How to report REPEATED MEASURES ANOVA output in tables on a word text file?

Question

I would like to export tables for the following result for a repeated measure anova:

Here the function which ANOVA test has been implemented

fAddANOVA = function(data) data %>% 
  ezANOVA(dv = .(value), wid = .(ID), within = .(COND)) %>% as_tibble()

And here the commands to explore ANOVA statistics

aov_stats <- df_join %>% group_by(signals) %>% 
  mutate(ANOVA = map(data, ~fAddANOVA(.x))) %>% 
  dplyr::select(., -data) %>% 
  unnest(ANOVA)

> aov_stats
# A tibble: 12 x 4
# Groups:   signals [12]
   signals     ANOVA$Effect  $DFn  $DFd      $F      $p $`p<.05`    $ges `Mauchly's Test~    $W     $p $`p<.05` `Sphericity Cor~  $GGe $`p[GG]` $`p[GG]<.05`  $HFe $`p[HF]` $`p[HF]<.05`
   <chr>       <chr>        <dbl> <dbl>   <dbl>   <dbl> <chr>      <dbl> <chr>            <dbl>  <dbl> <chr>    <chr>            <dbl>    <dbl> <chr>        <dbl>    <dbl> <chr>       
 1 P3FCz       COND             2    48  0.0440 9.57e-1 ""       3.38e-4 COND             0.938 0.480  ""       COND             0.942  9.50e-1 ""           1.02   9.57e-1 ""          
 2 P3Cz        COND             2    48  0.594  5.56e-1 ""       6.30e-3 COND             0.846 0.147  ""       COND             0.867  5.33e-1 ""           0.928  5.44e-1 ""          
 3 P3Pz        COND             2    48  5.18   9.22e-3 "*"      4.28e-2 COND             0.989 0.885  ""       COND             0.990  9.46e-3 "*"          1.08   9.22e-3 "*"         
 4 LPPearlyFCz COND             2    48  3.59   3.52e-2 "*"      2.40e-2 COND             0.997 0.965  ""       COND             0.997  3.54e-2 "*"          1.09   3.52e-2 "*"         
 5 LPPearlyCz  COND             2    48  7.09   2.00e-3 "*"      6.87e-2 COND             0.949 0.549  ""       COND             0.952  2.40e-3 "*"          1.03   2.00e-3 "*"         
 6 LPPearlyPz  COND             2    48 13.9    1.70e-5 "*"      1.14e-1 COND             0.948 0.544  ""       COND             0.951  2.53e-5 "*"          1.03   1.70e-5 "*"         
 7 LPP1FCz     COND             2    48  4.56   1.54e-2 "*"      2.92e-2 COND             0.849 0.151  ""       COND             0.868  2.02e-2 "*"          0.930  1.78e-2 "*"         
 8 LPP1Cz      COND             2    48  7.05   2.07e-3 "*"      6.37e-2 COND             0.823 0.107  ""       COND             0.850  3.65e-3 "*"          0.908  2.93e-3 "*"         
 9 LPP1Pz      COND             2    48 13.3    2.52e-5 "*"      9.94e-2 COND             0.774 0.0522 ""       COND             0.815  1.07e-4 "*"          0.867  7.14e-5 "*"         
10 LPP2FCz     COND             2    48  0.286  7.53e-1 ""       2.84e-3 COND             0.734 0.0285 "*"      COND             0.790  7.01e-1 ""           0.836  7.14e-1 ""          
11 LPP2Cz      COND             2    48  1.05   3.59e-1 ""       1.22e-2 COND             0.945 0.520  ""       COND             0.948  3.56e-1 ""           1.03   3.59e-1 ""          
12 LPP2Pz      COND             2    48  2.64   8.15e-2 ""       3.15e-2 COND             0.904 0.314  ""       COND             0.913  8.71e-2 ""           0.984  8.25e-2 ""          
> 

I kindly ask some suggestions for reporting results adopting this two visualizing methods

SOLUTION 1:

three splitted tables on a word doc containing:

1. ANOVA measures, ranging from the first to the eighth column;

2. Machly's Test statistics, from the ninth one to the twelweth columns as follows in the tibble so that also the column containing the signals which these statistics are referred to is reported as well;

3. Spherificity test, from the thirtheenth one to the ending column, always including the signals columns;

SOLUTION 2:

An one table in a way

1. to get rid of the redundant one (or COND)

2. and above of each results columns chunk (ANOVA(3-8), Mauchly's test (10-12) and Sphericity test(14-19)), grouping surmounting line with the names of the statistics that ranges refers to.

