--- title: "Study2 (article)" author: "Yuya Endo" date: "2020-09-18" output: html_document: theme: cerulean highlight: tango toc: true toc_depth: 3 toc_float: true self_contained: true --- ``` ## My Session info ## R version 4.0.2 (2020-06-22) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Catalina 10.15.4 Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib locale: ja_JP.UTF-8/ja_JP.UTF-8/ja_JP.UTF-8/C/ja_JP.UTF-8/ja_JP.UTF-8 attached base packages: grid stats graphics grDevices utils datasets methods base other attached packages: patchwork_1.0.0 ggstance_0.3.4 cjoint_2.1.0 survey_4.0 survival_3.1-12 Matrix_1.2-18 lmtest_0.9-37 zoo_1.8-7 sandwich_2.5-1 gt_0.2.0.5 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0 ``` #install any necessary packages, inserting package name as appropriate install.packages("packagename") ```{r, include = FALSE} knitr::opts_chunk$set( warning = FALSE, message = FALSE, comment = "", fig.align = "center" ) ``` ```{r road pkg} library(tidyverse) library(gt) library(cjoint) library(ggstance) library(patchwork) ``` ```{r read data} df_type1 <- read_csv("data/study2_data_type1.csv") df_type2 <- read_csv("data/study2_data_type2.csv") ``` ```{r df_conjoint} df_conjoint <- bind_rows(df_type1, df_type2) %>% mutate(across(where(is.character), as.factor)) ``` ```{r attribute_list} attribute_list <- list() attribute_list[["Sex"]] <- c("Male", "Female") attribute_list[["Age"]] <- c("44 years old","52 years old","60 years old", "68 years old","76 years old") attribute_list[["Race/Ethnicity"]] <- c("White","Black", "Hispanic","Asian American") attribute_list[["Marital status"]] <- c("Single","Married") attribute_list[["Parental status"]] <- c("No children","1 child","2 children") attribute_list[["Experience in legal profession"]] <- c("No experience","5 years", "10 years","15 years","20 years") attribute_list[["Law school ranking"]] <- c("Top 10 (Tier 1)", "50-100 (Tier 2)","151-200 (Tier 4)") attribute_list[["Party affiliation"]] <- c("Democratic Party","Republican Party") ``` ```{r conjoint_design} conjoint_design <- makeDesign(type = "constraints", attribute.levels = attribute_list) ``` ```{r baselines} baselines <- list() baselines[["Sex"]] <- c("Male") baselines[["Age"]] <- c("44 years old") baselines[["Race/Ethnicity"]] <- c("White") baselines[["Marital status"]] <- c("Single") baselines[["Parental status"]] <- c("No children") baselines[["Experience in legal profession"]] <- c("No experience") baselines[["Law school ranking"]] <- c("Top 10 (Tier 1)") baselines[["Party affiliation"]] <- c("Democratic Party") ``` ## Figure 2 ```{r acie_partisan} acie_partisan <- df_conjoint %>% drop_na(Partisanship) %>% filter(type == "type2") %>% amce(selected ~ (Sex + Age + `Race/Ethnicity` + `Marital status` + `Parental status` + `Experience in legal profession` + `Law school ranking` + `Party affiliation`) * Partisanship, data = ., cluster = TRUE, respondent.id = "respondentIndex", design = conjoint_design, baselines = baselines, respondent.varying = c("Partisanship")) ``` ```{r base_level function} base_level <- function(data, mod){ df_base_pa <- summary(mod)$baselines_amce %>% mutate( Level = str_c(Attribute, ":", "\n", "(", "Baseline = ", Level, ")") ) pa <- data %>% mutate( lwr = Estimate - 1.96 * `Std. Err`, upr = Estimate + 1.96 * `Std. Err` ) %>% bind_rows(df_base_pa) %>% mutate( Level = as.factor(Level), Level = factor(Level, levels = c("Republican Party", "Party affiliation:\n(Baseline = Democratic Party)", "151-200 (Tier 4)", "50-100 (Tier 2)", "Law school ranking:\n(Baseline = Top 10 (Tier 1))", "20 years", "15 years", "10 years", "5 years", "Experience in legal profession:\n(Baseline = No experience)", "2 children", "1 child", "Parental status:\n(Baseline = No children)", "Married", "Marital status:\n(Baseline = Single)", "76 years old", "68 years old", "60 years old", "52 years old", "Age:\n(Baseline = 44 years old)", "Hispanic", "Black", "Asian American", "Race/Ethnicity:\n(Baseline = White)", "Female", "Sex:\n(Baseline = Male)")) ) return(pa) } ``` ```{r} summary(acie_partisan)$table_values_amce ``` ```{r acie_mutate function} acie_mutate <- function(dat1, dat2, dat3, mod){ d1 <- dat1 %>% base_level(mod = mod) %>% mutate(Partisanship = "Democrat") d2 <- dat2 %>% base_level(mod = mod) %>% mutate(Partisanship = "Independent") d3 <- dat3 %>% base_level(mod = mod) %>% mutate(Partisanship = "Republican") dd <- bind_rows(d1, d2, d3) %>% mutate(Partisanship = factor(Partisanship, levels = c("Republican", "Independent", "Democrat"))) return(dd) } ``` ```{r df_acie_partisan1} df_acie_partisan <- acie_mutate(summary(acie_partisan)$Partisanship1amce, summary(acie_partisan)$Partisanship2amce, summary(acie_partisan)$Partisanship3amce, mod = acie_partisan) %>% mutate( judge_lab = if_else(Partisanship == "Democrat" & Level == "Female", "Judge's attributes", NA_character_), Partisanship = str_c("Respondent's\n Partisanship = ", Partisanship) ) ``` ```{r conjoint_plot function} conjoint_plot <- function(data, ylim, xlim, xlab, facet_vari = NULL, text_x, text_y){ pl <- data %>% ggplot(., aes(x = Estimate, y = Level, xmin = lwr, xmax = upr), col = "black") + geom_vline(xintercept = 0, size = .5, colour = "black", linetype = "dotted") + geom_pointrange() + coord_cartesian(ylim = ylim, xlim = xlim, clip = 'off') + labs(x = xlab, y = NULL) + theme(legend.position = "none", axis.text = element_text(size = 11), axis.title = element_text(size = 12), plot.margin = unit(c(1, 1, 0, 1), "lines")) if(is.null(facet_vari)){ pl <- pl + geom_text(x = text_x, y = text_y, hjust = 0, col = "black", size = 4, label = "Judge's attributes", show.legend = FALSE) return(pl) } else { facet_vari <- sym(facet_vari) pl <- pl + facet_wrap(facet_vari, ncol = 3) + geom_text(data = data, x = text_x, y = text_y, hjust = 0, col = "black", size = 4, label = data$judge_lab, show.legend = FALSE) return(pl) } } ``` ```{r pl_acie_partisan, fig.width=10, fig.height=10} pl_acie_partisan <- df_acie_partisan %>% ggplot(., aes(x = Estimate, y = Level, xmin = lwr, xmax = upr), col = "black") + geom_vline(xintercept = 0, size = .5, colour = "black", linetype = "dotted") + geom_pointrange() + facet_wrap(~ Partisanship, ncol = 3) + geom_text(data = df_acie_partisan, x = -.49, y = 26.7, hjust = 0, col = "black", size = 4, label = df_acie_partisan$judge_lab, show.legend = FALSE) + coord_cartesian(ylim = c(1, 26.5), xlim = c(-.2, .23), clip = 'off') + labs(x = "Change in Pr(Biased judge)", y = NULL) + theme(legend.position = "none", axis.text = element_text(size = 11), axis.title = element_text(size = 12), plot.margin = unit(c(1, 1, 0, 1), "lines")) pl_acie_partisan ``` ```{r save Figure2} ggsave("output_figure/paper/Figure2.png", pl_acie_partisan, width = 10, height = 10) ``` ## Figure 3 ```{r acie_partisan_case} acie_partisan_case <- df_conjoint %>% drop_na(Partisanship) %>% filter(type == "type2") %>% split(.$Condition) %>% map(~ amce(selected ~ (Sex + Age + `Race/Ethnicity` + `Marital status` + `Parental status` + `Experience in legal profession` + `Law school ranking` + `Party affiliation`) * Partisanship, data = ., cluster = TRUE, respondent.id = "respondentIndex", design = conjoint_design, baselines = baselines, respondent.varying = c("Partisanship")) ) ``` ```{r conjoint_mutate} conjoint_mutate <- function(data, p1 = "", p2 = "", p3 = "", xlim, xlab = ""){ cm <- data %>% mutate( Partisanship = c(rep(p1, nrow(.) / 3), rep(p2, nrow(.) / 3), rep(p3, nrow(.) / 3)), Partisanship = factor(Partisanship, levels = c("Republican", "Independent", "Democrat")), lwr = Estimate - 1.96 * `Std. Err`, upr = Estimate + 1.96 * `Std. Err` ) %>% filter(Level %in% c("Female", "Hispanic")) %>% mutate( judge = if_else(Level == "Female", "Female Judge", "Hispanic Judge") ) pl <- cm %>% mutate(judge = fct_rev(fct_inorder(judge))) %>% ggplot(aes(x = Estimate, y = Partisanship, shape = judge, color = judge, xmin = lwr, xmax = upr)) + geom_vline(xintercept = 0, size = 1, colour = "gray75", linetype = "solid") + geom_pointrangeh(position = position_dodgev(height = .75), size = .65) + labs(x = xlab, y = NULL) + xlim(xlim) + scale_colour_manual(name = "Judge", values = c(`Female Judge` = "black", `Hispanic Judge` = "grey60"), guide = guide_legend(reverse = TRUE)) + scale_shape_manual(name = "Judge", values = c(`Female Judge` = 15, `Hispanic Judge` = 17), guide = guide_legend(reverse = TRUE)) + theme(legend.position = "bottom", legend.key = element_rect(fill = "white"), axis.text = element_text(size = 11)) return(pl) } ``` ```{r} summary(acie_partisan_case$Conjoint1)$table_values_amce ``` ```{r pl_abortion} pl_abortion <- bind_rows(summary(acie_partisan_case$Conjoint1)$Partisanship1amce, summary(acie_partisan_case$Conjoint1)$Partisanship2amce, summary(acie_partisan_case$Conjoint1)$Partisanship3amce) %>% conjoint_mutate(p1 = "Democrat", p2 = "Independent", p3 = "Republican", xlim = c(-.11, .11), xlab = "Change in Pr(Biased judge)") pl_abortion ``` ```{r save Figure3} ggsave("output_figure/paper/Figure3.png", pl_abortion, width = 6, height = 2.8) ``` ## Part 3: Separated Conjoint Results for Immigration Case (Figure 4) ```{r} summary(acie_partisan_case$Conjoint2)$table_values_amce ``` ```{r pl_immigration} pl_immigration <- bind_rows(summary(acie_partisan_case$Conjoint2)$Partisanship1amce, summary(acie_partisan_case$Conjoint2)$Partisanship2amce, summary(acie_partisan_case$Conjoint2)$Partisanship3amce) %>% conjoint_mutate(p1 = "Democrat", p2 = "Independent", p3 = "Republican", xlim = c(-.23, .23), xlab = "Change in Pr(Biased judge)") pl_immigration ``` ```{r save Figure4} ggsave("output_figure/paper/Figure4.png", pl_immigration, width = 6, height = 2.8) ```