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DescribeBy格式化output

[英]DescribeBy formatting output

 原文  2021-09-29 08:58:31  1   2    r

问题描述

Since I could not find a suitable solution in other questions, I hope you guys can help me.由于我在其他问题中找不到合适的解决方案，希望大家能帮助我。 I would like the output of the describeBy() function from the psych package to not contain the rows "xxxx.Soll" and "xxxx.Transtyp", but just "xxxx.Feuchte" where xxxx is the respective number.我希望来自 psych package 的 describeBy() function 的 output 不包含行“xxxx.Soll”和“xxxx.Transtyp”，而只包含“xxxx.Feuchte”，其中 xxxx 是相应的数字。 Also, it would be nice to have the number in an extra column.此外，最好将数字放在额外的列中。 I would like to have the results in a neat dataframe so I can export them as.csv or display in a nice table with rmarkdown::paged_table or whatever.我希望将结果整理成 dataframe，这样我就可以将它们导出为 csv 或显示在带有 rmarkdown::paged_table 或其他任何内容的漂亮表格中。 This is my df:这是我的 df：

df <- structure(list(Datum = structure(c(18703, 18703, 18703, 18703, 
18703, 18703, 18703, 18703, 18724, 18724, 18724, 18724, 18724, 
18724, 18724, 18724, 18730, 18730, 18730, 18730, 18730, 18730, 
18730, 18730, 18744, 18744, 18744, 18744, 18744, 18744, 18744, 
18744, 18758, 18758, 18758, 18758, 18758, 18758, 18758, 18758, 
18774, 18774, 18774, 18774, 18774, 18774, 18774, 18774), class = "Date"), 
    Soll = c("1189", "119", "1192", "1202", "149", "172", "2484", 
    "552", "1189", "119", "1192", "1202", "149", "172", "2484", 
    "552", "1189", "119", "1192", "1202", "149", "172", "2484", 
    "552", "1189", "119", "1192", "1202", "149", "172", "2484", 
    "552", "1189", "119", "1192", "1202", "149", "172", "2484", 
    "552", "1189", "119", "1192", "1202", "149", "172", "2484", 
    "552"), Transtyp = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("2", 
    "5"), class = "factor"), Feuchte = c(33.8375, 35.459375, 
    36.6518518518519, 36.1193548387097, 37.8310344827586, 35.8935483870968, 
    33.3625, 36.5032258064516, 26.775, 26.0064516129032, 30.128125, 
    28.50625, 23.0645161290323, 21.45625, 25.340625, 26.446875, 
    30.4375, 31.0466666666667, 32.15625, 30.715625, 29.9875, 
    31.2290322580645, 29.084375, 29.9387096774194, 12.26875, 
    12.7925925925926, 14.6516129032258, 15.428125, 13.159375, 
    13.70625, 12.89375, 14.4, 14.078125, 10.6387096774194, 13.7896551724138, 
    17.071875, 9.690625, 11.6, 10.21875, 13.225, 19.83125, 17.2851851851852, 
    17.441935483871, 19.15, 20.24375, 22.3125, 14.2741935483871, 
    17.358064516129)), row.names = c(NA, -48L), groups = structure(list(
    Datum = structure(c(18703, 18703, 18703, 18703, 18703, 18703, 
    18703, 18703, 18724, 18724, 18724, 18724, 18724, 18724, 18724, 
    18724, 18730, 18730, 18730, 18730, 18730, 18730, 18730, 18730, 
    18744, 18744, 18744, 18744, 18744, 18744, 18744, 18744, 18758, 
    18758, 18758, 18758, 18758, 18758, 18758, 18758, 18774, 18774, 
    18774, 18774, 18774, 18774, 18774, 18774), class = "Date"), 
    Soll = c("1189", "119", "1192", "1202", "149", "172", "2484", 
    "552", "1189", "119", "1192", "1202", "149", "172", "2484", 
    "552", "1189", "119", "1192", "1202", "149", "172", "2484", 
    "552", "1189", "119", "1192", "1202", "149", "172", "2484", 
    "552", "1189", "119", "1192", "1202", "149", "172", "2484", 
    "552", "1189", "119", "1192", "1202", "149", "172", "2484", 
    "552"), .rows = structure(list(1L, 2L, 3L, 4L, 5L, 6L, 7L, 
        8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
        19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
        30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 
        41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, -48L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

The code I have so far is:我到目前为止的代码是：

df %>% ungroup() %>%
        select(-c("Datum")) %>%
        describeBy(., list(.$Soll, .$Transtyp))%>%
        do.call("rbind", .)

