| --- |
| 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) |
| ``` |
|
|
