. While past studies have documented how households adapt to poor water services, many of these coping strategies come at a significant personal cost. Here we examine the coping strategies and emotional impacts of unreliable water services among 400 women in Iztapalapa, Mexico City. Data were collected through surveys over the Fall of 2022 and Spring of 2023. We assessed household water access, water management practices, and emotional responses to local water services.
Results indicate that during acute water shortages, women can spend extended periods (several hours, or sometimes days) waiting for water trucks. Additionally, 57% of respondents reported feeling frustrated or angry about their water situation, while around 20% experienced family conflicts over water use or community-level conflicts around water management, often involving water vendors or government services.
This study offers one of the first in-depth examinations of how water insecurity specifically affects women in Iztapalapa, a densely populated region of Mexico City with severe water access challenges. The findings highlight the urgent need for policy interventions that address water insecurity with a gender-sensitive approach, recognizing the disproportionate burden placed on women as primary water managers in their households.
Number of individuals with HW_TOTAL > 12: 97 ( 24.25 %)Number of individuals with HW_TOTAL > 8: 172 ( 43 %)BOXPLOTS
# Create a categorical variable for HW_TOTAL threshold
df <- df %>%
mutate(HW_Group = ifelse(HW_TOTAL > 8, "Above 8", "Below 8"))
# Identify numeric columns for plotting (excluding HW_TOTAL and ID)
numeric_columns <- df %>%
select(where(is.numeric)) %>%
select(-c(HW_TOTAL, ID)) %>%
names()
# Create plots for all numeric variables
plot_list <- list()
for (var in numeric_columns) {
p <- ggplot(df, aes(x = HW_Group, y = .data[[var]], fill = HW_Group)) +
geom_boxplot(alpha = 0.7, outlier.shape = NA) +
geom_jitter(width = 0.03, alpha = 0.35) +
labs(title = paste("Comparison of", var, "by HWISE score group"),
x = "HW Group",
y = var) +
theme_minimal() +
theme(legend.position = "none")
plot_list[[var]] <- p
print(p) # Print each plot
}Warning: Removed 17 rows containing non-finite outside the scale range
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(`geom_point()`).
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cat("Generated comparison plots for all numeric variables based on HW_TOTAL.\n")Generated comparison plots for all numeric variables based on HW_TOTAL.BARPLOTS
# ========================
# Visualizing Categorical Variables
# ========================
# Create bar plots for categorical variables by HW_Group
cat_plot_list <- list()
for (var in cat_columns) {
p_cat <- ggplot(df, aes(x = .data[[var]], fill = HW_Group)) +
geom_bar(position = "dodge") +
labs(title = paste("Distribution of", var, "by HWISE score group"),
x = var,
y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels for readability
cat_plot_list[[var]] <- p_cat
print(p_cat) # Print each plot
}
cat("Generated comparison plots for all categorical variables based on HW_TOTAL.\n")Generated comparison plots for all categorical variables based on HW_TOTAL.## Explore HWISE total scores
# Ensure HW_Total is numeric
df$HW_TOTAL <- as.numeric(df$HW_TOTAL)
# Ensure categorical variables are factors
cat_columns <- grep("CAT$", names(df), value = TRUE)
df <- df %>%
mutate(across(all_of(cat_columns), as.factor))
# Get total number of individuals
total_individuals <- nrow(df)
# Count individuals with HW_Total > 12 and HW_Total > 8
count_hw_above_12 <- df %>%
filter(HW_TOTAL > 12) %>%
summarise(count = n())
count_hw_above_8 <- df %>%
filter(HW_TOTAL > 8) %>%
summarise(count = n())
# Calculate percentages
percent_hw_above_12 <- (count_hw_above_12$count / total_individuals) * 100
percent_hw_above_8 <- (count_hw_above_8$count / total_individuals) * 100
# Print results
cat("Number of individuals with HW_Total > 12:", count_hw_above_12$count,
"(", round(percent_hw_above_12, 2), "%)\n")Number of individuals with HW_Total > 12: 97 ( 24.25 %)cat("Number of individuals with HW_Total > 8:", count_hw_above_8$count,
"(", round(percent_hw_above_8, 2), "%)\n")Number of individuals with HW_Total > 8: 172 ( 43 %)# Create a categorical variable for HW_Total threshold
df <- df %>%
mutate(HW_Group = ifelse(HW_TOTAL > 8, "Above 8", "Below 8"))
"Identify numeric columns for plotting (excluding HW_Total and ID)"[1] "Identify numeric columns for plotting (excluding HW_Total and ID)"numeric_columns <- df %>%
select(where(is.numeric)) %>%
select(-c(HW_TOTAL, ID)) %>%
names()
# Create plots for all numeric variables
plot_list <- list()
for (var in numeric_columns) {
p <- ggplot(df, aes(x = HW_Group, y = .data[[var]], fill = HW_Group)) +
geom_boxplot(alpha = 0.7, outlier.shape = NA) +
geom_jitter(width = 0.1, alpha = 0.5) +
labs(title = paste("Comparison of", var, "by HWISE score group"),
x = "HW Group",
y = var) +
theme_minimal() +
theme(legend.position = "none")
plot_list[[var]] <- p
print(p) # Print each plot
}Warning: Removed 17 rows containing non-finite outside the scale range
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(`geom_point()`).
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cat("Generated comparison plots for all numeric variables based on HW_Total.\n")Generated comparison plots for all numeric variables based on HW_Total.# Subset only numeric columns
df_numeric <- df %>% select(all_of(numeric_columns))
# Compute correlation matrix (excluding NAs)
cor_matrix <- cor(df_numeric, use = "pairwise.complete.obs")
# ========================
# Heatmap using ggplot2
# ========================
# Convert the correlation matrix into a format suitable for ggplot2
cor_data <- melt(cor_matrix)
# Create heatmap plot
ggplot(cor_data, aes(Var1, Var2, fill = value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Correlation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Correlation Heatmap",
x = "", y = "")
sessionInfo()R version 4.4.2 (2024-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.3.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Detroit
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reshape2_1.4.4 tidyr_1.3.1 ggplot2_3.5.1 dplyr_1.1.4
loaded via a namespace (and not attached):
[1] gtable_0.3.6 jsonlite_1.8.9 compiler_4.4.2 promises_1.3.0
[5] tidyselect_1.2.1 Rcpp_1.0.13-1 stringr_1.5.1 git2r_0.35.0
[9] later_1.3.2 jquerylib_0.1.4 scales_1.3.0 yaml_2.3.10
[13] fastmap_1.2.0 plyr_1.8.9 R6_2.5.1 labeling_0.4.3
[17] generics_0.1.3 workflowr_1.7.1 knitr_1.49 tibble_3.2.1
[21] munsell_0.5.1 rprojroot_2.0.4 bslib_0.8.0 pillar_1.9.0
[25] rlang_1.1.4 utf8_1.2.4 cachem_1.1.0 stringi_1.8.4
[29] httpuv_1.6.15 xfun_0.49 fs_1.6.5 sass_0.4.9
[33] cli_3.6.3 withr_3.0.2 magrittr_2.0.3 grid_4.4.2
[37] digest_0.6.37 rstudioapi_0.17.1 lifecycle_1.0.4 vctrs_0.6.5
[41] evaluate_1.0.1 glue_1.8.0 farver_2.1.2 whisker_0.4.1
[45] colorspace_2.1-1 fansi_1.0.6 purrr_1.0.2 rmarkdown_2.29
[49] tools_4.4.2 pkgconfig_2.0.3 htmltools_0.5.8.1