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temperature_data_viz.R
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temperature_data_viz.R
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setwd("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019")
#scatterplot of 2019 data
ggplot(ghcn_lax_2019, aes(date, tmax)) + geom_point(colour="dark green", size = 1)
#scatterplot of 2018 data
ghcn_lax_2018 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2018.csv")
View(ghcn_lax_2018)
ggplot(ghcn_lax_2018, aes(date, tmax)) + geom_point(colour="dark blue", size = 1)
#combine and scatterplot 2018 and 2019 data
ghcn_lax_2018_2019 <- bind_rows(ghcn_lax_2018, ghcn_lax_2019)
View(ghcn_lax_2018_2019)
ggplot(ghcn_lax_2018_2019, aes(date, tmax)) + geom_point(colour="magenta", size = 1)
#install libraries
library(ggplot2)
library(dplyr)
library(readr)
#assign data from each year to variables
ghcn_lax_2019 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2019.csv")
ghcn_lax_2018 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2018.csv")
ghcn_lax_2017 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2017.csv")
ghcn_lax_2016 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2016.csv")
ghcn_lax_2015 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2015.csv")
ghcn_lax_2014 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2014.csv")
ghcn_lax_2013 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2013.csv")
ghcn_lax_2012 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2012.csv")
ghcn_lax_2011 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2011.csv")
ghcn_lax_2010 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2010.csv")
#combine the data from each year into one variable
ghcn_lax_2010_2019 <- bind_rows(ghcn_lax_2010, ghcn_lax_2011, ghcn_lax_2012, ghcn_lax_2013, ghcn_lax_2014, ghcn_lax_2015, ghcn_lax_2016, ghcn_lax_2017, ghcn_lax_2018, ghcn_lax_2019)
#export combined data as a csv file
write_csv(ghcn_lax_2010_2019, "/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_2010_2019.csv")
#scatterplot of combined (decade) data
ggplot(ghcn_lax_2010_2019, aes(y=tmax, x=date)) +
geom_point(colour="cornflowerblue", size = 0.5) +
ylab("Max Temperature") +
xlab("") +
scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
theme(panel.background = element_rect(fill="white"))
#save scatterplot
ggsave("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/scat_tmax_date_lax_2010_2019.pdf")
#import 1968 data to have something to bind other years' data to
ghcn_lax_1968 <- read_csv("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_1968.csv")
ghcn_lax_1968_2019 <- ghcn_lax_1968
#import each year of csv file starting at 1969
for (year in 1969:2019){
#temporary names
tempname <- paste0("ghcn_lax_", year)
csvname <- paste0("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_", year, ".csv")
#import
tempdata <- read_csv(csvname)
#bind data and overwrite ghcn_lax_1968_2019
ghcn_lax_1968_2019 <- bind_rows(ghcn_lax_1968_2019, tempdata)
#rename tempdata as tempname to start the loop over
assign(tempname, tempdata)
}
rm(tempdata, csvname, tempname, year)
#save data
write.csv(ghcn_lax_1968_2019, "/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/ghcn_lax_1968_2019.csv")
#create a scatterplot of all the years
ggplot(ghcn_lax_1968_2019, aes(y=tmax, x=date)) +
geom_point(colour="dark red", size = 0.5) +
labs(y = "Max Temperature", x = "") +
scale_x_date(date_breaks = "10 years", date_labels = "%Y") +
theme(panel.background = element_rect(fill="white"))
#save scatterplot
ggsave("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/scat_tmax_date_lax_1968_2019.pdf")
#goal: create period variable and table of averages
#install libraries
library(lubridate)
#make a copy of data
ghcn_group <- ghcn_lax_1968_2019
#add year variable
ghcn_group = mutate(ghcn_group, year = year(date))
#create period variable
ghcn_group <- mutate(ghcn_group, period = ifelse(year<1986, "1968-1985", "1986-2019"))
#group data
ghcn_group <- group_by(ghcn_group, period)
#average max temperature date per period
ghcn_period_avg <- summarise(ghcn_group, tmax = mean(tmax))
#ungroup
ungroup(ghcn_group)
#bar graph
ggplot(ghcn_period_avg, aes(y=tmax, x=period)) +
geom_bar(stat="identity", fill = "aquamarine2", width = 0.5) +
labs(y = "Max Temperature", x = "Period") +
theme(panel.background = element_rect(fill="black"))
ggsave("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/bar_tmax_period_lax_1968_2019.pdf")
#scatterplot
ggplot(ghcn_period_avg, aes(y=tmax, x=period)) +
geom_point(stat="identity", colour = "red3", size = 5) +
labs(y = "Maximum Temperature", x = "Time Period") +
ylim(70, 71) +
theme(panel.background = element_rect(fill="papayawhip"))
ggsave("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/scat_tmax_period_lax_1968_2019.pdf")
#now group data by year
ghcn_group <- group_by(ghcn_group, year)
#average max temperature date per year
ghcn_year_avg <- summarise(ghcn_group, tmax = mean(tmax))
#ungroup again
ungroup(ghcn_group)
#line graph
ggplot(ghcn_year_avg, aes(y=tmax, x=year)) +
geom_line(colour = "coral2") +
labs(y = "Maximum Temperature", x = "") +
#theme_minimal()
theme(panel.background = element_rect(fill="white"))
ggsave("/Users/cassidywood/Desktop/spring 2020/env 175/Project 2/ghcn_lax_1968_2019/line_tmax_year_lax_1968_2019.pdf")