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一个 ggplot 中的多个 RColorBrewer 调色板

发布于 2025-01-15 14:17:57 字数 12520 浏览 4 评论 0原文

我正在尝试将几个从较低色调到较暗色调的 RColorBrewer 调色板放入一个 ggplot 中。但到目前为止我还没有成功，我发现我只能使用一个。 我的数据集data：

data <- wrapr::build_frame(
   "ID"  , "Treatment", "conc"    , "relabs"   |
     1   , "A"        , "NK"      , 0.9552     |
     2   , "A"        , "NK"      , 1.016      |
     3   , "A"        , "NK"      , 1.069      |
     4   , "A"        , "NK"      , 1.029      |
     5   , "A"        , "NK"      , 0.9992     |
     6   , "A"        , "NK"      , 1.036      |
     7   , "A"        , "NK"      , 0.9867     |
     8   , "A"        , "NK"      , 0.9082     |
     9   , "A"        , "100 µM"  , 0.9549     |
     10  , "A"        , "100 µM"  , 0.9016     |
     11  , "A"        , "100 µM"  , 0.9058     |
     12  , "A"        , "100 µM"  , 0.9029     |
     13  , "A"        , "100 µM"  , 0.8595     |
     14  , "A"        , "100 µM"  , 0.8643     |
     15  , "A"        , "100 µM"  , 0.8687     |
     16  , "A"        , "100 µM"  , 0.9319     |
     17  , "A"        , "10 µM"   , 0.8128     |
     18  , "A"        , "10 µM"   , 0.805      |
     19  , "A"        , "10 µM"   , 0.7765     |
     20  , "A"        , "10 µM"   , 0.8065     |
     21  , "A"        , "10 µM"   , 0.8153     |
     22  , "A"        , "10 µM"   , 0.8045     |
     23  , "A"        , "10 µM"   , 0.7827     |
     24  , "A"        , "10 µM"   , 0.8017     |
     25  , "A"        , "10 µM X" , 0.00229    |
     26  , "A"        , "10 µM X" , 0.0002057  |
     27  , "A"        , "10 µM X" , -0.01033   |
     28  , "A"        , "10 µM X" , -0.003444  |
     29  , "A"        , "10 µM X" , -0.01401   |
     30  , "A"        , "10 µM X" , -0.007581  |
     31  , "A"        , "10 µM X" , -0.01063   |
     32  , "A"        , "10 µM X" , -0.01012   |
     33  , "A"        , "100 µM Y", 0.005991   |
     34  , "A"        , "100 µM Y", 0.01108    |
     35  , "A"        , "100 µM Y", 0.003925   |
     36  , "A"        , "100 µM Y", 0.02162    |
     37  , "A"        , "100 µM Y", 0.02916    |
     38  , "A"        , "100 µM Y", 0.01679    |
     39  , "A"        , "100 µM Y", 0.03044    |
     40  , "A"        , "100 µM Y", 0.01541    |
     41  , "B"        , "NK"      , 1.038      |
     42  , "B"        , "NK"      , 0.9651     |
     43  , "B"        , "NK"      , 0.9948     |
     44  , "B"        , "NK"      , 0.9688     |
     45  , "B"        , "NK"      , 0.9727     |
     46  , "B"        , "NK"      , 0.9985     |
     47  , "B"        , "NK"      , 1.035      |
     48  , "B"        , "NK"      , 1.027      |
     49  , "B"        , "100 µM"  , 0.3466     |
     50  , "B"        , "100 µM"  , 0.3429     |
     51  , "B"        , "100 µM"  , 0.3131     |
     52  , "B"        , "100 µM"  , 0.3302     |
     53  , "B"        , "100 µM"  , 0.3204     |
     54  , "B"        , "100 µM"  , 0.3265     |
     55  , "B"        , "100 µM"  , 0.3238     |
     56  , "B"        , "100 µM"  , 0.3425     |
     57  , "B"        , "10 µM"   , 0.7703     |
     58  , "B"        , "10 µM"   , 0.7484     |
     59  , "B"        , "10 µM"   , 0.76       |
     60  , "B"        , "10 µM"   , 0.7915     |
