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 166 140
18mo 142 166
24mo 126 123A total of \(n=\) 863 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 348 86
male 335 94The 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)Warning in wordcloud::wordcloud(words = wd_ct$word, freq = wd_ct$n, min.freq =
my_thresh, : daddy could not be fit on page. It will not be plotted.
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, : 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, : baby 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, : more 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 =
my_thresh, : thankyou 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, : 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, : bear 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, : 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 =
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, : box 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, : kiss 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, : 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 =
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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, : here 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, : letsgo 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, : 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, : 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, : 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, : toothbrush 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 =
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my_thresh, : chair 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, : frog 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, : drink 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, : 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, : 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, : 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, : balloon 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, : knee 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, : 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 =
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my_thresh, : bellybutton 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, : jump 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, : 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, : dirty 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, : head 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, : trash 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, : five 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, : juice 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, : 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, : bed 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, : cold 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, : shh 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, : out 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, : 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, : spoon 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, : brush 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, : tummy 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, : eye 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, : cookie 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, : paper 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, : dog 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, : hug 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, : 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, : 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, : dance 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, : cow 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, : grapes 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, : buttocks 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, : six 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, : run 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, : inside 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, : lion 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, : go 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, : bug 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, : flower 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, : 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, : sleep 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, : nightnight 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, : 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, : 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, : sock 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, : vroom 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, : 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, : plate 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, : face 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, : mine 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, : bottle 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, : pants 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, : 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, : 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, : 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, : happy 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, : daddy 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, : 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, : foot 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, : person 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, : penis 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$walk_mos_kea |> fivenum()[1] -2.93 11.70 12.72 14.27 19.00
hv |>
dplyr::filter(walk_mos_who > walk_mos_min, !is.na(walk_mos_who)) |>
ggplot() +
aes(walk_mos_who, fill = child_sex) +
geom_histogram(bins=12) +
theme(legend.position="bottom") +
theme(legend.title = element_blank())
message("Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum): ")Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum): hv$walk_mos_who |> fivenum()[1] -3.29 11.11 12.13 13.58 25.08
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 = 55.876, df = 648, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.8958008 0.9223662
sample estimates:
cor
0.910013 
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 = 15.211, df = 616, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.4627415 0.5776048
sample estimates:
cor
0.52254 
xtabs(formula = ~ age_group + feeding_breastfeed, data = hv) feeding_breastfeed
age_group donotknow no refused yes
12mo 0 16 0 290
18mo 1 15 0 292
24mo 0 12 1 231hv %>%
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 |
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 281 293 227
changed 4 3 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 16
verygood 79 71 64
excellent 212 215 162
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 169 211 190
yes 137 95 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 157 135 109
refused 0 0 1
yes 149 172 132Manual 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 18 12
birthhospital 249 229 169
birthhospital afterhome 39 49 46
birthhospital donotknow 1 1 1
donotknow 1 5 6
no 9 6 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 48 57 51
afterhome donotknow 0 1 0
birthhospital 72 81 61
birthhospital afterhome 29 33 30
birthhospital donotknow 1 1 1
donotknow 39 42 28
no 116 91 70
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 276 266 177
once 23 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 0 712 1 135
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 1 833 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
1 849 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
27 828 xtabs(formula = ~ mediause.home_technology.dvd, hv)mediause.home_technology.dvd
FALSE TRUE
567 288 xtabs(formula = ~ mediause.home_technology.computer, hv)mediause.home_technology.computer
FALSE TRUE
33 822 xtabs(formula = ~ mediause.home_technology.ipad, hv)mediause.home_technology.ipad
FALSE TRUE
232 623 xtabs(formula = ~ mediause.home_technology.educationalgame, hv)mediause.home_technology.educationalgame
FALSE TRUE
660 195 xtabs(formula = ~ mediause.home_technology.videogame, hv)mediause.home_technology.videogame
FALSE TRUE
353 502 xtabs(formula = ~ mediause_technology_child_tv, hv)mediause_technology_child_tv
no yes
121 705 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
246 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
588 233 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
349 274 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
57 138 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
433 69 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
132 108 139 414 61 xtabs(formula = ~ mediause_techuse_playtime, hv)mediause_techuse_playtime
likely neutral neverdothis notverylikely verylikely
284 260 26 236 48 xtabs(formula = ~ mediause_techuse_bedtime, hv)mediause_techuse_bedtime
likely neutral neverdothis notverylikely verylikely
54 50 468 249 30 xtabs(formula = ~ mediause_techuse_transportation, hv)mediause_techuse_transportation
likely neutral neverdothis notverylikely verylikely
118 84 375 236 38 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
305 551 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 21 148
TRUE 295 86xtabs(formula = ~ pets_indoors_outdoors, hv)pets_indoors_outdoors
indoors outdoors
518 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
101 752 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
138 718 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
207 647 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
115 741 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