Homework 1

Author

Jingyi Yang

1. Start Up

knitr::opts_chunk$set(echo = TRUE)
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(here)
here() starts at C:/8-601
library(readr)
library(readxl)
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

2. Import the data

health <- read_csv("C:\\8-601\\Final Project\\Health_conditions_among_children_under_age_18__by_selected_characteristics__United_States.csv")
Rows: 2744 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (8): INDICATOR, PANEL, UNIT, STUB_NAME, STUB_LABEL, YEAR, AGE, FLAG
dbl (8): PANEL_NUM, UNIT_NUM, STUB_NAME_NUM, STUB_LABEL_NUM, YEAR_NUM, AGE_N...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
health %>% print(n = 10, width = Inf)
# A tibble: 2,744 × 16
   INDICATOR                                    
   <chr>                                        
 1 Health conditions among children under age 18
 2 Health conditions among children under age 18
 3 Health conditions among children under age 18
 4 Health conditions among children under age 18
 5 Health conditions among children under age 18
 6 Health conditions among children under age 18
 7 Health conditions among children under age 18
 8 Health conditions among children under age 18
 9 Health conditions among children under age 18
10 Health conditions among children under age 18
   PANEL                                       PANEL_NUM
   <chr>                                           <dbl>
 1 Current asthma among persons under 18 years         1
 2 Current asthma among persons under 18 years         1
 3 ADHD among persons under 18 years                   3
 4 ADHD among persons under 18 years                   3
 5 ADHD among persons under 18 years                   3
 6 Current asthma among persons under 18 years         1
 7 Current asthma among persons under 18 years         1
 8 Current asthma among persons under 18 years         1
 9 Current asthma among persons under 18 years         1
10 Current asthma among persons under 18 years         1
   UNIT                       UNIT_NUM STUB_NAME STUB_NAME_NUM STUB_LABEL    
   <chr>                         <dbl> <chr>             <dbl> <chr>         
 1 Percent of children, crude        1 Total                 0 Under 18 years
 2 Percent of children, crude        1 Total                 0 Under 18 years
 3 Percent of children, crude        1 Age                   1 10-17 years   
 4 Percent of children, crude        1 Age                   1 10-17 years   
 5 Percent of children, crude        1 Age                   1 10-17 years   
 6 Percent of children, crude        1 Total                 0 Under 18 years
 7 Percent of children, crude        1 Total                 0 Under 18 years
 8 Percent of children, crude        1 Total                 0 Under 18 years
 9 Percent of children, crude        1 Total                 0 Under 18 years
10 Percent of children, crude        1 Total                 0 Under 18 years
   STUB_LABEL_NUM YEAR      YEAR_NUM AGE            AGE_NUM ESTIMATE    SE FLAG 
            <dbl> <chr>        <dbl> <chr>            <dbl>    <dbl> <dbl> <chr>
 1           0    1997-1999        1 Under 18 years     0       NA    NA   ...  
 2           0    2000-2002        2 Under 18 years     0       NA    NA   ...  
 3           1.22 1997-1999        1 10-17 years        2.2      7.6   0.2 <NA> 
 4           1.22 2000-2002        2 10-17 years        2.2      9     0.3 <NA> 
 5           1.22 2003-2005        3 10-17 years        2.2      8.9   0.3 <NA> 
 6           0    2003-2005        3 Under 18 years     0        8.7   0.2 <NA> 
 7           0    2006-2008        4 Under 18 years     0        9.3   0.2 <NA> 
 8           0    2007-2009        5 Under 18 years     0        9.4   0.2 <NA> 
 9           0    2008-2010        6 Under 18 years     0        9.5   0.2 <NA> 
10           0    2009-2011        7 Under 18 years     0        9.5   0.2 <NA> 
# ℹ 2,734 more rows

3. Clean the data

The data set includes 16 columns. The column “INDICATOR” consists of the information about the whole data set, which is “Health conditions among children under age 18” and is a characteristic variable. The column “PANEL” is a characteristic variable and includes information about various health issues. The column “PANEL_NUM” represents the code for different health issues. The column “UNIT” indicates the number in the data set focused on percentage. “UNIT_NUM” refers to the code representing the characteristic variables in the “UNIT” column. “STUB_NAME” includes different characteristics for the respondents, like age, race, sex, etc., and the “STUB_NAME_NUM” shows the codes related to them. “STUB_LABEL” means the sub-variables related to the characteristics, like different age groups, female and male for the gender, and column “STUB_LABEL_NUM” involves the number representing them. “YEAR” includes information about different year stages, “YEAR_NUM” represents them, “AGE” and “AGE_NUM” include different age stages and numbers represent them, “ESTIMATE” is the number percent of children, “SE” means the standard error. In the “FLAG” column, “—” means the data is not available, and “*” means the estimate might not be reliable.

To clean the data, delete the columns that include some repeat information, like the number representing the variables, and the columns that only include one information, like the “INDICATOR” column. Besides, arranging the table through “PANEL” and “YEAR” will make the table more in order and easier to understand.

After cleaning the table and referencing a document related to this data set, the research questions are clear about how age, sex, insurance status, poverty status, and race relate to the health condition of children who are under the age of 18.

health_clean <- health %>%
  select(- ("INDICATOR"),
         - ("PANEL_NUM"),
         - ("UNIT"),
         - ("UNIT_NUM"),
         - ("STUB_NAME_NUM"),
         - ("STUB_LABEL_NUM"),
         - ("YEAR_NUM"),
         - ("AGE_NUM"),
         - ("FLAG"),
         -("SE"))%>%
  arrange(PANEL, YEAR)
  

health_clean
# A tibble: 2,744 × 6
   PANEL                             STUB_NAME STUB_LABEL   YEAR  AGE   ESTIMATE
   <chr>                             <chr>     <chr>        <chr> <chr>    <dbl>
 1 ADHD among persons under 18 years Age       10-17 years  1997… 10-1…      7.6
 2 ADHD among persons under 18 years Age       5-17 years   1997… 5-17…      6.5
 3 ADHD among persons under 18 years Age       5-9 years    1997… 5-9 …      4.8
 4 ADHD among persons under 18 years Sex       Male         1997… Unde…      9.6
 5 ADHD among persons under 18 years Sex       Female       1997… Unde…      3.2
 6 ADHD among persons under 18 years Race      White only   1997… Unde…      7.1
 7 ADHD among persons under 18 years Race      Black or Af… 1997… Unde…      5  
 8 ADHD among persons under 18 years Race      American In… 1997… Unde…      8.5
 9 ADHD among persons under 18 years Race      Asian only   1997… Unde…      1.7
10 ADHD among persons under 18 years Race      Native Hawa… 1997… Unde…     NA  
# ℹ 2,734 more rows

4. Divided the data

Dividing the data through different “PANEL,” or various health issues, and then further dive based on “STUB_NAME,” or different characteristics.

4.1”Current asthma among persons under 18 years”

`Current asthma heath_under 18 years` <- health_clean%>%
filter(PANEL=="Current asthma among persons under 18 years", STUB_NAME== "Total")%>%
  arrange(YEAR)
 
`Current asthma heath_under 18 years`
# A tibble: 14 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Current asthma among persons under… Total     Under 18 … 1997… Unde…     NA  
 2 Current asthma among persons under… Total     Under 18 … 2000… Unde…     NA  
 3 Current asthma among persons under… Total     Under 18 … 2003… Unde…      8.7
 4 Current asthma among persons under… Total     Under 18 … 2006… Unde…      9.3
 5 Current asthma among persons under… Total     Under 18 … 2007… Unde…      9.4
 6 Current asthma among persons under… Total     Under 18 … 2008… Unde…      9.5
 7 Current asthma among persons under… Total     Under 18 … 2009… Unde…      9.5
 8 Current asthma among persons under… Total     Under 18 … 2010… Unde…      9.4
 9 Current asthma among persons under… Total     Under 18 … 2011… Unde…      9  
10 Current asthma among persons under… Total     Under 18 … 2012… Unde…      8.7
11 Current asthma among persons under… Total     Under 18 … 2013… Unde…      8.5
12 Current asthma among persons under… Total     Under 18 … 2014… Unde…      8.5
13 Current asthma among persons under… Total     Under 18 … 2015… Unde…      8.4
14 Current asthma among persons under… Total     Under 18 … 2016… Unde…      8.1
`Current asthma heath_age` <- health_clean%>%
  filter(PANEL=="Current asthma among persons under 18 years", STUB_NAME== "Age")%>%
  arrange(YEAR)

`Current asthma heath_age`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Current asthma among persons under… Age       0-4 years  1997… 0-4 …     NA  
 2 Current asthma among persons under… Age       5-17 years 1997… 5-17…     NA  
 3 Current asthma among persons under… Age       5-9 years  1997… 5-9 …     NA  
 4 Current asthma among persons under… Age       10-17 yea… 1997… 10-1…     NA  
 5 Current asthma among persons under… Age       0-4 years  2000… 0-4 …     NA  
 6 Current asthma among persons under… Age       5-17 years 2000… 5-17…     NA  
 7 Current asthma among persons under… Age       5-9 years  2000… 5-9 …     NA  
 8 Current asthma among persons under… Age       10-17 yea… 2000… 10-1…     NA  
 9 Current asthma among persons under… Age       0-4 years  2003… 0-4 …      6.1
10 Current asthma among persons under… Age       5-17 years 2003… 5-17…      9.6
# ℹ 46 more rows
`Current asthma heath_sex` <- health_clean%>%
  filter(PANEL=="Current asthma among persons under 18 years", STUB_NAME== "Sex")%>%
  arrange(YEAR)

`Current asthma heath_sex`
# A tibble: 28 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Current asthma among persons under… Sex       Male       1997… Unde…     NA  
 2 Current asthma among persons under… Sex       Female     1997… Unde…     NA  
 3 Current asthma among persons under… Sex       Male       2000… Unde…     NA  
 4 Current asthma among persons under… Sex       Female     2000… Unde…     NA  
 5 Current asthma among persons under… Sex       Male       2003… Unde…      9.9
 6 Current asthma among persons under… Sex       Female     2003… Unde…      7.3
 7 Current asthma among persons under… Sex       Male       2006… Unde…     10.7
 8 Current asthma among persons under… Sex       Female     2006… Unde…      7.8
 9 Current asthma among persons under… Sex       Male       2007… Unde…     10.8
10 Current asthma among persons under… Sex       Female     2007… Unde…      7.9
# ℹ 18 more rows
`Current asthma heath_race` <- health_clean%>%
  filter(PANEL=="Current asthma among persons under 18 years", STUB_NAME== "Race")%>%
  arrange(YEAR)

`Current asthma heath_race`
# A tibble: 84 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Current asthma among persons under… Race      White only 1997… Unde…       NA
 2 Current asthma among persons under… Race      Black or … 1997… Unde…       NA
 3 Current asthma among persons under… Race      American … 1997… Unde…       NA
 4 Current asthma among persons under… Race      Asian only 1997… Unde…       NA
 5 Current asthma among persons under… Race      Native Ha… 1997… Unde…       NA
 6 Current asthma among persons under… Race      2 or more… 1997… Unde…       NA
 7 Current asthma among persons under… Race      White only 2000… Unde…       NA
 8 Current asthma among persons under… Race      Black or … 2000… Unde…       NA
 9 Current asthma among persons under… Race      American … 2000… Unde…       NA
10 Current asthma among persons under… Race      Asian only 2000… Unde…       NA
# ℹ 74 more rows
`Current asthma heath_Hispanic origin and race` <- health_clean%>%
  filter(PANEL=="Current asthma among persons under 18 years", STUB_NAME== "Hispanic origin and race")%>%
  arrange(YEAR)

`Current asthma heath_Hispanic origin and race`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Current asthma among persons under… Hispanic… Hispanic … 1997… Unde…     NA  
 2 Current asthma among persons under… Hispanic… Not Hispa… 1997… Unde…     NA  
 3 Current asthma among persons under… Hispanic… Not Hispa… 1997… Unde…     NA  
 4 Current asthma among persons under… Hispanic… Not Hispa… 1997… Unde…     NA  
 5 Current asthma among persons under… Hispanic… Hispanic … 2000… Unde…     NA  
 6 Current asthma among persons under… Hispanic… Not Hispa… 2000… Unde…     NA  
 7 Current asthma among persons under… Hispanic… Not Hispa… 2000… Unde…     NA  
 8 Current asthma among persons under… Hispanic… Not Hispa… 2000… Unde…     NA  
 9 Current asthma among persons under… Hispanic… Hispanic … 2003… Unde…      7.6
10 Current asthma among persons under… Hispanic… Not Hispa… 2003… Unde…      8.9
# ℹ 46 more rows
`Current asthma heath_Percent of poverty level` <- health_clean%>%
  filter(PANEL=="Current asthma among persons under 18 years", STUB_NAME== "Percent of poverty level")%>%
  arrange(YEAR)

