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05_plots.R
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05_plots.R
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rm(list = ls())
library(tidyverse)
library(lubridate)
# theming
library(ggsci)
library(cowplot)
# SPECIFY TREATMENT AND CONTROL PERIODS
control_period <- c("20162017", '20172018', "20182019")
treatment_period <- "20192020"
# GENERATE PLOTTING FUNCTIONS
# read in and edit data
gen_model_df <- function(path_to_rds_file, sibling_FE = FALSE, type_name = NULL) {
read_and_edit <- function(path_to_rds_file, type_name) {
data <- readRDS(path_to_rds_file) %>%
mutate(type = type_name)
return(data)
}
full_models <- c()
model_types <- c("overall", "grade", "ses", "female", "ability")
for(i in model_types) {
print(i)
if(sibling_FE) {
data <- read_and_edit(paste0(models_path, "models_", i, "_",
paste0(control_period, collapse = ""), "_", treatment_period, ".rds"),
type_name = i) %>%
select(term, estimate, std.error, statistic, p.value, model, type) %>%
mutate_at(vars(-term, -model, -type), as.numeric)
full_models <- bind_rows(full_models, data)
} else {
data <- read_and_edit(paste0(models_path, "models_", i, "_",
paste0(control_period, collapse = ""), "_", treatment_period, ".rds"),
type_name = i) %>%
mutate(statistic = as.numeric(statistic), p.value = as.numeric(p.value),
conf.low = as.numeric(conf.low), conf.high = as.numeric(conf.high), df = as.numeric(df),
nobs = as.numeric(nobs), nclusters = as.numeric(nclusters))
full_models <- bind_rows(full_models, data)
}
}
return(full_models %>%
mutate(type = ifelse(type == "overall" & model!="ALL", model, type)) %>%
filter(term=="treat"|str_detect(term, "full_treat")) %>%
mutate(label = ifelse(model=="ALL" & type=="overall", "",
ifelse(type=="RW", "Maths",
ifelse(type=="TBL", "Reading",
ifelse(type=="SP", "Spelling",
ifelse(type=="DMT", "Info. Processing",
ifelse(str_detect(model, "7"), "Age 11",
ifelse(str_detect(model, "6"), "Age 10",
ifelse(str_detect(model, "5"), "Age 9",
ifelse(str_detect(model, "4"), "Age 8",
ifelse(type=="ses" & term=="treat", "High",
ifelse(type=="ses" & term=="full_treat:seslow", "Low",
ifelse(type=="ses" & term=="full_treat:seslowest", "Lowest",
ifelse(type=="female" & term=="treat", "Boys",
ifelse(type=="female" & term=="full_treat:female", "Girls",
ifelse(type=="ability" & term=="treat", "Top",
ifelse(type=="ability" & term=="full_treat:abilitymiddle",
"Middle",
ifelse(type=="ability" & term=="full_treat:abilitybottom",
"Bottom", NA))))))))))))))))),
label = factor(label, levels = c("", "Total","High", "Low", "Lowest", "Boys", "Girls",
"Maths", "Spelling", "Reading", "Top", "Middle", "Bottom",
"Age 8", "Age 9", "Age 10", "Age 11")),
type = ifelse(type == "overall", "Total",
ifelse(type %in% c('RW', 'TBL', 'SP'), "Subject",
ifelse(type == "ses", "Parental Educ.",
ifelse(type == "female", "Sex",
ifelse(type == "ability", "Prior Perf.",
ifelse(type == "grade", "School Grade", NA)))))),
type = factor(type, levels = c("", "Total", "Parental Educ.", "Sex",
"Prior Perf.","Subject", "School Grade"))))
}
# generate plots
make_plot <- function(full_models, matched = FALSE, sibling_FE = FALSE, DMT = FALSE, dodge = 0,
alt_treat_years = FALSE) {
if (DMT) {
full_models_all <- full_models %>%
filter(!type == "Subject")
g_start <- full_models_all %>%
ggplot(aes(x = reorder(label, -as.numeric(label)), y = estimate, color = type, shape = type,
alpha = group)) +
geom_point(size = 2.5, position = position_dodge(width=dodge)) +
geom_errorbar(aes(ymin =
estimate - 1.96 * std.error,
