r - ggplot2 - 要素の色と凡例の調整

Question

2 つの別々の測定装置で測定された放射能活動の傾向をプロットする短いスクリプトを作成しました。スクリプトを以下に示します

pkgLoad <- function(x)
  {
    if (!require(x,character.only = TRUE))
    {
      install.packages(x,dep=TRUE, repos='http://star-www.st-andrews.ac.uk/cran/')
      if(!require(x,character.only = TRUE)) stop("Package not found")
    }

  }

pkgLoad("ggplot2")
pkgLoad("XLConnect")
pkgLoad("reshape2")

#Load the workbook
wb<-loadWorkbook("CapintecQC.xlsx")

df_blue <-readWorksheet(wb, sheet = "Blue", startCol=1, endCol=6)
#sort date format
df_blue$Date <- as.Date(df_blue$Date , "%d/%m/%y")
df_blue[order(df_blue$Date ),]

df_gold <-readWorksheet(wb, sheet = "Gold", startCol=1, endCol=6)
df_gold$Date <- as.Date(df_gold$Date , "%d/%m/%y")
df_gold[order(df_gold$Date ),]

#Reference Cs-137 details
ref_activity <- 9.3
half_life <- 30.23
ref_date <- as.Date('06/01/08',format='%d/%m/%y')

blue_melt <- melt(df_blue[,c(1,2:6)], id="Date", value.name="Activity", variable.name="Isotope")

#Add new column to data frame with expected activity
df_gold["Exp_Act"] <- round(ref_activity*exp((-0.693/half_life)*as.numeric(difftime(df_gold$Date,ref_date))/365.25),3)
df_gold["Exp_Act_0.95"] <- 0.95 * df_gold$Exp_Act
df_gold["Exp_Act_1.05"] <- 1.05 * df_gold$Exp_Act 


gold_melt <- melt(df_gold[,c(1,2:6)], id="Date", value.name="Activity", variable.name="Isotope")

p <- ggplot( NULL )+geom_point(data = gold_melt, aes(x=Date,y=Activity, col=Isotope)) + geom_ribbon(data = df_gold, aes(x = Date, ymin = Exp_Act_0.95, ymax = Exp_Act_1.05), fill='blue', alpha=0.2) + geom_point(data = blue_melt, aes(x=Date,y=Activity, col=Isotope), shape=2) + theme_bw()
print(p)

ここに画像の説明を入力

私は R/ggplot2 の能力があまりありません。各放射性核種の測定された放射能が両方のデバイスで同じ色になるように最終的なプロットを表示したいと思います (つまり、Cs-137 は赤、99mTc は青)。グラフが異なる色をプロットするので、どうすればこれを行うことができますか?

また、伝説は喜ばしいものではありません。(i) Excel ヘッダーから取得される各核種のフォーマットは、Cs-137 から Cs.137 に変更されます。Cs-137、Tc-99m などをヘッダーとして使用するにはどうすればよいですか? (ii) 各放射性核種は凡例で複製されています - 各デバイスに 1 つ。最初のデータフレーム (df_gold) の凡例だけを表示したり、凡例にテキストを表示したりして、テキストの色をプロットのマーカーの色と一致させることは可能ですか?)

