Tables assignation for reiterated flextable-CodePudding

I've created a list of titles to be assigned to some tables as follows:

title <- c('P3(400-450).FCz', 'P3(400-450).Cz', 'P3(400-450).Pz',
           'LPPearly(500-700).FCz', 'LPPearly(500-700).Cz',
           'LPPearly(500-700).Pz', 'LPP1(500-1000).FCz', 
           'LPP1(500-1000).Cz', 'LPP1(500-1000).Pz', 
           'LPP2(1000-1500).FCz', 'LPP2(1000-1500).Cz',
           'LPP2(1000-1500).Pz', 'LPP2(1000-1500).POz')

list_1 <- paste0('lsmeans statics of ', title)
list_2 <- paste0('Contrasts of ', title)

titles_tables <-append(list_1, list_2)

If I try running the following code to attribute the proper title (that actaully should return a 13 couple of tables, respectively one for lsmeans statistics and for constrasts)

tables <- md %>% map(~.x %>% 
        map(~broom::tidy(.x) %>% flextable::flextable() %>% 
        colformat_double(digits = 2) %>% theme_box() %>% 
          valign(valign = "center") %>% autofit() %>% 
          set_caption(caption = titles_tables)))

I found that each table present just the name of the first title_tables list element. Anyone knows I could sort propeprly the names to each table?

The dataset is

> dput(head(out_long, 100))
structure(list(ID = c("01", "01", "01", "04", "04", "04", "06", 
"06", "06", "07", "07", "07", "08", "08", "08", "09", "09", "09", 
"10", "10", "10", "11", "11", "11", "12", "12", "12", "13", "13", 
"13", "15", "15", "15", "16", "16", "16", "17", "17", "17", "18", 
"18", "18", "19", "19", "19", "21", "21", "21", "22", "22", "22", 
"23", "23", "23", "25", "25", "25", "27", "27", "27", "28", "28", 
"28", "30", "30", "30", "44", "44", "44", "46", "46", "46", "49", 
"49", "49", "01", "01", "01", "04", "04", "04", "06", "06", "06", 
"07", "07", "07", "08", "08", "08", "09", "09", "09", "10", "10", 
"10", "11", "11", "11", "12"), GR = c("RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP"), SES = c("V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V"), COND = c("NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", 
"NEG-NOC", "NEU-NOC", "NEG-CTR"), signals = c("P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3Cz", "P3Cz", "P3Cz", "P3Cz", "P3Cz", 
"P3Cz", "P3Cz", "P3Cz", "P3Cz", "P3Cz", "P3Cz", "P3Cz", "P3Cz", 
"P3Cz", "P3Cz", "P3Cz", "P3Cz", "P3Cz", "P3Cz", "P3Cz", "P3Cz", 
"P3Cz", "P3Cz", "P3Cz", "P3Cz"), 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, -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)), row.names = c(NA, 
-100L), class = c("tbl_df", "tbl", "data.frame"))
>

Here the way I got the lsmeans statistics, which the tables are referring to:

 md1 <- out_long %>%
  group_by(signals) %>%
  do(fit = lmerTest::lmer(value ~ COND   (1 |ID), data = .)) %>% 
  pull(fit) %>% 
  lapply(., function(m) lsmeans(m, pairwise ~ COND, adjust="tukey"))

CodePudding user response：

Consider changing the append to

titles_tables <- Map(c, list_1, list_2)

as append does concatenation of the two lists into a vector, whereas we need to name the nested list elements with each corresponding elements of list_1 and list_2. So, it is better to keep it in a list of vectors by concatenating the corresponding elements.

and then use map2 (as mentioned in the comments). Here, it is a nested list. So, we need two map2. i.e. the first map2, gets the 'md1' and the corresponding list of titles_tables, and within the next map2, it will be looping over the corresponding individual elements

library(dplyr)
library(purrr)
library(flextable)
library(emmeans)
output <- md1 %>%
      map2(titles_tables[seq_along(md1)], ~{
            title <- .y
             .x %>% 
                       map2(title, ~broom::tidy(.x) %>% flextable::flextable() %>% 
                             colformat_double(digits = 2) %>% theme_box() %>% 
                             valign(valign = "center") %>% autofit() %>% 
                             set_caption(caption = .y))
             
             })

-check the output

output[[1]]$lsmeans
output[[1]]$contrasts