     61  , "B"        , "10 µM"   , 0.7664     |
     62  , "B"        , "10 µM"   , 0.7407     |
     63  , "B"        , "10 µM"   , 0.7726     |
     64  , "B"        , "10 µM"   , 0.8036     |
     65  , "B"        , "10 µM X" , -0.003965  |
     66  , "B"        , "10 µM X" , -0.001291  |
     67  , "B"        , "10 µM X" , 0.002101   |
     68  , "B"        , "10 µM X" , -0.001548  |
     69  , "B"        , "10 µM X" , 0.004782   |
     70  , "B"        , "10 µM X" , -0.006738  |
     71  , "B"        , "10 µM X" , -0.008429  |
     72  , "B"        , "10 µM X" , -0.009955  |
     73  , "B"        , "100 µM Y", 0.01063    |
     74  , "B"        , "100 µM Y", 0.008139   |
     75  , "B"        , "100 µM Y", 0.01149    |
     76  , "B"        , "100 µM Y", 0.01182    |
     77  , "B"        , "100 µM Y", 0.01418    |
     78  , "B"        , "100 µM Y", 0.009189   |
     79  , "B"        , "100 µM Y", 0.007849   |
     80  , "B"        , "100 µM Y", 0.0171     |
     81  , "C"        , "NK"      , 0.9342     |
     82  , "C"        , "NK"      , 1.033      |
     83  , "C"        , "NK"      , 0.9425     |
     84  , "C"        , "NK"      , 1          |
     85  , "C"        , "NK"      , 1.082      |
     86  , "C"        , "NK"      , 0.9697     |
     87  , "C"        , "NK"      , 1.069      |
     88  , "C"        , "NK"      , 0.9684     |
     89  , "C"        , "100 µM"  , 1.31       |
     90  , "C"        , "100 µM"  , 1.25       |
     91  , "C"        , "100 µM"  , 1.305      |
     92  , "C"        , "100 µM"  , 1.28       |
     93  , "C"        , "100 µM"  , 1.293      |
     94  , "C"        , "100 µM"  , 1.256      |
     95  , "C"        , "100 µM"  , 1.35       |
     96  , "C"        , "100 µM"  , 1.219      |
     97  , "C"        , "10 µM"   , 0.9741     |
     98  , "C"        , "10 µM"   , 1.066      |
     99  , "C"        , "10 µM"   , 0.9849     |
     100 , "C"        , "10 µM"   , 0.9737     |
     101 , "C"        , "10 µM"   , 0.9619     |
     102 , "C"        , "10 µM"   , 0.989      |
     103 , "C"        , "10 µM"   , 0.9821     |
     104 , "C"        , "10 µM"   , 1.026      |
     105 , "C"        , "10 µM X" , 0.137      |
     106 , "C"        , "10 µM X" , 0.1283     |
     107 , "C"        , "10 µM X" , 0.09757    |
     108 , "C"        , "10 µM X" , 0.1522     |
     109 , "C"        , "10 µM X" , 0.1411     |
     110 , "C"        , "10 µM X" , 0.1377     |
     111 , "C"        , "10 µM X" , 0.1222     |
     112 , "C"        , "10 µM X" , 0.1209     |
     113 , "C"        , "100 µM Y", -0.00434   |
     114 , "C"        , "100 µM Y", -0.009208  |
     115 , "C"        , "100 µM Y", 0.01106    |
     116 , "C"        , "100 µM Y", -0.0005099 |
     117 , "C"        , "100 µM Y", 0.001142   |
     118 , "C"        , "100 µM Y", -0.002433  |
     119 , "C"        , "100 µM Y", 0.009931   |
     120 , "C"        , "100 µM Y", -0.01025   |
     121 , "D"        , "NK"      , 1.046      |
     122 , "D"        , "NK"      , 1.032      |
     123 , "D"        , "NK"      , 0.9685     |
     124 , "D"        , "NK"      , 0.9981     |
     125 , "D"        , "NK"      , 1.005      |
     126 , "D"        , "NK"      , 1.001      |