ymax = estimate + 1.96 * std.error), width = 0, size = 0.7,
position = position_dodge(width=dodge)) +
scale_alpha_discrete(range = c(0.3, 1))
} else {
full_models_all <- full_models %>%
filter(str_detect(model, "ALL") | type == "Subject") %>%
mutate(group = "all")
g_start <- full_models_all %>%
ggplot(aes(x = reorder(label, -as.numeric(label)), y = estimate, color = type, shape = type)) +
geom_point(size = 2.5) +
geom_errorbar(aes(ymin =
estimate - 1.96 * std.error,
ymax = estimate + 1.96 * std.error), width = 0, size = 0.7)
}
g1 <- g_start +
geom_hline(yintercept = 0, linetype = 2, color = "darkgrey") +
cowplot::theme_cowplot(font_size = 17) +
coord_flip() +
scale_color_aaas() +
scale_fill_aaas() +
ylab("Learning loss (percentiles)") +
xlab("")
# ADD FACETS
if (matched) {
g1 <- g1 +
facet_grid(type ~ model_type, scales = "free", space = "free", switch="y") +
scale_y_continuous(minor_breaks = seq(-5.5 , 1.5, 1),
breaks = round(seq(-5, 1.5, 1), 2),
limits = c(-5.2, 1.2))
} else if (sibling_FE) {
g1 <- g1 +
facet_grid(type ~ ., scales = "free", space = "free", switch="y") +
scale_y_continuous(minor_breaks = seq(-10 , 2, 1),
breaks = round(seq(-10, 2, 1), 2),
limits = c(-10, 2))
} else if (alt_treat_years) {
g1 <- g1 +
facet_grid(type ~ ., scales = "free", space = "free", switch="y") +
scale_y_continuous(minor_breaks = seq(-5.5 , 2, 1),
breaks = round(seq(-5, 2, 1), 2),
limits = c(-5.2, 2))
} else {
g1 <- g1 +
facet_grid(type ~ ., scales = "free", space = "free", switch="y") +
scale_y_continuous(minor_breaks = seq(-5.5 , 1.5, 1),
breaks = round(seq(-5, 1.5, 1), 2),
limits = c(-5.2, 1.2))
}
plot <- g1 +
theme(panel.grid.major.x = element_line(size = 0.5, linetype = 'dotted',
colour = "lightgrey"),
panel.grid.minor.x = element_line(size = 0.25, linetype = 'dotted',
colour = "lightgrey"),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", hjust=0.5, vjust=0.5),
strip.background=element_rect(fill = NA, color = "black", size = 1.2),
legend.position = "none",
panel.spacing.x=unit(1, "lines"),
panel.spacing.y=unit(0.1, "lines"),
panel.border = element_rect(color = "lightgrey", fill = NA, size = 0.5),
plot.margin=grid::unit(c(0.5,0.5,0.1,0.1), "mm"))
return(plot)
}
# COEF PLOT (MAIN RESULTS): FIGURE 2 ------------------------------------------
models_path <- "data/final/main/"
plots_path <- "plots/main/"
full_models <- gen_model_df(models_path)
make_plot(full_models = full_models)
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
# COEF PLOT (ALL CONTROLS): APPENDIX -------------------------------------------
models_path <- "data/final/supp/r_all_controls/"
plots_path <- "plots/supp/r_all_controls/"
full_models <- gen_model_df(models_path)
make_plot(full_models = full_models)
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
# COEF PLOT (MATCHED): APPENDIX ------------------------------------------------
models_path <- "data/final/supp/r_matching/"
plots_path <- "plots/supp/r_matching/"
full_models <- gen_model_df(models_path) %>%
mutate(model_type = recode(model_type, "P-Score" = "Propensity Score", "E-Balance" = "Entropy Balance"))
make_plot(full_models = full_models, matched = T)
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
# COEF PLOT (SIBLING FIXED EFFECTS): APPENDIX ----------------------------------
models_path <- "data/final/supp/r_sibling_FE/"
plots_path <- "plots/supp/r_sibling_FE/"
full_models <- gen_model_df(models_path)
make_plot(full_models = full_models, sibling_FE = T)
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
# COEF PLOT (SCHOOL FIXED EFFECTS): APPENDIX -----------------------------------
models_path <- "data/final/supp/r_schools_FE/"
