Cdc Data Exercise

CDC Data Exploration: Quarterly Provisional Estimates for Selected Birth Indicators

This exercise utilizes different data processing techniques to explore CDC-produced data.

Description of the data

This dataset comes from the National Center for Health Statistics (NCHS) and is a part of the CDC’s National Vital Statistics System. The data is a quarterly release of provisional estimates of selected reproductive indicators including general fertility rates, age-specific birth rates, total and low-risk cesarean delivery rates, preterm birth rates, and other gestational age categories.

The dataset is available at the CDC website.

Reading in the data

Here, I loaded all the packages I will use for this data exercise, listed below. I also read in the data from the csv file, and I called the data cdcdata. We can see that there are 8 variables and 1100 observations. This dataset has multiple indicators stacked on top of each other. For example, within the “Topic” column, there are metrics for “Birth Rate”, “Gestational Age,” etc., each measuring a very different outcome. Instead of widening the dataset (splitting the data into multiple tables, one for each metric), I am going to make things easier by looking at one metric, “Age-specific Birth Rates,” which I will get to below.

#Loading packages
library(tidyverse) #This includes ggplot2, tidyr, readr, dplyr, stringr, purr, forcats
library(knitr)
library(here)
library(kableExtra)

#Reading in the csv file
cdcdata <- read_csv(here("cdcdata-exercise", "NCHS_VSRR_Quarterly_provisional_estimates_for_selected_birth_indicators_20240205.csv"))

#Checking the packaging (displaying first and last few rows of data)
nrow(cdcdata)

[1] 1100

ncol(cdcdata)

[1] 8

#Showing the structure
str(cdcdata)

spc_tbl_ [1,100 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ Year and Quarter       : chr [1:1100] "2023 Q3" "2023 Q3" "2023 Q3" "2023 Q3" ...
 $ Topic                  : chr [1:1100] "Birth Rates" "Birth Rates" "Birth Rates" "Birth Rates" ...
 $ Topic Subgroup         : chr [1:1100] "Age-specific Birth Rates" "Age-specific Birth Rates" "Age-specific Birth Rates" "Age-specific Birth Rates" ...
 $ Indicator              : chr [1:1100] "10-14 years" "10-14 years" "10-14 years" "10-14 years" ...
 $ Race Ethnicity Category: chr [1:1100] "All races and origins" "Hispanic" "Non-Hispanic Black" "Non-Hispanic White" ...
 $ Rate                   : num [1:1100] 0.2 0.3 0.4 0.1 13.4 21.3 19.7 8.7 56 78.3 ...
 $ Unit                   : chr [1:1100] "per 1,000 population" "per 1,000 population" "per 1,000 population" "per 1,000 population" ...
 $ Significant            : chr [1:1100] NA NA "*" NA ...
 - attr(*, "spec")=
  .. cols(
  ..   `Year and Quarter` = col_character(),
  ..   Topic = col_character(),
  ..   `Topic Subgroup` = col_character(),
  ..   Indicator = col_character(),
  ..   `Race Ethnicity Category` = col_character(),
  ..   Rate = col_double(),
  ..   Unit = col_character(),
  ..   Significant = col_character()
  .. )
 - attr(*, "problems")=<externalptr> 

#Looking at top/bottom of data
head(cdcdata)[, c(1:8)]

# A tibble: 6 × 8
  `Year and Quarter` Topic     `Topic Subgroup` Indicator Race Ethnicity Categ…¹
  <chr>              <chr>     <chr>            <chr>     <chr>                 
1 2023 Q3            Birth Ra… Age-specific Bi… 10-14 ye… All races and origins 
2 2023 Q3            Birth Ra… Age-specific Bi… 10-14 ye… Hispanic              
3 2023 Q3            Birth Ra… Age-specific Bi… 10-14 ye… Non-Hispanic Black    
4 2023 Q3            Birth Ra… Age-specific Bi… 10-14 ye… Non-Hispanic White    
5 2023 Q3            Birth Ra… Age-specific Bi… 15-19 ye… All races and origins 
6 2023 Q3            Birth Ra… Age-specific Bi… 15-19 ye… Hispanic              
# ℹ abbreviated name: ¹​`Race Ethnicity Category`
# ℹ 3 more variables: Rate <dbl>, Unit <chr>, Significant <chr>

tail(cdcdata)[, c(1:8)]

# A tibble: 6 × 8
  `Year and Quarter` Topic     `Topic Subgroup` Indicator Race Ethnicity Categ…¹
  <chr>              <chr>     <chr>            <chr>     <chr>                 
1 2021 Q1            NICU Adm… NICU Admission … NICU Adm… Non-Hispanic Black    
2 2021 Q1            NICU Adm… NICU Admission … NICU Adm… Non-Hispanic White    
3 2021 Q1            Source o… Medicaid         Medicaid  All races and origins 
4 2021 Q1            Source o… Medicaid         Medicaid  Hispanic              
5 2021 Q1            Source o… Medicaid         Medicaid  Non-Hispanic Black    
6 2021 Q1            Source o… Medicaid         Medicaid  Non-Hispanic White    
# ℹ abbreviated name: ¹​`Race Ethnicity Category`
# ℹ 3 more variables: Rate <dbl>, Unit <chr>, Significant <chr>

This dataset is pretty untidy. The “Year and Quarter” column is a combination of the year and the quarter. I will separate this column into two columns, “Year” and “Quarter”, and convert them to numeric. In addition, the “Topic” and “Topic Subgroup” columns have multiple indicators listed in the same column. I will be looking at the observations only for “Birth Rates” and will remove all other observations. I will also replace the space with an underscore in the “Topic” column where it contains “Birth Rates.”