     127 , "D"        , "NK"      , 0.9329     |
     128 , "D"        , "NK"      , 1.017      |
     129 , "D"        , "100 µM"  , 0.1012     |
     130 , "D"        , "100 µM"  , 0.1177     |
     131 , "D"        , "100 µM"  , 0.09581    |
     132 , "D"        , "100 µM"  , 0.09372    |
     133 , "D"        , "100 µM"  , 0.1143     |
     134 , "D"        , "100 µM"  , 0.1019     |
     135 , "D"        , "100 µM"  , 0.08676    |
     136 , "D"        , "100 µM"  , 0.09314    |
     137 , "D"        , "10 µM"   , 0.461      |
     138 , "D"        , "10 µM"   , 0.4717     |
     139 , "D"        , "10 µM"   , 0.4536     |
     140 , "D"        , "10 µM"   , 0.487      |
     141 , "D"        , "10 µM"   , 0.5137     |
     142 , "D"        , "10 µM"   , 0.4936     |
     143 , "D"        , "10 µM"   , 0.4574     |
     144 , "D"        , "10 µM"   , 0.4904     |
     145 , "D"        , "10 µM X" , -0.02192   |
     146 , "D"        , "10 µM X" , -0.02502   |
     147 , "D"        , "10 µM X" , -0.0238    |
     148 , "D"        , "10 µM X" , -0.01711   |
     149 , "D"        , "10 µM X" , -0.02345   |
     150 , "D"        , "10 µM X" , -0.01186   |
     151 , "D"        , "10 µM X" , -0.004447  |
     152 , "D"        , "10 µM X" , -0.01209   |
     153 , "D"        , "100 µM Y", -0.01495   |
     154 , "D"        , "100 µM Y", -0.01741   |
     155 , "D"        , "100 µM Y", -0.0101    |
     156 , "D"        , "100 µM Y", -0.007783  |
     157 , "D"        , "100 µM Y", 0.004533   |
     158 , "D"        , "100 µM Y", -0.01373   |
     159 , "D"        , "100 µM Y", -0.02207   |
     160 , "D"        , "100 µM Y", -0.01263   |
     161 , "E"        , "NK"      , 1.03       |
     162 , "E"        , "NK"      , 0.9683     |
     163 , "E"        , "NK"      , 0.9915     |
     164 , "E"        , "NK"      , 0.9887     |
     165 , "E"        , "NK"      , 1.019      |
     166 , "E"        , "NK"      , 1.007      |
     167 , "E"        , "NK"      , 0.9909     |
     168 , "E"        , "NK"      , 1.004      |
     169 , "E"        , "100 µM"  , 0.7583     |
     170 , "E"        , "100 µM"  , 0.8541     |
     171 , "E"        , "100 µM"  , 0.822      |
     172 , "E"        , "100 µM"  , 0.8506     |
     173 , "E"        , "100 µM"  , 0.8122     |
     174 , "E"        , "100 µM"  , 0.8442     |
     175 , "E"        , "100 µM"  , 0.831      |
     176 , "E"        , "100 µM"  , 0.8153     |
     177 , "E"        , "10 µM"   , 0.9815     |
     178 , "E"        , "10 µM"   , 0.9623     |
     179 , "E"        , "10 µM"   , 0.97       |
     180 , "E"        , "10 µM"   , 0.9798     |
     181 , "E"        , "10 µM"   , 0.967      |
     182 , "E"        , "10 µM"   , 0.9825     |
     183 , "E"        , "10 µM"   , 1.01       |
     184 , "E"        , "10 µM"   , 0.9284     |
     185 , "E"        , "10 µM X" , 0.2576     |
     186 , "E"        , "10 µM X" , 0.2454     |
     187 , "E"        , "10 µM X" , 0.2467     |
     188 , "E"        , "10 µM X" , 0.2544     |
     189 , "E"        , "100 µM Y", 0.005576   |
     190 , "E"        , "100 µM Y", 0.01025    |
     191 , "E"        , "100 µM Y", 0.00863    |
     192 , "E"        , "100 µM Y", 0.004152   )

