可挠性数据合并

Question

我想从这里合并(cbind或类似的)许多flextable对象：

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to此文：

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设立：

library(tidyverse)
library(flextable)
model1 <- data.frame("variable" = c(letters[1:3]),
                     "coeff" = c(20.3, 12.2, 10.1),
                     "ci" = c("19-20", "10-14", "10-11"))
model2 <- data.frame("variable" = c(letters[1:4]),
                     "coeff" = c(17, 10.1, 189, 200),
                     "ci" = c("12-20", "0.1-1.2", "100-200", "150-250"))
model3 <- data.frame("variable" = c(letters[1:5]),
                     "coeff" = c(1:5),
                     "ci" = c("1-3", "0.1-2.2", "2-4", "3-6", "2-9"))
t1 <- flextable(model1) %>% 
  add_header_row(values = c("", "model 1", "model 1"), top = T) %>% merge_at(i = 1:1, j = 2:3, part = "header")
t2 <- flextable(model2) %>% 
  add_header_row(values = c("", "model 2", "model 2"), top = T) %>% merge_at(i = 1:1, j = 2:3, part = "header")
t3 <- flextable(model3) %>% 
  add_header_row(values = c("", "model 3", "model 3"), top = T) %>% merge_at(i = 1:1, j = 2:3, part = "header")

我不知道如何有效地合并这些数据集，我可以使数据集具有相同的大小，以便至少可以使用cbind，但这意味着删除和重新排序具有相同名称的列会很麻烦：

model1[nrow(model1) + (nrow(model3) - nrow(model1)), ] <- NA
model2[nrow(model2) + (nrow(model3) - nrow(model2)), ] <- NA
df_bind <- cbind(model1, model2, model3)
df_bind

#   variable coeff    ci variable coeff      ci variable coeff      ci
# 1        a  20.3 19-20        a  17.0   12-20        a     1     1-3
# 2        b  12.2 10-14        b  10.1 0.1-1.2        b     2 0.1-2.2
# 3        c  10.1 10-11        c 189.0 100-200        c     3     2-4
# 4     <NA>    NA  <NA>        d 200.0 150-250        d     4     3-6
# 5     <NA>    NA  <NA>     <NA>    NA    <NA>        e     5     2-9

然后我想使用flextable命令：

flextable(df_bind)
#Error in flextable(df_bind) : duplicated col_keys

这是行不通的，因为他们有相同的列名。

我唯一能想到的方法就是重命名每一个dataframe列(例如重命名为variable1、variable2、variable3等)。然后合并，然后使用add_header_row重命名为原始名称，但是这是非常低效率的。

有更简单的解决办法吗？

谢谢

编辑

我似乎无法复制“本解决方案”。当我跑步时：

bind_rows(list(model_1 = model1, model_2 = model2, model_3 = model3), .id = 'model') %>%
  pivot_wider(id_cols = variable, names_from = model, values_from = c(coeff, ci)) %>%
  select(variable, coeff_model_1, ci_model_1, 
         coeff_model_2, ci_model_2, coeff_model_3, ci_model_3) %>%
  flextable(col_keys = my_header$col_keys) 

到目前为止，我得到了以下看起来还不错的信息：

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然后，当我添加以下两行时：

bind_rows(list(model_1 = model1, model_2 = model2, model_3 = model3), .id = 'model') %>%
  pivot_wider(id_cols = variable, names_from = model, values_from = c(coeff, ci)) %>%
  select(variable, coeff_model_1, ci_model_1, 
         coeff_model_2, ci_model_2, coeff_model_3, ci_model_3) %>%
  flextable(col_keys = my_header$col_keys) %>%
  set_header_df(mapping = my_header, key = "col_keys") %>%
  theme_booktabs() 

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然后

bind_rows(list(model_1 = model1, model_2 = model2, model_3 = model3), .id = 'model') %>%
  pivot_wider(id_cols = variable, names_from = model, values_from = c(coeff, ci)) %>%
  select(variable, coeff_model_1, ci_model_1, 
         coeff_model_2, ci_model_2, coeff_model_3, ci_model_3) %>%
  flextable(col_keys = my_header$col_keys) %>%
  set_header_df(mapping = my_header, key = "col_keys") %>%
  theme_booktabs() %>% 
  merge_v(part = "header") %>%
  merge_h(part = "header") 

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最后

bind_rows(list(model_1 = model1, model_2 = model2, model_3 = model3), .id = 'model') %>%
  pivot_wider(id_cols = variable, names_from = model, values_from = c(coeff, ci)) %>%
  select(variable, coeff_model_1, ci_model_1, 
         coeff_model_2, ci_model_2, coeff_model_3, ci_model_3) %>%
  flextable(col_keys = my_header$col_keys) %>%
  set_header_df(mapping = my_header, key = "col_keys") %>%
  theme_booktabs() %>% 
  merge_v(part = "header") %>%
  merge_h(part = "header") %>%
  align(align = "center", part = "all") %>%
  colformat_num(na_str = "")

