Culbert_Exercise9_Submission

Culbert_Exercise09.R

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+##Exercise 9: Plotting
+
+
+##Question 1:
+#find data on two related variables 
+#create data file into txt.
+#generate scatterplot of two variables
+#include trend line 
+
+#install ggplot package
+library(ggplot2)
+library(cowplot)
+
+#dataset compares CO2 emission vs years
+CO2.data <- read.table("CO2emissions.txt", header = TRUE, sep = "\t", stringsAsFactors = FALSE)
+CO2.data
+
+#create scatterplot with trendline
+ggplot(CO2.data, aes(x = as.numeric(Year), y = as.numeric(CO2.Emmissions)))  + 
+  geom_point() + 
+  xlab("Year") + 
+  ylab("CO2 Emissions (kilotons)") +
+  theme_classic() +
+  stat_smooth()
+
+
+##Question 2:
+#write script that generates two figures that summarize the data
+#show a barplot of the means of the four populations
+#show a scatterplot of the observations
+data <- read.csv("data.txt", header=TRUE)
+data
+
+#barplot
+ggplot(data, aes(x = region, y = observations)) +
+  stat_summary(fun.y = mean, geom = "bar") +
+  theme_classic() +
+  xlab("Region") +
+  ylab("Average Observations")
+
+#scatterplot
+ggplot(data, aes(x = region, y = observations)) +
+  geom_jitter() +
+  theme_classic() +
+  xlab("Region") +
+  ylab("Observations")
+
+##The scatterplot seems to offer a more accurate representation of the spread of the data
+##The scatterplot provides information about each observation point and the different densities
+##For example, the "South" region has two distinct clumps of data points, 
+##a higher density around values 20-30, and a lower density around 5
+##The barplot only reveals to us the average observation for each region
+##The barplot is a bit deceiving because it shows all the regions having observations around 15,
+##however, the scatterplot better reveals to us the variation across each region