## ----fig setup, include=FALSE------------------------------------------------- knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE) knitr::opts_chunk$set(fig.width = 9, fig.height = 5) ## ----lapop package------------------------------------------------------------ library(lapop) ## ----load data---------------------------------------------------------------- # Single-country Single-year AmericasBarometer 2023 Brazil data(bra23) # AmericasBarometer 2006-2023 Brazil Merge data(cm23) # AmericasBarometer 2023 Year Merge data(ym23) ## ----weight datasets, eval = TRUE--------------------------------------------- # Single-Country Single-Year bra23w <- lpr_data(bra23, wt = TRUE) print(bra23w) # Single-Country Multi-Year cm23w <- lpr_data(cm23) print(cm23w) # Multi-Country Single-Year ym23w <- lpr_data(ym23) print(ym23w) ## ----lapop fonts, eval=F------------------------------------------------------ # lapop::lapop_fonts() # Re-run font registration explicitly if needed ## ----time series, eval = TRUE, fig.alt = "Support for democracy decline a decade ago and remains comparatively low in Brazil"---- fig1.1_data <- lpr_ts(cm23w, outcome = "ing4", rec = c(5, 7), use_wave = TRUE) lapop_ts(fig1.1_data, ymin = 50, ymax = 80, main_title = "Support for democracy decline a decade ago and remains comparatively low in Brazil", subtitle = "% who support democracy") ## ----cross country, eval = TRUE, fig.alt = "In many countries, only about one in two adults support democracy"---- fig1.2_data <- lpr_cc(ym23w, outcome = "ing4", rec = c(5, 7)) lapop_cc(fig1.2_data, main_title = "In many countries, only about one in two adults support democracy", subtitle = "% who support democracy") ## ----mline, eval = TRUE, warnings=F, fig.alt = "Trust in executives has declined to a level similar to other political institutions"---- fig2.1_data <- lpr_mline(cm23w, outcome = c("b13", "b21", "b31"), rec = c(5, 7), rec2 = c(5, 7), rec3 = c(5, 7)) # Changing legend for readibility fig2.1_data$varlabel <- ifelse(fig2.1_data$varlabel == "b13", "National Legislature", ifelse(fig2.1_data$varlabel=="b21", "Political Parties", "Supreme Court")) lapop_mline(fig2.1_data, main_title = "Trust in executives has declined to a level similar to other \npolitical institutions", subtitle = "% who trust...") ## ----ccm, warnings = FALSE, eval = TRUE, fig.alt = "The public typically reports more trust in the armed forces than the police, but levels vary"---- fig2.3_data <- lpr_ccm(ym23w, outcome_vars = c("b12", "b18"), rec1 = c(5, 7), rec2 = c(5, 7)) # Changing legend for readibility fig2.3_data$var <- ifelse(fig2.3_data$var == "b12", "Armed Forces", "National Police") lapop_ccm(fig2.3_data, main_title = "The public typically reports more trust in the armed forces than the police,\nbut levels vary", subtitle = "% who trust...") ## ----mover, eval = TRUE, fig.alt = "Satisfaction with democracy is significantly lower among women, those with higher educational attainment, and the middle class"---- library(dplyr) ym23w$variables <- ym23w$variables %>% mutate( edrer = as.numeric(edre), gender = as.numeric(q1tc_r) ) %>% mutate(edrer = case_when( edrer <= 2 ~ "None/Primary", edrer %in% c(3, 4) ~ "Secondary", edrer > 4 ~ "Superior", TRUE ~ NA_character_ ), edrer = factor(edrer, levels = c("None/Primary", "Secondary", "Superior")), gender = ifelse(q1tc_r == 3, NA, q1tc_r)) %>% mutate(gender = case_when( gender == 1 ~ "Men", gender == 2 ~ "Women", TRUE ~ NA_character_ )) # Changing legend for readibility attributes(ym23w$variables$gender)$label <- "Gender" attributes(ym23w$variables$edrer)$label <- "Education" attributes(ym23w$variables$wealth)$label <- "Wealth" fig_mover <- lpr_mover(ym23w, outcome = "pn4", grouping_vars = c("gender", "edrer", "wealth"), rec = c(1, 2)) lapop_mover(fig_mover, main_title = "Satisfaction with democracy is significantly lower among women,\nthose with higher educational attainment, and the middle class", subtitle = "% who are satisfied with democracy") ## ----histogram, eval = TRUE, fig.alt = "On average in the LAC region, one in three say voting is the best way to influence change"---- spot32_data <- lpr_hist(ym23w, outcome = "vb21n") # Translating to English spot32_data$cat <- c("Vote", "Run for\noffice", "Protest", "Participate\nin local orgs.", "Other", "Change is\nimpossible") lapop_hist(spot32_data, main_title = "On average in the LAC region, one in three say voting is the best way \nto influence change", subtitle = "How do you believe you can best influence change?") ## ----dumbell, eval = TRUE, fig.alt = "Among those with emigration intentions, the percentage who say they are very likely to emigrate increased in Nicaragua and Guatemala"---- fig3.5_data <- lpr_dumb(ym23w, outcome = "q14f", rec = c(1, 1), over = c(2018, 2023), xvar = "pais", ttest = TRUE) # Select Countries fig3.5_data <- fig3.5_data[c(1:3,5:6), ] lapop_dumb(fig3.5_data, main_title = "Among those with emigration intentions, the percentage who say they are \nvery likely to emigrate increased in Nicaragua and Guatemala", subtitle = "% who say it is very likely they will emigrate", source = "LAPOP Lab, AmericasBarometer 2018/19 and 2023") ## ----stack, eval= TRUE, fig.alt = "Nicaragua has the highest percentage of individuals with migration intentions, while Haiti has the lowest"---- attributes(ym23w$variables$q14f)$label <- "Migration intentions" fig3.8_data <- lpr_stack(ym23w, outcome = "q14f", xvar = "pais", order = "hi-lo") lapop_stack(fig3.8_data, xvar = "xvar_label", source = "LAPOP Lab, AmericasBarometer 2023", main_title = "Nicaragua has the highest percentage of individuals with migration intentions,\nwhile Haiti has the lowest") ## ----reverse, eval = TRUE, warnings=F----------------------------------------- # For data.frames cm23$ing4r <- lpr_resc(cm23$ing4, reverse = TRUE, map=TRUE) # For LPR_DATA() objects cm23w$variables$ing4r <- lpr_resc(cm23w$variables$ing4, reverse = TRUE, map=TRUE)