In xuyiqing/panelView: Visualizing Panel Data
knitr::opts_chunk$set(
collapse = TRUE,
fig.width = 10,
fig.height = 7,
comment = "#>"
)
Syntax overview
#import xlxs as data.frame
library(httr)
url <- "https://yiqingxu.org/packages/panelview/syntax.xlsx"
a <- GET(url, write_disk(tf <- tempfile(fileext = ".xlsx")))
exceldata_R <- readxl::read_excel(tf)
dfdata_R = data.frame(exceldata_R)
knitr::kable(dfdata_R, "simple")
Note that Y, D, and X in the above table are merely labels; they can be any variables in a panel dataset.
Plot treatment & missingness
First, we show how to visualize the treatment conditions and missing values in a panel dataset. The treatment indicator may be dichotomous or have more than 2 levels. We first load the panelView package, which ships three datasets.
library(panelView)
data(panelView)
ls()
Using the turnout dataset (a balanced panel), we show the treatment status of Election Day Registration (EDR) in each state in a given year (Xu 2017). The first variable on the right-hand-side of the regression formula is designated as the treatment indicator. Including covariates may change the plot because of missing values in these covariates. The index option specifies the unit (group) and time indicators. We can change the labels (titles) of x- and y-axes through xlab and ylab, respectively.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
data = turnout, index = c("abb","year"),
xlab = "Year", ylab = "State")
We can use the by.timing option to sort units by the timing of receiving the treatment (then by the total number of periods exposed to the treatment). Users can also specify the background color using the background option and use various cex options to adjust fontsizes for texts appearing in the main title (cex.main), axes (cex.axis), axis labels (cex.lab), and legend (cex.legend).
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)
We can use the main option to change the title of the plot, use the axis.lab.gap option to change the gaps between labels on the x- and y-axes, and use the legend.labs option to change the labels shown in the legend.
For DID-type TSCS data with a dichotomous treatment indicator, we can distinguish the pre- and post-treatment periods for treated units by specifying pre.post = TURE (which is set as default prior to v.1.1.0):
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
data = turnout, index = c("abb","year"),
xlab = "Year", ylab = "State", pre.post = TRUE)
Again, we can change the labels in the legend. Make sure the length of legend.labs is the same as the number of treatment statuses (in this case, 3).
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)"))
We can remove the labels on the y-axis (or x-axis) by specifying axis.lab = "time" (or "unit"). Setting axis.lab = "off" will remove labels on both axes. The default is axis.lab = "both".
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
data = turnout, index = c("abb","year"),
main = "EDR Reform", axis.lab = "time")
Instead of specifying a formula, we can also directly give the variable name of the treatment indicator:
panelview(D = "policy_edr", data = turnout, index = c("abb","year"),
xlab = "Year", ylab = "State", pre.post = TRUE,
main = "EDR Reform", axis.lab = "time")
We can change the colors of the bricks for the controls and treated using the color option. Colors should be specified in the order of "treated-pre", "treated-post" and "control"---plus "missing" if the dataset contains missing values---if pre.post = TRUE and in the order of "control", "treated" if pre.post = FALSE:
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")
If the time variable is not evenly distributed, we can use leave.gap = TRUE to show the gaps in time using white bars. Otherwise, panelview will skip the time gaps and leave an warning "Time is not evenly distributed (possibly due to missing data)."
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)
For a panel dataset in which the treatment may switch on and off, we do not differentiate between pre- and post-treatment statuses. To demonstrate how panelview can be used in a more general setting, the following plot uses the capacity dataset, which is used to investigate the effect of democracy, the treatment, on state capacity, the outcome (Wang and Xu 2018). From the figure below, we see quite a few cases of democratic reversals and that there are many missing values.
panelview(Capacity ~ demo + lnpop + lngdp,
data = capacity, index = c("ccode", "year"),
main = "Democracy and State Capacity",
axis.lab.gap = c(2,10))
Sorting units based on the first period a unit receives the treatment gives a more appealing visual; axis.lab.angle allows users to change the look of the labels on the x-axis:
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")
Collapse units by treatment histories
When the number of units is large, sometimes it is helpful to collapse units based on their treatment histories. We can do so by setting collapse.history = TRUE. See two examples below. The units are sorted by the timing of first getting the treatment if by.timing = TRUE or by the group size of each unique treatment history, which is shown on the y-axis.
