abresler · July 4, 2023 18:56 · Jan 3, 2015 · Jan 3, 2015 · Jan 3, 2015 · Jan 3, 2015
diff --git a/tufte b/tufte
@@ -69,7 +69,6 @@ p <- ggplot(past, aes(newday, temp)) +
 				panel.border = element_blank(),
 				panel.background = element_blank(),
 				axis.ticks = element_blank(),
-				#axis.text = element_blank(),
 				axis.title = element_blank()) +
 	geom_linerange(past, mapping=aes(x=newday, ymin=lower, ymax=upper), colour = "wheat2", alpha=.1)
 

diff --git a/tufte b/tufte
@@ -62,9 +62,6 @@ dgr_fmt <- function(x, ...) {
 # create y-axis variable
 a <- dgr_fmt(seq(-10,100, by=10))
 
-# create a small dataframe to represent legend symbol for 2014 temperature
-data <- data.frame(x=seq(175,182),y=rnorm(8,15,2))
-
 p <- ggplot(past, aes(newday, temp)) +
 	theme(plot.background = element_blank(),
 				panel.grid.minor = element_blank(),

diff --git a/tufte b/tufte
@@ -1,4 +1,3 @@
-## Modified and Inspired by http://rpubs.com/bradleyboehmke/weather_graphic
 library(dplyr)
 library(tidyr)
 library(magrittr)

