This page shows some simple visualizations of the cleaned aggregate home visit questionnaire data file.
Load dependencies.
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(wordcloud))
suppressPackageStartupMessages(library(RColorBrewer))
source("../R/mcdi_viz_wordcloud.R")
source("../R/ecbq_plot.R")
source("../R/ecbq_plot_chunk.R")Load saved “latest” data file.
hv <- readr::read_csv("../data/csv/home_visit/agg/PLAY-non-mcdi-kobo-latest.csv", show_col_types = FALSE)Child age in months (age_group) by child_sex.
Note: The child’s exact age in months is part of the Databrary-related data. That is on the work plan.
home_visit_filtered <- hv |>
dplyr::filter(!is.na(age_group),
!is.na(child_sex))xtabs(formula = ~ age_group + child_sex,
data = home_visit_filtered) child_sex
age_group female male
12mo 169 146
18mo 152 171
24mo 130 124A total of \(n=\) 892 mother-infant dyads have been tested. This includes training and pilot visits.
home_visit_filtered |>
ggplot() +
aes(age_group, fill = child_sex) +
geom_bar() +
theme(legend.position = "bottom") +
theme(legend.title = element_blank())
To calculate cumulative visits, we have to add an index variable
df <- home_visit_filtered |>
dplyr::select(test_date, site_id) |>
dplyr::mutate(test_date = as.Date(test_date)) |>
dplyr::arrange(test_date) |>
dplyr::mutate(n_visits = seq_along(test_date))df |>
dplyr::filter(!is.na(test_date),
!is.na(n_visits),
!is.na(site_id)) |>
ggplot() +
aes(test_date, n_visits) +
geom_point()
df <- hv |>
dplyr::mutate(language_child = stringr::str_replace_all(language_child, " ", "_"))
xtabs(formula = ~ child_sex + language_child, data = df) language_child
child_sex english english_spanish
female 364 87
male 346 95The current workflow seems to omit Spanish-only families. This must be investigated further.
We have temporarily disabled these visualizations while we conform them to a newer workflow.
eng_12 <-
readr::read_csv(
file.path(here::here(), "data/csv/home_visit/agg/mcdi_english_12_combined.csv"),
col_types = readr::cols(.default = 'c'),
show_col_types = FALSE
)There are some duplicate columns we must merge.
eng_12_long <- eng_12 |>
dplyr::mutate(bath = case_when(!is.na(bath_1) ~ bath_1,
!is.na(bath_2) ~ bath_2,
!is.na(bath_3) ~ bath_3,
!is.na(bath_4) ~ bath_4)) |>
dplyr::select(-contains("bath_")) |>
dplyr::mutate(choo_choo = case_when(!is.na(choo_choo) ~ choo_choo,
!is.na(choochoo) ~ choochoo)) |>
dplyr::select(-choochoo) |>
dplyr::mutate(I = case_when(!is.na(I) ~ I,
!is.na(i) ~ i)) |>
dplyr::select(-i) |>
dplyr::mutate(mommy = case_when(!is.na(mommy_1) ~ mommy_1,
!is.na(mommy_2) ~ mommy_2)) |>
dplyr::select(-contains("mommy_")) |>
dplyr::mutate(outside = case_when(!is.na(outside_1) ~ outside_1,
!is.na(outside_2) ~ outside_2)) |>
dplyr::select(-contains("outside_")) |>
dplyr::mutate(please = case_when(!is.na(please_1) ~ please_1,
!is.na(please_2) ~ please_2)) |>
dplyr::select(-contains("please_")) |>
dplyr::mutate(telephone = case_when(!is.na(telephone) ~ telephone,
!is.na(telelphone) ~ telelphone)) |>
dplyr::select(-telelphone) |>
tidyr::pivot_longer(cols = !(play_id | site_id | subject_number),
names_to = "word",
values_to = "understands_or_says")
xtabs(~ word + understands_or_says, eng_12_long) understands_or_says
word understands understands_says
a 8 0
airplane 26 0
allgone 85 11
also 3 0
animal 19 0
apple 2 0
arms 33 0
away 8 0
baabaa 15 20
baby 84 9
babysitter 3 0
ball 89 25
balloon 30 7
banana 2 0
bath 109 7
be 1 0
bear 24 5
bed 58 1
beepbeep 0 0
bellybutton 2 0
bib 60 0
bicycle 17 1
big 22 2
bird 32 0
bite 46 1
blanket 37 1
blow 29 0
book 101 17
boots 5 0
bottle 78 36
box 26 1
boy 14 0
bread 19 0
break 2 0
broken 4 0
broom 11 1
brother 24 8
brush 36 1
bunny 34 0
button 11 1
bye 91 57
cake 10 1
candy 3 0
car 59 7
cat 49 17
cereal 11 1
chair 29 0
cheese 1 0
chicken 24 1
choo_choo 12 4
church 9 0
clockwatch 17 1
close 47 0
cockadoodledoo 11 2
comb 14 0
cookie 18 3
couch 32 0
cow 29 5
crib 3 0
cry 42 0
cup 59 4
daddy 38 110
dance 80 0
dark 9 0
diaper 89 3
dirty 26 1
dish 3 0
dog 66 34
doll 26 1
dont 62 2
door 42 2
draw 8 0
drawer 2 0
dress 13 0
drum 31 0
duck 33 4
ears 0 0
eat 109 4
egg 45 2
elephant 35 0
eye 37 3
face 1 0
fall 20 0
family 19 0
fast 15 0
feed 25 0
finger 37 0
finish 23 4
firetruck 17 0
flower 25 0
foot 55 0
for 4 0
frog 22 0
girl 11 0
give 1 0
glass 2 0
glasses 38 2
go 46 7
goodmorning 68 0
grandma 73 15
grandpa 62 16
hair 53 1
hammer 4 0
hand 56 0
hat 30 4
have 16 1
head 43 0
help 37 1
here 34 1
hi 82 62
hisher 8 0
hit 29 0
home 46 0
horse 23 0
hot 48 4
how 0 0
hurry 2 0
hurt 24 1
I 12 1
icecream 17 0
in 59 2
juice 4 0
jump 26 0
keys 38 0
kick 22 1
kiss 98 1
kitchen 31 0
kitty 52 13
lady 4 0
lamp 6 0
leg 21 0
light 41 5
lion 23 1
look 77 1
man 2 1
many 6 0
me 16 2
meat 10 0
medicine 19 0
meow 43 13
milk 93 12
mine 27 0
mommy 52 99
money 4 0
monkey 33 0
moo 1 0
moon 11 2
more 73 20
mouse 9 0
mouth 53 3
nap 3 0
necklace 10 0
new 2 0
night 33 3
nightnight 69 9
none 12 0
nose 52 3
now 13 0
on 41 3
onetwothree 39 1
open 69 0
other 3 0
ouch 48 5
out 35 1
outside 80 0
pajamas 42 0
pants 33 1
paper 17 0
party 8 0
pattycake 45 0
pen 14 0
penguin 11 0
picture 34 0
pillow 25 0
plant 16 0
play 66 1
please 32 2
pretty 11 1
purse 7 0
push 34 0
put 61 0
quackquack 21 14
radio 1 0
rain 10 0
refrigerator 25 0
rock 12 1
say 21 0
scissors 1 0
see 46 0
shh 76 10
