oes_plot {oesr}

Create OES Plot

Description

Plot experimental results using OES style

Usage

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.

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.

Value

A plot; if save = TRUE, a file containing a plot.

Author(s)

Miles Williams

Examples

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()