panelView

https://yiqingxu.org/packages/panelview/
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### Install panelView from GitHub (R)

Source: https://yiqingxu.org/packages/panelview/reference/index

Installs the development version of the panelView package from GitHub using the devtools package. Requires devtools to be installed first.

```R
if (!requireNamespace("devtools", quietly = TRUE)) {
    install.packages('devtools', repos = 'http://cran.us.r-project.org')
}

devtools::install_github('xuyiqing/panelView')
```

--------------------------------

### Install panelView from CRAN and GitHub

Source: https://context7.com/context7/yiqingxu_packages_panelview/llms.txt

Instructions for installing the panelView R package. It can be installed from CRAN for the stable version or from GitHub for the development version using the devtools package.

```r
install.packages('panelView')
```

```r
# if not already installed
install.packages('devtools', repos = 'http://cran.us.r-project.org')

# note: "V" is capitalized in package name
devtools::install_github('xuyiqing/panelView')
```

--------------------------------

### Install panelView from GitHub using devtools (R)

Source: https://yiqingxu.org/packages/panelview/index

Installs the development version of the panelView package from GitHub. Requires the 'devtools' package, which may need to be installed first. Ensures the latest features and bug fixes are available.

```R
if not already installed
install.packages('devtools', repos = 'http://cran.us.r-project.org') 

# note: "V" is capitalized
devtools::install_github('xuyiqing/panelView')
```

--------------------------------

### Load panelView and example datasets

Source: https://context7.com/context7/yiqingxu_packages_panelview/llms.txt

Loads the panelView package and its included example datasets. Three datasets are available: 'turnout', 'capacity', and 'simdata', each suitable for different types of panel data analysis.

```r
library(panelView)
data(panelView)

# Three datasets are included:
# - turnout: State-level voter turnout data (balanced panel)
# - capacity: Cross-national democracy and state capacity data
# - simdata: Simulated dataset with discrete outcome variable
```

--------------------------------

### Install panelView Package from GitHub

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Installs the development version of the panelView package directly from GitHub using the devtools package. This allows access to the latest features and bug fixes.

```r
install.packages('devtools', repos = 'http://cran.us.r-project.org') # if not already installed
devtools::install_github('xuyiqing/panelView')
```

--------------------------------

### Install panelView from CRAN (R)

Source: https://yiqingxu.org/packages/panelview/index

Installs the stable version of the panelView package from the Comprehensive R Archive Network (CRAN). This is the recommended method for most users.

```R
install.packages('panelView')
```

--------------------------------

### Panelview with Continuous Treatment and No Grid

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This example visualizes a continuous treatment variable using panelview and demonstrates how to disable grid lines by setting `gridOff = TRUE`. This is useful when the treatment has many levels and is better interpreted as continuous.

```r
panelview(Capacity ~ polity2 + lngdp, 
          data = capacity, index = c("ccode", "year"), 
          axis.lab.gap = c(2,10), main = "Regime Type", 
          gridOff = TRUE)
```

--------------------------------

### Remove panelView Package (R)

Source: https://yiqingxu.org/packages/panelview/reference/index

Provides commands to remove the panelView package, useful for troubleshooting installation issues. It includes variations for potential naming differences.

```R
remove.packages('panelView')
# or if the above fails:
# remove.packages('panelview') # package name "panelview" no longer in use
```

--------------------------------

### Collapse Units by Treatment Histories with Panelview

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This R code demonstrates collapsing units by their treatment histories using panelview. The 'collapse.history = TRUE' argument groups units with similar treatment patterns. Two examples are provided: one sorted by treatment timing and another by cohort size.

```r
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, 
          data = turnout, index = c("abb","year"), axis.lab.angle = 90,
          by.timing = TRUE, collapse.history = "TRUE") 

          # sorted by treatment timing

```

```r
panelview(Capacity ~ demo + lnpop + lngdp, data = capacity, 
          index = c("ccode", "year"), axis.lab.gap = c(2,0), 
          axis.lab.angle = 90, collapse.history = "TRUE") 

          # sorted by cohort size

```

--------------------------------

### Visualize Panel Data using panelView in R

Source: https://yiqingxu.org/packages/panelview/reference/reference/panelView

This example demonstrates how to use the panelview function from the panelview R package to visualize treatment and outcome data. It requires the panelview library and a dataset named 'turnout'. The function visualizes the specified outcome and treatment variables across units and time, with options to set indexing variables.

```r
library(panelView)
data(panelView)
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
          data = turnout,
          index = c("abb","year"))

