How do you pull nested data from the same source to make a tibble?
women %>% lm(height~1+.,.) %>% .$fitted.values # THIS WORKS
women %>% lm(height~1+.,.) %>% .$model # THIS WORKS
women %>% lm(height~1+.,.) %>% as_tibble(.$model, .$fitted.values) # THIS DOESN'T WORK
Pipes generally put the Left-Hand Side into the Right-Hand Side as the first argument, and that’s Not Ideal in this case. If you use the data pronoun . to manually specify position, the pipe sometimes doesn’t implicitly include it as the first argument—but exactly when that happens is a little tricky.
if you enclose that last part of the pipe in braces, you can ensure that the pronoun isn’t placed first:
women %>% lm(height~1+.,.) %>% {as_tibble(.$model, .$fitted.values)}
This way the pipe evaluates to as_tibble(.$model, .$fitted.value) and not as_tibble(., .$model, .$fitted.value). (I only just learnt this trick reading your post! Wish I’d known it earlier 
)
An alternative to @rensa 's answer is to use the %$% operator from magrittr. It exposes only the variables from the LHS object in the RHS object (Note that model is a data.frame and fitted.values is a vector so you need to turn fitted.values into a data.frame to bind them).
library(dplyr)
library(magrittr)
women %>%
lm(height ~ 1 + ., data = .) %$%
bind_cols(model, tibble(fitted.values))
#> height weight fitted.values
#> 1 58 115 58.75712
#> 2 59 117 59.33162
#> 3 60 120 60.19336
#> 4 61 123 61.05511
#> 5 62 126 61.91686
#> 6 63 129 62.77861
#> 7 64 132 63.64035
#> 8 65 135 64.50210
#> 9 66 139 65.65110
#> 10 67 142 66.51285
#> 11 68 146 67.66184
#> 12 69 150 68.81084
#> 13 70 154 69.95984
#> 14 71 159 71.39608
#> 15 72 164 72.83233
But of course the easiest way to do this specific example might be to use broom::augment().
women %>%
lm(height ~ 1 + ., data = .) %>%
broom::augment()
#> height weight .fitted .se.fit .resid .hat .sigma
#> 1 58 115 58.75712 0.2002641 -0.75711680 0.20712077 0.3866824
#> 2 59 117 59.33162 0.1879640 -0.33161526 0.18245976 0.4456021
#> 3 60 120 60.19336 0.1703858 -0.19336294 0.14992863 0.4539882
#> 4 61 123 61.05511 0.1541707 -0.05511062 0.12274998 0.4576921
#> 5 62 126 61.91686 0.1397938 0.08314170 0.10092380 0.4573072
#> 6 63 129 62.77861 0.1278767 0.22139402 0.08445008 0.4531106
#> 7 64 132 63.64035 0.1191595 0.35964634 0.07332884 0.4451279
#> 8 65 135 64.50210 0.1143764 0.49789866 0.06756007 0.4331457
#> 9 66 139 65.65110 0.1149121 0.34890175 0.06819443 0.4459639
#> 10 67 142 66.51285 0.1204412 0.48715407 0.07491476 0.4340420
#> 11 68 146 67.66184 0.1336164 0.33815716 0.09220125 0.4464007
#> 12 69 150 68.81084 0.1517996 0.18916026 0.11900325 0.4542973
#> 13 70 154 69.95984 0.1734227 0.04016335 0.15532075 0.4578334
#> 14 71 159 71.39608 0.2036096 -0.39608278 0.21409880 0.4394719
#> 15 72 164 72.83233 0.2360451 -0.83232892 0.28774483 0.3587713
#> .cooksd .std.resid
#> 1 0.4876664537 -1.93227145
#> 2 0.0775185018 -0.83346757
#> 3 0.0200312397 -0.47660030
#> 4 0.0012509293 -0.13371569
#> 5 0.0022285683 0.19926427
#> 6 0.0127513262 0.52581582
#> 7 0.0285208433 0.84902718
#> 8 0.0497413772 1.17176170
#> 9 0.0246883563 0.82138974
#> 10 0.0536443631 1.15102361
#> 11 0.0330356154 0.80655238
#> 12 0.0141663366 0.45798528
#> 13 0.0009067618 0.09930995
#> 14 0.1404228626 -1.01533829
#> 15 1.0146442939 -2.24122754
Created on 2018-01-31 by the reprex package (v0.1.1.9000).