df_gold 構造体

structure(list(Date = structure(c(15708, 15709, 15712, 15713, 
15714, 15715, 15716, 15719, 15720, 15721, 15722, 15723, 15726, 
15727, 15729, 15730, 15733, 15734, 15735, 15736, 15740, 15741, 
15743, 15747, 15748, 15749, 15750, 15751, 15754, 15755, 15756, 
15757, 15758, 15761, 15762, 15764, 15765, 15768, 15769, 15770, 
15771, 15772, 15775, 15776, 15777, 15779, 15782, 15783, 15784, 
15785, 15786, 15789, 15790, 15791, 15792, 15797, 15798, 15799, 
15800), class = "Date"), Cs..137 = c(8.2, 8.1, 8.1, 8.1, 8.1, 
8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 
8.1, 8.1, 8.1, 8.2, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8, 8.2, 
8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 
8.1, 8.1, 8.1, 8, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 8.1, 
8.1, 8.1), In..111 = c(6.49, 6.47, 6.48, 6.43, 6.49, 6.51, 6.5, 
6.47, 6.48, 6.4, 6.48, 6.48, 6.48, 6.49, 6.49, 6.47, 6.48, 6.48, 
6.5, 6.47, 6.49, 6.55, 6.46, 6.49, 6.48, 6.48, 6.46, 6.48, 6.49, 
6.44, 6.49, 6.46, 6.45, 6.46, 6.46, 6.43, 6.49, 6.47, 6.45, 6.43, 
6.44, 6.44, 6.44, 6.46, 6.45, 6.47, 6.45, 6.43, 6.44, 6.47, 6.45, 
6.46, 6.45, 6.46, 6.39, 6.46, 6.44, 6.42, 6.41), I..123 = c(6.97, 
6.94, 6.96, 6.91, 6.92, 6.95, 6.93, 6.92, 6.93, 7, 6.97, 6.96, 
6.96, 6.94, 6.98, 6.97, 6.95, 6.95, 6.94, 6.96, 6.97, 7.01, 6.92, 
7, 6.98, 6.97, 6.91, 6.99, 6.95, 6.88, 6.96, 6.91, 6.91, 6.93, 
6.94, 6.94, 6.97, 6.93, 6.93, 6.93, 6.96, 6.94, 6.94, 6.92, 6.93, 
6.91, 6.93, 6.92, 6.92, 6.91, 6.91, 6.89, 6.92, 6.9, 6.9, 6.91, 
6.91, 6.9, 6.9), I..131 = c(10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 
10.5, 10.5, 10.5, 10.8, 10.5, 10.6, 10.5, 10.5, 10.5, 10.5, 10.5, 
10.5, 10.5, 10.5, 10.5, 10.6, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 
10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.4, 
10.5, 10.4, 10.5, 10.5, 10.5, 10.4, 10.5, 10.4, 10.4, 10.5, 10.4, 
10.4, 10.4, 10.4, 10.4, 10.3, 10.5, 10.5, 10.5, 10.6), Tc..99m = c(15, 
15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15.1, 15, 15, 15.1, 15, 
15, 15, 15, 15.1, 15, 15.1, 15, 15, 15, 15, 15, 15, 15, 15, 15, 
15, 15, 15, 15, 14.9, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 
14.9, 14.8, 14.9, 14.9, 14.9, 14.9, 15, 15, 14.8, 15, 15, 15, 
15), Exp_Act = c(8.294, 8.293, 8.292, 8.291, 8.291, 8.29, 8.29, 
8.288, 8.288, 8.287, 8.287, 8.286, 8.285, 8.284, 8.283, 8.283, 
8.281, 8.28, 8.28, 8.279, 8.277, 8.277, 8.276, 8.274, 8.273, 
8.273, 8.272, 8.272, 8.27, 8.27, 8.269, 8.269, 8.268, 8.266, 
8.266, 8.265, 8.264, 8.263, 8.262, 8.262, 8.261, 8.261, 8.259, 
8.259, 8.258, 8.257, 8.256, 8.255, 8.255, 8.254, 8.254, 8.252, 
8.251, 8.251, 8.25, 8.248, 8.247, 8.247, 8.246), Exp_Act_0.95 = c(7.8793, 
7.87835, 7.8774, 7.87645, 7.87645, 7.8755, 7.8755, 7.8736, 7.8736, 
7.87265, 7.87265, 7.8717, 7.87075, 7.8698, 7.86885, 7.86885, 
7.86695, 7.866, 7.866, 7.86505, 7.86315, 7.86315, 7.8622, 7.8603, 
7.85935, 7.85935, 7.8584, 7.8584, 7.8565, 7.8565, 7.85555, 7.85555, 
7.8546, 7.8527, 7.8527, 7.85175, 7.8508, 7.84985, 7.8489, 7.8489, 
7.84795, 7.84795, 7.84605, 7.84605, 7.8451, 7.84415, 7.8432, 
7.84225, 7.84225, 7.8413, 7.8413, 7.8394, 7.83845, 7.83845, 7.8375, 
7.8356, 7.83465, 7.83465, 7.8337), Exp_Act_1.05 = c(8.7087, 8.70765, 
8.7066, 8.70555, 8.70555, 8.7045, 8.7045, 8.7024, 8.7024, 8.70135, 
8.70135, 8.7003, 8.69925, 8.6982, 8.69715, 8.69715, 8.69505, 
8.694, 8.694, 8.69295, 8.69085, 8.69085, 8.6898, 8.6877, 8.68665, 
8.68665, 8.6856, 8.6856, 8.6835, 8.6835, 8.68245, 8.68245, 8.6814, 
8.6793, 8.6793, 8.67825, 8.6772, 8.67615, 8.6751, 8.6751, 8.67405, 
8.67405, 8.67195, 8.67195, 8.6709, 8.66985, 8.6688, 8.66775, 
8.66775, 8.6667, 8.6667, 8.6646, 8.66355, 8.66355, 8.6625, 8.6604, 
8.65935, 8.65935, 8.6583)), row.names = c(NA, -59L), .Names = c("Date", 
"Cs..137", "In..111", "I..123", "I..131", "Tc..99m", "Exp_Act", 
"Exp_Act_0.95", "Exp_Act_1.05"), class = "data.frame")