data_summary <-
data %>%
  group_by(Treatment, conc) %>%
  dplyr::summarize(relabs_avg            = mean(relabs),
            relabs_sd             = sd(relabs),
            relabs_median         = median(relabs),
            relabs_mad            = mad(relabs),
            relabs_q1             = quantile(relabs, probs = c(0.25)),
            relabs_q3             = quantile(relabs, probs = c(0.75)),
            size                  = n()) %>%
  dplyr::mutate(across(where(is.numeric), ~round(., digits = 3)))

data_summary

alpha <- 0.05

data_full <-
data %>% 
    group_by(Treatment, conc) %>% 
   dplyr:: summarize(mean = mean(relabs),
                     median = median(relabs),
                     lower = mean(relabs) - qt(1- alpha/2, (n() - 1))*sd(relabs)/sqrt(n()),
                     upper = mean(relabs) + qt(1- alpha/2, (n() - 1))*sd(relabs)/sqrt(n()))

data_full

df<- merge(data_summary, data_full)
df

df_t_test <-
df_full  %>% 
  group_by(Treatment, conc) %>% 
  do(tidy(t.test(.$relabs, 
                 mu = 1     , 
                 alt = "less",
                 conf.level = 0.95, var.equal = FALSE)))
df_t_test

df_full<- merge(data, df)
df_full

df_full<- merge(data_full, df_t_test)
df_full

我当前使用的：

df_full$Label <- NA
df_full$Label[df_full$mean <0]<-'ND'
df_full$Label[df_full$p.value<0.001 & is.na(df_full$Label)]<-'***'
df_full$Label[df_full$p.value<0.01 & is.na(df_full$Label)]<-'**'
df_full$Label[df_full$p.value<0.05 & is.na(df_full$Label)]<-'*'

breaks_y =c(0, 0.25, 0.5, 0.75, 1, 1.25, 1.5)

df_full$Label <- NA
df_full$Label[df_full$mean <0]<-'ND'
df_full$Label[df_full$p.value<0.001 & is.na(df_full$Label)]<-'***'
df_full$Label[df_full$p.value<0.01 & is.na(df_full$Label)]<-'**'
df_full$Label[df_full$p.value<0.05 & is.na(df_full$Label)]<-'*'

plot <- 
ggplot(df_full, aes(x = factor (Treatment, level = c("A","B", "C", "D", "E")), y = mean, fill = conc)) +
geom_col(color = "black",  position = position_dodge(0.8), width = 0.7) +
geom_errorbar(aes(ymax = upper, ymin = lower), width = 0.27, position = position_dodge(0.8), color = "black", size = 0.7) +
geom_text(aes(label = Label, group = conc),size = 3, position = position_dodge(width =0.8), color = "black", vjust =-2) +
labs(x = "Treatment", y = "XXX", title = "YYY ", color = "ZZZ", fill = "ZZZ") +
scale_y_continuous(limits = c(0, 1.5), breaks = breaks_y) +
  theme_bw() +
  theme(axis.text = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face ="bold"),
    axis.title.x = element_text(size = 12, face ="bold"))

plot + scale_fill_brewer(palette = "Blues")

有没有办法将调色板“蓝色”放在A Treatment上，将“灰色”放在B Treatment上等等？或者某种我找不到的手动方法？

原文

I am trying to put Several RColorBrewer Palettes that goes from lower tones to darker tones in one ggplot. But so far I've been unsuccessful and I've found that I can only use one.
My data set data:

