#0301上課程式碼：
#資料(data)、值(valus)、變數(varible)、物件(object)
# R 是以物件導向為主的程式語言, 在R中每一樣 “東西”, 都可視為 “物件”(object),
##一、R語言資料集合:
#1.向量(vector)
#2.清單(list)
#3.矩陣(matrix)
#4.陣列(array)
#5.因子(factor)
#6.資料框(dataframe)
# 資料分析基本上是 ：產生資料物件、命名、使用函式對物件運算操作
#建立向量資料====
id <- c("001","002","003","004","005")
name <- c("superman", "batman", "spiderman", "wolverine", "wonderwoman")
height <- c(190, 188, 178, 150, 183)
weight <- c(102, 95, 76, 88, 59)
gender <- c("Male", "Male","Male","Male","Female")
#二、R 的最基本物件是向量====
# 向量是由包含相同 “模式” 的元素 (element) 組成，
# 向量物件的基本元素的類型(basic mode) 主要分成
# 六大向量(vector)類型
# 1.數值向量（numeric）：包含 "integer", 整數型向量 與 "double", 倍精確度型向量。
# 2.整數向量（integer）
# 3.文字向量（character）
# 4.邏輯值向量（logical）
# 5.日期向量（Date）
# 6.日期時間向量（POSIXct）
#用class()查詢資料類型====
class(name)
## [1] "character"
class(height)
## [1] "numeric"
class(gender)
## [1] "character"
class(id)
## [1] "character"
class(height)
## [1] "numeric"
q <- height > 170
q
## [1] TRUE TRUE TRUE FALSE TRUE
class(q)
## [1] "logical"
#str()查看資料結構====
#summary()查看變數統計量
str(name)
## chr [1:5] "superman" "batman" "spiderman" "wolverine" "wonderwoman"
str(height)
## num [1:5] 190 188 178 150 183
#用[ ]取出向量資料
name
## [1] "superman" "batman" "spiderman" "wolverine" "wonderwoman"
name[2]
## [1] "batman"
name[3:5]
## [1] "spiderman" "wolverine" "wonderwoman"
name[-1]
## [1] "batman" "spiderman" "wolverine" "wonderwoman"
name[-c(1,3,5)]
## [1] "batman" "wolverine"
height
## [1] 190 188 178 150 183
height[height > 170]
## [1] 190 188 178 183
#將向量資料組合為data frame(數據框)
heroes <-data.frame(id,gender, height, weight, name)
#顯示在console區
heroes
## id gender height weight name
## 1 001 Male 190 102 superman
## 2 002 Male 188 95 batman
## 3 003 Male 178 76 spiderman
## 4 004 Male 150 88 wolverine
## 5 005 Female 183 59 wonderwoman
#看資料結構
str(heroes)