shirt 3 0
shoe 59 2
sing 30 0
sink 12 0
sister 19 2
sit 61 0
sky 22 0
sleep 71 0
smile 49 1
soap 19 0
sock 51 3
soft 34 0
some 5 0
spoon 57 0
squirrel 18 0
stairs 50 0
stop 4 0
store 13 0
street 8 0
stroller 41 0
sun 21 1
swing 1 0
table 27 0
take 27 0
teddybear 30 3
telephone 48 0
television 23 0
thankyou 56 9
them 3 0
there 26 1
this 22 5
throw 52 0
tickle 49 2
tiger 15 2
tired 57 0
toast 6 0
today 4 0
tomorrow 3 0
tongue 31 0
tooth 29 1
touch 48 0
towel 26 1
toy 83 0
train 20 1
trash 25 1
tree 31 5
truck 41 2
turtle 17 2
uh_oh 69 34
uhoh 7 2
vroom 19 25
wait 50 0
walk 65 0
want 34 0
wash 30 0
water 95 13
where 57 1
who 20 1
window 34 0
woofwoof 51 26
write 2 0
yes 83 13
yumyum 65 19word_ct <- eng_12_long |>
dplyr::filter(!is.na(understands_or_says)) |>
dplyr::filter(understands_or_says == "understands") |>
dplyr::count(word, sort = TRUE)
xtabs(~ word + understands_or_says, eng_12_long) understands_or_says
word understands understands_says
a 8 0
airplane 26 0
allgone 85 11
also 3 0
animal 19 0
apple 2 0
arms 33 0
away 8 0
baabaa 15 20
baby 84 9
babysitter 3 0
ball 89 25
balloon 30 7
banana 2 0
bath 109 7
be 1 0
bear 24 5
bed 58 1
beepbeep 0 0
bellybutton 2 0
bib 60 0
bicycle 17 1
big 22 2
bird 32 0
bite 46 1
blanket 37 1
blow 29 0
book 101 17
boots 5 0
bottle 78 36
box 26 1
boy 14 0
bread 19 0
break 2 0
broken 4 0
broom 11 1
brother 24 8
brush 36 1
bunny 34 0
button 11 1
bye 91 57
cake 10 1
candy 3 0
car 59 7
cat 49 17
cereal 11 1
chair 29 0
cheese 1 0
chicken 24 1
choo_choo 12 4
church 9 0
clockwatch 17 1
close 47 0
cockadoodledoo 11 2
comb 14 0
cookie 18 3
couch 32 0
cow 29 5
crib 3 0
cry 42 0
cup 59 4
daddy 38 110
dance 80 0
dark 9 0
diaper 89 3
dirty 26 1
dish 3 0
dog 66 34
doll 26 1
dont 62 2
door 42 2
draw 8 0
drawer 2 0
dress 13 0
drum 31 0
duck 33 4
ears 0 0
eat 109 4
egg 45 2
elephant 35 0
eye 37 3
face 1 0
fall 20 0
family 19 0
fast 15 0
feed 25 0
finger 37 0
finish 23 4
firetruck 17 0
flower 25 0
foot 55 0
for 4 0
frog 22 0
girl 11 0
give 1 0
glass 2 0
glasses 38 2
go 46 7
goodmorning 68 0
grandma 73 15
grandpa 62 16
hair 53 1
hammer 4 0
hand 56 0
hat 30 4
have 16 1
head 43 0
help 37 1
here 34 1
hi 82 62
hisher 8 0
hit 29 0
home 46 0
horse 23 0
hot 48 4
how 0 0
hurry 2 0
hurt 24 1
I 12 1
icecream 17 0
in 59 2
juice 4 0
jump 26 0
keys 38 0
kick 22 1
kiss 98 1
kitchen 31 0
kitty 52 13
lady 4 0
lamp 6 0
leg 21 0
light 41 5
lion 23 1
look 77 1
man 2 1
many 6 0
me 16 2
meat 10 0
medicine 19 0
meow 43 13
milk 93 12
mine 27 0
mommy 52 99
money 4 0
monkey 33 0
moo 1 0
moon 11 2
more 73 20
mouse 9 0
mouth 53 3
nap 3 0
necklace 10 0
new 2 0
night 33 3
nightnight 69 9
none 12 0
nose 52 3
now 13 0
on 41 3
onetwothree 39 1
open 69 0
other 3 0
ouch 48 5
out 35 1
outside 80 0
pajamas 42 0
pants 33 1
paper 17 0
party 8 0
pattycake 45 0
pen 14 0
penguin 11 0
picture 34 0
pillow 25 0
plant 16 0
play 66 1
please 32 2
pretty 11 1
purse 7 0
push 34 0
put 61 0
quackquack 21 14
radio 1 0
rain 10 0
refrigerator 25 0
rock 12 1
say 21 0
scissors 1 0
see 46 0
shh 76 10
shirt 3 0
shoe 59 2
sing 30 0
sink 12 0
sister 19 2
sit 61 0
sky 22 0
sleep 71 0
smile 49 1
soap 19 0
sock 51 3
soft 34 0
some 5 0
spoon 57 0
squirrel 18 0
stairs 50 0
stop 4 0
store 13 0
street 8 0
stroller 41 0
sun 21 1
swing 1 0
table 27 0
take 27 0
teddybear 30 3
telephone 48 0
television 23 0
thankyou 56 9
them 3 0
there 26 1
this 22 5
throw 52 0
tickle 49 2
tiger 15 2
tired 57 0
toast 6 0
today 4 0
tomorrow 3 0
tongue 31 0
tooth 29 1
touch 48 0
towel 26 1
toy 83 0
train 20 1
trash 25 1
tree 31 5
truck 41 2
turtle 17 2
uh_oh 69 34
uhoh 7 2
vroom 19 25
wait 50 0
walk 65 0
want 34 0
wash 30 0
water 95 13
where 57 1
who 20 1
window 34 0
woofwoof 51 26
write 2 0
yes 83 13
yumyum 65 19# rcb_color_paired <- RColorBrewer::brewer.pal(12, 'Paired')
# wordcloud::wordcloud(words = word_ct$word, freq = word_ct$n, min.freq = quant_25, colors = rcb_color_paired)
mcdi_viz_wordcloud(word_ct, n_participants = dim(eng_12)[1], quantile = .50)
eng_18 <-
readr::read_csv(
file.path(here::here(), "data/csv/home_visit/agg/mcdi_english_18_combined.csv"),
col_types = readr::cols(.default = 'c'),
show_col_types = FALSE
)word_ct <- eng_18 |>
dplyr::mutate(wet = case_when(!is.na(wet_1) ~ wet_1,
!is.na(wet_2) ~ wet_2)) |>
dplyr::select(-contains("wet_")) |>
dplyr::mutate(rain = case_when(!is.na(rain_1) ~ rain_1,
!is.na(rain_2) ~ rain_2,
!is.na(rain_3) ~ rain_3)) |>
dplyr::select(-contains("rain_")) |>
dplyr::mutate(leg = case_when(!is.na(leg_1) ~ leg_1,
!is.na(leg_2) ~ leg_2)) |>
dplyr::rename(candy = candy_1) |>
tidyr::pivot_longer(cols = !(play_id | site_id | subject_number),
names_to = "word",
values_to = "says") |>
dplyr::filter(!is.na(says)) |>
dplyr::filter(says == TRUE) |>
dplyr::count(word, sort = TRUE)
word_ct# A tibble: 354 × 2
word n
<chr> <int>
1 daddy 136
2 mommy 133
3 bye 127
4 uhoh 122
5 ball 118
6 hi 118
7 no 117
8 banana 106
9 woofwoof 106
10 yes 100
# ℹ 344 more rows#wordcloud::wordcloud(words = word_ct$word, freq = word_ct$n, min.freq = quant_25, colors = rcb_color_paired)
mcdi_viz_wordcloud(word_ct, n_participants = dim(eng_18)[1], quantile = .50)
eng_24 <-
readr::read_csv(
file.path(here::here(), "data/csv/home_visit/agg/mcdi_english_24_combined.csv"),
col_types = readr::cols(.default = 'c'),
show_col_types = FALSE
)word_ct <- eng_24 |>
tidyr::pivot_longer(cols = !(play_id | site_id | subject_number),