data <- wrapr::build_frame(
   "ID"  , "Treatment", "conc"    , "relabs"   |
     1   , "A"        , "NK"      , 0.9552     |
     2   , "A"        , "NK"      , 1.016      |
     3   , "A"        , "NK"      , 1.069      |
     4   , "A"        , "NK"      , 1.029      |
     5   , "A"        , "NK"      , 0.9992     |
     6   , "A"        , "NK"      , 1.036      |
     7   , "A"        , "NK"      , 0.9867     |
     8   , "A"        , "NK"      , 0.9082     |
     9   , "A"        , "100 µM"  , 0.9549     |
     10  , "A"        , "100 µM"  , 0.9016     |
     11  , "A"        , "100 µM"  , 0.9058     |
     12  , "A"        , "100 µM"  , 0.9029     |
     13  , "A"        , "100 µM"  , 0.8595     |
     14  , "A"        , "100 µM"  , 0.8643     |
     15  , "A"        , "100 µM"  , 0.8687     |
     16  , "A"        , "100 µM"  , 0.9319     |
     17  , "A"        , "10 µM"   , 0.8128     |
     18  , "A"        , "10 µM"   , 0.805      |
     19  , "A"        , "10 µM"   , 0.7765     |
     20  , "A"        , "10 µM"   , 0.8065     |
     21  , "A"        , "10 µM"   , 0.8153     |
     22  , "A"        , "10 µM"   , 0.8045     |
     23  , "A"        , "10 µM"   , 0.7827     |
     24  , "A"        , "10 µM"   , 0.8017     |
     25  , "A"        , "10 µM X" , 0.00229    |
     26  , "A"        , "10 µM X" , 0.0002057  |
     27  , "A"        , "10 µM X" , -0.01033   |
     28  , "A"        , "10 µM X" , -0.003444  |
     29  , "A"        , "10 µM X" , -0.01401   |
     30  , "A"        , "10 µM X" , -0.007581  |
     31  , "A"        , "10 µM X" , -0.01063   |
     32  , "A"        , "10 µM X" , -0.01012   |
     33  , "A"        , "100 µM Y", 0.005991   |
     34  , "A"        , "100 µM Y", 0.01108    |
     35  , "A"        , "100 µM Y", 0.003925   |
     36  , "A"        , "100 µM Y", 0.02162    |
     37  , "A"        , "100 µM Y", 0.02916    |
     38  , "A"        , "100 µM Y", 0.01679    |
     39  , "A"        , "100 µM Y", 0.03044    |
     40  , "A"        , "100 µM Y", 0.01541    |
     41  , "B"        , "NK"      , 1.038      |
     42  , "B"        , "NK"      , 0.9651     |
     43  , "B"        , "NK"      , 0.9948     |
     44  , "B"        , "NK"      , 0.9688     |
     45  , "B"        , "NK"      , 0.9727     |
     46  , "B"        , "NK"      , 0.9985     |
     47  , "B"        , "NK"      , 1.035      |
     48  , "B"        , "NK"      , 1.027      |
     49  , "B"        , "100 µM"  , 0.3466     |
     50  , "B"        , "100 µM"  , 0.3429     |
     51  , "B"        , "100 µM"  , 0.3131     |
     52  , "B"        , "100 µM"  , 0.3302     |
     53  , "B"        , "100 µM"  , 0.3204     |
     54  , "B"        , "100 µM"  , 0.3265     |
     55  , "B"        , "100 µM"  , 0.3238     |
     56  , "B"        , "100 µM"  , 0.3425     |
     57  , "B"        , "10 µM"   , 0.7703     |
     58  , "B"        , "10 µM"   , 0.7484     |
     59  , "B"        , "10 µM"   , 0.76       |
     60  , "B"        , "10 µM"   , 0.7915     |
     61  , "B"        , "10 µM"   , 0.7664     |
     62  , "B"        , "10 µM"   , 0.7407     |
     63  , "B"        , "10 µM"   , 0.7726     |
     64  , "B"        , "10 µM"   , 0.8036     |
     65  , "B"        , "10 µM X" , -0.003965  |
     66  , "B"        , "10 µM X" , -0.001291  |
     67  , "B"        , "10 µM X" , 0.002101   |
     68  , "B"        , "10 µM X" , -0.001548  |
     69  , "B"        , "10 µM X" , 0.004782   |
     70  , "B"        , "10 µM X" , -0.006738  |
     71  , "B"        , "10 µM X" , -0.008429  |
     72  , "B"        , "10 µM X" , -0.009955  |
     73  , "B"        , "100 µM Y", 0.01063    |
     74  , "B"        , "100 µM Y", 0.008139   |
     75  , "B"        , "100 µM Y", 0.01149    |
     76  , "B"        , "100 µM Y", 0.01182    |
     77  , "B"        , "100 µM Y", 0.01418    |
     78  , "B"        , "100 µM Y", 0.009189   |
     79  , "B"        , "100 µM Y", 0.007849   |
     80  , "B"        , "100 µM Y", 0.0171     |
     81  , "C"        , "NK"      , 0.9342     |
     82  , "C"        , "NK"      , 1.033      |
     83  , "C"        , "NK"      , 0.9425     |
     84  , "C"        , "NK"      , 1          |
     85  , "C"        , "NK"      , 1.082      |
     86  , "C"        , "NK"      , 0.9697     |
     87  , "C"        , "NK"      , 1.069      |
     88  , "C"        , "NK"      , 0.9684     |
     89  , "C"        , "100 µM"  , 1.31       |
     90  , "C"        , "100 µM"  , 1.25       |
     91  , "C"        , "100 µM"  , 1.305      |
     92  , "C"        , "100 µM"  , 1.28       |
     93  , "C"        , "100 µM"  , 1.293      |
     94  , "C"        , "100 µM"  , 1.256      |
     95  , "C"        , "100 µM"  , 1.35       |
