centrality_description.md
In statsExpressions: Tidy Dataframes and Expressions with Statistical Details

centrality description works as expected - no missing data

Code
  select(df, -expression)
Output
  # A tibble: 12 x 14
     Species    Sepal.Length std.dev   iqr conf.low conf.high   min   max skewness
     <fct>             <dbl>   <dbl> <dbl>    <dbl>     <dbl> <dbl> <dbl>    <dbl>
   1 setosa             5.01   0.352 0.400     4.93      5.09   4.3   5.8    0.120
   2 versicolor         5.94   0.516 0.7       5.82      6.05   4.9   7      0.105
   3 virginica          6.59   0.636 0.750     6.46      6.75   4.9   7.9    0.118
   4 setosa             5     NA     0.400     4.9       5.1    4.3   5.8    0.120
   5 versicolor         5.9   NA     0.7       5.65      6.1    4.9   7      0.105
   6 virginica          6.5   NA     0.750     6.32      6.7    4.9   7.9    0.118
   7 setosa             5      0.352 0.400     4.92      5.09   4.3   5.8    0.120
   8 versicolor         5.91   0.516 0.7       5.81      6.07   4.9   7      0.105
   9 virginica          6.55   0.636 0.750     6.42      6.71   4.9   7.9    0.118
  10 setosa             5.02  NA     0.400     5.00      5.05   4.3   5.8    0.120
  11 versicolor         5.75  NA     0.7       5.63      5.85   4.9   7      0.105
  12 virginica          6.40  NA     0.750     6.34      6.42   4.9   7.9    0.118
     kurtosis n.obs missing.obs n.expression              mad
        <dbl> <int>       <int> <chr>                   <dbl>
   1  -0.253     50           0 "setosa\n(n = 50)"     NA    
   2  -0.533     50           0 "versicolor\n(n = 50)" NA    
   3   0.0329    50           0 "virginica\n(n = 50)"  NA    
   4  -0.253     50           0 "setosa\n(n = 50)"      0.297
   5  -0.533     50           0 "versicolor\n(n = 50)"  0.519
   6   0.0329    50           0 "virginica\n(n = 50)"   0.593
   7  -0.253     50           0 "setosa\n(n = 50)"     NA    
   8  -0.533     50           0 "versicolor\n(n = 50)" NA    
   9   0.0329    50           0 "virginica\n(n = 50)"  NA    
  10  -0.253     50           0 "setosa\n(n = 50)"     NA    
  11  -0.533     50           0 "versicolor\n(n = 50)" NA    
  12   0.0329    50           0 "virginica\n(n = 50)"  NA

Code
  df[["expression"]]
Output
  [[1]]
  list(widehat(mu)[mean] == "5.01")

  [[2]]
  list(widehat(mu)[mean] == "5.94")

  [[3]]
  list(widehat(mu)[mean] == "6.59")

  [[4]]
  list(widehat(mu)[median] == "5.000")

  [[5]]
  list(widehat(mu)[median] == "5.900")

  [[6]]
  list(widehat(mu)[median] == "6.500")

  [[7]]
  list(widehat(mu)[trimmed] == "5.000")

  [[8]]
  list(widehat(mu)[trimmed] == "5.910")

  [[9]]
  list(widehat(mu)[trimmed] == "6.547")

  [[10]]
  list(widehat(mu)[MAP] == "5.02")

  [[11]]
  list(widehat(mu)[MAP] == "5.75")

  [[12]]
  list(widehat(mu)[MAP] == "6.40")