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
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 a subset of units
Sometimes a dataset has many units and we only want to take a peak of a subset of the units. panelview allows users to specify the units to be be shown by specifying the show.id (units in their alphabetical order) or id (original unit ids recoded in the "unit" variable) options. In the following figure, we plot the treatment statuses of the first 25 units.
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))
If the formula is not provided, we can use the argument D to specify the treatment indicator. For example:
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")
Below we plot the treatment statuses of the next 25 units by calling their names, i.e., their ccode. Note that several countries are removed from the plot due to missing values in the specified variables.
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$ccode)[26:50], by.timing = TRUE)
Plot missingness only
Starting from v.1.1.8, we allow users to plot missingness in by setting type(missing) or type(miss).
capacity %>% panelview(Capacity ~ 1,
index = c("ccode","year"),
axis.lab="off", type = "miss")
We can also directly supply a variable name without specifying a formula:
capacity %>% panelview(Y = "Capacity",
index = c("ccode", "year"),
axis.lab.gap = c(2,10),
type = "miss")
When leave.gap = TRUE, time gaps are shown in the plot when the time variable is not evenly distributed (the default is FALSE):
capacity$demo[capacity$year == "1960" | capacity$year == "1980"] <- NA
capacity$lngdp[capacity$year == "1990"] <- NA
panelview(1 ~ demo + lngdp, data = capacity, index = c("ccode","year"),
type = "missing", leave.gap = TRUE, axis.lab.gap = c(2,10))
>2 treatment conditions
Starting from v.1.1.0, panelview supports TSCS data with more than 2 treatment levels.
For example, we create a measure of regime type with three treatment levels.
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")
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"))
This is equivalent to:
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"))
If the number of treatment levels is greater than 5, then the treatment indicator
will be regarded as a continuous variable. We can remove the grid lines by
specifying gridOff = TRUE:
panelview(Capacity ~ polity2 + lngdp,
data = capacity, index = c("ccode", "year"),
axis.lab.gap = c(2,10), main = "Regime Type",
gridOff = TRUE)
We can also change the colors using the option color:
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")
Plot an outcome (or any variable)
The second main function of panelview is to plot the raw outcome variable of a panel dataset in a time-series fashion. The syntax is very similar except that we need to specify type = "outcome". We can control the ranges of the data to be shown by supplying xlim and ylim. Different colors represent different treatment conditions.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
data = turnout, index = c("abb","year"),
type = "outcome", main = "EDR Reform and Turnout",
ylim = c(0,100),xlab = "Year", ylab = "Turnout")
To turn off the coloring for observations in the pretreatment period, set pre.post = FALSE.
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")
Similarly, we can specify the labels shown in the legend using legend.labs. Note that we can also turn off legends by specifying legendOff = TRUE.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
data = turnout, index = c("abb","year"),
type = "outcome", main = "EDR Reform and Turnout",
legend.labs = c("Control States","Treated States (before EDR)",
"Treated States (after EDR)"))
We do not use the black and white theme by specifying theme.bw = FALSE.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
data = turnout, index = c("abb","year"), type = "outcome",
main = "EDR Reform and Turnout", theme.bw = FALSE)
And we can change the colors for observations under different treatment statuses using the option color and change the legend labels using legend.labs.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
data = turnout, index = c("abb","year"),
type = "outcome", main = "EDR Reform and Turnout",
color = c("lightblue", "blue", "#99999950"),
legend.labs = c("Control States","Treated States (before EDR)",
"Treated States (after EDR)"))
In cases with a staggered treatment and a continous outcome, we can collapse units based on cohort defined the timing of getting the treatment by setting by.cohort = TRUE. In the turnout data, there are five cohorts:
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)
Again, we can specify which unit(s) we want to take a look at:
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
data = turnout, index = c("abb","year"),
type = "outcome", main = "EDR Reform and Turnout (AL, AR, CT)",
id = c("AL", "AR", "CT"))
To better understand the data, sometimes we want to plot the outcome based on whether the treatment status has changed during the observed time period. We can simply add an option by.group = TRUE. The algorithm will analyze the data and automatically put each unit into different groups, e.g. (1) always treated, (2) always in control, (3) treatment status changed. Users can adjust the fontsizes of the title and subtitles using the cex.main and cex.main.sub options, respectively.