```

--------------------------------

### Visualize treatment reversals with panelView

Source: https://context7.com/context7/yiqingxu_packages_panelview/llms.txt

Plots panel data where treatments can switch on and off over time, illustrating treatment reversals. This example uses the 'capacity' dataset and visualizes the relationship between 'demo' (treatment) and state capacity, with axis label gaps customized.

```r
# Democracy data with treatment reversals
panelview(Capacity ~ demo + lnpop + lngdp,
          data = capacity,
          index = c("ccode", "year"),
          axis.lab.gap = c(2, 10),
          main = "Democracy and State Capacity")

# axis.lab.gap: show every 2nd x-axis label, every 10th y-axis label
# Missing values are shown in grey
```

--------------------------------

### Panelview with Continuous Treatment and Custom Colors

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This example shows how to apply custom colors to a panelview plot when dealing with a continuous treatment variable. The `color` argument accepts a vector of colors, and a `background` color can also be specified.

```r
panelview(Capacity ~ polity2 + lngdp, 
          data = capacity, index = c("ccode", "year"), 
          axis.lab.gap = c(2,10), main = "Regime Type", 
          color = c("yellow", "orange","red","purple","brown"), background = "white")
```

--------------------------------

### Bivariate plots by unit for discrete outcomes with panelview

Source: https://context7.com/context7/yiqingxu_packages_panelview/llms.txt

This set of examples focuses on creating bivariate plots separated by unit, specifically when dealing with discrete outcomes. It showcases different combinations of discrete and continuous treatments and demonstrates how to control the plot style (e.g., points or lines) for discrete treatments using `outcome.type = "discrete"` and `style` arguments.

```r
# Discrete outcome + discrete treatment by unit
panelview(Y ~ D,
          data = simdata,
          index = c("id", "time"),
          type = "bivariate",
          by.unit = TRUE,
          theme.bw = FALSE,
          outcome.type = "discrete",
          id = unique(simdata$id)[1:12])
```

```r
# Continuous treatment + discrete outcome by unit
simdata$x <- seq(0.001, 0.9, 0.001)
panelview(Y ~ x,
          data = simdata,
          index = c("id", "time"),
          type = "bivar",
          by.unit = TRUE,
          outcome.type = "discrete",
          lwd = 0.4,
          id = unique(simdata$id)[1:12])
```

```r
# Use line style for discrete treatment
panelview(Y ~ D,
          data = simdata,
          index = c("id", "time"),
          type = "bivar",
          by.unit = TRUE,
          outcome.type = "discrete",
          style = "line",
          theme.bw = FALSE,
          lwd = 0.4,
          id = unique(simdata$id)[1:20])
```

--------------------------------

### Visualize Any Variable in Panel Data using panelView

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Demonstrates how to use panelView to visualize any variable in a panel dataset by omitting the treatment indicator, starting from v1.0.3. It shows the basic syntax for plotting an outcome variable.

```r
panelView(turnout ~ 1, data = turnout, index = c("abb","year"), type = "outcome", main = "Turnout", ylim = c(0,100), xlab = "Year", ylab = "Turnout")
```

--------------------------------

### Remove panelView Package (R)

Source: https://yiqingxu.org/packages/panelview/index

Provides commands to remove the panelView package from the R environment. This is useful for troubleshooting installation issues or ensuring a clean reinstallation.

```R
remove.packages('panelView')
```

```R
# or
remove.packages('panelview') # package name "panelview" no longer in use
```

--------------------------------

### Panelview with 3 Treatment Levels (Categorical)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This example demonstrates creating a custom categorical treatment variable with three levels (Autocracy, Hybrid, Democracy) and visualizing it using panelview. It sets custom colors and legend labels for clarity. The `panelview` function is used with a formula interface.

```r
demo2 <- rep(0, dim(capacity)[1])
demo2[which(capacity$polity2 < -0.5)] <- -1
demo2[which(capacity$polity2 > 0.5)] <- 1
capacity$demo2 <- demo2

panelview(Capacity ~ demo2 + lngdp, 
          data = capacity, index = c("ccode", "year"), 
          axis.lab.gap = c(2,10), 
          main = "Regime Type")
```

```r
library(RColorBrewer) 
mycol<-brewer.pal(3,"Set1")[c(1,3,2)]
panelview(Capacity ~ demo2, data = capacity, 
          index = c("ccode", "year"), axis.lab.gap = c(2), 
          main = "Regime Type", axis.lab = c("time"), 
          color = mycol, legend.labs = c("Autocracy", "Hybrid", "Democracy"))
```

```r
panelview(D = "demo2", data = capacity, index = c("ccode", "year"), 
          axis.lab.gap = c(2), main = "Regime Type", 
          axis.lab = c("time"), color = mycol, 
          legend.labs = c("Autocracy", "Hybrid", "Democracy"))
```

--------------------------------

### panelView with Continuous Treatment Variable (R)

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Illustrates using panelView with a continuous treatment variable, which is automatically handled when the number of treatment levels exceeds 5. This example shows how to turn off grid lines and customize color gradients for continuous treatments.

```r
panelView(Capacity ~ polity2 + lngdp, data = capacity, index = c("ccode", "year"), axis.lab.gap = c(2,10), main = "Regime Type", gridOff = TRUE)
```

```r
panelView(Capacity ~ polity2 + lngdp, data = capacity, index = c("ccode", "year"), axis.lab.gap = c(2,10), main = "Regime Type", color = c("yellow", "red"), background = "white")
```