## 'data.frame': 5 obs. of 5 variables:
## $ id : chr "001" "002" "003" "004" ...
## $ gender: chr "Male" "Male" "Male" "Male" ...
## $ height: num 190 188 178 150 183
## $ weight: num 102 95 76 88 59
## $ name : chr "superman" "batman" "spiderman" "wolverine" ...
#看摘要
summary(heroes)
## id gender height weight
## Length:5 Length:5 Min. :150.0 Min. : 59
## Class :character Class :character 1st Qu.:178.0 1st Qu.: 76
## Mode :character Mode :character Median :183.0 Median : 88
## Mean :177.8 Mean : 84
## 3rd Qu.:188.0 3rd Qu.: 95
## Max. :190.0 Max. :102
## name
## Length:5
## Class :character
## Mode :character
##
##
##
##存成.RData格式====
save(heroes, file = "heros.RData")#請填入"heroes.RData"
#清空物件，然後再讀取一次.RData
load("heros.RData")
###用[ ] 取出data frame中的資料, 其中[ 列, 欄]====
heroes[2, ]#取出第二列資料
## id gender height weight name
## 2 002 Male 188 95 batman
heroes[,2]#取出第二欄資料
## [1] "Male" "Male" "Male" "Male" "Female"
heroes[5,]##取出第五列
## id gender height weight name
## 5 005 Female 183 59 wonderwoman
heroes[3,4]##取出第三列第四欄資料
## [1] 76
#用欄位名稱取出某欄的資料，有兩種方法：
heroes[["name"]]
## [1] "superman" "batman" "spiderman" "wolverine" "wonderwoman"
##或是
heroes$name
## [1] "superman" "batman" "spiderman" "wolverine" "wonderwoman"
heroes$height
## [1] 190 188 178 150 183
mean(heroes$height)
## [1] 177.8
heroes[1:3,]
## id gender height weight name
## 1 001 Male 190 102 superman
## 2 002 Male 188 95 batman
## 3 003 Male 178 76 spiderman
heroes[heroes$name=="spiderman",]#找出名字為"spiderman"的所有資料
## id gender height weight name
## 3 003 Male 178 76 spiderman
heroes[heroes$name!="spiderman",]#找出除了"spiderman"以外的所有資料
## id gender height weight name
## 1 001 Male 190 102 superman
## 2 002 Male 188 95 batman
## 4 004 Male 150 88 wolverine
## 5 005 Female 183 59 wonderwoman
heroes[heroes$height>180,]#找出身高大於180的所有資料
## id gender height weight name
## 1 001 Male 190 102 superman
## 2 002 Male 188 95 batman
## 5 005 Female 183 59 wonderwoman
heroes$height
## [1] 190 188 178 150 183
sort(heroes$height)#排序由小到大
## [1] 150 178 183 188 190
sort(heroes$height, decreasing = T)#排序由大到小
## [1] 190 188 183 178 150
#畫圖一
barplot(sort(heroes$height, decreasing = T))
#畫圖二
barplot(sort(heroes$height, decreasing = T),#要畫圖的向量資料
main = "Heroes", #大標題
sub = "by Hung Weitse", #次標題
names.arg = c("superman", "batman", "spiderman", "wonderwoman", "wolverine"), #資料名稱
xlab = "name", #x軸標籤
ylab = "height", #y軸標籤
col = c(6:10))#使用內建第6至10號顏色, colors(查詢R內建顏色)
# 亞洲四小龍(Four Asian Tigers)====
# https://zh.wikipedia.org/wiki/%E4%BA%9A%E6%B4%B2%E5%9B%9B%E5%B0%8F%E9%BE%99
#建立資料
nation <- c("Korea","Taiwan","HongKong","Singapore")
growth <- c(0.36,0.28,0.83,1.40)
#合併nation和growth為data frame
nation <- data.frame(nation,growth)
#畫長條圖
nation$growth
## [1] 0.36 0.28 0.83 1.40
sort(nation$growth, decreasing = T)#排序由大到小
## [1] 1.40 0.83 0.36 0.28
barplot(sort(nation$growth, decreasing = T))
barplot(sort(nation$growth, decreasing = T),#要畫圖的向量資料
main = "population growth rate", #大標題
sub = "by Hung Weitse", #次標題
names.arg = c("Singapore","HongKong","Korea","Taiwan"), #資料名稱
xlab = "nation", #x軸標籤
ylab = "pgr", #y軸標籤
col = c(11:14))#使用內建第6至10號顏色, colors(查詢R內建顏色)
###資料組合與切割====
df <- iris
#檢視資料
#查看完整資料
#查看資料結構
str(df)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#查看資料摘要
summary(df)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
#查看資料欄位名稱
names(df)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
#查看第三欄，第1到10筆資料
df[1:10,3]
## [1] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5
#查看欄位名稱為(Species)的資料
df$Species
## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica
#修改欄位名稱, 請填入"花萼長", "花萼寬", "花瓣長", "花瓣寬", "品種")
names(df) <-c("花萼長","花萼寬","花瓣長","花瓣寬","品種")
#找出"花萼長"大於等於7的所有資料
df[df$花萼長>7,]
## 花萼長 花萼寬 花瓣長 花瓣寬 品種
## 103 7.1 3.0 5.9 2.1 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 136 7.7 3.0 6.1 2.3 virginica
#計算"花萼長"大於等於7的資料有幾筆？
nrow(df[df$花萼長>7,])
## [1] 12
#查看「品種」欄位為setosa資料
df[df$品種=="setosa",]
## 花萼長 花萼寬 花瓣長 花瓣寬 品種
## 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
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
#將[品種]欄位中為setosa資料，另存成一個物件(df_setosa)，
df_setosa <- df[df$品種=="setosa",]
#將物件(df_setosa)另存為.RData格式
save(df_setosa,file = "df_setosa.RData")
#測驗====
#讀取heroes_stats.RData
load("heroes_stats.RData")
#檢視資料
#查看完整資料
#查看資料結構
str(heroes_stats)
## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 428 obs. of 9 variables:
