Data Description

[第一組範例]是內建資料集 Diamonds。~54,000 round diamonds from http://www.diamondse.info/

[第二組範例]是內建資料集 WorldPhones。

[設定所需的函式庫(libraries)以及載入資料]

install.packages("ggplot2")
install.packages("ggplot2movies")
library(ggplot2)
library(reshape2)
library(ggplot2movies)

data(diamonds) #第一組data
data(WorldPhones) #第二組data
data(movies)  #第三組data

ggplot(data=diamonds, aes(x=cut)) + geom_bar() #a quick example 

PART 1.三大圖型概述

[畫圖基本概念]你要決定到底是要Factor or Numeric?

[第一大類] bar plot

str(diamonds)
## Classes 'tbl_df', 'tbl' and 'data.frame':    53940 obs. of  10 variables:
##  $ carat  : num  0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
##  $ depth  : num  61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num  55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int  326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num  3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num  3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num  2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
head(diamonds)
##   carat       cut color clarity depth table price    x    y    z
## 1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48
summary(diamonds)
##      carat               cut        color        clarity     
##  Min.   :0.2000   Fair     : 1610   D: 6775   SI1    :13065  
##  1st Qu.:0.4000   Good     : 4906   E: 9797   VS2    :12258  
##  Median :0.7000   Very Good:12082   F: 9542   SI2    : 9194  
##  Mean   :0.7979   Premium  :13791   G:11292   VS1    : 8171  
##  3rd Qu.:1.0400   Ideal    :21551   H: 8304   VVS2   : 5066  
##  Max.   :5.0100                     I: 5422   VVS1   : 3655  
##                                     J: 2808   (Other): 2531  
##      depth           table           price             x         
##  Min.   :43.00   Min.   :43.00   Min.   :  326   Min.   : 0.000  
##  1st Qu.:61.00   1st Qu.:56.00   1st Qu.:  950   1st Qu.: 4.710  
##  Median :61.80   Median :57.00   Median : 2401   Median : 5.700  
##  Mean   :61.75   Mean   :57.46   Mean   : 3933   Mean   : 5.731  
##  3rd Qu.:62.50   3rd Qu.:59.00   3rd Qu.: 5324   3rd Qu.: 6.540  
##  Max.   :79.00   Max.   :95.00   Max.   :18823   Max.   :10.740  
##                                                                  
##        y                z         
##  Min.   : 0.000   Min.   : 0.000  
##  1st Qu.: 4.720   1st Qu.: 2.910  
##  Median : 5.710   Median : 3.530  
##  Mean   : 5.735   Mean   : 3.539  
##  3rd Qu.: 6.540   3rd Qu.: 4.040  
##  Max.   :58.900   Max.   :31.800  
## 
ggplot(data=diamonds, aes(x=cut)) + geom_bar() #x must be of type factor

ggp <- ggplot(data=diamonds[1:5,], aes(x=cut)) + geom_bar() #geom_bar drops category with no occurrence
ggp

ggplot(data=diamonds, aes(x=cut)) + geom_bar() + coord_flip() #horizontal bar

ggp <- ggp + xlab("Cut") + ylab("Count") + ggtitle("Hello ggplot!") #change label naming
ggp

ggp + geom_bar(fill="snow", color="black") # change color and see colors() if you're picky

1-1.我們可以事先做一點點計算(plot counts as is )

diamonds_precounted <- as.data.frame(table(diamonds$cut, dnn=c("Cut")))
diamonds_precounted
##         Cut  Freq
## 1      Fair  1610
## 2      Good  4906
## 3 Very Good 12082
## 4   Premium 13791
## 5     Ideal 21551
ggplot(diamonds_precounted, aes(x=Cut, y=Freq)) + geom_bar(stat="identity") # default is "bin"

1-2.有關於stat=“identity”

A.row should be unique: otherwise counts will be summed up

B.missing label will be present at default: differ from stat=“bin”

C.negative bar is allowed

diamonds[1:5,]
##   carat     cut color clarity depth table price    x    y    z
## 1  0.23   Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21 Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23    Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29 Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31    Good     J     SI2  63.3    58   335 4.34 4.35 2.75
ggplot(diamonds[1:5,], aes(x=cut, y=depth)) + geom_bar(stat="identity")

ggplot(diamonds_precounted, aes(x=reorder(Cut, -Freq), y=Freq)) + 
  geom_bar(stat='identity') # The order is determined by factor levels

1-3.stack grouping

ggplot(data=diamonds, aes(x=color, fill=cut)) + geom_bar() #by fill

ggplot(data=diamonds, aes(x=color, color=cut)) + geom_bar() #by color