names_to = "word",
values_to = "says") |>
dplyr::filter(!is.na(says)) |>
dplyr::filter(says == TRUE) |>
dplyr::count(word, sort = TRUE)mcdi_viz_wordcloud(word_ct, n_participants = dim(eng_24)[1], quantile = .50)Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : butterfly could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
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my_thresh, : truck could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : couch could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : hat could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : down could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : this could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : cracker could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : open could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : door could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : baabaa could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : bye could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : egg could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : wet_1 could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : potty could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : please could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : ear could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : mommy could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : tooth could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : house could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : boat could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : hand could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : keys could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : hi could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : yes could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : swing could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : look could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : cup could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : big could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : car could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : choochoo could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : orange could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : hot could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : star could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : walk could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : rock could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : quackquack could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : bathtub could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
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my_thresh, : circle could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : woofwoof could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : allgone could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : ouch could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : no could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : banana could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : cat could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : mouth could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : apple could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : ball could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : monkey could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : where could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : leg_1 could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : cake could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : rain_2 could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : wash could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : milk could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : tree could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : eat could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : airplane could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : yogurt could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : diaper could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : towel could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : fast could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : duck could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : soap could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : horse could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : see could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : one could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : on could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : blanket could not be fit on page. It will not be plotted.Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : sun could not be fit on page. It will not be plotted.
Here are some rare words (quantile < 0.20).
rare_word_ct <- word_ct |>
dplyr::filter(n < dim(eng_24)[1]*.25)
#
# wordcloud::wordcloud(words = rare_word_ct$word, freq = rare_word_ct$n)
mcdi_viz_wordcloud(rare_word_ct, n_participants = dim(eng_24)[1], quantile = .20)Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : meat could not be fit on page. It will not be plotted.
