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Impact_evaluation_PCW_121417.Rmd
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Impact_evaluation_PCW_121417.Rmd
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---
title: "Preliminary results of the impact evaluation paper"
author: "Mani Rouhi Rad"
date: '`r format(Sys.Date(), "%B %d, %Y")`'
#output: pdf_document
#output: md_document
output:
html_document:
css: custom.css
# keep_md: true
#fontsize: 12pt
---
```{r, include=FALSE, warning=FALSE}
library(rgeos)
library(rgdal)
library(plyr)
library(reshape2)
library(dplyr)
library(data.table)
library(ggplot2)
library(scales)
library(R.matlab)
library(sp)
library(sf)
library(maptools)
library(broom)
library(quantreg)
library(foreign)
library(raster)
library(lmtest)
library(RColorBrewer)
library(readstata13)
library(parallel)
library(stargazer)
library(AER)
library(texreg)
library(gridExtra)
library(grid)
library(directlabels)
```
```{r setup, include=FALSE}
# knitr::opts_knit$set(root.dir = 'C:/Users/mr2284/Dropbox/Panama S2/Impact Evaluation')
knitr::opts_knit$set(root.dir = 'C:/Users/mr2284/Dropbox/Research/Panama Canal-My material/Codes/IE/IE_PCW')
# knitr::opts_chunk$set(fig.width=12, fig.height=8, fig.path='./Figures and Tables', echo=FALSE, warning=FALSE, message=FALSE, cache=TRUE)
```
```{r, include=FALSE, warning=FALSE}
simpleCap <- function(x) { # 1) Function defined to make all caps to propper
paste(substring(x, 1,1), tolower(substring(x, 2)), # 2)
sep="") } # 3)
FN_area_sp = function(input_sp, scenario){ # 1) function defined to find the area
foo = st_as_sf(input_sp)
a = as.data.table(input_sp@data)
b = as.data.table(st_area(foo))
b[, paste(scenario, "ha", sep="_"):=as.numeric(x*0.0001)]
b=b[, 2]
output_data=data.table(a, b)
return(output_data)
}
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
grid_arrange_shared_legend <- function(...) {
plots <- list(...)
g <- ggplotGrob(plots[[1]] + theme(legend.position="bottom"))$grobs
legend <- g[[which(sapply(g, function(x) x$name) == "guide-box")]]
lheight <- sum(legend$height)
grid.arrange(
do.call(arrangeGrob, lapply(plots, function(x)
x + theme(legend.position="none"))),
legend,
ncol = 1,
heights = unit.c(unit(1, "npc") - lheight, lheight))
# heights=unit(c(2, 2, 2), c("in", "in", "in")))
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
######## Input data ######
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# I. River discharge
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# river_discharge=data.table(read.csv("./Data/Map Area_update.csv"))
river_discharge=data.table(read.csv("./Data/Map Area2.csv"))
river_discharge=melt(river_discharge, id=c("River", "Year", "Day"), variable.name = "month", value.name = "discharge_m3_s")
river_discharge=river_discharge[complete.cases(Day)]
river_discharge[ , month:=as.character(month)]
river_discharge[ , month:=match(month,month.abb)]
# river_discharge[, foo_date:=as.character(paste(Year, month, Day, sep = "/"))]
# river_discharge[, date_read := as.Date(foo_date, "%Y/%b/%d")]
river_discharge=river_discharge[,.(River, year=Year, month, day=Day, discharge_m3_s)]
river_discharge[, discharge_m3_s:=as.numeric(discharge_m3_s)]
river_discharge[, River:=gsub("Cano", "Cano Quebrado", River)]
river_discharge[, River:=gsub("Ciri", "Ciri grande", River)]
river_discharge[, unique(River)]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# II. River - watershed connection
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
river_names = readOGR(dsn="./Data/Rivers",layer="Rivers_PCWCopy")
river_names=data.table(river_names@data)
river_names=river_names[,.(NOMBRE, SUBCUENC_2)]
river_names[, River:=simpleCap(NOMBRE)]
spanish_cano=river_names[, unique(River)][3]
river_names[, River:=gsub(spanish_cano, "Cano Quebrado", River)]
river_names[, unique(River)]
setkey(river_names, River)
setkey(river_discharge, River)
river_discharge=river_names[river_discharge]
river_discharge=river_discharge[complete.cases(NOMBRE)]
river_discharge=river_discharge[,.(River, SUBCUENC_2, year, month, day, discharge_m3_s)]
weird_char = unique(river_discharge$SUBCUENC_2)[1]
weird_char = substr(weird_char, 1, 4) # win
# weird_char = substr(weird_char, 1, 3) # mac
river_discharge[, SUBCUENC_2 := gsub(weird_char, "Rio", SUBCUENC_2)]
river_discharge[, SUBCUENC_2 := gsub("Boquerón", "Boqueron", SUBCUENC_2)]
river_discharge[, SUBCUENC_2 := gsub("Caño", "Cano", SUBCUENC_2)]
weird_char_ciri=unique(river_discharge$SUBCUENC_2)[4]
weird_char_ciri=substr(weird_char_ciri, 5, 8)
weird_char_pequeni=unique(river_discharge$SUBCUENC_2)[6]
weird_char_pequeni=substr(weird_char_pequeni, 5, 13)
river_discharge[, SUBCUENC_2 := gsub(weird_char_ciri, "Ciri", SUBCUENC_2)] # win
river_discharge[, SUBCUENC_2 := gsub(weird_char_pequeni, "Pequeni", SUBCUENC_2)] # win