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)
If we want to arrange the subfigures in a row rather than in a column, use the by.group.side = TRUE option instead of by.group = TRUE.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor,
data = turnout, index = c("abb","year"),
type = "outcome", main = "EDR Reform and Turnout",
by.group.side = TRUE, cex.main = 20, cex.main.sub = 15)
Ignore treatment status
Starting from v 1.0.3, we allow users to omit the treatment indicator. Therefore, panelview can in fact be used to visualize any variable in a panel dataset.
panelview(turnout ~ 1, data = turnout, index = c("abb","year"),
type = "outcome", main = "Turnout",
ylim = c(0,100), xlab = "Year", ylab = "Turnout")
Alternatively, by setting ignore.treat = TRUE. The right-hand-side variables may change the plot by introducing missing values.
panelview(Y = "turnout", data = turnout,
index = c("abb","year"), type = "outcome",
main = "Turnout", ylim = c(0,100),
xlab = "Year", ylab = "Turnout")
Outcome trajectories by cohort
Starting from v 1.1.11, users can use the by.cohort = TRUE option to plot the average outcome trajectories of units with same treatment history (if the number of unique treatment history is less than 20). This type of plots can help users diagnose the extent to which treatment effect heterogeneity may cause biases of certain estimators (e.g. the two-way fixed effects estimators).
panelview(turnout ~ policy_edr+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)"))
Discrete outcomes
We can accommodate discrete variables by setting outcome.type = "discrete". Below is an example using the simdata dataset, in which the outcome variable takes three values: 0, 1, and 2.
panelview(Y ~ D, data = simdata, index = c("id", "time"),
outcome.type = "discrete",
type = "outcome", xlim = c(8, 15))
We split the sample based on changes in treatment status and use the black and white theme:
panelview(Y ~ D, data = simdata, index = c("id", "time"),
by.group = TRUE, outcome.type = "discrete",
type = "outcome", xlim = c(8, 15))
If the treatment indicator has more than 2 treatment levels or a continuous variable (e.g. polity2), then treatment status will not be shown on the "outcome" plot:
panelview(Capacity ~ polity2 + lngdp,
data = capacity, index = c("ccode", "year"),
main = "Measuring State Capacity",
type = "outcome", legendOff = TRUE)
Plot bivariate relationships
Visualize time series of the outcome and treatment in one figure by specifying type = "bivar" or type = "bivariate". For continuous variable, we use line plot as default; for discrete variable, we use bar plot. To plot connected lines ( "connected" or "c" ), lines ( "line" or "l" ), or bars ( "bar" or "b" ) rather than the default, please add style = c("",""), where the first element defines the outcome style, and the second defines the treatment style.
In aggregate
When type = "bivariate", panelview plots the averages of D and Y against time with the default option by.unit = FALSE.
Below are two examples with a continuous outcome and a discrete treatment. In the first example, style = c("c","b") represents a connected line plot for the outcome and a bar plot for the treatment---the default is style = c("l","b"), in which "l" represents a line plot. In the second example, we adjust the y-axes ranges using ylim = list(c( , ), c( , )), in which the first vector specifies the range for the average outcome and the second vector specifies the range for the average treatment.