`Current asthma heath_Percent of poverty level`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Current asthma among persons under… Percent … Below 100% 1997… Unde…     NA  
 2 Current asthma among persons under… Percent … 100%-199%  1997… Unde…     NA  
 3 Current asthma among persons under… Percent … 200%-399%  1997… Unde…     NA  
 4 Current asthma among persons under… Percent … 400% or m… 1997… Unde…     NA  
 5 Current asthma among persons under… Percent … Below 100% 2000… Unde…     NA  
 6 Current asthma among persons under… Percent … 100%-199%  2000… Unde…     NA  
 7 Current asthma among persons under… Percent … 200%-399%  2000… Unde…     NA  
 8 Current asthma among persons under… Percent … 400% or m… 2000… Unde…     NA  
 9 Current asthma among persons under… Percent … 100%-199%  2003… Unde…      8.6
10 Current asthma among persons under… Percent … Below 100% 2003… Unde…     10.4
# ℹ 46 more rows
`Current asthma heath_insurance` <- health_clean%>%
  filter(PANEL=="Current asthma among persons under 18 years", STUB_NAME== "Health insurance status at the time of interview")%>%
  arrange(YEAR)

`Current asthma heath_insurance`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Current asthma among persons under… Health i… Insured    1997… Unde…       NA
 2 Current asthma among persons under… Health i… Insured: … 1997… Unde…       NA
 3 Current asthma among persons under… Health i… Insured: … 1997… Unde…       NA
 4 Current asthma among persons under… Health i… Uninsured  1997… Unde…       NA
 5 Current asthma among persons under… Health i… Insured    2000… Unde…       NA
 6 Current asthma among persons under… Health i… Insured: … 2000… Unde…       NA
 7 Current asthma among persons under… Health i… Insured: … 2000… Unde…       NA
 8 Current asthma among persons under… Health i… Uninsured  2000… Unde…       NA
 9 Current asthma among persons under… Health i… Insured    2003… Unde…        9
10 Current asthma among persons under… Health i… Insured: … 2003… Unde…        8
# ℹ 46 more rows
`Current asthma heath` <-  bind_rows(`Current asthma heath_under 18 years`, `Current asthma heath_age`, `Current asthma heath_sex`, `Current asthma heath_race`, `Current asthma heath_Hispanic origin and race`, `Current asthma heath_Percent of poverty level`, `Current asthma heath_insurance`)

`Current asthma heath`
# A tibble: 350 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Current asthma among persons under… Total     Under 18 … 1997… Unde…     NA  
 2 Current asthma among persons under… Total     Under 18 … 2000… Unde…     NA  
 3 Current asthma among persons under… Total     Under 18 … 2003… Unde…      8.7
 4 Current asthma among persons under… Total     Under 18 … 2006… Unde…      9.3
 5 Current asthma among persons under… Total     Under 18 … 2007… Unde…      9.4
 6 Current asthma among persons under… Total     Under 18 … 2008… Unde…      9.5
 7 Current asthma among persons under… Total     Under 18 … 2009… Unde…      9.5
 8 Current asthma among persons under… Total     Under 18 … 2010… Unde…      9.4
 9 Current asthma among persons under… Total     Under 18 … 2011… Unde…      9  
10 Current asthma among persons under… Total     Under 18 … 2012… Unde…      8.7
# ℹ 340 more rows

4.2 “Asthma attack in last 12 months among persons under 18 years”

`Asthma attack in past 12 months_under 18 years` <- health_clean%>%
filter(PANEL=="Asthma attack in last 12 months among persons under 18 years", STUB_NAME== "Total")%>%
  arrange(YEAR)
 
`Asthma attack in past 12 months_under 18 years`
# A tibble: 14 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Asthma attack in last 12 months am… Total     Under 18 … 1997… Unde…      5.4
 2 Asthma attack in last 12 months am… Total     Under 18 … 2000… Unde…      5.7
 3 Asthma attack in last 12 months am… Total     Under 18 … 2003… Unde…      5.4
 4 Asthma attack in last 12 months am… Total     Under 18 … 2006… Unde…      5.5
 5 Asthma attack in last 12 months am… Total     Under 18 … 2007… Unde…      5.4
 6 Asthma attack in last 12 months am… Total     Under 18 … 2008… Unde…      5.6
 7 Asthma attack in last 12 months am… Total     Under 18 … 2009… Unde…      5.6
 8 Asthma attack in last 12 months am… Total     Under 18 … 2010… Unde…      5.5
 9 Asthma attack in last 12 months am… Total     Under 18 … 2011… Unde…      5.3
10 Asthma attack in last 12 months am… Total     Under 18 … 2012… Unde…      4.9
11 Asthma attack in last 12 months am… Total     Under 18 … 2013… Unde…      4.5
12 Asthma attack in last 12 months am… Total     Under 18 … 2014… Unde…      4.4
13 Asthma attack in last 12 months am… Total     Under 18 … 2015… Unde…      4.5
14 Asthma attack in last 12 months am… Total     Under 18 … 2016… Unde…      4.5
`Asthma attack in past 12 months_age` <- health_clean%>%
  filter(PANEL=="Asthma attack in last 12 months among persons under 18 years", STUB_NAME== "Age")%>%
  arrange(YEAR)

`Asthma attack in past 12 months_age`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Asthma attack in last 12 months am… Age       0-4 years  1997… 0-4 …      4.3
 2 Asthma attack in last 12 months am… Age       5-17 years 1997… 5-17…      5.7
 3 Asthma attack in last 12 months am… Age       5-9 years  1997… 5-9 …      5.6
 4 Asthma attack in last 12 months am… Age       10-17 yea… 1997… 10-1…      5.8
 5 Asthma attack in last 12 months am… Age       0-4 years  2000… 0-4 …      4.7
 6 Asthma attack in last 12 months am… Age       5-17 years 2000… 5-17…      6.1
 7 Asthma attack in last 12 months am… Age       5-9 years  2000… 5-9 …      6.3
 8 Asthma attack in last 12 months am… Age       10-17 yea… 2000… 10-1…      5.9
 9 Asthma attack in last 12 months am… Age       0-4 years  2003… 0-4 …      4.2
10 Asthma attack in last 12 months am… Age       5-17 years 2003… 5-17…      5.8
# ℹ 46 more rows
`Asthma attack in past 12 months_sex` <- health_clean%>%
  filter(PANEL=="Asthma attack in last 12 months among persons under 18 years", STUB_NAME== "Sex")%>%
  arrange(YEAR)

`Asthma attack in past 12 months_sex`
# A tibble: 28 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Asthma attack in last 12 months am… Sex       Male       1997… Unde…      6.2
 2 Asthma attack in last 12 months am… Sex       Female     1997… Unde…      4.5
 3 Asthma attack in last 12 months am… Sex       Male       2000… Unde…      6.6
 4 Asthma attack in last 12 months am… Sex       Female     2000… Unde…      4.7
 5 Asthma attack in last 12 months am… Sex       Male       2003… Unde…      6.3
 6 Asthma attack in last 12 months am… Sex       Female     2003… Unde…      4.4
 7 Asthma attack in last 12 months am… Sex       Male       2006… Unde…      6.2
 8 Asthma attack in last 12 months am… Sex       Female     2006… Unde…      4.7
 9 Asthma attack in last 12 months am… Sex       Male       2007… Unde…      6.2
10 Asthma attack in last 12 months am… Sex       Female     2007… Unde…      4.6
# ℹ 18 more rows
`Asthma attack in past 12 months_race` <- health_clean%>%
  filter(PANEL=="Asthma attack in last 12 months among persons under 18 years", STUB_NAME== "Race")%>%
  arrange(YEAR)

`Asthma attack in past 12 months_race`
# A tibble: 84 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Asthma attack in last 12 months am… Race      White only 1997… Unde…      5  
 2 Asthma attack in last 12 months am… Race      Black or … 1997… Unde…      7  
 3 Asthma attack in last 12 months am… Race      American … 1997… Unde…      6.4
 4 Asthma attack in last 12 months am… Race      Asian only 1997… Unde…      4.3
 5 Asthma attack in last 12 months am… Race      Native Ha… 1997… Unde…     NA  
 6 Asthma attack in last 12 months am… Race      2 or more… 1997… Unde…     NA  
 7 Asthma attack in last 12 months am… Race      White only 2000… Unde…      5.2
 8 Asthma attack in last 12 months am… Race      Black or … 2000… Unde…      8  
 9 Asthma attack in last 12 months am… Race      American … 2000… Unde…      8.7
10 Asthma attack in last 12 months am… Race      Asian only 2000… Unde…      4.7
# ℹ 74 more rows
`Asthma attack in past 12 months_Hispanic origin and race` <- health_clean%>%
  filter(PANEL=="Asthma attack in last 12 months among persons under 18 years", STUB_NAME== "Hispanic origin and race")%>%
  arrange(YEAR)

`Asthma attack in past 12 months_Hispanic origin and race`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Asthma attack in last 12 months am… Hispanic… Hispanic … 1997… Unde…      4.8
 2 Asthma attack in last 12 months am… Hispanic… Not Hispa… 1997… Unde…      5.5
 3 Asthma attack in last 12 months am… Hispanic… Not Hispa… 1997… Unde…      5.1
 4 Asthma attack in last 12 months am… Hispanic… Not Hispa… 1997… Unde…      7  
 5 Asthma attack in last 12 months am… Hispanic… Hispanic … 2000… Unde…      4.2
 6 Asthma attack in last 12 months am… Hispanic… Not Hispa… 2000… Unde…      6  
 7 Asthma attack in last 12 months am… Hispanic… Not Hispa… 2000… Unde…      5.5
 8 Asthma attack in last 12 months am… Hispanic… Not Hispa… 2000… Unde…      7.9
 9 Asthma attack in last 12 months am… Hispanic… Hispanic … 2003… Unde…      4.6
10 Asthma attack in last 12 months am… Hispanic… Not Hispa… 2003… Unde…      5.6
# ℹ 46 more rows
`Asthma attack in past 12 months_Percent of poverty level` <- health_clean%>%
  filter(PANEL=="Asthma attack in last 12 months among persons under 18 years", STUB_NAME== "Percent of poverty level")%>%
  arrange(YEAR)

`Asthma attack in past 12 months_Percent of poverty level`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Asthma attack in last 12 months am… Percent … Below 100% 1997… Unde…      6.1
 2 Asthma attack in last 12 months am… Percent … 100%-199%  1997… Unde…      5.3
 3 Asthma attack in last 12 months am… Percent … 200%-399%  1997… Unde…      5  
 4 Asthma attack in last 12 months am… Percent … 400% or m… 1997… Unde…      5.2
 5 Asthma attack in last 12 months am… Percent … Below 100% 2000… Unde…      7.1
 6 Asthma attack in last 12 months am… Percent … 100%-199%  2000… Unde…      5.4
 7 Asthma attack in last 12 months am… Percent … 200%-399%  2000… Unde…      5.3
 8 Asthma attack in last 12 months am… Percent … 400% or m… 2000… Unde…      5.5
 9 Asthma attack in last 12 months am… Percent … Below 100% 2003… Unde…      6.5
10 Asthma attack in last 12 months am… Percent … 100%-199%  2003… Unde…      5.2
# ℹ 46 more rows
`Asthma attack in past 12 months_insurance` <- health_clean%>%
  filter(PANEL=="Asthma attack in last 12 months among persons under 18 years", STUB_NAME== "Health insurance status at the time of interview")%>%
  arrange(YEAR)

`Asthma attack in past 12 months_insurance`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Asthma attack in last 12 months am… Health i… Insured    1997… Unde…      5.6
 2 Asthma attack in last 12 months am… Health i… Insured: … 1997… Unde…      5  
 3 Asthma attack in last 12 months am… Health i… Insured: … 1997… Unde…      7.7
 4 Asthma attack in last 12 months am… Health i… Uninsured  1997… Unde…      3.9
 5 Asthma attack in last 12 months am… Health i… Insured    2000… Unde…      5.9
 6 Asthma attack in last 12 months am… Health i… Insured: … 2000… Unde…      5.3
 7 Asthma attack in last 12 months am… Health i… Insured: … 2000… Unde…      7.7
 8 Asthma attack in last 12 months am… Health i… Uninsured  2000… Unde…      4.3
 9 Asthma attack in last 12 months am… Health i… Insured    2003… Unde…      5.6
10 Asthma attack in last 12 months am… Health i… Insured: … 2003… Unde…      5  
# ℹ 46 more rows
`Asthma attack in past 12 months` <-  bind_rows(`Asthma attack in past 12 months_under 18 years`, `Asthma attack in past 12 months_age`, `Asthma attack in past 12 months_sex`, `Asthma attack in past 12 months_race`, `Asthma attack in past 12 months_Hispanic origin and race`, `Asthma attack in past 12 months_Percent of poverty level`, `Asthma attack in past 12 months_insurance`)

