I know how to use methods like system of linear equations to show that any vector in $R^n$ can be expressed in the form of unique linear combinations of a given set of n linearly independent vectors, but how to expand this result to that any given set of exactly n linearly independent vectors must form a basis of $R^n$ space? How to understand it conceptually?
I saw there are similar questions, but the answers in those posts do not really convince me.
Thanks in advance.
I'm not sure if this answers your question but in order for a set of vectors to be a basis of a vector space it has to be linear independent and has to span the entire space. Now if you're given a set of $n$ linearly independent vectors in $\mathbb{R}^n$ you only have to show that it spans $\mathbb{R}^n$. But as you said yourself this is indeed the case since you can express any vector as a linear combination of vectors from your set.
If that isn't satisfactory, you could also think about it this way: Suppose you have $n$ linearly independent vectors $(v_1, \dots , v_n)$ and another vector $w$ not in the span of your $(v_1, \dots , v_n)$. Then $(v_1, \dots , v_n,w)$ is linear dependent since it's a set of $n+1$ vectors in a vector space of dimension $n$. This means there are $\mu, \lambda_i \in \mathbb{R}, i=1,\dots,n$ not all $0$ with $0=\mu w + \sum_{i=1}^{n} \lambda_i v_i$. Now $\mu \neq 0$ because otherwise $(v_1, \dots , v_n)$ would be linear dependent. Therefore $w=-\frac{1}{\mu} \sum_{i=1}^{n} \lambda_i v_i$ but this means $w$ is in the span of $(v_1, \dots , v_n)$, a contradiction.