## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(undidR)
## ----schematic, echo=FALSE, fig.align='center', out.width='90%', fig.cap="Schematic of the UNDID framework."----
knitr::include_graphics("figures/undidR_schematic.png")
## -----------------------------------------------------------------------------
# First, an initializing CSV is created detailing the silos
# and their treatment times. Control silos (here, 73 and 46)
# should be labelled with "control".
init <- create_init_csv(silo_names = c("73", "46", "71", "58"),
start_times = "1989",
end_times = "2000",
treatment_times = c("control", "control",
"1991", "1991"))
init
# After the initializing CSV file is created, `create_diff_df()`
# can be called. This creates the empty differences data frame which
# will then be filled out at each individual silo for its respective portion.
init_filepath <- normalizePath(file.path(tempdir(), "init.csv"),
winslash = "/", mustWork = FALSE)
empty_diff_df <- create_diff_df(init_filepath, date_format = "yyyy",
freq = "yearly")
empty_diff_df
## -----------------------------------------------------------------------------
# The initializing CSV for staggered adoption is created in the same way.
# When `create_diff_df()` is run, it will automatically detect whether or not
# the initial setup is for a common adoption or staggered adoption scenario.
init <- create_init_csv(silo_names = c("73", "46", "54", "23", "86", "32",
"71", "58", "64", "59", "85", "57"),
start_times = "1989",
end_times = "2000",
treatment_times = c(rep("control", 6),
"1991", "1993", "1996", "1997",
"1997", "1998"))
init
# Creating the empty differences data frame and associated CSV file is
# the same for the case of staggered adoption as it is for common adoption.
init_filepath <- normalizePath(file.path(tempdir(), "init.csv"),
winslash = "/", mustWork = FALSE)
empty_diff_df <- create_diff_df(init_filepath, date_format = "yyyy",
freq = "yearly",
covariates = c("asian", "black", "male"))
head(empty_diff_df, 4)
## -----------------------------------------------------------------------------
# When calling `undid_stage_two()`, ensure that the `time_column` of
# the `silo_df` contains only character values, i.e. date strings.
silo_data <- silo71
silo_data$year <- as.character(silo_data$year)
empty_diff_filepath <- system.file("extdata/common", "empty_diff_df.csv",
package = "undidR")
stage2 <- undid_stage_two(empty_diff_filepath, silo_name = "71",
silo_df = silo_data, time_column = "year",
outcome_column = "coll", silo_date_format = "yyyy")
head(stage2$diff_df, 4)
head(stage2$trends_data, 4)
## -----------------------------------------------------------------------------
# Here we can see that calling `undid_stage_two()` for staggered adoption
# is no different than calling `undid_stage_two()` for common adoption.
silo_data <- silo71
silo_data$year <- as.character(silo_data$year)
empty_diff_filepath <- system.file("extdata/staggered", "empty_diff_df.csv",
package = "undidR")
stage2 <- undid_stage_two(empty_diff_filepath, silo_name = "71",
silo_df = silo_data, time_column = "year",
outcome_column = "coll", silo_date_format = "yyyy")
head(stage2$diff_df, 4)
head(stage2$trends_data, 4)
## ----fig.width=6, fig.height=4, out.width = "70%", dpi=300, fig.align="center"----
# `undid_stage_three()`, given a `dir_path`, will search that folder
# for all CSV files that begin with "filled_diff_df_" and stitch
# them together in order to compute the group level ATTs, aggregate ATT
# and associated standard errors and p-values.
dir_path <- system.file("extdata/common", package = "undidR")
results <- undid_stage_three(dir_path, covariates = FALSE)
results
# We can also view a parallel trends plot of our silos by callling
# `plot_parallel_trends()`. This function will search a folder for all
# CSV files that start with "trends_data" and combine them. The combined
# data is returned by the function.
trends <- plot_parallel_trends(
dir_path,
simplify_legend = FALSE, # Setting to `FALSE` puts every silo in legend
lwd = 3, # Set line width
ylim = c(0, 1), # (min, max) of y-axis
xdates = as.Date(c("1989-01-01",
"1991-01-01",
"1993-01-01", # Explicity declare dates on x-axis
"1995-01-01",
"1997-01-01",
"1999-01-01")),
date_format = "%Y", # Specify x-axis date format
ylabels = c(0, 0.25, 0.5, 0.75, 1), # Values on y-axis
legend_location = "topleft", # Where should the legend go?
legend_on = TRUE, # Defaults `TRUE`, can be set to `FALSE` to omit legend
ylab = "coll", # y-axis title
treatment_colour = c("red", # Set colours of treatment silos
"coral"),
control_colour = c("#2fa7cf", # Colours can be entered as hexcodes
"skyblue")
)
head(trends, 4)
## ----fig.width=6, fig.height=4, out.width = "70%", dpi=300, fig.align="center"----
# When calling `undid_stage_three()` for staggered adoption it is
# important to specify the aggregation method, `agg`.
dir_path <- system.file("extdata/staggered", package = "undidR")
results <- undid_stage_three(dir_path, agg = "silo", covariates = TRUE,
nperm = 501)
# The `nperm` parameter specifies how many permutations
# to consider for randomization inference.
results
# If we have many silos, but want to view a parallel trends plot
# we can set `combine = TRUE` when calling `plot_parallel_trends()`
# which will combine all treatment silos into a single line, and all
# control silos into a single line.
trends <- plot_parallel_trends(
dir_path,
combine = TRUE, # Plots one line for control and one line for treated
simplify_legend = TRUE, # Reduces legend to simply "Control" and "Treated"
lwd = 3, # Set line width
ylim = c(0.2, 0.6), # (min, max) of y-axis
xdates = as.Date(c("1989-01-01",
"1991-01-01",
"1993-01-01", # Explicity declare dates on x-axis
"1995-01-01",
"1997-01-01",
"1999-01-01")),
date_format = "%Y", # Specify x-axis date format
ylabels = c(0.2, 0.4, 0.6), # Values on y-axis
legend_location = "topright", # Where should the legend go?
treatment_indicator_lwd = 2, # line width for the treatment indicator lines
treatment_indicator_alpha = 0.2, # transparency for treatment indicator lines
treatment_indicator_col = "red" # colour for treatment indicator lines
)
head(trends, 4)