如何将4个单独的数据集包装起来？

Question

因此，我在尝试将4种不同的数据集包装起来时遇到了一些麻烦。每个数据集代表一个参与者，但我不知道如何组合这些数据集，这样就可以对它们进行面封装并共享x轴。以下是每个参与者的数据集：

p03

structure(list(day = structure(c(18880, 18880, 18908, 18908, 
18911, 18912, 18912, 18913, 18913, 18914, 18914, 18915, 18915, 
18916, 18916, 18917, 18917, 18918, 18918, 18919, 18920, 18921, 
18921, 18922, 18922, 18923, 18924), class = "Date"), trialtype = c("go", 
"nogo", "go", "nogo", "go", "go", "nogo", "go", "nogo", "go", 
"nogo", "go", "nogo", "go", "nogo", "go", "nogo", "go", "nogo", 
"go", "go", "go", "nogo", "go", "nogo", "go", "go"), mean = c(0.453363414634146, 
0.21005, 0.63720350877193, 0.8809, 0.624077192982456, 0.581956842105263, 
0.63065, 0.588196842105263, 0.607075, 0.540253289473684, 0.73215, 
0.529153097345133, 0.583133333333333, 0.538794736842105, 0.4409, 
0.522421428571429, 0.4792, 0.552129824561403, 0.5282, 0.512742105263158, 
0.473664912280702, 0.528580701754386, 0.544266666666667, 0.490643859649123, 
0.49955, 0.440738596491228, 0.475708771929825)), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -27L), groups = structure(list(
    day = structure(c(18880, 18908, 18911, 18912, 18913, 18914, 
    18915, 18916, 18917, 18918, 18919, 18920, 18921, 18922, 18923, 
    18924), class = "Date"), .rows = structure(list(1:2, 3:4, 
        5L, 6:7, 8:9, 10:11, 12:13, 14:15, 16:17, 18:19, 20L, 
        21L, 22:23, 24:25, 26L, 27L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -16L), .drop = TRUE))

p04

structure(list(day = structure(c(18880, 18880, 18918, 18918, 
18919, 18920, 18922, 18923), class = "Date"), trialtype = c("go", 
"nogo", "go", "nogo", "go", "go", "go", "go"), mean = c(0.453363414634146, 
0.21005, 0.51259649122807, 0.5512, 0.631542105263158, 0.635184210526316, 
0.718966666666667, 0.795638596491228)), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -8L), groups = structure(list(
    day = structure(c(18880, 18918, 18919, 18920, 18922, 18923
    ), class = "Date"), .rows = structure(list(1:2, 3:4, 5L, 
        6L, 7L, 8L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -6L), .drop = TRUE))

p05

structure(list(day = structure(c(18924, 18924, 18925, 18925, 
18926, 18926, 18927, 18927, 18928, 18928, 18929, 18929, 18930, 
18930, 18932, 18933, 18933, 18934, 18935, 18935, 18936, 18936, 
18937, 18937, 18940, 18941, 18941, 18942, 18944, 18944, 18945, 
18945, 18948, 18950, 18951, 18951), class = "Date"), trialtype = c("go", 
"nogo", "go", "nogo", "go", "nogo", "go", "nogo", "go", "nogo", 
"go", "nogo", "go", "nogo", "go", "go", "nogo", "go", "go", "nogo", 
"go", "nogo", "go", "nogo", "go", "go", "nogo", "go", "go", "nogo", 
"go", "nogo", "go", "go", "go", "nogo"), mean = c(0.693312280701754, 
0.5485, 0.589122807017544, 1.26686666666667, 0.542644444444444, 
0.3142, 0.607622807017544, 1.0739, 0.562494642857143, 0.45075, 
0.522236842105263, 1.4405, 0.585782456140351, 0.574333333333333, 
0.549415789473684, 0.499456140350877, 0.89835, 0.571619298245614, 
0.836829824561404, 0.8192, 0.881107692307692, 2.16316666666667, 
0.600214035087719, 0.6608, 0.665928571428571, 0.633136363636364, 
0.4519, 0.558714035087719, 0.7840875, 1.20543333333333, 0.597352631578947, 
1.564875, 0.517835087719298, 0.533582456140351, 0.526968421052632, 
1.31243333333333)), class = c("grouped_df", "tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -36L), groups = structure(list(
    day = structure(c(18924, 18925, 18926, 18927, 18928, 18929, 
    18930, 18932, 18933, 18934, 18935, 18936, 18937, 18940, 18941, 
    18942, 18944, 18945, 18948, 18950, 18951), class = "Date"), 
    .rows = structure(list(1:2, 3:4, 5:6, 7:8, 9:10, 11:12, 13:14, 
        15L, 16:17, 18L, 19:20, 21:22, 23:24, 25L, 26:27, 28L, 
        29:30, 31:32, 33L, 34L, 35:36), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -21L), .drop = TRUE))

