Data visualization for missing data I am a designer and am trying to plot a set of data over time. For example,
Day1     Day2     Day3      Day4      Day5
 10       53       21        67        38

I am using a normal line chart to plot this, but when there is no data available for a day or two in between (like below, for example) the developers are assuming it to be zero and the plot actually dips down to zero and goes up. I somehow feel this is not correct.
Day1     Day2     Day3      Day4      Day5
 10                          67        38

When I use MS Excel to plot the above data (with missing values in between), it draws a line from 67 to 38 (Line Chart). If I enter say, 25 in Day2, I see a line from 10 to 25 and then a gap then from 67 to 38.


*

*Now, my question is, is it correct to simply join the line from 25 (Day2) to 67 (Day3), so that I may get a continuous graph?


I see some designs containing splines instead if straight lines connecting two points. This is visually appealing, but I know splines are used to for data interpolation (correct me if I am wrong) and not in such cases as I have described.


*Can I still plot the known points using a spline? Is this acceptable?


(https://dribbble.com/shots/2062935-File-Dashboard-Free-PSD/attachments/369112)
I am sorry if my questions are lame, because my knowledge in statistics and data visualization is less.  
 A: I would honestly simply leave data points without information empty. In R:
foo <- structure(c(10,NA,NA,67,38),.Names=paste0("Day",1:5))
plot(foo,xaxt="n",xlab="",ylab="",pch=19,type="o",
  ylim=c(0,max(foo,na.rm=TRUE)))
axis(1,seq_along(foo),names(foo))



Anything else is defensible if it reflects information you have about your data. For instance, if your database recorded sales and your store was open on days 2 & 3, but nobody wanted to buy your widgets, then you can validly infer and plot zeros. (If the store was closed or you were out of stock in widgets, you should not, since any demand could not have been satisfied.)
You could linearly interpolate if this is a "good guess" at what "really" happened during the periods with no data. Of course, what is a "good guess" will depend on your specific situation.
I would not use splines, unless I had a very good reason. Linear interpolation is simpler, and one should always use a simpler approach unless a more complex one like splines is warranted (Occam's razor). Plus, higher-order splines can explode, depending on your specific data.