#This section was written with help from ChatGPT.

#Get column names
colnames(cdcdata)

[1] "Year and Quarter"        "Topic"                  
[3] "Topic Subgroup"          "Indicator"              
[5] "Race Ethnicity Category" "Rate"                   
[7] "Unit"                    "Significant"            

#Separate the "Year and Quarter" column into "Year" and "Quarter" columns in cdcdata
cdcdata <- cdcdata %>%
  separate("Year and Quarter", into = c("Year", "Quarter"), sep = " Q")

#Convert "Year" and "Quarter" columns to numeric
cdcdata <- cdcdata %>%
  mutate(
    Year = as.numeric(Year),
    Quarter = case_when(
      Quarter == "1" ~ 1,
      Quarter == "2" ~ 2,
      Quarter == "3" ~ 3,
      Quarter == "4" ~ 4
    )
  )

#Rename observations "Birth Rates"
cdcdata <- cdcdata %>%
  mutate(Topic = ifelse(Topic == "Birth Rates", str_replace(Topic, " ", "_"), Topic))

# Filter to include only observations where the "Topic" column contains "Birth_Rates"
cdcdata1 <- cdcdata %>% filter(Topic == "Birth_Rates")

#Print the modified cdcdata dataset
print(cdcdata1)

# A tibble: 572 × 9
    Year Quarter Topic   `Topic Subgroup` Indicator Race Ethnicity Categ…¹  Rate
   <dbl>   <dbl> <chr>   <chr>            <chr>     <chr>                  <dbl>
 1  2023       3 Birth_… Age-specific Bi… 10-14 ye… All races and origins    0.2
 2  2023       3 Birth_… Age-specific Bi… 10-14 ye… Hispanic                 0.3
 3  2023       3 Birth_… Age-specific Bi… 10-14 ye… Non-Hispanic Black       0.4
 4  2023       3 Birth_… Age-specific Bi… 10-14 ye… Non-Hispanic White       0.1
 5  2023       3 Birth_… Age-specific Bi… 15-19 ye… All races and origins   13.4
 6  2023       3 Birth_… Age-specific Bi… 15-19 ye… Hispanic                21.3
 7  2023       3 Birth_… Age-specific Bi… 15-19 ye… Non-Hispanic Black      19.7
 8  2023       3 Birth_… Age-specific Bi… 15-19 ye… Non-Hispanic White       8.7
 9  2023       3 Birth_… Age-specific Bi… 20-24 ye… All races and origins   56  
10  2023       3 Birth_… Age-specific Bi… 20-24 ye… Hispanic                78.3
# ℹ 562 more rows
# ℹ abbreviated name: ¹​`Race Ethnicity Category`
# ℹ 2 more variables: Unit <chr>, Significant <chr>

With our new dataset, called cdcdata_birth_rates, we can see that the “Year and Quarter” column has been separated into “Year” and “Quarter” columns, and the “Topic” column has been modified to replace the space with an underscore where it contains “Birth Rates.” We can also see that the “Topic” column contains only “Birth Rates” observations. This gives us 9 columns (variables) and 572 rows (observations).

Additionally, the “Topic” and “Subtopic” columns are redundant in that the “Subtopic” column is the true indicator we are looking for, and we can remove the “Topic” column. The same issue we had in the “Topic” column is also present in the “Topic Subgroup” column. We will remove the “Topic” column and filter the dataset to include only observations where the “Topic Subgroup” column is exactly “Age-specific Birth Rates.” We will also replace “Age-specific Birth Rates” with “Age_Specific” in the “Topic Subgroup” column.

#This section was written with help from ChatGPT.

#Remove the "Topic" column
cdcdata1 <- cdcdata1 %>%
  select(-Topic)

#Filter the dataset to include only observations where the "Topic Subgroup" column is exactly "Age-specific Birth Rates"
cdcdata1 <- cdcdata1 %>%
  filter(`Topic Subgroup` == "Age-specific Birth Rates")

#Replace "Age-specific Birth Rates" with "Age_Specific" in the "Topic Subgroup" column
cdcdata1 <- cdcdata1 %>%
  mutate(`Topic Subgroup` = str_replace(`Topic Subgroup`, "Age-specific Birth Rates", "Age_Specific"))

#Remove the "Topic Subgroup" column and rename dataframe
cdcdata2 <- cdcdata1 %>%
  select(-`Topic Subgroup`)

#Print the modified cdcdata dataset
print(cdcdata2)