# 1. Continuous Y, discrete D:
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout,
index = c("abb","year"),
type = "bivariate",
style = c("c","b"),
main = "EDR Reform and Turnout", ylab = "Turnout")
panelview(lnpop ~ demo, data = capacity, index = c("country", "year"),
ylim = list(c(8, 9.4), c(0.25, 0.6)), type = "bivar")
With discrete outcome and treatment variables, the default is style = c("b","b"):
# 2. Discrete Y, discrete D:
panelview(Y ~ D, data = simdata, index = c("id", "time"),
type = "bivar", theme.bw = FALSE, outcome.type = "discrete")
When both variables are continuous, the default is style = c("l","l"):
# 3. Continuous Y, continuous D:
panelview(lnpop ~ polity2, data = capacity, index = c("country", "year"),
ylim = list(c(8.75,9.4), c(-0.2, 0.35)), type = "bivar")
When both variables are discrete, the default is style = c("l","b"):
# 4. Discrete Y, continuous D
simdata$x <- seq(0.001,0.9,0.001)
panelview(Y ~ x, data = simdata, index = c("id", "time"),
type = "bivar", outcome.type = "discrete")
Another example. Option lwd controls the line width (default is 0.2):
## Line the discete treatment
# 1. Y: continuous; D: discrete
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout,
index = c("abb","year"), xlab = "EDR", ylab = "Turnout", type = "bivar",
style = c("line","connected"), color = c(2, 3), lwd = 0.4)
By each unit
We plot Y and D against time for each unit using by.unit = TRUE. Below are two examples with continuous outcome and discrete treatment. We arrange four subgraphs in one row:
# 1. Continuous Y, discrete D:
panelview(turnout ~ policy_edr, data = turnout,
index = c("abb","year"), ylab = "Turnout", type = "bivar",
by.unit = TRUE, show.id = c(1:12))
Another example:
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))
If the outcome is discrete, we can plot outcome and treatment against time in the same figure, indicating outcome.type = "discrete":
# 2. Discrete Y, discrete D:
panelview(Y ~ D, data = simdata,
index = c("id", "time"),
type = "bivariate",
by.unit = TRUE,
theme.bw = FALSE,
outcome.type = "discrete",
id = unique(simdata$id)[1:12])
When both variables are continuous:
# 3. Continuous Y, continuous D:
panelview(lnpop ~ polity2, data = capacity,
index = c("country","year"),
type = "bivar",
by.unit = TRUE,
theme.bw = FALSE,
color = c("blue","red"),
show.id = c(1:12))
With discrete outcome and continuous treatment:
# 4. Discrete Y, continuous D
simdata$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$id)[1:12])
Adding style = "line" to plot both variables in lines:
## Line the discete treatment
# 1. Y: continuous; D: discrete
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout,
index = c("abb","year"),
type = "bivar",
by.unit = TRUE,
style = "line",
theme.bw = FALSE,
lwd = 0.5,
show.id = c(1:12),
ylab = "Turnout")
# 2. Y: Discrete; D: discrete
panelview(Y ~ D, data = simdata,
index = c("id", "time"),
type = "bivar",
by.unit = TRUE,
outcome.type = "discrete",
style = "line",
theme.bw = FALSE,
lwd = 0.4,
id = unique(simdata$id)[1:20])
xuyiqing/panelView documentation built on Oct. 1, 2023, 3:58 a.m.
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knitr::opts_chunk$set( collapse = TRUE, fig.width = 10, fig.height = 7, comment = "#>" )
Syntax overview
#import xlxs as data.frame library(httr) url <- "https://yiqingxu.org/packages/panelview/syntax.xlsx" a <- GET(url, write_disk(tf <- tempfile(fileext = ".xlsx"))) exceldata_R <- readxl::read_excel(tf) dfdata_R = data.frame(exceldata_R) knitr::kable(dfdata_R, "simple")
Note that Y, D, and X in the above table are merely labels; they can be any variables in a panel dataset.
Plot treatment & missingness
First, we show how to visualize the treatment conditions and missing values in a panel dataset. The treatment indicator may be dichotomous or have more than 2 levels. We first load the panelView package, which ships three datasets.
library(panelView) data(panelView) ls()
Using the turnout dataset (a balanced panel), we show the treatment status of Election Day Registration (EDR) in each state in a given year (Xu 2017). The first variable on the right-hand-side of the regression formula is designated as the treatment indicator. Including covariates may change the plot because of missing values in these covariates. The index option specifies the unit (group) and time indicators. We can change the labels (titles) of x- and y-axes through xlab and ylab, respectively.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), xlab = "Year", ylab = "State")
We can use the by.timing option to sort units by the timing of receiving the treatment (then by the total number of periods exposed to the treatment). Users can also specify the background color using the background option and use various cex options to adjust fontsizes for texts appearing in the main title (cex.main), axes (cex.axis), axis labels (cex.lab), and legend (cex.legend).
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)
We can use the main option to change the title of the plot, use the axis.lab.gap option to change the gaps between labels on the x- and y-axes, and use the legend.labs option to change the labels shown in the legend.
