Demographics table plot elements

read df

Characteristic Helmet Status p-value2
No, N = 1131 Yes, N = 271
Age at Admission 10 (8, 13) 10 (8, 14) 0.6
Race 0.017
African American/Black 29 (26%) 4 (15%)
American Indian/Alaskan Native 7 (6.2%) 0 (0%)
Caucasian 59 (52%) 23 (85%)
Hispanic 17 (15%) 0 (0%)
Other 1 (0.9%) 0 (0%)
Sex 0.009
Female 23 (20%) 12 (44%)
Male 90 (80%) 15 (56%)
Insurance.Status 0.020
None 18 (16%) 4 (15%)
Private 28 (25%) 14 (52%)
Public 67 (59%) 9 (33%)
Collision Type 0.074
Bicycle Only 67 (59%) 21 (78%)
Motor Vehicle Collision 46 (41%) 6 (22%)
Loss.of.Consciousness 0.6
No 65 (58%) 17 (63%)
Unknown 5 (4.4%) 2 (7.4%)
Yes 43 (38%) 8 (30%)
Head Injury 0.015
No 50 (44%) 19 (70%)
Yes 63 (56%) 8 (30%)
Head/Neck Injury 0.14
No 62 (55%) 19 (70%)
Yes 51 (45%) 8 (30%)
Spine.Injury 0.4
No 107 (95%) 24 (89%)
Yes 6 (5.3%) 3 (11%)
1 Median (IQR); n (%)
2 Wilcoxon rank sum test; Fisher's exact test; Pearson's Chi-squared test 
#helmet use 01 = Yes, 02 = No, 03 = Unknown
#sex 1 = female 2 = male
#tbl_summary(df[,c(3:5,9,15)],label = `Helmet.Use` ~ "Neuroradiologic Findings",by="Helmet.Use") %>%
#  bold_labels()  %>%add_p()

colfunc <- colorRampPalette(c("lightskyblue", "dodgerblue4"))
colfunc(3)
## [1] "#87CEFA" "#4B8EC2" "#104E8B"

Bar graphs for overall helmeting rates

Bar graphs for sub group helmeting rates: sex, race, insurance status, injury type, mechanism of crash, income status (I go back and forth with this one because the results we have are kind of mixed)

Bar graphs for sub group helmeting rates: sex, race, insurance status, injury type, mechanism of crash, income status (I go back and forth with this one because the results we have are kind of mixed) Bar graphs for sub group helmeting rates: injury type, mechanism of crash, income status

some ideas for plot arrangement

p3 + p3a

p4 + p4a

p6 + p8

#p8 + p9
p3 + p4 + p6 + p8 + plot_layout(ncol = 2, guides = "collect")

Bar graph of the subset (n = 52) specifically in vehicular accidents (referred to in last paragraph of results section) Bar graph to compare our helmeting rates to other studies (if possible)

#df_car <- subset(df3, Mechanism.of.Injury!="2")