# A tibble: 352 × 7
    Year Quarter Indicator   `Race Ethnicity Category`  Rate Unit    Significant
   <dbl>   <dbl> <chr>       <chr>                     <dbl> <chr>   <chr>      
 1  2023       3 10-14 years All races and origins       0.2 per 1,… <NA>       
 2  2023       3 10-14 years Hispanic                    0.3 per 1,… <NA>       
 3  2023       3 10-14 years Non-Hispanic Black          0.4 per 1,… *          
 4  2023       3 10-14 years Non-Hispanic White          0.1 per 1,… <NA>       
 5  2023       3 15-19 years All races and origins      13.4 per 1,… *          
 6  2023       3 15-19 years Hispanic                   21.3 per 1,… *          
 7  2023       3 15-19 years Non-Hispanic Black         19.7 per 1,… *          
 8  2023       3 15-19 years Non-Hispanic White          8.7 per 1,… *          
 9  2023       3 20-24 years All races and origins      56   per 1,… *          
10  2023       3 20-24 years Hispanic                   78.3 per 1,… *          
# ℹ 342 more rows

Now, we have 7 columns (variables) and 352 observations, since we are only looking at age-specific birth rates, we don’t need a column with that information.

Now let’s clean up the rest of the column names and observation entries, removing spaces and recategorizing variables.

#This section was written with help from ChatGPT.

#Renaming columns
cdcdata2 <- cdcdata2 %>%
  rename(Age_Years = Indicator)

cdcdata2 <- cdcdata2 %>%
  rename(Race_Ethnicity = `Race Ethnicity Category`)

#Define the specific levels for the "Race_Ethnicity" category
race_ethnicity_levels <- c("All races and origins", "Hispanic", "Non-Hispanic Black", "Non-Hispanic White")

#Convert the "Race Ethnicity Category" column to a factor variable with custom levels
cdcdata2$Race_Ethnicity <- factor(cdcdata2$Race_Ethnicity, levels = race_ethnicity_levels)

#View the levels of the "Race_Ethnicity" column
levels(cdcdata2$Race_Ethnicity)

[1] "All races and origins" "Hispanic"              "Non-Hispanic Black"   
[4] "Non-Hispanic White"   

#Get unique age category levels from the "Age_Years" column
age_levels <- unique(cdcdata2$Age_Years)

#Convert the "Age_Years" column to a factor variable with custom levels
cdcdata2$Age_Years <- factor(cdcdata2$Age_Years, levels = age_levels)

#View the levels of the "Age_Years" column
levels(cdcdata2$Age_Years)

[1] "10-14 years" "15-19 years" "20-24 years" "25-29 years" "30-34 years"
[6] "35-39 years" "40-44 years" "45+ years"  

#Print the modified cdcdata dataset
print(cdcdata2)

# A tibble: 352 × 7
    Year Quarter Age_Years   Race_Ethnicity         Rate Unit        Significant
   <dbl>   <dbl> <fct>       <fct>                 <dbl> <chr>       <chr>      
 1  2023       3 10-14 years All races and origins   0.2 per 1,000 … <NA>       
 2  2023       3 10-14 years Hispanic                0.3 per 1,000 … <NA>       
 3  2023       3 10-14 years Non-Hispanic Black      0.4 per 1,000 … *          
 4  2023       3 10-14 years Non-Hispanic White      0.1 per 1,000 … <NA>       
 5  2023       3 15-19 years All races and origins  13.4 per 1,000 … *          
 6  2023       3 15-19 years Hispanic               21.3 per 1,000 … *          
 7  2023       3 15-19 years Non-Hispanic Black     19.7 per 1,000 … *          
 8  2023       3 15-19 years Non-Hispanic White      8.7 per 1,000 … *          
 9  2023       3 20-24 years All races and origins  56   per 1,000 … *          
10  2023       3 20-24 years Hispanic               78.3 per 1,000 … *          
# ℹ 342 more rows

We can see that we have multiple missing entries for the “Significant” Column. From the data dictionary on the website, the CDC defines the “Significant” variable as follows: “An asterisk (*) indicates that estimates for the most recent quarter are significantly different from the same quarter of the previous year.” This variable is not relevant for this analysis, so we can remove this column from the dataset. When CDC calculated rates, they had population size in each group, and thus could calculate significance.

But first, let’s just check how many observations are missing an asterisk in the “Significant” column.

# Count the number of NA values in the "Significant" column
na_count <- sum(is.na(cdcdata2$Significant))

# Print the number of NA values
print(na_count)

[1] 327

We can see that 327 of the 352 observations were not significantly different from the same quarter of the previous year.

Now, let’s remove that column/variable altogether.