crawl_mos_min <- 4
walk_mos_min <- 6hv |>
dplyr::select(site_id, participant_ID, crawl_mos) |>
dplyr::filter(crawl_mos < crawl_mos_min) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()| site_id | participant_ID | crawl_mos |
|---|---|---|
| CHOPH | 003 | 3.72 |
| PRINU | 020 | -5.69 |
hv |>
dplyr::select(site_id, participant_ID, walk_mos_kea) |>
dplyr::filter(walk_mos_kea < walk_mos_min) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()| site_id | participant_ID | walk_mos_kea |
|---|---|---|
| CSUFL | 011 | 0.76 |
| UMIAM | 018 | 2.76 |
| PRINU | 020 | -2.93 |
hv |>
dplyr::select(site_id, participant_ID, walk_mos_who) |>
dplyr::filter(walk_mos_who < walk_mos_min) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()| site_id | participant_ID | walk_mos_who |
|---|---|---|
| PRINU | 020 | -3.29 |
hv |>
dplyr::filter(crawl_mos > crawl_mos_min, !is.na(crawl_mos)) |>
ggplot() +
aes(crawl_mos, fill = child_sex) +
geom_histogram(bins = 12) +
theme(legend.position = "bottom") +
theme(legend.title = element_blank())
hv |>
dplyr::filter(walk_mos_kea > walk_mos_min, !is.na(walk_mos_kea)) |>
ggplot() +
aes(walk_mos_kea, fill = child_sex) +
theme(legend.position="bottom") +
geom_histogram(bins = 10)
message("Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum): ")Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum): hv$walk_mos_kea |> fivenum()[1] -2.930 11.700 12.750 14.285 19.000
hv |>
dplyr::filter(walk_mos_who > walk_mos_min, !is.na(walk_mos_who)) |>
ggplot() +
aes(walk_mos_who, fill = child_sex) +
geom_histogram(bins=12) +
theme(legend.position="bottom") +
theme(legend.title = element_blank())
message("Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum): ")Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum): hv$walk_mos_who |> fivenum()[1] -3.290 11.110 12.145 13.580 25.080
filtered_walk_mos <- hv |>
dplyr::filter(walk_mos_who > walk_mos_min, !is.na(walk_mos_who),
walk_mos_kea > walk_mos_min, !is.na(walk_mos_kea))
filtered_walk_mos |>
ggplot() +
aes(walk_mos_who, walk_mos_kea, color = child_sex) +
geom_point() +
geom_smooth(method = "lm") +
xlim(8, 18) +
ylim(8, 18) +
theme(legend.position = "bottom") +
theme(aspect.ratio = 1) +
theme(legend.title = element_blank()) -> walk_p
ggExtra::ggMarginal(
walk_p,
play_loco,
walk_mos_who,
walk_mos_kea,
type = "density",
margins = "both",
groupColour = TRUE,
groupFill = TRUE
)`geom_smooth()` using formula = 'y ~ x'Warning: Removed 9 rows containing non-finite outside the scale range
(`stat_smooth()`).`geom_smooth()` using formula = 'y ~ x'Warning: Removed 9 rows containing non-finite outside the scale range
(`stat_smooth()`).Warning: Removed 9 rows containing missing values or values outside the scale range
(`geom_point()`).Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_smooth()`).cor.test(filtered_walk_mos$walk_mos_who, filtered_walk_mos$walk_mos_kea)
Pearson's product-moment correlation
data: filtered_walk_mos$walk_mos_who and filtered_walk_mos$walk_mos_kea
t = 57.466, df = 671, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.8979533 0.9236007
sample estimates:
cor
0.9116606 
filtered_walk_crawl_mos <- hv |>
dplyr::filter(crawl_mos > crawl_mos_min, !is.na(crawl_mos),
walk_mos_kea > walk_mos_min, !is.na(walk_mos_kea))
filtered_walk_crawl_mos |>
ggplot() +
aes(crawl_mos, walk_mos_kea, color = child_sex) +
geom_point() +
geom_smooth(method = "lm") +
theme(legend.position = "bottom") +
theme(aspect.ratio = 1) +
theme(legend.title = element_blank()) -> walk_crawl_p
ggExtra::ggMarginal(
walk_crawl_p,
filtered_walk_crawl_mos,
crawl_mos,
walk_mos_kea,
type = "density",
margins = "both",
groupColour = TRUE,
groupFill = TRUE
)`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'cor.test(filtered_walk_crawl_mos$crawl_mos, filtered_walk_crawl_mos$walk_mos_kea)
Pearson's product-moment correlation
data: filtered_walk_crawl_mos$crawl_mos and filtered_walk_crawl_mos$walk_mos_kea
t = 15.31, df = 639, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.4590090 0.5725085
sample estimates:
cor
0.5180353 
xtabs(formula = ~ age_group + feeding_breastfeed, data = hv) feeding_breastfeed
age_group donotknow no refused yes
12mo 0 17 0 298
18mo 1 15 0 307
24mo 0 13 1 235hv %>%
dplyr::select(child_sex, feeding_solidfood_age) |>
dplyr::filter(feeding_solidfood_age < 12) |>
ggplot() +
aes(x = feeding_solidfood_age, color = child_sex, fill = child_sex) +
geom_histogram(bins = 6) +
xlab("Introduction of solid foods (mos)") +
theme(legend.title = element_blank())
There are some unlikely values here.
hv |>
dplyr::select(participant_ID,
feeding_solidfood_age) |>
dplyr::filter(feeding_solidfood_age > 12 | feeding_solidfood_age < 3) %>%
knitr::kable(format = 'html')| participant_ID | feeding_solidfood_age |
|---|---|
| 996 | 999 |
| 006 | 990 |
| 012 | 990 |
| 031 | 995 |
| 001 | 0 |
| 027 | 990 |
A preliminary look at feeding_comments shows that there are some names mentioned. So, we do not view them here.
We should consider flagging these in the QA process.
These data only focus on child_sleeping_position. Other sleeping data are in the screening questionnaire.
xtabs(formula = ~ child_sleeping_position + age_group, data = hv) age_group
child_sleeping_position 12mo 18mo 24mo
back 290 307 232
changed 4 4 3
donotknow 0 1 0
other 4 1 1
refused 0 0 1
side 5 3 6
sitting 2 0 1
stomach_down 1 1 2
stomach_side 9 6 3child_health_rating <- hv |>
# dplyr::select(child_sex, age_group, child_health) |>
dplyr::filter(!is.na(child_health)) |>
dplyr::mutate(child_health = factor(child_health,
c("poor",
"fair",
"good",
"verygood",
"excellent",
"donotknow",
"refused"),
ordered = TRUE))
xtabs(formula = ~ child_health + age_group, data = child_health_rating) age_group
child_health 12mo 18mo 24mo
poor 0 0 0
fair 2 2 1
good 13 19 17
verygood 80 73 65
excellent 220 228 165
donotknow 0 1 0
refused 0 0 1recent_vax <- hv |>
dplyr::select(child_sex, age_group, child_recent_vaccination) |>
dplyr::filter(!is.na(child_recent_vaccination))
xtabs(formula = ~ child_recent_vaccination + age_group, data = recent_vax) age_group
child_recent_vaccination 12mo 18mo 24mo
donotknow 0 2 4
no 174 224 195
yes 141 97 50seen_specialist <- hv |>
dplyr::select(child_sex, age_group, child_medical_specialist) |>
dplyr::filter(!is.na(child_medical_specialist))
xtabs(formula = ~ child_medical_specialist + age_group, data = seen_specialist) age_group
child_medical_specialist 12mo 18mo 24mo
donotknow 0 1 2
no 160 141 112
refused 0 0 1
yes 155 181 134Manual inspection shows some possible identifying information (names) in child_medical_specialist_comments.