## $ Name : chr "3-D Man" "A-Bomb" "Abe Sapien" "Abin Sur" ...
## $ Alignment : Factor w/ 3 levels "bad","good","neutral": 2 2 2 2 1 1 2 2 2 1 ...
## $ Intelligence: num 50 38 88 50 63 88 63 10 75 50 ...
## $ Strength : num 31 100 14 90 80 100 10 8 28 85 ...
## $ Speed : num 43 17 35 53 53 83 12 13 38 100 ...
## $ Durability : num 32 80 42 64 90 99 100 5 80 85 ...
## $ Power : num 25 17 35 84 55 100 71 5 72 100 ...
## $ Combat : num 52 64 85 65 95 56 64 20 95 40 ...
## $ Total : num 233 316 299 406 436 526 320 61 388 460 ...
## - attr(*, "spec")=List of 3
## ..$ cols :List of 9
## .. ..$ Name : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Alignment : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ Intelligence: list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ Strength : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ Speed : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ Durability : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ Power : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ Combat : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ Total : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr [1:2] "collector_guess" "collector"
## ..$ delim : chr ","
## ..- attr(*, "class")= chr "col_spec"
## - attr(*, "problems")=<externalptr>
#查看資料摘要
summary(heroes_stats)
## Name Alignment Intelligence Strength
## Length:428 bad :121 Min. : 6.00 Min. : 4.00
## Class :character good :296 1st Qu.: 50.00 1st Qu.: 10.00
## Mode :character neutral: 11 Median : 63.00 Median : 32.00
## Mean : 62.69 Mean : 40.78
## 3rd Qu.: 75.00 3rd Qu.: 73.50
## Max. :113.00 Max. :100.00
## Speed Durability Power Combat
## Min. : 8.00 Min. : 5.00 Min. : 5.00 Min. : 6.00
## 1st Qu.: 23.00 1st Qu.: 28.00 1st Qu.: 34.00 1st Qu.: 42.00
## Median : 33.00 Median : 58.00 Median : 57.50 Median : 64.00
## Mean : 38.36 Mean : 59.00 Mean : 57.06 Mean : 60.76
## 3rd Qu.: 50.00 3rd Qu.: 89.25 3rd Qu.: 75.25 3rd Qu.: 80.00
## Max. :100.00 Max. :120.00 Max. :100.00 Max. :101.00
## Total
## Min. : 61.0
## 1st Qu.:240.5
## Median :306.0
## Mean :318.7
## 3rd Qu.:384.5
## Max. :581.0
#查看資料欄位名稱
names(heroes_stats)
## [1] "Name" "Alignment" "Intelligence" "Strength" "Speed"
## [6] "Durability" "Power" "Combat" "Total"
#查看第一、二、九欄，第1到5筆資料
heroes_stats[1:5,c(1,2,9)]
## Name Alignment Total
## 1 3-D Man good 233
## 2 A-Bomb good 316
## 3 Abe Sapien good 299
## 4 Abin Sur good 406
## 5 Abomination bad 436
#查看欄位名稱為Alignment的資料
heroes_stats$Alignment
## [1] good good good good bad bad good good good
## [10] bad good good bad good good good bad good
## [19] good bad bad good good good good bad good
## [28] good good good good good bad good bad good
## [37] good good good good good good good good good
## [46] good good good good bad good good good neutral
## [55] bad good good good good bad good good bad
## [64] bad good good good bad bad bad good good
## [73] good good good bad good bad good good good
## [82] bad good good good good good good bad good
## [91] good good bad bad good good good good good
## [100] good bad good good good good good good bad
## [109] good good good good neutral bad good neutral bad
## [118] bad good good bad good good good bad bad
## [127] bad good bad good bad good bad good good
## [136] bad bad good bad bad good good good good
## [145] good good good good good good good bad neutral
## [154] good good bad good bad bad bad good good
## [163] good bad bad good good good good bad good
## [172] good good good good good good good good good
## [181] good good good good bad good good good good
## [190] good good good bad good good good good good
## [199] good good good good bad good good neutral bad
## [208] good bad bad good good bad good bad bad
## [217] good bad bad good bad good bad good good
## [226] bad bad bad bad bad good good good bad
## [235] good bad good bad neutral good good bad good
## [244] good good bad good good bad good good good
## [253] bad good bad good good good good good good
## [262] good bad bad bad good good good bad bad
## [271] good good good good bad bad good good good
## [280] good good good good good good good good good
## [289] bad good good bad good good good good bad
## [298] bad good good bad good bad good good good
## [307] bad good neutral good neutral good bad good bad
## [316] good bad bad bad good good good good good
## [325] good good bad good neutral good good bad good
## [334] good bad bad bad good good good good good
## [343] good bad good good good good bad bad bad
## [352] good good good bad good good good good good
## [361] good good good good good good good good good
## [370] good good good bad good good bad bad good
## [379] good bad good neutral good good good good good
## [388] good bad good bad neutral good good bad good
## [397] bad bad bad bad bad bad good good good
## [406] bad bad good good bad good good good good
## [415] bad good good good good good good good good
## [424] good good good good bad
## Levels: bad good neutral
#找出"Power"大於95的所有資料
heroes_stats[heroes_stats$power>95,]
## [1] Name Alignment Intelligence Strength Speed
## [6] Durability Power Combat Total
## <0 rows> (or 0-length row.names)
#計算""Power"大於95的的資料有幾筆？
nrow(heroes_stats[heroes_stats$power>95,])
## [1] 0
#修改欄位名稱為中文
names(heroes_stats) <-c("結盟","智力","抵抗力","速度","耐用性","力量","戰鬥","總和")