`Asthma attack in past 12 months`
# A tibble: 350 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Asthma attack in last 12 months am… Total     Under 18 … 1997… Unde…      5.4
 2 Asthma attack in last 12 months am… Total     Under 18 … 2000… Unde…      5.7
 3 Asthma attack in last 12 months am… Total     Under 18 … 2003… Unde…      5.4
 4 Asthma attack in last 12 months am… Total     Under 18 … 2006… Unde…      5.5
 5 Asthma attack in last 12 months am… Total     Under 18 … 2007… Unde…      5.4
 6 Asthma attack in last 12 months am… Total     Under 18 … 2008… Unde…      5.6
 7 Asthma attack in last 12 months am… Total     Under 18 … 2009… Unde…      5.6
 8 Asthma attack in last 12 months am… Total     Under 18 … 2010… Unde…      5.5
 9 Asthma attack in last 12 months am… Total     Under 18 … 2011… Unde…      5.3
10 Asthma attack in last 12 months am… Total     Under 18 … 2012… Unde…      4.9
# ℹ 340 more rows

4.3 “ADHD among persons under 18 years”

`hyperactivity disorder_under 18 years` <- health_clean%>%
filter(PANEL=="ADHD among persons under 18 years", STUB_NAME== "Total")%>%
  arrange(YEAR)
 
`hyperactivity disorder_under 18 years`
# A tibble: 0 × 6
# ℹ 6 variables: PANEL <chr>, STUB_NAME <chr>, STUB_LABEL <chr>, YEAR <chr>,
#   AGE <chr>, ESTIMATE <dbl>
`hyperactivity disorder_age` <- health_clean%>%
  filter(PANEL=="ADHD among persons under 18 years", STUB_NAME== "Age")%>%
  arrange(YEAR)

`hyperactivity disorder_age`
# A tibble: 42 × 6
   PANEL                             STUB_NAME STUB_LABEL  YEAR   AGE   ESTIMATE
   <chr>                             <chr>     <chr>       <chr>  <chr>    <dbl>
 1 ADHD among persons under 18 years Age       10-17 years 1997-… 10-1…      7.6
 2 ADHD among persons under 18 years Age       5-17 years  1997-… 5-17…      6.5
 3 ADHD among persons under 18 years Age       5-9 years   1997-… 5-9 …      4.8
 4 ADHD among persons under 18 years Age       10-17 years 2000-… 10-1…      9  
 5 ADHD among persons under 18 years Age       5-17 years  2000-… 5-17…      7.5
 6 ADHD among persons under 18 years Age       5-9 years   2000-… 5-9 …      5.2
 7 ADHD among persons under 18 years Age       10-17 years 2003-… 10-1…      8.9
 8 ADHD among persons under 18 years Age       5-17 years  2003-… 5-17…      7.6
 9 ADHD among persons under 18 years Age       5-9 years   2003-… 5-9 …      5.6
10 ADHD among persons under 18 years Age       5-17 years  2006-… 5-17…      8.5
# ℹ 32 more rows
`hyperactivity disorder_sex` <- health_clean%>%
  filter(PANEL=="ADHD among persons under 18 years", STUB_NAME== "Sex")%>%
  arrange(YEAR)

`hyperactivity disorder_sex`
# A tibble: 28 × 6
   PANEL                             STUB_NAME STUB_LABEL YEAR    AGE   ESTIMATE
   <chr>                             <chr>     <chr>      <chr>   <chr>    <dbl>
 1 ADHD among persons under 18 years Sex       Male       1997-1… Unde…      9.6
 2 ADHD among persons under 18 years Sex       Female     1997-1… Unde…      3.2
 3 ADHD among persons under 18 years Sex       Male       2000-2… Unde…     10.8
 4 ADHD among persons under 18 years Sex       Female     2000-2… Unde…      4.2
 5 ADHD among persons under 18 years Sex       Male       2003-2… Unde…     10.7
 6 ADHD among persons under 18 years Sex       Female     2003-2… Unde…      4.4
 7 ADHD among persons under 18 years Sex       Male       2006-2… Unde…     12  
 8 ADHD among persons under 18 years Sex       Female     2006-2… Unde…      4.9
 9 ADHD among persons under 18 years Sex       Male       2007-2… Unde…     12.3
10 ADHD among persons under 18 years Sex       Female     2007-2… Unde…      5.5
# ℹ 18 more rows
`hyperactivity disorder_race` <- health_clean%>%
  filter(PANEL=="ADHD among persons under 18 years", STUB_NAME== "Race")%>%
  arrange(YEAR)

`hyperactivity disorder_race`
# A tibble: 84 × 6
   PANEL                             STUB_NAME STUB_LABEL   YEAR  AGE   ESTIMATE
   <chr>                             <chr>     <chr>        <chr> <chr>    <dbl>
 1 ADHD among persons under 18 years Race      White only   1997… Unde…      7.1
 2 ADHD among persons under 18 years Race      Black or Af… 1997… Unde…      5  
 3 ADHD among persons under 18 years Race      American In… 1997… Unde…      8.5
 4 ADHD among persons under 18 years Race      Asian only   1997… Unde…      1.7
 5 ADHD among persons under 18 years Race      Native Hawa… 1997… Unde…     NA  
 6 ADHD among persons under 18 years Race      2 or more r… 1997… Unde…     NA  
 7 ADHD among persons under 18 years Race      White only   2000… Unde…      8.1
 8 ADHD among persons under 18 years Race      Black or Af… 2000… Unde…      7  
 9 ADHD among persons under 18 years Race      American In… 2000… Unde…     NA  
10 ADHD among persons under 18 years Race      Asian only   2000… Unde…     NA  
# ℹ 74 more rows
`hyperactivity disorder_Hispanic origin and race` <- health_clean%>%
  filter(PANEL=="ADHD among persons under 18 years", STUB_NAME== "Hispanic origin and race")%>%
  arrange(YEAR)

`hyperactivity disorder_Hispanic origin and race`
# A tibble: 56 × 6
   PANEL                             STUB_NAME   STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                             <chr>       <chr>      <chr> <chr>    <dbl>
 1 ADHD among persons under 18 years Hispanic o… Hispanic … 1997… Unde…      3.6
 2 ADHD among persons under 18 years Hispanic o… Not Hispa… 1997… Unde…      7  
 3 ADHD among persons under 18 years Hispanic o… Not Hispa… 1997… Unde…      7.7
 4 ADHD among persons under 18 years Hispanic o… Not Hispa… 1997… Unde…      5  
 5 ADHD among persons under 18 years Hispanic o… Hispanic … 2000… Unde…      4.2
 6 ADHD among persons under 18 years Hispanic o… Not Hispa… 2000… Unde…      8.2
 7 ADHD among persons under 18 years Hispanic o… Not Hispa… 2000… Unde…      9  
 8 ADHD among persons under 18 years Hispanic o… Not Hispa… 2000… Unde…      6.8
 9 ADHD among persons under 18 years Hispanic o… Hispanic … 2003… Unde…      4.6
10 ADHD among persons under 18 years Hispanic o… Not Hispa… 2003… Unde…      8.3
# ℹ 46 more rows
`hyperactivity disorder_Percent of poverty level` <- health_clean%>%
  filter(PANEL=="ADHD among persons under 18 years", STUB_NAME== "Percent of poverty level")%>%
  arrange(YEAR)

`hyperactivity disorder_Percent of poverty level`
# A tibble: 56 × 6
   PANEL                             STUB_NAME   STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                             <chr>       <chr>      <chr> <chr>    <dbl>
 1 ADHD among persons under 18 years Percent of… Below 100% 1997… Unde…      7.2
 2 ADHD among persons under 18 years Percent of… 100%-199%  1997… Unde…      6.7
 3 ADHD among persons under 18 years Percent of… 200%-399%  1997… Unde…      6.2
 4 ADHD among persons under 18 years Percent of… 400% or m… 1997… Unde…      6.1
 5 ADHD among persons under 18 years Percent of… Below 100% 2000… Unde…      8.2
 6 ADHD among persons under 18 years Percent of… 100%-199%  2000… Unde…      7.5
 7 ADHD among persons under 18 years Percent of… 200%-399%  2000… Unde…      7.7
 8 ADHD among persons under 18 years Percent of… 400% or m… 2000… Unde…      7.1
 9 ADHD among persons under 18 years Percent of… Below 100% 2003… Unde…      8.4
10 ADHD among persons under 18 years Percent of… 100%-199%  2003… Unde…      7.8
# ℹ 46 more rows
`hyperactivity disorder_insurance` <- health_clean%>%
  filter(PANEL=="ADHD among persons under 18 years", STUB_NAME== "Health insurance status at the time of interview")%>%
  arrange(YEAR)

`hyperactivity disorder_insurance`
# A tibble: 56 × 6
   PANEL                             STUB_NAME   STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                             <chr>       <chr>      <chr> <chr>    <dbl>
 1 ADHD among persons under 18 years Health ins… Insured    1997… Unde…      6.7
 2 ADHD among persons under 18 years Health ins… Insured: … 1997… Unde…      5.9
 3 ADHD among persons under 18 years Health ins… Insured: … 1997… Unde…     10.5
 4 ADHD among persons under 18 years Health ins… Uninsured  1997… Unde…      4.8
 5 ADHD among persons under 18 years Health ins… Insured    2000… Unde…      7.8
 6 ADHD among persons under 18 years Health ins… Insured: … 2000… Unde…      7  
 7 ADHD among persons under 18 years Health ins… Insured: … 2000… Unde…     10.7
 8 ADHD among persons under 18 years Health ins… Uninsured  2000… Unde…      5.4
 9 ADHD among persons under 18 years Health ins… Insured    2003… Unde…      7.8
10 ADHD among persons under 18 years Health ins… Insured: … 2003… Unde…      7  
# ℹ 46 more rows
`hyperactivity disorder` <-  bind_rows(`hyperactivity disorder_under 18 years`, `hyperactivity disorder_age`, `hyperactivity disorder_sex`, `hyperactivity disorder_race`, `hyperactivity disorder_Hispanic origin and race`, `hyperactivity disorder_Percent of poverty level`, `hyperactivity disorder_insurance`)

`hyperactivity disorder`
# A tibble: 322 × 6
   PANEL                             STUB_NAME STUB_LABEL  YEAR   AGE   ESTIMATE
   <chr>                             <chr>     <chr>       <chr>  <chr>    <dbl>
 1 ADHD among persons under 18 years Age       10-17 years 1997-… 10-1…      7.6
 2 ADHD among persons under 18 years Age       5-17 years  1997-… 5-17…      6.5
 3 ADHD among persons under 18 years Age       5-9 years   1997-… 5-9 …      4.8
 4 ADHD among persons under 18 years Age       10-17 years 2000-… 10-1…      9  
 5 ADHD among persons under 18 years Age       5-17 years  2000-… 5-17…      7.5
 6 ADHD among persons under 18 years Age       5-9 years   2000-… 5-9 …      5.2
 7 ADHD among persons under 18 years Age       10-17 years 2003-… 10-1…      8.9
 8 ADHD among persons under 18 years Age       5-17 years  2003-… 5-17…      7.6
 9 ADHD among persons under 18 years Age       5-9 years   2003-… 5-9 …      5.6
10 ADHD among persons under 18 years Age       5-17 years  2006-… 5-17…      8.5
# ℹ 312 more rows

4.4 “Serious emotional or behavioral difficulties among persons under 18 years

`Serious emotional_under 18 years` <- health_clean%>%
filter(PANEL=="Serious emotional or behavioral difficulties among persons under 18 years", STUB_NAME== "Total")%>%
  arrange(YEAR)
 
`Serious emotional_under 18 years`
# A tibble: 0 × 6
# ℹ 6 variables: PANEL <chr>, STUB_NAME <chr>, STUB_LABEL <chr>, YEAR <chr>,
#   AGE <chr>, ESTIMATE <dbl>
`Serious emotional_age` <- health_clean%>%
  filter(PANEL=="Serious emotional or behavioral difficulties among persons under 18 years", STUB_NAME== "Age")%>%
  arrange(YEAR)

`Serious emotional_age`
# A tibble: 42 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Serious emotional or behavioral di… Age       5-17 years 1997… 5-17…     NA  
 2 Serious emotional or behavioral di… Age       5-9 years  1997… 5-9 …     NA  
 3 Serious emotional or behavioral di… Age       10-17 yea… 1997… 10-1…     NA  
 4 Serious emotional or behavioral di… Age       5-17 years 2000… 5-17…     NA  
 5 Serious emotional or behavioral di… Age       5-9 years  2000… 5-9 …     NA  
 6 Serious emotional or behavioral di… Age       10-17 yea… 2000… 10-1…     NA  
 7 Serious emotional or behavioral di… Age       5-17 years 2003… 5-17…      5.1
 8 Serious emotional or behavioral di… Age       5-9 years  2003… 5-9 …      4.3
 9 Serious emotional or behavioral di… Age       10-17 yea… 2003… 10-1…      5.6
10 Serious emotional or behavioral di… Age       5-17 years 2006… 5-17…      5.4
# ℹ 32 more rows
`Serious emotional_sex` <- health_clean%>%
  filter(PANEL=="Serious emotional or behavioral difficulties among persons under 18 years", STUB_NAME== "Sex")%>%
  arrange(YEAR)