plots_path <- "plots/supp/r_schools_FE/"
full_models <- gen_model_df(models_path)
make_plot(full_models = full_models)
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
# COEF PLOT (NOT CONTROLLING FOR DAYS BETWEEN TESTS): APPENDIX -----------------------------------
models_path <- "data/final/supp/r_exclude_days/"
plots_path <- "plots/supp/r_exclude_days/"
full_models <- gen_model_df(models_path)
make_plot(full_models = full_models)
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
# COEF PLOT (SCHOOLS IN NEIGHBOURHOODS WITH LOW SHARE OF NON-WESTERN RESIDENTS): APPENDIX ----------
models_path <- "data/final/supp/r_low_pnw/"
plots_path <- "plots/supp/r_low_pnw/"
full_models <- gen_model_df(models_path)
make_plot(full_models = full_models)
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
# COEF PLOT (ONLY STUDENTS WITH NON-MISSING DVS): APPENDIX ----------
models_path <- "data/final/supp/r_nonmissing_dvs/"
plots_path <- "plots/supp/r_nonmissing_dvs/"
full_models <- gen_model_df(models_path)
make_plot(full_models = full_models)
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
# COEF PLOT (ONLY SCHOOLS WHERE >=75% OF STUDENTS TOOK A TEST DURING PANDEMIC): APPENDIX ----------
models_path <- "data/final/supp/r_schools_75/"
plots_path <- "plots/supp/r_schools_75/"
full_models <- gen_model_df(models_path)
make_plot(full_models = full_models)
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
# COEF PLOT (ALTERNATIVE CONTROL YEARS): APPENDIX -------------------------------------------
models_path <- "data/final/supp/r_alt_control_years/"
plots_path <- "plots/supp/r_alt_control_years/"
control_periods <- list(c("20162017", "20172018"),
c("20162017", "20182019"),
c("20172018", "20182019"),
c("20162017"), c("20172018"), c("20182019"))
treatment_period <- "20192020"
for(control_period in control_periods) {
full_models <- gen_model_df(models_path)
print(make_plot(full_models = full_models))
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
}
# COEF PLOT (ALTERNATIVE TREATMENT YEARS): APPENDIX -------------------------------------------
models_path <- "data/final/supp/p_alt_treat_years/"
plots_path <- "plots/supp/p_alt_treat_years/"
control_periods <- list(c("20172018", "20182019"),
c("20162017", "20182019"),
c("20162017", "20172018"))
treatment_periods <- list(c("20162017"),c("20172018"), c("20182019"))
for(i in 1:length(treatment_periods)) {
models_path <- "data/final/supp/p_alt_treat_years/"
plots_path <- "plots/supp/p_alt_treat_years/"
control_period <- control_periods[[i]]
treatment_period <- treatment_periods[[i]]
full_models <- gen_model_df(models_path)
print(make_plot(full_models = full_models, alt_treat_years = TRUE))
ggsave(paste0(plots_path, "ll_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 7, height = 9)
}
# RE-CLASSIFY CORRECT TREATMENT AND CONTROL YEARS
control_period <- c("20162017", "20172018", "20182019")
treatment_period <- c("20192020")
# NON-CURRICULAR CONTENT (DMT TESTS) APPENDIX ------------------------------------------------------
# COMPARISON OF EFFECT OF PANDEMIC FOR TESTS OF CURRICULAR VS NON-CURRICULAR CONTENT
models_path <- "data/final/main/"
full_models <- gen_model_df(models_path) %>%
mutate(group = "all")
full_models_dmt <- full_models %>%
filter(str_detect(model, "DMT")) %>%
mutate(type = replace_na(type, "Total"),
label = replace_na(label, "")) %>%
mutate(group = "dmt")
full_models_dmt_all <- rbind(full_models_dmt, full_models %>%
filter(str_detect(model, "ALL")))
make_plot(full_models = full_models_dmt_all, DMT = T, dodge = 0.5)
