在R中重新排序資料框

我想重新排序 r 中的一些表。

我從 lmer() 和 confint() 函式中提取了所有值。也許還有另一種方法可以直接總結 lmer()-results？（各種 lmer 函式）

例如，我的表格如下所示：

我需要的是這種風格的表格：

我怎樣才能做到這一點？

這是一個資料框嘗試的代碼：

loc_map_column <- c("RA","RA","RA","LV","LV","LV","LV","LA","LA","RV","RV","RV","RV")
loc_stim_column <- c("NSR","loc_stimLA","loc_stimRA","NSR","loc_stimLV","loc_stimRA","loc_stimRV","NSR","loc_stimRA","NSR","loc_stimLA","loc_stimLV","loc_stimRA")
logVelocity <- c(-0.502600888,-0.034630279,-0.691259575,-0.182731047,-0.236928272, -0.913238563,-0.042520352,0.007310606,-0.340983839,-0.351529452,-0.622877246,-0.175692036,-0.535687639)
confidence_lower <- c(0.2, 0.3, 0.1, 0.4, 0.22, 0.11, 0.33, 0.44, 0.63, 0.133, 0.311, 0.01, 0.001)
confidence_upper <- c(0.2, 0.3, 0.1, 0.4, 0.22, 0.11, 0.33, 0.44, 0.63, 0.133, 0.311, 0.01, 0.001)
p_value <- c(0.2, 0.3, 0.1, 0.4, 0.22, 0.11, 0.33, 0.44, 0.63, 0.133, 0.311, 0.01, 0.001)

summarydf <- data.frame(loc_map_column, loc_stim_column, logVelocity, confidence_lower, confidence_upper, p_value)

uj5u.com熱心網友回復：

tidyr根據您提供的資料，我們可以通過庫分兩步完成此操作。

library(tidyr)

第一步是使資料更長，我們使用函式來做到這一點pivot_longer，選擇將所有數值列放入一個具有函式的列中where：

longer <- pivot_longer(summarydf, cols = where(is.numeric), names_to = 'measure')
longer 

這給了我們：

# A tibble: 52 x 4
   loc_map_column loc_stim_column measure            value
   <chr>          <chr>           <chr>              <dbl>
 1 RA             NSR             logVelocity      -0.503 
 2 RA             NSR             confidence_lower  0.2   
 3 RA             NSR             confidence_upper  0.2   
 4 RA             NSR             p_value           0.2   
 5 RA             loc_stimLA      logVelocity      -0.0346
 6 RA             loc_stimLA      confidence_lower  0.3   
 7 RA             loc_stimLA      confidence_upper  0.3   
 8 RA             loc_stimLA      p_value           0.3   
 9 RA             loc_stimRA      logVelocity      -0.691 
10 RA             loc_stimRA      confidence_lower  0.1   
# ... with 42 more rows

我們現在需要再擴大一點，我們使用函式pivot_wider. 我們希望該列loc_map_column成為數字列的名稱。在上一步中，所有數字列都映射到名為“ value”的列

pivot_wider(longer,  names_from = loc_map_column, values_from = value)

我們可以用管道運算子 ( ) 將這兩個步驟合二為一%>%，得到

summarydf %>%
  pivot_longer(cols = where(is.numeric), names_to = 'measure') %>%
  pivot_wider(names_from = loc_map_column, values_from = value)

這給出了輸出

# A tibble: 24 x 6
   loc_stim_column measure               RA     LV       LA     RV
   <chr>           <chr>              <dbl>  <dbl>    <dbl>  <dbl>
 1 NSR             logVelocity      -0.503  -0.183  0.00731 -0.352
 2 NSR             confidence_lower  0.2     0.4    0.44     0.133
 3 NSR             confidence_upper  0.2     0.4    0.44     0.133
 4 NSR             p_value           0.2     0.4    0.44     0.133
 5 loc_stimLA      logVelocity      -0.0346 NA     NA       -0.623
 6 loc_stimLA      confidence_lower  0.3    NA     NA        0.311
 7 loc_stimLA      confidence_upper  0.3    NA     NA        0.311
 8 loc_stimLA      p_value           0.3    NA     NA        0.311
 9 loc_stimRA      logVelocity      -0.691  -0.913 NA       -0.536
10 loc_stimRA      confidence_lower  0.1     0.11  NA        0.001
# ... with 14 more rows

uj5u.com熱心網友回復：

有一些可用的格式來試驗你的資料框會很有幫助。但是，我會使用庫中的group_by()和的組合pivot_wider()：dplyr和tidyr.

https://tidyr.tidyverse.org/reference/pivot_wider.html

希望它作為一個開始會有所幫助......如果您提供一些復制粘貼格式，我可以嘗試一下。

uj5u.com熱心網友回復：

也許有一種方法可以讓它更清晰，但是這段代碼可以完成你正在尋找的東西。你可以試試這樣的

data <- data.frame(a = c(1:13),
                   loc_map_column = c("RA","RA","RA","LV","LV","LV","LV",
                                      "LA","LA","RV","RV","RV","RV"),
                   loc_stim_column = c("NSR","loc_stimLA","loc_stimRA","NSR",
                                       "loc_stimLV","loc_stimRA","loc_stimRV","NSR",
                                       "loc_stimRA","NSR","loc_stimLA","loc_stimLV",
                                       "loc_stimRA"),
                   logVelocity = c(-0.502600888,-0.034630279,-0.691259575,-0.182731047,
                                   -0.236928272,-0.913238563,-0.042520352,0.007310606,
                                   -0.340983839,-0.351529452,-0.622877246,-0.175692036,
                                   -0.535687639),
                   confidence_lower = c(-0.8253034,-0.034630279,-0.691259575,-0.182731047,
                                        -0.236928272,-0.913238563,-0.042520352,0.007310606,
                                        -0.340983839,-0.351529452,-0.622877246,-0.175692036,
                                        -0.535687639),
                   confidence_upper = c(-0.1798744,0.3684858,-0.30230958,0.46092692,
                                        0.53562934,-0.13692249,0.77911468,0.32826335,
                                        0.09108281,0.18298935,0.07870576,0.4567075,
                                        0.10106017),
                   p_value = c(0.0404513,2.084996E-29,0.00000002582846,0.5829759,
                               0.4102663,2.867447E-27,0.1212858,0.9650069,
                               0.000000001413686,0.2710587,0.00142231,0.0004382634,
                               0.0004286051))
data <- data[,-1]


dtalogV <- data %>% group_by(loc_stim_column) %>% 
  pivot_wider(names_from = loc_map_column,values_from = logVelocity,
              id_cols = loc_stim_column)
dtaconfL <- data %>% group_by(loc_stim_column) %>% 
  pivot_wider(names_from = loc_map_column,values_from = confidence_lower,
              id_cols = loc_stim_column)
dtaconfU <- data %>% group_by(loc_stim_column) %>% 
  pivot_wider(names_from = loc_map_column,values_from = confidence_upper,
              id_cols = loc_stim_column)
dtaPval <- data %>% group_by(loc_stim_column) %>% 
  pivot_wider(names_from = loc_map_column,values_from = p_value,
              id_cols = loc_stim_column)

varnames <- c(rep("logVelocity",length(dtalogV)),rep("confidence_lower",length(dtaconfU)),
                  rep("confidence_upper",length(dtaPval)),rep("p_value",length(dtaconfL)))


databind <-  rbind(dtalogV,dtaconfL,dtaconfU,dtaPval)
final <- data.frame(databind,varnames=varnames)
final <- final[,c(1,6,2:5)]

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