Question

我需要有关R中情节的帮助。

我得到一个带有源“ data_small”的图。我现在有第二个来源“ data_big”，我想将其覆盖在同一图中。

两个来源都有“ risk_datatheft_likelihood”和“ risk_datatheft_damage”列

有什么主意吗？第二个来源应以不同的颜色显示。

"query": {
        "range" : {
            "salary" : {
                "gte" : "1000",
                "lt" :  "3000"
            }
        }
    }

Answer 1

我认为您可能只想将两个数据集的行与一个新变量绑定以区别它们，就像这样：

bind_rows(mutate(data_big, size="big"),
          mutate(data_small, size="small"))

这会产生类似以下内容的小标题

：

# A tibble: 10 x 3
   risk_datatheft_likelihood risk_datatheft_damage size 
   <chr>                                     <int> <chr>
 1 B                                             3 big  
 2 B                                             2 big  
 3 C                                             4 big  
 4 A                                             1 big  
 5 D                                             5 big  
 6 C                                             4 small
 7 A                                             2 small
 8 B                                             3 small
 9 C                                             4 small
10 D                                             1 small

现在，您可以使用size作为颜色美感了：

bind_rows(mutate(data_big, size="big"),
          mutate(data_small, size="small")) %>%
    ggplot(aes(risk_datatheft_likelihood, risk_datatheft_damage)) +
    geom_jitter(aes(col=size))

这是完整的代码，后跟成绩单，因为您在重现我的解决方案时遇到困难。

完整代码：

library(tidyverse)

data_big <- read_delim("data_big.csv", delim=";")
data_small <- read_delim("data_small.csv", delim=";")

# run the plot using one multiline command:
bind_rows(mutate(data_big, size="big"),
          mutate(data_small, size="small")) %>%
    ggplot(aes(risk_datatheft_likelihood, risk_datatheft_damage)) +
    geom_jitter(aes(col=size))

# alternatively, save the combined data first
data_combined <- bind_rows(mutate(data_big, size="big"),
                           mutate(data_small, size="small"))

# and run the plot separately
ggplot(data_combined,
       aes(risk_datatheft_likelihood, risk_datatheft_damage)) +
    geom_jitter(aes(col=size)) +
    labs(x = "damage", y = "likelihood",
         title = "Risk Map", subtitle = "Datatheft") +
    theme_classic() +
    theme(legend.position="bottom") +
    geom_hline(yintercept = 0.5, color="red") +
    geom_hline(yintercept = 1.5) +
    geom_hline(yintercept = 2.5) +
    geom_hline(yintercept = 3.5) +
    geom_hline(yintercept = 4.5) +
    geom_vline(xintercept = 0.5, color="red") +
    geom_vline(xintercept = 1.5) +
    geom_vline(xintercept = 2.5) +
    geom_vline(xintercept = 3.5) +
    geom_vline(xintercept = 4.5)

和完整的成绩单：

> library(tidyverse)
[... stuff about loading tidyverse ...]
> 
> data_big <- read_delim("data_big.csv", delim=";")
Parsed with column specification:
cols(
  risk_datatheft_likelihood = col_character(),
  risk_datatheft_damage = col_integer()
)
> data_small <- read_delim("data_small.csv", delim=";")
Parsed with column specification:
cols(
  risk_datatheft_likelihood = col_character(),
  risk_datatheft_damage = col_integer()
)
> 
> # run the plot using one multiline command:
> bind_rows(mutate(data_big, size="big"),
+           mutate(data_small, size="small")) %>%
+     ggplot(aes(risk_datatheft_likelihood, risk_datatheft_damage)) +
+     geom_jitter(aes(col=size))
> 
> # alternatively, save the combined data first
> data_combined <- bind_rows(mutate(data_big, size="big"),
+                            mutate(data_small, size="small"))
> 
> # and run the plot separately
> ggplot(data_combined,
+        aes(risk_datatheft_likelihood, risk_datatheft_damage)) +
+     geom_jitter(aes(col=size)) +
+     labs(x = "damage", y = "likelihood",
+          title = "Risk Map", subtitle = "Datatheft") +
+     theme_classic() +
+     theme(legend.position="bottom") +
+     geom_hline(yintercept = 0.5, color="red") +
+     geom_hline(yintercept = 1.5) +
+     geom_hline(yintercept = 2.5) +
+     geom_hline(yintercept = 3.5) +
+     geom_hline(yintercept = 4.5) +
+     geom_vline(xintercept = 0.5, color="red") +
+     geom_vline(xintercept = 1.5) +
+     geom_vline(xintercept = 2.5) +
+     geom_vline(xintercept = 3.5) +
+     geom_vline(xintercept = 4.5)
>

Answer 2

非常感谢您的帮助！我不是很确定，但是这种结合也可以：

bind_rows(mutate(data_big, size="big"),
          mutate(data_small, size="small")) %>%
  ggplot(aes(risk_datatheft_likelihood, risk_datatheft_damage)) +
  geom_jitter(aes(col=size)) +
  labs(x = "risk_datatheft_likelihood", y = "risk_datatheft_damage",
       title = "Risk Map", subtitle = "Risiko: Datatheft") +
  theme_classic() +
  theme(legend.position="bottom") +
  geom_hline(yintercept = 0.5, color="red") +
  geom_hline(yintercept = 1.5) +
  geom_hline(yintercept = 2.5) +
  geom_hline(yintercept = 3.5) +
  geom_hline(yintercept = 4.5) +
  geom_vline(xintercept = 0.5, color="red") +
  geom_vline(xintercept = 1.5) +
  geom_vline(xintercept = 2.5) +
  geom_vline(xintercept = 3.5) +
  geom_vline(xintercept = 4.5)

R两幅图叠加

2 个答案: