Automated data analysis in R or Python (PCA)

I would like a script, or collection of scripts, to identify "causal" factors in a time series data set. The process I'm envisioning is something like: 1. Load data from SQL into data frame (e.g., sales volume, products, weather, geography, prices, etc.) 2. Given a response variable (sales) among time series data, do the following: a. identify correlation among variables and control for (remove) spurious correlation b. run variable selection (PCA, random forest) c. create visualizations of predictors d. identify predictors that are influential or have big impact on response variable, or even their causal effect. e. rank variable by importance (visualize variable importance as a graph) The scripts should be configured so they can run without interaction. For example, the script would be passed a response variable, a date range, and a list of columns to evaluate. The script would create a correlation matrix and identify influential factors as well as a script can, then write the output to a file. I have a preference for python, but R would be fine if necessary. Thanks!