`Serious emotional_sex`
# A tibble: 28 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Serious emotional or behavioral di… Sex       Male       1997… Unde…     NA  
 2 Serious emotional or behavioral di… Sex       Female     1997… Unde…     NA  
 3 Serious emotional or behavioral di… Sex       Male       2000… Unde…     NA  
 4 Serious emotional or behavioral di… Sex       Female     2000… Unde…     NA  
 5 Serious emotional or behavioral di… Sex       Male       2003… Unde…      6.1
 6 Serious emotional or behavioral di… Sex       Female     2003… Unde…      4.1
 7 Serious emotional or behavioral di… Sex       Male       2006… Unde…      6.9
 8 Serious emotional or behavioral di… Sex       Female     2006… Unde…      3.8
 9 Serious emotional or behavioral di… Sex       Male       2007… Unde…      6.9
10 Serious emotional or behavioral di… Sex       Female     2007… Unde…      4  
# ℹ 18 more rows
`Serious emotional_race` <- health_clean%>%
  filter(PANEL=="Serious emotional or behavioral difficulties among persons under 18 years", STUB_NAME== "Race")%>%
  arrange(YEAR)

`Serious emotional_race`
# A tibble: 84 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Serious emotional or behavioral di… Race      White only 1997… Unde…       NA
 2 Serious emotional or behavioral di… Race      Black or … 1997… Unde…       NA
 3 Serious emotional or behavioral di… Race      American … 1997… Unde…       NA
 4 Serious emotional or behavioral di… Race      Asian only 1997… Unde…       NA
 5 Serious emotional or behavioral di… Race      Native Ha… 1997… Unde…       NA
 6 Serious emotional or behavioral di… Race      2 or more… 1997… Unde…       NA
 7 Serious emotional or behavioral di… Race      White only 2000… Unde…       NA
 8 Serious emotional or behavioral di… Race      Black or … 2000… Unde…       NA
 9 Serious emotional or behavioral di… Race      American … 2000… Unde…       NA
10 Serious emotional or behavioral di… Race      Asian only 2000… Unde…       NA
# ℹ 74 more rows
`Serious emotional_Hispanic origin and race` <- health_clean%>%
  filter(PANEL=="Serious emotional or behavioral difficulties among persons under 18 years", STUB_NAME== "Hispanic origin and race")%>%
  arrange(YEAR)

`Serious emotional_Hispanic origin and race`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Serious emotional or behavioral di… Hispanic… Hispanic … 1997… Unde…     NA  
 2 Serious emotional or behavioral di… Hispanic… Not Hispa… 1997… Unde…     NA  
 3 Serious emotional or behavioral di… Hispanic… Not Hispa… 1997… Unde…     NA  
 4 Serious emotional or behavioral di… Hispanic… Not Hispa… 1997… Unde…     NA  
 5 Serious emotional or behavioral di… Hispanic… Hispanic … 2000… Unde…     NA  
 6 Serious emotional or behavioral di… Hispanic… Not Hispa… 2000… Unde…     NA  
 7 Serious emotional or behavioral di… Hispanic… Not Hispa… 2000… Unde…     NA  
 8 Serious emotional or behavioral di… Hispanic… Not Hispa… 2000… Unde…     NA  
 9 Serious emotional or behavioral di… Hispanic… Hispanic … 2003… Unde…      3.8
10 Serious emotional or behavioral di… Hispanic… Not Hispa… 2003… Unde…      5.4
# ℹ 46 more rows
`Serious emotional_Percent of poverty level` <- health_clean%>%
  filter(PANEL=="Serious emotional or behavioral difficulties among persons under 18 years", STUB_NAME== "Percent of poverty level")%>%
  arrange(YEAR)

`Serious emotional_Percent of poverty level`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Serious emotional or behavioral di… Percent … Below 100% 1997… Unde…     NA  
 2 Serious emotional or behavioral di… Percent … 100%-199%  1997… Unde…     NA  
 3 Serious emotional or behavioral di… Percent … 200%-399%  1997… Unde…     NA  
 4 Serious emotional or behavioral di… Percent … 400% or m… 1997… Unde…     NA  
 5 Serious emotional or behavioral di… Percent … Below 100% 2000… Unde…     NA  
 6 Serious emotional or behavioral di… Percent … 100%-199%  2000… Unde…     NA  
 7 Serious emotional or behavioral di… Percent … 200%-399%  2000… Unde…     NA  
 8 Serious emotional or behavioral di… Percent … 400% or m… 2000… Unde…     NA  
 9 Serious emotional or behavioral di… Percent … Below 100% 2003… Unde…      7.4
10 Serious emotional or behavioral di… Percent … 100%-199%  2003… Unde…      5.4
# ℹ 46 more rows
`Serious emotional_insurance` <- health_clean%>%
  filter(PANEL=="Serious emotional or behavioral difficulties among persons under 18 years", STUB_NAME== "Health insurance status at the time of interview")%>%
  arrange(YEAR)

`Serious emotional_insurance`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Serious emotional or behavioral di… Health i… Insured    1997… Unde…     NA  
 2 Serious emotional or behavioral di… Health i… Insured: … 1997… Unde…     NA  
 3 Serious emotional or behavioral di… Health i… Insured: … 1997… Unde…     NA  
 4 Serious emotional or behavioral di… Health i… Uninsured  1997… Unde…     NA  
 5 Serious emotional or behavioral di… Health i… Insured    2000… Unde…     NA  
 6 Serious emotional or behavioral di… Health i… Insured: … 2000… Unde…     NA  
 7 Serious emotional or behavioral di… Health i… Insured: … 2000… Unde…     NA  
 8 Serious emotional or behavioral di… Health i… Uninsured  2000… Unde…     NA  
 9 Serious emotional or behavioral di… Health i… Insured    2003… Unde…      5.2
10 Serious emotional or behavioral di… Health i… Insured: … 2003… Unde…      4.1
# ℹ 46 more rows
`Serious emotional` <-  bind_rows(`Serious emotional_under 18 years`, `Serious emotional_age`, `Serious emotional_sex`, `Serious emotional_race`, `Serious emotional_Hispanic origin and race`, `Serious emotional_Percent of poverty level`, `Serious emotional_insurance`)

`Serious emotional`
# A tibble: 322 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Serious emotional or behavioral di… Age       5-17 years 1997… 5-17…     NA  
 2 Serious emotional or behavioral di… Age       5-9 years  1997… 5-9 …     NA  
 3 Serious emotional or behavioral di… Age       10-17 yea… 1997… 10-1…     NA  
 4 Serious emotional or behavioral di… Age       5-17 years 2000… 5-17…     NA  
 5 Serious emotional or behavioral di… Age       5-9 years  2000… 5-9 …     NA  
 6 Serious emotional or behavioral di… Age       10-17 yea… 2000… 10-1…     NA  
 7 Serious emotional or behavioral di… Age       5-17 years 2003… 5-17…      5.1
 8 Serious emotional or behavioral di… Age       5-9 years  2003… 5-9 …      4.3
 9 Serious emotional or behavioral di… Age       10-17 yea… 2003… 10-1…      5.6
10 Serious emotional or behavioral di… Age       5-17 years 2006… 5-17…      5.4
# ℹ 312 more rows

4.5 “Food allergy among persons under 18 years”

`Food allergy_under 18 years` <- health_clean%>%
filter(PANEL=="Food allergy among persons under 18 years", STUB_NAME== "Total")%>%
  arrange(YEAR)
 
`Food allergy_under 18 years`
# A tibble: 14 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Food allergy among persons under 1… Total     Under 18 … 1997… Unde…      3.4
 2 Food allergy among persons under 1… Total     Under 18 … 2000… Unde…      3.6
 3 Food allergy among persons under 1… Total     Under 18 … 2003… Unde…      3.8
 4 Food allergy among persons under 1… Total     Under 18 … 2006… Unde…      4.3
 5 Food allergy among persons under 1… Total     Under 18 … 2007… Unde…      4.6
 6 Food allergy among persons under 1… Total     Under 18 … 2008… Unde…      4.8
 7 Food allergy among persons under 1… Total     Under 18 … 2009… Unde…      5.1
 8 Food allergy among persons under 1… Total     Under 18 … 2010… Unde…      5.2
 9 Food allergy among persons under 1… Total     Under 18 … 2011… Unde…      5.6
10 Food allergy among persons under 1… Total     Under 18 … 2012… Unde…      5.6
11 Food allergy among persons under 1… Total     Under 18 … 2013… Unde…      5.6
12 Food allergy among persons under 1… Total     Under 18 … 2014… Unde…      5.8
13 Food allergy among persons under 1… Total     Under 18 … 2015… Unde…      6.1
14 Food allergy among persons under 1… Total     Under 18 … 2016… Unde…      6.4
`Food allergy_age` <- health_clean%>%
  filter(PANEL=="Food allergy among persons under 18 years", STUB_NAME== "Age")%>%
  arrange(YEAR)

`Food allergy_age`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Food allergy among persons under 1… Age       0-4 years  1997… 0-4 …      3.8
 2 Food allergy among persons under 1… Age       5-17 years 1997… 5-17…      3.3
 3 Food allergy among persons under 1… Age       5-9 years  1997… 5-9 …      3.1
 4 Food allergy among persons under 1… Age       10-17 yea… 1997… 10-1…      3.4
 5 Food allergy among persons under 1… Age       0-4 years  2000… 0-4 …      4  
 6 Food allergy among persons under 1… Age       5-17 years 2000… 5-17…      3.4
 7 Food allergy among persons under 1… Age       5-9 years  2000… 5-9 …      3.6
 8 Food allergy among persons under 1… Age       10-17 yea… 2000… 10-1…      3.3
 9 Food allergy among persons under 1… Age       0-4 years  2003… 0-4 …      4.3
10 Food allergy among persons under 1… Age       5-17 years 2003… 5-17…      3.6
# ℹ 46 more rows
`Food allergy_sex` <- health_clean%>%
  filter(PANEL=="Food allergy among persons under 18 years", STUB_NAME== "Sex")%>%
  arrange(YEAR)

`Food allergy_sex`
# A tibble: 28 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Food allergy among persons under 1… Sex       Male       1997… Unde…      3.4
 2 Food allergy among persons under 1… Sex       Female     1997… Unde…      3.5
 3 Food allergy among persons under 1… Sex       Male       2000… Unde…      3.7
 4 Food allergy among persons under 1… Sex       Female     2000… Unde…      3.4
 5 Food allergy among persons under 1… Sex       Male       2003… Unde…      3.8
 6 Food allergy among persons under 1… Sex       Female     2003… Unde…      3.8
 7 Food allergy among persons under 1… Sex       Male       2006… Unde…      4.3
 8 Food allergy among persons under 1… Sex       Female     2006… Unde…      4.2
 9 Food allergy among persons under 1… Sex       Male       2007… Unde…      4.6
10 Food allergy among persons under 1… Sex       Female     2007… Unde…      4.5
# ℹ 18 more rows
`Food allergy_race` <- health_clean%>%
  filter(PANEL=="Food allergy among persons under 18 years", STUB_NAME== "Race")%>%
  arrange(YEAR)

`Food allergy_race`
# A tibble: 84 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Food allergy among persons under 1… Race      White only 1997… Unde…      3.5
 2 Food allergy among persons under 1… Race      Black or … 1997… Unde…      3.1
 3 Food allergy among persons under 1… Race      American … 1997… Unde…     NA  
 4 Food allergy among persons under 1… Race      Asian only 1997… Unde…      3.9
 5 Food allergy among persons under 1… Race      Native Ha… 1997… Unde…     NA  
 6 Food allergy among persons under 1… Race      2 or more… 1997… Unde…     NA  
 7 Food allergy among persons under 1… Race      White only 2000… Unde…      3.6
 8 Food allergy among persons under 1… Race      Black or … 2000… Unde…      3  
 9 Food allergy among persons under 1… Race      American … 2000… Unde…      4.8
10 Food allergy among persons under 1… Race      Asian only 2000… Unde…      4.4
# ℹ 74 more rows
`Food allergy_Hispanic origin and race` <- health_clean%>%
  filter(PANEL=="Food allergy among persons under 18 years", STUB_NAME== "Hispanic origin and race")%>%
  arrange(YEAR)

`Food allergy_Hispanic origin and race`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Food allergy among persons under 1… Hispanic… Hispanic … 1997… Unde…      2.1
 2 Food allergy among persons under 1… Hispanic… Not Hispa… 1997… Unde…      3.7
 3 Food allergy among persons under 1… Hispanic… Not Hispa… 1997… Unde…      3.8
 4 Food allergy among persons under 1… Hispanic… Not Hispa… 1997… Unde…      3.1
 5 Food allergy among persons under 1… Hispanic… Hispanic … 2000… Unde…      2.5
 6 Food allergy among persons under 1… Hispanic… Not Hispa… 2000… Unde…      3.8
 7 Food allergy among persons under 1… Hispanic… Not Hispa… 2000… Unde…      3.9
 8 Food allergy among persons under 1… Hispanic… Not Hispa… 2000… Unde…      3.1
 9 Food allergy among persons under 1… Hispanic… Hispanic … 2003… Unde…      2.8
10 Food allergy among persons under 1… Hispanic… Not Hispa… 2003… Unde…      4  
# ℹ 46 more rows
`Food allergy_Percent of poverty level` <- health_clean%>%
  filter(PANEL=="Food allergy among persons under 18 years", STUB_NAME== "Percent of poverty level")%>%
  arrange(YEAR)

