R：将一个奇怪的长表格格式化为宽格式

Question

我有一些在代码块中找到的格式的数据。

dat <- data.frame(fruit=c('apple',NA,NA,NA,NA,NA,NA,NA,NA,
                          'pear',NA,NA,NA,NA,NA,NA,NA,NA,
                          'orange',NA,NA,NA,NA,NA,NA,NA,NA),     
                  fertiliser=c('phosphorus',NA,NA,
                               'potassium',NA,NA,
                               'Nitrogen',NA,NA,
                               'phosphorus',NA,NA,
                               'potassium',NA,NA,
                               'Nitrogen',NA,NA,
                               'phosphorus',NA,NA,
                               'potassium',NA,NA,
                               'Nitrogen',NA,NA),
                  method=rep(c('a','b','c'),9),
                  no_of_fruits=c(rep(5,3),rep(2,3),rep(6,3),rep(8,3),rep(7,3),rep(4,3),rep(3,3),rep(1,3),rep(9,3)),
                  mean=c(rnorm(27, 50, 20)),
                  lower_ci=c(rnorm(27,20,20)),
                  upper_ci=c(rnorm(27,80,20)),
                  pval=c(rnorm(27,0.005,0.001)),
                  x=c(rnorm(1,0.004,0.05),NA,NA,
                      rnorm(1,0.004,0.05),NA,NA,
                      rnorm(1,0.004,0.05),NA,NA,
                      rnorm(1,0.004,0.05),NA,NA,
                      rnorm(1,0.004,0.05),NA,NA,
                      rnorm(1,0.004,0.05),NA,NA,
                      rnorm(1,0.004,0.05),NA,NA,
                      rnorm(1,0.004,0.05),NA,NA,
                      rnorm(1,0.004,0.05),NA,NA),
                  y=c(rnorm(1,0.004,0.0003),NA,NA,
                      rnorm(1,0.004,0.0003),NA,NA,
                      rnorm(1,0.004,0.0003),NA,NA,
                      rnorm(1,0.004,0.0003),NA,NA,
                      rnorm(1,0.004,0.0003),NA,NA,
                      rnorm(1,0.004,0.0003),NA,NA,
                      rnorm(1,0.004,0.0003),NA,NA,
                      rnorm(1,0.004,0.0003),NA,NA,
                      rnorm(1,0.004,0.0003),NA,NA))

上面的数据框中有 27 行数据，但我希望新数据集中总共有 6 行：6 行水果，每个水果行 3 行肥料。

要将数据转换为长格式，我希望原始

dat

数据框中方法行中的每个单元格成为一个单独的列，因此新数据集中的每个单独行将是：

| fruit | fertiliser | no_of_fruits | a_mean | a_lower_ci | a_upper_ci | a_pval | a_x | a_y | b_mean | b_lower_ci | b_upper_ci | b_pval | b_x | b_y | c_mean | c_lower_ci | c_upper_ci | c_pval | c_x | c_y | 

希望这是有道理的！

我尝试使用 for 循环来解决这个问题，但我的尝试似乎很笨拙......

library(tidyverse)

methods <- unique(dat$method)

fert <- unique(dat$fertiliser) # with NAs
fert <- na.omit(fert) # without NAs

fru <- unique(dat$fruit) # with NAs
fru <- na.omit(fru) # without NAs

dat_2 <- data.frame()
longer_row <- NULL

p = 1
# for loop to go through each row in finalised_results_children
for(x in p:length(dat)){
  # the above for loop goes through each fruit:
  # apple, pear, orange
    for(a in 1:length(methods)){
      # the above for loop goes through each of 3 methods: 
      # a,b,c
      method = dat[a,3]
      results=dat[x,4:8]
      
      # rename columns to e.g. 'a_mean'
      columns = colnames(dat[,4:8]) %>%
                str_replace_all(., ' ', '_')
      columns = paste(method, columns, sep = '_')
      colnames(results) = columns
      
      # this seems to be where the problem is!
      # we want to create a (1x(5*3 methods) + x + y) row
      if(is.null(longer_row)){
        longer_row = results
        longer_row = cbind(data.frame(fruit=fru[x]), data.frame(fertiliser=fer[x]), longer_row, dat[x, 9:10])
      }else{
        longer_row = cbind(longer_row, results)
      }
      
    }
    #
    dat_2 <- rbind(dat_2, longer_row)
    longer_row = NULL
  p = p+3
}

任何帮助将不胜感激！

Answer 1

library(tidyverse)

fill(dat,fruit, fertiliser) %>%
    pivot_wider(names_from = method, values_from = mean:y,
                            names_vary = 'slowest',
                            names_glue = '{method}_{.value}')
# A tibble: 9 × 21
  fruit  fertiliser no_of_fruits a_mean a_lower_ci a_upper_ci  a_pval      a_x     a_y b_mean
  <chr>  <chr>             <dbl>  <dbl>      <dbl>      <dbl>   <dbl>    <dbl>   <dbl>  <dbl>
1 apple  phosphorus            5   54.6       15.7       66.6 0.00507  0.0541  0.00445   64.6
2 apple  potassium             2   35.2       27.8       75.8 0.00492  0.00354 0.00406   27.6
3 apple  Nitrogen              6   54.5       20.6       40.5 0.00420 -0.00259 0.00457   40.9
4 pear   phosphorus            8   56.4      -28.2       58.7 0.00558 -0.00305 0.00354   49.2
5 pear   potassium             7   55.3       25.9      116.  0.00413 -0.0480  0.00388   73.4
6 pear   Nitrogen              4   34.8       13.7       95.8 0.00415  0.0385  0.00390   60.2
7 orange phosphorus            3   17.3       44.9      137.  0.00492  0.00892 0.00477   29.5
8 orange potassium             1   18.3       40.5       72.1 0.00480  0.0525  0.00398   56.0
9 orange Nitrogen              9   45.3       41.2       63.4 0.00454 -0.00916 0.00390   39.1
# ℹ 11 more variables: b_lower_ci <dbl>, b_upper_ci <dbl>, b_pval <dbl>, b_x <dbl>, b_y <dbl>,
#   c_mean <dbl>, c_lower_ci <dbl>, c_upper_ci <dbl>, c_pval <dbl>, c_x <dbl>, c_y <dbl>