     96  , "C"        , "100 µM"  , 1.219      |
     97  , "C"        , "10 µM"   , 0.9741     |
     98  , "C"        , "10 µM"   , 1.066      |
     99  , "C"        , "10 µM"   , 0.9849     |
     100 , "C"        , "10 µM"   , 0.9737     |
     101 , "C"        , "10 µM"   , 0.9619     |
     102 , "C"        , "10 µM"   , 0.989      |
     103 , "C"        , "10 µM"   , 0.9821     |
     104 , "C"        , "10 µM"   , 1.026      |
     105 , "C"        , "10 µM X" , 0.137      |
     106 , "C"        , "10 µM X" , 0.1283     |
     107 , "C"        , "10 µM X" , 0.09757    |
     108 , "C"        , "10 µM X" , 0.1522     |
     109 , "C"        , "10 µM X" , 0.1411     |
     110 , "C"        , "10 µM X" , 0.1377     |
     111 , "C"        , "10 µM X" , 0.1222     |
     112 , "C"        , "10 µM X" , 0.1209     |
     113 , "C"        , "100 µM Y", -0.00434   |
     114 , "C"        , "100 µM Y", -0.009208  |
     115 , "C"        , "100 µM Y", 0.01106    |
     116 , "C"        , "100 µM Y", -0.0005099 |
     117 , "C"        , "100 µM Y", 0.001142   |
     118 , "C"        , "100 µM Y", -0.002433  |
     119 , "C"        , "100 µM Y", 0.009931   |
     120 , "C"        , "100 µM Y", -0.01025   |
     121 , "D"        , "NK"      , 1.046      |
     122 , "D"        , "NK"      , 1.032      |
     123 , "D"        , "NK"      , 0.9685     |
     124 , "D"        , "NK"      , 0.9981     |
     125 , "D"        , "NK"      , 1.005      |
     126 , "D"        , "NK"      , 1.001      |
     127 , "D"        , "NK"      , 0.9329     |
     128 , "D"        , "NK"      , 1.017      |
     129 , "D"        , "100 µM"  , 0.1012     |
     130 , "D"        , "100 µM"  , 0.1177     |
     131 , "D"        , "100 µM"  , 0.09581    |
     132 , "D"        , "100 µM"  , 0.09372    |
     133 , "D"        , "100 µM"  , 0.1143     |
     134 , "D"        , "100 µM"  , 0.1019     |
     135 , "D"        , "100 µM"  , 0.08676    |
     136 , "D"        , "100 µM"  , 0.09314    |
     137 , "D"        , "10 µM"   , 0.461      |
     138 , "D"        , "10 µM"   , 0.4717     |
     139 , "D"        , "10 µM"   , 0.4536     |
     140 , "D"        , "10 µM"   , 0.487      |
     141 , "D"        , "10 µM"   , 0.5137     |
     142 , "D"        , "10 µM"   , 0.4936     |
     143 , "D"        , "10 µM"   , 0.4574     |
     144 , "D"        , "10 µM"   , 0.4904     |
     145 , "D"        , "10 µM X" , -0.02192   |
     146 , "D"        , "10 µM X" , -0.02502   |
     147 , "D"        , "10 µM X" , -0.0238    |
     148 , "D"        , "10 µM X" , -0.01711   |
     149 , "D"        , "10 µM X" , -0.02345   |
     150 , "D"        , "10 µM X" , -0.01186   |
     151 , "D"        , "10 µM X" , -0.004447  |
     152 , "D"        , "10 µM X" , -0.01209   |
     153 , "D"        , "100 µM Y", -0.01495   |
     154 , "D"        , "100 µM Y", -0.01741   |
     155 , "D"        , "100 µM Y", -0.0101    |
     156 , "D"        , "100 µM Y", -0.007783  |
     157 , "D"        , "100 µM Y", 0.004533   |
     158 , "D"        , "100 µM Y", -0.01373   |
     159 , "D"        , "100 µM Y", -0.02207   |
     160 , "D"        , "100 µM Y", -0.01263   |
     161 , "E"        , "NK"      , 1.03       |
     162 , "E"        , "NK"      , 0.9683     |
     163 , "E"        , "NK"      , 0.9915     |
     164 , "E"        , "NK"      , 0.9887     |
     165 , "E"        , "NK"      , 1.019      |
     166 , "E"        , "NK"      , 1.007      |
     167 , "E"        , "NK"      , 0.9909     |
     168 , "E"        , "NK"      , 1.004      |
     169 , "E"        , "100 µM"  , 0.7583     |
     170 , "E"        , "100 µM"  , 0.8541     |
     171 , "E"        , "100 µM"  , 0.822      |
     172 , "E"        , "100 µM"  , 0.8506     |
     173 , "E"        , "100 µM"  , 0.8122     |
     174 , "E"        , "100 µM"  , 0.8442     |
     175 , "E"        , "100 µM"  , 0.831      |
     176 , "E"        , "100 µM"  , 0.8153     |
     177 , "E"        , "10 µM"   , 0.9815     |
     178 , "E"        , "10 µM"   , 0.9623     |
     179 , "E"        , "10 µM"   , 0.97       |
     180 , "E"        , "10 µM"   , 0.9798     |
     181 , "E"        , "10 µM"   , 0.967      |
     182 , "E"        , "10 µM"   , 0.9825     |
     183 , "E"        , "10 µM"   , 1.01       |
     184 , "E"        , "10 µM"   , 0.9284     |
     185 , "E"        , "10 µM X" , 0.2576     |
     186 , "E"        , "10 µM X" , 0.2454     |
     187 , "E"        , "10 µM X" , 0.2467     |
     188 , "E"        , "10 µM X" , 0.2544     |
     189 , "E"        , "100 µM Y", 0.005576   |
     190 , "E"        , "100 µM Y", 0.01025    |
     191 , "E"        , "100 µM Y", 0.00863    |
     192 , "E"        , "100 µM Y", 0.004152   )