#Remove the "Significant" column
cdcdata2 <- cdcdata2 %>%
  select(-Significant)

#Print the new dataset
print(cdcdata2)

# A tibble: 352 × 6
    Year Quarter Age_Years   Race_Ethnicity         Rate Unit                
   <dbl>   <dbl> <fct>       <fct>                 <dbl> <chr>               
 1  2023       3 10-14 years All races and origins   0.2 per 1,000 population
 2  2023       3 10-14 years Hispanic                0.3 per 1,000 population
 3  2023       3 10-14 years Non-Hispanic Black      0.4 per 1,000 population
 4  2023       3 10-14 years Non-Hispanic White      0.1 per 1,000 population
 5  2023       3 15-19 years All races and origins  13.4 per 1,000 population
 6  2023       3 15-19 years Hispanic               21.3 per 1,000 population
 7  2023       3 15-19 years Non-Hispanic Black     19.7 per 1,000 population
 8  2023       3 15-19 years Non-Hispanic White      8.7 per 1,000 population
 9  2023       3 20-24 years All races and origins  56   per 1,000 population
10  2023       3 20-24 years Hispanic               78.3 per 1,000 population
# ℹ 342 more rows

So, for our final dataset, we have 352 observations and 6 variables! However, in our exporatory analysis, we are going to look at the average of the rates over the entire 11 quarter time period, so we will drop the variables “Year”, “Quarter”, and “Unit” since they are not relevant for this analysis. All units are the same (per 1,000 population). This gives us 3 variables (Age_Years, Race_Ethnicity, and Rate).

#Remove the "Year", "Quarter", "Unit" columns
cdcdata2 <- cdcdata2 %>%
  select(-Year, -Quarter, -Unit)

#Exploratory/Descriptive Analysis of Clean Data

Now let’s begin to explore the data. We can’t show the percentage of observations in each category because we only have rate and not the population size. We also can’t summarize the variables in a way that can be described in a distribution because we don’t have the population size, just the final rates for each group.

We can ask: within each Race_Ethnicity category (All races and origins, Hispanic, Non-Hispanic Black, and Non-Hispanic White), how does the birth rate change across age groups during this 11-quarter period?

First, let’s summarize the data so that we get the mean and standard deviation for each age group according to race_ethnicity.

#Collapse data by Age_Years and Race_Ethnicity
cdcdata2_agg <- cdcdata2 %>%
  group_by(Age_Years, Race_Ethnicity) %>%
  summarise_all(list(mean = mean, sd = sd)) %>%
  ungroup()

#Make a  table using knitr
knitr::kable(cdcdata2_agg, caption = "Mean and Standard Deviation of Birth Rates by Age")

Mean and Standard Deviation of Birth Rates by Age

Age_Years	Race_Ethnicity	mean	sd
10-14 years	All races and origins	0.2000000	0.0000000
10-14 years	Hispanic	0.3000000	0.0000000
10-14 years	Non-Hispanic Black	0.3545455	0.0522233
10-14 years	Non-Hispanic White	0.1000000	0.0000000
15-19 years	All races and origins	13.8181818	0.3789939
15-19 years	Hispanic	21.3000000	0.3193744
15-19 years	Non-Hispanic Black	21.1636364	1.1595454
15-19 years	Non-Hispanic White	9.2181818	0.3341203
20-24 years	All races and origins	60.2818182	1.9898835
20-24 years	Hispanic	81.8909091	1.8019182
20-24 years	Non-Hispanic Black	76.3454545	4.4362966
20-24 years	Non-Hispanic White	50.6272727	1.8363501
25-29 years	All races and origins	92.6090909	1.1076593
25-29 years	Hispanic	110.3090909	3.8386077
25-29 years	Non-Hispanic Black	90.9818182	1.5879661
25-29 years	Non-Hispanic White	90.5363636	0.9254974
30-34 years	All races and origins	96.4363636	1.3086426
30-34 years	Hispanic	99.2727273	3.9678939
30-34 years	Non-Hispanic Black	79.1909091	0.7879778
30-34 years	Non-Hispanic White	99.9000000	1.4303846
35-39 years	All races and origins	53.8363636	1.5711605
35-39 years	Hispanic	56.8000000	2.8722813
35-39 years	Non-Hispanic Black	49.0000000	1.5244671
35-39 years	Non-Hispanic White	52.0727273	1.1816014
40-44 years	All races and origins	12.1909091	0.4678772
40-44 years	Hispanic	14.8636364	0.7500303
40-44 years	Non-Hispanic Black	13.2454545	0.6486349
40-44 years	Non-Hispanic White	10.4454545	0.2769969
45+ years	All races and origins	1.0090909	0.0943880
45+ years	Hispanic	1.0636364	0.1206045
45+ years	Non-Hispanic Black	1.2909091	0.1445998
45+ years	Non-Hispanic White	0.8363636	0.0809040

print(cdcdata2_agg)

# A tibble: 32 × 4
   Age_Years   Race_Ethnicity          mean     sd
   <fct>       <fct>                  <dbl>  <dbl>
 1 10-14 years All races and origins  0.2   0     
 2 10-14 years Hispanic               0.3   0     
 3 10-14 years Non-Hispanic Black     0.355 0.0522
 4 10-14 years Non-Hispanic White     0.1   0     
 5 15-19 years All races and origins 13.8   0.379 
 6 15-19 years Hispanic              21.3   0.319 
 7 15-19 years Non-Hispanic Black    21.2   1.16  
 8 15-19 years Non-Hispanic White     9.22  0.334 
 9 20-24 years All races and origins 60.3   1.99  
10 20-24 years Hispanic              81.9   1.80  
# ℹ 22 more rows

These are all stable measures because the SDs are small.

For each race/ethnic group, I will plot the mean rate for each age group. I am not plotting the rate for each age group and race/ethnic group over time because that would result in 32 lines, and that would be a very messy figure. Instead, I will plot the mean rate for each age group for each race/ethnic group for the entire period.