river_discharge[, SUBCUENC_2 := gsub("Gatún", "Gatun", SUBCUENC_2)]
river_discharge[, unique(SUBCUENC_2)]
river_discharge[year==2016 & River=="Gatun",.N, by="month"]
river_discharge[year==2016 & River=="Chagres",.N, by="month"]
river_discharge[,.N, by=River]
# Except Indio, which will be thrown out, the panel is almost balanced. Only Chagres has more data which may mean there is "extra" data
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# II. Hectares in each region-year
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
PIEA = readOGR(dsn="./Data/PIEA", layer="PIEA_polygons")
watershed_original = readOGR(dsn="./Data/Watersheds", layer="Watersheds_PCW")
watershed_original = gBuffer(watershed_original, byid = TRUE, width = 0)
watershed_PIEA_final = raster::intersect(PIEA, watershed_original)
# writeOGR(watershed_PIEA_final,
# dsn="./Data/PIEA", layer="PIEA_by_watershed", driver="ESRI Shapefile")
watershed_PIEA_final=FN_area_sp(watershed_PIEA_final, "area_PIEA_watershed")
watershed_PIEA_final=watershed_PIEA_final[,.(Uso, SUBCUENC_2, area_PIEA_watershed_ha, area_watershed_ha=area_Water)]
watershed_PIEA_final[, Uso:=gsub(": Programa PIEA", "", Uso)]
watershed_PIEA_final_by_land_use = watershed_PIEA_final[, sum(area_PIEA_watershed_ha), by="Uso"]
watershed_PIEA_final_by_watershed= watershed_PIEA_final[, .(sum(area_PIEA_watershed_ha), sum(area_watershed_ha)), by="SUBCUENC_2"]
watershed_PIEA_final_by_watershed[, percent_area:=V1/V2*100]
setkey(watershed_PIEA_final_by_watershed, percent_area)
watershed_PIEA_final[, SUBCUENC_2 := gsub(weird_char, "Rio", SUBCUENC_2)]
watershed_PIEA_final[, SUBCUENC_2 := gsub("Boquerón", "Boqueron", SUBCUENC_2)]
watershed_PIEA_final[, SUBCUENC_2 := gsub("Caño", "Cano", SUBCUENC_2)]
watershed_PIEA_final[, SUBCUENC_2 := gsub(weird_char_ciri, "Ciri", SUBCUENC_2)]
watershed_PIEA_final[, SUBCUENC_2 := gsub(weird_char_pequeni, "Pequeni", SUBCUENC_2)] # mac
watershed_PIEA_final[, SUBCUENC_2 := gsub("Gatún", "Gatun", SUBCUENC_2)]
watershed_PIEA_final[, unique(SUBCUENC_2)]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# III. Rainfall (Other climatic variables?)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
################
# rainfall_PIEA=read.dta13("./Data/Rainfall/acp_15Minstorms_Steve_v18112016.dta")
# rainfall_PIEA=data.table(rainfall_PIEA)
# rainfall_PIEA[,month:=substr(date, 4, 5)]
# rainfall_PIEA[,day :=substr(date, 1, 2)]
# rainfall_PIEA[,total_rain_day:=sum(Totalrain), by=c("site", "Year", "month", "day")]
# rainfall_PIEA=unique(rainfall_PIEA, by=c("site", "Year", "month", "day"))
# rainfall_PIEA[,month:=as.numeric(month)]
# rainfall_PIEA[,Year :=as.numeric(Year)]
# rainfall_PIEA[,day :=as.numeric(day)]
################
rainfall_PIEA2=read.csv("./Data/Rainfall/ACP_Rainfall_hourly.csv")
rainfall_PIEA2=data.table(rainfall_PIEA2)
rainfall_PIEA2[,DATETIME.dd.mm.yyyy.hh.mm.:=as.character(DATETIME.dd.mm.yyyy.hh.mm.)]
rainfall_PIEA2[, day:=substr(DATETIME.dd.mm.yyyy.hh.mm., 1, regexpr('/', DATETIME.dd.mm.yyyy.hh.mm.)-1)]
rainfall_PIEA2[,first_foo:=substr(DATETIME.dd.mm.yyyy.hh.mm., regexpr('/', DATETIME.dd.mm.yyyy.hh.mm.)+1, 20)]
rainfall_PIEA2[, month:=substr(first_foo, 1, regexpr('/', first_foo)-1)]
rainfall_PIEA2[,first_foo:=substr(first_foo, regexpr('/', first_foo)+1, 20)]
rainfall_PIEA2[, year:=substr(first_foo, 1, regexpr(' ', first_foo)-1)]
rainfall_PIEA2[, day:= as.numeric(as.character(day))]
rainfall_PIEA2[, month:=as.numeric(as.character(month))]
rainfall_PIEA2[, year:= as.numeric(as.character(year))]
rainfall_PIEA2=rainfall_PIEA2[, .(Station, year, month, day, total_rain_day=Rain..mm.)]
rainfall_PIEA2[, total_rain_day:=sum(total_rain_day), by=c("Station", "year", "month", "day")]
rainfall_PIEA2=unique(rainfall_PIEA2, by=c("Station", "year", "month", "day"))
####### check these two for rainfall before 2004
rainfall_PIEA2[,unique(Station)]
rainfall_PIEA2[Station== "CHORRO", .N, by="year"]
# for (i in 1:nrow(rivers_used)) {
# temp=rainfall_PIEA2[Station== rivers_used[i], .N, by="year"]
# print(rivers_used[i])
# print(temp)
# readline(prompt="Press [enter] to continue")
# }
######## only SANMIGUEL and CHAGRECITO don't have enough data. Unfortunately, ZANGUENA only has data starting 2004. This is in Cano Quebrado catchment so would have been helpful.
setnames(rainfall_PIEA2, "Station", "site")
###############
rainfall_stations = readOGR(dsn="./Data/Rainfall/stations", layer="stations")
# foo1 = watershed_original[!is.na(over(rainfall_stations, as(watershed_original,"SpatialPolygons"))),]
bar_1=over(rainfall_stations, as(watershed_original,"SpatialPolygons"))
bar_1=data.table(bar_1)
setnames(bar_1, "bar_1", "over_id")
bar_2=data.table(bar_1, rainfall_stations@data)
bar_2=bar_2[complete.cases(over_id)]
bar_3=data.table(watershed_original@data)