p07

structure(list(day = structure(c(19210, 19210, 19211, 19211, 
19212, 19212, 19213, 19213, 19214, 19214, 19215, 19215, 19216, 
19216, 19217, 19217, 19218, 19218, 19219, 19221, 19222, 19223, 
19223, 19224, 19225, 19226, 19226, 19227, 19227, 19228, 19228, 
19229, 19229, 19230, 19231, 19231, 19232, 19232, 19238, 19238, 
19239, 19239, 19240, 19241, 19242, 19242, 19243), class = "Date"), 
    trialtype = c("go", "nogo", "go", "nogo", "go", "nogo", "go", 
    "nogo", "go", "nogo", "go", "nogo", "go", "nogo", "go", "nogo", 
    "go", "nogo", "go", "go", "go", "go", "nogo", "go", "go", 
    "go", "nogo", "go", "nogo", "go", "nogo", "go", "nogo", "go", 
    "go", "nogo", "go", "nogo", "go", "nogo", "go", "nogo", "go", 
    "go", "go", "nogo", "go"), mean = c(0.496695906432749, 0.54765, 
    0.44894269005848, 0.406666666666667, 0.444126315789474, 0.3496, 
    0.442609356725146, 0.4528, 0.462447953216374, 0.4262, 0.47875730994152, 
    0.464266666666667, 0.459146783625731, 0.261666666666667, 
    0.479398830409357, 0.4199, 0.48975730994152, 0.2944, 0.511256140350877, 
    0.49047485380117, 0.543529239766082, 0.513159064327485, 0.5597, 
    0.51296783625731, 0.478275438596491, 0.492419298245614, 0.1061, 
    0.49356432748538, 0.4948, 0.534784795321637, 0.5097, 0.509980701754386, 
    0.2998, 0.505461988304094, 0.52029649122807, 0.52315, 0.52303216374269, 
    0.5337, 0.517447368421053, 0.3997, 0.517894152046784, 0.696366666666667, 
    0.511729824561403, 0.5346, 0.515273684210526, 0.508, 0.552337426900585
    )), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -47L), groups = structure(list(day = structure(c(19210, 
19211, 19212, 19213, 19214, 19215, 19216, 19217, 19218, 19219, 
19221, 19222, 19223, 19224, 19225, 19226, 19227, 19228, 19229, 
19230, 19231, 19232, 19238, 19239, 19240, 19241, 19242, 19243
), class = "Date"), .rows = structure(list(1:2, 3:4, 5:6, 7:8, 
    9:10, 11:12, 13:14, 15:16, 17:18, 19L, 20L, 21L, 22:23, 24L, 
    25L, 26:27, 28:29, 30:31, 32:33, 34L, 35:36, 37:38, 39:40, 
    41:42, 43L, 44L, 45:46, 47L), ptype = integer(0), class = c("vctrs_list_of", 
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -28L), .drop = TRUE))

从数据集中，我开始为每个参与者创建单独的数据可视化，如下所示：

p03_dropped_rt <- p03_response_times %>%
  drop_na()

#convert day into date object 

p03_dropped_rt$day <- as.Date(p03_dropped_rt$day)

head(p03_dropped_rt)

#create a line graph for p03 to show this data but have the x axis as just days 

          
p3 <- p03_dropped_rt %>% 
  group_by(trialtype) %>% 
  arrange(day) %>% 
  mutate(days = paste0("day",row_number())) %>% 
  ggplot(aes(x = fct_inorder(days), y = mean, group = trialtype)) +
  geom_line(aes(color = trialtype)) +
  geom_point(aes(color = trialtype)) +
  theme(axis.text.x = element_text(angle = 60, vjust = 0.5)) +
  labs(title=" P03s Average response time of Go/No-go Trials",
       x = "Day of training",
       y = "Average Response Time (s) ",
       color = "Trial Type")

p3

# repeat for p04 

#drop missing values 

p04_dropped_rt <- p04_response_times %>%
  drop_na()

