---
title: "Plotting results"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Plotting results}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 4.5)
options(sdid.num.threads = 2)
```

The plot functions take the result of `sdid()` and return ggplot objects, which draw when printed and can be extended with `+`. They require ggplot2, and `sdid_units_plot()` uses ggrepel, when it is installed, to keep its labels apart.

```{r}
library(sdid)
data(california_prop99)
fit = sdid(california_prop99, "PacksPerCapita", "State", "Year", "treated",
           vce = "placebo", seed = 1213, graph = FALSE, quietly = TRUE)
```

## Trends

`sdid_plot()` draws the treated units' average and the synthetic control. The red dashed line marks treatment. The gray band at the bottom shows the time weights with their scale on the right-hand axis.

```{r}
sdid_plot(fit)
```

`style = "effects"` adds a diagram of the comparison behind the estimate, with an arrow marking the effect.

```{r}
sdid_plot(fit, style = "effects")
```

## Individual units

`spaghetti.units` adds the trajectories of chosen units, here the three controls with the largest weights.

```{r}
w = weights(fit)$omega
top = w$unit[order(-w$`1989`)][1:3]
sdid_plot(fit, spaghetti.units = top, spaghetti.label.size = 3)
```

`spaghetti.matrices` adds any other trajectories you want, as a list with one matrix per estimate. Each row is a trajectory with one column per period, and its row name becomes the label. Here we add the plain average of all controls, which is what difference-in-differences compares against.

```{r}
Y = fit$setup$Y
average = rbind("Average of controls" = colMeans(Y[w$unit, , drop = FALSE]))
sdid_plot(fit, spaghetti.matrices = list(average), spaghetti.label.size = 3)
```

## Comparing estimators

A named list of results draws each in its own panel, stacked and labeled by its name.

```{r, fig.height = 9}
estimates = lapply(c(SDID = "sdid", SC = "sc", DID = "did"), function(m) {
  sdid(california_prop99, "PacksPerCapita", "State", "Year", "treated",
       method = m, vce = "noinference", graph = FALSE, quietly = TRUE)
})
sdid_plot(estimates)
```

Synthetic control puts no weight on the periods before treatment, so its panel has no band, and difference-in-differences weighs them equally. Each band is drawn on its own scale. `lambda.comparable = TRUE` draws them on a common scale instead, where a weight of 1 fills the band, so that their heights can be compared across panels.

## Ticks

By default each axis gets about five ticks, and a year axis only gets whole years. To choose the ticks yourself, use `x.breaks` and `y.breaks`. Adding a ggplot scale to the plot instead would replace the package's own.

```{r}
sdid_plot(fit, x.breaks = seq(1970, 2000, by = 5))
```

## Unit weights

`sdid_units_plot()` shows what each control contributes. For every control, it computes the difference-in-differences estimate using that control alone; the overall estimate is the weighted average of these. The default scatter plots each control's weight against its own estimate, and controls with no weight are drawn as open circles.

```{r, fig.height = 5.5}
sdid_units_plot(fit)
```

Heavily weighted controls sit on the right. If they cluster around the red line, they agree with each other. `type = "bars"` shows only the weighted controls, largest weight first, with each bar running from the overall estimate to that control's own estimate.

```{r, fig.height = 7}
sdid_units_plot(fit, type = "bars")
```

`label.units` sets which controls get labels: all of them, only the weighted ones, or the `n` with the largest weights. A staggered result gets one panel per cohort, with ten labels each, unless `cohort` picks a single cohort.

## Placebo tests

`sdid_placebo_plot()` draws the estimate above an in-time placebo, which pretends treatment began earlier, at `placebo.time`, using only the periods before the real treatment. If there was no effect before the real treatment, the placebo's two lines should keep tracking each other after the red line that marks the placebo treatment.

```{r, fig.height = 7}
sdid_placebo_plot(fit, placebo.time = 1981)
```

`sdid_placebo()` returns the placebo estimate itself.

```{r}
sdid_placebo(fit, placebo.time = 1981)
```