#查看欄位名稱為Alignment的資料
heroes_stats$結盟
## [1] "3-D Man" "A-Bomb" "Abe Sapien"
## [4] "Abin Sur" "Abomination" "Abraxas"
## [7] "Adam Monroe" "Agent Bob" "Agent Zero"
## [10] "Air-Walker" "Alan Scott" "Alfred Pennyworth"
## [13] "Amazo" "Angel" "Angel Salvadore"
## [16] "Animal Man" "Annihilus" "Ant-Man"
## [19] "Ant-Man II" "Anti-Monitor" "Apocalypse"
## [22] "Aquababy" "Aqualad" "Aquaman"
## [25] "Archangel" "Arclight" "Ares"
## [28] "Ariel" "Armor" "Atlas"
## [31] "Atom Girl" "Aurora" "Azazel"
## [34] "Azrael" "Bane" "Banshee"
## [37] "Bantam" "Batgirl IV" "Batgirl VI"
## [40] "Batman" "Batman II" "Battlestar"
## [43] "Beast" "Beast Boy" "Ben 10"
## [46] "Beta Ray Bill" "Beyonder" "Big Barda"
## [49] "Big Daddy" "Big Man" "Bill Harken"
## [52] "Bird-Brain" "Bishop" "Bizarro"
## [55] "Black Adam" "Black Bolt" "Black Cat"
## [58] "Black Knight III" "Black Lightning" "Black Mamba"
## [61] "Black Panther" "Black Widow" "Blackout"
## [64] "Blackwing" "Blade" "Bling!"
## [67] "Blink" "Blizzard II" "Blob"
## [70] "Bloodaxe" "Bloodhawk" "Blue Beetle III"
## [73] "Boom Boom" "Booster Gold" "Box IV"
## [76] "Brainiac" "Brainiac 5" "Bullseye"
## [79] "Bumblebee" "Bushido" "Cable"
## [82] "Callisto" "Cameron Hicks" "Cannonball"
## [85] "Captain America" "Captain Britain" "Captain Marvel"
## [88] "Captain Marvel II" "Carnage" "Catwoman"
## [91] "Century" "Chamber" "Chameleon"
## [94] "Changeling" "Chuck Norris" "Claire Bennet"
## [97] "Cloak" "Colossus" "Crystal"
## [100] "Cyborg" "Cyborg Superman" "Cyclops"
## [103] "Dagger" "Daphne Powell" "Daredevil"
## [106] "Darkhawk" "Darkman" "Darkseid"
## [109] "Darkstar" "Dash" "Dazzler"
## [112] "Deadman" "Deadpool" "Deadshot"
## [115] "Deathlok" "Deathstroke" "Demogoblin"
## [118] "Destroyer" "DL Hawkins" "Doc Samson"
## [121] "Doctor Doom" "Doctor Fate" "Doctor Strange"
## [124] "Domino" "Doomsday" "Doppelganger"
## [127] "Dormammu" "Dr Manhattan" "Ego"
## [130] "Elastigirl" "Electro" "Elektra"
## [133] "Elle Bishop" "Elongated Man" "Emma Frost"
## [136] "Evilhawk" "Exodus" "Falcon"
## [139] "Fallen One II" "Faora" "Feral"
## [142] "Fin Fang Foom" "Firebird" "Firelord"
## [145] "Firestar" "Firestorm" "Flash I"
## [148] "Flash II" "Flash III" "Flash IV"
## [151] "Forge" "Frenzy" "Galactus"
## [154] "Gambit" "Gary Bell" "General Zod"
## [157] "Ghost Rider" "Giganta" "Goblin Queen"
## [160] "Gog" "Goku" "Gravity"