`Food allergy_Percent of poverty level`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Food allergy among persons under 1… Percent … Below 100% 1997… Unde…      3.3
 2 Food allergy among persons under 1… Percent … 100%-199%  1997… Unde…      3  
 3 Food allergy among persons under 1… Percent … 200%-399%  1997… Unde…      3.2
 4 Food allergy among persons under 1… Percent … 400% or m… 1997… Unde…      4.2
 5 Food allergy among persons under 1… Percent … Below 100% 2000… Unde…      3.2
 6 Food allergy among persons under 1… Percent … 100%-199%  2000… Unde…      3.4
 7 Food allergy among persons under 1… Percent … 200%-399%  2000… Unde…      3.4
 8 Food allergy among persons under 1… Percent … 400% or m… 2000… Unde…      4  
 9 Food allergy among persons under 1… Percent … Below 100% 2003… Unde…      3.3
10 Food allergy among persons under 1… Percent … 100%-199%  2003… Unde…      3.8
# ℹ 46 more rows
`Food allergy_insurance` <- health_clean%>%
  filter(PANEL=="Food allergy among persons under 18 years", STUB_NAME== "Health insurance status at the time of interview")%>%
  arrange(YEAR)

`Food allergy_insurance`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Food allergy among persons under 1… Health i… Insured    1997… Unde…      3.5
 2 Food allergy among persons under 1… Health i… Insured: … 1997… Unde…      3.5
 3 Food allergy among persons under 1… Health i… Insured: … 1997… Unde…      3.6
 4 Food allergy among persons under 1… Health i… Uninsured  1997… Unde…      2.6
 5 Food allergy among persons under 1… Health i… Insured    2000… Unde…      3.7
 6 Food allergy among persons under 1… Health i… Insured: … 2000… Unde…      3.7
 7 Food allergy among persons under 1… Health i… Insured: … 2000… Unde…      3.7
 8 Food allergy among persons under 1… Health i… Uninsured  2000… Unde…      2.4
 9 Food allergy among persons under 1… Health i… Insured    2003… Unde…      3.9
10 Food allergy among persons under 1… Health i… Insured: … 2003… Unde…      4  
# ℹ 46 more rows
`Food allergy` <-  bind_rows(`Food allergy_under 18 years`, `Food allergy_age`, `Food allergy_sex`, `Food allergy_race`, `Food allergy_Hispanic origin and race`, `Food allergy_Percent of poverty level`, `Food allergy_insurance`)

`Food allergy`
# A tibble: 350 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Food allergy among persons under 1… Total     Under 18 … 1997… Unde…      3.4
 2 Food allergy among persons under 1… Total     Under 18 … 2000… Unde…      3.6
 3 Food allergy among persons under 1… Total     Under 18 … 2003… Unde…      3.8
 4 Food allergy among persons under 1… Total     Under 18 … 2006… Unde…      4.3
 5 Food allergy among persons under 1… Total     Under 18 … 2007… Unde…      4.6
 6 Food allergy among persons under 1… Total     Under 18 … 2008… Unde…      4.8
 7 Food allergy among persons under 1… Total     Under 18 … 2009… Unde…      5.1
 8 Food allergy among persons under 1… Total     Under 18 … 2010… Unde…      5.2
 9 Food allergy among persons under 1… Total     Under 18 … 2011… Unde…      5.6
10 Food allergy among persons under 1… Total     Under 18 … 2012… Unde…      5.6
# ℹ 340 more rows

4.6 “Skin allergy among persons under 18 years”

`Skin allergy_under 18 years` <- health_clean%>%
filter(PANEL=="Skin allergy among persons under 18 years", STUB_NAME== "Total")%>%
  arrange(YEAR)
 
`Skin allergy_under 18 years`
# A tibble: 14 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Skin allergy among persons under 1… Total     Under 18 … 1997… Unde…      7.4
 2 Skin allergy among persons under 1… Total     Under 18 … 2000… Unde…      8.1
 3 Skin allergy among persons under 1… Total     Under 18 … 2003… Unde…      9.6
 4 Skin allergy among persons under 1… Total     Under 18 … 2006… Unde…     10.1
 5 Skin allergy among persons under 1… Total     Under 18 … 2007… Unde…     10.7
 6 Skin allergy among persons under 1… Total     Under 18 … 2008… Unde…     12  
 7 Skin allergy among persons under 1… Total     Under 18 … 2009… Unde…     12.5
 8 Skin allergy among persons under 1… Total     Under 18 … 2010… Unde…     12.5
 9 Skin allergy among persons under 1… Total     Under 18 … 2011… Unde…     12.2
10 Skin allergy among persons under 1… Total     Under 18 … 2012… Unde…     11.8
11 Skin allergy among persons under 1… Total     Under 18 … 2013… Unde…     11.8
12 Skin allergy among persons under 1… Total     Under 18 … 2014… Unde…     11.9
13 Skin allergy among persons under 1… Total     Under 18 … 2015… Unde…     12.5
14 Skin allergy among persons under 1… Total     Under 18 … 2016… Unde…     12.7
`Skin allergy_age` <- health_clean%>%
  filter(PANEL=="Skin allergy among persons under 18 years", STUB_NAME== "Age")%>%
  arrange(YEAR)

`Skin allergy_age`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Skin allergy among persons under 1… Age       0-4 years  1997… 0-4 …      8.1
 2 Skin allergy among persons under 1… Age       5-17 years 1997… 5-17…      7.2
 3 Skin allergy among persons under 1… Age       5-9 years  1997… 5-9 …      7.5
 4 Skin allergy among persons under 1… Age       10-17 yea… 1997… 10-1…      7.1
 5 Skin allergy among persons under 1… Age       0-4 years  2000… 0-4 …      8.7
 6 Skin allergy among persons under 1… Age       5-17 years 2000… 5-17…      7.9
 7 Skin allergy among persons under 1… Age       5-9 years  2000… 5-9 …      8.6
 8 Skin allergy among persons under 1… Age       10-17 yea… 2000… 10-1…      7.5
 9 Skin allergy among persons under 1… Age       0-4 years  2003… 0-4 …     11  
10 Skin allergy among persons under 1… Age       5-17 years 2003… 5-17…      9.1
# ℹ 46 more rows
`Skin allergy_sex` <- health_clean%>%
  filter(PANEL=="Skin allergy among persons under 18 years", STUB_NAME== "Sex")%>%
  arrange(YEAR)

`Skin allergy_sex`
# A tibble: 28 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Skin allergy among persons under 1… Sex       Male       1997… Unde…      7.3
 2 Skin allergy among persons under 1… Sex       Female     1997… Unde…      7.6
 3 Skin allergy among persons under 1… Sex       Male       2000… Unde…      7.9
 4 Skin allergy among persons under 1… Sex       Female     2000… Unde…      8.4
 5 Skin allergy among persons under 1… Sex       Male       2003… Unde…      9.5
 6 Skin allergy among persons under 1… Sex       Female     2003… Unde…      9.8
 7 Skin allergy among persons under 1… Sex       Male       2006… Unde…      9.7
 8 Skin allergy among persons under 1… Sex       Female     2006… Unde…     10.5
 9 Skin allergy among persons under 1… Sex       Male       2007… Unde…     10.6
10 Skin allergy among persons under 1… Sex       Female     2007… Unde…     10.8
# ℹ 18 more rows
`Skin allergy_race` <- health_clean%>%
  filter(PANEL=="Skin allergy among persons under 18 years", STUB_NAME== "Race")%>%
  arrange(YEAR)

`Skin allergy_race`
# A tibble: 84 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Skin allergy among persons under 1… Race      White only 1997… Unde…      7.1
 2 Skin allergy among persons under 1… Race      Black or … 1997… Unde…      9  
 3 Skin allergy among persons under 1… Race      American … 1997… Unde…      4.1
 4 Skin allergy among persons under 1… Race      Asian only 1997… Unde…      8  
 5 Skin allergy among persons under 1… Race      Native Ha… 1997… Unde…     NA  
 6 Skin allergy among persons under 1… Race      2 or more… 1997… Unde…     NA  
 7 Skin allergy among persons under 1… Race      White only 2000… Unde…      7.6
 8 Skin allergy among persons under 1… Race      Black or … 2000… Unde…     10.4
 9 Skin allergy among persons under 1… Race      American … 2000… Unde…      9.1
10 Skin allergy among persons under 1… Race      Asian only 2000… Unde…      8.4
# ℹ 74 more rows
`Skin allergy_Hispanic origin and race` <- health_clean%>%
  filter(PANEL=="Skin allergy among persons under 18 years", STUB_NAME== "Hispanic origin and race")%>%
  arrange(YEAR)

`Skin allergy_Hispanic origin and race`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Skin allergy among persons under 1… Hispanic… Hispanic … 1997… Unde…      5.5
 2 Skin allergy among persons under 1… Hispanic… Not Hispa… 1997… Unde…      7.8
 3 Skin allergy among persons under 1… Hispanic… Not Hispa… 1997… Unde…      7.5
 4 Skin allergy among persons under 1… Hispanic… Not Hispa… 1997… Unde…      9  
 5 Skin allergy among persons under 1… Hispanic… Hispanic … 2000… Unde…      5.6
 6 Skin allergy among persons under 1… Hispanic… Not Hispa… 2000… Unde…      8.7
 7 Skin allergy among persons under 1… Hispanic… Not Hispa… 2000… Unde…      8.2
 8 Skin allergy among persons under 1… Hispanic… Not Hispa… 2000… Unde…     10.4
 9 Skin allergy among persons under 1… Hispanic… Hispanic … 2003… Unde…      7.2
10 Skin allergy among persons under 1… Hispanic… Not Hispa… 2003… Unde…     10.2
# ℹ 46 more rows
`Skin allergy_Percent of poverty level` <- health_clean%>%
  filter(PANEL=="Skin allergy among persons under 18 years", STUB_NAME== "Percent of poverty level")%>%
  arrange(YEAR)

`Skin allergy_Percent of poverty level`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Skin allergy among persons under 1… Percent … Below 100% 1997… Unde…      7.3
 2 Skin allergy among persons under 1… Percent … 100%-199%  1997… Unde…      7.2
 3 Skin allergy among persons under 1… Percent … 200%-399%  1997… Unde…      7.3
 4 Skin allergy among persons under 1… Percent … 400% or m… 1997… Unde…      7.9
 5 Skin allergy among persons under 1… Percent … Below 100% 2000… Unde…      7.1
 6 Skin allergy among persons under 1… Percent … 100%-199%  2000… Unde…      7.6
 7 Skin allergy among persons under 1… Percent … 200%-399%  2000… Unde…      8.5
 8 Skin allergy among persons under 1… Percent … 400% or m… 2000… Unde…      8.8
 9 Skin allergy among persons under 1… Percent … Below 100% 2003… Unde…      9  
10 Skin allergy among persons under 1… Percent … 100%-199%  2003… Unde…      8.7
# ℹ 46 more rows
`Skin allergy_insurance` <- health_clean%>%
  filter(PANEL=="Skin allergy among persons under 18 years", STUB_NAME== "Health insurance status at the time of interview")%>%
  arrange(YEAR)

`Skin allergy_insurance`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Skin allergy among persons under 1… Health i… Insured    1997… Unde…      7.7
 2 Skin allergy among persons under 1… Health i… Insured: … 1997… Unde…      7.4
 3 Skin allergy among persons under 1… Health i… Insured: … 1997… Unde…      8.4
 4 Skin allergy among persons under 1… Health i… Uninsured  1997… Unde…      5.9
 5 Skin allergy among persons under 1… Health i… Insured    2000… Unde…      8.5
 6 Skin allergy among persons under 1… Health i… Insured: … 2000… Unde…      8.5
 7 Skin allergy among persons under 1… Health i… Insured: … 2000… Unde…      8.4
 8 Skin allergy among persons under 1… Health i… Uninsured  2000… Unde…      5.3
 9 Skin allergy among persons under 1… Health i… Insured    2003… Unde…     10  
10 Skin allergy among persons under 1… Health i… Insured: … 2003… Unde…     10.1
# ℹ 46 more rows
`Skin allergy` <-  bind_rows(`Skin allergy_under 18 years`, `Skin allergy_age`, `Skin allergy_sex`, `Skin allergy_race`, `Skin allergy_Hispanic origin and race`, `Skin allergy_Percent of poverty level`, `Skin allergy_insurance`)

