# Create OES Plot Plot experimental results using OES style ```r oes_plot( prep, font = "sans", device = "pdf", treatment_fill = "#F2C446", control_fill = "#2E9AC4", digits = 3, report_stars = TRUE, xlab, ylab, title = "Outcomes under Treatment", save = FALSE, name_save = "figure1.png", base_size = 12, width = 3.1, height = 4, dpi_forplot = 300 ) ``` ## Arguments - `prep`: A tidy tibble of estimates to plot, such as the output from `oes_prep()`. - `font`: Optional string giving font; defaults to `"sans"`. - `device`: Set device for loading fonts. Default is `"pdf"`. - `treatment_fill`: Bar color for treatment conditions. - `control_fill`: Bar color for control condition. - `digits`: Integer representing number of digits after decimal point to report. Defaults to 3. - `report_stars`: Logical indicating whether to display asterisks for statistical significance. Defaults to `TRUE`. - `xlab`: String providing the $x$-axis label. - `ylab`: String providing the $y$-axis label. - `title`: String providing the plot title. - `save`: Logical indicating whether to save the plot. Defaults to `FALSE`. If `TRUE`, the plot is not returned, but a file is saved using `ggsave`. - `name_save`: File name for saved plot. - `base_size`: Base font size for plot. - `width`: Width of saved plot (in inches). Use 6.8 for multiple columns. - `height`: Height of saved plot (in inches). - `dpi_forplot`: Resolution of saved plot. ## Returns A plot; if `save = TRUE`, a file containing a plot. ## Details oes_plot plots the observed response mean of a control group and the predicted response means of one or more treatment groups based on OES guidance on data reporting and visualization best-practice. Read more about this OES guidance at https://oes.gsa.gov/assets/files/reporting-statistical-results.pdf. ## Examples ```r data(df_oes) # Single binary treatment: fit <- lm(y1 ~ x1, df_oes) # Multiple treatment conditions: fit2 <- lm(y2 ~ x2, df_oes) # Using HC2 SE's from lm_robust(): fit_robust <- estimatr::lm_robust(y1 ~ x1, df_oes) fit_robust2 <- estimatr::lm_robust(y2 ~ x2, df_oes) # Using covariates and lm(): fit_covars <- lm(y2 ~ x2 + z1 + z2 + z3, df_oes) # Using covariates and lm_robust(): fit_covars_robust <- estimatr::lm_robust(y2 ~ x2 + z1 + z2 + z3, df_oes) # Specify treatment_arms: oes_prep(fit, treatment_arms = 1) |> oes_plot() # Specify treatment_vars: fit |> oes_prep(treatment_vars = "x1") |> oes_plot() # Specify treatment_arms: oes_prep(fit2, treatment_arms = 3) |> oes_plot() # Specify treatment_vars: fit2 |> oes_prep(treatment_vars = c("x21", "x22", "x23")) |> oes_plot() # Specify custom treatment_labels: prep_w_labels <- oes_prep(fit2, treatment_arms = 3, treatment_labels = c( "Email", "Email +\nReward", "Email +\nRisk"), control_label = "Status Quo") oes_plot(prep_w_labels) # Using objects from estimatr::lm_robust(): oes_prep(fit_robust, treatment_arms = 1) |> oes_plot() oes_prep(fit_robust2, treatment_arms = 3) |> oes_plot() # Specify covariates with lm: oes_prep(fit_covars, treatment_arms = 3) |> oes_plot() # Specify covariates with lm_robust(): oes_prep(fit_covars_robust, treatment_arms = 3) |> oes_plot() # For the Lin estimator, a manual version of lm_lin(): m.mat <- cbind(y2 = df_oes$y2, model.matrix(y2 ~ x2 + z1 + z2 + z3, df_oes)[, -1]) m.mat <- dplyr::mutate_at( data.frame(m.mat), .vars = c('z1', 'z2', 'z31', 'z32', 'z33', 'z34', 'z35'), function(x) x - mean(x) ) fit_lin <- estimatr::lm_robust(y2 ~ (x21 + x22 + x23) * (z1 + z2 + z31 + z32 + z33 + z34 + z35), m.mat) oes_prep(fit_lin, treatment_arms = 3) |> oes_plot() ``` ## Author(s) Miles Williams

oes_plot function