--------------------------------

### General panelView Plotting

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

A basic example of using panelView to plot a panel dataset without specific pre-post differentiation. This is suitable for datasets where treatment status can change over time. The function visualizes the relationship between an outcome and treatment variables.

```r
panelView(Capacity ~ demo + lnpop + lngdp, data = capacity, index = c("ccode", "year"), axis.lab.gap = c(2,10), main = "Democracy and State Capacity")
```

--------------------------------

### Accommodate Discrete Outcomes in panelView

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Shows how to handle discrete outcome variables in panelView by setting `outcome.type = "discrete"`. This example uses the `simdata` dataset with an outcome variable having three values (0, 1, 2).

```r
panelView(Y ~ D, data = simdata, index = c("id", "time"), by.group = FALSE, outcome.type = "discrete", type = "outcome", xlim = c(8, 15))
```

--------------------------------

### Plot Discrete Outcomes with Continuous Treatment

Source: https://context7.com/context7/yiqingxu_packages_panelview/llms.txt

This example illustrates plotting discrete outcomes when the treatment variable is continuous or has multiple levels. In such cases, `panelview` typically uses a single color for all observations and disables treatment coloring to avoid confusion.

```r
# When treatment has >2 levels, no treatment coloring
panelview(Capacity ~ polity2 + lngdp,
          data = capacity,
          index = c("ccode", "year"),
          main = "Measuring State Capacity",
          type = "outcome",
          legendOff = TRUE)
```

--------------------------------

### Plot Outcome Trajectories by Cohort

Source: https://context7.com/context7/yiqingxu_packages_panelview/llms.txt

This example visualizes average outcome trajectories for each treatment cohort using `by.cohort = TRUE`. It groups units based on their unique treatment histories and plots the average outcome for each group, aiding in the diagnosis of treatment effect heterogeneity.

```r
# Show average outcome for each treatment cohort
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
          data = turnout,
          index = c("abb", "year"),
          type = "outcome",
          main = "EDR Reform and Turnout",
          by.cohort = TRUE,
          ylim = c(40, 80),
          legend.labs = c("Control States",
                          "Treated States (before EDR)",
                          "Treated States (after EDR)"))
```

--------------------------------

### Group Outcome Plots by Treatment Status

Source: https://context7.com/context7/yiqingxu_packages_panelview/llms.txt

This example shows how to group outcome plots by treatment status using `by.group = TRUE`. It creates separate subpanels for 'always treated', 'always control', and 'switchers' (units that change treatment status), allowing for a clear comparison of treatment effects across different groups.

```r
# Split into subpanels: always treated, always control, switchers
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
          data = turnout,
          index = c("abb", "year"),
          type = "outcome",
          main = "EDR Reform and Turnout",
          by.group = TRUE,
          cex.main = 20,
          cex.main.sub = 15)
```

--------------------------------

### Plot Discrete Outcomes with Jittered Points

Source: https://context7.com/context7/yiqingxu_packages_panelview/llms.txt

This example demonstrates plotting discrete outcome variables using `outcome.type = "discrete"`. The `jittered` points help visualize the distribution of discrete values (e.g., 0, 1, 2) over time, with colors indicating treatment status.

```r
# Plot discrete outcome with jittered points
panelview(Y ~ D,
          data = simdata,
          index = c("id", "time"),
          outcome.type = "discrete",
          type = "outcome",
          xlim = c(8, 15))
```

--------------------------------

### Load panelView and Sample Data

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Loads the panelView library and makes the built-in 'turnout', 'capacity', and 'simdata' datasets available for use. This is a preliminary step before performing any visualizations.

```r
library(panelView)
data(panelView)
ls()
```

--------------------------------

### Load panelView Package and Data

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Loads the panelView R package and its associated datasets. This is a prerequisite for using the package's visualization functions. It also lists the available datasets after loading.

```r
library(panelView)
#> ## See bit.ly/panelview4r for more info.
#> ## Report bugs -> yiqingxu@stanford.edu.
data(panelView)
ls()
#> [1] "a"           "capacity"    "dfdata_R"    "exceldata_R" "simdata"    
#> [6] "tf"          "turnout"     "url"
```

--------------------------------

### Panelview with General Settings: Democracy and State Capacity

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This R code demonstrates the panelview function for a general setting with the 'capacity' dataset. It visualizes the effect of democracy (treatment) on state capacity (outcome), including population and GDP. Key arguments include the formula, data, indexing, main title, and axis label gap.

```r
panelview(Capacity ~ demo + lnpop + lngdp, 
          data = capacity, index = c("ccode", "year"), 
          main = "Democracy and State Capacity",
          axis.lab.gap = c(2,10))
```