`Skin allergy`
# A tibble: 350 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Skin allergy among persons under 1… Total     Under 18 … 1997… Unde…      7.4
 2 Skin allergy among persons under 1… Total     Under 18 … 2000… Unde…      8.1
 3 Skin allergy among persons under 1… Total     Under 18 … 2003… Unde…      9.6
 4 Skin allergy among persons under 1… Total     Under 18 … 2006… Unde…     10.1
 5 Skin allergy among persons under 1… Total     Under 18 … 2007… Unde…     10.7
 6 Skin allergy among persons under 1… Total     Under 18 … 2008… Unde…     12  
 7 Skin allergy among persons under 1… Total     Under 18 … 2009… Unde…     12.5
 8 Skin allergy among persons under 1… Total     Under 18 … 2010… Unde…     12.5
 9 Skin allergy among persons under 1… Total     Under 18 … 2011… Unde…     12.2
10 Skin allergy among persons under 1… Total     Under 18 … 2012… Unde…     11.8
# ℹ 340 more rows

4.7 “Hay fever or respiratory allergy among persons under 18 years”

`Hay fever_under 18 years` <- health_clean%>%
filter(PANEL=="Hay fever or respiratory allergy among persons under 18 years", STUB_NAME== "Total")%>%
  arrange(YEAR)
 
`Hay fever_under 18 years`
# A tibble: 14 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Hay fever or respiratory allergy a… Total     Under 18 … 1997… Unde…     17.5
 2 Hay fever or respiratory allergy a… Total     Under 18 … 2000… Unde…     17.7
 3 Hay fever or respiratory allergy a… Total     Under 18 … 2003… Unde…     17.3
 4 Hay fever or respiratory allergy a… Total     Under 18 … 2006… Unde…     16.5
 5 Hay fever or respiratory allergy a… Total     Under 18 … 2007… Unde…     16.6
 6 Hay fever or respiratory allergy a… Total     Under 18 … 2008… Unde…     17  
 7 Hay fever or respiratory allergy a… Total     Under 18 … 2009… Unde…     17  
 8 Hay fever or respiratory allergy a… Total     Under 18 … 2010… Unde…     16.8
 9 Hay fever or respiratory allergy a… Total     Under 18 … 2011… Unde…     16.5
10 Hay fever or respiratory allergy a… Total     Under 18 … 2012… Unde…     16  
11 Hay fever or respiratory allergy a… Total     Under 18 … 2013… Unde…     15.6
12 Hay fever or respiratory allergy a… Total     Under 18 … 2014… Unde…     15.1
13 Hay fever or respiratory allergy a… Total     Under 18 … 2015… Unde…     15.2
14 Hay fever or respiratory allergy a… Total     Under 18 … 2016… Unde…     14.7
`Hay fever_age` <- health_clean%>%
  filter(PANEL=="Hay fever or respiratory allergy among persons under 18 years", STUB_NAME== "Age")%>%
  arrange(YEAR)

`Hay fever_age`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Hay fever or respiratory allergy a… Age       0-4 years  1997… 0-4 …     10.7
 2 Hay fever or respiratory allergy a… Age       5-17 years 1997… 5-17…     19.9
 3 Hay fever or respiratory allergy a… Age       5-9 years  1997… 5-9 …     17.3
 4 Hay fever or respiratory allergy a… Age       10-17 yea… 1997… 10-1…     21.6
 5 Hay fever or respiratory allergy a… Age       0-4 years  2000… 0-4 …     10.4
 6 Hay fever or respiratory allergy a… Age       5-17 years 2000… 5-17…     20.3
 7 Hay fever or respiratory allergy a… Age       5-9 years  2000… 5-9 …     18.1
 8 Hay fever or respiratory allergy a… Age       10-17 yea… 2000… 10-1…     21.7
 9 Hay fever or respiratory allergy a… Age       0-4 years  2003… 0-4 …     10.1
10 Hay fever or respiratory allergy a… Age       5-17 years 2003… 5-17…     20  
# ℹ 46 more rows
`Hay fever_sex` <- health_clean%>%
  filter(PANEL=="Hay fever or respiratory allergy among persons under 18 years", STUB_NAME== "Sex")%>%
  arrange(YEAR)

`Hay fever_sex`
# A tibble: 28 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Hay fever or respiratory allergy a… Sex       Male       1997… Unde…     18.6
 2 Hay fever or respiratory allergy a… Sex       Female     1997… Unde…     16.3
 3 Hay fever or respiratory allergy a… Sex       Male       2000… Unde…     18.8
 4 Hay fever or respiratory allergy a… Sex       Female     2000… Unde…     16.5
 5 Hay fever or respiratory allergy a… Sex       Male       2003… Unde…     18.9
 6 Hay fever or respiratory allergy a… Sex       Female     2003… Unde…     15.6
 7 Hay fever or respiratory allergy a… Sex       Male       2006… Unde…     17.9
 8 Hay fever or respiratory allergy a… Sex       Female     2006… Unde…     15.1
 9 Hay fever or respiratory allergy a… Sex       Male       2007… Unde…     18.1
10 Hay fever or respiratory allergy a… Sex       Female     2007… Unde…     15.1
# ℹ 18 more rows
`Hay fever_race` <- health_clean%>%
  filter(PANEL=="Hay fever or respiratory allergy among persons under 18 years", STUB_NAME== "Race")%>%
  arrange(YEAR)

`Hay fever_race`
# A tibble: 84 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Hay fever or respiratory allergy a… Race      White only 1997… Unde…     17.9
 2 Hay fever or respiratory allergy a… Race      Black or … 1997… Unde…     16.2
 3 Hay fever or respiratory allergy a… Race      American … 1997… Unde…     15.2
 4 Hay fever or respiratory allergy a… Race      Asian only 1997… Unde…     15.3
 5 Hay fever or respiratory allergy a… Race      Native Ha… 1997… Unde…     NA  
 6 Hay fever or respiratory allergy a… Race      2 or more… 1997… Unde…     NA  
 7 Hay fever or respiratory allergy a… Race      White only 2000… Unde…     18.5
 8 Hay fever or respiratory allergy a… Race      Black or … 2000… Unde…     15.6
 9 Hay fever or respiratory allergy a… Race      American … 2000… Unde…     16.4
10 Hay fever or respiratory allergy a… Race      Asian only 2000… Unde…     12.6
# ℹ 74 more rows
`Hay fever_Hispanic origin and race` <- health_clean%>%
  filter(PANEL=="Hay fever or respiratory allergy among persons under 18 years", STUB_NAME== "Hispanic origin and race")%>%
  arrange(YEAR)

`Hay fever_Hispanic origin and race`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Hay fever or respiratory allergy a… Hispanic… Hispanic … 1997… Unde…     12.4
 2 Hay fever or respiratory allergy a… Hispanic… Not Hispa… 1997… Unde…     18.4
 3 Hay fever or respiratory allergy a… Hispanic… Not Hispa… 1997… Unde…     19.1
 4 Hay fever or respiratory allergy a… Hispanic… Not Hispa… 1997… Unde…     16.3
 5 Hay fever or respiratory allergy a… Hispanic… Hispanic … 2000… Unde…     12.4
 6 Hay fever or respiratory allergy a… Hispanic… Not Hispa… 2000… Unde…     18.8
 7 Hay fever or respiratory allergy a… Hispanic… Not Hispa… 2000… Unde…     19.9
 8 Hay fever or respiratory allergy a… Hispanic… Not Hispa… 2000… Unde…     15.5
 9 Hay fever or respiratory allergy a… Hispanic… Hispanic … 2003… Unde…     12.8
10 Hay fever or respiratory allergy a… Hispanic… Not Hispa… 2003… Unde…     18.3
# ℹ 46 more rows
`Hay fever_Percent of poverty level` <- health_clean%>%
  filter(PANEL=="Hay fever or respiratory allergy among persons under 18 years", STUB_NAME== "Percent of poverty level")%>%
  arrange(YEAR)

`Hay fever_Percent of poverty level`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Hay fever or respiratory allergy a… Percent … Below 100% 1997… Unde…     14.3
 2 Hay fever or respiratory allergy a… Percent … 100%-199%  1997… Unde…     15.4
 3 Hay fever or respiratory allergy a… Percent … 200%-399%  1997… Unde…     18.5
 4 Hay fever or respiratory allergy a… Percent … 400% or m… 1997… Unde…     20.3
 5 Hay fever or respiratory allergy a… Percent … Below 100% 2000… Unde…     14  
 6 Hay fever or respiratory allergy a… Percent … 100%-199%  2000… Unde…     15.6
 7 Hay fever or respiratory allergy a… Percent … 200%-399%  2000… Unde…     18.1
 8 Hay fever or respiratory allergy a… Percent … 400% or m… 2000… Unde…     21.1
 9 Hay fever or respiratory allergy a… Percent … Below 100% 2003… Unde…     14.2
10 Hay fever or respiratory allergy a… Percent … 100%-199%  2003… Unde…     16  
# ℹ 46 more rows
`Hay fever_insurance` <- health_clean%>%
  filter(PANEL=="Hay fever or respiratory allergy among persons under 18 years", STUB_NAME== "Health insurance status at the time of interview")%>%
  arrange(YEAR)

`Hay fever_insurance`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Hay fever or respiratory allergy a… Health i… Insured    1997… Unde…     18  
 2 Hay fever or respiratory allergy a… Health i… Insured: … 1997… Unde…     18.8
 3 Hay fever or respiratory allergy a… Health i… Insured: … 1997… Unde…     15  
 4 Hay fever or respiratory allergy a… Health i… Uninsured  1997… Unde…     14.3
 5 Hay fever or respiratory allergy a… Health i… Insured    2000… Unde…     18.3
 6 Hay fever or respiratory allergy a… Health i… Insured: … 2000… Unde…     19.2
 7 Hay fever or respiratory allergy a… Health i… Insured: … 2000… Unde…     16  
 8 Hay fever or respiratory allergy a… Health i… Uninsured  2000… Unde…     12.6
 9 Hay fever or respiratory allergy a… Health i… Insured    2003… Unde…     17.7
10 Hay fever or respiratory allergy a… Health i… Insured: … 2003… Unde…     18.5
# ℹ 46 more rows
`Hay fever` <-  bind_rows(`Hay fever_under 18 years`, `Hay fever_age`, `Hay fever_sex`, `Hay fever_race`, `Hay fever_Hispanic origin and race`, `Hay fever_Percent of poverty level`, `Hay fever_insurance`)

`Hay fever`
# A tibble: 350 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Hay fever or respiratory allergy a… Total     Under 18 … 1997… Unde…     17.5
 2 Hay fever or respiratory allergy a… Total     Under 18 … 2000… Unde…     17.7
 3 Hay fever or respiratory allergy a… Total     Under 18 … 2003… Unde…     17.3
 4 Hay fever or respiratory allergy a… Total     Under 18 … 2006… Unde…     16.5
 5 Hay fever or respiratory allergy a… Total     Under 18 … 2007… Unde…     16.6
 6 Hay fever or respiratory allergy a… Total     Under 18 … 2008… Unde…     17  
 7 Hay fever or respiratory allergy a… Total     Under 18 … 2009… Unde…     17  
 8 Hay fever or respiratory allergy a… Total     Under 18 … 2010… Unde…     16.8
 9 Hay fever or respiratory allergy a… Total     Under 18 … 2011… Unde…     16.5
10 Hay fever or respiratory allergy a… Total     Under 18 … 2012… Unde…     16  
# ℹ 340 more rows

4.8 “Ear infections among persons under 18 years”

`ear infections_under 18 years` <- health_clean%>%
filter(PANEL=="Ear infections among persons under 18 years", STUB_NAME== "Total")%>%
  arrange(YEAR)
 
`ear infections_under 18 years`
# A tibble: 14 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Ear infections among persons under… Total     Under 18 … 1997… Unde…      7.1
 2 Ear infections among persons under… Total     Under 18 … 2000… Unde…      6.7
 3 Ear infections among persons under… Total     Under 18 … 2003… Unde…      5.8
 4 Ear infections among persons under… Total     Under 18 … 2006… Unde…      5.5
 5 Ear infections among persons under… Total     Under 18 … 2007… Unde…      5.5
 6 Ear infections among persons under… Total     Under 18 … 2008… Unde…      5.7
 7 Ear infections among persons under… Total     Under 18 … 2009… Unde…      5.6
 8 Ear infections among persons under… Total     Under 18 … 2010… Unde…      5.6
 9 Ear infections among persons under… Total     Under 18 … 2011… Unde…      5.6
10 Ear infections among persons under… Total     Under 18 … 2012… Unde…      5.2
11 Ear infections among persons under… Total     Under 18 … 2013… Unde…      5  
12 Ear infections among persons under… Total     Under 18 … 2014… Unde…      4.6
13 Ear infections among persons under… Total     Under 18 … 2015… Unde…      4.4
14 Ear infections among persons under… Total     Under 18 … 2016… Unde…      4.4
`ear infections_age` <- health_clean%>%
  filter(PANEL=="Ear infections among persons under 18 years", STUB_NAME== "Age")%>%
  arrange(YEAR)