setkey(bar_2, over_id)
setkey(bar_3, OBJECTID_1)
bar= bar_2[bar_3]
bar=bar[complete.cases(CODENAME)]
setkey(bar, CODENAME)
rainfall_stations=bar[,.(OBJECTID, over_id, CODENAME, SUBCUENC_2)]
rainfall_stations[, CODENAME:=gsub(" ", "", CODENAME, fixed = TRUE)]
setkey(rainfall_stations, CODENAME)
setkey(rainfall_PIEA2, site)
rainfall_PIEA2=rainfall_stations[rainfall_PIEA2]
rainfall_PIEA2=rainfall_PIEA2[complete.cases(CODENAME) & complete.cases(SUBCUENC_2)]
rainfall_PIEA2=rainfall_PIEA2[,.(Station= CODENAME, SUBCUENC_2, date=as.Date(paste(year, month, day, sep = "-")), year, month, day, total_rain_day)]
# rainfall_PIEA2[, date_read:=as.Date(date, "dd/mm/yyyy")]
# rainfall_PIEA2[, date_read:=as.Date(date, "%d/%m/%Y")]
#
# ggplot(data = rainfall_PIEA2[Station=="CANONES"], aes(x=date_read, y=total_rain_day))+
# geom_line(size=1.1)+
# # coord_cartesian(ylim=c(0, 400)) +
# scale_x_date(labels = date_format("%m-%Y")) #+
# # scale_y_discrete(breaks=seq(0, 400, 100)) # Ticks from 0-10, every .25
# rainfall_PIEA2[, unique(Station)]
rainfall_PIEA2[, unique(SUBCUENC_2)]
# watershed_PIEA_final[, unique(SUBCUENC_2)]
# river_discharge[, unique(SUBCUENC_2)]
# str(river_discharge)
# str(rainfall_PIEA2)
rainfall_PIEA2[SUBCUENC_2== rainfall_PIEA2[, unique(SUBCUENC_2)][6], unique(Station)]
rainfall_PIEA2[Station == rainfall_PIEA2[SUBCUENC_2==rainfall_PIEA2[, unique(SUBCUENC_2)][6], unique(Station)]]
# rainfall_PIEA[Station == rainfall_PIEA2[SUBCUENC_2==rainfall_PIEA2[, unique(SUBCUENC_2)][6], unique(Station)]]
### looks good \/
rainfall_PIEA2[, SUBCUENC_2 := gsub(weird_char, "Rio", SUBCUENC_2)]
rainfall_PIEA2[, SUBCUENC_2 := gsub("Boquerón", "Boqueron", SUBCUENC_2)]
rainfall_PIEA2[, SUBCUENC_2 := gsub("Caño", "Cano", SUBCUENC_2)]
rainfall_PIEA2[, SUBCUENC_2 := gsub(weird_char_ciri, "Ciri", SUBCUENC_2)] # win
rainfall_PIEA2[, SUBCUENC_2 := gsub(weird_char_pequeni, "Pequeni", SUBCUENC_2)] # win
rainfall_PIEA2[, SUBCUENC_2 := gsub("Gatún", "Gatun", SUBCUENC_2)]
rainfall_PIEA2[, unique(SUBCUENC_2)]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# IV. Seasons
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
seasons=read.csv("./Data/seasons.csv")
seasons=data.table(seasons)
seasons[, Dry_season_start_date := as.Date(Dry_season_start, "%d-%b-%y")]
seasons[, Dry_season_end_date := as.Date(Dry_season_end, "%d-%b-%y")]
seasons=seasons[,.(YEAR, Dry_season_start_date, Dry_season_end_date)]
seasons[, Dry_season_length := Dry_season_end_date - Dry_season_start_date]
setkey(seasons, YEAR)
setkey(river_discharge, year)
river_discharge=river_discharge[seasons]
river_discharge=river_discharge[complete.cases(River)]
river_discharge[, date_disch:=as.Date(paste(year, month, day, sep = "/"))]
river_discharge=river_discharge[complete.cases(date_disch)]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# V. Merge
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
setkey(rainfall_PIEA2, SUBCUENC_2, year, month, day)
setkey(river_discharge, SUBCUENC_2, year, month, day)
unique(rainfall_PIEA2, by="SUBCUENC_2")
unique(river_discharge, by="SUBCUENC_2")
# setkey(watershed_PIEA_final, SUBCUENC_2, year, month, day)
# setkey(watershed_PIEA_final, SUBCUENC_2, Uso)
# data_reg = river_discharge[rainfall_PIEA2, allow.cartesian=T, all.x=T]
# data_reg = rainfall_PIEA2[river_discharge, allow.cartesian=T]
# data_reg = data_reg[complete.cases(date_read)]
# data_reg = data_reg[complete.cases(total_rain_day) & complete.cases(discharge_m3_s)]
data_reg = merge(river_discharge, rainfall_PIEA2, by.x=c("SUBCUENC_2", "year", "month", "day"), by.y=c("SUBCUENC_2", "year", "month", "day") , all.x=T)
# data_reg = data_reg[year > 2004]
data_reg = data_reg[year > 2003]
data_reg = data_reg[,.(SUBCUENC_2, River, year, month, day, Dry_season_start_date, Dry_season_end_date, discharge_m3_s, Station, total_rain_day)]
data_reg[, date_read := as.Date(paste(year, month, day, sep = "-"))]
data_reg=data_reg[complete.cases(date_read)]
data_reg[is.na(total_rain_day), total_rain_day:=0]
# rivers_used=data_reg[, unique(Station), by="SUBCUENC_2"]
# rivers_used=rivers_used[,2]
data_reg[, day_of_dry_season := date_read - Dry_season_start_date]
data_reg[, day_of_wet_season := date_read - (Dry_season_end_date+1)]
data_reg[, day_of_season := ifelse(date_read < Dry_season_end_date+1, day_of_dry_season, day_of_wet_season)]
data_reg[, dry_dummy := ifelse(date_read < Dry_season_end_date+1, 1, 0)]
data_reg=data_reg[year<2016]
######## I think merging discharge and rainfall should be done more carefully
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# VI. land use data
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# These are the areas that Blair estimated. I double chekced Trinidad for 2012 and the estimates look good. So I am using the estimates.