## [163] "Green Arrow" "Green Goblin" "Green Goblin II"
## [166] "Groot" "Guy Gardner" "Hal Jordan"
## [169] "Hancock" "Harley Quinn" "Havok"
## [172] "Hawk" "Hawkeye" "Hawkeye II"
## [175] "Hawkgirl" "Hellboy" "Hellcat"
## [178] "Hercules" "Hit-Girl" "Hope Summers"
## [181] "Hulk" "Human Torch" "Huntress"
## [184] "Husk" "Hydro-Man" "Hyperion"
## [187] "Iceman" "Impulse" "Ink"
## [190] "Invisible Woman" "Iron Fist" "Iron Man"
## [193] "Iron Monger" "Isis" "Jack of Hearts"
## [196] "Jack-jack" "Jean Grey" "Jennifer Kale"
## [199] "Jim Powell" "JJ Powell" "John Stewart"
## [202] "John Wraith" "Joker" "Jolt"
## [205] "Jubilee" "Juggernaut" "Junkpile"
## [208] "Justice" "Kang" "Kevin 11"
## [211] "Kick-Ass" "Kid Flash" "Killer Croc"
## [214] "Kilowog" "Kingpin" "Klaw"
## [217] "Kool-Aid Man" "Kraven II" "Kraven the Hunter"
## [220] "Krypto" "Leader" "Leech"
## [223] "Lex Luthor" "Light Lass" "Lightning Lad"
## [226] "Lightning Lord" "Living Brain" "Lizard"
## [229] "Lobo" "Loki" "Longshot"
## [232] "Luke Cage" "Luna" "Mach-IV"
## [235] "Machine Man" "Magneto" "Magog"
## [238] "Magus" "Man-Bat" "Man-Thing"
## [241] "Man-Wolf" "Mandarin" "Martian Manhunter"
## [244] "Marvel Girl" "Master Chief" "Match"
## [247] "Matt Parkman" "Maverick" "Maxima"
## [250] "Maya Herrera" "Medusa" "Meltdown"
## [253] "Mephisto" "Mera" "Metallo"
## [256] "Metron" "Micah Sanders" "Micro Lad"
## [259] "Mimic" "Misfit" "Miss Martian"
## [262] "Mister Fantastic" "Mister Freeze" "Mister Mxyzptlk"
## [265] "Molten Man" "Monica Dawson" "Moon Knight"
## [268] "Moonstone" "Morlun" "Moses Magnum"
## [271] "Mr Incredible" "Ms Marvel" "Ms Marvel II"
## [274] "Multiple Man" "Mysterio I" "Mystique"
## [277] "Namora" "Namorita" "Naruto Uzumaki"
## [280] "Nick Fury" "Nightcrawler" "Nightwing"
## [283] "Niki Sanders" "Nina Theroux" "Northstar"
## [286] "Nova" "Nova" "Offspring"
## [289] "Onslaught" "Oracle (DC)" "Osiris"
## [292] "Penguin" "Peter Petrelli" "Phantom Girl"
## [295] "Phoenix" "Plastic Man" "Plastique"
## [298] "Poison Ivy" "Power Girl" "Professor X"
## [301] "Proto-Goblin" "Punisher" "Pyro"
## [304] "Question" "Quicksilver" "Quill"
## [307] "Ra's Al Ghul" "Rachel Pirzad" "Raven"
## [310] "Red Arrow" "Red Hood" "Red Hulk"
## [313] "Red Mist" "Red Robin" "Red Skull"
## [316] "Red Tornado" "Rhino" "Rick Flag"
## [319] "Riddler" "Robin I" "Robin III"
## [322] "Robin V" "Rocket