It just looks really messy and I cant figure out which variable belongs to which group.它看起来真的很乱，我无法弄清楚哪个变量属于哪个组。 Also, I tried rownames_to_columns to better work with the names, but I get an error.. Any help is really appreciated!此外，我尝试使用 rownames_to_columns 来更好地使用名称，但我收到错误消息。非常感谢任何帮助！ Cheers干杯

2 个解决方案

解决方案1
 2  2021-09-29 09:23:29

Are you looking for such a solution?您正在寻找这样的解决方案吗？

library(psych)
describeBy(
  df,
 list(df$Soll, df$Transtyp) 
)

output: output：

 Descriptive statistics by group 
: 1189
: 2
  vars  n mean sd median trimmed mad min max range skew kurtosis se
1   NA NA   NA NA     NA      NA  NA  NA  NA    NA   NA       NA NA
-------------------------------------------------------------------------------------------------------- 
: 119
: 2
  vars  n mean sd median trimmed mad min max range skew kurtosis se
1   NA NA   NA NA     NA      NA  NA  NA  NA    NA   NA       NA NA
-------------------------------------------------------------------------------------------------------- 
: 1192
: 2
  vars  n mean sd median trimmed mad min max range skew kurtosis se
1   NA NA   NA NA     NA      NA  NA  NA  NA    NA   NA       NA NA
-------------------------------------------------------------------------------------------------------- 
: 1202
: 2
  vars  n mean sd median trimmed mad min max range skew kurtosis se
1   NA NA   NA NA     NA      NA  NA  NA  NA    NA   NA       NA NA
-------------------------------------------------------------------------------------------------------- 
: 149
: 2
  vars  n mean sd median trimmed mad min max range skew kurtosis se
1   NA NA   NA NA     NA      NA  NA  NA  NA    NA   NA       NA NA
-------------------------------------------------------------------------------------------------------- 
: 172
: 2
  vars  n mean sd median trimmed mad min max range skew kurtosis se
1   NA NA   NA NA     NA      NA  NA  NA  NA    NA   NA       NA NA
-------------------------------------------------------------------------------------------------------- 
: 2484
: 2
  vars  n mean sd median trimmed mad min max range skew kurtosis se
1   NA NA   NA NA     NA      NA  NA  NA  NA    NA   NA       NA NA
-------------------------------------------------------------------------------------------------------- 
: 552
: 2
  vars  n mean sd median trimmed mad min max range skew kurtosis se
1   NA NA   NA NA     NA      NA  NA  NA  NA    NA   NA       NA NA
-------------------------------------------------------------------------------------------------------- 
: 1189
: 5
          vars n  mean   sd median trimmed   mad   min   max range  skew kurtosis   se
Datum        1 6   NaN   NA     NA     NaN    NA   Inf  -Inf  -Inf    NA       NA   NA
Soll*        2 6  1.00 0.00    1.0    1.00  0.00  1.00  1.00  0.00   NaN      NaN 0.00
Transtyp*    3 6  2.00 0.00    2.0    2.00  0.00  2.00  2.00  0.00   NaN      NaN 0.00
Feuchte      4 6 22.87 8.85   23.3   22.87 12.13 12.27 33.84 21.57 -0.02       -2 3.61
-------------------------------------------------------------------------------------------------------- 
: 119
: 5
          vars n mean    sd median trimmed   mad   min   max range skew kurtosis   se
Datum        1 6  NaN    NA     NA     NaN    NA   Inf  -Inf  -Inf   NA       NA   NA
Soll*        2 6  1.0  0.00   1.00     1.0  0.00  1.00  1.00  0.00  NaN      NaN 0.00
Transtyp*    3 6  2.0  0.00   2.00     2.0  0.00  2.00  2.00  0.00  NaN      NaN 0.00
Feuchte      4 6 22.2 10.15  21.65    22.2 13.53 10.64 35.46 24.82 0.09       -2 4.14
-------------------------------------------------------------------------------------------------------- 
: 1192
: 5
          vars n  mean   sd median trimmed   mad   min   max range skew kurtosis   se
Datum        1 6   NaN   NA     NA     NaN    NA   Inf  -Inf  -Inf   NA       NA   NA
Soll*        2 6  1.00 0.00   1.00    1.00  0.00  1.00  1.00  0.00  NaN      NaN 0.00
Transtyp*    3 6  2.00 0.00   2.00    2.00  0.00  2.00  2.00  0.00  NaN      NaN 0.00
Feuchte      4 6 24.14 9.99  23.79   24.14 12.98 13.79 36.65 22.86 0.07    -2.14 4.08
-------------------------------------------------------------------------------------------------------- 
: 1202
: 5
          vars n mean   sd median trimmed   mad   min   max range skew kurtosis   se
Datum        1 6  NaN   NA     NA     NaN    NA   Inf  -Inf  -Inf   NA       NA   NA
Soll*        2 6  1.0 0.00   1.00     1.0  0.00  1.00  1.00  0.00  NaN      NaN 0.00
Transtyp*    3 6  2.0 0.00   2.00     2.0  0.00  2.00  2.00  0.00  NaN      NaN 0.00
Feuchte      4 6 24.5 8.44  23.83    24.5 10.11 15.43 36.12 20.69 0.16    -1.99 3.44
-------------------------------------------------------------------------------------------------------- 
: 149
: 5
          vars n  mean    sd median trimmed   mad  min   max range skew kurtosis   se
Datum        1 6   NaN    NA     NA     NaN    NA  Inf  -Inf  -Inf   NA       NA   NA
Soll*        2 6  1.00  0.00   1.00    1.00  0.00 1.00  1.00  0.00  NaN      NaN 0.00
Transtyp*    3 6  2.00  0.00   2.00    2.00  0.00 2.00  2.00  0.00  NaN      NaN 0.00
Feuchte      4 6 22.33 10.47  21.65   22.33 12.47 9.69 37.83 28.14  0.2     -1.7 4.27
-------------------------------------------------------------------------------------------------------- 
: 172
: 5
          vars n mean   sd median trimmed   mad  min   max range skew kurtosis   se
Datum        1 6  NaN   NA     NA     NaN    NA  Inf  -Inf  -Inf   NA       NA   NA
Soll*        2 6  1.0 0.00   1.00     1.0  0.00  1.0  1.00  0.00  NaN      NaN 0.00
Transtyp*    3 6  2.0 0.00   2.00     2.0  0.00  2.0  2.00  0.00  NaN      NaN 0.00
Feuchte      4 6 22.7 9.51  21.88    22.7 12.99 11.6 35.89 24.29 0.16    -1.83 3.88
-------------------------------------------------------------------------------------------------------- 
: 2484
: 5
          vars n  mean   sd median trimmed mad   min   max range skew kurtosis   se
Datum        1 6   NaN   NA     NA     NaN  NA   Inf  -Inf  -Inf   NA       NA   NA
Soll*        2 6  1.00 0.00   1.00    1.00   0  1.00  1.00  0.00  NaN      NaN 0.00
Transtyp*    3 6  2.00 0.00   2.00    2.00   0  2.00  2.00  0.00  NaN      NaN 0.00
Feuchte      4 6 20.86 9.63  19.81   20.86  12 10.22 33.36 23.14 0.11    -2.07 3.93
-------------------------------------------------------------------------------------------------------- 
: 552
: 5
          vars n  mean   sd median trimmed   mad   min  max range skew kurtosis   se
Datum        1 6   NaN   NA     NA     NaN    NA   Inf -Inf  -Inf   NA       NA   NA
Soll*        2 6  1.00 0.00    1.0    1.00  0.00  1.00  1.0  0.00  NaN      NaN 0.00
Transtyp*    3 6  2.00 0.00    2.0    2.00  0.00  2.00  2.0  0.00  NaN      NaN 0.00
Feuchte      4 6 22.98 9.42   21.9   22.98 11.52 13.22 36.5 23.28 0.22    -1.91 3.85