centrality description works as expected - missing data

Code
  select(df_na, -expression)
Output
  # A tibble: 16 x 14
     condition desire std.dev   iqr conf.low conf.high   min   max skewness
     <chr>      <dbl>   <dbl> <dbl>    <dbl>     <dbl> <dbl> <dbl>    <dbl>
   1 HDHF        7.85    2.47   4       7.50      8.20   0      10   -1.13 
   2 HDLF        6.74    3.11   5       6.22      7.26   0      10   -0.740
   3 LDHF        7.38    2.52   3.5     7.01      7.88   0.5    10   -0.947
   4 LDLF        5.72    2.71   4       5.34      6.10   0      10   -0.132
   5 HDHF        8.75   NA      4       8         9.88   0      10   -1.13 
   6 HDLF        8      NA      5       6         8.5    0      10   -0.740
   7 LDHF        8      NA      3.5     7.25      8.5    0.5    10   -0.947
   8 LDLF        6      NA      4       5         6.25   0      10   -0.132
   9 HDHF        8.47    2.47   4       7.73      8.67   0      10   -1.13 
  10 HDLF        7.32    3.11   5       6.35      7.77   0      10   -0.740
  11 LDHF        7.88    2.52   3.5     7.12      8.12   0.5    10   -0.947
  12 LDLF        5.72    2.71   4       5.27      6.35   0      10   -0.132
  13 HDHF        9.98   NA      4       9.97      9.99   0      10   -1.13 
  14 HDLF        9.73   NA      5       9.10      9.92   0      10   -0.740
  15 LDHF        9.85   NA      3.5     9.82      9.97   0.5    10   -0.947
  16 LDLF        5.99   NA      4       5.58      6.26   0      10   -0.132
     kurtosis n.obs missing.obs n.expression       mad
        <dbl> <int>       <int> <chr>            <dbl>
   1    0.486    92           0 "HDHF\n(n = 92)" NA   
   2   -0.663    91           0 "HDLF\n(n = 91)" NA   
   3    0.160    91           0 "LDHF\n(n = 91)" NA   
   4   -0.761    93           0 "LDLF\n(n = 93)" NA   
   5    0.486    92           0 "HDHF\n(n = 92)"  1.85
   6   -0.663    91           0 "HDLF\n(n = 91)"  2.97
   7    0.160    91           0 "LDHF\n(n = 91)"  2.97
   8   -0.761    93           0 "LDLF\n(n = 93)"  2.97
   9    0.486    92           0 "HDHF\n(n = 92)" NA   
  10   -0.663    91           0 "HDLF\n(n = 91)" NA   
  11    0.160    91           0 "LDHF\n(n = 91)" NA   
  12   -0.761    93           0 "LDLF\n(n = 93)" NA   
  13    0.486    92           0 "HDHF\n(n = 92)" NA   
  14   -0.663    91           0 "HDLF\n(n = 91)" NA   
  15    0.160    91           0 "LDHF\n(n = 91)" NA   
  16   -0.761    93           0 "LDLF\n(n = 93)" NA

Code
  df_na[["expression"]]
Output
  [[1]]
  list(widehat(mu)[mean] == "7.85")

  [[2]]
  list(widehat(mu)[mean] == "6.74")

  [[3]]
  list(widehat(mu)[mean] == "7.38")

  [[4]]
  list(widehat(mu)[mean] == "5.72")

  [[5]]
  list(widehat(mu)[median] == "8.750")

  [[6]]
  list(widehat(mu)[median] == "8.000")

  [[7]]
  list(widehat(mu)[median] == "8.000")

  [[8]]
  list(widehat(mu)[median] == "6.000")

  [[9]]
  list(widehat(mu)[trimmed] == "8.473")

  [[10]]
  list(widehat(mu)[trimmed] == "7.318")

  [[11]]
  list(widehat(mu)[trimmed] == "7.882")

  [[12]]
  list(widehat(mu)[trimmed] == "5.719")

  [[13]]
  list(widehat(mu)[MAP] == "9.98")

  [[14]]
  list(widehat(mu)[MAP] == "9.73")

  [[15]]
  list(widehat(mu)[MAP] == "9.85")

  [[16]]
  list(widehat(mu)[MAP] == "5.99")

centrality description works when variable is named `variable`

Code
  select(res, -expression)
Output
  # A tibble: 3 x 11
    variable    wt std.dev   iqr   min   max skewness kurtosis n.obs missing.obs
       <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl> <int>       <int>
  1        4  2.29   0.570 0.945  1.51  3.19    0.404  -0.851     11           0
  2        6  3.12   0.356 0.67   2.62  3.46   -0.363  -2.08       7           0
  3        8  4.00   0.759 0.865  3.17  5.42    1.24    0.0780    14           0
    n.expression 
    <chr>        
  1 "4\n(n = 11)"
  2 "6\n(n = 7)" 
  3 "8\n(n = 14)"

Code
  res[["expression"]]
Output
  [[1]]
  list(widehat(mu)[mean] == "2.29")

  [[2]]
  list(widehat(mu)[mean] == "3.12")

  [[3]]
  list(widehat(mu)[mean] == "4.00")