--------------------------------

### Turn Off Pre-Post Distinction in Outcome Plots

Source: https://context7.com/context7/yiqingxu_packages_panelview/llms.txt

This example demonstrates how to disable the pre-post distinction in panelview outcome plots. It is useful when you want to view the overall trend without emphasizing the period before and after a specific treatment.

```r
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
          data = turnout,
          index = c("abb", "year"),
          pre.post = FALSE,
          type = "outcome",
          main = "EDR Reform and Turnout",
          ylim = c(0, 100),
          xlab = "Year",
          ylab = "Turnout")
```

--------------------------------

### Basic Panel Visualization with panelView

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Generates a basic visualization of treatment status (EDR policy) over time for states using the 'turnout' dataset. It specifies the treatment variables, the dataset, and unit/time identifiers. Customizes x and y axis labels.

```r
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, 
          data = turnout, index = c("abb","year"), 
          xlab = "Year", ylab = "State")
```

--------------------------------

### panelView: Control unit display with display.all

Source: https://yiqingxu.org/packages/panelview/reference/news/index

Adds the `display.all` option to panelView. If set to TRUE, all units are displayed; otherwise, if the number of units exceeds 500, a random sample of 500 units is presented.

```r
panelView(..., display.all = TRUE)
```

--------------------------------

### PanelView with Continuous Treatment and Custom Colors (R)

Source: https://yiqingxu.org/packages/panelview/reference/articles/tutorial

Illustrates using panelview with a continuous treatment variable ('polity2') which is automatically handled when the number of treatment levels exceeds a threshold. It also shows how to customize the plot's appearance by specifying colors and background, and how to remove grid lines.

```R
panelview(Capacity ~ polity2 + lngdp, 
          data = capacity, index = c("ccode", "year"), 
          axis.lab.gap = c(2,10), main = "Regime Type", 
          gridOff = TRUE)
```

```R
panelview(Capacity ~ polity2 + lngdp, 
          data = capacity, index = c("ccode", "year"), 
          axis.lab.gap = c(2,10), main = "Regime Type", 
          color = c("yellow", "orange","red","purple","brown"), background = "white")
```

--------------------------------

### Panel Visualization with Pre/Post Treatment Distinction

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Visualizes treatment status, explicitly distinguishing between pre- and post-treatment periods for treated units. This is useful for Difference-in-Differences analysis. It uses the 'turnout' dataset and specifies unit/time identifiers.

```r
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, 
          data = turnout, index = c("abb","year"), 
          xlab = "Year", ylab = "State", pre.post = TRUE)
```

--------------------------------

### Plot Subset of Units with Panelview (show.id)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This R code illustrates how to plot a subset of units using the 'show.id' argument in panelview. It displays the treatment statuses for the first 25 units in the 'capacity' dataset, allowing for focused analysis of specific units.

```r
panelview(Capacity ~ demo + lnpop + lngdp, data = capacity, 
          index = c("ccode", "year"), axis.lab.gap = c(2,0), 
          main = "Democracy and State Capacity", show.id = c(1:25))
```

--------------------------------

### Advanced Panel Visualization with panelView Options

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Enhances the panel visualization by sorting units by treatment timing, setting a white background, and adjusting various font sizes (main title, axes, axis labels, legend). It also provides custom labels for the legend.

```r
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, 
          data = turnout, index = c("abb","year"), 
          xlab = "Year", ylab = "State", by.timing = TRUE, 
          legend.labs = c("No EDR", "EDR"), background = "white", 
          cex.main = 20, cex.axis= 8, cex.lab = 12, cex.legend = 12)
#> Specified labels in the order of: Under Control, Under Treatment.
```

--------------------------------

### Visualize Panel Data with panelview

Source: https://yiqingxu.org/packages/panelview/reference/panelView

This code snippet demonstrates how to use the panelview function from the panelview R package to visualize panel data. It shows the basic syntax for specifying the outcome variable, treatment variables, data source, and indexing variables. The function allows for customization of various plot elements through arguments like cex.lab, cex.legend, and by.unit.

```r
library(panelView)
data(panelView)
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
          data = turnout,
          index = c("abb","year"))


```

--------------------------------

### PanelView Plot Collapsing Treatment Histories (Sorted by Cohort Size)

Source: https://yiqingxu.org/packages/panelview/reference/articles/tutorial

Visualizes democracy and state capacity, collapsing units by treatment histories and sorting them by cohort size. This aids in understanding group dynamics in the 'capacity' dataset.

```R
panelview(Capacity ~ demo + lnpop + lngdp, data = capacity, 
          index = c("ccode", "year"), axis.lab.gap = c(2,0), 
          axis.lab.angle = 90, collapse.history = "TRUE") 
          # sorted by cohort size
```