diff --git a/tufte b/tufte
@@ -0,0 +1,170 @@
+## Modified and Inspired by http://rpubs.com/bradleyboehmke/weather_graphic
+library(dplyr)
+library(tidyr)
+library(magrittr)
+library(ggplot2)
+"http://academic.udayton.edu/kissock/http/Weather/gsod95-current/NYNEWYOR.txt" %>%
+	read.table() %>% data.frame %>% tbl_df -> data
+names(data) <- c("month", "day", "year", "temp")
+
+data %>%
+	group_by(year, month) %>%
+	arrange(day) %>%
+	ungroup() %>%
+	group_by(year) %>%
+	mutate(newday = seq(1, length(day))) %>%   # label days as 1:365 (will represent x-axis)
+	ungroup() %>%
+	filter(temp != -99 & year != 2014) %>%     # filter out missing data (identified with '-99' value) & current year data
+	group_by(newday) %>%
+	mutate(upper = max(temp), # identify max value for each day
+				 lower = min(temp), # identify min value for each day
+				 avg = mean(temp),  # calculate mean value for each day
+				 se = sd(temp)/sqrt(length(temp))) %>%  # calculate standard error of mean
+	mutate(avg_upper = avg+(2.101*se),  # calculate 95% CI for mean
+				 avg_lower = avg-(2.101*se)) %>%  # calculate 95% CI for mean
+	ungroup() -> past
+
+# create dataframe that represents current year data
+data %>%
+	group_by(year, month) %>%
+	arrange(day) %>%
+	ungroup() %>%
+	group_by(year) %>%
+	mutate(newday = seq(1, length(day))) %>%  # create matching x-axis as historical data
+	ungroup() %>%
+	filter(temp != -99 & year == 2014) -> present   # filter out missing data & select current year data
+
+# create dataframe that represents the lowest temp for each day for the historical data
+pastlows <- past %>%
+	group_by(newday) %>%
+	summarise(Pastlow = min(temp)) # identify lowest temp for each day from 1975-2013
+
+# create dataframe that identifies the days in 2014 in which the temps were lower than all previous 19 years
+presentlows <- present %>%
+	left_join(pastlows) %>%  # merge historical lows to current year low data
+	mutate(record = ifelse(temp<Pastlow, "Y", "N")) %>% # identifies if current year was record low
+	filter(record == "Y")  # filter for days that represent current year record lows
+
+# create dataframe that represents the highest temp for each day for the historical data
+pasthighs <- past %>%
+	group_by(newday) %>%
+	summarise(Pasthigh = max(temp))  # identify highest temp for each day from 1975-2013
+
+# create dataframe that identifies the days in 2014 in which the temps were higher than all previous 19 years
+presenthighs <- present %>%
+	left_join(pasthighs) %>%  # merge historical highs to current year low data
+	mutate(record = ifelse(temp>Pasthigh, "Y", "N")) %>% # identifies if current year was record high
+	filter(record == "Y")  # filter for days that represent current year record highs
+
+dgr_fmt <- function(x, ...) {
+	parse(text = paste(x, "*degree", sep = ""))
+}
+
+# create y-axis variable
+a <- dgr_fmt(seq(-10,100, by=10))
+
+# create a small dataframe to represent legend symbol for 2014 temperature
+data <- data.frame(x=seq(175,182),y=rnorm(8,15,2))
+
+p <- ggplot(past, aes(newday, temp)) +
+	theme(plot.background = element_blank(),
+				panel.grid.minor = element_blank(),
+				panel.grid.major = element_blank(),
+				panel.border = element_blank(),
+				panel.background = element_blank(),
+				axis.ticks = element_blank(),
+				#axis.text = element_blank(),
+				axis.title = element_blank()) +
+	geom_linerange(past, mapping=aes(x=newday, ymin=lower, ymax=upper), colour = "wheat2", alpha=.1)
+
+#p
+#Next, we can add the data that represents the 95% confidence interval around the daily mean temperatures for 1975-2013.
+p <- p +
+	geom_linerange(past, mapping=aes(x=newday, ymin=avg_lower, ymax=avg_upper), colour = "wheat4")
+#p
+
+p <- p +
+	geom_line(present, mapping=aes(x=newday, y=temp, group=1)) +
+	geom_vline(xintercept = 0, colour = "wheat3", linetype=1, size=1)
+
+#p
+
+p <- p + geom_hline(yintercept = -10, colour = "white", linetype=1) +
+	geom_hline(yintercept = 0, colour = "white", linetype=1) +
+	geom_hline(yintercept = 10, colour = "white", linetype=1) +
+	geom_hline(yintercept = 20, colour = "white", linetype=1) +
+	geom_hline(yintercept = 30, colour = "white", linetype=1) +
+	geom_hline(yintercept = 40, colour = "white", linetype=1) +
+	geom_hline(yintercept = 50, colour = "white", linetype=1) +
+	geom_hline(yintercept = 60, colour = "white", linetype=1) +
+	geom_hline(yintercept = 70, colour = "white", linetype=1) +
+	geom_hline(yintercept = 80, colour = "white", linetype=1) +
+	geom_hline(yintercept = 90, colour = "white", linetype=1) +
+	geom_hline(yintercept = 100, colour = "white", linetype=1)
+
+p <- p +
+	geom_vline(xintercept = 31, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 59, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 90, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 120, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 151, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 181, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 212, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 243, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 273, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 304, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 334, colour = "wheat3", linetype=3, size=.5) +
+	geom_vline(xintercept = 365, colour = "wheat3", linetype=3, size=.5)
+
+p <- p +
+	coord_cartesian(ylim = c(-10,100)) +
+	scale_y_continuous(breaks = seq(-10,100, by=10), labels = a) +
+	scale_x_continuous(expand = c(0, 0),
+										 breaks = c(15,45,75,105,135,165,195,228,258,288,320,350),
+										 labels = c("January", "February", "March", "April",
+										 					 "May", "June", "July", "August", "September",
+										 					 "October", "November", "December"))
+
+#Step 7
+
+p <- p +
+	geom_point(data=presentlows, aes(x=newday, y=temp), colour="blue3") +
+	geom_point(data=presenthighs, aes(x=newday, y=temp), colour="firebrick3")
+
+p <- p +
+	ggtitle("New York City's Weather in 2014") +
+	theme(plot.title=element_text(face="bold",hjust=.012,vjust=.8,colour="#3C3C3C",size=20)) +
+	annotate("text", x = 35, y = 98, label = "temperature in Fahrenheit", size=4, fontface="bold")
+
+p +
+	annotate("text", x = 63, y = 93,
+					 label = "Data represents average daily temperatures. Accessible data dates back to", size=3, colour="gray30") +
+	annotate("text", x = 59.5, y = 90,
+					 label = "January 1, 1975. Data for 2014 is only available through December 16.", size=3, colour="gray30") +
+	annotate("text", x = 61, y = 87,
+					 label = "Average temperature for the year was 54.8° making 2014 the 6th coldest", size=3, colour="gray30") +
+	annotate("text", x = 61, y = 84, label = "year since 1995", size=3, colour="gray30") -> p
+
+p +
+	annotate("segment", x = 22, xend = 32, y = 12, yend = 3, colour = "blue3") +
+	annotate("text", x = 50, y = 2, label = "We had 25 days that were the", size=3, colour="blue3") +
+	annotate("text", x = 50, y = 0, label = "coldest since 1995", size=3, colour="blue3") +
+	annotate("segment", x = 169, xend = 169, y = 83, yend = 92, colour = "firebrick3") +
+	annotate("text", x = 169, y = 95, label = "We had 4 days that were the", size=3, colour="firebrick3") +
+	annotate("text", x = 169, y = 93, label = "hottest since 1995", size=3, colour="firebrick3") -> p
+
+present %>% filter(newday %in% c(180:185)) %>% select(x = newday, y =  temp) %>% data.frame -> legend_data
+legend_data$y - 65 -> legend_data$y
+p +
+	annotate("segment", x = 182, xend = 182, y = 5, yend = 25, colour = "wheat2", size=3) +
+	annotate("segment", x = 182, xend = 182, y = 12, yend = 18, colour = "wheat4", size=3) +
+	geom_line(data=legend_data, aes(x=x,y=y)) +
+	annotate("segment", x = 184, xend = 186, y = 17.7, yend = 17.7, colour = "wheat4", size=.5) +
+	annotate("segment", x = 184, xend = 186, y = 12.2, yend = 12.2, colour = "wheat4", size=.5) +
+	annotate("segment", x = 185, xend = 185, y = 12.2, yend = 17.7, colour = "wheat4", size=.5) +
+	annotate("text", x = 196, y = 14.75, label = "NORMAL RANGE", size=2, colour="gray30") +
+	annotate("text", x = 165, y = 14.75, label = "2014 TEMPERATURE", size=2, colour="gray30") +
+	annotate("text", x = 193, y = 25, label = "RECORD HIGH", size=2, colour="gray30") +
+	annotate("text", x = 193, y = 5, label = "RECORD LOW", size=2, colour="gray30")
+
+## Exported as an SVG of 1200 width and 525 height