Thank you very much in advance

In case I let the dataset below

      > dput(head(df_join))
structure(list(signals = c("P3FCz", "P3Cz", "P3Pz", "LPPearlyFCz", 
"LPPearlyCz", "LPPearlyPz"), data = list(structure(list(ID = structure(c(1L, 
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 
6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 11L, 11L, 
11L, 12L, 12L, 12L, 13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 
16L, 16L, 16L, 17L, 17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 
20L, 20L, 21L, 21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 
24L, 25L, 25L, 25L), .Label = c("01", "04", "06", "07", "08", 
"09", "10", "11", "12", "13", "15", "16", "17", "18", "19", "21", 
"22", "23", "25", "27", "28", "30", "44", "46", "49"), class = "factor"), 
    GR = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), SES = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), 
    COND = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", "NEG-NOC", "NEU-NOC"
    ), class = "factor"), value = c(-11.6312151716924, -11.1438413285935, 
    -3.99591470944713, -0.314155675382471, 0.238885648959708, 
    5.03749946898385, -0.213621915029167, -2.96032491743069, 
    -1.97168681693488, -2.83109425298642, 1.09291198163802, -6.692991645215, 
    4.23849942428043, 2.9898889629932, 3.5510699900835, 9.57481668808606, 
    5.4167795618285, 1.7067607715475, -6.13036076093477, -2.82955734597919, 
    -2.50672211111696, 0.528517585832501, 8.16418133488309, 1.88777321897925, 
    -7.73588468896919, -9.83058052401056, -6.97442700196932, 
    1.27327945355082, 2.11962397764132, 0.524299677616254, -1.83310726842883, 
    0.658810483381172, -0.261373488428192, 4.37524298634374, 
    0.625555654900511, 3.19617639836154, 0.0405517582137798, 
    -3.29357103412113, -0.381435057304614, -5.73445509910268, 
    -6.1129152355645, -2.45744234877604, 2.95352732001065, 0.527721249096473, 
    1.91803490989119, -3.46703346467546, -2.40438419043702, -5.35374408162217, 
    -7.27028665849262, -7.1532211375959, -5.39955520296854, 2.65765002364624, 
    0.372495441513391, 6.24433066412776, 1.85698518142405, -0.564454675803529, 
    -0.068523080368053, -7.04782633579147, -4.52263283590558, 
    -6.62134671432544, 4.56661945182626, 3.05859761335498, 2.02997952225347, 
    -6.10523962206958, -0.521871236969702, -3.97851995684846, 
    -2.61258020387919, -4.13974828699279, -3.9210032516844, -4.63162466544638, 
    -4.36762718685405, -6.71005969834916, -4.22719611676328, 
    -0.229916506217565, -5.69725200870146)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -75L)), structure(list(
    ID = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 
    4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 
    9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 
    13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
    17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 
    21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 
    25L, 25L), .Label = c("01", "04", "06", "07", "08", "09", 
    "10", "11", "12", "13", "15", "16", "17", "18", "19", "21", 
    "22", "23", "25", "27", "28", "30", "44", "46", "49"), class = "factor"), 
    GR = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), SES = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), 
    COND = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", "NEG-NOC", "NEU-NOC"
    ), class = "factor"), value = c(-5.16524399006139, -5.53112490175437, 
    0.621502123415388, 2.23100741241039, 3.96990710862955, 7.75899775608441, 
    -1.30019374375434, -3.59899040898949, -1.92340529575071, 
    2.19344184533265, 5.87900720863083, -5.92378937757888, 2.44958531767688, 
    3.10043497883256, 1.65779442628225, 13.7118233181713, 6.86178446511352, 
    5.31481098188172, -4.13240668697805, 0.162182285588285, 0.142083484505352, 
    5.42592103255673, 14.5496375672716, 4.52018125654081, -2.40677805475299, 
    -5.3832670295207, -1.55736964635117, 3.48359241788107, 4.23167123533126, 
    2.00051785325202, 1.48755216347718, 2.37269462739372, 1.30346907198835, 
    3.89476490634811, 1.87516303240986, 4.36353100770575, 1.9413417416824, 
    -2.22114447555529, -0.015852062711641, -2.76146409940467, 
    -3.51627712447581, 1.01799377568815, 1.74783962328435, 1.1303870721987, 
    2.16398550183836, -3.31557794753334, -1.83920975041768, -6.06703163736936, 
    -8.1566939611461, -9.23030396302541, -4.35545141573936, 0.906302081219897, 
    0.45401759063429, 3.80236232314171, 4.0336657306528, 2.0185967445137, 
    0.835589319243251, -4.6805488231028, -1.20746167339041, -5.50475999427345, 
    4.96594373869991, 4.1349308440931, 3.00187233307059, -5.61465293602653, 
    0.544596077279702, -5.20450410570445, -0.0325220589039272, 
    -2.28038421035601, -2.01375702882255, -1.6547144697087, -0.619979893871085, 