hearing_tested <- hv |>
dplyr::select(child_sex, age_group, child_hearing_tested) |>
dplyr::filter(!is.na(child_hearing_tested))
xtabs(formula = ~ child_hearing_tested + age_group, data = hearing_tested) age_group
child_hearing_tested 12mo 18mo 24mo
afterhome 7 20 13
birthhospital 252 235 173
birthhospital afterhome 42 53 46
birthhospital donotknow 1 1 1
donotknow 2 5 6
no 11 9 9
refused 0 0 1vision_tested <- hv |>
dplyr::select(child_sex, age_group, child_vision_tested) |>
dplyr::filter(!is.na(child_vision_tested))
xtabs(formula = ~ child_vision_tested + age_group, data = vision_tested) age_group
child_vision_tested 12mo 18mo 24mo
afterhome 50 61 51
afterhome donotknow 0 1 0
birthhospital 74 82 61
birthhospital afterhome 29 37 31
birthhospital donotknow 1 1 1
donotknow 41 42 28
no 119 97 74
no donotknow 1 2 2
refused 0 0 1illness_allergy <- hv |>
dplyr::select(child_sex, age_group, contains(c("allergies", "ear_infection",
"asthma",
"respiratory",
"gastrointestinal")))
illness_allergy |>
tidyr::pivot_longer(!c('child_sex', 'age_group'),
names_to = "type",
values_to = "response") |>
dplyr::filter(!is.na(response)) |>
dplyr::mutate(response = factor(response,
c("no", "yes", "donotknow", "refused"))) |>
ggplot() +
aes(x = response, fill = child_sex) +
geom_bar() +
facet_grid(cols = vars(type), rows = vars(age_group)) +
scale_x_discrete(guide = guide_axis(angle = 90)) +
xlab("") +
theme(legend.position = "bottom", legend.title = element_blank())
xtabs(formula = ~ child_injury_times + age_group, data = hv) age_group
child_injury_times 12mo 18mo 24mo
donotknow 1 2 1
never 284 281 182
once 24 31 56
refused 0 0 1
threemore 3 1 1
twice 3 8 8smoking_drinking <- hv |>
dplyr::select(age_group,
child_sex,
contains("smoking"),
contains("drinking")
)
xtabs(formula = ~ mom_pregnant_smoking + mom_pregnant_drinking, smoking_drinking) mom_pregnant_drinking
mom_pregnant_smoking donotknow no refused yes
donotknow 2 1 0 0
no 0 738 1 138
refused 0 0 1 0
yes 0 1 0 2xtabs(formula = ~ smoking_house + smoking_car, smoking_drinking) smoking_car
smoking_house donotknow no refused yes
donotknow 1 1 0 0
no 1 862 0 3
refused 0 4 1 0
yes 0 11 0 3The following may contain identifiable data.
smoking_drinking |>
dplyr::select(smoking_comments) |>
dplyr::filter(!is.na(smoking_comments)) |>
knitr::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ mom_smoking_now, smoking_drinking)mom_smoking_now
donotknow no refused yes
1 878 4 4 smoking_drinking$mom_smoking_now_amount[!is.na(smoking_drinking$mom_smoking_now_amount)][1] 4 3 1 10The following may contain identifiable data.
smoking_drinking |>
dplyr::select(mom_drinking_comments) |>
dplyr::filter(!is.na(mom_drinking_comments)) |>
knitr::kable(format = 'html') |>
kableExtra::kable_classic()phq4 <- hv |>
dplyr::select(child_sex, age_group, contains("phq4_")) |>
dplyr::select(-contains("comments"))
names(phq4) <- gsub(pattern = "phq4_", replacement = "", names(phq4))
phq4 |>
tidyr::pivot_longer(!c('child_sex', 'age_group'),
names_to = "question",
values_to = "response") |>
dplyr::filter(!is.na(response)) |>
dplyr::mutate(response = factor(
response,
c("notatall", "severaldays", "morethanhalf", "nearly")
), ordered = TRUE) |>
ggplot() +
aes(x = response, fill = child_sex) +
geom_bar() +
facet_grid(cols = vars(question), rows = vars(age_group)) +
scale_x_discrete(guide = guide_axis(angle = 90)) +
xlab("") +
theme(legend.position = "bottom", legend.title = element_blank())
The next section provides visualizations of the Rothbart Early Childhood Behavior Questionnaire (ECBQ) data related to child temperament.
ecbq <- hv |>
dplyr::select(child_sex, age_group, contains("ecbq"))
ecbq_complete <- ecbq |>
tidyr::complete() |>
dplyr::select(-ecbq_comments)
ecbq_vars <- names(ecbq)[stringr::str_detect(names(ecbq_complete),"ecbq_")]
# Drop comments
ecbq_vars <- ecbq_vars[!stringr::str_detect(ecbq_vars, "ecbq_comments")]
# Read questions CSV
ecbq_qs_all <- readr::read_csv(file = file.path(here::here(), "data/csv/_meta", "ecbq_questions.csv"), show_col_types = FALSE)
# Make questions data frame
ecbq_qs_df <- ecbq_qs_all |>
dplyr::mutate(var_name = ecbq_vars)Define helper functions.
