使用geom_smooth繪制具有三個分類變數和一個數值變數的表

我有這張表，其中包含三個分類變數和一個數值變數：

df <- structure(list(Q = c("q_pol", "q_wh", "q_pol", "q_wh"), 
                     median_all = c(0.667362125626559, 0.624735641188929, 0.548153075210995, 0.398574206026083), 
                     median_half = c(-0.350314785114947,1.42461790732669, 0.372537880024059, 0.44085155122463), 
                     median_third = c(-0.93389146143506,0.236025246988988, -1.02912771930043, 0.0361894830862238), 
                     median_quart = c(-0.112157689065904,  0.704777764871505, -0.848709176683769, 1.24452019211073), 
                     Partcpt = c("Not_Answerer", "Not_Answerer", "Answerer", "Answerer")), 
                class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -4L)) 

我想可視化median*列中的值如何分布在三個分類變數上，使用geom_smooth. 為了到達那里，我一直在這樣做：

library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
df %>%
  # cast all `median*`variables longer:
  pivot_longer(-c(Q, Partcpt)) %>%
  # rename:
  rename(slope_range = name) %>%
  # simplify labels:
  mutate(slope_range = str_replace(slope_range, ".*_(.*)$", "\\1")) %>%
  # convert slope_range to numerical variable:
  mutate(slope_range_N = case_when(
    slope_range == "all" ~ 1,
    slope_range == "half" ~ 2,
    slope_range == "third" ~ 3,
    TRUE                    ~ 4)
  ) %>%
  # plot:
ggplot( 
       aes(x = slope_range_N, y = value, color = Q))   
  geom_smooth(method = "loess")

這里有兩個問題：首先，轉換slope_range為數字似乎不專業；其次，更重要的是，生成的圖沒有顯示valueby的分布Partcpt。如何將其作為情節中的第四個變數包含在內？

編輯：

也許以下內容有助于解決方案，基本思想是將Q值和Partcpt值轉換為單個列（而不是兩個不同的列）：

# df with `Q`:
df_Q <- df1 %>% 
  select(Q, slope_range,  value, slope_range_N) %>%
  rename(Cat = Q)

# df with `Partcpt`
df_Partcpt <- df1 %>% 
  select(Partcpt, slope_range,  value, slope_range_N) %>%
  rename(Cat = Partcpt)

# bind:
plot_df <- bind_rows(df_Q, df_Partcpt)

# plot:
ggplot(plot_df,
       aes(x = slope_range_N, y = value, color = Cat))   
  geom_smooth(method = "loess", span = 0.4, se = FALSE)

Just how to have merely two colors for the two Qvalues and two line types for the two Partcptvalues, I don't know.

uj5u.com熱心網友回復：

這部分只是對資料爭論的改進。看起來您過度考慮/過度設計了您的流程。當您重塑資料時，從斜率變數的因子水平中獲取數字，您可以在pivot_longer. 然后映射到線型。需要注意的一點是，每個斜率-參與者-Q 組合只有一個觀察結果，因此您的 LOESS 模型在觀察結果如此之少的情況下并不完全合適（您會收到大量關于此的警告）。如果需要，您可以改為使用樣條曲線。

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

df_long <- df %>%
  pivot_longer(c(-Q, -Partcpt), names_to = "slope_range", 
               names_transform = list(slope_range = ~as.numeric(as.factor(.))))

ggplot(df_long, aes(x = slope_range, y = value, color = Q, linetype = Partcpt))  
  geom_smooth(method = loess, se = FALSE)  
  guides(linetype = guide_legend(override.aes = list(color = "black")))

loess如果需要，您可以將引數傳遞給。但是，通常對于這樣的事情，我通常更喜歡自己在更大的范圍內進行建模。我認為這類似于geom_smooth引擎蓋下的操作，但直接訪問它可能很有用。在這里，我將為每個 Q 參與者組合制作 LOESS 模型，然后使用這些模型來預測slope_range. 然后geom_line直接使用。geom_smooth和之間的默認線寬不同geom_line，但您可以輕松調整。

df_modeled <- df_long %>%
  group_by(Q, Partcpt) %>%
  nest() %>%
  mutate(loess_mod = purrr::map(data, ~loess(value ~ slope_range, data = .)),
         x = purrr::map(data, ~seq(min(.$slope_range), max(.$slope_range), by = 0.1)),
         pred = purrr::map(loess_mod, ~predict(., newdata = unlist(x)))) %>%
  unnest(pred, x)

head(df_modeled)
#> # A tibble: 6 × 6
#> # Groups:   Q, Partcpt [1]
#>   Q     Partcpt      data             loess_mod     x   pred
#>   <chr> <chr>        <list>           <list>    <dbl>  <dbl>
#> 1 q_pol Not_Answerer <tibble [4 × 2]> <loess>     1   0.667 
#> 2 q_pol Not_Answerer <tibble [4 × 2]> <loess>     1.1 0.598 
#> 3 q_pol Not_Answerer <tibble [4 × 2]> <loess>     1.2 0.496 
#> 4 q_pol Not_Answerer <tibble [4 × 2]> <loess>     1.3 0.373 
#> 5 q_pol Not_Answerer <tibble [4 × 2]> <loess>     1.4 0.236 
#> 6 q_pol Not_Answerer <tibble [4 × 2]> <loess>     1.5 0.0949

ggplot(df_modeled, aes(x = x, y = pred, color = Q, linetype = Partcpt))  
  geom_line()  
  guides(linetype = guide_legend(override.aes = list(color = "black")))

uj5u.com熱心網友回復：

在編輯之前，我可以通過 Partcpt 映射線型來獲得兩種顏色的四條線和虛線與實線。我所做的只是添加linetype=Partcpt到aes()通話中。

library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
df %>%
  # cast all `median*`variables longer:
  pivot_longer(-c(Q, Partcpt)) %>%
  # rename:
  rename(slope_range = name) %>%
  # simplify labels:
  mutate(slope_range = str_replace(slope_range, ".*_(.*)$", "\\1")) %>%
  # convert slope_range to numerical variable:
  mutate(slope_range_N = case_when(
    slope_range == "all" ~ 1,
    slope_range == "half" ~ 2,
    slope_range == "third" ~ 3,
    TRUE                    ~ 4)
  ) %>%
  # plot:
  ggplot( 
    aes(x = slope_range_N, y = value, color = Q, linetype=Partcpt))   
  geom_smooth(method = "loess", se=FALSE)  
  scale_color_manual(values = c("black", "grey")) # Line changes colours

繪圖輸出如下所示：

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