Last updated: 2019-09-20

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Rmd b824099 dleelab 2019-09-20 created 
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
library(ggplot2)

icecream <- read.table("data/icecream.txt")
dim(icecream)
[1] 200   5
icecream$ice_cream <- as.factor(icecream$ice_cream)

copier <- read.table("data/CH01PR20.txt")
dim(copier)
[1] 45  2
colnames(copier)=c("minutes","number")

puzzle.summary <- icecream %>% group_by(ice_cream) %>% summarise(Mean=mean(puzzle),
            Variance=var(puzzle))%>%as.data.frame()

Graphical plots

Traditional R plotting

Density plot, boxplot

plot(density(copier$minutes),xlab="minutes")#,ylab="density")

plot(density(icecream$puzzle),xlab="puzzle score")

boxplot(video~ice_cream, data=icecream)

Scatter plot

plot(x=copier$number,y=copier$minutes)

plot(puzzle~video, data=icecream)#response againt predictors

# by default, it's x axis first, then y axis. or you can specify

Correlation matrix

head(iris)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
cor(iris[,1:3])
             Sepal.Length Sepal.Width Petal.Length
Sepal.Length    1.0000000  -0.1175698    0.8717538
Sepal.Width    -0.1175698   1.0000000   -0.4284401
Petal.Length    0.8717538  -0.4284401    1.0000000
pairs(iris[,1:3])

Advanced ploting - ggplot2

#install.packages("ggplot2")
library(ggplot2)

Density plot
Run the first layer, then add extra layers, use + to add extra layers

p <- ggplot(data=copier, mapping=aes(x=minutes)) + 
  geom_density() +
  xlab("Minutes used") +
  ggtitle("This is a density plot of minutes") +
  theme(plot.title = element_text(hjust = 0.5))
p

Add mean line(vertical line)

p + geom_vline(aes(xintercept=mean(minutes)),
              color="red", linetype="dashed", size=2) #change dotted, or size

A geom is the geometrical object that a plot uses to represent data. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on. Scatterplots break the trend; they use the point geom. As we see above, you can use different geoms to plot the same data.

Boxplot

p <- ggplot(icecream, aes(x=ice_cream, y=puzzle)) + 
  geom_boxplot()
p

Similar method

q <- ggplot(icecream) + 
  geom_boxplot(aes(x=ice_cream, y=puzzle) )
q

Add summary stats

q + geom_point(data=puzzle.summary,aes(x=ice_cream, y=Mean), shape=18, col="blue", size=3)

p + stat_summary(fun.y=mean, geom="point", shape=7,col="red", size=4)

q + stat_summary(aes(x=ice_cream, y=puzzle),fun.y=mean, geom="point", shape=7,col="red", size=4)

#a little bit different, q don't have the aes settings, just different ways to do the calculation.

Scatter plot - copier data

p <- ggplot(copier,aes(x=number, y=minutes)) + 
  geom_point()
p

Change theme

p + theme_bw()

p + theme_classic()

Scatter plot - icecream data

p <- ggplot(icecream,aes(x=video, y=puzzle, col=ice_cream,shape=ice_cream)) +
  geom_point()
p

How about mark it by gender

icecream$female=as.factor(icecream$female)

p <- ggplot(icecream,aes(x=video, y=puzzle, col=female,shape=female)) +
  geom_point()
p

p <- ggplot(icecream,aes(x=ice_cream, y=puzzle, col=female,shape=female)) +
  geom_point()
p

Many other ways to customize the plot

p <- ggplot(icecream, aes(x=ice_cream, y=puzzle,fill=ice_cream)) + 
  geom_boxplot()
p

p <- ggplot(icecream, aes(x=ice_cream, y=puzzle,fill=female)) + 
  geom_boxplot()
p

p+scale_fill_hue(l=70, c=80) #many other ways to change the color/theme/type, etc


sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.2.1 dplyr_0.8.3  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       knitr_1.24       whisker_0.3-2    magrittr_1.5    
 [5] workflowr_1.4.0  munsell_0.5.0    tidyselect_0.2.5 colorspace_1.4-1
 [9] R6_2.4.0         rlang_0.4.0      stringr_1.4.0    tools_3.6.1     
[13] grid_3.6.1       gtable_0.3.0     xfun_0.9         withr_2.1.2     
[17] git2r_0.26.1     htmltools_0.3.6  lazyeval_0.2.2   yaml_2.2.0      
[21] rprojroot_1.3-2  digest_0.6.20    assertthat_0.2.1 tibble_2.1.3    
[25] crayon_1.3.4     purrr_0.3.2      fs_1.3.1         glue_1.3.1      
[29] evaluate_0.14    rmarkdown_1.15   labeling_0.3     stringi_1.4.3   
[33] compiler_3.6.1   pillar_1.4.2     scales_1.0.0     backports_1.1.4 
[37] pkgconfig_2.0.2