#convert day into date object 

p04_dropped_rt$day <- as.Date(p04_dropped_rt$day)

head(p04_dropped_rt)

#create a line graph to show these results 

p4 <- p04_dropped_rt %>% 
  group_by(trialtype) %>% 
  arrange(day) %>% 
  mutate(days = paste0("day",row_number())) %>% 
  ggplot(aes(x = fct_inorder(days), y = mean, group = trialtype)) +
  geom_line(aes(color = trialtype)) +
  geom_point(aes(color = trialtype)) +
  theme(axis.text.x = element_text(angle = 60, vjust = 0.5)) +
  labs(title=" P04s Average response time of Go/No-go Trials",
       x = "Day of training",
       y = "Average Response Time (s) ",
       color = "Trial Type")

p4

#repeat for p05 and drop missing values 

p05_dropped_rt <- p05_response_times %>%
  drop_na()

#convert day into date object 

p05_dropped_rt$day <- as.Date(p05_dropped_rt$day)

head(p05_dropped_rt)

#create a line graph to show this 

p5 <- p05_dropped_rt %>% 
  group_by(trialtype) %>% 
  arrange(day) %>% 
  mutate(days = paste0("day",row_number())) %>% 
  ggplot(aes(x = fct_inorder(days), y = mean, group = trialtype)) +
  geom_line(aes(color = trialtype)) +
  geom_point(aes(color = trialtype)) +
  theme(axis.text.x = element_text(angle = 60, vjust = 0.5)) +
  labs(title=" P05s Average response time of Go/No-go Trials",
       x = "Day of training",
       y = "Average Response Time (s) ",
       color = "Trial Type")

p5

# repeat for p07 and drop missing values 

p07_dropped_rt <- p07_response_times %>%
  drop_na()

# convert day to date object 

p07_dropped_rt$day <- as.Date(p07_dropped_rt$day)

head(p07_dropped_rt)

#create a line graph to show this 

p7 <- p07_dropped_rt %>% 
  group_by(trialtype) %>% 
  arrange(day) %>% 
  mutate(days = paste0("day",row_number())) %>% 
  ggplot(aes(x = fct_inorder(days), y = mean, group = trialtype)) +
  geom_line(aes(color = trialtype)) +
  geom_point(aes(color = trialtype)) +
  theme(axis.text.x = element_text(angle = 60, vjust = 0.5)) +
  labs(title=" P07s Average response time of Go/No-go Trials",
       x = "Day of training",
       y = "Average Response Time (s) ",
       color = "Trial Type")

p7

我不知道该如何包装，所以我尝试了‘gg排列’。然而，这导致图形被令人难以置信地展开，并没有外观良好的视觉效果。如果你想看的话，我在最后附了一张照片。

有人能帮我吗？

​

​

我试图按以下方式绑定这些行

bind_rows(list(p3, p4, p5, p7), .id="patient") %>% 
  group_by(trialtype) %>% 
  arrange(day) %>% 
  mutate(days = paste0("day",row_number())) %>% 
  ggplot(aes(x = fct_inorder(days), y = mean, group = trialtype)) +
  geom_line(aes(color = trialtype)) +
  geom_point(aes(color = trialtype)) +
  theme(axis.text.x = element_text(angle = 60, vjust = 0.5)) +
  labs(title="Average response time of Go/No-go Trials",
       x = "Day of training",
       y = "Average Response Time (s) ",
       color = "Trial Type") + facet_wrap(patient)

它产生了这个错误

Error in `bind_rows()`:
! Argument 1 must be a data frame or a named atomic vector.

Answer 1

我不知道你在做什么，但显然是不对的。

bind_rows(list(p03, p04, p05, p07), .id="patient") %>% 
  group_by(trialtype) %>% 
  arrange(day) %>% 
  mutate(days = paste0("day",row_number())) %>% 
  ggplot(aes(x = fct_inorder(days), y = mean, group = trialtype)) +
  geom_line(aes(color = trialtype)) +
  geom_point(aes(color = trialtype)) +
  theme(axis.text.x = element_text(angle = 60, vjust = 0.5)) +
  labs(title="Average response time of Go/No-go Trials",
       x = "Day of training",
       y = "Average Response Time (s) ",
       color = "Trial Type") + 
  facet_wrap(vars(patient))

给出

​

​

若要访问联机帮助，请使用?。例如，> ?facet_wrap。或者在RStudio帮助面板顶部的搜索栏中键入函数的名称。或者使用谷歌。