df_blue 構造体

structure(list(Date = structure(c(15790, 15791, 15792, 15797, 
15798, 15799, 15800), class = "Date"), Cs.137 = c(8.1, 8.2, 8.2, 
8.2, 8.2, 8.2, 8.2), I.123 = c(6.82, 6.85, 6.91, 6.84, 6.82, 
6.82, 6.83), I.131 = c(10.5, 10.6, 10.6, 10.5, 10.6, 10.6, 10.6
), In.111 = c(6.35, 6.45, 6.43, 6.37, 6.38, 6.4, 6.37), X99m.Tc = c(15, 
15, 15.1, 15.1, 15.1, 15.1, 15.1)), .Names = c("Date", "Cs.137", 
"I.123", "I.131", "In.111", "X99m.Tc"), row.names = c(NA, -7L
), class = "data.frame")

score 1 · Accepted Answer

私のアプローチは、両方のデータフレームをバインドしてから、デバイスの名前 (金または青) を含む新しい列を追加することです。

df<-rbind(gold_melt,blue_melt)
df$device<-rep(c("gold","blue"),c(nrow(gold_melt),nrow(blue_melt)))

recode()ライブラリの関数を使用して、car名前をIsotope本来あるべきように変更します。

df$Isotope<-recode(df$Isotope,"c('Cs..137','Cs.137')='Cs-137';
       c('I..123','I.123')='I-123';
       c('I..131','I.131')='I-131';
       c('In..111','In.111')='In-111'
       ;c('Tc..99m','X99m.Tc')='Tc-99m'")

geom_point()これで、新しいデータフレームを使用するための呼び出しが 1 回必要になります。shape=deviceデバイスごとに異なる形状を取得するためにも追加しました。

ggplot(NULL) + 
  geom_point(data=df,aes(x=Date,y=Activity, col=Isotope,shape=device))+
  geom_ribbon(data = df_gold, aes(x = Date, ymin = Exp_Act_0.95, ymax = Exp_Act_1.05), fill='blue', alpha=0.2)

ここに画像の説明を入力

score 1 · Accepted Answer

伝説を一緒に「融合」したい場合に備えて、Didzisの答えを構築してください：

df <- transform(df, device = factor(device, levels=unique(device)), 
                          grp = paste(Isotope, device, sep="_"))

require(RColorBrewer)
ggplot() + geom_point(data = df, aes(x = Date, y = Activity, 
                       colour=grp, shape = grp, fill=grp)) + 
geom_ribbon(data = df_gold, aes(x = Date, ymin = Exp_Act_0.95, 
                    ymax = Exp_Act_1.05), fill='blue', alpha=0.2) + 
scale_shape_manual("", values=rep(c(21,24), 5)) + 
scale_fill_manual("", values=rep(brewer.pal(5, "Set1"), each=2)) + 
scale_colour_manual("", values=rep(brewer.pal(5, "Set1"), each=2))

ここに画像の説明を入力

r - ggplot2 - 要素の色と凡例の調整

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