Cramers v when r = 1 and k = 2 I have just read that 'Cramér's V may also be applied to goodness of fit chi-squared models when there is a 1×k table (e.g.: r=1)'. It then goes on to say 'In this case k is taken as the number of optional outcomes and it functions as a measure of tendency towards a single outcome.'  The source  is https://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V
So if Cramer’s v = sqrt (x2/n * min k – 1, r – 1) what would I multiply by in my case where k = 2 and r = 1?
 A: If you divide the chi-square value by N and (k - 1), you will get a statistic [that ranges from 0 to 1 when conducting a chi-square goodness of fit test with equally distributed theoretical probabilities (e.g. 0.50 and 0.50 in your case). See this source.  See also the examples and documentation for this function.
A: While Mangiafico's response more than answers the question, I wish to add a bit of clarification which I have found personally useful.  (And my hope is to record in one place some of the information I have located on this topic.)
In brief, I was recently teaching a mathematical statistics course, and on the online homework system I use, I added a question to an assignment on multinomial distributions and goodness-of-fit tests.  Curiously, it was a question I had written some time ago.  When students started complaining that the question wasn't grading correctly, I did some investigating.  And this is how I located this thread.
First, the statistic being referred to in this thread does not quite appear to me to be the Cramér's $V$.  And, it took some time to locate a relatively easily accessible reference, but I did locate one.
Lomax & Hahs-Vaughn (2012) introduce an effect size (not labelled $V$ or such) for a multinomial goodness-of-fit (GoF) test (p.220)
$$\text{effect size} = \frac{\chi^2}{N(J-1)}$$
where $\chi^2$ is the test statistic for the GoF test, $N$ is the total sample size, and $J$ is the number of distinct categories.  The reason I'm reluctant to label this as a version of Cramér's V is because it does not include the square-root, and this is not an association problem.  (For reference, Cramér's $V$ is a measure of association—not fit—and is given by
$$V=\sqrt{\frac{\chi^2}{N \cdot \min(r-1,c-1)}}$$
as indicated here https://en.wikipedia.org/wiki/Effect_size#Other_metrics.)
The other common effect size that is reported for general chi-square tests is Cohen's W (https://en.wikipedia.org/wiki/Effect_size#Cohen's_w), which can be expressed as
$$W = \sqrt{\sum_{i=1}^J \frac{(p_j-\pi_j)^2}{\pi_j}}$$
or equivlalently
$$W = \sqrt{\frac{\chi^2}{N}}.$$
While this value can range from 0 to values larger than 1, the effect size proposed here does indeed range from 0 to 1, if the assumption is for an equiprobably distribution among the $J$ categories.
So, in summary, there appear to be three effect sizes that are related to each other in some fashion.  The first two are for fit to a multinomial distributions, and the last is for an association.  Summarized here, they are

*

*for a $1\times J$ table: Cohen's $W = \sqrt{\frac{\chi^2}{N}}$

*for a $1\times J$ table: quasi-$V = \frac{\chi^2}{N\cdot(J-1)}$

*for a $r\times c$ table: Cramér's $V = \sqrt{\frac{\chi^2}{N \cdot \min(r-1,c-1)}}$

As a brief addendum, I include here the proof that this quasi-$V$ obtains the (maximal) value of 1 if the expected frequencies are based on $J$ equiprobable categories and all of the frequency appears in only one of the $J$ categories.
$$\begin{align}V &= \frac{\chi^2}{N\cdot(J-1)} \\
&= \frac{1}{N\cdot(J-1)} \left(\frac{(N-\frac{N}{J})^2}{\frac{N}{J}} + (J-1)\frac{(0-\frac{N}{J})^2}{\frac{N}{J}}\right) \\
&= \frac{J}{J-1} \left((1-\frac{1}{J})^2 + (J-1)(\frac{1}{J})^2\right)\\
&= \frac{J}{J-1} \left(\left(\frac{J-1}{J}\right)^2 + \frac{J-1}{J^2}\right)\\
&= \frac{J-1}{J} + \frac{1}{J} \\
&= 1\end{align}$$
