Add a rug representation to plot

Here is a possible implementation of rug representation using ListPlot. Maybe implementation from @MarcoB is more efficient.

jitter function

Here is a implementation of jitter function:

jitter[x_] := Module[{r, z, xx, d}, r = {Min[x], Max[x]};
  z = First@Differences[r];
  z = If[z == 0, Abs[r], z];
  z = If[z == 0, 1, z];
  xx = DeleteDuplicates@Sort@Round[x, 10^(-3 + Floor@Log10[z])];
  d = Differences[xx];
  d = If[Length@d > 0, Min@d, If[xx != 0, xx/10, z/10]];
  x + RandomReal[{-Abs[d]/5, Abs[d]/5}, Length@x]
  ]

Based on this, rug plot can be implemented as

ListPlot[{Thread@{x, y}, Tuples@{jitter@x, {1.5}}}, 
 PlotMarkers -> {{}, {"|", 6}}, PlotStyle -> Black]

Density plot vs. rug

@Jens thinks density plot is more informative. I agree with the idea that density plot are easy to read, but rug provides more details of the data than density estimation. Most of the time, this is a bad thing, because we do not want to represent to much information in one graph. However, I think there are cases, rug is more suitable. Following is density estimation and is rug representation of wt in mtcars dataset. I think it is not so bad to have a rug representation near the axes.

Show[{SmoothHistogram[x], 
  ListPlot[Tuples@{jitter@x, {0.02}}, PlotMarkers -> {"|", 8}]}] 

Just to illustrate the point I made in the comment, let's take a data set where the points stack up vertically, and verify what it looks like if we visualize their density by means of a color gradient as in the question One-dimensional heatmap. You first have to copy the definition of heatMap from the second code block in my answer, and then execute this:

iris = ExampleData[{"Statistics", "FisherIris"}][[All, 1 ;; 2]];

h = Show[heatMap[Map[{#, 0} &, iris[[All, 1]]], 
    "Points" -> 2 Length[iris], "Radius" -> {1, .01}, 
    PlotRange -> {{4, 8}, {0, .1}}, PlotRangePadding -> 0, 
    FrameLabel -> None, 
    ColorFunction -> (ColorData["SiennaTones"][1 - #] &)], 
   Frame -> None, PlotRangePadding -> None, ImagePadding -> None, 
   AspectRatio -> Full];

ListPlot[iris, Prolog -> Inset[h, {4, 0}, {4, 0}, 4], 
 PlotRange -> {{4, 8}, {0, 4.5}}, Frame -> True]

This replacement rug is made with a color gradient (SiennaTones) that indicates clustering of data points by darker shading. I didn't automate the choice of plot parameters yet, but it could be done if you think it's useful. The example shows that bandwidth is not a problem because I use a Gaussian filter where the radius can simply be chosen to be as small as needed to achieve the maximal resolution.

Edit

Here is another example, where the data are distributed more irregularly:

mtcars = Import["mtcars.csv"];
x = Drop[mtcars[[All, 7]], 1];
y = Drop[mtcars[[All, 2]], 1];

h2 = Show[
   heatMap[Map[{#, 0} &, x], "Points" -> 10 Length[x], 
    "Radius" -> {1, .01}, PlotRange -> {{1.5, 4.5}, {0, .1}}, 
    PlotRangePadding -> 0, FrameLabel -> None, 
    ColorFunction -> (ColorData["SiennaTones"][1 - #] &)], 
   Frame -> None, PlotRangePadding -> None, ImagePadding -> None, 
   AspectRatio -> Full];

ListPlot[Thread[List[x, y]], 
 Prolog -> Inset[h2, {1.5, 0}, {1.5, 0}, 3], 
 PlotRange -> {{1.5, 4.5}, {0, 40}}, Frame -> True]

Here, I had to use more sampling points (option "Points") because the data are more closely spaced in some places.