# Helper function to retrieve long question given short variable name
retrieve_long_q <- function(this_var= "rothbart_unfamiliarperson", data_dict = ecbq_qs_df) {
assertthat::is.string(this_var)
data_dict |>
filter(var_name == this_var) |>
select(question) |>
as.character()
}
# Helper plot function
my_ecbq_plot <- function(var_lbl = "rothbart_unfamiliarperson", df) {
stopifnot(is.data.frame(df))
stopifnot(is.character(var_lbl))
df <- df |> dplyr::select(child_sex, age_group, {{ var_lbl }})
df <- df |>
dplyr::filter(!is.na(.data[[var_lbl]])) |>
dplyr::mutate(rating = factor(
.data[[var_lbl]],
c(
"never",
"very_rarely",
"less_than_half",
"about_half",
"more_than_half",
"almost_always",
"always"
),
ordered = TRUE
))
ggplot(df) +
aes(x = rating, fill = child_sex) +
geom_bar() +
facet_grid(cols = vars(age_group), rows = vars(child_sex)) +
# https://stackoverflow.com/questions/1330989/rotating-and-spacing-axis-labels-in-ggplot2
scale_x_discrete(guide = guide_axis(angle = 90)) +
theme(legend.position = "none") +
ggtitle(paste0("Ratings of '", {{var_lbl}}, "'")) +
xlab("")
}
# Helper chunk function
return_section <- function(var, data) {
chunk_hdr <- knitr::knit_expand(text = c("### Responses for: `{this_var}`", "\n"),
this_var = var)
# Build fig.cap from ground up
fig_name <- paste0("fig-dist-", var)
fig_cap <- paste0("'Distribution of responses to `", var, "`'")
fig_caption <- paste0("fig.cap = ", fig_cap)
plot_chunk_hdr <- paste0("```{r ",
fig_name,
", echo = FALSE, warning = FALSE, ",
fig_caption,
"}")
plot_chunk <- c(plot_chunk_hdr, "print(my_ecbq_plot(var, data))", "```")
question_long <-
paste0("\n**Q**: '", retrieve_long_q(var), "'", "\n")
knitr::knit_child(
text = c(chunk_hdr, question_long, plot_chunk),
envir = environment(),
quiet = TRUE
)
}these_vars <- names(df)
res <- invisible(lapply(ecbq_vars, return_section, data = ecbq_complete))
cat(unlist(res), sep = "\n")ecbq_unfamiliarpersonQ: ‘When approached by an unfamiliar person in a public space (for example, the grocery store), how often did your child cling to a parent?’
ecbq_unfamiliarperson
ecbq_troubletaskQ: ‘While having trouble completing a task (e.g. building, drawing, dressing), how often did your child get easily irritated?’
ecbq_troubletask
ecbq_companyofchildQ: ‘When a familiar child came to your home, how often did your child seek out the company of the child?’
ecbq_companyofchild
ecbq_choiceactivitiesQ: ‘When offered a choice of activities, how often did your child decide what to do very quickly and go for it?’
ecbq_choiceactivities
ecbq_quietlysungQ: ‘During daily or evening quiet time with you and your child, how often did your child enjoy just being quietly sung to?’
ecbq_quietlysung
ecbq_playingoutdoorsQ: ‘While playing outdoors, how often did your child choose to take chances for the fun and excitement of it?’
ecbq_playingoutdoors
ecbq_morethan10Q: ‘While engaged in play with his/her favorite toy, how often did your child play for more than 10 minutes?’
ecbq_morethan10
ecbq_respondingremarksQ: ‘While engaged in play with his/her favorite toy, how often did your child continue to play while at the same time responding to your remarks or questions?’
ecbq_respondingremarks
ecbq_excitedlovedadultsQ: ‘When told that loved adults would visit, how often did your child get very excited?’
ecbq_excitedlovedadults
ecbq_fiddlehairQ: ‘During quiet activities, such as reading a story, how often did your child fiddle with his/her hair, clothing, etc.?’
ecbq_fiddlehair
ecbq_roughrowdyQ: ‘While playing indoors, how often did your child like rough and rowdy games?’
ecbq_roughrowdy
ecbq_rockedhuggedQ: ‘When being gently rocked or hugged, how often did your child seem eager to get away?’
ecbq_rockedhugged
ecbq_involvednewactivityQ: ‘When encountering a new activity, how often did your child get involved immediately?’
ecbq_involvednewactivity
ecbq_tirequicklyQ: ‘When engaged in an activity requiring attention, such as building with blocks, how often did your child tire of the activity relatively quickly?’
ecbq_tirequickly
ecbq_callattentionQ: ‘During everyday activities, how often did your child pay attention to you right away when you called to him/her?’
ecbq_callattention
ecbq_tagsQ: ‘During everyday activities, how often did your child seem to be irritated by tags in his/her clothes?’
ecbq_tags
ecbq_noisyenvironmentQ: ‘During everyday activities, how often did your child become bothered by sound while in a noisy environment?’
ecbq_noisyenvironment
ecbq_energyQ: ‘During everyday activities, how often did your child seem full of energy, even in the evening?’
ecbq_energy
ecbq_vehiclesQ: ‘While in a public place, how often did your child seem afraid of large, noisy vehicles?’
ecbq_vehicles
ecbq_activeQ: ‘When playing outdoors with other children, how often did your child seem to be one of the most active children?’
ecbq_active
ecbq_forbiddenQ: ‘When told no, how often did your child stop the forbidden activity?’
ecbq_forbidden
ecbq_sadlytearfulQ: ‘When told no, how often did your child become sadly tearful?’
ecbq_sadlytearful
ecbq_downblueQ: ‘Following an exciting activity or event, how often did your child seem to feel down or blue?’
ecbq_downblue
ecbq_runhouseQ: ‘When playing indoors, how often did your child run through the house?’
ecbq_runhouse
ecbq_excitingeventQ: ‘Before an exciting event (such as receiving a new toy), how often did your child get very excited about getting it?’
ecbq_excitingevent
ecbq_tempertantrumQ: ‘When he/she asked for something and you said no, how often did your child have a temper tantrum?’
ecbq_tempertantrum
ecbq_waitpatientlyQ: ‘When asked to wait for a desirable item (such as ice cream), how often did your child wait patiently?’
ecbq_waitpatiently
ecbq_rockedsmileQ: ‘When being gently rocked, how often did your child smile?’
ecbq_rockedsmile
ecbq_moldQ: ‘While being held on your lap, how often did your child mold to your body?’
ecbq_mold
ecbq_interactadultQ: ‘When a familiar adult, such as a relative or a friend, visited your home, how often did your child want to interact with the adult?’
ecbq_interactadult
ecbq_carefulQ: ‘When asked to do so, how often was your child able to be careful with something breakable?’
ecbq_careful
ecbq_enternewplaceQ: ‘When visiting a new place, how often did your child not want to enter?’
ecbq_enternewplace
ecbq_crymorethan3Q: ‘When he/she was upset, how often did your child cry for more than 3 minutes, even when being comforted?’