#
land_use=read.csv("./Data/Land Covers/land_use_over_time_R_input.csv")
land_use=data.table(land_use)
land_use[, unique(River)]
land_use=land_use[River!="Las Cascadas"]
land_use=land_use[,foo:=ifelse(X2003==0 & X2008==0 & X2012==0, 1, 0)]
land_use=land_use[foo==0]
land_use=land_use[Land.use!="Water"]
forest=land_use[Land.use=="Mature Forests" | Land.use=="Secondary Forests" | Land.use=="Forest Plantations"]
cols=colnames(forest)[3:5]
forest[, (cols):=lapply(.SD, sum), by="River", .SDcols=cols]
forest=unique(forest, by="River")
forest=forest[,.(River, f_2003=X2003, f_2008=X2008, f_2012=X2012)]
non_forest=land_use[Land.use=="Pastures" | Land.use=="Crops" | Land.use=="Scrub" | Land.use=="Kans Grass" | Land.use=="Towns" | Land.use=="Barren Soil"]
cols=colnames(non_forest)[3:5]
non_forest[, (cols):=lapply(.SD, sum), by="River", .SDcols=cols]
non_forest=unique(non_forest, by="River")
non_forest=non_forest[,.(River, n_2003=X2003, n_2008=X2008, n_2012=X2012)]
setkey(forest, River)
setkey(non_forest, River)
net=forest[non_forest]
net[, X2003:=f_2003 - n_2003]
net[, X2008:=f_2008 - n_2008]
net[, X2012:=f_2012 - n_2012]
net=net[,.(River, type="net", X2003, X2008, X2012)]
land_use_fig = copy(land_use)
land_use_fig[, type:=ifelse(Land.use=="Mature Forests" | Land.use=="Secondary Forests" | Land.use=="Forest Plantations", "forest",
ifelse(Land.use=="Pastures" | Land.use=="Crops" | Land.use=="Scrub" | Land.use=="Kans Grass" | Land.use=="Towns" | Land.use=="Barren Soil", "non_forest"
, "other"))]
land_use_fig=land_use_fig[type=="forest" | type== "non_forest"]
col_sum=colnames(land_use_fig)[3:5]
land_use_fig[,(col_sum):=lapply(.SD, sum), by=c("River", "type"), .SDcols=col_sum]
land_use_fig=unique(land_use_fig, by=c("type", "River"))
land_use_fig=land_use_fig[,.(River, type, X2003, X2008, X2012)]
land_use_fig=rbind(land_use_fig, net)
setkey(land_use_fig, River)
land_use_fig=land_use_fig[,.(River, type, X2003, X2008, X2012)]
land_use_fig=melt(land_use_fig, id=c("River", "type"))
land_use_fig[, variable:=ifelse(variable=="X2003", 2003, ifelse(variable=="X2008", 2008, 2012))]
foo_d=land_use_fig[variable==2003, value]
land_use_fig[, foo:=foo_d]
land_use_fig[, foo:=value - foo]
lu_fig1<- ggplot(land_use_fig[type=="forest"], aes(x=factor(variable), y=foo, group=River, col=River))+
geom_point(size=2)+
geom_line(size=1.2)+
theme_bw()+
xlab("Year")+
ylab("Ha")+
ggtitle(paste("forest land"))+
scale_colour_discrete(guide = 'none') +
scale_x_discrete(expand=c(0, 1)) +
geom_dl(aes(label = River), method = list(dl.trans(x = x + 0.2), "last.points", cex = 0.8))
lu_fig3<- ggplot(land_use_fig[type=="net"], aes(x=factor(variable), y=foo, group=River, col=River))+
geom_point(size=2)+
geom_line(size=1.2)+
theme_bw()+
xlab("Year")+
ylab("Ha")+
ggtitle(paste("net forest cover"))+
scale_colour_discrete(guide = 'none') +
scale_x_discrete(expand=c(0, 1)) +
geom_dl(aes(label = River), method = list(dl.trans(x = x + 0.2), "last.points", cex = 0.8))
lu_fig2 <- ggplot(land_use_fig[type=="non_forest"], aes(x=factor(variable), y=foo, group=River, col=River))+
geom_point(size=2)+
geom_line(size=1.2)+
theme_bw()+
xlab("Year")+
ylab("Ha")+
ggtitle(paste("non forest land"))+
scale_colour_discrete(guide = 'none') +
scale_x_discrete(expand=c(0, 1)) +
geom_dl(aes(label = River), method = list(dl.trans(x = x + 0.2), "last.points", cex = 0.8))
###### >>>>> I need to get back to this and add them to the figure with ESE estimates as bars <<<<<<<<<
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ####
######## Plot PIEA polygons ###########
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
county_fills <- readOGR(dsn="C:/Users/mr2284/Dropbox/Research/Panama Canal-My material/Codes/IE/IE_PCW/Data/Watersheds",layer="watersheds_original")
county_fills@data$id = rownames(county_fills@data)
county_fills_2= county_fills@data
PIEA@data$id = rownames(PIEA@data)
PIEA_2= PIEA@data
# install.packages("gpclib")
# gpclibPermit() ## It should return TRUE
county_fills_1 <- tidy(county_fills, region = "id")
fills.df = join(county_fills_1, county_fills@data, by="id")
PIEA_1 <- tidy(PIEA, region = "id")
pills.df = join(PIEA_1, PIEA@data, by="id")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ####
######## Data Analysis ###########
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
trin = watershed_PIEA_final_by_watershed[,SUBCUENC_2][25]
ciri = watershed_PIEA_final_by_watershed[,SUBCUENC_2][31]
cano = watershed_PIEA_final_by_watershed[,SUBCUENC_2][3]
Boqueron = watershed_PIEA_final_by_watershed[,SUBCUENC_2][4]
Gatun = watershed_PIEA_final_by_watershed[,SUBCUENC_2][8]
watershed_PIEA_west = watershed_PIEA_final_by_watershed[SUBCUENC_2==trin | SUBCUENC_2==ciri | SUBCUENC_2==cano | SUBCUENC_2==Boqueron | SUBCUENC_2==Gatun]
watershed_PIEA_west = watershed_PIEA_west[ SUBCUENC_2==trin, SUBCUENC_2:="Trinidad"]
watershed_PIEA_west = watershed_PIEA_west[ SUBCUENC_2==ciri, SUBCUENC_2:="Ciri grande"]
watershed_PIEA_west = watershed_PIEA_west[ SUBCUENC_2==cano, SUBCUENC_2:="Cano Quebrado"]
watershed_PIEA_west = watershed_PIEA_west[ SUBCUENC_2==Gatun, SUBCUENC_2:="Gatun"]
watershed_PIEA_west = watershed_PIEA_west[ SUBCUENC_2==Boqueron, SUBCUENC_2:="Boqueron"]
river_discharge_west= river_discharge[River=="Trinidad" | River=="Ciri grande" | River=="Cano Quebrado" | River=="Boqueron" | River=="Gatun"]