    -4.48258340054462, -1.42281778522059, 2.62315679073783, -4.13736508533355
    )), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-75L)), structure(list(ID = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 
3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 
8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 
13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 21L, 
21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 25L, 25L
), .Label = c("01", "04", "06", "07", "08", "09", "10", "11", 
"12", "13", "15", "16", "17", "18", "19", "21", "22", "23", "25", 
"27", "28", "30", "44", "46", "49"), class = "factor"), GR = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), 
    SES = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), COND = structure(c(1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", 
    "NEG-NOC", "NEU-NOC"), class = "factor"), value = c(11.8802266972569, 
    12.1053426662461, 12.955441582096, 15.0981004360619, 15.4046229884164, 
    16.671036999147, 3.13771453335467, -0.0892565159000666, 2.15365554736525, 
    13.6778924406572, 14.3862738306396, 6.86762877785576, 7.47946451329025, 
    8.93405130318593, 8.45962311067909, 23.4166601996042, 15.1868092142896, 
    9.97183712753913, 6.267521071803, 10.142198458411, 10.6320358418368, 
    12.9998037913548, 20.7052065690674, 11.8852179570666, 15.7899796085713, 
    7.50729833890206, 14.3076172484818, 9.93797956768228, 10.7693238464384, 
    5.04681800218272, 5.16656503460515, 7.87875085817396, 2.29899409536951, 
    10.0135486953849, 5.48278706243332, 7.81908431468528, 8.64382513728869, 
    3.35777109534179, 3.47474629234488, 4.35678644331281, 3.47085321062162, 
    6.56231512354717, 4.93825547529124, 7.33985613752315, 6.81966900599588, 
    6.54487921689425, 7.25872117706077, 1.10301223694429, -0.856423579793706, 
    -0.887835692028378, -0.931653372049331, 5.6617683754256, 
    2.29939831067085, 5.1554825066748, 6.59026080217083, 3.0741733363644, 
    1.80359068950898, 1.63892755704177, 3.857933716935, 0.769316188513939, 
    10.7031907391191, 9.53278894637555, 8.01071628743378, 6.04891324234645, 
    11.1964453850602, 3.46633322373091, 14.4393884282958, 11.2339563353478, 
    7.74933708914689, 7.1182095475238, 7.39260082121406, 0.627435381320771, 
    9.15473202689768, 13.6559037433263, 7.14786907480758)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -75L)), structure(list(
    ID = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 
    4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 
    9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 
    13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
    17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 
    21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 
    25L, 25L), .Label = c("01", "04", "06", "07", "08", "09", 
    "10", "11", "12", "13", "15", "16", "17", "18", "19", "21", 
    "22", "23", "25", "27", "28", "30", "44", "46", "49"), class = "factor"), 
    GR = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), SES = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), 
    COND = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", "NEG-NOC", "NEU-NOC"
    ), class = "factor"), value = c(-11.7785042972793, -9.14927207125904, 
    -7.58190508537766, -4.01515836011381, -6.60165385653499, 
    -2.02861964460179, 4.46729570509601, 2.54036572774646, 2.22923889930115, 
    -0.883620011106743, -2.63569087592267, -2.0629672230873, 
    1.14544537612393, 2.08056674659401, 0.0422658298956365, 13.2986259796748, 
    5.06669915366333, 3.93467692474742, 0.0229069420708053, 4.31923128857779, 
    0.237726051904304, 1.89972383690448, 3.2371880079134, 0.318100791495115, 
    -8.08292381883298, -5.73174008540523, -15.7998485301436, 
    1.75469999857951, 0.677370118816266, -1.8397955509895, 2.55445787016256, 
    -0.380810453692585, 0.62462329496673, 2.61316333850434, 2.68202480583985, 
    1.76690658846479, 0.148635887703097, -0.958853757041888, 
    -3.17305964093897, -7.82526758429289, -6.58557573679886, 
    -4.39207076049089, 2.36752476749952, 0.594715760553033, -0.29794568443312, 
    -4.5365387390683, 0.196832250811775, -2.70852853745588, 0.498995124872827, 
    0.165171574219401, 0.269498974991661, 0.901948386281446, 
    -2.45955661653299, 1.63525170542944, 0.155897732673534, 1.8491735212703, 
    -0.856727109535223, -1.16182571974245, 1.07658425742917, 
    -2.21433585407388, 4.3385479368043, 4.40588599635354, 0.127710423625772, 
    -6.26956613362656, -1.17658595005389, -7.25886366924741, 
    -0.888293709383838, -2.14177059335841, -2.42141595261389, 
    -2.958120275175, -5.1274001953303, -5.32347488769128, -4.41290818553442, 
    -1.21404719262173, -4.23649270310915)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -75L)), structure(list(
    ID = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 