ecbq_crymorethan3
ecbq_easilysoothedQ: ‘When he/she was upset, how often did your child become easily soothed?’
ecbq_easilysoothed
ecbq_busyotherQ: ‘When you were busy, how often did your child find another activity to do when asked?’
ecbq_busyother
ecbq_differentpeopleQ: ‘When around large gatherings of familiar adults or children, how often did your child enjoy playing with a number of different people?’
ecbq_differentpeople
We make use of the ggcorrplot package for some simple visualizations of the ECBQ data.
drop_rothbart_lbl <- function(x) {
stringr::str_remove(x, "rothbart_")
}
make_ecbq_lvls_numeric <- function(x) {
nas <- stringr::str_detect(x, "na")
x[nas] <- NA
x |>
stringr::str_replace_all("never", "1") |>
stringr::str_replace_all("very_rarely", "2") |>
stringr::str_replace_all("less_than_half", "3") |>
stringr::str_replace_all("about_half", "4") |>
stringr::str_replace_all("more_than_half", "5") |>
stringr::str_replace_all("almost_always", "6") |>
stringr::str_replace_all("always", "7") |>
as.numeric()
}
recode_ecbq_lvls <- function(df_col, df) {
df |>
dplyr::case_match(.data[[df_col]], "never" ~ 1,
"very_rarely" ~ 2,
"less_than_half" ~ 3,
"about_half" ~ 4,
"more_than_half" ~ 5,
"almost_always" ~ 6,
"always" ~ 7,
"na" ~ NA)
}
make_ecbq_lvls_nonNA <- function(x) {
these_na_rows <- stringr::str_detect(x, "na")
x[these_na_rows] <- NA
x
}
clean_ecbq_var <- function(x) {
x |>
make_ecbq_lvls_numeric() |>
make_ecbq_lvls_nonNA() |>
as.numeric()
}
ecbq_only <- ecbq_complete |>
dplyr::select(-child_sex, -age_group) |>
dplyr::rename_with(drop_rothbart_lbl)
make_numeric_ecbq <- function(var_lbl, df) {
stopifnot(is.data.frame(df))
stopifnot(is.character(var_lbl))
df <- df |>
dplyr::select({{ var_lbl }})
df
}
# ecbq_only <- ecbq_only |>
# purrr::map(names(ecbq), make_rothbart_lvls_numeric)
# ecbq_corr <- round(cor(ecbq_only), 1)xtabs(formula = ~ mediause.home_technology.tv, hv)mediause.home_technology.tv
FALSE TRUE
30 854 xtabs(formula = ~ mediause.home_technology.dvd, hv)mediause.home_technology.dvd
FALSE TRUE
589 295 xtabs(formula = ~ mediause.home_technology.computer, hv)mediause.home_technology.computer
FALSE TRUE
33 851 xtabs(formula = ~ mediause.home_technology.ipad, hv)mediause.home_technology.ipad
FALSE TRUE
243 641 xtabs(formula = ~ mediause.home_technology.educationalgame, hv)mediause.home_technology.educationalgame
FALSE TRUE
684 200 xtabs(formula = ~ mediause.home_technology.videogame, hv)mediause.home_technology.videogame
FALSE TRUE
370 514 xtabs(formula = ~ mediause_technology_child_tv, hv)mediause_technology_child_tv
no yes
125 727 The following may contain identifiable data.
hv |>
dplyr::select(mediause_tv_how) |>
dplyr::filter(!is.na(mediause_tv_how)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ mediause_technology_child_dvd, hv)mediause_technology_child_dvd
no yes
253 42 The following may contain identifiable data.
hv |>
dplyr::select(mediause_dvd_how) |>
dplyr::filter(!is.na(mediause_dvd_how)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ mediause_technology_child_computer, hv)mediause_technology_child_computer
no yes
612 238 The following may contain identifiable data.
hv |>
dplyr::select(mediause_computer_how) |>
dplyr::filter(!is.na(mediause_computer_how)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ mediause_technology_child_ipad, hv)mediause_technology_child_ipad
no yes
357 284 The following may contain identifiable data.
hv |>
dplyr::select(mediause_ipad_how) |>
dplyr::filter(!is.na(mediause_ipad_how)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ mediause_technology_child_educational, hv)mediause_technology_child_educational
no yes
58 142 The following may contain identifiable data.
hv |>
dplyr::select(mediause_educational_how) |>
dplyr::filter(!is.na(mediause_educational_how)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ mediause_technology_child_videogame, hv)mediause_technology_child_videogame
no yes
443 71 The following may contain identifiable data.
hv |>
dplyr::select(mediause_videogame_how) |>
dplyr::filter(!is.na(mediause_videogame_how)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()The following may contain identifiable data.
Moreover, this is a free-text field that requires further processing to be visualized.
hv |>
dplyr::select(mediause_tv_hrs_per_day) |>
dplyr::filter(!is.na(mediause_tv_hrs_per_day)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ mediause_techuse_meals, hv)mediause_techuse_meals
likely neutral neverdothis notverylikely verylikely
139 115 142 425 62 xtabs(formula = ~ mediause_techuse_playtime, hv)mediause_techuse_playtime
likely neutral neverdothis notverylikely verylikely
292 270 29 242 50 xtabs(formula = ~ mediause_techuse_bedtime, hv)mediause_techuse_bedtime
likely neutral neverdothis notverylikely verylikely
55 53 485 257 30 xtabs(formula = ~ mediause_techuse_transportation, hv)mediause_techuse_transportation
likely neutral neverdothis notverylikely verylikely
121 86 385 247 41 The following may contain identifiable data.
hv |>
dplyr::select(mediause_comments) |>
dplyr::filter(!is.na(mediause_comments)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ pets_at_home, hv)pets_at_home
no yes
313 572 hv$pets_types_number[!is.na(hv$pets_types_number)] |> head(20) [1] "1 dog"
[2] "1 cat"
[3] "1 Turtle"
[4] "2 dogs"
[5] "2 dogs"
[6] "2 pugs and 2 fish"
[7] "1 cat"
[8] "2 Dogs"
[9] "1 dog"
[10] "3 cats"
[11] "2 Cats, 1 bearded dragon, 1 snake, 1 tarantula and 1 frog - (just had a fish but it recently passed away)"
[12] "2 cats"
[13] "7 rabbits"
[14] "2 cats"
[15] "1 dog"
[16] "3 dogs"
[17] "1 dog, 1 cat"
[18] "2 dogs"
[19] "1 dog, 1 cat"
[20] "1 cat" These data need further cleaning to be visualized properly.