river_discharge_west=river_discharge_west[date_disch < Dry_season_end_date]
river_discharge_west[, tot_discharge := discharge_m3_s*24*3600] # M3
setkey(watershed_PIEA_west, SUBCUENC_2)
setkey(river_discharge_west, River)
watershed_PIEA_west=watershed_PIEA_west[river_discharge_west]
data_pre=watershed_PIEA_west[year < 2008]
data_pre[, discharge_per_ha := tot_discharge/V2] # m3 per day per hectare
data_pre[, extra_discharge := discharge_per_ha * V1 * .2] # m3 per day for the entire catchment under 20% ESE
data_pre[, mean_extra_discharge :=mean(extra_discharge), by=c("SUBCUENC_2", "month")]
data_pre[, sd_extra_discharge :=sd(extra_discharge), by=c("SUBCUENC_2", "month")]
data_pre=unique(data_pre, by=c("SUBCUENC_2", "month"))
data_pre=data_pre[,.(SUBCUENC_2, month, mean_extra_discharge, sd_extra_discharge)]
summary_PIEA=watershed_PIEA_west[,.(Watershed=SUBCUENC_2, PIEA_hecatres=V1, Watershed_hectares=V2, percent_area_in_PIEA=percent_area)]
summary_PIEA=unique(summary_PIEA, by="Watershed")
summary_PIEA=rbind(summary_PIEA, list("Chagres", 0, 0, 0), list("Pequeni", 0, 0, 0))
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ####
# 0. Teeatment before and after ####
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#################
data_reg_copy_daily = copy(data_reg)
data_reg_copy_daily[,total_rain_day:=mean(total_rain_day), by=c("River", "year", "month", "day")]
data_reg_copy_daily=unique(data_reg_copy_daily, by=c("River", "year", "month", "day"))
data_reg_copy_daily[, tot_discharge := discharge_m3_s*24*3600] # total daily Million m3
data_reg_copy_daily[, lag.rain_day := c(NA, total_rain_day[-.N]), by=c("SUBCUENC_2", "year")]
data_reg_copy_daily[, lag.rain_day_2 := c(NA, lag.rain_day[-.N]), by=c("SUBCUENC_2", "year")]
data_reg_copy_daily[, rain_past_2_days := lag.rain_day + lag.rain_day_2]
data_reg_copy_daily[, lag.discharge := c(NA, tot_discharge[-.N]), by=c("SUBCUENC_2", "year")]
data_reg_daily_dry = data_reg_copy_daily[dry_dummy==1]
data_reg_daily_wet = data_reg_copy_daily[dry_dummy==0]
#
# FN_before_after_by_river <- function(year_pre, year_post, River_tr) {
# # dt=data_reg_copy_daily[(River=="Trinidad" | River=="Ciri grande" | River=="Cano Quebrado")]
# dt=data_reg_copy_daily[(River==River_tr)]
# dt = dt[year < year_pre | year > year_post][ ,year_treat:= ifelse(year>2007, 1, 0)]
# coefs_DiD = data.table(month=1:12, coefficient_DiD = rep(0 , 12), standard_error_DiD= rep(0, 12))
# for(i in 1:12){
# DiD_prepost = lm(tot_discharge ~ year_treat + total_rain_day + lag.rain_day + day_of_season + lag.discharge,
# data = dt[ month == i])
# beta_DiD = summary(DiD_prepost)$coefficients[2,1]
# std_error= summary(DiD_prepost)$coefficients[2,2]
# coefs_DiD[i, 2] = beta_DiD
# coefs_DiD[i, 3] = std_error
# }
#
# dt_ESE=data_pre[SUBCUENC_2==River_tr]
#
# l=ggplot(data = coefs_DiD, aes(x=factor(month), y=coefficient_DiD))+
# geom_errorbar(aes(ymin=coefficient_DiD - 2.866 * standard_error_DiD , ymax=coefficient_DiD + 2.866 * standard_error_DiD), width=.1) +
# geom_line(aes(group=1)) +
# geom_point() +
# geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
# width=.2, col="red") +
# theme_bw()+
# xlab("month")+
# ylab("Diff in Diff Coefficient")+
# ggtitle(paste("Before", year_pre, "and after", year_post, "\nfor", River_tr, sep = " "))
# return(l)
# }
#
#
FN_before_after_by_river <- function(year_pre, year_post, River_tr) {
# dt=data_reg_copy_daily[(River=="Trinidad" | River=="Ciri grande" | River=="Cano Quebrado")]
dt=data_reg_copy_daily[River==River_tr & month < 5]
dt = dt[year < year_pre | year > year_post][ ,year_treat:= ifelse(year>2007, 1, 0)]
dt_pre=dt[year_treat==0 ,.(year, month, day, tot_discharge)]
dt_pos=dt[year_treat==1 ,.(year, month, day, tot_discharge)]
dt_pre[,mean_disch_pre:=mean(tot_discharge), by="month"]
dt_pre[,foo:=.N, by="month"]
dt_pre[,sd_disch_pre := sd(tot_discharge), by= "month"]
dt_pre[,sd_disch_pre := (sd_disch_pre)^2/foo]
dt_pre=unique(dt_pre, by="month")
dt_pre=dt_pre[,.(month, mean_disch_pre, sd_disch_pre)]
dt_pos[,mean_disch_pos:=mean(tot_discharge), by="month"]
dt_pos[,foo:=.N, by="month"]
dt_pos[,sd_disch_pos := sd(tot_discharge), by= "month"]
dt_pos[,sd_disch_pos := (sd_disch_pos)^2/foo]
dt_pos=unique(dt_pos, by="month")
dt_pos=dt_pos[,.(month, mean_disch_pos, sd_disch_pos)]
setkey(dt_pre, month)
setkey(dt_pos, month)
dt=dt_pre[dt_pos]
dt[, sd:=sqrt(sd_disch_pre + sd_disch_pos)]
dt[, mean:=mean_disch_pos - mean_disch_pre]
dt_ESE=data_pre[SUBCUENC_2==River_tr & month < 5]
l=ggplot(data = dt, aes(x=factor(month), y=mean))+
geom_errorbar(aes(ymin=mean - 1.96 * sd , ymax=mean + 1.96 * sd), width=.1) +
geom_line(aes(group=1)) +
geom_point() +
geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
width=.2, col="red") +
theme_bw()+
xlab("month")+
ylab("Change in daily discharge (m3)")+
ggtitle(paste("Before", year_pre, "and after", year_post, "\nfor", River_tr, sep = " "))
return(l)
}
## West Side
a1=FN_before_after_by_river(2008, 2012, "Trinidad")
a3=FN_before_after_by_river(2008, 2012, "Ciri grande")
a2=FN_before_after_by_river(2008, 2012, "Cano Quebrado")
a4=FN_before_after_by_river(2008, 2012, "Boqueron")
a5=FN_before_after_by_river(2008, 2012, "Chagres")
a6=FN_before_after_by_river(2008, 2012, "Gatun")