--------------------------------

### Plot Treatment Status in Panel Data using panelView (R)

Source: https://yiqingxu.org/packages/panelview/index

Demonstrates how to use the panelView function to plot the treatment status across different units and time periods in a panel dataset. Requires the panelview package and sample data.

```R
library(panelView)
data(panelView)
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, 
          data = turnout, index = c("abb","year"), 
          xlab = "Year",ylab = "State")
```

--------------------------------

### Panel Visualization with Custom Colors and Treatment Timing

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Visualizes panel data using custom colors for treated (pre and post) and control groups, and sorts units by treatment timing. Requires the dplyr package for data manipulation. Colors are specified for control and treated groups.

```r
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
turnout %>% panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, 
                      color = c("#B0C4DE","white"), by.timing = TRUE, 
                      index = c("abb","year"), xlab = "Year", ylab = "State")
#> Specified colors in the order of: Under Control, Under Treatment.
```

--------------------------------

### Visualize Treatment Effects with Panelview

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This R code visualizes treatment effects in a panel dataset using the panelview function. It specifies the outcome and treatment variables, the data source, and indexing variables. The 'type' argument is set to 'treat' to indicate treatment visualization, and 'leave.gap' is set to TRUE to show gaps in time series.

```r
turnout2 <- turnout[!(turnout$year=="1924" | turnout$year=="1928" | turnout$year == "1940"),]
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, 
          data = turnout2, index = c("abb","year"), 
          type = "treat", leave.gap = TRUE)
```

--------------------------------

### Panelview with Sorted Units and X-axis Labels

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This R code showcases the panelview function with options to sort units by treatment timing and customize x-axis labels. The 'by.timing = TRUE' argument sorts units based on their first treatment period, and 'axis.lab.angle = 90' rotates the x-axis labels for better readability.

```r
panelview(Capacity ~ demo + lnpop + lngdp, 
          data = capacity, index = c("ccode", "year"), 
          main = "Democracy and State Capacity: Treatment Status", 
          axis.lab.angle = 90, by.timing = TRUE, axis.lab = "time")
```

--------------------------------

### Panel Visualization with Custom Legend and Pre/Post

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Generates a panel visualization that distinguishes pre- and post-treatment periods and allows for custom labels in the legend. The legend labels correspond to 'Control States', 'Treated States (before EDR)', and 'Treated States (after EDR)'.

```r
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, 
          data = turnout, index = c("abb","year"), 
          xlab = "Year", ylab = "State", by.timing = TRUE, 
          pre.post = TRUE, 
          legend.labs = c("Control States", "Treated States (before EDR)", 
                          "Treated States (after EDR)"))
#> Specified labels in the order of: Controls, Treated (Pre), Treated (Post).
```

--------------------------------

### BibTeX Citation for Panelview Package

Source: https://yiqingxu.org/packages/panelview/reference/authors

This snippet provides the BibTeX entry for citing the panelView R package and panelview Stata package. It includes all necessary details such as title, authors, journal, year, volume, number, pages, and DOI for academic referencing.

```bibtex
@Article{
  title = {Panel Data Visualization in {R} ({panelView}) and {Stata} ({panelview})},
  author = {Hongyu Mou and Licheng Liu and Yiqing Xu},
  journal = {Journal of Statistical Software},
  year = {2023},
  volume = {107},
  number = {7},
  pages = {1--20},
  doi = {10.18637/jss.v107.i07},
}
```

--------------------------------

### Panel Visualization using Direct Treatment Variable

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Generates a panel visualization by directly specifying the treatment indicator variable ('policy_edr') instead of using a formula. Includes pre- and post-treatment distinction and custom axis label control.

```r
panelview(D = "policy_edr", data = turnout, index = c("abb","year"), 
          xlab = "Year", ylab = "State", pre.post = TRUE,
          main = "EDR Reform", axis.lab = "time")
```

--------------------------------

### Plot Subset of Units with Panelview (id)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This R code demonstrates plotting a subset of units by specifying their original IDs using the 'id' argument in panelview. It visualizes the treatment statuses for units 26 through 50, sorted by treatment timing.

```r
panelview(Capacity ~ demo + lnpop + lngdp, 
          data = capacity, 
          index = c("ccode", "year"), 
          axis.lab.gap = c(2,0), 
          main = "Democracy and State Capacity", 
          id = unique(capacity$ccode)[26:50], by.timing = TRUE)
#> List of units removed from dataset: 260 265 342 346 347
```

