Plotting Multiple IRFs using ggplot and gridExtra

General
1

Hi, I have run 5 seperate IRFs from a VAR model and I'm struggling to figure out how to plot them in a grid using ggplot and gridExtra

To my knowledge, you can only ggplot from a dataframe. So that's my first question, how can I get these 5 IRF's into a dataframe?

Once I have them in a dataframe, what is the easiest way to plot all five IRFs in grid format in 1 row of panels?

I have reproduced my code below

## Estimate VAR Model

var.est.debt_arg <- VAR(debt_arg,p=1,type="both",season=NULL)
summary(var.est.debt_arg)

## Impulse Response Functions

debtarg_1 <- irf(var.est.debt_arg,response="pfdebt_arg",impulse="sp",n.ahead=40,ortho=TRUE,boot=TRUE)
 # response of pfdebt to s&p shock

debtarg_2 <- irf(var.est.debt_arg,response="pfdebt_arg",impulse="m1_us",n.ahead=40,ortho=TRUE,boot=TRUE)
# response of pfdebt to us M1 Shock

debtarg_3 <- irf(var.est.debt_arg,response="pfdebt_arg",impulse="m1_arg",n.ahead=40,ortho=TRUE,boot=TRUE)
# response of pfdebt to a domestic m1 shock

debtarg_4 <- irf(var.est.debt_arg,response="pfdebt_arg",impulse="eq_arg",n.ahead=40,ortho=TRUE,boot=TRUE)
# response of pfdebt to equity market price shock

debtarg_5 <- irf(var.est.debt_arg,response="pfdebt_arg",impulse="pfequity_arg",n.ahead=40,ortho=TRUE,boot=TRUE)
# response of pfdebt to pfequity shocks

For understanding purposes, I've also reproduced my dataset (debt_arg) here

structure(c(43.0909967382336, 26.9790446803372, 16.967092620984, 
29.5051405616314, 4.17318850227895, -4.84876355707382, -24.0607156164264, 
-17.9926676757792, -26.8046197351318, -39.2565717944843, -39.308523853837, 
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-121.2682841506, -95.2702362099528, -62.4721882693054, -53.5841403286581, 
-38.7460923880107, -22.6580444473633, -42.4199965067158, -1.6919485660685, 
46.4060993745788, 27.4541473152261, 172.152195255873, 181.360243196521, 
176.898291137168, 309.076339077816, 336.794387018463, 196.02243495911, 
395.660482899758, 464.988530840405, 494.556578781053, 396.9146267217, 
505.832674662347, 524.940722602995, 519.918770543642, 420.396818484289, 
349.224866424937, 186.772914365584, 184.720962306232, 40.1990102468787, 
87.1570581875262, 42.2151061281736, -182.786845931179, -248.408797990532, 
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66295018.1798717, -94357525.4859095, 41697675.4063964, 22833247.3850187, 
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1596119677.49392, 514743198.781707, 102026577.988947, -292454396.807919, 
-130885581.879457, -187033564.604513, 195110655.737268), .Dim = c(113L, 
6L), .Dimnames = list(NULL, c("sp", "m1_us", "m1_arg", "eq_arg", 
"pfdebt_arg", "pfequity_arg")), .Tsp = c(1991, 2019, 4), class = c("mts", 
"ts", "matrix"))

If anyone could assist with this, I'd appreciate it!!

2

Here's how I went about getting it faceted if that's enough for you. The real trouble is just wrangling the objects to get the data to play nicely with ggplot.

library(tidyverse)

bound <- list(debtarg_1, debtarg_2, debtarg_3, debtarg_4, debtarg_5) %>% 
  map(., `[`, 1:3) %>% 
  unlist(., recursive = FALSE) %>% 
  bind_cols(.) %>% 
  rownames_to_column(var = "n") %>% 
  pivot_longer(-n) %>% 
  mutate(n = as.numeric(n))

ready <- bound %>% 
  mutate(distinction = case_when(as.numeric(str_extract(name, ".$")) == 1 ~ "Lower bound",
                                 as.numeric(str_extract(name, ".$")) == 2 ~ "Upper bound",
                                 TRUE ~ "Point estimate")) %>%
  mutate(name = if_else(str_detect(name, "us"),
                        "us",
                        name)) %>%  
  mutate(name = str_extract(name, "^.{2}"))

ready %>% 
  ggplot(aes(x = n, y = value, color = distinction)) +
  geom_line() +
  facet_wrap(~name)

image
image701×533 7.09 KB

I'll add to my answer within the hour for a more thorough explanation :slight_smile:

3

So here's a more thorough walkthrough, with the end-result being arranged via gridExtra.

So first of all, while it wasn't included in your code, it seems that you're using the vars and tsDyn packages. My solution relies on purrr, dplyr, tibble, ggplot2, stringr, and tidyr, all of which are part of the tidyverse. So it's easiest just to load the tidyverse, as well as vars, tsDyn and of course gridExtra.

library(tidyverse)
library(vars)
library(tsDyn)
library(gridExtra)

The code you shared

(only change to the code is that I've assigned the data to debt_arg)

