| library(maps) | |
| unemp <- read.csv('unemployment09.csv',header=FALSE, stringsAsFactors=FALSE, | |
| col.names=c("blsid", "stfips", "cofips", "name", "year", "pop1", "pop2", "unempraw", "unemppct")) | |
| unemp$mpname <- tolower(paste(state.name[match(sub("^.*([A-Z][A-Z])$","\\1",unemp$name,fixed=FALSE),state.abb)], | |
| sub("^(.*) (County|[Cc]ity|Parish), ..$","\\1", unemp$name),sep=",")) | |
| unemp$ri <- as.numeric(cut(unemp$unemppct,c(seq(0,10,by=2),max(unemp$unemppct)))) |
| doInstall <- TRUE # Change to FALSE if you don't want packages installed. | |
| toInstall <- c("ggplot2", "reshape2", "RColorBrewer") | |
| if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")} | |
| lapply(toInstall, library, character.only = TRUE) | |
| # Generate a random matrix | |
| # This can be any type of numeric matrix, | |
| # though we often see heatmaps of square correlation matrices. | |
| nRow <- 9 | |
| nCol <- 16 |
| # Brian Abelson @brianabelson | |
| # Harmony Institute | |
| # December 5, 2012 | |
| # lda is a wrapper for lda.collapsed.gibbs.sampler in the "lda" package | |
| # it fits topic models using latent dirichlet allocation | |
| # it provides arguments for cleaning the input text and tuning the parameters of the model | |
| # it also returns alot of useful information about the topics/documents in a format that you can easily join back to your original data | |
| # this allows you to easily model outcomes based on the distribution of topics within a collection of texts |
| # load the package and data set "Teams" | |
| install.packages("Lahman") | |
| library("Lahman") | |
| data(Teams) | |
| # | |
| # | |
| # CREATE LEAGUE SUMMARY TABLES | |
| # ============================ | |
| # | |
| # select a sub-set of teams from 1901 [the establishment of the American League] forward to 2012 |
Press minus + shift + s and return to chop/fold long lines!
| library(rgdal) | |
| library(dplyr) | |
| library(readr) | |
| library(stringi) | |
| library(stringr) | |
| library(tidyr) | |
| library(grid) | |
| library(scales) | |
| library(ggplot2) | |
| library(ggthemes) |
| seasonStart | group | total | share | barTops | barBases | ||
|---|---|---|---|---|---|---|---|
| 1 | 1992 | Other EU | 22536 | 0.024715160408366 | 0.024715160408366 | 0 | |
| 2 | 1992 | non-EU | 41531 | 0.0455469172399649 | 0.070262077648331 | 0.024715160408366 | |
| 3 | 1992 | Ireland | 63597 | 0.0697466301247273 | 0.140008707773058 | 0.070262077648331 | |
| 4 | 1992 | Rest of UK | 146838 | 0.16103677334237 | 0.301045481115428 | 0.140008707773058 | |
| 5 | 1992 | England | 637327 | 0.698954518884572 | 1 | 0.301045481115428 | |
| 6 | 1993 | Other EU | 27526 | 0.0301765248869175 | 0.0301765248869175 | 0 | |
| 7 | 1993 | non-EU | 62492 | 0.0685094599009391 | 0.0986859847878566 | 0.0301765248869175 | |
| 8 | 1993 | Ireland | 54388 | 0.0596251120958247 | 0.158311096883681 | 0.0986859847878566 | |
| 9 | 1993 | Rest of UK | 124047 | 0.135991694494204 | 0.294302791377885 | 0.158311096883681 |
| lut_match <- function(LUT, code_field, desc_col = 2, is_NA = "NA", no_code = "N/A"){ | |
| require(stringr) | |
| require(splitstackshape) | |
| # this loops through all unique codes in code_field. Potentially slow | |
| code_field[which(code_field == "")] <- NA | |
| unique_code <- unique(code_field) | |
| coded <- code_field | |
| for(i in seq_along(unique_code)){ | |
| if(is.na(unique_code[i])){ | |
| coded[is.na(coded)] <- is_NA # set this to whatever works |
| --- | |
| title: "Visualizing the Clinton Email Network in R" | |
| author: "hrbrmstr" | |
| date: "`r Sys.Date()`" | |
| output: html_document | |
| --- | |
| ```{r include=FALSE} | |
| knitr::opts_chunk$set( | |
| collapse=TRUE, | |
| comment="#>", |
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