--------------------------------

### panelView with More Than Two Treatment Conditions (R)

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Demonstrates using panelView to visualize panel data with multiple (more than two) discrete treatment levels. It shows how to prepare data for multi-level treatments and customize plot aesthetics like colors and labels. Supports custom axis labels and direct specification of the treatment variable 'D'.

```r
demo2 <- rep(0, dim(capacity)[1])
demo2[which(capacity$polity2 < -0.5)] <- -1
demo2[which(capacity$polity2 > 0.5)] <- 1
capacity$demo2 <- demo2

panelView(Capacity ~ demo2 + lngdp, data = capacity, index = c("ccode", "year"), axis.lab.gap = c(2,10), main = "Regime Type")
```

```r
library(RColorBrewer) 
mycol<-brewer.pal(3,"Set1")[c(1,3,2)]
panelView(Capacity ~ demo2, data = capacity, index = c("ccode", "year"), axis.lab.gap = c(2), main = "Regime Type", axis.lab = c("time"), color = mycol, legend.labs = c("Autocracy", "Hybrid", "Democracy"))
```

```r
panelView(D = "demo2", data = capacity, index = c("ccode", "year"), axis.lab.gap = c(2), main = "Regime Type", axis.lab = c("time"), color = mycol, legend.labs = c("Autocracy", "Hybrid", "Democracy"))
```

--------------------------------

### Visualize Discrete Outcomes Grouped by Treatment Status in panelView

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Demonstrates visualizing discrete outcomes grouped by treatment status using `by.group = TRUE` in panelView, along with applying a black and white theme (`theme.bw = TRUE`).

```r
panelView(Y ~ D, data = simdata, index = c("id", "time"), by.group = TRUE, outcome.type = "discrete", type = "outcome",  xlim = c(8, 15), theme.bw = TRUE)
```

--------------------------------

### Plot Treatment Status in Panel Data (R)

Source: https://yiqingxu.org/packages/panelview/reference/index

Demonstrates how to use the panelview function to visualize treatment status in a panel dataset. It requires the panelView package and a dataset named 'turnout'. The function visualizes the relationship between turnout and policy variables, indexed by state and year.

```R
library(panelView)
data(turnout)
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, 
          data = turnout, index = c("abb","year"), 
          xlab = "Year", ylab = "State")
```

--------------------------------

### PanelView: Continuous Y, Discrete D (Line Style)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Plots a continuous outcome variable against multiple discrete treatment variables using a line style plot. Requires the 'panelview' package. Input data should include 'turnout' and treatment columns, indexed by 'abb' and 'year'.

```r
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, 
          index = c("abb","year"),
          type = "bivar",
          by.unit = TRUE,
          style = "line",
          theme.bw = FALSE,
          lwd = 0.5,
          show.id = c(1:12),
          ylab = "Turnout")
```

--------------------------------

### Plot Missingness with Panelview (type = 'miss')

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This R code demonstrates plotting missing values in a panel dataset using panelview with 'type = "miss"'. It visualizes missingness for the 'Capacity' variable, specifying indexing and disabling axis labels for clarity.

```r
capacity %>% panelview(Capacity ~ 1, 
                       index = c("ccode","year"), 
                       axis.lab="off", type = "miss")
```

--------------------------------

### Plot Treatment Status (Pre/Post with Custom Labels)

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Combines the pre/post distinction with custom legend labels and unit sorting by treatment timing. This provides a detailed view of treatment adoption and its timing relative to control and treated groups.

```r
panelView(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), xlab = "Year", ylab = "State", by.timing = TRUE, pre.post = TRUE, legend.labs = c("Control States", "Treated States (before EDR)", "Treated States (after EDR)"))
```

--------------------------------

### PanelView: Continuous Y, Discrete D (Bivar Plot)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Plots a continuous outcome variable against a discrete treatment variable using a bivariate plot. Requires the 'panelview' package. Input data should include 'turnout' and 'policy_edr' columns, indexed by 'abb' and 'year'.

```r
panelview(turnout ~ policy_edr, data = turnout, 
          index = c("abb","year"), ylab = "Turnout", type = "bivar",
          by.unit = TRUE,  show.id = c(1:12))
```

--------------------------------

### PanelView: Discrete Y, Continuous D (Bivar Plot)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Plots a discrete outcome variable against a continuous treatment variable using a bivariate plot. Requires the 'panelview' package and specifies 'outcome.type = "discrete"'. Input data should include 'Y' and 'x' columns, indexed by 'id' and 'time'.

```r
simdata$x <- seq(0.001,0.9,0.001)
panelview(Y ~ x, data = simdata,
          index = c("id", "time"),
          type = "bivar",
          by.unit = TRUE,
          outcome.type = "discrete",
          lwd = 0.4,
          id = unique(simdata$id)[1:12])
```

--------------------------------

### Plot Treatment Indicator with Panelview (D argument)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

This R code shows how to plot a treatment indicator directly using the 'D' argument in panelview when a full formula is not provided. It visualizes the 'demo' treatment indicator for the first 25 units, specifying the 'type' as 'treat'.

```r
panelview(D = "demo", data = capacity, index = c("ccode", "year"), 
          axis.lab.gap = c(2,0), main = "Democracy and State Capacity", 
          show.id = c(1:25), type = "treat")
```