For DID-type TSCS data with a dichotomous treatment indicator, we can distinguish the pre- and post-treatment periods for treated units by specifying pre.post = TURE (which is set as default prior to v.1.1.0):
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), xlab = "Year", ylab = "State", pre.post = TRUE)
Again, we can change the labels in the legend. Make sure the length of legend.labs is the same as the number of treatment statuses (in this case, 3).
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)"))
We can remove the labels on the y-axis (or x-axis) by specifying axis.lab = "time" (or "unit"). Setting axis.lab = "off" will remove labels on both axes. The default is axis.lab = "both".
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), main = "EDR Reform", axis.lab = "time")
Instead of specifying a formula, we can also directly give the variable name of the treatment indicator:
panelview(D = "policy_edr", data = turnout, index = c("abb","year"), xlab = "Year", ylab = "State", pre.post = TRUE, main = "EDR Reform", axis.lab = "time")
We can change the colors of the bricks for the controls and treated using the color option. Colors should be specified in the order of "treated-pre", "treated-post" and "control"---plus "missing" if the dataset contains missing values---if pre.post = TRUE and in the order of "control", "treated" if pre.post = FALSE:
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")
If the time variable is not evenly distributed, we can use leave.gap = TRUE to show the gaps in time using white bars. Otherwise, panelview will skip the time gaps and leave an warning "Time is not evenly distributed (possibly due to missing data)."
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)
For a panel dataset in which the treatment may switch on and off, we do not differentiate between pre- and post-treatment statuses. To demonstrate how panelview can be used in a more general setting, the following plot uses the capacity dataset, which is used to investigate the effect of democracy, the treatment, on state capacity, the outcome (Wang and Xu 2018). From the figure below, we see quite a few cases of democratic reversals and that there are many missing values.
panelview(Capacity ~ demo + lnpop + lngdp, data = capacity, index = c("ccode", "year"), main = "Democracy and State Capacity", axis.lab.gap = c(2,10))
Sorting units based on the first period a unit receives the treatment gives a more appealing visual; axis.lab.angle allows users to change the look of the labels on the x-axis:
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")
Collapse units by treatment histories
When the number of units is large, sometimes it is helpful to collapse units based on their treatment histories. We can do so by setting collapse.history = TRUE. See two examples below. The units are sorted by the timing of first getting the treatment if by.timing = TRUE or by the group size of each unique treatment history, which is shown on the y-axis.
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
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 a subset of units
Sometimes a dataset has many units and we only want to take a peak of a subset of the units. panelview allows users to specify the units to be be shown by specifying the show.id (units in their alphabetical order) or id (original unit ids recoded in the "unit" variable) options. In the following figure, we plot the treatment statuses of the first 25 units.
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))
If the formula is not provided, we can use the argument D to specify the treatment indicator. For example:
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")
Below we plot the treatment statuses of the next 25 units by calling their names, i.e., their ccode. Note that several countries are removed from the plot due to missing values in the specified variables.
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$ccode)[26:50], by.timing = TRUE)
Plot missingness only
Starting from v.1.1.8, we allow users to plot missingness in by setting type(missing) or type(miss).
capacity %>% panelview(Capacity ~ 1, index = c("ccode","year"), axis.lab="off", type = "miss")
We can also directly supply a variable name without specifying a formula:
capacity %>% panelview(Y = "Capacity", index = c("ccode", "year"), axis.lab.gap = c(2,10), type = "miss")
When leave.gap = TRUE, time gaps are shown in the plot when the time variable is not evenly distributed (the default is FALSE):
capacity$demo[capacity$year == "1960" | capacity$year == "1980"] <- NA capacity$lngdp[capacity$year == "1990"] <- NA panelview(1 ~ demo + lngdp, data = capacity, index = c("ccode","year"), type = "missing", leave.gap = TRUE, axis.lab.gap = c(2,10))
>2 treatment conditions
Starting from v.1.1.0, panelview supports TSCS data with more than 2 treatment levels. For example, we create a measure of regime type with three treatment levels.
