#2.1 A worked example ex- We’ll build a a complex graph; by starting with a simple graph and adding additional elements, one at a time.to explore the relationship between wages (wage) and experience (expr).

# load data
data(CPS85 , package = "mosaicData")

##2.1.1 ggplot ex- It specifies the data frame containing the data to be plotted the mapping of the variables to visual properties of the graph. (The mappings are placed within the aes function (where aes stands for aesthetics).)

# load data
data(CPS85 , package = "mosaicData")

#2.1.2 geoms EX- Geoms objects (points, lines, bars, etc.) that can be placed on a graph. Geom_point creats scatter plot. In ggplot2 graphs, functions are chained together using the + sign to build a final plot.

# specify dataset and mapping
library(ggplot2)
ggplot(data = CPS85,
       mapping = aes(x = exper, y = wage))

# delete outlier
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
plotdata <- filter(CPS85, wage < 40)

# redraw scatterplot
ggplot(data = plotdata,
       mapping = aes(x = exper, y = wage)) +
  geom_point()

#2.1.3 grouping EX- variables can be mapped to the color, shape, size, transparency, and other visual characteristics of geometric objects. This allows groups of observations to be superimposed in a single graph.

# indicate sex using color
ggplot(data = plotdata,
       mapping = aes(x = exper, 
                     y = wage,
                     color = sex)) +
  geom_point(alpha = .7,
             size = 3) +
  geom_smooth(method = "lm", 
              se = FALSE, 
              size = 1.5)

#2.1.4 scales EX- Scales control how variables are mapped to the visual characteristics of the plot. Scale functions (which start with scale_) allow you to modify this mapping

# modify the x and y axes and specify the colors to be used
ggplot(data = plotdata,
       mapping = aes(x = exper, 
                     y = wage,
                     color = sex)) +
  geom_point(alpha = .7,
             size = 3) +
  geom_smooth(method = "lm", 
              se = FALSE, 
              size = 1.5) +
  scale_x_continuous(breaks = seq(0, 60, 10)) +
  scale_y_continuous(breaks = seq(0, 30, 5),
                     label = scales::dollar) +
  scale_color_manual(values = c("indianred3", 
                                "cornflowerblue"))

#2.1.5 facets EX-Facets reproduce a graph for each level a given variable (or combination of variables). Facets are created using functions that start with facet_

# reproduce plot for each level of job sector
ggplot(data = plotdata,
       mapping = aes(x = exper, 
                     y = wage,
                     color = sex)) +
  geom_point(alpha = .7) +
  geom_smooth(method = "lm", 
              se = FALSE) +
  scale_x_continuous(breaks = seq(0, 60, 10)) +
  scale_y_continuous(breaks = seq(0, 30, 5),
                     label = scales::dollar) +
  scale_color_manual(values = c("indianred3", 
                                "cornflowerblue")) +
  facet_wrap(~sector)

#2.1.6 labels EX- The labs function provides customized labels for the axes and legends. Additionally, a custom title, subtitle, and caption can be added.

# add informative labels
ggplot(data = plotdata,
       mapping = aes(x = exper, 
                     y = wage,
                     color = sex)) +
  geom_point(alpha = .7) +
  geom_smooth(method = "lm", 
              se = FALSE) +
  scale_x_continuous(breaks = seq(0, 60, 10)) +
  scale_y_continuous(breaks = seq(0, 30, 5),
                     label = scales::dollar) +
  scale_color_manual(values = c("indianred3", 
                                "cornflowerblue")) +
  facet_wrap(~sector) +
  labs(title = "Relationship between wages and experience",
       subtitle = "Current Population Survey",
       caption = "source: http://mosaic-web.org/",
       x = " Years of Experience",
       y = "Hourly Wage",
       color = "Gender")

#2.1.7 themes EX- Theme functions (which start with theme_) control background colors, fonts, grid-lines, legend placement, and other non-data related features of the graph.

# use a minimalist theme
ggplot(data = plotdata,
       mapping = aes(x = exper, 
                     y = wage,
                     color = sex)) +
  geom_point(alpha = .6) +
  geom_smooth(method = "lm", 
              se = FALSE) +
  scale_x_continuous(breaks = seq(0, 60, 10)) +
  scale_y_continuous(breaks = seq(0, 30, 5),
                     label = scales::dollar) +
  scale_color_manual(values = c("indianred3", 
                                "cornflowerblue")) +
  facet_wrap(~sector) +
  labs(title = "Relationship between wages and experience",
       subtitle = "Current Population Survey",
       caption = "source: http://mosaic-web.org/",
       x = " Years of Experience",
       y = "Hourly Wage",
       color = "Gender") +
  theme_minimal()

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