# save this plot in new folder, since it doesnt feature in main analysis
models_path <- "data/final/supp/r_dmt/"
plots_path <- "plots/supp/r_dmt/"
ggsave(paste0(plots_path, "dmt_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 9, height = 7.8)
# NON-CURRICULAR CONTENT (DMT TESTS), SES ONLY (FIG 4) ---------------------------------------------
# COMPARISON OF EFFECT OF PANDEMIC FOR TESTS OF CURRICULAR VS NON-CURRICULAR CONTENT
models_path <- "data/final/main/"
plots_path <- "plots/main/"
full_models <- gen_model_df(models_path) %>%
filter(type %in% c(NA, "Total", "Parental Educ.")) %>%
mutate(type = ifelse(type=="Parental Educ.", "Parental Education", as.character(type))) %>%
mutate(type = factor(type, levels = c("Total", "Parental Education"))) %>%
mutate(group = "all")
full_models_dmt <- full_models %>%
filter(str_detect(model, "DMT")) %>%
mutate(type = replace_na(type, "Total"),
label = replace_na(label, "")) %>%
mutate(group = "dmt")
full_models_dmt_all <- rbind(full_models_dmt, full_models %>%
filter(str_detect(model, "ALL")))
make_plot(full_models = full_models_dmt_all, DMT = T) +
theme(axis.line = element_line(colour = 'black', size = 0.2),
panel.spacing.x=unit(0.04, "lines"),
panel.border = element_rect(color = "lightgrey", fill = NA, size = 0.5),
strip.text = element_text(size = 11),
strip.background=element_rect(fill = NA, color = "black", size = 0.8),
axis.title.x = element_text(size = 12),
axis.text.x = element_text(size = 11),
axis.text.y = element_text(size = 11),
axis.title.y = element_text(size = 12))
# save
ggsave(paste0(plots_path, "ll_dmt_ses_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 4.5, height = 3)
# NON-CURRICULAR CONTENT (DMT TESTS), IN PREVIOUS YEARS: APPENDIX ----------------------------------
# COMPARISON OF EFFECT OF PANDEMIC FOR TESTS OF CURRICULAR VS NON-CURRICULAR CONTENT
control_periods <- list(c("20172018", "20182019"),
c("20162017", "20182019"),
c("20162017", "20172018"))
treatment_periods <- list(c("20162017"),c("20172018"), c("20182019"))
for(i in 1:length(treatment_periods)) {
# load data
models_path <- "data/final/supp/p_alt_treat_years/"
plots_path <- "plots/supp/p_alt_treat_years/"
control_period <- control_periods[[i]]
treatment_period <- treatment_periods[[i]]
full_models <- gen_model_df(models_path) %>%
mutate(group = "all")
full_models_dmt <- full_models %>%
filter(str_detect(model, "DMT")) %>%
mutate(type = replace_na(type, "Total"),
label = replace_na(label, "")) %>%
mutate(group = "dmt")
full_models_dmt_all <- rbind(full_models_dmt, full_models %>%
filter(str_detect(model, "ALL")))
print(make_plot(full_models = full_models_dmt_all, DMT = T, dodge = 0.5,
alt_treat_years = T))
# save this plot in new folder, since it is mainly focused on non-curricular content
models_path <- "data/final/supp/r_dmt/"
plots_path <- "plots/supp/r_dmt/"
ggsave(paste0(plots_path, "dmt_full_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 9, height = 7.8)
}
# SCHOOL RANDOM EFFECTS: APPENDIX ----------------------------------------------
models_path <- "data/final/supp/r_schools_ml/"
plots_path <- "plots/supp/r_schools_ml/"
df <- readRDS(paste0(models_path, "models_overall_201620172017201820182019_20192020.rds"))
plot_re <- function(df, re_type = "controls") {
ggplot(df %>% filter(RE_model == re_type) , aes(x = School, y = Treat, color = Sig)) + geom_point(size=0.3) +
geom_errorbar(aes(x = School, ymin = lb_treat, ymax = ub_treat), size=0.2,
alpha=0.4) +
geom_hline(yintercept=0, linetype='dashed') +
theme_bw() + scale_color_aaas() +