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")
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"))
This is equivalent to:
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"))
If the number of treatment levels is greater than 5, then the treatment indicator
will be regarded as a continuous variable. We can remove the grid lines by
specifying gridOff = TRUE:
panelview(Capacity ~ polity2 + lngdp, data = capacity, index = c("ccode", "year"), axis.lab.gap = c(2,10), main = "Regime Type", gridOff = TRUE)
We can also change the colors using the option color:
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")
Plot an outcome (or any variable)
The second main function of panelview is to plot the raw outcome variable of a panel dataset in a time-series fashion. The syntax is very similar except that we need to specify type = "outcome". We can control the ranges of the data to be shown by supplying xlim and ylim. Different colors represent different treatment conditions.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), type = "outcome", main = "EDR Reform and Turnout", ylim = c(0,100),xlab = "Year", ylab = "Turnout")
To turn off the coloring for observations in the pretreatment period, set pre.post = FALSE.
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")
Similarly, we can specify the labels shown in the legend using legend.labs. Note that we can also turn off legends by specifying legendOff = TRUE.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), type = "outcome", main = "EDR Reform and Turnout", legend.labs = c("Control States","Treated States (before EDR)", "Treated States (after EDR)"))
We do not use the black and white theme by specifying theme.bw = FALSE.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), type = "outcome", main = "EDR Reform and Turnout", theme.bw = FALSE)
And we can change the colors for observations under different treatment statuses using the option color and change the legend labels using legend.labs.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), type = "outcome", main = "EDR Reform and Turnout", color = c("lightblue", "blue", "#99999950"), legend.labs = c("Control States","Treated States (before EDR)", "Treated States (after EDR)"))
In cases with a staggered treatment and a continous outcome, we can collapse units based on cohort defined the timing of getting the treatment by setting by.cohort = TRUE. In the turnout data, there are five cohorts:
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)
Again, we can specify which unit(s) we want to take a look at:
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), type = "outcome", main = "EDR Reform and Turnout (AL, AR, CT)", id = c("AL", "AR", "CT"))
To better understand the data, sometimes we want to plot the outcome based on whether the treatment status has changed during the observed time period. We can simply add an option by.group = TRUE. The algorithm will analyze the data and automatically put each unit into different groups, e.g. (1) always treated, (2) always in control, (3) treatment status changed. Users can adjust the fontsizes of the title and subtitles using the cex.main and cex.main.sub options, respectively.
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)
If we want to arrange the subfigures in a row rather than in a column, use the by.group.side = TRUE option instead of by.group = TRUE.
panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), type = "outcome", main = "EDR Reform and Turnout", by.group.side = TRUE, cex.main = 20, cex.main.sub = 15)
Ignore treatment status
Starting from v 1.0.3, we allow users to omit the treatment indicator. Therefore, panelview can in fact be used to visualize any variable in a panel dataset.
panelview(turnout ~ 1, data = turnout, index = c("abb","year"), type = "outcome", main = "Turnout", ylim = c(0,100), xlab = "Year", ylab = "Turnout")
Alternatively, by setting ignore.treat = TRUE. The right-hand-side variables may change the plot by introducing missing values.
panelview(Y = "turnout", data = turnout, index = c("abb","year"), type = "outcome", main = "Turnout", ylim = c(0,100), xlab = "Year", ylab = "Turnout")
Outcome trajectories by cohort
Starting from v 1.1.11, users can use the by.cohort = TRUE option to plot the average outcome trajectories of units with same treatment history (if the number of unique treatment history is less than 20). This type of plots can help users diagnose the extent to which treatment effect heterogeneity may cause biases of certain estimators (e.g. the two-way fixed effects estimators).
panelview(turnout ~ policy_edr+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)"))
Discrete outcomes
We can accommodate discrete variables by setting outcome.type = "discrete". Below is an example using the simdata dataset, in which the outcome variable takes three values: 0, 1, and 2.
panelview(Y ~ D, data = simdata, index = c("id", "time"), outcome.type = "discrete", type = "outcome", xlim = c(8, 15))
We split the sample based on changes in treatment status and use the black and white theme:
panelview(Y ~ D, data = simdata, index = c("id", "time"), by.group = TRUE, outcome.type = "discrete", type = "outcome", xlim = c(8, 15))
If the treatment indicator has more than 2 treatment levels or a continuous variable (e.g. polity2), then treatment status will not be shown on the "outcome" plot:
panelview(Capacity ~ polity2 + lngdp, data = capacity, index = c("ccode", "year"), main = "Measuring State Capacity", type = "outcome", legendOff = TRUE)
Plot bivariate relationships
Visualize time series of the outcome and treatment in one figure by specifying type = "bivar" or type = "bivariate". For continuous variable, we use line plot as default; for discrete variable, we use bar plot. To plot connected lines ( "connected" or "c" ), lines ( "line" or "l" ), or bars ( "bar" or "b" ) rather than the default, please add style = c("",""), where the first element defines the outcome style, and the second defines the treatment style.