    4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 
    9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 
    13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
    17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 
    21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 
    25L, 25L), .Label = c("01", "04", "06", "07", "08", "09", 
    "10", "11", "12", "13", "15", "16", "17", "18", "19", "21", 
    "22", "23", "25", "27", "28", "30", "44", "46", "49"), class = "factor"), 
    GR = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), SES = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), 
    COND = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", "NEG-NOC", "NEU-NOC"
    ), class = "factor"), value = c(-5.96429031525769, -5.10918437158799, 
    -2.81732229625975, -1.43557366487622, -3.14872157912645, 
    0.160393685024631, 3.52155765271648, 2.10437989449921, 2.70693992810407, 
    5.49897156207812, 5.81171180245335, -1.37301251388987, -0.434363848460157, 
    2.87987510596148, -1.27152670283348, 17.2093269365993, 7.79412746755931, 
    8.11964589961276, 4.95253363860044, 9.50695673265293, 4.15235381401148, 
    6.1294488368639, 8.01447499455337, 0.783414018677801, -1.24197194087055, 
    -0.487178595894761, -9.79031812534203, 4.22150266269492, 
    4.20139847550095, 0.208005397351335, 4.19096721581768, 0.815283302847055, 
    1.48137456347872, 2.0809543999959, 4.35199943309111, 2.84860039832237, 
    3.05879540677983, 2.11976068962167, -0.269002712326028, -2.77155065610474, 
    -2.59002218694999, 0.17928456999128, 2.24515223348079, 1.88805943988563, 
    -0.0920286086411814, -2.00968595029144, 2.59427260100332, 
    -1.27622011197768, 0.588399071755827, -1.43982473126936, 
    1.96978732491278, -0.338674980283045, -1.86484698930706, 
    -0.0154791822607025, 2.55036185373462, 4.42520405730058, 
    -0.599156247027551, 1.60091251589958, 4.7367320574401, -0.192490723623988, 
    4.8452288234686, 5.71745745981867, 1.02554478706585, -4.5951256708181, 
    1.1704842909792, -7.42770276334892, 3.15655538248828, -0.639830772856786, 
    -0.345116641695513, -0.0391030568720636, -2.61585906518491, 
    -2.71685194532693, -1.7348388034111, 1.00287124847525, -2.4844653851482
    )), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-75L)), structure(list(ID = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 
3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 
8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 
13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 21L, 
21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 25L, 25L
), .Label = c("01", "04", "06", "07", "08", "09", "10", "11", 
"12", "13", "15", "16", "17", "18", "19", "21", "22", "23", "25", 
"27", "28", "30", "44", "46", "49"), class = "factor"), GR = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), 
    SES = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), COND = structure(c(1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", 
    "NEG-NOC", "NEU-NOC"), class = "factor"), value = c(8.23981597718437, 
    9.51261484648731, 9.42367409925817, 5.06332653216481, 5.02619159395405, 
    9.07903916629231, 7.56089165217984, 5.49719893790597, 4.91476855238182, 
    13.0320953572069, 10.8414516494484, 5.86927622259489, 3.25309970442897, 
    4.6847880297099, 2.71096740085175, 25.567439566524, 16.3241813617706, 
    13.0990192799703, 11.9200281736866, 14.6901305277101, 9.67397418905514, 
    10.2974302220899, 12.0768070828642, 5.9401530589224, 12.4817579327688, 
    12.419526465857, 1.00612108990875, 9.63063375751153, 10.5631237176538, 
    3.08031473770521, 3.35694102903017, 4.28046277054405, -0.133592200169464, 
    6.9103658689166, 7.64737651416791, 6.75669517393108, 8.5369185279747, 
    7.08645126073423, 4.47409706618326, 4.39617687043259, 3.27924738047746, 
    6.06169418872804, 5.34939694712468, 5.58288092654703, 4.85729686493463, 
    7.38032829587839, 11.7259526759912, 4.95764559864061, 6.24066579989613, 
    3.49843659402445, 4.07498375647916, 3.55732294589389, 1.33918111568512, 
    0.956782967443242, 2.32002496709926, 3.15289777246607, -0.832211906889126, 
    6.39254974438057, 7.0533787627062, 2.97245026797807, 6.23573445580928, 
    7.6052386193207, 2.98791225155534, 3.10850022259445, 8.12060882554471, 
    -0.00459651443883508, 13.5899217198075, 9.93070913311253, 
    8.10285456644801, 5.04464304009428, 2.02262615478956, 1.0510618938653, 
    5.62233873107127, 10.1193593084848, 5.87476640145049)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -75L)))), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -6L), groups = structure(list(
    signals = c("LPPearlyCz", "LPPearlyFCz", "LPPearlyPz", "P3Cz", 
    "P3FCz", "P3Pz"), .rows = structure(list(5L, 4L, 6L, 2L, 
        1L, 3L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -6L), .drop = TRUE))
> 