#ChatGPT helped me with this section.

#Plotting a bar graph
p1 <- ggplot(cdcdata2_agg, aes(x = Age_Years, y = mean, fill = Race_Ethnicity)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Mean Rate by Age Group and Race/Ethnicity",
       x = "Age Group",
       y = "Mean Rate",
       fill = "Race/Ethnicity") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_fill_brewer(palette = "Set2")  # Choose a color palette if needed
plot(p1)

#Saving figure
figure_file <- here("cdcdata-exercise", "mean_rate_bar.png")
ggsave(filename = figure_file, plot=p1)

#Plotting a line graph
p2 <- ggplot(cdcdata2_agg, aes(x = Age_Years, y = mean, color = Race_Ethnicity, group = Race_Ethnicity)) +
  geom_line() +
  labs(title = "Mean Rate by Age Group and Race/Ethnicity",
       x = "Age Group",
       y = "Mean Rate",
       color = "Race/Ethnicity") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_color_brewer(palette = "Set2")  # Choose a color palette if needed
plot(p2)

#Saving figure
figure_file <- here("cdcdata-exercise", "mean_rate_line.png")
ggsave(filename = figure_file, plot=p2)

These graphs tell us how birth rates change as women get older among different race_ethnicity categories. Unsurprisingly, despite differences among racial_ethnic categories, the overall trend remains the same. Birth rates are highest among women in the 25-29 and 30-34 age groups.

Rachel Robertson contributed to this portion of the exercise.

Creating a Synthetic Data Set Based on Birth Rates for Age Categories and Race/Ethnicity

Finding Number of Entries for Each Factor Variable

Cassia previously narrowed the data to 352 rows and 3 columns. The column rate represents a double (numeric variable in R), while the columns Age_Years and Race_Ethnicity both represent factors. As described by Cassia, the factor levels for Age_Years include: 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-45, and 45+ (years). The factor levels for Race_Ethnicity include:“All races and origins”, “Hispanic”, “Non-Hispanic Black”, “Non-Hispanic White”.

However, I do not know how entries are within each of these levels. I will need to know how many entries are in each ethnicity group and corresponding age group to make sure the demographic factors of the synthetic data align with the original data.

I will start by checking how many entries are within each factor level for age and race/ethnicity, separately. I will do this by using the group_by() function and tally() from dplyr.

n_age <- cdcdata2 %>% #Create new age category data frame
    group_by(Age_Years) %>% #group the values for age_years
    tally() #Tally values in each age_years level
n_race <- cdcdata2 %>% #Create new race/ethnicity data frame
    group_by(Race_Ethnicity) %>% #group the values for Race/ethnicity
    tally() #Tally values in each race/ethnicity level
    print(n_age)

# A tibble: 8 × 2
  Age_Years       n
  <fct>       <int>
1 10-14 years    44
2 15-19 years    44
3 20-24 years    44
4 25-29 years    44
5 30-34 years    44
6 35-39 years    44
7 40-44 years    44
8 45+ years      44

    print(n_race)#show the tables

# A tibble: 4 × 2
  Race_Ethnicity            n
  <fct>                 <int>
1 All races and origins    88
2 Hispanic                 88
3 Non-Hispanic Black       88
4 Non-Hispanic White       88

The age entries are evenly distributed with 44 entries for each age category.There are 88 entries per each race/ ethnicity group. This means that the new data frame will include an even distribution of age and race/ethnicity.

I will now check how many entries for rate are within each aggregate race/ethnicity + age category. I will use the group_by() function from dplyr, similar to what Cassia previously used.

cdcdata2_agg2 <- cdcdata2 %>%
  group_by(Age_Years, Race_Ethnicity) %>% #Create an aggregate data frame with age and race/ethnicity groupped together
  tally() #Take a count fo how many entries there are per groupping
  print(cdcdata2_agg2)

# A tibble: 32 × 3
# Groups:   Age_Years [8]
   Age_Years   Race_Ethnicity            n
   <fct>       <fct>                 <int>
 1 10-14 years All races and origins    11
 2 10-14 years Hispanic                 11
 3 10-14 years Non-Hispanic Black       11
 4 10-14 years Non-Hispanic White       11
 5 15-19 years All races and origins    11
 6 15-19 years Hispanic                 11
 7 15-19 years Non-Hispanic Black       11
 8 15-19 years Non-Hispanic White       11
 9 20-24 years All races and origins    11
10 20-24 years Hispanic                 11
# ℹ 22 more rows

There are 11 entries for each Age and Race/Ethnicity group. This means that 11 individuals for each race/ethnicity group were also part of the same age group.

Synthetic Data for Birth Rate dependent on Age and Race

I will begin to make a synthetic data frame that reflects the trends of the original data frame, but with 1056 observations. I wanted to choose an n close to 1000. I chose 1056 specifically because it is a multiple of 11 (the smallest number of entries in all factor levels) and of 352 (the total number of rows left from the original data set).