`ear infections_age`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Ear infections among persons under… Age       0-4 years  1997… 0-4 …     13.7
 2 Ear infections among persons under… Age       5-17 years 1997… 5-17…      4.8
 3 Ear infections among persons under… Age       5-9 years  1997… 5-9 …      7.1
 4 Ear infections among persons under… Age       10-17 yea… 1997… 10-1…      3.2
 5 Ear infections among persons under… Age       0-4 years  2000… 0-4 …     12.8
 6 Ear infections among persons under… Age       5-17 years 2000… 5-17…      4.5
 7 Ear infections among persons under… Age       5-9 years  2000… 5-9 …      6.9
 8 Ear infections among persons under… Age       10-17 yea… 2000… 10-1…      2.9
 9 Ear infections among persons under… Age       0-4 years  2003… 0-4 …     11  
10 Ear infections among persons under… Age       5-17 years 2003… 5-17…      3.8
# ℹ 46 more rows
`ear infections_sex` <- health_clean%>%
  filter(PANEL=="Ear infections among persons under 18 years", STUB_NAME== "Sex")%>%
  arrange(YEAR)

`ear infections_sex`
# A tibble: 28 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Ear infections among persons under… Sex       Male       1997… Unde…      7.3
 2 Ear infections among persons under… Sex       Female     1997… Unde…      6.9
 3 Ear infections among persons under… Sex       Male       2000… Unde…      6.9
 4 Ear infections among persons under… Sex       Female     2000… Unde…      6.5
 5 Ear infections among persons under… Sex       Male       2003… Unde…      5.9
 6 Ear infections among persons under… Sex       Female     2003… Unde…      5.6
 7 Ear infections among persons under… Sex       Male       2006… Unde…      5.7
 8 Ear infections among persons under… Sex       Female     2006… Unde…      5.4
 9 Ear infections among persons under… Sex       Male       2007… Unde…      5.6
10 Ear infections among persons under… Sex       Female     2007… Unde…      5.4
# ℹ 18 more rows
`ear infections_race` <- health_clean%>%
  filter(PANEL=="Ear infections among persons under 18 years", STUB_NAME== "Race")%>%
  arrange(YEAR)

`ear infections_race`
# A tibble: 84 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Ear infections among persons under… Race      White only 1997… Unde…      7.4
 2 Ear infections among persons under… Race      Black or … 1997… Unde…      5.9
 3 Ear infections among persons under… Race      American … 1997… Unde…     10.8
 4 Ear infections among persons under… Race      Asian only 1997… Unde…      3.7
 5 Ear infections among persons under… Race      Native Ha… 1997… Unde…     NA  
 6 Ear infections among persons under… Race      2 or more… 1997… Unde…     NA  
 7 Ear infections among persons under… Race      White only 2000… Unde…      7.2
 8 Ear infections among persons under… Race      Black or … 2000… Unde…      5  
 9 Ear infections among persons under… Race      American … 2000… Unde…      6.3
10 Ear infections among persons under… Race      Asian only 2000… Unde…      2.6
# ℹ 74 more rows
`ear infections_Hispanic origin and race` <- health_clean%>%
  filter(PANEL=="Ear infections among persons under 18 years", STUB_NAME== "Hispanic origin and race")%>%
  arrange(YEAR)

`ear infections_Hispanic origin and race`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Ear infections among persons under… Hispanic… Hispanic … 1997… Unde…      6.1
 2 Ear infections among persons under… Hispanic… Not Hispa… 1997… Unde…      7.3
 3 Ear infections among persons under… Hispanic… Not Hispa… 1997… Unde…      7.7
 4 Ear infections among persons under… Hispanic… Not Hispa… 1997… Unde…      5.9
 5 Ear infections among persons under… Hispanic… Hispanic … 2000… Unde…      6.7
 6 Ear infections among persons under… Hispanic… Not Hispa… 2000… Unde…      6.7
 7 Ear infections among persons under… Hispanic… Not Hispa… 2000… Unde…      7.3
 8 Ear infections among persons under… Hispanic… Not Hispa… 2000… Unde…      4.9
 9 Ear infections among persons under… Hispanic… Hispanic … 2003… Unde…      6.2
10 Ear infections among persons under… Hispanic… Not Hispa… 2003… Unde…      5.7
# ℹ 46 more rows
`ear infections_Percent of poverty level` <- health_clean%>%
  filter(PANEL=="Ear infections among persons under 18 years", STUB_NAME== "Percent of poverty level")%>%
  arrange(YEAR)

`ear infections_Percent of poverty level`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Ear infections among persons under… Percent … Below 100% 1997… Unde…      8.3
 2 Ear infections among persons under… Percent … 100%-199%  1997… Unde…      7.1
 3 Ear infections among persons under… Percent … 200%-399%  1997… Unde…      6.8
 4 Ear infections among persons under… Percent … 400% or m… 1997… Unde…      6.6
 5 Ear infections among persons under… Percent … Below 100% 2000… Unde…      7.9
 6 Ear infections among persons under… Percent … 100%-199%  2000… Unde…      6.8
 7 Ear infections among persons under… Percent … 200%-399%  2000… Unde…      6.5
 8 Ear infections among persons under… Percent … 400% or m… 2000… Unde…      6.1
 9 Ear infections among persons under… Percent … Below 100% 2003… Unde…      6.7
10 Ear infections among persons under… Percent … 100%-199%  2003… Unde…      5.7
# ℹ 46 more rows
`ear infections_insurance` <- health_clean%>%
  filter(PANEL=="Ear infections among persons under 18 years", STUB_NAME== "Health insurance status at the time of interview")%>%
  arrange(YEAR)

`ear infections_insurance`
# A tibble: 56 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Ear infections among persons under… Health i… Insured    1997… Unde…      7.3
 2 Ear infections among persons under… Health i… Insured: … 1997… Unde…      6.6
 3 Ear infections among persons under… Health i… Insured: … 1997… Unde…     10.2
 4 Ear infections among persons under… Health i… Uninsured  1997… Unde…      5.9
 5 Ear infections among persons under… Health i… Insured    2000… Unde…      6.9
 6 Ear infections among persons under… Health i… Insured: … 2000… Unde…      6.4
 7 Ear infections among persons under… Health i… Insured: … 2000… Unde…      8.7
 8 Ear infections among persons under… Health i… Uninsured  2000… Unde…      4.9
 9 Ear infections among persons under… Health i… Insured    2003… Unde…      5.8
10 Ear infections among persons under… Health i… Insured: … 2003… Unde…      5.2
# ℹ 46 more rows
`ear infections` <-  bind_rows(`ear infections_under 18 years`, `ear infections_age`, `ear infections_sex`, `ear infections_race`, `ear infections_Hispanic origin and race`, `ear infections_Percent of poverty level`, `ear infections_insurance`)

`ear infections`
# A tibble: 350 × 6
   PANEL                               STUB_NAME STUB_LABEL YEAR  AGE   ESTIMATE
   <chr>                               <chr>     <chr>      <chr> <chr>    <dbl>
 1 Ear infections among persons under… Total     Under 18 … 1997… Unde…      7.1
 2 Ear infections among persons under… Total     Under 18 … 2000… Unde…      6.7
 3 Ear infections among persons under… Total     Under 18 … 2003… Unde…      5.8
 4 Ear infections among persons under… Total     Under 18 … 2006… Unde…      5.5
 5 Ear infections among persons under… Total     Under 18 … 2007… Unde…      5.5
 6 Ear infections among persons under… Total     Under 18 … 2008… Unde…      5.7
 7 Ear infections among persons under… Total     Under 18 … 2009… Unde…      5.6
 8 Ear infections among persons under… Total     Under 18 … 2010… Unde…      5.6
 9 Ear infections among persons under… Total     Under 18 … 2011… Unde…      5.6
10 Ear infections among persons under… Total     Under 18 … 2012… Unde…      5.2
# ℹ 340 more rows

5 Analysis the data

Analysis of the data through mean, median, standard deviation, frequency, and proportion for the categorical variables, like the different characteristics of the respondents.

5.1 “Current asthma among persons under 18 years”

 `Current asthma heath_summary` <- `Current asthma heath`%>%
  group_by(STUB_NAME, STUB_LABEL)%>%
  summarise(mean_number= mean(ESTIMATE, na.rm = T), median_number= median(ESTIMATE, na.rm = T), sd_number=sd(ESTIMATE, na.rm = T))%>%
  ungroup()
`summarise()` has grouped output by 'STUB_NAME'. You can override using the
`.groups` argument.
`Current asthma heath_summary`
# A tibble: 25 × 5
   STUB_NAME                      STUB_LABEL mean_number median_number sd_number
   <chr>                          <chr>            <dbl>         <dbl>     <dbl>
 1 Age                            0-4 years         5.38          5.8      0.967
 2 Age                            10-17 yea…       10.6          10.6      0.339
 3 Age                            5-17 years       10.3          10.4      0.427
 4 Age                            5-9 years         9.74          9.85     0.725
 5 Health insurance status at th… Insured           9.11          9.1      0.585
 6 Health insurance status at th… Insured: …       11.2          11.4      0.880
 7 Health insurance status at th… Insured: …        7.85          7.85     0.438
 8 Health insurance status at th… Uninsured         6.62          6.75     0.480
 9 Hispanic origin and race       Hispanic …        8             7.9      0.467
10 Hispanic origin and race       Not Hispa…        9.18          9.05     0.566
# ℹ 15 more rows
freq_table_STUB_NAME <-table(`Current asthma heath`$"STUB_NAME")
freq_table_STUB_NAME

                                             Age 
                                              56 
Health insurance status at the time of interview 
                                              56 
                        Hispanic origin and race 
                                              56 
                        Percent of poverty level 
                                              56 
                                            Race 
                                              84 
                                             Sex 
                                              28 
                                           Total 
                                              14 
prop_table_STUB_NAME <- freq_table_STUB_NAME%>% prop.table()
prop_table_STUB_NAME

                                             Age 
                                            0.16 
Health insurance status at the time of interview 
                                            0.16 
                        Hispanic origin and race 
                                            0.16 
                        Percent of poverty level 
                                            0.16 
                                            Race 
                                            0.24 
                                             Sex 
                                            0.08 
                                           Total 
                                            0.04 

5.2 “Asthma attack in last 12 months among persons under 18 years”

 `Asthma attack in past 12 months_summary` <- `Asthma attack in past 12 months`%>%
  group_by(STUB_NAME, STUB_LABEL)%>%
  summarise(mean_number= mean(ESTIMATE, na.rm = T), median_number= median(ESTIMATE, na.rm = T), sd_number=sd(ESTIMATE, na.rm = T))%>%
  ungroup()
`summarise()` has grouped output by 'STUB_NAME'. You can override using the
`.groups` argument.
`Asthma attack in past 12 months_summary`
# A tibble: 25 × 5
   STUB_NAME                      STUB_LABEL mean_number median_number sd_number
   <chr>                          <chr>            <dbl>         <dbl>     <dbl>
 1 Age                            0-4 years         3.76          4.2      0.755
 2 Age                            10-17 yea…        5.48          5.7      0.389
 3 Age                            5-17 years        5.68          5.85     0.408
 4 Age                            5-9 years         5.99          6.2      0.553
 5 Health insurance status at th… Insured           5.3           5.6      0.535
 6 Health insurance status at th… Insured: …        6.54          6.8      0.803
 7 Health insurance status at th… Insured: …        4.63          4.85     0.527
 8 Health insurance status at th… Uninsured         3.74          3.7      0.303
 9 Hispanic origin and race       Hispanic …        4.44          4.5      0.373
10 Hispanic origin and race       Not Hispa…        5.36          5.55     0.500
# ℹ 15 more rows
freq_table_STUB_NAME <-table(`Asthma attack in past 12 months`$"STUB_NAME")
freq_table_STUB_NAME

                                             Age 
                                              56 
Health insurance status at the time of interview 
                                              56 
                        Hispanic origin and race 
                                              56 
                        Percent of poverty level 
                                              56 
                                            Race 
                                              84 
                                             Sex 
                                              28 
                                           Total 
                                              14 
prop_table_STUB_NAME <- freq_table_STUB_NAME%>% prop.table()
prop_table_STUB_NAME

                                             Age 
                                            0.16 
Health insurance status at the time of interview 
                                            0.16 
                        Hispanic origin and race 
                                            0.16 
                        Percent of poverty level 
                                            0.16 
                                            Race 
                                            0.24 
                                             Sex 
                                            0.08 
                                           Total 
                                            0.04 

5.3 “ADHD among persons under 18 years”