Raccoon" "Rogue"
## [325] "Ronin" "Rorschach" "Sabretooth"
## [328] "Sage" "Sandman" "Sasquatch"
## [331] "Savage Dragon" "Scarecrow" "Scarlet Spider"
## [334] "Scarlet Spider II" "Scarlet Witch" "Scorpia"
## [337] "Scorpion" "Sentry" "Shadow King"
## [340] "Shadowcat" "Shang-Chi" "Shatterstar"
## [343] "She-Hulk" "Shocker" "Shriek"
## [346] "Sif" "Silver Surfer" "Silverclaw"
## [349] "Sinestro" "Siren" "Siryn"
## [352] "Skaar" "Snowbird" "Sobek"
## [355] "Solomon Grundy" "Songbird" "Space Ghost"
## [358] "Spawn" "Spider-Girl" "Spider-Man"
## [361] "Spider-Woman" "Spider-Woman III" "Spyke"
## [364] "Stardust" "Starfire" "Stargirl"
## [367] "Steel" "Stephanie Powell" "Storm"
## [370] "Sub-Mariner" "Sunspot" "Superboy"
## [373] "Superboy-Prime" "Supergirl" "Superman"
## [376] "Swamp Thing" "Sylar" "Synch"
## [379] "Tempest" "Thanos" "The Cape"
## [382] "The Comedian" "Thing" "Thor"
## [385] "Thor Girl" "Thunderbird" "Thunderstrike"
## [388] "Thundra" "Tiger Shark" "Tigra"
## [391] "Tinkerer" "Toad" "Toxin"
## [394] "Triplicate Girl" "Two-Face" "Ultragirl"
## [397] "Ultron" "Utgard-Loki" "Vanisher"
## [400] "Venom" "Venom II" "Venom III"
## [403] "Vindicator II" "Violet" "Vision"
## [406] "Vulture" "Walrus" "War Machine"
## [409] "Warlock" "Warp" "Warpath"
## [412] "Wasp" "Watcher" "Wildfire"
## [415] "Willis Stryker" "Winter Soldier" "Wolfsbane"
## [418] "Wolverine" "Wonder Girl" "Wonder Man"
## [421] "Wonder Woman" "X-23" "X-Man"
## [424] "Yellowjacket" "Yellowjacket II" "Ymir"
## [427] "Zatanna" "Zoom"
#查看[Alignment]欄位中為good資料
heroes_stats[heroes_stats$結盟=="good",]
## [1] 結盟 智力 抵抗力 速度 耐用性 力量 戰鬥 總和 <NA>
## <0 rows> (or 0-length row.names)
#將[Alignment]欄位中為good資料，另存成一個物件(heroes_good)，
heroes_good <- heroes_stats[heroes_stats$結盟=="good",]
#將物件(heroes_good)另存為.RData格式
save(heroes_good, file = "heroes_good.RData")
#[Name]欄位中有"man"的hero
heroes_stats[heroes_stats$Name=="man",]
## [1] 結盟 智力 抵抗力 速度 耐用性 力量 戰鬥 總和 <NA>
## <0 rows> (or 0-length row.names)
#請選擇五個超級英雄，以長條圖呈現他們的綜合能力值（Total）
heroes <- c("3-D Man","A-Bomb","Abe Sapien","Abin Sur","Abomination")
total<- c(233,316,299,406,436)
data <- data.frame(heroes,total)
barplot(sort(data$total, decreasing = T),#要畫圖的向量資料
main = "綜合能力值", #大標題
sub = "綜合能力值by minjay", #次標題
names.arg = c("3-D Man","A-Bomb","Abe Sapien","Abin
Sur","Abomination"), #資料名稱
xlab = "heroes", #x軸標籤
ylab = "total", #y軸標籤
col = c(28:32))