I will create an empty data frame with the same variables, but renamed without using the “_”

set.seed(5) #Set seed for replicability
n_mothers <- 1056 #Make there a total of 1056 participants (mothers) in this survey
syn_cdcdata <- data.frame(
  AgeYears = factor(n_mothers),
  RaceEthnicity = factor(n_mothers),
  Rate = numeric(n_mothers)
) #create a new data frame called syn_cdcdata with a total of 1056 entries for each column 

I will start by defining the AgeYears and RaceEthnicity factor levels and ensure that they are evenly distributed. I told Chat GPT: “I want to make a synthetic factor variable in an existing blank data frame. The factor variable should have the same of entries at each level. Let the total data entries be 1000 and each factor level be a, b, and c.” The output was not entirely what I needed, but I learned that I can create an entries per level variable for each factor level by making an equation of the total divided by the number of levels.I also learned that I can add “,each = number of entries variable” into the rep function to specify the even number of entries for each level. There will be 4 levels for raceethnicity and 8 for ageyears.

syn_cdcdata$AgeYears <- rep(c("10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-45", "45+"), each = n_mothers/8) #Make the ageyear factor levels as specified by with an equal number of entries for each level
syn_cdcdata$RaceEthnicity <- rep(c("All races and origins", "Hispanic", "Non-Hispanic Black", "Non-Hispanic White"), length.out = n_mothers) #Make the raceethnicity factor levels, I kept length.out instead of each so that there were different age levels within each race level
str(syn_cdcdata) #checking the number of total entries

'data.frame':   1056 obs. of  3 variables:
 $ AgeYears     : chr  "10-14" "10-14" "10-14" "10-14" ...
 $ RaceEthnicity: chr  "All races and origins" "Hispanic" "Non-Hispanic Black" "Non-Hispanic White" ...
 $ Rate         : num  0 0 0 0 0 0 0 0 0 0 ...

# Check that there is an equal number of levels for each factor
n_ageyears <- syn_cdcdata %>% #Create new age category data frame
    group_by(AgeYears) %>% #group the values for age_years
    tally() #Tally values in each age_years level
n_raceeth <- syn_cdcdata %>% #Create new race/ethnicity data frame
    group_by(RaceEthnicity) %>% #group the values for Race/ethnicity
    tally() #Tally values in each race/ethnicity level
    print(n_ageyears)

# A tibble: 8 × 2
  AgeYears     n
  <chr>    <int>
1 10-14      132
2 15-19      132
3 20-24      132
4 25-29      132
5 30-34      132
6 35-39      132
7 40-45      132
8 45+        132

    print(n_raceeth)#show the tables

# A tibble: 4 × 2
  RaceEthnicity             n
  <chr>                 <int>
1 All races and origins   264
2 Hispanic                264
3 Non-Hispanic Black      264
4 Non-Hispanic White      264

There is the correct number of entries for each factor level, but they are character variables rather than factors now, so I will change both variables back to factors.

syn_cdcdata$AgeYears <- factor(syn_cdcdata$AgeYears) #Set ageyears to a factor
syn_cdcdata$RaceEthnicity <- factor(syn_cdcdata$RaceEthnicity) #set raceethnicity to a factor
str(syn_cdcdata) #check the structure

'data.frame':   1056 obs. of  3 variables:
 $ AgeYears     : Factor w/ 8 levels "10-14","15-19",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ RaceEthnicity: Factor w/ 4 levels "All races and origins",..: 1 2 3 4 1 2 3 4 1 2 ...
 $ Rate         : num  0 0 0 0 0 0 0 0 0 0 ...

Since they are now factor variables, we will add the birth rates for each ageyears + raceethnicity group using a normal distribution and the means and standard deviations provided in Cassia’s exploratory analysis.

I had no clue how to create rates that would match each corresponding two factors of ageyears and raceethnicity, so I told ChatGPT: “I want to create a synthetic rate variable in a data frame. The rate is dependent on a combination of two factor variables. I have the mean and standard deviation of each combination of factor levels. I want the rate variable to be normally distributed.”

I was given a base function and explanation for each function. I then had to ask it to give me a combination of factor levels for a 4X8 matrices. Then, I had to ask for it to account for both factor variables in the mean_index and sd_index. I fill in this function with the names specific to my data frame in the chunk below.

Even with all of this back and forth with ChatGPT, the code it output gave me several blank rows for the rate value. Instead, I decided to go to the class question channel and ask for help there. Zayne Billings suggested that I use a regression model in the format y = intercept + b1 * age + b2 * race + b12 * age * gender + e. Assuming that the rate for age is normally distributed. I will find the mean for the entire factor of age. Assuming that this is impacted by race (maybe race is an effect modifier to this distribution) I will examine the mean rate for each race. Then I will find the interaction term with the aggregate function.

