#讀取資料，命名為"x"
x<- read.csv("strays.csv", stringsAsFactors = TRUE)
#查看資料結構
summary(x)
## id type gender size color age
## Min. : 26250 狗:1048 F:534 BIG :181 咖啡色: 13 ADULT:886
## 1st Qu.: 50182 貓: 34 M:548 MEDIUM:713 白色 : 26 CHILD:196
## Median :129335 SMALL :188 花色 : 88
## Mean :120020 虎斑色:104
## 3rd Qu.:179406 黃色 :249
## Max. :248935 黑色 :602
##
## county
## 新北市 :220
## 南投縣 :188
## 高雄市 :172
## 臺南市 : 97
## 臺北市 : 84
## 桃園市 : 74
## (Other):247
#重新命名欄位名稱
colnames(x) #檢視欄位名稱
## [1] "id" "type" "gender" "size" "color" "age" "county"
colnames(x) <- c("id", "type", "gender", "size", "color","age","country")
###類別資料分析###
#問題：犬與貓毛色分析的量長條圖
#次數分配表
t <- table(x$type,x$color)
t
##
## 咖啡色 白色 花色 虎斑色 黃色 黑色
## 狗 13 26 75 93 246 595
## 貓 0 0 13 11 3 7
barplot(t)
barplot(t, beside =T )
#加上圖例與上色
rownames(t)
## [1] "狗" "貓"
label <- rownames(t)
label
## [1] "狗" "貓"
barplot(t,
beside =T ,
legend.text =label,
xlab = "size",
main = "犬與貓毛色分析的長條圖",
sub = "by Yong-Sheng",
col =c(39,11))
#問題：各縣市流浪動物數量長條圖
#次數分配表
table(x$type)
##
## 狗 貓
## 1048 34
table(x$country)
##
## 南投縣 嘉義市 嘉義縣 基隆市 宜蘭縣 屏東縣 彰化縣 新北市 新竹市 新竹縣 桃園市
## 188 2 1 15 3 40 4 220 63 6 74
## 澎湖縣 臺中市 臺北市 臺南市 臺東縣 花蓮縣 苗栗縣 金門縣 雲林縣 高雄市
## 15 11 84 97 3 2 3 49 30 172
k <-table(x$type,x$country)
k.t <- prop.table(k,2)
k.t
##
## 南投縣 嘉義市 嘉義縣 基隆市 宜蘭縣 屏東縣
## 狗 0.984042553 1.000000000 1.000000000 0.933333333 1.000000000 1.000000000
## 貓 0.015957447 0.000000000 0.000000000 0.066666667 0.000000000 0.000000000
##
## 彰化縣 新北市 新竹市 新竹縣 桃園市 澎湖縣
## 狗 1.000000000 1.000000000 1.000000000 1.000000000 0.959459459 1.000000000
## 貓 0.000000000 0.000000000 0.000000000 0.000000000 0.040540541 0.000000000
##
## 臺中市 臺北市 臺南市 臺東縣 花蓮縣 苗栗縣
## 狗 0.909090909 0.845238095 0.948453608 1.000000000 1.000000000 1.000000000
## 貓 0.090909091 0.154761905 0.051546392 0.000000000 0.000000000 0.000000000
##
## 金門縣 雲林縣 高雄市
## 狗 1.000000000 0.766666667 0.994186047
## 貓 0.000000000 0.233333333 0.005813953
#將次數變成百分比(乘以100)
k.t <- k.t*100
k.t
##
## 南投縣 嘉義市 嘉義縣 基隆市 宜蘭縣 屏東縣
## 狗 98.4042553 100.0000000 100.0000000 93.3333333 100.0000000 100.0000000
## 貓 1.5957447 0.0000000 0.0000000 6.6666667 0.0000000 0.0000000
##
## 彰化縣 新北市 新竹市 新竹縣 桃園市 澎湖縣
## 狗 100.0000000 100.0000000 100.0000000 100.0000000 95.9459459 100.0000000
## 貓 0.0000000 0.0000000 0.0000000 0.0000000 4.0540541 0.0000000
##
## 臺中市 臺北市 臺南市 臺東縣 花蓮縣 苗栗縣
## 狗 90.9090909 84.5238095 94.8453608 100.0000000 100.0000000 100.0000000
## 貓 9.0909091 15.4761905 5.1546392 0.0000000 0.0000000 0.0000000
##
## 金門縣 雲林縣 高雄市
## 狗 100.0000000 76.6666667 99.4186047
## 貓 0.0000000 23.3333333 0.5813953
#四捨五入至小數2位
k.t <- round(k.t,2)
k.t
##
## 南投縣 嘉義市 嘉義縣 基隆市 宜蘭縣 屏東縣 彰化縣 新北市 新竹市 新竹縣
## 狗 98.40 100.00 100.00 93.33 100.00 100.00 100.00 100.00 100.00 100.00
## 貓 1.60 0.00 0.00 6.67 0.00 0.00 0.00 0.00 0.00 0.00
##
## 桃園市 澎湖縣 臺中市 臺北市 臺南市 臺東縣 花蓮縣 苗栗縣 金門縣 雲林縣
## 狗 95.95 100.00 90.91 84.52 94.85 100.00 100.00 100.00 100.00 76.67
## 貓 4.05 0.00 9.09 15.48 5.15 0.00 0.00 0.00 0.00 23.33
##
## 高雄市
## 狗 99.42
## 貓 0.58
barplot(k)
