response to https://gist.github.com/timelyportfolio/0841126c91beb83114b3

challenge.R

library(dplyr)     # These two need that it is magrittr@dev that is installed.
library(magrittr)  # i.e. the dev branch @ GitHub, not master.  
library(ggplot2)
library(mgcv)

"http://zevross.com/blog/wp-content/uploads/2014/08/chicago-nmmaps.csv" %>%
  read.csv(as.is = TRUE) %>%
  mutate(date = as.Date(date)) %>%
  filter(date > as.Date("1996-12-31")) %>%
  mutate(year = substring(date, 1, 4)) %T>%
  {
    thegam <<- gam(o3 ~ s(temp), data = .) %T>%
    plot(residuals=TRUE, main="Yuck, not a nice plot")
  } %T>%
  {
    t <- data.frame(temp = seq(min(.$temp), max(.$temp), length = 300))
    predict(
        thegam
      , type    = "terms"
      , newdata = t
      , se.fit  = TRUE
    ) %T>% 
    {
      plot(t$temp, .$fit, type = "n", lwd = 3, xlim = c(-3, 90), ylim = c(-20, 30),
           main = "Ahhh, definitely better",
           ylab = paste("s(temp,", round(sum(thegam$edf[-1]),2), ")", sep = ""))
    } %T>% 
    { 
      polygon(c(t$temp, rev(t$temp)), 
              c(.$fit + 1.96*.$se.fit, rev(.$fit - 1.96*.$se.fit)), 
              col = "grey", border = NA)
    } %T>%
    { 
      lines(t$temp, .$fit, lwd = 2)   
    }
  } %T>%
  { origdata;
    predict(thegam, type = "terms") + residuals(thegam) %T>%
    points(origdata$temp, ., pch = 16, col = rgb(0, 0, 1, 0.25))
    { rug(.$temp) }
  }