labs(title=paste0("Estimated school-level treatment effects including 95% CI"),
x="Schools", y="Estimated school-level treatment effect") + cowplot::theme_cowplot(font_size = 15) + theme(legend.position = "none")
}
ggsave(paste0(plots_path, "school_re_woc.pdf"), plot_re(df, "base"), width=10, height=7)
ggsave(paste0(plots_path, "school_re_wc.pdf"), plot_re(df, "controls"), width=10, height=7)
schools <- readRDS("data/raw/schools.rds")
base_df_school <- df %>%
filter(RE_model == "base") %>%
left_join(schools)
controls_df_school <- df %>%
filter(RE_model == "controls") %>%
left_join(schools)
## for proportion non-western
var = "school_weight"
bins = 21
gen_binned_scatter <- function(df, var = "prop_non_western", bins = 25, xlabel="x",
xlim = c(0, 0.8), ylim = c(-6, -1.5)) {
df$var <- df[[var]]
bins_var <- quantile(df$var, seq(0, 1, 1 / bins), na.rm=T)
bins_var <- bins_var[!duplicated(bins_var)]
gg <- ggplot(df, aes(y = Treat, x = var)) +
stat_smooth(method='lm')
gg_data <- data.frame(ggplot_build(gg)$data[[1]])
return(ggplot(df, aes(y = Treat, x = var)) + stat_summary_bin(fun.y="mean", breaks = bins_var, color="black") +
geom_smooth(method = "lm", color="black", fill = NA) + cowplot::theme_cowplot(font_size= 19.5) +
geom_ribbon(data=gg_data, aes(x=x, ymin = ymin, ymax = ymax), fill = "NA", linetype = 2, color="black", inherit.aes = F) +
labs(x=xlabel, y="Estimated school-level treatment effect",
title = "") + coord_cartesian(xlim = xlim, ylim = ylim))
}
ggsave(paste0(plots_path, "school_pnw_ALL_25_woc.pdf"),
gen_binned_scatter(base_df_school, "prop_non_western", 25, xlabel = "Proportion non-Western",
xlim = c(0, 0.8), ylim = c(-6, -1.5)))
ggsave(paste0(plots_path, "school_pnw_ALL_25_wc.pdf"),
gen_binned_scatter(controls_df_school, "prop_non_western", 25, xlabel = "Proportion non-Western",
xlim = c(0, 0.8), ylim = c(-6, -1.5)))
ggsave(paste0(plots_path, "school_sw_ALL_21_woc.pdf"),
gen_binned_scatter(base_df_school, "school_weight", 21, xlabel = "School disadvantage",
xlim = c(18, 40), ylim = c(-6, -1.5)))
ggsave(paste0(plots_path, "school_sw_ALL_21_wc.pdf"),
gen_binned_scatter(controls_df_school, "school_weight", 21, xlabel = "School disadvantage",
xlim = c(18, 40), ylim = c(-6, -1.5)))
# RE-CLASSIFY CORRECT TREATMENT AND CONTROL YEARS
control_period <- c("20162017", "20172018", "20182019")
treatment_period <- c("20192020")
# PLOT THE EFFECT OF SES ACROSS ALL SPECIFICATIONS --------------------------------------------
control_period <- c("20162017", "20172018", "20182019")
treatment_period <- c("20192020")
# Main model
models_path <- "data/final/main/"
plots_path <- "plots/main/"
models_main <- gen_model_df(models_path) %>%
mutate(spec = "Baseline")
# Full controls
models_path <- "data/final/supp/r_all_controls/"
models_control <- gen_model_df(models_path) %>%
mutate(spec = "All Controls")
# Matched
models_path <- "data/final/supp/r_matching/"
models_matched <- gen_model_df(models_path) %>%
mutate(spec = model_type,
spec = ifelse(spec == "E-Balance", "Entropy Bal.",
ifelse(spec == "P-Score", "Prop-Score", spec)))
# Schools with 75% Test Attendance
models_path <- "data/final/supp/r_schools_75/"
models_schools_75 <- gen_model_df(models_path) %>%
mutate(spec = "75% Schools")
# School Fixed Effects
models_path <- "data/final/supp/r_schools_FE/"
models_schools_FE <- gen_model_df(models_path) %>%
mutate(spec = "School FE")
# Family FE
models_path <- "data/final/supp/r_sibling_FE/"
models_sibling_FE <- gen_model_df(models_path) %>%
mutate(spec = "Family FE")
models <- bind_rows(models_main, models_control, models_matched, models_schools_75,