Rubberize

Answer 1

As for Solution 1:

### Solution 1
library(officer)
library(flextable)

tab_1 <- aov_stats %>% select(signals, ANOVA) %>% as.data.frame()
tab_1 <- flextable(cbind(tab_1[, 1], tab_1[, 2]) %>% rename(signals = `tab_1[, 1]`))
tab_1 <- set_caption(tab_1, "1. ANOVA")

tab_2 <- aov_stats %>% select(signals, `Mauchly's Test for Sphericity`) %>% as.data.frame()
tab_2 <- flextable(cbind(tab_2[, 1], tab_2[, 2]) %>% rename(signals = `tab_2[, 1]`))
tab_2 <- set_caption(tab_2, "2. Mauchly's Test for Sphericity")

tab_3 <- aov_stats %>% select(signals, `Sphericity Corrections`) %>% as.data.frame()
tab_3 <- flextable(cbind(tab_3[, 1], tab_3[, 2]) %>% rename(signals = `tab_3[, 1]`))
tab_3 <- set_caption(tab_2, "3. Sphericity Corrections")

word_export <- read_docx()
body_add_flextable(word_export, tab_1, align = "left", split = FALSE)
body_add_par(word_export, value = "")
body_add_flextable(word_export, tab_2, align = "left", split = FALSE)
body_add_par(word_export, value = "")
body_add_flextable(word_export, tab_3, align = "left", split = FALSE)
print(word_export, 'ANOVA.docx')

Edit: Solution 2:

### Solution 2
library(flextable)

tab <- aov_stats %>% as.data.frame()
cols <- colnames(cbind(tab[, 1], tab[, 2], tab[, 3], tab[, 4]))[-c(9,13)]
cols <- replace(cols, cols == "tab[, 1]", "signals")

tab <- flextable(cbind(tab[, 1], tab[, 2], tab[, 3], tab[, 4]) %>% setNames(1:19) %>% select(-c(9, 13)))
tab <- delete_part(tab, part = "header")
tab <- add_header_row(tab, values = cols, colwidths = rep(1, 17))
tab <- add_header_row(tab, values = c("", "ANOVA", "Mauchly's Test for Sphericity.", "Sphericity Corrections."), colwidths = c(2, 6, 3, 6))
tab <- theme_box(tab)

Torquemada

Thank you so much. Exaclty what I intended. If you don't mind I would learning something about solution 2. I am pretty curious and impressed how grounded you are – Rubberize

Alright, so I did have some questions about Solution 2. Do you want to keep 1 column for COND or remove all 3 columns? And do you mean the second part as hwo you for example have merged cells in Excel? And if so do you want that within R or in your export file? And if the latter is the case what export format are you looking for? – Torquemada

1) I do want to the the first coulmn for COND, and remove the other teo that are redundant. 2) If you mean for second part, those columns from Mauchly's test till the last column, then yes (I hope to not have misunderstood whwt you intend, but I got that instead of separated tables, thete will a single one with the all statistics gather together and following each other). 3) let's try in R, just in case I'm insterested to export in word (4) then I let you know,. Hope to have been clear. Thanks ;) – Rubberize

Please just take in mind that above each statistics group there would need a line above with the names that statistics refers – Rubberize

Good evening, did you have any success ? :) – Rubberize

Didn't have a look at it yet. Weekend is usually weekend for me ;) Had a look at it now. I think you will like the look of it. Let me know if you have any questions about the code, it looks a bit more abstract than solution 1 I can imagine – Torquemada

It is lovely. Thank ypou so much. Just in case I would like to rewrite this command all in one pipe chain, what do you suggest. I mean I've tried doing it with the first solution but it seems there some problem of matching columns numnbers to be replaced – Rubberize

Some of it you can get through a pipe but not all. The whole word doc part you can put in a pipeline for example but after that I think it becomes pretty difficult to do so. I can dig into it a bit more but I don't think a whole lot more can be done – Torquemada