Part of the purrr resolution by Isabella R. Ghement, Ph.D.

More purrr goodness: map2 and pwalk

#=======================================================================
# Install required packages 
#=======================================================================

install.packages("ggridges")

#=======================================================================
# Load required packages 
#=======================================================================

library(ggplot2)
library(dplyr)

library(ggridges)

library(ggthemes)

library(Hmisc)

library(purrr)

#=======================================================================
# Load airquality data set which comes with R
#=======================================================================

data(airquality)

head(airquality)

?airquality

str(airquality)

#=======================================================================
# Prepare airquality data set for further analysis 
#=======================================================================

# convert Month variable from integer to factor

airquality$Month <- factor(airquality$Month, 
                           levels=5:9, 
                           labels=month.name[5:9])

# assign a label and units to Ozone variable  

label(airquality$Ozone) <- "Ozone" 
units(airquality$Ozone) <- "ppb"
 
# assign a label and units to Wind variable  

label(airquality$Wind) <- "Wind" 
units(airquality$Wind) <- "mph"

# assign a label and units to Temp variable  

label(airquality$Temp) <- "Temperature" 
units(airquality$Temp) <- "degrees F"

# assign a label to Month variable   

label(airquality$Month) <- "Month"


#=======================================================================
# Define a custom function which will be used to produce joyplots 
# (e.g., joyplots of Ozone for the months of May, June, July, August and 
# September). Joyplots (or density ridgeline plots) are partially overlapping 
# density plots that create the impression of a mountain range when considered together.  
# See https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html
# for more details on this type of plots.  
#=======================================================================

joyplots <-  function(data, x, y) {
   
   .e = environment()

   data <- dplyr::select_(data, .dots=c(x,y) %>% unname)

   head(data)

   names(data) <- c("x","y")

   data.nonmiss <- data[complete.cases(data), ]

   ggplot(data=data.nonmiss, aes(x=x,y=y, fill=y, height = ..density..), environment=.e) +
     geom_density_ridges(rel_min_height = 0.005, stat = "density") +
      scale_y_discrete(expand = c(0.01, 0)) +
       scale_x_continuous(expand = c(0.01, 0)) +
        scale_fill_few() +
         xlab(paste0(label(data.nonmiss$x), " (",units(data.nonmiss$x),")")) +
          ylab(paste0(label(data.nonmiss$y))) + 
           theme_minimal() + 
            theme(legend.position="none")

}

#=======================================================================
# Test the joyplots() function for the Ozone variable 
#=======================================================================

joyplots(airquality, x="Ozone", y="Month")

#=======================================================================
# Apply the joyplot() function simultaneously to the variables 
# Ozone, Wind and Temp from the airquality data set using the 
# map2() function from the purrr package. Assign relevant names to 
# the resulting plots, which will be stored into a list called plots.  
#=======================================================================

xvars <- c("Ozone", "Wind", "Temp")   # specify all variables for which we need to build joyplots
yvars <- c("Month")                   # specify the Month variable

plots <- map2(xvars, yvars, joyplots, data=airquality)   # apply joyplots simultaneously to every combination of elements of xvars and yvars

names(plots) <- xvars   # name the plots

plots

#=======================================================================
# Use the pwalk() function from the purrr package to export each of the 
# plots from the list of plots to a pdf file. The names of all pdf files 
# are specified in the filenames object.  The files are saved with 
# ggsave() in the current working directory.
#=======================================================================

filenames <- stringr::str_c(names(plots), ".pdf")   # create the filenames under which the plots will be saved

filenames

pwalk(list(filenames, plots), ggsave, path = getwd())   # save the plots under these filenames with pwalk()

getwd()   # pdf files are saved here