#I started by askign chatGPT "How do I find the mean rate for one factor variable?"
mean_rate <- aggregate(cdcdata2$Rate, by = list(cdcdata2$Age_Years), FUN = mean) #aggregate the Rate and Age_years variables and use the mean function
colnames(mean_rate) <- c("Factor", "Mean_Rate") #put the two new variables together in a new dataframe
print(mean_rate) #print the new columns

       Factor  Mean_Rate
1 10-14 years  0.2386364
2 15-19 years 16.3750000
3 20-24 years 67.2863636
4 25-29 years 96.1090909
5 30-34 years 93.7000000
6 35-39 years 52.9272727
7 40-44 years 12.6863636
8   45+ years  1.0500000

#I will now use the same function for the race variable
mean_rate2 <- aggregate(cdcdata2$Rate, by = list(cdcdata2$Race_Ethnicity), FUN = mean) #aggregate the Rate and race_ethnicity variables and use the mean function
colnames(mean_rate2) <- c("Factor2", "Mean_Rate2") #put the two new variables together in a new dataframe
print(mean_rate2) #print the new columns

                Factor2 Mean_Rate2
1 All races and origins   41.29773
2              Hispanic   48.22500
3    Non-Hispanic Black   41.44659
4    Non-Hispanic White   39.21705

I also know that I will need to find the residual sd to get e which is e = rnorm(length_of_vector, 0, residual_sd). I use a linear model to find the residual_sd.

model1 <- lm(Rate ~ Race_Ethnicity + Age_Years + Race_Ethnicity * Age_Years, data = cdcdata2) #make a linear model predicting rate with an interaction term of race * age
summary(model1) #Show the model summary

Call:
lm(formula = Rate ~ Race_Ethnicity + Age_Years + Race_Ethnicity * 
    Age_Years, data = cdcdata2)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.2455 -0.2568  0.0364  0.6432  5.5545 

Coefficients:
                                                       Estimate Std. Error
(Intercept)                                             0.20000    0.49771
Race_EthnicityHispanic                                  0.10000    0.70387
Race_EthnicityNon-Hispanic Black                        0.15455    0.70387
Race_EthnicityNon-Hispanic White                       -0.10000    0.70387
Age_Years15-19 years                                   13.61818    0.70387
Age_Years20-24 years                                   60.08182    0.70387
Age_Years25-29 years                                   92.40909    0.70387
Age_Years30-34 years                                   96.23636    0.70387
Age_Years35-39 years                                   53.63636    0.70387
Age_Years40-44 years                                   11.99091    0.70387
Age_Years45+ years                                      0.80909    0.70387
Race_EthnicityHispanic:Age_Years15-19 years             7.38182    0.99542
Race_EthnicityNon-Hispanic Black:Age_Years15-19 years   7.19091    0.99542
Race_EthnicityNon-Hispanic White:Age_Years15-19 years  -4.50000    0.99542
Race_EthnicityHispanic:Age_Years20-24 years            21.50909    0.99542
Race_EthnicityNon-Hispanic Black:Age_Years20-24 years  15.90909    0.99542
Race_EthnicityNon-Hispanic White:Age_Years20-24 years  -9.55455    0.99542
Race_EthnicityHispanic:Age_Years25-29 years            17.60000    0.99542
Race_EthnicityNon-Hispanic Black:Age_Years25-29 years  -1.78182    0.99542
Race_EthnicityNon-Hispanic White:Age_Years25-29 years  -1.97273    0.99542
Race_EthnicityHispanic:Age_Years30-34 years             2.73636    0.99542
Race_EthnicityNon-Hispanic Black:Age_Years30-34 years -17.40000    0.99542
Race_EthnicityNon-Hispanic White:Age_Years30-34 years   3.56364    0.99542
Race_EthnicityHispanic:Age_Years35-39 years             2.86364    0.99542
Race_EthnicityNon-Hispanic Black:Age_Years35-39 years  -4.99091    0.99542
Race_EthnicityNon-Hispanic White:Age_Years35-39 years  -1.66364    0.99542
Race_EthnicityHispanic:Age_Years40-44 years             2.57273    0.99542
Race_EthnicityNon-Hispanic Black:Age_Years40-44 years   0.90000    0.99542
Race_EthnicityNon-Hispanic White:Age_Years40-44 years  -1.64545    0.99542
Race_EthnicityHispanic:Age_Years45+ years              -0.04545    0.99542
Race_EthnicityNon-Hispanic Black:Age_Years45+ years     0.12727    0.99542
Race_EthnicityNon-Hispanic White:Age_Years45+ years    -0.07273    0.99542
                                                      t value Pr(>|t|)    
(Intercept)                                             0.402 0.688070    
Race_EthnicityHispanic                                  0.142 0.887113    
Race_EthnicityNon-Hispanic Black                        0.220 0.826350    
Race_EthnicityNon-Hispanic White                       -0.142 0.887113    
Age_Years15-19 years                                   19.348  < 2e-16 ***
Age_Years20-24 years                                   85.359  < 2e-16 ***
Age_Years25-29 years                                  131.287  < 2e-16 ***
Age_Years30-34 years                                  136.725  < 2e-16 ***
Age_Years35-39 years                                   76.202  < 2e-16 ***
Age_Years40-44 years                                   17.036  < 2e-16 ***
Age_Years45+ years                                      1.149 0.251213    
Race_EthnicityHispanic:Age_Years15-19 years             7.416 1.09e-12 ***
Race_EthnicityNon-Hispanic Black:Age_Years15-19 years   7.224 3.72e-12 ***
Race_EthnicityNon-Hispanic White:Age_Years15-19 years  -4.521 8.69e-06 ***
Race_EthnicityHispanic:Age_Years20-24 years            21.608  < 2e-16 ***
Race_EthnicityNon-Hispanic Black:Age_Years20-24 years  15.982  < 2e-16 ***
Race_EthnicityNon-Hispanic White:Age_Years20-24 years  -9.598  < 2e-16 ***
Race_EthnicityHispanic:Age_Years25-29 years            17.681  < 2e-16 ***
Race_EthnicityNon-Hispanic Black:Age_Years25-29 years  -1.790 0.074397 .  
Race_EthnicityNon-Hispanic White:Age_Years25-29 years  -1.982 0.048357 *  
Race_EthnicityHispanic:Age_Years30-34 years             2.749 0.006318 ** 
Race_EthnicityNon-Hispanic Black:Age_Years30-34 years -17.480  < 2e-16 ***
Race_EthnicityNon-Hispanic White:Age_Years30-34 years   3.580 0.000397 ***
Race_EthnicityHispanic:Age_Years35-39 years             2.877 0.004287 ** 
Race_EthnicityNon-Hispanic Black:Age_Years35-39 years  -5.014 8.85e-07 ***
Race_EthnicityNon-Hispanic White:Age_Years35-39 years  -1.671 0.095643 .  
Race_EthnicityHispanic:Age_Years40-44 years             2.585 0.010193 *  
Race_EthnicityNon-Hispanic Black:Age_Years40-44 years   0.904 0.366602    
Race_EthnicityNon-Hispanic White:Age_Years40-44 years  -1.653 0.099307 .  
Race_EthnicityHispanic:Age_Years45+ years              -0.046 0.963607    
Race_EthnicityNon-Hispanic Black:Age_Years45+ years     0.128 0.898342    
Race_EthnicityNon-Hispanic White:Age_Years45+ years    -0.073 0.941803    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.651 on 320 degrees of freedom
Multiple R-squared:  0.9983,    Adjusted R-squared:  0.9981 
F-statistic:  6056 on 31 and 320 DF,  p-value: < 2.2e-16