a7=FN_before_after_by_river(2008, 2012, "Pequeni")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ####
# 0. Monthly regressions for the entire watershed ####
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#################
FN_coefficients_DiD_monthly <- function(year_pre, year_post) {
# dt=data_reg_copy_daily[(River=="Trinidad" | River=="Ciri grande" | River=="Cano Quebrado")]
dt=copy(data_reg_copy_daily)
dt = dt[year < year_pre | year > year_post][ ,year_treat:= ifelse(year>2007, 1, 0)]
dt=dt[, treatment:=ifelse(River=="Trinidad" | River=="Ciri grande", 1, 0)]
coefs_DiD = data.table(month=1:12, coefficient_DiD = rep(0 , 12), standard_error_DiD= rep(0, 12))
for(i in 1:12){
# DiD_prepost = lm(tot_discharge ~ year_treat + total_rain_day + lag.rain_day + day_of_season + lag.discharge,
# data = dt[ month == i])
DiD_prepost = lm(tot_discharge ~ treatment + year_treat + treatment:year_treat + total_rain_day + lag.rain_day + day_of_season
+ factor(year)
, data = dt[ month == i])
beta_DiD = summary(DiD_prepost)$coefficients[nrow(summary(DiD_prepost)$coefficients),1]
std_error= summary(DiD_prepost)$coefficients[nrow(summary(DiD_prepost)$coefficients),2]
coefs_DiD[i, 2] = beta_DiD
coefs_DiD[i, 3] = std_error
}
# dt_ESE=data_pre[SUBCUENC_2==River_tr]
l=ggplot(data = coefs_DiD, aes(x=factor(month), y=coefficient_DiD, group=1))+
geom_errorbar(aes(ymin=coefficient_DiD - 2.866 * standard_error_DiD , ymax=coefficient_DiD + 2.866 * standard_error_DiD), width=.1) +
geom_line() +
geom_point() +
theme_bw()+
# geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
# width=.2, col="red") +
xlab("month")+
ylab("Diff in Diff Coefficient")+
ggtitle(paste("Before", year_pre, "and after", year_post, sep = " "))
return(l)
}
b1=FN_coefficients_DiD_monthly(2008, 2012)
b2=FN_coefficients_DiD_monthly(2007, 2012)
b3=FN_coefficients_DiD_monthly(2008, 2013)
b4=FN_coefficients_DiD_monthly(2007, 2013)
FN_coefficients_DiD_monthly_lag <- function(year_pre, year_post) {
# dt=data_reg_copy_daily[(River=="Trinidad" | River=="Ciri grande" | River=="Cano Quebrado")]
dt=copy(data_reg_copy_daily)
dt = dt[year < year_pre | year > year_post][ ,year_treat:= ifelse(year>2007, 1, 0)]
dt=dt[, treatment:=ifelse(River=="Trinidad" | River=="Ciri grande", 1, 0)]
coefs_DiD = data.table(month=1:12, coefficient_DiD = rep(0 , 12), standard_error_DiD= rep(0, 12))
for(i in 1:12){
# DiD_prepost = lm(tot_discharge ~ year_treat + total_rain_day + lag.rain_day + day_of_season + lag.discharge,
# data = dt[ month == i])
DiD_prepost = lm(tot_discharge ~ treatment + year_treat + treatment:year_treat + total_rain_day + lag.rain_day + day_of_season
+ factor(year) + lag.discharge
, data = dt[ month == i])
beta_DiD = summary(DiD_prepost)$coefficients[nrow(summary(DiD_prepost)$coefficients),1]
std_error= summary(DiD_prepost)$coefficients[nrow(summary(DiD_prepost)$coefficients),2]
coefs_DiD[i, 2] = beta_DiD
coefs_DiD[i, 3] = std_error
}
# dt_ESE=data_pre[SUBCUENC_2==River_tr]
l=ggplot(data = coefs_DiD, aes(x=factor(month), y=coefficient_DiD, group=1))+
geom_errorbar(aes(ymin=coefficient_DiD - 2.866 * standard_error_DiD , ymax=coefficient_DiD + 2.866 * standard_error_DiD), width=.1) +
geom_line() +
geom_point() +
# geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
# width=.2, col="red") +
theme_bw()+
xlab("month")+
ylab("Diff in Diff Coefficient")+
ggtitle(paste("Before", year_pre, "and after", year_post, sep = " "))
return(l)
}
b5=FN_coefficients_DiD_monthly_lag(2008, 2012)
b6=FN_coefficients_DiD_monthly_lag(2007, 2012)
b7=FN_coefficients_DiD_monthly_lag(2008, 2013)
b8=FN_coefficients_DiD_monthly_lag(2007, 2013)
### changing treatment year to 2010
FN_coefficients_DiD_monthly_lag2 <- function(year_pre, year_post) {
# dt=data_reg_copy_daily[(River=="Trinidad" | River=="Ciri grande" | River=="Cano Quebrado")]
dt=copy(data_reg_copy_daily)
dt = dt[year < year_pre | year > year_post][ ,year_treat:= ifelse(year>2009, 1, 0)]
dt=dt[, treatment:=ifelse(River=="Trinidad" | River=="Ciri grande", 1, 0)]
coefs_DiD = data.table(month=1:12, coefficient_DiD = rep(0 , 12), standard_error_DiD= rep(0, 12))
for(i in 1:12){
# DiD_prepost = lm(tot_discharge ~ year_treat + total_rain_day + lag.rain_day + day_of_season + lag.discharge,
# data = dt[ month == i])
DiD_prepost = lm(tot_discharge ~ treatment + year_treat + treatment:year_treat + total_rain_day + lag.rain_day + day_of_season
+ factor(year) + lag.discharge
, data = dt[ month == i])
beta_DiD = summary(DiD_prepost)$coefficients[nrow(summary(DiD_prepost)$coefficients),1]
std_error= summary(DiD_prepost)$coefficients[nrow(summary(DiD_prepost)$coefficients),2]
coefs_DiD[i, 2] = beta_DiD
coefs_DiD[i, 3] = std_error
}
# dt_ESE=data_pre[SUBCUENC_2==River_tr]
l=ggplot(data = coefs_DiD, aes(x=factor(month), y=coefficient_DiD, group=1))+
geom_errorbar(aes(ymin=coefficient_DiD - 2.866 * standard_error_DiD , ymax=coefficient_DiD + 2.866 * standard_error_DiD), width=.1) +
geom_line() +
geom_point() +
# geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
# width=.2, col="red") +
theme_bw()+
xlab("month")+
ylab("Diff in Diff Coefficient")+
ggtitle(paste("Before", year_pre, "and after", year_post, sep = " "))
return(l)
}
b15=FN_coefficients_DiD_monthly_lag2(2010, 2012)
b16=FN_coefficients_DiD_monthly_lag2(2008, 2012)
b17=FN_coefficients_DiD_monthly_lag2(2010, 2013)