--------------------------------

### PanelView: Continuous Y, Discrete D (Bivar Plot with ylim)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Plots a continuous outcome variable against a discrete treatment variable using a bivariate plot, with specified y-axis limits for both outcome and treatment. Requires the 'panelview' package. Input data should include 'lnpop' and 'demo' columns, indexed by 'country' and 'year'.

```r
panelview(lnpop ~ demo, data = capacity,
          index = c("country","year"), type = "bivar",
          by.unit = TRUE, ylim = list(c(8,10), c(-2, 2)),
          show.id = c(1:16))
```

--------------------------------

### PanelView: Continuous Y, Continuous D (Bivar Plot)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Plots a continuous outcome variable against a continuous treatment variable using a bivariate plot. Requires the 'panelview' package. Input data should include 'lnpop' and 'polity2' columns, indexed by 'country' and 'year'.

```r
panelview(lnpop ~ polity2, data = capacity,
          index = c("country","year"),
          type = "bivar",
          by.unit = TRUE,
          theme.bw = FALSE,
          color = c("blue","red"),
          show.id = c(1:12))
```

--------------------------------

### Sort Units by Treatment Timing in panelView

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Demonstrates how to sort units based on their first treatment period using the 'by.timing = TRUE' option in panelView. This provides a more visually appealing arrangement of units, highlighting the timing of treatment effects. It also allows for setting axis labels.

```r
panelView(Capacity ~ demo + lnpop + lngdp, data = capacity, index = c("ccode", "year"), axis.lab.gap = 2, main = "Democracy and State Capacity: Treatment Status", by.timing = TRUE, axis.lab = "time")
```

--------------------------------

### Plot Discrete Outcomes with PanelView in R

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Accommodates discrete outcome variables by setting `outcome.type = "discrete"`. Shows how to plot discrete outcomes with a binary treatment, and also how to split the sample by changes in treatment status using `by.group = TRUE`.

```r
panelview(Y ~ D, data = simdata, index = c("id", "time"), 
          outcome.type = "discrete", 
          type = "outcome", xlim = c(8, 15))
```

```r
panelview(Y ~ D, data = simdata, index = c("id", "time"),
          by.group = TRUE, outcome.type = "discrete",
          type = "outcome",  xlim = c(8, 15))
```

--------------------------------

### PanelView: Discrete Y, Discrete D (Line Style)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Plots a discrete outcome variable against a discrete treatment variable using a line style plot. Requires the 'panelview' package and specifies 'outcome.type = "discrete"'. Input data should include 'Y' and 'D' columns, indexed by 'id' and 'time'.

```r
panelview(Y ~ D, data = simdata,
          index = c("id", "time"),
          type = "bivar",
          by.unit = TRUE,
          outcome.type = "discrete",
          style = "line",
          theme.bw = FALSE,
          lwd = 0.4,
          id = unique(simdata$id)[1:20])
```

--------------------------------

### Panel data visualization function signature in R

Source: https://yiqingxu.org/packages/panelview/reference/reference/panelView

This R code snippet displays the function signature for `panelview`, a core function for visualizing panel data. It details numerous arguments for controlling data input, plot aesthetics, and specific visualization types. Dependencies include the R programming language and potentially other data visualization or panel data analysis packages.

```r
panelview(data, formula = NULL, Y = NULL, D = NULL,
            X = NULL, index,
            ignore.treat = FALSE, type = "treat",
            outcome.type = "continuous",
            treat.type = NULL, by.group = FALSE, by.group.side = FALSE, 
            by.timing = FALSE, theme.bw = TRUE,
            xlim = NULL, ylim = NULL,
            xlab = NULL, ylab = NULL,
            gridOff = FALSE, legendOff = FALSE,
            legend.labs = NULL, main = NULL,
            pre.post = NULL, id = NULL, show.id = NULL,
            color = NULL, axis.adjust = FALSE, axis.lab = "both",
            axis.lab.gap = c(0, 0), axis.lab.angle = NULL, shade.post = FALSE,
            cex.main = 15, cex.main.sub = 12, cex.axis = 8,
            cex.axis.x = NULL, cex.axis.y = NULL,
            cex.lab = 12, cex.legend = 12, background = NULL,
            style = NULL, by.unit = FALSE, lwd = 0.2, leave.gap = FALSE,
            display.all = NULL, by.cohort = FALSE,
            collapse.history = NULL, report.missing = FALSE)
```

--------------------------------

### PanelView Plot Collapsing Treatment Histories (Sorted by Timing)

Source: https://yiqingxu.org/packages/panelview/reference/articles/tutorial

Collapses units based on their treatment histories in the 'turnout' dataset, sorting them by the timing of their first treatment. This is useful for datasets with many units.

```R
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, 
          data = turnout, index = c("abb","year"), axis.lab.angle = 90,
          by.timing = TRUE, collapse.history = "TRUE") 
          # sorted by treatment timing
```

