| library(xml2) | |
| orig<-read_xml("https://itsalocke.com/sitemap-1.xml") | |
| xml_find_all( orig,"//loc") |
Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
The package includes all the following forms of documentation:
| myString<-"Hey Steph!" |
| a<-50:1 | |
| a[1:5] | |
| a[a<25] | |
| a[-1] | |
| a[-(40:50)] | |
| a[c(FALSE,TRUE)] | |
| b<-c(11:50,1:10) | |
| b[b %% 2] | |
| b[as.logical(b %% 2)] |
| This post provides an overview of performing diagnostic and performance evaluation on logistic regression models in R. After training a statistical model, it’s important to understand how well that model did in regards to it’s accuracy and predictive power. The following content will provide the background and theory to ensure that the right technique are being utilized for evaluating logistic regression models in R. | |
| Logistic Regression Example | |
| We will use the GermanCredit dataset in the caret package for this example. It contains 62 characteristics and 1000 observations, with a target variable (Class) that is allready defined. The response variable is coded 0 for bad consumer and 1 for good. It’s always recommended that one looks at the coding of the response variable to ensure that it’s a factor variable that’s coded accurately with a 0/1 scheme or two factor levels in the right order. The first step is to partition the data into training and testing sets. | |
| ``` | |
| library(caret) | |
| data(GermanCredit) | |
| Train <- cr |
| library(googlesheets) | |
| library(purrr) | |
| library(data.table) | |
| library(magrittr) | |
| suppressMessages(gs_auth(token = "c:\\Users/steph/Documents/googlesheets_token.rds", verbose = FALSE)) | |
| mySheet<-"https://docs.google.com/spreadsheets/d/1QfvTK58YO7kBIzI81JQ7rNLcTVbH0hDiP6qZ724GyoA/edit?usp=sharing" | |
| mySheet %>% | |
| gs_url() %>% | |
| gs_ws_ls() %>% |
| LineItem | B2016 | B2017 | TblChange | Total | Net | Title | Type | Dept | IgnoreRow | |
|---|---|---|---|---|---|---|---|---|---|---|
| Gain/Loss on Revaluation of Euro | 0 | 0 | 3 | FALSE | FALSE | FALSE | Revenue | Corporate Administration | FALSE | |
| Interest - Miscellaneous | 11e3 | 11e3 | 3 | FALSE | FALSE | FALSE | Revenue | Corporate Administration | FALSE | |
| Misc FY Adjustments | 0 | 0 | 3 | FALSE | FALSE | FALSE | Revenue | Corporate Administration | FALSE | |
| Unrealized Gain/Loss on Investments | 0 | 0 | 3 | FALSE | FALSE | FALSE | Revenue | Corporate Administration | FALSE | |
| Accounting - General | 3500 | 5e3 | 4 | FALSE | FALSE | FALSE | Expense | Corporate Administration | FALSE | |
| Annual Report | 37 | 37 | 4 | FALSE | FALSE | FALSE | Expense | Corporate Administration | FALSE | |
| Audit | 25e3 | 25e3 | 4 | FALSE | FALSE | FALSE | Expense | Corporate Administration | FALSE | |
| Authorize.Net Fees | 1e3 | 1e3 | 4 | FALSE | FALSE | FALSE | Expense | Corporate Administration | FALSE | |
| Bank Charges | 9e3 | 9e3 | 4 | FALSE | FALSE | FALSE | Expense | Corporate Administration | FALSE |
| --- | |
| title: "Untitled" | |
| output: | |
| flexdashboard::flex_dashboard: | |
| orientation: columns | |
| vertical_layout: fill | |
| runtime: shiny | |
| --- | |
| ```{r setup, include=FALSE} |
| library(dplyr) | |
| library(readr) | |
| library(DiagrammeR) | |
| staff <- read_csv( | |
| "ID,Name,Title,ManagerID | |
| 1,Jane,CEO, | |
| 2,Bill,CIO,1 | |
| 3,Lydia,CTO,1 | |
| 4,Jack,CMO,1 |
| library(dplyr) | |
| library(readr) | |
| library(DiagrammeR) | |
| staff <- read_csv( | |
| "ID,Name,Title,ManagerID | |
| 1,Jane,CEO, | |
| 2,Bill,CIO,1 | |
| 3,Lydia,CTO,1 | |
| 4,Jack,CMO,1 |