b18=FN_coefficients_DiD_monthly_lag2(2007, 2013)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ####
# 0. Annual regressions for the entire watershed for the dry season ####
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#################
FN_coefficients_DiD_annual_dry <- function(year_pre) {
dt=copy(data_reg_copy_daily)
dt=dt[dry_dummy==1]
coefs_DiD=data.table()
for (i in year_pre:2015) {
dt_1=rbind(dt[year< year_pre], dt[year== i])
dt_1[, year_treat:=ifelse(year==i, 1, 0)]
dt_1=dt_1[, treatment:=ifelse(River=="Trinidad" | River=="Ciri grande", 1, 0)]
DiD_annual = lm(tot_discharge ~ treatment + year_treat + treatment:year_treat + total_rain_day + lag.rain_day + day_of_season
# + factor(year) + lag.discharge
, data = dt_1)
beta_DiD = summary(DiD_annual)$coefficients[nrow(summary(DiD_annual)$coefficients),1]
std_DiD = summary(DiD_annual)$coefficients[nrow(summary(DiD_annual)$coefficients),2]
dt_2=data.table(year=i, beta_DiD, std_DiD)
coefs_DiD=rbind(coefs_DiD, dt_2)
}
l=ggplot(data = coefs_DiD, aes(x=factor(year), y=beta_DiD, group=1))+
geom_errorbar(aes(ymin=beta_DiD - 1.96 * std_DiD , ymax=beta_DiD + 1.96 * std_DiD), width=.1) +
geom_line() +
geom_point() +
# geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
# width=.2, col="red") +
theme_bw()+
xlab("month")+
ylab("Diff in Diff Coefficient")+
ggtitle("change in dry season discharge\nwithout lagged discharge")
return(l)
}
c1=FN_coefficients_DiD_annual_dry(2008)
FN_coefficients_DiD_annual_lag_dry <- function(year_pre) {
dt=copy(data_reg_copy_daily)
dt=dt[dry_dummy==1]
coefs_DiD=data.table()
for (i in year_pre:2015) {
dt_1=rbind(dt[year< year_pre], dt[year== i])
dt_1[, year_treat:=ifelse(year==i, 1, 0)]
dt_1=dt_1[, treatment:=ifelse(River=="Trinidad" | River=="Ciri grande", 1, 0)]
DiD_annual = lm(tot_discharge ~ treatment + year_treat + treatment:year_treat + total_rain_day + lag.rain_day + day_of_season
+ lag.discharge #+ factor(year)
, data = dt_1)
beta_DiD = summary(DiD_annual)$coefficients[nrow(summary(DiD_annual)$coefficients),1]
std_DiD = summary(DiD_annual)$coefficients[nrow(summary(DiD_annual)$coefficients),2]
dt_2=data.table(year=i, beta_DiD, std_DiD)
coefs_DiD=rbind(coefs_DiD, dt_2)
}
l=ggplot(data = coefs_DiD, aes(x=factor(year), y=beta_DiD, group=1))+
geom_errorbar(aes(ymin=beta_DiD - 1.96 * std_DiD , ymax=beta_DiD + 1.96 * std_DiD), width=.1) +
geom_line() +
geom_point() +
# geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
# width=.2, col="red") +
theme_bw()+
xlab("month")+
ylab("Diff in Diff Coefficient")+
ggtitle("change in dry season discharge\nwith lagged discharge")
return(l)
}
d1=FN_coefficients_DiD_annual_lag_dry(2008)
FN_coefficients_DiD_annual_wet <- function(year_pre) {
dt=copy(data_reg_copy_daily)
dt=dt[dry_dummy==0]
coefs_DiD=data.table()
for (i in year_pre:2015) {
dt_1=rbind(dt[year< year_pre], dt[year== i])
dt_1[, year_treat:=ifelse(year==i, 1, 0)]
dt_1=dt_1[, treatment:=ifelse(River=="Trinidad" | River=="Ciri grande", 1, 0)]
DiD_annual = lm(tot_discharge ~ treatment + year_treat + treatment:year_treat + total_rain_day + lag.rain_day + day_of_season
# + factor(year) + lag.discharge
, data = dt_1)
beta_DiD = summary(DiD_annual)$coefficients[nrow(summary(DiD_annual)$coefficients),1]
std_DiD = summary(DiD_annual)$coefficients[nrow(summary(DiD_annual)$coefficients),2]
dt_2=data.table(year=i, beta_DiD, std_DiD)
coefs_DiD=rbind(coefs_DiD, dt_2)
}
l=ggplot(data = coefs_DiD, aes(x=factor(year), y=beta_DiD, group=1))+
geom_errorbar(aes(ymin=beta_DiD - 1.96 * std_DiD , ymax=beta_DiD + 1.96 * std_DiD), width=.1) +
geom_line() +
geom_point() +
# geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
# width=.2, col="red") +
theme_bw()+
xlab("month")+
ylab("Diff in Diff Coefficient")+
ggtitle("change in wet season discharge\nwithout lagged discharge")
return(l)
}
c2=FN_coefficients_DiD_annual_wet(2008)
FN_coefficients_DiD_annual_lag_wet <- function(year_pre) {
dt=copy(data_reg_copy_daily)
dt=dt[dry_dummy==0]
coefs_DiD=data.table()
for (i in year_pre:2015) {
dt_1=rbind(dt[year< year_pre], dt[year== i])
dt_1[, year_treat:=ifelse(year==i, 1, 0)]
dt_1=dt_1[, treatment:=ifelse(River=="Trinidad" | River=="Ciri grande", 1, 0)]
DiD_annual = lm(tot_discharge ~ treatment + year_treat + treatment:year_treat + total_rain_day + lag.rain_day + day_of_season
+ lag.discharge #+ factor(year)
, data = dt_1)
beta_DiD = summary(DiD_annual)$coefficients[nrow(summary(DiD_annual)$coefficients),1]
std_DiD = summary(DiD_annual)$coefficients[nrow(summary(DiD_annual)$coefficients),2]
dt_2=data.table(year=i, beta_DiD, std_DiD)
coefs_DiD=rbind(coefs_DiD, dt_2)
}
l=ggplot(data = coefs_DiD, aes(x=factor(year), y=beta_DiD, group=1))+
geom_errorbar(aes(ymin=beta_DiD - 1.96 * std_DiD , ymax=beta_DiD + 1.96 * std_DiD), width=.1) +
geom_line() +
geom_point() +
# geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
# width=.2, col="red") +
theme_bw()+
xlab("month")+
ylab("Diff in Diff Coefficient")+
ggtitle("change in wet season discharge\nwithout lagged discharge")
return(l)
}
d2=FN_coefficients_DiD_annual_lag_wet(2008)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ####
# 0. Monthly regressions for the entire watershed for finding N ####
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#################