data_summary <-
data %>%
  group_by(Treatment, conc) %>%
  dplyr::summarize(relabs_avg            = mean(relabs),
            relabs_sd             = sd(relabs),
            relabs_median         = median(relabs),
            relabs_mad            = mad(relabs),
            relabs_q1             = quantile(relabs, probs = c(0.25)),
            relabs_q3             = quantile(relabs, probs = c(0.75)),
            size                  = n()) %>%
  dplyr::mutate(across(where(is.numeric), ~round(., digits = 3)))

data_summary

alpha <- 0.05

data_full <-
data %>% 
    group_by(Treatment, conc) %>% 
   dplyr:: summarize(mean = mean(relabs),
                     median = median(relabs),
                     lower = mean(relabs) - qt(1- alpha/2, (n() - 1))*sd(relabs)/sqrt(n()),
                     upper = mean(relabs) + qt(1- alpha/2, (n() - 1))*sd(relabs)/sqrt(n()))

data_full

df<- merge(data_summary, data_full)
df

df_t_test <-
df_full  %>% 
  group_by(Treatment, conc) %>% 
  do(tidy(t.test(.$relabs, 
                 mu = 1     , 
                 alt = "less",
                 conf.level = 0.95, var.equal = FALSE)))
df_t_test

df_full<- merge(data, df)
df_full

df_full<- merge(data_full, df_t_test)
df_full

What I'm currently using:

df_full$Label <- NA
df_full$Label[df_full$mean <0]<-'ND'
df_full$Label[df_full$p.value<0.001 & is.na(df_full$Label)]<-'***'
df_full$Label[df_full$p.value<0.01 & is.na(df_full$Label)]<-'**'
df_full$Label[df_full$p.value<0.05 & is.na(df_full$Label)]<-'*'

breaks_y =c(0, 0.25, 0.5, 0.75, 1, 1.25, 1.5)

df_full$Label <- NA
df_full$Label[df_full$mean <0]<-'ND'
df_full$Label[df_full$p.value<0.001 & is.na(df_full$Label)]<-'***'
df_full$Label[df_full$p.value<0.01 & is.na(df_full$Label)]<-'**'
df_full$Label[df_full$p.value<0.05 & is.na(df_full$Label)]<-'*'

plot <- 
ggplot(df_full, aes(x = factor (Treatment, level = c("A","B", "C", "D", "E")), y = mean, fill = conc)) +
geom_col(color = "black",  position = position_dodge(0.8), width = 0.7) +
geom_errorbar(aes(ymax = upper, ymin = lower), width = 0.27, position = position_dodge(0.8), color = "black", size = 0.7) +
geom_text(aes(label = Label, group = conc),size = 3, position = position_dodge(width =0.8), color = "black", vjust =-2) +
labs(x = "Treatment", y = "XXX", title = "YYY ", color = "ZZZ", fill = "ZZZ") +
scale_y_continuous(limits = c(0, 1.5), breaks = breaks_y) +
  theme_bw() +
  theme(axis.text = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face ="bold"),
    axis.title.x = element_text(size = 12, face ="bold"))

plot + scale_fill_brewer(palette = "Blues")

Is there a way to put color palette "Blues" on A Treatment, "Greys" on B Treatment and so on? Or some kind of manual way to do that I wasn't able to find?

