mbannert

# Postgis data type in my case is WSG84: geom | geometry(Point,4326) 
library(jsonlite)
library(RPostgreSQL)
source("dbConnect.R") # create some connection object called con

p <- dbGetQuery(con,
                "SELECT id,
                postal_code,
                st_asGeoJSON(geom)
                FROM sometable

mbannert

library(fmsb)
# all data taken from basketball-reference.com
curry_now <- read.table(text="Season,Age,Tm,Lg,Pos,G,GS,MP,FG,FGA,FG%,3P,3PA,3P%,2P,2PA,2P%,eFG%,FT,FTA,FT%,ORB,DRB,TRB,AST,STL,BLK,TOV,PF,PTS
2015-16,27,GSW,NBA,PG,20,20,34.3,10.6,20.2,.524,5.1,11.1,.459,5.5,9.1,.602,.650,5.8,6.2,.943,0.5,4.6,5.1,6.0,2.4,0.2,3.6,1.7,32.0",sep=",",dec=".",header=T)

jordan_93 <- read.table(text="Season,Age,Tm,Lg,Pos,G,GS,MP,FG,FGA,FG%,3P,3PA,3P%,2P,2PA,2P%,eFG%,FT,FTA,FT%,ORB,DRB,TRB,AST,STL,BLK,TOV,PF,PTS
1992-93,29,CHI,NBA,SG,78,78,39.3,12.7,25.7,.495,1.0,2.9,.352,11.7,22.7,.514,.515,6.1,7.3,.837,1.7,5.0,6.7,5.5,2.8,0.8,2.7,2.4,32.6
",
                        header = T,sep=",",dec=".")

mbannert

# load packages - install from CRAN if you don't have the package:
# install.packages("waffle")
library(waffle)

# all data taken from 
# basketball-reference.com - keep up the great work!
dubs <- c("X3p" = 251,"X2p" = 552 ,"FT" = 333) 
dubs_points_scored <- dubs * c(3,2,1)
names(dubs_points_scored) <- c("by 3s","by 2s","by FTs")

mbannert

# just an implementation example that does not make a whole lot of sense... 

library(MASS)
y <- mtcars$mpg
ll <- list(cars1 = mtcars[,-1], cars2 = mtcars[,-1]*100)
out_put_list <- lapply(ll,function(x){
  pca <- prcomp(x,scale.=T)
  pca_scores <- as.data.frame(pca$x)
  # maybe give back the result and do it 
  # multiple dfs

mbannert

library(RJSONIO)
library(RCurl)
fx <- getURL("http://www.quandl.com/api/v1/datasets/BOE/XUDLBK68")
fx_df <- data.frame(date = as.Date(unlist(lapply(fromJSON(fx)$data,"[[",1))),
           EURCHF = unlist(lapply(fromJSON(fx)$data,"[[",2)))
# save("fx_df",file="presentation/data/fx_df_temp.RData")

plot(fx_df,main = "Swiss Franc per Euro Exchange Rate",
     xlab = "Source: Quandl API (BOE)",ylab = "",type="l")
     

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setMethod("dbIsValid", "PostgreSQLConnection", function(dbObj, ...) {
  isValid <- tryCatch({dbGetInfo(dbObj)},
                      error = function(e) NULL)
  !is.null(isValid)  
})

mbannert

# getfame makes use of year from the tis package... 
# and thus conflicts with data.table... if the fame package would import instead of 
# depending on tis, everything would be solved... but it doesn't so this
# patch helps out. 
# Hat tip to Richie cotton for helping me out in this SO thread
# http://stackoverflow.com/questions/26462903/how-to-handle-masking-conflicts-in-r-package-the-right-way
# The remaining problem with Richie's ideas was that getfame contains numerous not exported 
# function so I had to hunt them down and add a fame:::functionname to it... 
# here's the patch, TSget won't fail after loading data.table. YAY!
assignInNamespace(

mbannert

rgb2hex <- function(r,g,b) sprintf('#%s',paste(as.hexmode(c(r,g,b)),collapse = ''))

rgb2hex(255,0,0)
# returns '#ff0000'

mbannert

# generate data, variables will be name X1 .... X8 
# dimension: 1200 rows, 7 cols
daten <- data.frame(matrix(rnorm(8400),ncol = 7))
# 8th col is just sequential id 1-1200
daten$id <- as.numeric(row.names(daten))


# assume data are sorted, 
# add another column that indicates
# the subset

mbannert

# load data from an SPSS file
library(foreign)
votematch<-read.spss("ioannis.sav", to.data.frame=TRUE, reencode="UTF-8")

# explore the loaded dataset
head(votematch)
dim(votematch)
names(votematch)
str(votematch)