A note of forcats

It might be desirable to reorder these variables by something other than id. For example, we have the true mean saved in our dataframe. We can sort the id factors using the forcats::fct_reorder() function (see forcats tidyverse page). I’ve found it useful so I’m posting this bit here (you’re welcome future Len).

ggplot(data=df2,aes(x=mysamp,
                    y=forcats::fct_reorder(factor(id),mean), # reorder the factor by mean
                                           fill=..x..))+
  geom_joy_gradient()+
  scale_fill_viridis()+
    theme_joy()+
    theme(legend.position="none",plot.caption=element_text(hjust=0,size=9),
          axis.title=element_text(size=12),
          plot.title=element_text(size=24))+
    labs(x="X variable",y="ID",caption="@lenkiefer", title="Joyplot")

## Picking joint bandwidth of 0.188

Beeswarms

The joyplots are really cool, but there are other plots to show distributions. Plots like beeswarm](https://github.com/eclarke/ggbeeswarm) plots. Let’s make one using our data.

ggplot(data=df2,aes(x=mysamp,
                    y=factor(id),
                    color=mysamp))+
  geom_quasirandom(alpha=0.82)+
  scale_color_viridis()+
    theme_joy()+
    theme(legend.position="none",plot.caption=element_text(hjust=0,size=9),
          axis.title=element_text(size=12),
          plot.title=element_text(size=24))+
    labs(x="X variable",y="ID",caption="@lenkiefer", title="Beeswarm plot")

Joyswarms in the wild

These could be useful out in the wild. I’ve been experimenting with some real world data (check my Twitter feed). And in the future, maybe I’ll share some examples here.

Oh wait, nevermind. We’ll do one now.

I just saw a tweet from the St. Louis Federal Reserve on how long new homes are staying on the market.

How long has it taken for new houses to be sold? See months on market since 1975 https://t.co/BEN6kcATjQ pic.twitter.com/yVP49SxCwT

— St. Louis Fed (@stlouisfed) August 3, 2017

That’s a great candidate for a joyswarm plot. Let’s make one.

We’ll use the approach I outlined here to get the data. Let’s download the data and plot a time series.

library(quantmod)

## Loading required package: xts

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## 
## Attaching package: 'xts'

## The following objects are masked from 'package:dplyr':
## 
##     first, last

## Loading required package: TTR

## Version 0.4-0 included new data defaults. See ?getSymbols.

df.fred= getSymbols('MNMFS',src='FRED', auto.assign=F) 

## 'getSymbols' currently uses auto.assign=TRUE by default, but will
## use auto.assign=FALSE in 0.5-0. You will still be able to use
## 'loadSymbols' to automatically load data. getOption("getSymbols.env")
## and getOption("getSymbols.auto.assign") will still be checked for
## alternate defaults.
## 
## This message is shown once per session and may be disabled by setting 
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.

df.fred = data.frame(date=time(df.fred), coredata(df.fred) )

mycaption="@lenkiefer Source: U.S. Bureau of the Census, Median Number of Months on Sales Market for Newly Completed Homes [MNMFS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/MNMFS, August 3, 2017."

# Wrap caption 120 characters:
mycaption <- paste0(strwrap(mycaption, 80), sep="", collapse="\n")

ggplot(data=df.fred,aes(x=date,y=MNMFS))+geom_line()+
    theme_minimal()+theme(plot.caption=element_text(hjust=0),
                        legend.position="top")+
  guides(linetype=F)+
  scale_color_viridis(name="Variable",discrete=T,end=0.8)+
  labs(x="",
       x="Months, Not Seasonally Adjusted",
       title="Median Number of Months on Sales Market for Newly Completed Homes",
       caption=mycaption     )

## Warning: The plyr::rename operation has created duplicates for the
## following name(s): (`x`)

These data are not seasonally adjusted, so you can see the pronounced seasonal variation. You can also see large volatility around the Great Recession. Let’s first create a joyplot using month on the Y axis. We’ll also add dots showing the 2017 values.

ggplot(data=df.fred,
       aes(y=forcats::fct_reorder(factor(as.character(date,format="%B")),
                                  -month(date)),
           x=MNMFS, fill=..x..))+
  geom_joy_gradient(rel_min_height=0.01)+
  theme_joy()+
  theme(plot.caption=element_text(hjust=0),
        legend.position="top")+
  geom_point(data=dplyr::filter(df.fred,year(date)==2017))+
  scale_fill_viridis(name="Median months on market",option="C",direction=-1)+
  labs(y="",
       x="Months, Not Seasonally Adjusted",
       title="Median Number of Months on Sales Market for Newly Completed Homes",
       subtitle="Distribution Jan 1975-Jun 2017\nBlack dots=2017 values",
       caption=mycaption)

## Picking joint bandwidth of 0.536

Here we can see that the 2017 values are quite low by historical standards.

Let’s try a joyswarm:

ggplot(data=dplyr::filter(df.fred,month(date)<7),
       aes(y=forcats::fct_reorder(factor(as.character(date,format="%B")),
                                  -month(date)),
           x=MNMFS,
           color=MNMFS))+
  geom_joy(rel_min_height=0.01,alpha=0.6,fill="lightgray")+
  geom_quasirandom(alpha=0.8)+
  theme_joy()+
  theme(plot.caption=element_text(hjust=0),
        legend.position="top")+
  geom_point(data=dplyr::filter(df.fred,year(date)==2017),color="black",size=1.5)+
  scale_color_viridis(name="Median months on market",option="C",direction=-1)+
  labs(y="",
       x="Months, Not Seasonally Adjusted",
       title="Median Number of Months on Sales Market for Newly Completed Homes",
       subtitle="Distribution Jan 1975-Jun 2017\nBlack dots=2017 values",
       caption=mycaption)

## Picking joint bandwidth of 0.493

ggplot(data=df.fred,
       aes(x=forcats::fct_reorder(factor(as.character(date,format="%B")),
                                  -month(date)),
           
           y=MNMFS,color=MNMFS,
           group=month(date)))+
  theme_joy()+
  coord_flip()+
  theme(plot.caption=element_text(hjust=0),
        legend.position="top")+
  geom_boxplot(color=viridis::plasma(5)[3])+
    geom_point(data=dplyr::filter(df.fred,year(date)==2017),color="black",size=1.5)+
  scale_color_viridis(name="Median months on market",option="C",direction=1)+
  labs(x="",y="Months, Not Seasonally Adjusted",
       title="Median Number of Months on Sales Market for Newly Completed Homes",
       subtitle="Distribution Jan 1975-Jun 2017\nBlack dots=2017 values",
       caption=mycaption)