models_schools_FE, models_sibling_FE)
# PLOT MODEL
g1 <- models %>%
filter(term=="treat"|str_detect(term, "full_treat")) %>%
filter(str_detect(model, "ALL")) %>%
filter(type %in% c("Total", "Parental Educ.")) %>%
mutate(type = recode(type, "Parental Educ." = "Parental Education"),
spec = factor(spec, levels = c("Baseline", "All Controls", "Entropy Bal.", "Prop-Score",
"75% Schools", "School FE", "Family FE"))) %>%
ggplot(aes(x = reorder(label, -as.numeric(label)), y = estimate, color = spec,
group = spec,
shape = spec)) +
geom_point(size = 2.5, position = position_dodge(width=-0.8)) +
geom_errorbar(aes(ymin =
estimate - 1.96 * std.error,
ymax = estimate + 1.96 * std.error), width = 0, size = 0.7,
position = position_dodge(width=-0.8)) +
geom_hline(yintercept = 0, linetype = 2, color = "darkgrey") +
facet_grid(type ~ ., scales = "free", space = "free", switch="y") +
scale_shape_manual(values=1:length(unique(models$spec))) +
scale_y_continuous(minor_breaks = seq(-6.5 , 1.5, 0.5),
breaks = round(seq(-6, 1, 1), 2),
limits = c(-6.3, 1.2)) +
cowplot::theme_cowplot(font_size = 23) +
coord_flip() +
scale_color_aaas() +
scale_fill_aaas() +
ylab("Learning loss (percentiles)") +
xlab("")
g1 +
theme(panel.grid.major.x = element_line(size = 0.5, linetype = 'dotted',
colour = "lightgrey"),
panel.grid.minor.x = element_line(size = 0.25, linetype = 'dotted',
colour = "lightgrey"),
strip.placement = "outside",
strip.text.y = element_text(face = "bold", hjust=0.5, vjust=0.5),
strip.background=element_rect(fill = NA, color = "black", size = 1.5),
legend.position = "right",
legend.title = element_blank(),
panel.spacing.x=unit(0.08, "lines"),
panel.spacing.y=unit(0.1, "lines"),
panel.border = element_rect(color = "lightgrey", fill = NA, size = 0.5),
plot.margin=grid::unit(c(0.5,0.5,0.1,0.1), "mm"))
# save plot
ggsave(paste0(plots_path, "ll_ses_", paste(control_period, collapse = ""), "_", treatment_period, ".pdf"),
width = 9, height = 8)
# SPECIFICATION CURVES (APPENDIX) ------------------------------------------------------------------
# FUNCTIONS TO CREATE TOP AND BOTTOM PLOT OF SPECIFICATION CURVE
make_coef_plot <- function(data, dep_var) {
# plot specification curve
g1 <- data %>%
filter(str_detect(outcome, dep_var)) %>%
ggplot(aes(x = h_order, y = estimate)) +
geom_ribbon(aes(fill = outcome,
ymin = estimate - 1.96* std.error,
ymax = estimate + 1.96* std.error, ), size = 0.2, alpha = 0.5) +
geom_point(aes(color = outcome), shape = 21, size = 0.2) +
ylab("Treatment Coefficient") +
scale_color_aaas(guide = FALSE) +
scale_fill_aaas(guide = FALSE) +
geom_hline(yintercept = 0, color = "black", size = 1, linetype = 2) +
facet_wrap(~outcome, scales = "free_x") +
theme_bw() +
theme(axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = 14),
strip.text = element_text(size = 16),
axis.title.y = element_text(size = 14),
strip.background=element_rect(fill = NA, color = "black", size = 0.8))
if(treatment_period=="20192020"){
g1 <- g1 +
ylim(c(-4.15, 2))
} else if(treatment_period=="20182019"){
g1 <- g1 + ylim(c(-4.1, 2))
}
if(str_detect(dep_var, "Maths")){g1 <- g1 +
scale_color_manual(guide = FALSE, values = c("#EE0000FF")) +
scale_fill_manual(guide = FALSE, values = c("#EE0000FF"))}
if(str_detect(dep_var, "Reading")){g1 <- g1 +
scale_color_manual(guide = FALSE, values = c("#008B45FF")) +
scale_fill_manual(guide = FALSE, values = c("#008B45FF"))}
if(str_detect(dep_var, "Spelling")){g1 <- g1 +
scale_color_manual(guide = FALSE, values = c("#631879FF")) +
scale_fill_manual(guide = FALSE, values = c("#631879FF"))}
return(g1)
