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# Jared Knowles jknowles

Created Feb 20, 2020
How to order bar charts in ggplot2
View barplots.R
 # Load ggplot2 library(ggplot2) # Load example data data(mtcars) # Create a character vector of car names mtcars\$name <- row.names(mtcars) # Plot car names by mpg ggplot(mtcars, aes(x = name, y = mpg)) +
Created Jun 17, 2019
Exploring student composition effects on test score growth of school average scores. Example for SDP 2019 Regression course.
View sdp_reg_exercise_1_example_2019.R
 ################################################################################################### ## Title: Regression Module Assignment 1 ## Exploring Student Composition Effects on Test Score Growth ## Author: Jared E. Knowles, Civilytics Consulting ## Date: 6/12/2019 ## Last Updated: 6/17/2019 ################################################################################################### # ---------------------------------------------------------------------------- # Load the data
Last active Dec 4, 2018
Functions to Support CPE and Census Data Alignment
View cpe_functions.R
 ################################################################################ # Functions to find the data ################################################################################ # Finders # Simple functions that take the ID code from CPE (e.g. 49-00039) and look up # the respective data for it in the structure provided in teh competition find_police_shape <- function(dept_id, kaggle_kernel = FALSE) { if(kaggle_kernel == TRUE) { prefix = "../input/cpe-data/"
Created Sep 16, 2018
 ############################################################################### ## SDP Fall Workshop Predictive Analytics ## Advanced / Additional Code Snippets for Working with PA Data and Models ## Author: Jared E. Knowles ## Date: 09/14/2018 ## You do not need to use all or even any of this code. The code does not need to ## be run together. This is just a survey of some additional techniques/tricks you ## can do in R to make explaining predictive models and complex data easier. ## As always - your needs and approaches may different. ################################################################################
Created Sep 16, 2018
 // Additional tools for machine learning and predictive analytics in stata /* Author: Jared Knowles Date: 09/12/2018 Purpose: Survey of some additional code helpful in conducting and explaining or demonstrating predictive analytics to stakeholders. You do not need to run all of this code - this is a survey of commands that tackle different techniques. Pick and choose what might be most useful to you. */
Created May 30, 2018 — forked from lecy/datausa_census_api.md
Building Census Dataset in R Using datausa.io API
View datausa_census_api.rmd
 # Using the dataUSA.io API for Census Data in R This gist contains some notes on constructing a query for census and economic data from the [DataUSA.io](http://datausa.io/) site. This is a quick-start guide to their API; for in-depth documentation check out their [API documentation](https://github.com/DataUSA/datausa-api/wiki/Overview). A great way to learn how to structure a query is to visit a specific datausa.io page and click on the "Options" button on top of any graph, then select "API" to see the query syntax that created the graph. ![Analytics](https://ga-beacon.appspot.com/UA-27835807-2/gist-id?pixel) ## Example Use
Created Mar 6, 2018
Robust Prediction Intervals for LM
View robust_predict.lm.R
 predict.robust <- function(model, data, robust_vcov = NULL, level = 0.95, interval = "prediction"){ # adapted from # https://stackoverflow.com/questions/38109501/how-does-predict-lm-compute-confidence-interval-and-prediction-interval # model is an lm object from r # data is the dataset to predict from # robust_vcov must be a robust vcov matrix created by V <- sandwich::vcovHC(model, ...) # level = the % of the confidence interval, default is 95% # interval = either "prediction" or "confidence" - prediction includes uncertainty about the model itself if(is.null(robust_vcov)){
Last active Sep 10, 2017
R Helper functions for the Philadelphia SDP Cohort 8 Predictive Analytics Workshop
View helper_funcs.R
 # Calculate the AUC of a GLM model easily # Jared Knowles # model = a fitted glm in R # newdata = an optional data.frame of new fitted values auc.glm <- function(model, newdata = NULL){ if(missing(newdata)){ resp <- model\$y # if(class(resp) == "numeric"){ # resp <- factor(resp) # }
Created Apr 27, 2014
Here are some things you can do with Gists in GistBox.
View 0_reuse_code.js
 // Use Gists to store code you would like to remember later on console.log(window); // log the "window" object to the console
Last active Aug 29, 2015 — forked from skranz/s_dplyr
View s_dplyr.R
 # Helper functions that allow string arguments for dplyr's data modification functions like arrange, select etc. # Author: Sebastian Kranz # Examples are below #' Modified version of dplyr's filter that uses string arguments #' @export s_filter = function(.data, ...) { eval.string.dplyr(.data,"filter", ...) }