1. Introduction to ggplot2 Package (with Scatter Plot)
2. Bar Chart
3. Tree Map
4. Correlation Plot

ggplot is a powerful package for more advanced visualization.

The functions in ggplot package build up a group in layer: starting with a simple graph -> adding additional elements/functions with +, one at a time.

We will first load a data from a package using the following code, then load the library of ggplot2

install.packages('mosaicData')

library('mosaicData')
data(CPS85, package = "mosaicData")
# load library 
library(ggplot2)
head(CPS85,5)

##   wage educ race sex hispanic south married exper union age   sector
## 1  9.0   10    W   M       NH    NS Married    27   Not  43    const
## 2  5.5   12    W   M       NH    NS Married    20   Not  38    sales
## 3  3.8   12    W   F       NH    NS  Single     4   Not  22    sales
## 4 10.5   12    W   F       NH    NS Married    29   Not  47 clerical
## 5 15.0   12    W   M       NH    NS Married    40 Union  58    const

The first function in building a graph is ggplot function. It specifies the - data frame containing the data to be plotted - the mapping of the variables to visual properties of the graph (where aes stands for aesthetics).

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

Geoms: geometric objects (points, lines, bars, etc.) of a graph. They are added using functions that start with geom_. In this example, geom_point function is used to create a scatterplot. In ggplot2 graphs, functions are chained together using the + sign to build a final plot.

    ggplot(data = CPS85,
           mapping = aes(x = exper, y = wage)) + 
    geom_point() #This is how we add datapoints to the plot

Next, let’s add a line of best fit. We can do this with the geom_smooth function. Options control

• the type of line (linear, quadratic, nonparametric)

• the thickness of the line - the line’s color

• the presence or absence of a confidence interval. Here we request a linear regression (method = lm) line (where lm stands for linear model).

    # add a line of best fit.
    ggplot(data = plotdata,
           mapping = aes(x = exper, y = wage)) +
      geom_point(color = "cornflowerblue",alpha = .7,size = 3) +
      geom_smooth(method = "lm") #added another layer of smooth line of best fit and specified linear regression method

## `geom_smooth()` using formula = 'y ~ x'

Variables can be mapped to the color, shape, size, transparency, etc. 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)) + 
  #using sex variable to differentiate colors
      geom_point(alpha = .7, size = 3) +
      geom_smooth(method = "lm", se = FALSE, linewidth = 1.5)

It appears that men tend to make more money than women. Additionally, there may be a stronger relationship between experience and wages for men than than for women.

To explore the relationship between age and income, create the following graph, age as x axis, and wage as y axis. Group the outcome based on their marital status (adjust the scatterplot’s color based on married variable)

The distribution of a single categorical variable is typically plotted with a bar chart, a pie chart, or (less commonly) a tree map.

The Marriage dataset contains the marriage records of 98 individuals in Mobile County, Alabama. Below, a bar chart is used to display the distribution of wedding participants by race.

library(ggplot2)
data(Marriage, package = "mosaicData")

ggplot(Marriage, aes(x = race)) + 
  geom_bar(fill = "cornflowerblue", 
           color="black") +
  labs(x = "Race", 
       y = "Frequency", 
       title = "Participants by race")

library(ggplot2)
library(tidyverse)
df = read_csv("HousePrices.csv")

ImageData <- df %>%
  group_by(air_cond) %>%
  summarise(n_house = n(), avg_price = mean(price))
ImageData

## # A tibble: 2 × 3
##   air_cond n_house avg_price
##   <chr>      <int>     <dbl>
## 1 No          1093   187022.
## 2 Yes          635   254904.

ggplot(ImageData, aes(air_cond, avg_price)) + 
  geom_col(colour = "cornflowerblue",fill = "cornflowerblue") +
  labs(x = "Whether house have AC", y = "Average Price", 
       title = "Price by AC")+
  coord_cartesian(ylim=c(150000,270000))# set y limits

Tree map is an alternative to pie chart, but it also can handle catgorical variables with many levels.

#install.packages("treemapify")
library(treemapify)
data(Marriage, package = "mosaicData")

# create a treemap of marriage officials
plotdata <- Marriage %>%
  count(officialTitle)

Correlation plots help you to visualize the pairwise relationships between a set of quantitative variables by displaying their correlations using color or shading. We will first calculate correlation

#Load data
data(SaratogaHouses, package="mosaicData")
# select numeric variables
df <- dplyr::select_if(SaratogaHouses, is.numeric)
# calulate the correlations
r <- cor(df, use="complete.obs")
round(r,2)

##            price lotSize   age landValue livingArea pctCollege bedrooms
## price       1.00    0.16 -0.19      0.58       0.71       0.20     0.40
## lotSize     0.16    1.00 -0.02      0.06       0.16      -0.03     0.11
## age        -0.19   -0.02  1.00     -0.02      -0.17      -0.04     0.03
## landValue   0.58    0.06 -0.02      1.00       0.42       0.23     0.20
## livingArea  0.71    0.16 -0.17      0.42       1.00       0.21     0.66
## pctCollege  0.20   -0.03 -0.04      0.23       0.21       1.00     0.16
## bedrooms    0.40    0.11  0.03      0.20       0.66       0.16     1.00
## fireplaces  0.38    0.09 -0.17      0.21       0.47       0.25     0.28
## bathrooms   0.60    0.08 -0.36      0.30       0.72       0.18     0.46
## rooms       0.53    0.14 -0.08      0.30       0.73       0.16     0.67
##            fireplaces bathrooms rooms
## price            0.38      0.60  0.53
## lotSize          0.09      0.08  0.14
## age             -0.17     -0.36 -0.08
## landValue        0.21      0.30  0.30
## livingArea       0.47      0.72  0.73
## pctCollege       0.25      0.18  0.16
## bedrooms         0.28      0.46  0.67
## fireplaces       1.00      0.44  0.32
## bathrooms        0.44      1.00  0.52
## rooms            0.32      0.52  1.00

library(ggplot2)
library(ggcorrplot)
ggcorrplot(r, colors = c("blue", "white", "red")) # Plot the correlation

We can also customize the output.

• hc.order = TRUE reorders the variables, placing variables with similar correlation patterns together.

• type = "lower" plots the lower portion of the correlation matrix.

• lab = TRUE overlays the correlation coefficients (as text) on the plot.

ggcorrplot(r, 
           hc.order = TRUE, 
           type = "lower",
           lab = TRUE)