在R中將matplot（）轉換爲ggplot2函數？

Question

我正在嘗試使用ggplot2包創建此圖。目前，我知道如何使這種情節只能用matplot()功能：

matplot(t(security_paths[[3]]), type='l') 

電流輸入矩陣security_paths[[3]]是這樣的，除了30列，而不是15：

  V1  V2  V3  V4  V5  V6  V7  V8 
result.2 100 99.37178 98.61707 98.98689 99.90287 100.04947 99.40548 101.40779 
result.7 100 100.11730 99.59974 99.53214 100.19915 101.56142 101.75984 101.47623 
result.2.1 100 100.50476 101.76885 102.49223 104.60058 104.77955 105.85920 105.75034 
result.7.1 100 101.69973 101.41755 100.29977 100.24582 100.76930 100.92308 100.36429 
result.2.2 100 98.53694 100.43020 100.44469 100.38406 99.97830 99.79855 99.11653 
result.7.2 100 98.93675 97.90757 97.00043 97.97458 97.42952 95.74721 96.13461 
result.2.3 100 98.80080 98.00636 99.07521 99.28543 99.87422 101.05531 100.83643 
result.7.3 100 100.11288 99.70116 100.24362 100.40603 101.00101 101.00941 102.78160 
result.2.4 100 99.34431 99.31093 100.05931 98.51813 98.16358 97.77950 98.66993 
result.7.4 100 100.26578 101.02704 101.49346 102.44187 101.28351 101.22310 100.94201 
        V9  V10  V11  V12  V13  V14  V15 
result.2 100.10401 100.06043 103.46456 105.82758 106.42934 108.01544 107.92772 
result.7 102.92835 101.83701 100.98723 101.30874 102.94760 102.75872 103.40619 
result.2.1 105.49117 106.64736 107.61651 108.06622 107.60332 107.10505 108.26500 
result.7.1 101.06239 101.85163 103.28536 102.75925 101.97034 101.49260 100.95205 
result.2.2 99.00879 97.64125 97.43550 96.66271 97.50641 96.30086 96.30059 
result.7.2 95.99714 94.05973 95.44348 96.60735 94.97913 94.42340 93.57966 
result.2.3 101.96880 103.12590 102.94677 104.25266 103.28532 101.56988 101.94995 
result.7.3 102.68475 103.05124 103.07985 103.74680 103.46858 101.71314 100.02625 
result.2.4 96.76381 97.58092 96.82826 96.00925 97.87556 98.61583 96.78436 
result.7.4 100.37194 99.71654 100.32579 100.39055 100.44717 101.25647 101.86222 

如何在ggplot2中使用繪圖功能重新創建matplot圖形？

注意

給出的數據不匹配的圖形，但它是相同的格式。

Answer 1

我沒有檢查，看看別人使用dplyr/tidyr（如果他們這麼做，有人請告訴我，我會刪除此並標記了一個問題，重複）：

dat <- read.table(text="   V1  V2  V3  V4  V5  V6  V7  V8 
result.2 100 99.37178 98.61707 98.98689 99.90287 100.04947 99.40548 101.40779 
result.7 100 100.11730 99.59974 99.53214 100.19915 101.56142 101.75984 101.47623 
result.2.1 100 100.50476 101.76885 102.49223 104.60058 104.77955 105.85920 105.75034 
result.7.1 100 101.69973 101.41755 100.29977 100.24582 100.76930 100.92308 100.36429 
result.2.2 100 98.53694 100.43020 100.44469 100.38406 99.97830 99.79855 99.11653 
result.7.2 100 98.93675 97.90757 97.00043 97.97458 97.42952 95.74721 96.13461 
result.2.3 100 98.80080 98.00636 99.07521 99.28543 99.87422 101.05531 100.83643 
result.7.3 100 100.11288 99.70116 100.24362 100.40603 101.00101 101.00941 102.78160 
result.2.4 100 99.34431 99.31093 100.05931 98.51813 98.16358 97.77950 98.66993 
result.7.4 100 100.26578 101.02704 101.49346 102.44187 101.28351 101.22310 100.94201") 

library(dplyr) 
library(tidyr) 
library(ggplot2) 

dat %>% 
    add_rownames() %>% 
    gather(reading, value, -rowname) %>% 
    group_by(rowname) %>% 
    mutate(x=1:n()) %>% 
    ggplot(aes(x=x, y=value, group=rowname)) + 
    geom_line(aes(color=rowname))