barplot(k, beside =T )
#加上圖例與上色
rownames(k)
## [1] "狗" "貓"
label <- rownames(k)
label
## [1] "狗" "貓"
barplot(k,
beside =T ,
legend.text =label,
xlab = "縣市",
main = "各縣市流浪動物數量長條圖",
sub = "by Yong-Sheng",
col =c(31,14))
#三、犬與貓毛色分析的圓餅圖
r <-table(x$type,x$color)
r
##
## 咖啡色 白色 花色 虎斑色 黃色 黑色
## 狗 13 26 75 93 246 595
## 貓 0 0 13 11 3 7
r.t <-prop.table(r)
#將次數變成百分比(乘以100)
r.t <- r.t*100
r.t
##
## 咖啡色 白色 花色 虎斑色 黃色 黑色
## 狗 1.2014787 2.4029575 6.9316081 8.5951941 22.7356747 54.9907579
## 貓 0.0000000 0.0000000 1.2014787 1.0166359 0.2772643 0.6469501
#四捨五入至小數2位
r.t <- round(r.t,2)
r.t
##
## 咖啡色 白色 花色 虎斑色 黃色 黑色
## 狗 1.20 2.40 6.93 8.60 22.74 54.99
## 貓 0.00 0.00 1.20 1.02 0.28 0.65
o <- r.t[1,] # 狗資料
z <- r.t[2,] # 貓資料
o
## 咖啡色 白色 花色 虎斑色 黃色 黑色
## 1.20 2.40 6.93 8.60 22.74 54.99
z
## 咖啡色 白色 花色 虎斑色 黃色 黑色
## 0.00 0.00 1.20 1.02 0.28 0.65
# par()是圖形控制函數，mfrow = c(1,2)表示建立一個1x2的空間，用來呈現後續的圖
par(mfrow = c(1,2))
pie(o, main = "Dog" )
pie(z, main = "Cat" )
dev.off() #離開par()
## null device
## 1
#畫圓餅圖並加上資料標籤
pie_category <- colnames(r.t)
pie_category
## [1] "咖啡色" "白色" "花色" "虎斑色" "黃色" "黑色"
o_label <- paste(pie_category, o,"%", sep = "")
o_label
## [1] "咖啡色1.2%" "白色2.4%" "花色6.93%" "虎斑色8.6%" "黃色22.74%"
## [6] "黑色54.99%"
z_label <- paste(pie_category, z,"%", sep = "")
z_label
## [1] "咖啡色0%" "白色0%" "花色1.2%" "虎斑色1.02%" "黃色0.28%"
## [6] "黑色0.65%"
par(mfrow = c(1,2))# c(1,2)，表示建立一個1x2的空間，用來呈現後續的圖
pie(o, labels =o_label , main = "Dog")
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pie(z, labels =z_label , main = "Cat")
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## Warning in text.default(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, adj
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## Warning in text.default(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, adj
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## Warning in text.default(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, adj
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## Warning in text.default(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, adj
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## Warning in text.default(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, adj =
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## Warning in text.default(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, adj
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## Warning in text.default(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, adj
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## Warning in text.default(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, adj
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## Warning in text.default(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, adj
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dev.off()
## null device
## 1