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蓝颜夕 2025-01-22 14:17:57

您确实必须创建自己的组合 Brewer 调色板，并将其应用于两个分组变量（conc 和 Treatment）的交互

fills <- c(sapply(c("Blues", "Greys", "Purples", "Oranges", "Greens"),
                function(x) brewer.pal(5, x)))

ggplot(df_full, aes(x = factor (Treatment, level = c("A","B", "C", "D", "E")), 
                    y = mean, fill = interaction(conc, Treatment))) +
geom_col(color = "black",  position = position_dodge(0.8), width = 0.7) +
geom_errorbar(aes(ymax = upper, ymin = lower), width = 0.27, 
              position = position_dodge(0.8), color = "black", size = 0.7) +
geom_text(aes(label = Label, group = conc),size = 3, 
          position = position_dodge(width =0.8), color = "black", vjust =-2) +
labs(x = "Treatment", y = "XXX", title = "YYY ", color = "ZZZ", fill = "ZZZ") +
scale_y_continuous(limits = c(0, 1.5), breaks = breaks_y) +
  theme_bw() +
  theme(axis.text = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face ="bold"),
    axis.title.x = element_text(size = 12, face ="bold")) +
  scale_fill_manual(values = fills)

在此输入图片描述

的当然，这里的问题是你的传奇现在很笨拙。然而，对于离散色标，很难解决这个问题。

实现类似效果的最简洁方法可能是根据处理进行填充，并使用 alpha 比例表示 conc

ggplot(df_full, aes(x = factor (Treatment, level = c("A","B", "C", "D", "E")), 
                    y = mean, fill = Treatment, alpha = conc)) +
geom_col(color = "black",  position = position_dodge(0.8), width = 0.7) +
geom_errorbar(aes(ymax = upper, ymin = lower), width = 0.27, 
              position = position_dodge(0.8), color = "black", size = 0.7) +
geom_text(aes(label = Label, group = conc),size = 3, 
          position = position_dodge(width =0.8), color = "black", vjust =-2) +
labs(x = "Treatment", y = "XXX", title = "YYY ", color = "ZZZ", fill = "ZZZ") +
scale_y_continuous(limits = c(0, 1.5), breaks = breaks_y) +
  theme_bw() +
  theme(axis.text = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face ="bold"),
    axis.title.x = element_text(size = 12, face ="bold")) +
  scale_fill_brewer(palette ="Spectral" ) +
  guides(fill = guide_none())

在此处输入图像描述

You would really have to create your own combined Brewer palette and apply it to the interaction of the two grouping variables (conc and Treatment)

fills <- c(sapply(c("Blues", "Greys", "Purples", "Oranges", "Greens"),
                function(x) brewer.pal(5, x)))

ggplot(df_full, aes(x = factor (Treatment, level = c("A","B", "C", "D", "E")), 
                    y = mean, fill = interaction(conc, Treatment))) +
geom_col(color = "black",  position = position_dodge(0.8), width = 0.7) +
geom_errorbar(aes(ymax = upper, ymin = lower), width = 0.27, 
              position = position_dodge(0.8), color = "black", size = 0.7) +
geom_text(aes(label = Label, group = conc),size = 3, 
          position = position_dodge(width =0.8), color = "black", vjust =-2) +
labs(x = "Treatment", y = "XXX", title = "YYY ", color = "ZZZ", fill = "ZZZ") +
scale_y_continuous(limits = c(0, 1.5), breaks = breaks_y) +
  theme_bw() +
  theme(axis.text = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face ="bold"),
    axis.title.x = element_text(size = 12, face ="bold")) +
  scale_fill_manual(values = fills)

enter image description here

Of course, the problem here is that your legend is now quite unwieldy. However, for a discrete color scale, it's difficult to get round this.

Possibly the cleanest way to achieve a similar effect is to fill according to treatment and use the alpha scale for conc

ggplot(df_full, aes(x = factor (Treatment, level = c("A","B", "C", "D", "E")), 
                    y = mean, fill = Treatment, alpha = conc)) +
geom_col(color = "black",  position = position_dodge(0.8), width = 0.7) +
geom_errorbar(aes(ymax = upper, ymin = lower), width = 0.27, 
              position = position_dodge(0.8), color = "black", size = 0.7) +
geom_text(aes(label = Label, group = conc),size = 3, 
          position = position_dodge(width =0.8), color = "black", vjust =-2) +
labs(x = "Treatment", y = "XXX", title = "YYY ", color = "ZZZ", fill = "ZZZ") +
scale_y_continuous(limits = c(0, 1.5), breaks = breaks_y) +
  theme_bw() +
  theme(axis.text = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face ="bold"),
    axis.title.x = element_text(size = 12, face ="bold")) +
  scale_fill_brewer(palette ="Spectral" ) +
  guides(fill = guide_none())

enter image description here

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