.Rapp.history

setwd("/Users/rbtrichler/Documents/AidData/Git Repos/kfw2_amazon_conflict")#
#essential spatial view packages (load and project shapefiles etc...)#
library(rgdal)#
library(sp)#
#tools for spatial objects#
library(rgeos)#
library(maptools)#
#tools for data manipulation#
library(reshape2)#
#library that handles matching#
library(MatchIt)#
#library that has old sci functions (like timeRangeTrend)#
library(SCI)#
shp_file <- "Processed_Data/shpfilecross.shp"#
dta_shp = readShapePoly(shp_file)#
#df_dta_shp = as.data.frame(dta_shp)#
#
#deletes the Pop_2000_x variable and renames Pop_2000_y to Pop_2000#
dta_shp@data$Pop_2000_x <- NULL#
names(dta_shp@data)[names(dta_shp@data)=="Pop_2000_y"] <- "Pop_2000"#
#
#fills in 0s for NAs in lfreq_tota and ifreq_tota#
dta_shp@data$lfreq_tota[is.na(dta_shp@data$lfreq_tota)] <- 0#
dta_shp@data$ifreq_tota[is.na(dta_shp@data$ifreq_tota)]<-0#
#
#Impute 2014 ntl value#
dta_shp@data$ntl_2013[is.na(dta_shp@data$ntl_2013)] <- 5555#
dta_shp@data$ntl2014trend <- timeRangeTrend(dta_shp,"ntl_[0-9][0-9][0-9][0-9]",2009,2013,"id")#
dta_shp@data$ntl2014trend[dta_shp@data$ntl2014trend == "NaN"] <- NA#
dta_shp@data$ntl_2014 <- (dta_shp@data$ntl2014trend + dta_shp@data$ntl_2013)#
dta_shp@data$ntl_2014[dta_shp@data$ntl_2014 < 0] <- 0#
dta_shp@data$ntl_2013[dta_shp@data$ntl_2013 == 5555] <- NA#
#Fills in the previous Pop value for the missing Pop values#
dta_shp@data$Pop_2003 <- dta_shp@data$Pop_2000#
dta_shp@data$Pop_2004 <- dta_shp@data$Pop_2000#
#
dta_shp@data$Pop_2006 <- dta_shp@data$Pop_2005#
dta_shp@data$Pop_2007 <- dta_shp@data$Pop_2005#
dta_shp@data$Pop_2008 <- dta_shp@data$Pop_2005#
dta_shp@data$Pop_2009 <- dta_shp@data$Pop_2005#
#
dta_shp@data$Pop_2011 <- dta_shp@data$Pop_2010#
dta_shp@data$Pop_2012 <- dta_shp@data$Pop_2010#
dta_shp@data$Pop_2013 <- dta_shp@data$Pop_2010#
dta_shp@data$Pop_2014 <- dta_shp@data$Pop_2010#
#Turn dta_shp@data into dataframe#
df<-dta_shp@data#
#
#Drop all variables for years outside of 2003-2014#
df<- df[, -grep("(19[0-9][0-9])", names(df))]#
df<- df[, -grep("(2000)", names(df))]#
df<- df[, -grep("(2001)", names(df))]#
df<- df[, -grep("(2002)", names(df))]#
df<- df[, -grep("(2015)", names(df))]#
df<- df[, -grep("(2020)", names(df))]#
#
#Drop all AvgD and AvgDist varialbes (many NAs, not used for analysis)#
df<-df[,-grep("(AvgD)",names(df))]#
#
#Add underscore to ifreq and lfreq variables to match other variables with yearly measures#
#
for (i in 2:length(df))#
{#
  colnames(df)[i] <- sub("lfreq","lfreq_",colnames(df)[i])#
}#
#
for (i in 2:length(df))#
{#
  colnames(df)[i] <- sub("ifreq","ifreq_",colnames(df)[i])#
}
View(df)
View(dta_shp@data)