Project_12-7

nitrofen <- read.csv("Rdatasets-master/csv/datasets/LifeCycleSavings.csv")
head(LifeCycleSavings)

##              sr pop15 pop75     dpi ddpi
## Australia 11.43 29.35  2.87 2329.68 2.87
## Austria   12.07 23.32  4.41 1507.99 3.93
## Belgium   13.17 23.80  4.43 2108.47 3.82
## Bolivia    5.75 41.89  1.67  189.13 0.22
## Brazil    12.88 42.19  0.83  728.47 4.56
## Canada     8.79 31.72  2.85 2982.88 2.43

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.4.2

# Basic scatter plot
ggplot(LifeCycleSavings, aes(x=pop15, y=sr)) + geom_point()

# Change the point size, and shape
ggplot(LifeCycleSavings, aes(x=pop15, y=sr)) +
  geom_point(size=2, shape=23)

ggplot(LifeCycleSavings, aes(x=pop15, y=sr)) +
  geom_point() + 
  geom_text(label=rownames(LifeCycleSavings))

geom_smooth(method="auto", se=TRUE, fullrange=FALSE, level=0.95)

## geom_smooth: na.rm = FALSE
## stat_smooth: na.rm = FALSE, fullrange = FALSE, level = 0.95, method = auto, formula = y ~ x, se = TRUE
## position_identity

# Add the regression line
ggplot(LifeCycleSavings, aes(x=pop15, y=dpi)) + 
  geom_point()+
  geom_smooth(method=lm)

# Remove the confidence interval
ggplot(LifeCycleSavings, aes(x=pop15, y=dpi)) + 
  geom_point()+
  geom_smooth(method=lm, se=FALSE)

# Loess method
ggplot(LifeCycleSavings, aes(x=pop15, y=dpi)) + 
  geom_point()+
  geom_smooth()

## `geom_smooth()` using method = 'loess'

# Add marginal rugs
ggplot(LifeCycleSavings, aes(x=pop15, y=dpi)) +
  geom_point() + geom_rug()

# Change colors
ggplot(LifeCycleSavings, aes(x=pop15, y=dpi, color=dpi)) +
  geom_point() + geom_rug()

# Add marginal rugs using lifecyclesavings data
ggplot(LifeCycleSavings, aes(x=pop15, y=dpi)) +
  geom_point() + geom_rug()

# Add the regression line
ggplot(LifeCycleSavings, aes(x=pop15, y=dpi)) + 
  geom_point()+
  geom_smooth(method=lm)

# Remove the confidence interval
ggplot(LifeCycleSavings, aes(x=pop15, y=dpi)) + 
  geom_point()+
  geom_smooth(method=lm, se=FALSE)

# Loess method
ggplot(LifeCycleSavings, aes(x=pop15, y=dpi)) + 
  geom_point()+
  geom_smooth()

## `geom_smooth()` using method = 'loess'

library(ggplot2)
# Basic barplot
p<-ggplot(data=LifeCycleSavings, aes(x=rownames(LifeCycleSavings), y=dpi)) +
  geom_bar(stat="identity")
p + scale_fill_discrete(h = c(180, 360), c = 150, l = 80)

# Horizontal bar plot
p + coord_flip()

p<-ggplot(data=LifeCycleSavings, aes(x=rownames(LifeCycleSavings), y=dpi)) +
  geom_bar(stat="identity")

# Outside bars
ggplot(data=LifeCycleSavings, aes(x=rownames(LifeCycleSavings), y=dpi)) +
  geom_bar(stat="identity", fill="steelblue")+
  geom_text(label=rownames(LifeCycleSavings), vjust=-0.3, size=3.5)+
  theme_minimal()

# One ellipse arround all points
ggplot(LifeCycleSavings, aes(rownames(LifeCycleSavings), dpi))+
  geom_point()+
  stat_ellipse()

## Too few points to calculate an ellipse
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## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

# Ellipse by groups
p <- ggplot(LifeCycleSavings, aes(rownames(LifeCycleSavings), dpi, color = dpi > 3))+
  geom_point()
p + stat_ellipse()

## Too few points to calculate an ellipse

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## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

# Change the type of ellipses: possible values are "t", "norm", "euclid"
p + stat_ellipse(type = "norm")

## Too few points to calculate an ellipse

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## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

ggplot(LifeCycleSavings, aes(dpi)) +
geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(LifeCycleSavings, aes(log(dpi), fill = cut(log(dpi), 100))) +
geom_histogram(show.legend = FALSE) +
scale_fill_discrete(h = c(240, 10))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p <- ggplot(LifeCycleSavings, aes(log(dpi), fill = cut(log(dpi), 100))) +
geom_histogram(show.legend = FALSE) +
theme_minimal() +
labs(time = "Variable X", y = "age") +
ggtitle("Histogram of Dpi")

p + scale_fill_discrete(h = c(180, 360), c = 150, l = 80)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(, aes(time, fill = cut(time, 100))) + geom_histogram() fill=“chartreuse”

Project_12-7

vigil

December 7, 2017