}
# BOTTOM PLOT
# Function to create a specification plot for a single category.
make_spec_plot <- function(data, category, dep_var = "Composite") {
# category = spec_cols[1] # DEBUG
specs <- data %>%
filter(outcome==dep_var) %>%
dplyr::select(h_order, category) %>%
pivot_longer(starts_with(category), names_prefix = paste0(category, "_")) %>%
mutate(name = factor(name, levels = rev(unique(name))))
if(is.numeric(specs$value)){
spec_plot <- ggplot(specs, aes(x = h_order, y = name, alpha = value)) +
geom_point(shape = "|", size = 0.7) +
scale_alpha_continuous(guide = FALSE, range = c(0, 1)) +
scale_fill_aaas(guide = FALSE) +
theme_bw() +
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(), axis.line.x = element_blank(), axis.text.x = element_blank(),
axis.title.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(),
axis.text.y = element_text(size = 13),
strip.text = element_text(size = 16))
} else {
spec_plot <- ggplot(specs, aes(x = h_order, y = value)) +
geom_point(shape = "|", size = 0.7) +
scale_alpha_continuous(guide = FALSE, range = c(0, 1)) +
scale_fill_aaas(guide = FALSE) +
theme_bw() +
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(), axis.line.x = element_blank(), axis.text.x = element_blank(),
axis.title.y = element_blank(), axis.ticks.y = element_blank(), axis.line.y = element_blank(),
axis.text.y = element_text(size = 13),
strip.text = element_text(size = 16))
}
}
# RUN ANALYSIS ------------------------
# Specify location of data and plots destination
models_path <- "data/final/supp/r_spec_curve/"
plots_path <- "plots/supp/r_spec_curve/"
# specify periods to loop over
control_periods <- list( c("20162017", "20172018"),
c("20162017", "20172018", "20182019"))
treatment_periods <- list(c("20182019"), c("20192020"))
for(i in 1:length(control_periods)){
# loop over main and placebo models
control_period <- control_periods[[i]]
treatment_period <- treatment_periods[[i]]
# load data
load(paste0("data/final/supp/r_spec_curve/complete_models_",
paste(control_period, collapse = ""), "_", treatment_period, ".Rda"))
dep_vars <- c("Composite", "Maths", "Reading", "Spelling")
for(dv in dep_vars) {
coef_plot_all <- make_coef_plot(complete_models, dv)
if(treatment_period=="20182019") {
dv <- paste0("Placebo (", dv, ")")
# correct coding mistake in sample period from 2020 to 2019
complete_models <- complete_models %>%
mutate(period = gsub("2019-2020", "2018-2019", period),
period = gsub("2017-2020", "2017-2019", period))
}
spec_plots_all <- lapply(list(
c("Parental Education", "Sex", "Prior Performance",
"School Grade", "Year", "Days between tests"),
names(complete_models[str_detect(names(complete_models), " x ")]),
c("type"),
c("period")), make_spec_plot, data = complete_models, dep_var = dv)
combined_plot <- plot_grid(plotlist = c(list(coef_plot_all), spec_plots_all),
labels = c("", "Controls", "Interactions", "Fixed-Effects", "Sample Period"),
label_size = 14, label_fontface = "italic", vjust = 0.5, hjust = -0.1,
rel_heights = c(1.7, 0.6, 1.1, 0.3, 0.2), align = "v", ncol = 1)
ggsave(combined_plot,
file = paste0(plots_path, "spec_curve_", dv, "_", paste(control_period, collapse = ""),
"_", treatment_period, ".pdf"), width = 10, height = 12)
}
}