`hyperactivity disorder_summary` <- `hyperactivity disorder`%>%
  group_by(STUB_NAME, STUB_LABEL)%>%
  summarise(mean_number= mean(ESTIMATE, na.rm = T), median_number= median(ESTIMATE, na.rm = T), sd_number=sd(ESTIMATE, na.rm = T))%>%
  ungroup()
`summarise()` has grouped output by 'STUB_NAME'. You can override using the
`.groups` argument.
`hyperactivity disorder_summary`
# A tibble: 23 × 5
   STUB_NAME                      STUB_LABEL mean_number median_number sd_number
   <chr>                          <chr>            <dbl>         <dbl>     <dbl>
 1 Age                            10-17 yea…       11.1          11.8      1.61 
 2 Age                            5-17 years        9.35          9.75     1.36 
 3 Age                            5-9 years         6.58          6.65     0.977
 4 Health insurance status at th… Insured           9.65         10.0      1.38 
 5 Health insurance status at th… Insured: …       12.6          13.1      1.20 
 6 Health insurance status at th… Insured: …        8.11          8.25     1.06 
 7 Health insurance status at th… Uninsured         5.84          5.85     0.534
 8 Hispanic origin and race       Hispanic …        5.61          5.7      1.12 
 9 Hispanic origin and race       Not Hispa…       10.4          10.9      1.60 
10 Hispanic origin and race       Not Hispa…        9.85          9.95     2.33 
# ℹ 13 more rows
freq_table_STUB_NAME <-table(`hyperactivity disorder`$"STUB_NAME")
freq_table_STUB_NAME

                                             Age 
                                              42 
Health insurance status at the time of interview 
                                              56 
                        Hispanic origin and race 
                                              56 
                        Percent of poverty level 
                                              56 
                                            Race 
                                              84 
                                             Sex 
                                              28 
prop_table_STUB_NAME <- freq_table_STUB_NAME%>% prop.table()
prop_table_STUB_NAME

                                             Age 
                                      0.13043478 
Health insurance status at the time of interview 
                                      0.17391304 
                        Hispanic origin and race 
                                      0.17391304 
                        Percent of poverty level 
                                      0.17391304 
                                            Race 
                                      0.26086957 
                                             Sex 
                                      0.08695652 

5.4 “Serious emotional or behavioral difficulties among persons under 18 years

`Serious emotional_summary` <- `Serious emotional`%>%
  group_by(STUB_NAME, STUB_LABEL)%>%
  summarise(mean_number= mean(ESTIMATE, na.rm = T), median_number= median(ESTIMATE, na.rm = T), sd_number=sd(ESTIMATE, na.rm = T))%>%
  ungroup()
`summarise()` has grouped output by 'STUB_NAME'. You can override using the
`.groups` argument.
`Serious emotional_summary`
# A tibble: 23 × 5
   STUB_NAME                      STUB_LABEL mean_number median_number sd_number
   <chr>                          <chr>            <dbl>         <dbl>     <dbl>
 1 Age                            10-17 yea…        5.92          5.9      0.230
 2 Age                            5-17 years        5.58          5.6      0.209
 3 Age                            5-9 years         5.02          5.1      0.289
 4 Health insurance status at th… Insured           5.72          5.75     0.217
 5 Health insurance status at th… Insured: …        8.64          8.55     0.483
 6 Health insurance status at th… Insured: …        4.08          4.1      0.111
 7 Health insurance status at th… Uninsured         3.83          3.75     0.481
 8 Hispanic origin and race       Hispanic …        4.08          4.25     0.343
 9 Hispanic origin and race       Not Hispa…        5.99          6        0.247
10 Hispanic origin and race       Not Hispa…        6.04          6.05     0.485
# ℹ 13 more rows
freq_table_STUB_NAME <-table(`Serious emotional`$"STUB_NAME")
freq_table_STUB_NAME

                                             Age 
                                              42 
Health insurance status at the time of interview 
                                              56 
                        Hispanic origin and race 
                                              56 
                        Percent of poverty level 
                                              56 
                                            Race 
                                              84 
                                             Sex 
                                              28 
prop_table_STUB_NAME <- freq_table_STUB_NAME%>% prop.table()
prop_table_STUB_NAME

                                             Age 
                                      0.13043478 
Health insurance status at the time of interview 
                                      0.17391304 
                        Hispanic origin and race 
                                      0.17391304 
                        Percent of poverty level 
                                      0.17391304 
                                            Race 
                                      0.26086957 
                                             Sex 
                                      0.08695652 

5.5 “Food allergy among persons under 18 years”

`Food allergy_summary` <- `Food allergy`%>%
  group_by(STUB_NAME, STUB_LABEL)%>%
  summarise(mean_number= mean(ESTIMATE, na.rm = T), median_number= median(ESTIMATE, na.rm = T), sd_number=sd(ESTIMATE, na.rm = T))%>%
  ungroup()
`summarise()` has grouped output by 'STUB_NAME'. You can override using the
`.groups` argument.
`Food allergy_summary`
# A tibble: 25 × 5
   STUB_NAME                      STUB_LABEL mean_number median_number sd_number
   <chr>                          <chr>            <dbl>         <dbl>     <dbl>
 1 Age                            0-4 years         5.17          5.15     0.763
 2 Age                            10-17 yea…        4.83          5.1      0.993
 3 Age                            5-17 years        4.91          5.2      1.02 
 4 Age                            5-9 years         5.05          5.35     1.12 
 5 Health insurance status at th… Insured           5.07          5.25     0.932
 6 Health insurance status at th… Insured: …        4.69          4.7      0.748
 7 Health insurance status at th… Insured: …        5.26          5.5      1.06 
 8 Health insurance status at th… Uninsured         4.06          4.4      0.883
 9 Hispanic origin and race       Hispanic …        3.84          3.7      0.955
10 Hispanic origin and race       Not Hispa…        5.31          5.65     0.986
# ℹ 15 more rows
freq_table_STUB_NAME <-table(`Serious emotional`$"STUB_NAME")
freq_table_STUB_NAME

                                             Age 
                                              42 
Health insurance status at the time of interview 
                                              56 
                        Hispanic origin and race 
                                              56 
                        Percent of poverty level 
                                              56 
                                            Race 
                                              84 
                                             Sex 
                                              28 
prop_table_STUB_NAME <- freq_table_STUB_NAME%>% prop.table()
prop_table_STUB_NAME

                                             Age 
                                      0.13043478 
Health insurance status at the time of interview 
                                      0.17391304 
                        Hispanic origin and race 
                                      0.17391304 
                        Percent of poverty level 
                                      0.17391304 
                                            Race 
                                      0.26086957 
                                             Sex 
                                      0.08695652 

5.6 “Skin allergy among persons under 18 years”

`Skin allergy_summary` <- `Skin allergy`%>%
  group_by(STUB_NAME, STUB_LABEL)%>%
  summarise(mean_number= mean(ESTIMATE, na.rm = T), median_number= median(ESTIMATE, na.rm = T), sd_number=sd(ESTIMATE, na.rm = T))%>%
  ungroup()
`summarise()` has grouped output by 'STUB_NAME'. You can override using the
`.groups` argument.
`Skin allergy_summary`
# A tibble: 25 × 5
   STUB_NAME                      STUB_LABEL mean_number median_number sd_number
   <chr>                          <chr>            <dbl>         <dbl>     <dbl>
 1 Age                            0-4 years        12.6          13.4       2.05
 2 Age                            10-17 yea…        9.84         10.4       1.35
 3 Age                            5-17 years       10.6          11.4       1.58
 4 Age                            5-9 years        11.8          12.7       1.91
 5 Health insurance status at th… Insured          11.4          12         1.64
 6 Health insurance status at th… Insured: …       11.5          12.2       1.68
 7 Health insurance status at th… Insured: …       11.2          11.9       1.60
 8 Health insurance status at th… Uninsured         8.7           9.35      1.83
 9 Hispanic origin and race       Hispanic …        8.99          9.6       1.79
10 Hispanic origin and race       Not Hispa…       11.8          12.6       1.79
# ℹ 15 more rows
freq_table_STUB_NAME <-table(`Skin allergy`$"STUB_NAME")
freq_table_STUB_NAME

                                             Age 
                                              56 
Health insurance status at the time of interview 
                                              56 
                        Hispanic origin and race 
                                              56 
                        Percent of poverty level 
                                              56 
                                            Race 
                                              84 
                                             Sex 
                                              28 
                                           Total 
                                              14 
prop_table_STUB_NAME <- freq_table_STUB_NAME%>% prop.table()
prop_table_STUB_NAME

                                             Age 
                                            0.16 
Health insurance status at the time of interview 
                                            0.16 
                        Hispanic origin and race 
                                            0.16 
                        Percent of poverty level 
                                            0.16 
                                            Race 
                                            0.24 
                                             Sex 
                                            0.08 
                                           Total 
                                            0.04 

5.7 “Hay fever or respiratory allergy among persons under 18 years”

`Hay fever_summary` <- `Hay fever`%>%
  group_by(STUB_NAME, STUB_LABEL)%>%
  summarise(mean_number= mean(ESTIMATE, na.rm = T), median_number= median(ESTIMATE, na.rm = T), sd_number=sd(ESTIMATE, na.rm = T))%>%
  ungroup()
`summarise()` has grouped output by 'STUB_NAME'. You can override using the
`.groups` argument.
`Hay fever_summary`
# A tibble: 25 × 5
   STUB_NAME                      STUB_LABEL mean_number median_number sd_number
   <chr>                          <chr>            <dbl>         <dbl>     <dbl>
 1 Age                            0-4 years         9.91          10.2     0.953
 2 Age                            10-17 yea…       20.1           20.3     1.08 
 3 Age                            5-17 years       18.9           19       0.990
 4 Age                            5-9 years        16.9           17       0.938
 5 Health insurance status at th… Insured          16.7           17.0     1.07 
 6 Health insurance status at th… Insured: …       14.5           14.5     0.772
 7 Health insurance status at th… Insured: …       17.9           18.4     1.15 
 8 Health insurance status at th… Uninsured        13.6           14       1.09 
 9 Hispanic origin and race       Hispanic …       12.5           12.4     0.485
10 Hispanic origin and race       Not Hispa…       17.5           17.8     1.01 
# ℹ 15 more rows
freq_table_STUB_NAME <-table(`Hay fever`$"STUB_NAME")
freq_table_STUB_NAME

                                             Age 
                                              56 
Health insurance status at the time of interview 
                                              56 
                        Hispanic origin and race 
                                              56 
                        Percent of poverty level 
                                              56 
                                            Race 
                                              84 
                                             Sex 
                                              28 
                                           Total 
                                              14 
prop_table_STUB_NAME <- freq_table_STUB_NAME%>% prop.table()
prop_table_STUB_NAME

                                             Age 
                                            0.16 
Health insurance status at the time of interview 
                                            0.16 
                        Hispanic origin and race 
                                            0.16 
                        Percent of poverty level 
                                            0.16 
                                            Race 
                                            0.24 
                                             Sex 
                                            0.08 
                                           Total 
                                            0.04 

5.8 “Ear infections among persons under 18 years”

`ear infections_summary` <- `ear infections`%>%
  group_by(STUB_NAME, STUB_LABEL)%>%
  summarise(mean_number= mean(ESTIMATE, na.rm = T), median_number= median(ESTIMATE, na.rm = T), sd_number=sd(ESTIMATE, na.rm = T))%>%
  ungroup()
`summarise()` has grouped output by 'STUB_NAME'. You can override using the
`.groups` argument.
`ear infections_summary`
# A tibble: 25 × 5
   STUB_NAME                      STUB_LABEL mean_number median_number sd_number
   <chr>                          <chr>            <dbl>         <dbl>     <dbl>
 1 Age                            0-4 years        10.4          10.4      1.48 
 2 Age                            10-17 yea…        2.38          2.3      0.329
 3 Age                            5-17 years        3.64          3.6      0.531
 4 Age                            5-9 years         5.62          5.75     0.883
 5 Health insurance status at th… Insured           5.59          5.7      0.803
 6 Health insurance status at th… Insured: …        7.02          7.2      1.37 
 7 Health insurance status at th… Insured: …        4.81          4.65     0.790
 8 Health insurance status at th… Uninsured         4.34          4.4      0.798
 9 Hispanic origin and race       Hispanic …        5.73          5.85     0.719
10 Hispanic origin and race       Not Hispa…        5.41          5.4      0.811
# ℹ 15 more rows
freq_table_STUB_NAME <-table(`ear infections`$"STUB_NAME")
freq_table_STUB_NAME

                                             Age 
                                              56 
Health insurance status at the time of interview 
                                              56 
                        Hispanic origin and race 
                                              56 
                        Percent of poverty level 
                                              56 
                                            Race 
                                              84 
                                             Sex 
                                              28 
                                           Total 
                                              14 
prop_table_STUB_NAME <- freq_table_STUB_NAME%>% prop.table()
prop_table_STUB_NAME

                                             Age 
                                            0.16 
Health insurance status at the time of interview 
                                            0.16 
                        Hispanic origin and race 
                                            0.16 
                        Percent of poverty level 
                                            0.16 
                                            Race 
                                            0.24 
                                             Sex 
                                            0.08 
                                           Total 
                                            0.04