解决方案2
 1 已采纳  2021-09-29 11:24:22

Wouldn't it be just easier to calculate all the statistics that you need for each group yourself.自己计算每个组所需的所有统计数据不是更容易吗？

library(dplyr)
library(psych)
  
result <-  df %>%
    group_by(Soll, Transtyp) %>%
    summarise(n = n(), 
              mean = mean(Feuchte, na.rm = TRUE), 
              sd = sd(Feuchte, na.rm = TRUE), 
              median = median(Feuchte, na.rm = TRUE),
              mad = mad(Feuchte, na.rm = TRUE), 
              min = min(Feuchte, na.rm = TRUE), 
              max = max(Feuchte, na.rm = TRUE), 
              median = median(Feuchte, na.rm = TRUE), 
              skew = skew(Feuchte, na.rm = TRUE), 
              kurtosis = e1071::kurtosis(Feuchte, na.rm = TRUE), 
              .groups = 'drop'
              )

result

#  Soll  Transtyp     n  mean    sd median   mad   min   max    skew kurtosis
#  <chr> <fct>    <int> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
#1 1189  5            6  22.9  8.85   23.3  12.1 12.3   33.8 -0.0213    -2.00
#2 119   5            6  22.2 10.1    21.6  13.5 10.6   35.5  0.0916    -2.00
#3 1192  5            6  24.1  9.99   23.8  13.0 13.8   36.7  0.0720    -2.14
#4 1202  5            6  24.5  8.44   23.8  10.1 15.4   36.1  0.157     -1.99
#5 149   5            6  22.3 10.5    21.7  12.5  9.69  37.8  0.200     -1.70
#6 172   5            6  22.7  9.51   21.9  13.0 11.6   35.9  0.159     -1.83
#7 2484  5            6  20.9  9.63   19.8  12.0 10.2   33.4  0.112     -2.07
#8 552   5            6  23.0  9.42   21.9  11.5 13.2   36.5  0.222     -1.91

You may then use write.csv or similar to write the results to csv.然后您可以使用write.csv或类似的方法将结果写入 csv。

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