hv_pets <- hv |>
dplyr::mutate(dogs = stringr::str_detect(pets_types_number, "[dD]og"),
cats = stringr::str_detect(pets_types_number, "[cC]at"))xtabs(formula = ~ dogs + cats, hv_pets) cats
dogs FALSE TRUE
FALSE 21 154
TRUE 305 91xtabs(formula = ~ pets_indoors_outdoors, hv)pets_indoors_outdoors
indoors outdoors
538 31 There are some identifiable elements in the comments_pets field.
hv |>
dplyr::select(comments_pets) |>
dplyr::filter(!is.na(comments_pets)) |>
knitr::kable(format = 'html') |>
kableExtra::kable_classic()div_labor_levels <- c(0:4)
div_labor_lbls <- c("never", "some_of_the_time", "about_half_the_time", "most_of_the_time", "all_of_the_time")
source("../R/div_labor_plot.R")div_labor_plot("laundry_self", hv)
div_labor_plot("laundry_partner", hv)
div_labor_plot("laundry_otherperson", hv)
div_labor_plot_by_person("laundry", hv)
There may be identifiable elements in the next field, so we do not render it.
hv |>
dplyr::select(labor_laundry_otherperson) |>
dplyr::filter(!is.na(labor_laundry_otherperson)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()div_labor_plot("cleaning_self", hv)
div_labor_plot("cleaning_partner", hv)
div_labor_plot("cleaning_otherperson", hv)
div_labor_plot_by_person("cleaning", hv)
There may be identifiable elements in the next field, so we do not render it.
hv |>
dplyr::select(labor_cleaning_otherperson) |>
dplyr::filter(!is.na(labor_cleaning_otherperson)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()div_labor_plot("dishes_self", hv)
div_labor_plot("dishes_partner", hv)
div_labor_plot("dishes_otherperson", hv)
div_labor_plot_by_person("dishes", hv)
There may be identifiable elements in the next field, so we do not render it.
hv |>
dplyr::select(labor_dishes_otherperson) |>
dplyr::filter(!is.na(labor_dishes_otherperson)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()div_labor_plot("cooking_self", hv)
div_labor_plot("cooking_partner", hv)
div_labor_plot("cooking_otherperson", hv)
div_labor_plot_by_person("cooking", hv)
There may be identifiable elements in the next field, so we do not render it.
hv |>
dplyr::select(labor_cooking_otherperson) |>
dplyr::filter(!is.na(labor_cooking_otherperson)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()div_labor_plot("feeding_self", hv)
div_labor_plot("feeding_partner", hv)
div_labor_plot("feeding_otherperson", hv)
The call to div_labor_plot_by_person() fails with “feeding” because that string is found in other non-division-of-labor variables.
div_labor_plot_by_person("feeding", hv)There may be identifiable elements in the next field, so we do not render it.
hv |>
dplyr::select(labor_feeding_otherperson) |>
dplyr::filter(!is.na(labor_feeding_otherperson)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()div_labor_plot("droppick_self", hv)
div_labor_plot("droppick_partner", hv)
div_labor_plot("droppick_otherperson", hv)
div_labor_plot_by_person("droppick", hv)
There may be identifiable elements in the next field, so we do not render it.
div_labor_plot("dishes_self", hv)hv |>
dplyr::select(labor_feeding_otherperson) |>
dplyr::filter(!is.na(labor_feeding_otherperson)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()div_labor_plot("bed_self", hv)
div_labor_plot("bed_partner", hv)
div_labor_plot("bed_otherperson", hv)
div_labor_plot_by_person("bed", hv)
There may be identifiable elements in the next field, so we do not render it.
hv |>
dplyr::select(labor_bed_otherperson) |>
dplyr::filter(!is.na(labor_bed_otherperson)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()div_labor_plot("disciplining_self", hv)
div_labor_plot("disciplining_partner", hv)
div_labor_plot("disciplining_otherperson", hv)
div_labor_plot_by_person("disciplining", hv)
There may be identifiable elements in the next field, so we do not render it.
hv |>
dplyr::select(labor_disciplining_otherperson) |>
dplyr::filter(!is.na(labor_disciplining_otherperson)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()There may be identifiable elements in the next field, so we do not render it.
hv |>
dplyr::select(comments_division_labor) |>
dplyr::filter(!is.na(comments_division_labor)) |>
kableExtra::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ typical_behavior, hv)typical_behavior
no yes
106 776 The following code will permit visualization of the comments, but the data should be checked for identifiable information before putting it in this document.
hv |>
dplyr::select(typical_behavior_specifics) |>
dplyr::filter(!is.na(typical_behavior_specifics)) |>
knitr::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ typical_activities, hv)typical_activities
no yes
145 740 The following code will permit visualization of the comments, but the data should be checked for identifiable information before putting it in this document.
hv |>
dplyr::select(typical_activities_specifics) |>
dplyr::filter(!is.na(typical_activities_specifics)) |>
knitr::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ typical_nightmorning, hv)typical_nightmorning
no yes
218 665 The following code will permit visualization of the comments, but the data should be checked for identifiable information before putting it in this document.
hv |>
dplyr::select(typical_nightmorning_specifics) |>
dplyr::filter(!is.na(typical_nightmorning_specifics)) |>
knitr::kable(format = 'html') |>
kableExtra::kable_classic()xtabs(formula = ~ typical_otherthanme, hv)typical_otherthanme
no yes
120 765 The following code will permit visualization of the comments, but the data should be checked for identifiable information before putting it in this document.
hv |>
dplyr::select(typical_otherthanme_specifics) |>
dplyr::filter(!is.na(typical_otherthanme_specifics)) |>
knitr::kable(format = 'html') |>
kableExtra::kable_classic()
Comments
Manual inspection shows that the comments field has some names. We omit printing the comments here until we can be assured that there is no identifiying information in the comments.
Code