If you look for approximate but accurate approximations, consider instead the function
$$f(x)=\log \left(2( r+2)\, x^{r+1}\right)-\log (x+1)$$which is much closer to linearity than the original one.
Starting at $x=1$, the first iterate of Newton method gives
$$\color{blue}{x_0=1-\frac{2 \log (r+2)}{2 r+1}}$$
What we have is $f(x_0)<0$ and $f''(x_0)<0$. So by Darboux theorem, Newton method will converge without any overshoot to the solution.
Now, compute the first iterate of a Newton-like method of order $n$; the solution is explicit.
Here are the results for $n=2,3,4$ corresponding to "named" methods
$$\left(
\begin{array}{ccccc}
r &\text{Newton} &\text{Halley} &\text{Householder} & \text{solution} \\
10 & 0.7893256697 & 0.7897861894 & 0.7897835658 & 0.7897836101 \\
20 & 0.8600829302 & 0.8601537395 & 0.8601535887 & 0.8601535896 \\
30 & 0.8926124870 & 0.8926347324 & 0.8926347063 & 0.8926347078 \\
40 & 0.9118567862 & 0.9118663299 & 0.9118663227 & 0.9118663230 \\
50 & 0.9247493155 & 0.9247542027 & 0.9247542001 & 0.9247542002 \\
60 & 0.9340638411 & 0.9340666479 & 0.9340666468 & 0.9340666468 \\
70 & 0.9411457486 & 0.9411474958 & 0.9411474953 & 0.9411474953 \\
80 & 0.9467327605 & 0.9467339152 & 0.9467339149 & 0.9467339149 \\
90 & 0.9512656601 & 0.9512664593 & 0.9512664591 & 0.9512664591 \\
100 & 0.9550251563 & 0.9550257301 & 0.9550257300 & 0.9550257300 \\
\end{array}
\right)$$
Using the next order, we have more than ten exact decimals.
Edit
It is possible to generate better and better $x_0$'s using higher order methods. For example, Halley method would give
$$x_0=1-\frac{4 (2 r+1) \log (r+2)}{2 (2 r+1)^2+(4 r+3) \log (r+2)}$$.
We still have $f(x_0^{(n)})<0$ and $f''(x_0^{(n)})<0$
Playing with the order of the method, for $r=10$, this would give
$$\left(
\begin{array}{cc}
n & x_0^{(n)} & \text{method} \\
2 & 0.7633422238 &\text{Newton} \\
3 & 0.7889144456 &\text{Halley} \\
4 & 0.7896940032 &\text{Householder} \\
5 & 0.7897718056 &\text{no name}\\
6 & 0.7897818574 &\text{no name}\\
7 & 0.7897833306 &\text{no name}\\
8 & 0.7897835634 &\text{no name}\\
\cdots & \cdots \\
\infty & 0.7897836101 &\text{solution}\\
\end{array}
\right)$$
The problem is that the formulae become quickly messy. What can be done is to let $t=(r+2)$ and expand as a series for large values of $t$. This gives, without any loss of accuracy,
$$\color{blue}{x_0=1-\frac{T}{t}\sum_{n=0}^\infty (-1)^n\, \frac{P_n(T)}{b_n\,t^n}}\qquad \text{with}\qquad T=\log(t)$$ where the first $b_n$ are
$$\{1,2,24,48,960,2880\}$$ and the polynomials
$$\left(
\begin{array}{cc}
n & P_n(T) \\
0 & 1 \\
1 & T-3 \\
2 & 4 T^2-39 T+54 \\
3 & 2 T^3-42 T^2+189 T-162 \\
4 & 8 T^4-295 T^3+2910 T^2-8100 T+4860 \\
5 & 4 T^5-225 T^4+4050 T^3-24975 T^2+48600 T-21870 \\
\end{array}
\right)$$