| ##programmer: JEY | |
| ##2/28/2013 | |
| ##District-month forecasts | |
| ##note: this takes about 5 minutes to run. It throws lots of errors due to many all zero time series, but it will eventually complete and build predictions | |
| rm(list=ls()) | |
| library(foreign) | |
| library(forecast) | |
| library(sos) | |
| data<-read.dta("raw_AFG.dta") |
| #useful functions for psychometrics and personality research | |
| #a growing compilation of simple functions that I have found useful for doing personality research | |
| #last updated Feb 14, 2005 | |
| #by William Revelle | |
| #includes | |
| #alpha.scale #find coefficient alpha for a scale and a dataframe of items | |
| #describe give means, sd, skew, n, and se | |
| #summ.stats #basic summary statistics by a grouping variable | |
| #error.crosses (error bars in two space) | |
| #skew find skew |
| # removes characters from multiple data frame columns | |
| # example removes * , % characters | |
| dw <- data.frame(gsub("\\*|,|%", "", as.matrix(dw)), stringsAsFactors = FALSE) |
Graphiques pour la CJC.
stats_esr.csv : extrait du RERS 2012, tableaux 6.4.2 et 8.18.1.
jce.R : extrait de l'enquête CJC "Les jeunes chercheurs étrangers en France. Résultats de l’enquête 2010", 2012, section VII.3. Les données sont générées directement dans le code.
vacataires.tsv : extrait de MESR-DGRH, Étude sur la situation des personnels enseignants non permanents de l’enseignement supérieur, 2009, tableaux 2 et 17. Variables utilisées :
| require(downloader) | |
| require(foreign) | |
| require(ggplot2) | |
| # download Quality of Government Standard dataset | |
| file = "data/qog-cs.txt" | |
| if(!file.exists(file)) { | |
| if(!file.exists(dta <- "data/qog-cs.dta")) { | |
| url = "http://www.qogdata.pol.gu.se/data/qog_std_cs.dta" | |
| download(url, dta, mode = "wb") |
| stats.table <- function(data, continuous, categorical, | |
| file = "stats.txt", digits = 1) { | |
| if(!require(pastecs)) stop("Install.packages('pastecs') first.") | |
| # continuous summary (n, mean, sd, min, max) of selected variable columns | |
| con.stats <- round(t(stat.desc(data[, continuous]))[, c(1, 9, 13, 4:5)], digits) | |
| # categorical summary (n, frequencies) of selected variable columns | |
| cat.stats <- lapply(data[, categorical], FUN = function(x) { | |
| data.frame(x <- table(x), percent = round(100 * x / nrow(data), digits))[-3] |
| # Target data source. | |
| link = "https://raw.github.com/briatte/ida/master/data/beijing.aqi.2013.txt" | |
| file = "data/beijing.aqi.2013.txt" | |
| if(!file.exists(file)) download(link, file) | |
| # Read CSV file. | |
| bp <- read.csv(file, stringsAsFactors = FALSE) | |
| # Check result. | |
| head(bp) | |
| bp$time <- strptime(bp$time, format = "%Y-%m-%d %T") | |
| ggplot(data = bp, aes(x = time, y = PM)) + |
| require(ggplot2) | |
| require(plyr) | |
| require(xlsx) | |
| # http://www.calculatedriskblog.com/2013/05/april-employment-report-165000-jobs-75.html | |
| # https://andrewpwheeler.wordpress.com/2013/03/18/the-junk-charts-challenge-remaking-a-great-line-chart-in-spss/ | |
| # http://www.minneapolisfed.org/publications_papers/studies/recession_perspective/ | |
| link = "http://www.minneapolisfed.org/publications_papers/studies/recession_perspective/data/historical_recessions_recoveries_data_05_03_2013.xls" | |
| file = "data/us.recessions.4807.xls" |
| ### Takes as input a Google Docs spreadsheet | |
| ### Outputs national and regional level estimates of support | |
| ### over time | |
| ### Load libraries | |
| library(R2WinBUGS) | |
| library(rjags) | |
| library(RCurl) | |
| library(reshape) | |
| library(foreach) |
| ## Please note, this script is entirely derivative of work done | |
| ## by Simon Jackman for his article `Pooling the Polls...' | |
| ## Aust. J. Pol. Science, 40(4): 499-517 | |
| ## Libraries | |
| library(reshape) | |
| library(rjags) | |
| library(R2WinBUGS) | |
| library(ggplot2) | |
| library(scales) |