--------------------------------

### Customize Brick Colors in panelView

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Illustrates how to change the colors of control and treated bricks in panelView plots using the 'color' option. The order of colors depends on the 'pre.post' argument. This functionality allows for enhanced visual distinction of different treatment statuses.

```r
library(dplyr)
turnout %>%
  panelView(turnout ~ policy_edr + policy_mail_in + policy_motor, color = c("#B0C4DE","white"), by.timing = TRUE, index = c("abb","year"), xlab = "Year", ylab = "State")
```

--------------------------------

### Visualize Any Variable: Ignore Treatment Status

Source: https://yiqingxu.org/packages/panelview/reference/articles/tutorial

Visualizes any variable by setting 'ignore.treat = TRUE', which can introduce missing values depending on the right-hand-side variables. Uses 'turnout' data with 'abb' and 'year' as index.

```R
panelview(Y = "turnout", data = turnout,
          index = c("abb","year"), type = "outcome",
          main = "Turnout", ylim = c(0,100), 
          xlab = "Year", ylab = "Turnout")
```

--------------------------------

### panelView: Add collapse.history for treatment history plots

Source: https://yiqingxu.org/packages/panelview/reference/news/index

Introduces the `collapse.history` argument to the `treat` plot in the panelView R package. This feature allows users to collapse treatment history, simplifying the visualization of treatment effects over time. No external dependencies are noted.

```r
panelView(..., collapse.history = TRUE)
```

--------------------------------

### Visualize binary treatment adoption patterns with panelView

Source: https://context7.com/context7/yiqingxu_packages_panelview/llms.txt

Generates a basic treatment status plot using the panelView function. This visualization helps understand when and which units receive treatment over time. The first right-hand side variable in the formula is treated as the primary indicator.

```r
library(panelView)
data(panelView)

# Basic treatment status plot
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
          data = turnout,
          index = c("abb", "year"),
          xlab = "Year",
          ylab = "State")

# The first RHS variable (policy_edr) is the treatment indicator
# Additional variables (policy_mail_in, policy_motor) are covariates
# Missing values in covariates affect the visualization
```

--------------------------------

### Visualize Any Variable with Panelview in R (Ignoring Treatment)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Visualizes any variable in a panel dataset by omitting the treatment indicator or setting 'ignore.treat = TRUE'. This allows for general time-series plotting of variables without focusing on treatment effects. Requires 'panelview' package.

```r
panelview(turnout ~ 1, data = turnout, index = c("abb","year"), 
          type = "outcome", main = "Turnout", 
          ylim = c(0,100), xlab = "Year", ylab = "Turnout")
```

```r
panelview(Y = "turnout", data = turnout,
          index = c("abb","year"), type = "outcome",
          main = "Turnout", ylim = c(0,100), 
          xlab = "Year", ylab = "Turnout")
```

--------------------------------

### Plot Treatment Status (Basic)

Source: https://yiqingxu.org/packages/panelview/panelView_1.1

Visualizes the treatment status (e.g., EDR policies) and missing values for each unit (state) over time (year) using the 'turnout' dataset. The formula specifies the outcome and treatment variables, and 'index' defines the unit and time identifiers. X and Y axis labels can be customized.

```r
panelView(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), xlab = "Year", ylab = "State")
```

--------------------------------

### Visualize Bivariate Relationships by Unit with PanelView

Source: https://yiqingxu.org/packages/panelview/reference/articles/tutorial

Plots outcome (Y) and treatment (D) against time for each unit individually. This is achieved by setting `by.unit = TRUE`. The function arranges multiple subgraphs in a row for comparison.

```r
...
```

--------------------------------

### Visualize Any Variable: No Treatment Indicator

Source: https://yiqingxu.org/packages/panelview/reference/articles/tutorial

Visualizes any variable in a panel dataset by omitting the treatment indicator, effectively allowing plotting of non-treatment related variables. Uses 'turnout' data with 'abb' and 'year' as index.

```R
panelview(turnout ~ 1, data = turnout, index = c("abb","year"), 
          type = "outcome", main = "Turnout", 
          ylim = c(0,100), xlab = "Year", ylab = "Turnout")
```

--------------------------------

### PanelView: Discrete Y, Discrete D (Bivariate Plot)

Source: https://yiqingxu.org/packages/panelview/articles/tutorial

Plots a discrete outcome variable against a discrete treatment variable using a bivariate plot. Requires the 'panelview' package and specifies 'outcome.type = "discrete"'. Input data should include 'Y' and 'D' columns, indexed by 'id' and 'time'.

```r
panelview(Y ~ D, data = simdata,
          index = c("id", "time"),
          type = "bivariate",
          by.unit = TRUE,
          theme.bw = FALSE,
          outcome.type = "discrete",
          id = unique(simdata$id)[1:12])
```
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