​

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R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default
    
attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] flextable_0.5.11 forcats_0.5.0    stringr_1.4.0    dplyr_1.0.2      purrr_0.3.4      readr_1.3.1      tidyr_1.1.2      tibble_3.0.3    
 [9] ggplot2_3.3.2    tidyverse_1.3.0 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.3        lubridate_1.7.9   lattice_0.20-38   assertthat_0.2.1  digest_0.6.25     utf8_1.1.4        R6_2.4.1          cellranger_1.1.0 
 [9] backports_1.1.5   reprex_0.3.0      evaluate_0.14     httr_1.4.1        pillar_1.4.3      gdtools_0.2.1     rlang_0.4.8       readxl_1.3.1     
[17] uuid_0.1-4        rstudioapi_0.11   data.table_1.12.8 rmarkdown_2.1     munsell_0.5.0     tinytex_0.20      broom_0.5.5       compiler_3.6.3   
[25] modelr_0.1.6      xfun_0.12         pkgconfig_2.0.3   systemfonts_0.1.1 base64enc_0.1-3   htmltools_0.4.0   tidyselect_1.1.0  fansi_0.4.1      
[33] crayon_1.3.4      dbplyr_1.4.2      withr_2.1.2       grid_3.6.3        nlme_3.1-144      jsonlite_1.6.1    gtable_0.3.0      lifecycle_0.2.0  
[41] DBI_1.1.0         magrittr_1.5      scales_1.1.0      zip_2.1.1         cli_2.0.2         stringi_1.4.6     fs_1.3.2          xml2_1.2.5       
[49] ellipsis_0.3.0    generics_0.0.2    vctrs_0.3.5       tools_3.6.3       glue_1.4.2        officer_0.3.14    hms_0.5.3         yaml_2.2.1       
[57] colorspace_1.4-1  rvest_0.3.5       knitr_1.28        haven_2.2.0

除了R的版本之外，我看不出sessionInfo有多大的不同，我还将officer更新为officer_0.3.14版本，但没有什么不同。看来set_header_df和colformat_num似乎工作不正常。

有什么想法吗？

Answer 1

也许这会有帮助。

使用列名以及标头标签设置标头。

使用bind_rows将所有模型放入长形式，并添加一列表示结果属于哪个模型。

使用pivot_wider将格式改为宽形式以供表示。之后，select将对您的列进行排序。

flextable set_header_df将根据列名(取自my_header键)提供标题标签。

library(tidyverse)
library(stringr)
library(flextable)

my_header <- data.frame(
  col_keys = c("variable", "coeff_model_1", "ci_model_1", 
               "coeff_model_2", "ci_model_2", "coeff_model_3", "ci_model_3"),
  line1 = "Table 1",
  line2 = c("", rep("Model 1", 2), rep("Model 2", 2), rep("Model 3", 2)),
  line3 = c("Variable", "Coeff", "CI", "Coeff", "CI", "Coeff", "CI"),
  stringsAsFactors = FALSE
)

bind_rows(list(model_1 = model1, model_2 = model2, model_3 = model3), .id = 'model') %>%
  pivot_wider(id_cols = variable, names_from = model, values_from = c(coeff, ci)) %>%
  select(variable, coeff_model_1, ci_model_1, 
         coeff_model_2, ci_model_2, coeff_model_3, ci_model_3) %>%
  flextable(col_keys = my_header$col_keys) %>%
  set_header_df(mapping = my_header, key = "col_keys") %>%
  theme_booktabs() %>% 
  merge_v(part = "header") %>%
  merge_h(part = "header") %>%
  align(align = "center", part = "all") %>%
  colformat_num(na_str = "")

输出

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会话信息

R> sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] flextable_0.5.11 forcats_0.5.0    stringr_1.4.0    dplyr_1.0.2      purrr_0.3.4      readr_1.3.1     
 [7] tidyr_1.1.2      tibble_3.0.3     ggplot2_3.3.2    tidyverse_1.3.0 

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0  xfun_0.16         haven_2.3.1       colorspace_1.4-1  vctrs_0.3.2       generics_0.0.2   
 [7] htmltools_0.5.0   viridisLite_0.3.0 yaml_2.2.1        base64enc_0.1-3   utf8_1.1.4        plotly_4.9.2.1   
[13] rlang_0.4.7       pillar_1.4.4      glue_1.4.2        withr_2.2.0       DBI_1.1.0         gdtools_0.2.2    
[19] dbplyr_1.4.3      uuid_0.1-4        modelr_0.1.7      readxl_1.3.1      lifecycle_0.2.0   munsell_0.5.0    
[25] gtable_0.3.0      cellranger_1.1.0  zip_2.1.1         rvest_0.3.5       htmlwidgets_1.5.1 evaluate_0.14    
[31] knitr_1.30        fansi_0.4.1       broom_0.7.0       Rcpp_1.0.5        backports_1.1.6   scales_1.1.1     
[37] jsonlite_1.7.1    fs_1.4.1          systemfonts_0.2.1 hms_0.5.3         digest_0.6.25     stringi_1.4.6    
[43] grid_4.0.2        cli_2.0.2         tools_4.0.2       magrittr_1.5      lazyeval_0.2.2    crayon_1.3.4     
[49] pkgconfig_2.0.3   ellipsis_0.3.1    data.table_1.13.0 xml2_1.3.2        reprex_0.3.0      lubridate_1.7.9  
[55] rmarkdown_2.2     officer_0.3.14    assertthat_0.2.1  httr_1.4.2        rstudioapi_0.11   R6_2.4.1         
[61] compiler_4.0.2