jmrphy

users<-c("screenname1", "screenname2", "screenname3")

out<-NULL

for (i in 1:length(users)){
  user<-users[i]
  friends <- tryCatch(getFriends(screen_name=user, oauth="~/Dropbox/credentials/twitter"),
                error=function(error_message) {
                 message(error_message)
                  return(NA)

jmrphy

require(foreign)

### Define the URL where the dataset (.csv) is located
url<-"https://dl.dropboxusercontent.com/u/20498362/uk_social_attitudes_survey_2011/uk_social_attitudes_survey_2011.csv?raw=1"

### Read in the data from the url we just defined, call it "data"
data <- read.csv(url)


### Some Generic variables

jmrphy

### Define the URL where the dataset is located
url<-"http://dl.dropboxusercontent.com/u/20498362/uk_health_survey_2011/uk_health_survey_2011_2.csv"

### Read the dataset straight from the web, name the dataframe "data"
df <- read.csv(url)

summary(df$compm13)  # quick descriptive stats
summary(df$dnoft)    # quick descriptive stats
summary(df$Age)      # quick descriptive stats
summary(df$Sex)      # quick descriptive stats

jmrphy

require(sp)
require(RColorBrewer)
require(memisc)

# get spatial data for Great Britain
con <- url("http://biogeo.ucdavis.edu/data/gadm2/R/GBR_adm2.RData")
print(load(con))
close(con)

gadm$NAME_1<-as.factor(gadm$NAME_1)

jmrphy

### Starter script for analyzing the Eurobarometer Trend data (1970-2002).

### Important: For this script to work, make sure to first set your working directory
### to a directory containing the Eurobarometer Trend dataset,
### named "eurobarometer_trends.dta." You can obtain this from their website or download
### the data file here: https://www.dropbox.com/s/5bdhel8l7c5r59z/eurobarometer_trends.dta?dl=0

### Outline:
### 1. Identify variables of interest and clean them up
### 2. Estimate a regression model of what makes people turnout for EP elections

jmrphy

# Some absolute basics of the R programming language.
# No data necessary, just load R and walk through this script.


####################################
### Warm up with some arithmetic ###
####################################

50 + 50   # addition
150 - 50  # subtraction

jmrphy

############################################################
### 1. Download and install the packages you want to use ###
############################################################

### Packages are just collections of code written by other R users to simplify common
### tasks. For instance, R is very good at reading all kinds of data files.
### There is a package called "foreign" that lets you read in various data files
### with hardly any effort. We need it here because we have a .dta datafile.
### In the future, when you need to install a certain package, use this code but 
### replace "foreign" in both lines with the name of your package.

jmrphy

x<-read.csv("tweets_#gazaunderattack.csv", header=FALSE, stringsAsFactors=FALSE)
x$username<-x$V2
x$text<-x$V5

#########################################
#### Nice Time-Series Plot ####
#########################################
library(ggplot2)
x$date <- strptime(x$V4, "%a, %d %b %Y %H:%M:%S %z", tz = "EST")
x$date <- as.POSIXct(x$date, tz = "EST")