FN_coefficients_DiD_monthly_nobs <- function(year_pre, year_post) {
dt=copy(data_reg_copy_daily)
dt = dt[year < year_pre | year > year_post][ ,year_treat:= ifelse(year>2007, 1, 0)]
dt=dt[, treatment:=ifelse(River=="Trinidad" | River=="Ciri grande", 1, 0)]
coefs_DiD = data.table(month=1:12, coefficient_DiD = rep(0 , 12), standard_error_DiD= rep(0, 12))
for(i in 1:12){
DiD_prepost = lm(tot_discharge ~ treatment + year_treat + treatment:year_treat + total_rain_day + lag.rain_day + day_of_season
+ factor(year), data = dt[ month == i])
beta_DiD = summary(DiD_prepost)$coefficients[nrow(summary(DiD_prepost)$coefficients),1]
std_error= summary(DiD_prepost)$coefficients[nrow(summary(DiD_prepost)$coefficients),2]
coefs_DiD[i, 2] = beta_DiD
coefs_DiD[i, 3] = std_error
}
coefs_DiD[, CI_lower:=coefficient_DiD - 2.866 * standard_error_DiD]
coefs_DiD[, CI_upper:=coefficient_DiD + 2.866 * standard_error_DiD]
nnss=nobs(DiD_prepost)
coefs_DiD[, m:= ceiling(((2.866 * standard_error_DiD) * sqrt(nnss-2)/coefficient_DiD)^2) + 2]
coefs_DiD[, CI_lower_corrected:=coefficient_DiD - 2.866 * standard_error_DiD * sqrt(nnss-2)/sqrt(m-2)]
coefs_DiD[, CI_upper_corrected:=coefficient_DiD + 2.866 * standard_error_DiD * sqrt(nnss-2)/sqrt(m-2)]
coefs_DiD[month > 4, c("CI_lower_corrected", "CI_upper_corrected"):=0]
coefs_DiD[, m:=ifelse(coefficient_DiD < 0, "", m)]
l=ggplot(data = coefs_DiD[month<5], aes(x=factor(month), y=coefficient_DiD, group=1))+
geom_errorbar(aes(ymin=CI_lower_corrected , ymax=CI_upper_corrected), width=.1) +
geom_line() +
geom_point() +
geom_text(aes(label=m),vjust=-1)+
theme_bw()+
# geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
# width=.2, col="red") +
xlab("month")+
ylab("Diff in Diff Coefficient")+
ggtitle(paste("Before", year_pre, "and after", year_post, "\nwithout lagged discharge", "\nN= ", nnss,sep = " "))
return(l)
}
b21=FN_coefficients_DiD_monthly_nobs(2008, 2012)
FN_coefficients_DiD_monthly_lag_nobs <- function(year_pre, year_post) {
# dt=data_reg_copy_daily[(River=="Trinidad" | River=="Ciri grande" | River=="Cano Quebrado")]
dt=copy(data_reg_copy_daily)
dt = dt[year < year_pre | year > year_post][ ,year_treat:= ifelse(year>2007, 1, 0)]
dt=dt[, treatment:=ifelse(River=="Trinidad" | River=="Ciri grande", 1, 0)]
coefs_DiD = data.table(month=1:12, coefficient_DiD = rep(0 , 12), standard_error_DiD= rep(0, 12))
for(i in 1:12){
DiD_prepost = lm(tot_discharge ~ treatment + year_treat + treatment:year_treat + total_rain_day + lag.rain_day + day_of_season
+ factor(year) + lag.discharge
, data = dt[ month == i])
beta_DiD = summary(DiD_prepost)$coefficients[nrow(summary(DiD_prepost)$coefficients),1]
std_error= summary(DiD_prepost)$coefficients[nrow(summary(DiD_prepost)$coefficients),2]
coefs_DiD[i, 2] = beta_DiD
coefs_DiD[i, 3] = std_error
}
coefs_DiD[, CI_lower:=coefficient_DiD - 2.866 * standard_error_DiD]
coefs_DiD[, CI_upper:=coefficient_DiD + 2.866 * standard_error_DiD]
nnss=nobs(DiD_prepost)
coefs_DiD[, m:= ceiling(((2.866 * standard_error_DiD) * sqrt(nnss-2)/coefficient_DiD)^2) + 2]
coefs_DiD[, CI_lower_corrected:=coefficient_DiD - 2.866 * standard_error_DiD * sqrt(nnss-2)/sqrt(m-2)]
coefs_DiD[, CI_upper_corrected:=coefficient_DiD + 2.866 * standard_error_DiD * sqrt(nnss-2)/sqrt(m-2)]
coefs_DiD[month > 4, c("CI_lower_corrected", "CI_upper_corrected"):=0]
coefs_DiD[, m:=ifelse(coefficient_DiD < 0, "", m)]
l=ggplot(data = coefs_DiD[month<5], aes(x=factor(month), y=coefficient_DiD, group=1))+
geom_errorbar(aes(ymin=CI_lower_corrected , ymax=CI_upper_corrected), width=.1) +
geom_line() +
geom_point() +
geom_text(aes(label=m),vjust=-1)+
theme_bw()+
# geom_errorbar(data = dt_ESE, aes(y=mean_extra_discharge, ymin=mean_extra_discharge - 1.96 * sd_extra_discharge , ymax=mean_extra_discharge + 1.96 * sd_extra_discharge),
# width=.2, col="red") +
xlab("month")+
ylab("Diff in Diff Coefficient")+
ggtitle(paste("Before", year_pre, "and after", year_post, "\nwith lagged discharge", "\nN= ", nnss,sep = " "))
return(l)
}
b22=FN_coefficients_DiD_monthly_lag_nobs(2008, 2012)
FN_coefficients_DiD_annual_dry_nobs <- function(year_pre) {
dt=copy(data_reg_copy_daily)
dt=dt[dry_dummy==1]
coefs_DiD=data.table()
for (i in year_pre:2015) {
dt_1=rbind(dt[year< year_pre], dt[year== i])
dt_1[, year_treat:=ifelse(year==i, 1, 0)]
dt_1=dt_1[, treatment:=ifelse(River=="Trinidad" | River=="Ciri grande", 1, 0)]
DiD_annual = lm(tot_discharge ~ treatment + year_treat + treatment:year_treat + total_rain_day + lag.rain_day + day_of_season
# + factor(year) + lag.discharge
, data = dt_1)
coefficient_DiD = summary(DiD_annual)$coefficients[nrow(summary(DiD_annual)$coefficients),1]
standard_error_DiD = summary(DiD_annual)$coefficients[nrow(summary(DiD_annual)$coefficients),2]
dt_2=data.table(year=i, coefficient_DiD, standard_error_DiD)
coefs_DiD=rbind(coefs_DiD, dt_2)
}
coefs_DiD[, CI_lower:=coefficient_DiD - 1.96 * standard_error_DiD]
coefs_DiD[, CI_upper:=coefficient_DiD + 1.96 * standard_error_DiD]
nnss=nobs(DiD_annual)
coefs_DiD[, m:= ceiling(((1.96 * standard_error_DiD) * sqrt(nnss-2)/coefficient_DiD)^2) + 2]
coefs_DiD[, CI_lower_corrected:=coefficient_DiD - 1.96 * standard_error_DiD * sqrt(nnss-2)/sqrt(m-2)]
coefs_DiD[, CI_upper_corrected:=coefficient_DiD + 1.96 * standard_error_DiD * sqrt(nnss-2)/sqrt(m-2)]
coefs_DiD[, m:=ifelse(coefficient_DiD < 0, "", m)]