As an alternative to @davis answer, you can also use some of the tidyverse tools to get all this workflow done keeping the structure of a table.
library(tidyverse)
tab <- women %>%
as_tibble() %>%
nest() %>%
mutate(model = map(data, ~ lm(height ~ 1 + ., data = .x)),
augmented = map(model, broom::augment))
tab
#> # A tibble: 1 x 3
#> data model augmented
#> <list> <list> <list>
#> 1 <tibble [15 x 2]> <S3: lm> <data.frame [15 x 9]>
You can see that everything is stored in the tibble, inside list column. Notice
the model column that contains the lm objects in a list.
you can then use unnest to get the result table you want.
tab %>%
unnest(augmented)
#> # A tibble: 15 x 9
#> height weight .fitted .se.fit .resid .hat .sigma .cooksd .std.resid
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 58.0 115 58.8 0.200 -0.757 0.207 0.387 0.488 -1.93
#> 2 59.0 117 59.3 0.188 -0.332 0.182 0.446 0.0775 -0.833
#> 3 60.0 120 60.2 0.170 -0.193 0.150 0.454 0.0200 -0.477
#> 4 61.0 123 61.1 0.154 -0.0551 0.123 0.458 0.00125 -0.134
#> 5 62.0 126 61.9 0.140 0.0831 0.101 0.457 0.00223 0.199
#> 6 63.0 129 62.8 0.128 0.221 0.0845 0.453 0.0128 0.526
#> 7 64.0 132 63.6 0.119 0.360 0.0733 0.445 0.0285 0.849
#> 8 65.0 135 64.5 0.114 0.498 0.0676 0.433 0.0497 1.17
#> 9 66.0 139 65.7 0.115 0.349 0.0682 0.446 0.0247 0.821
#> 10 67.0 142 66.5 0.120 0.487 0.0749 0.434 0.0536 1.15
#> 11 68.0 146 67.7 0.134 0.338 0.0922 0.446 0.0330 0.807
#> 12 69.0 150 68.8 0.152 0.189 0.119 0.454 0.0142 0.458
#> 13 70.0 154 70.0 0.173 0.0402 0.155 0.458 0.000907 0.0993
#> 14 71.0 159 71.4 0.204 -0.396 0.214 0.439 0.140 -1.02
#> 15 72.0 164 72.8 0.236 -0.832 0.288 0.359 1.01 -2.24
or without using broom
women %>%
as_tibble() %>%
nest() %>%
mutate(lm = map(data, ~ lm(height ~ 1 + ., data = .x)),
fitted = map(lm, "fitted.values")) %>%
unnest(data, fitted)
#> # A tibble: 15 x 3
#> fitted height weight
#> <dbl> <dbl> <dbl>
#> 1 58.8 58.0 115
#> 2 59.3 59.0 117
#> 3 60.2 60.0 120
#> 4 61.1 61.0 123
#> 5 61.9 62.0 126
#> 6 62.8 63.0 129
#> 7 63.6 64.0 132
#> 8 64.5 65.0 135
#> 9 65.7 66.0 139
#> 10 66.5 67.0 142
#> 11 67.7 68.0 146
#> 12 68.8 69.0 150
#> 13 70.0 70.0 154
#> 14 71.4 71.0 159
#> 15 72.8 72.0 164
This kind of workflow allow to apply model by group for example
# dummy dataset
women %>%
as_tibble() %>%
mutate(type = "C1") %>%
bind_rows(women %>% mutate(type = "C2")) %>%
# nesting by type, get list column of one element per type
nest(-type) %>%
# applying function using map to iterate through list column
mutate(model = map(data, ~ lm(height ~ 1 + ., data = .x)),
augmented = map(model, broom::augment))
#> # A tibble: 2 x 4
#> type data model augmented
#> <chr> <list> <list> <list>
#> 1 C1 <tibble [15 x 2]> <S3: lm> <data.frame [15 x 9]>
#> 2 C2 <tibble [15 x 2]> <S3: lm> <data.frame [15 x 9]>
Created on 2018-01-31 by the reprex package (v0.1.1.9000).