Your data

debt_arg <- structure(c(43.0909967382336, 26.9790446803372, 16.967092620984, 
            29.5051405616314, 4.17318850227895, -4.84876355707382, -24.0607156164264, 
            -17.9926676757792, -26.8046197351318, -39.2565717944843, -39.308523853837, 
            -46.8804759131896, -89.3024279725422, -101.794380031895, -106.046332091248, 
            -121.2682841506, -95.2702362099528, -62.4721882693054, -53.5841403286581, 
            -38.7460923880107, -22.6580444473633, -42.4199965067158, -1.6919485660685, 
            46.4060993745788, 27.4541473152261, 172.152195255873, 181.360243196521, 
            176.898291137168, 309.076339077816, 336.794387018463, 196.02243495911, 
            395.660482899758, 464.988530840405, 494.556578781053, 396.9146267217, 
            505.832674662347, 524.940722602995, 519.918770543642, 420.396818484289, 
            349.224866424937, 186.772914365584, 184.720962306232, 40.1990102468787, 
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            -223.860750049884, -246.332702109237, -160.484654168589, -130.956606227942, 
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            -30.4666406614001, -5.22859272075266, 86.9494552198946, 34.457503160542, 
            108.705551101189, 158.723599041837, 141.341646982484, 55.3496949231317, 
            -67.5222571362212, 72.7557908044264, 129.453838745074, 106.911886685721, 
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            -130885581.879457, -187033564.604513, 195110655.737268), .Dim = c(113L, 
                                                                              6L), .Dimnames = list(NULL, c("sp", "m1_us", "m1_arg", "eq_arg", 
                                                                                                            "pfdebt_arg", "pfequity_arg")), .Tsp = c(1991, 2019, 4), class = c("mts", 
                                                                                                                                                                               "ts", "matrix"))

Your computation

## Estimate VAR Model

var.est.debt_arg <- VAR(debt_arg,p=1,type="both",season=NULL)
summary(var.est.debt_arg)

## Impulse Response Functions

debtarg_1 <- irf(var.est.debt_arg,response="pfdebt_arg",impulse="sp",n.ahead=40,ortho=TRUE,boot=TRUE)
 # response of pfdebt to s&p shock

debtarg_2 <- irf(var.est.debt_arg,response="pfdebt_arg",impulse="m1_us",n.ahead=40,ortho=TRUE,boot=TRUE)
# response of pfdebt to us M1 Shock

debtarg_3 <- irf(var.est.debt_arg,response="pfdebt_arg",impulse="m1_arg",n.ahead=40,ortho=TRUE,boot=TRUE)
# response of pfdebt to a domestic m1 shock

debtarg_4 <- irf(var.est.debt_arg,response="pfdebt_arg",impulse="eq_arg",n.ahead=40,ortho=TRUE,boot=TRUE)
# response of pfdebt to equity market price shock

debtarg_5 <- irf(var.est.debt_arg,response="pfdebt_arg",impulse="pfequity_arg",n.ahead=40,ortho=TRUE,boot=TRUE)
# response of pfdebt to pfequity shocks

Wrangling the data from varirf objects to tidy dataframes

bound <- list(debtarg_1, debtarg_2, debtarg_3, debtarg_4, debtarg_5) %>% 
  map(., `[`, 1:3) %>% # This has to do accessing the data as vectors rather than a bunch of varirf objects
  unlist(., recursive = FALSE) %>% 
  bind_cols(.) %>% 
  rownames_to_column(var = "n") %>% # This column will refer to the periods going forward (n.ahead)
  pivot_longer(-n) %>% #this relies on the new tidyr package
  mutate(n = as.numeric(n)) # for later on when we plot it

ready <- bound %>% 
  mutate(distinction = case_when(as.numeric(str_extract(name, ".$")) == 1 ~ "Lower bound", # varirf denotes the lower bound with 1
                                 as.numeric(str_extract(name, ".$")) == 2 ~ "Upper bound", # varirf denotes the upper bound with 2
                                 TRUE ~ "Point estimate")) %>% # Any name that doesn't end with 1 or 2 is the point estimate
  mutate(name = if_else(str_detect(name, "us"), # As a convenience ...
                        "us", # I'm renaming the US M1 money supply shock to "us". 
                        name), # That way we can do the next thing...
         name = str_extract(name, "^.{2}"), #Which is to keep only the first two letters of each name.
         linetype_var = if_else(str_detect(distinction, "bound"), # Here we distinguish the point estimate from the up/low bounds
                                "2",
                                "1"))

Plotting the whole thing

So to create a list of plots, we first split our dataframe into a list of dataframes. We then apply a function consisting of our ggplot code to each dataframe in the list using the map function from the purrr package.


# We create a list of plots by resplitting the data into lists
# And apply a function consisting of our ggplot call to each dataframe in the list

plot_list <- ready %>%
  group_by(name) %>% 
  group_split() %>% 
  map(function(.) {ggplot(., aes(x = n, y = value, color = linetype_var, linetype = linetype_var, group = distinction)) +
  geom_line() +
  facet_wrap(~name) +
  theme_bw() +
  theme(legend.position = "none") +
  scale_color_manual(values = c("red", "black"))})

# Here's where gridExtra comes in 
marrangeGrob(plot_list, nrow = 5, ncol = 1)

And this is what we get.

image
image425×964 8.77 KB

Hope that answers the question. Don't hesitate to follow up if somethings unclear (or if I've misunderstood your intentions). And welcome to the RStudio Community board :slight_smile:

2 Likes
4

That goes above and beyond. I was going to ask for an improved reproducible example, called a reprex. (Kudos, tho' to @tanga94 for posting most of it!)

1 Like
5

Works like a charm! Thank you so much for this! Really helpful platform :slight_smile:

3 Likes
6

Great! For the benefit of those who follow, it would be helpful to mark @Hlynur's answer as the solution

1 Like
7

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