The solutions above don't really achieve the idea of a rug plot as it is commonly used by statisticians. Clearly there are cases when it is not appropriate (such as on scatter plots with lots of overlapping data points), but then there are other cases (esp histograms and smooth histograms) where it is insightful (and expected by journal publishers). But "whether to use" and "how to make" are different questions.

A standard rug plot can be achieved by using Line with Scaled offset coordinates in an Epilog, and setting your PlotRangePadding accordingly.

First, here are two random datasets.

Data1 = RandomVariate[NormalDistribution[0.35, 0.05], 200];
Data2 = RandomVariate[NormalDistribution[0.55, 0.05], 60];
DataPoints = Flatten[{Data1, Data2}];

To get the rug plot you just add some extra padding to the bottom of the histogram and draw your lines in the extra space using Epilog.

Histogram[DataPoints,PlotRangePadding->{Scaled[0.02],{Scaled[0.06],Scaled[0.03]}},
Epilog->{AbsoluteThickness[0.01],Line[Table[{Scaled[{0,-0.01},{i,0}],Scaled[{0,-0.03},{i,0}]},{i,DataPoints}]]}]

Using this method will scale the location and heights of the plot and lines regardless of the y-scale (e.g., whether it is a PDF or a histogram of counts). It is also easy to stack multiple sets of lines of different colors to represent multiple distributions. You can also easily adjust the line appearance according to your preferences.

"DistributionAxes" -> "Lines"

You can use DensityHistogram with suboption "DistributionAxes" -> "Lines" and ListPlot of the data as Epilog:

SeedRandom[1]
dt = RandomReal[1, {50, 2}];

DensityHistogram[dt, Method -> {"DistributionAxes" -> "Lines"}, 
 BaseStyle -> FaceForm[], 
 Epilog -> ListPlot[dt, PlotStyle -> {Red, PointSize[Large]}][[1]]]

Alternatively, with sufficiently many equal-sized bins or sufficiently small bin widths, we can use ChartElementFunction -> "Point" in DensityHistogram to get a ListPlot of data without using Epilog:

DensityHistogram[dt, {100, 100}, 
 Method -> {"DistributionAxes" -> "Lines"}, ColorFunction -> (Red &), 
 ChartBaseStyle -> PointSize[Large], ChartElementFunction -> "Point"]

Another example:

dist1 = BinormalDistribution[{1, 1}, {1, 1}, 1/2];
dist2 = BinormalDistribution[{5, 5}, {1, 1}, -1/2]; dt2 = 
 RandomVariate[MixtureDistribution[{3, 2}, {dist1, dist2}], 300];
DensityHistogram[dt2, Method -> {"DistributionAxes" -> "Lines"}, 
 BaseStyle -> FaceForm[], 
 Epilog -> ListPlot[dt2, PlotStyle -> {Red, PointSize[Large]}][[1]]]

UnivariateDataRug

Statistics`DataDistributionUtilities`UnivariateDataRug[dt[[All, 1]]]

With some processing (to remove arrows and to change orientation), the output of Statistics`DataDistributionUtilities`UnivariateDataRug can be used to construct data rugs for the vertical and horizontal axes.

ClearAll[rugF]
rugF[dir : ("horizontal" | "vertical") : "horizontal"] := 
  Module[{rule = If[dir === "horizontal", 
       Thread[{{x_, 0}, {x_, 1}} :> {{x, -.025}, {x, -.075}}], 
       Thread[{{x_, 0}, {x_, 1}} :> {{-.025, x}, {-.075, x}}]]}, 
    Statistics`DataDistributionUtilities`UnivariateDataRug[#] /. 
      Arrow[x_] :> {CapForm["Butt"], Line[x]} /. rule ] &;

Show[ListPlot[dt, PlotStyle -> PointSize[Large]], 
 rugF["vertical"][dt[[All, 2]]], rugF[][dt[[All, 1]]], 
 AspectRatio -> 1, Frame -> True, AxesOrigin -> {0, 0}, 
 PlotRangePadding -> {{.1, Scaled[.02]}, {.1, Scaled[.02]}}]