In aggregate
When type = "bivariate", panelview plots the averages of D and Y against time with the default option by.unit = FALSE.
Below are two examples with a continuous outcome and a discrete treatment. In the first example, style = c("c","b") represents a connected line plot for the outcome and a bar plot for the treatment---the default is style = c("l","b"), in which "l" represents a line plot. In the second example, we adjust the y-axes ranges using ylim = list(c( , ), c( , )), in which the first vector specifies the range for the average outcome and the second vector specifies the range for the average treatment.
# 1. Continuous Y, discrete D: panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), type = "bivariate", style = c("c","b"), main = "EDR Reform and Turnout", ylab = "Turnout")
panelview(lnpop ~ demo, data = capacity, index = c("country", "year"), ylim = list(c(8, 9.4), c(0.25, 0.6)), type = "bivar")
With discrete outcome and treatment variables, the default is style = c("b","b"):
# 2. Discrete Y, discrete D: panelview(Y ~ D, data = simdata, index = c("id", "time"), type = "bivar", theme.bw = FALSE, outcome.type = "discrete")
When both variables are continuous, the default is style = c("l","l"):
# 3. Continuous Y, continuous D: panelview(lnpop ~ polity2, data = capacity, index = c("country", "year"), ylim = list(c(8.75,9.4), c(-0.2, 0.35)), type = "bivar")
When both variables are discrete, the default is style = c("l","b"):
# 4. Discrete Y, continuous D simdata$x <- seq(0.001,0.9,0.001) panelview(Y ~ x, data = simdata, index = c("id", "time"), type = "bivar", outcome.type = "discrete")
Another example. Option lwd controls the line width (default is 0.2):
## Line the discete treatment # 1. Y: continuous; D: discrete panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), xlab = "EDR", ylab = "Turnout", type = "bivar", style = c("line","connected"), color = c(2, 3), lwd = 0.4)
By each unit
We plot Y and D against time for each unit using by.unit = TRUE. Below are two examples with continuous outcome and discrete treatment. We arrange four subgraphs in one row:
# 1. Continuous Y, discrete D: panelview(turnout ~ policy_edr, data = turnout, index = c("abb","year"), ylab = "Turnout", type = "bivar", by.unit = TRUE, show.id = c(1:12))
Another example:
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))
If the outcome is discrete, we can plot outcome and treatment against time in the same figure, indicating outcome.type = "discrete":
# 2. Discrete Y, discrete D: panelview(Y ~ D, data = simdata, index = c("id", "time"), type = "bivariate", by.unit = TRUE, theme.bw = FALSE, outcome.type = "discrete", id = unique(simdata$id)[1:12])
When both variables are continuous:
# 3. Continuous Y, continuous D: panelview(lnpop ~ polity2, data = capacity, index = c("country","year"), type = "bivar", by.unit = TRUE, theme.bw = FALSE, color = c("blue","red"), show.id = c(1:12))
With discrete outcome and continuous treatment:
# 4. Discrete Y, continuous D simdata$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$id)[1:12])
Adding style = "line" to plot both variables in lines:
## Line the discete treatment # 1. Y: continuous; D: discrete panelview(turnout ~ policy_edr + policy_mail_in + policy_motor, data = turnout, index = c("abb","year"), type = "bivar", by.unit = TRUE, style = "line", theme.bw = FALSE, lwd = 0.5, show.id = c(1:12), ylab = "Turnout")
# 2. Y: Discrete; D: discrete panelview(Y ~ D, data = simdata, index = c("id", "time"), type = "bivar", by.unit = TRUE, outcome.type = "discrete", style = "line", theme.bw = FALSE, lwd = 0.4, id = unique(simdata$id)[1:20])
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