The residual sd= 1.651

Now I will create a rate variable that corresponds to each interaction term (combination of levels). To do this more simply, I had a back and forth with ChatGPT. The original prompt was “If I have to make several equations formatted like : y = intercept + b1 * age + b2 * race + b12 * age * gender + e, Can I produce several equations at once by using the c() function for each b1, b2, and b12?”

# Define  the coefficients by the means provided above
b1_values <- c(0.2386364, 16.3750000, 67.2863636,  96.1090909, 93.7000000, 52.9272727, 12.6863636, 1.0500000)  # Means to different levels of age
b2_values <- c(41.29773, 48.22500, 41.44659, 39.21705)  # Means corresponding to different levels of race
b12_values <- c(0.2, 0.3, 0.3545, 0.1, 13.8182, 21.3, 21.1636, 9.2182, 60.2818, 81.8909, 76.3455, 50.6273, 92.6091, 110.3091, 90.9818, 90.5364, 96.4364,99.2727, 79.1909, 99.9, 53.8364, 56.8, 49, 52.0727, 12.1909, 14.8636, 13.2455, 10.4455, 1.0091, 1.0636, 1.2909, 0.8364)  #Interaction means for b12 given by Cassia's analysis above
e <- rnorm(1056, 0, 1.651) #define the error using residual sd

# Convert the variables to numeric
syn_cdcdata$AgeYears <- as.integer(syn_cdcdata$AgeYears)
syn_cdcdata$RaceEthnicity <- as.integer(syn_cdcdata$RaceEthnicity)

# Repeat all of the coefficient values to have a total of 1056 values
b1_values <- rep(b1_values, length.out = 1056)
b2_values <- rep(b2_values, length.out = 1056)
b12_values <- rep(b12_values, length.out = 1056)

# Generate rate values and store them in the output data frame
syn_cdcdata$Rate <- numeric(nrow(syn_cdcdata))  # Initialize the Rate column

for (i in seq_len(nrow(syn_cdcdata))) {
  b1 <- b1_values[syn_cdcdata$AgeYears[i]]
  b2 <- b2_values[syn_cdcdata$RaceEthnicity[i]]
  b12 <- b12_values[i]
  
  Rate <- b1 * syn_cdcdata$AgeYears[i] + b2 * syn_cdcdata$RaceEthnicity[i] + b12 * syn_cdcdata$AgeYears[i] * syn_cdcdata$RaceEthnicity[i] + e[i]
  
  syn_cdcdata$Rate[i] <- Rate
}

# View the output data frame
print(syn_cdcdata)

     AgeYears RaceEthnicity       Rate
1           1             1   40.34811
2           1             2   99.57421
3           1             3  123.56909
4           1             4  157.62264
5           1             1   58.18016
6           1             2  138.29324
7           1             3  187.28966
8           1             4  192.93064
9           1             1  101.34635
10          1             2  260.69845
11          1             3  355.64172
12          1             4  358.29230
13          1             1  132.36174
14          1             2  317.04675
15          1             3  395.75433
16          1             4  519.02297
17          1             1  136.98660
18          1             2  291.62831
19          1             3  362.54870
20          1             4  556.27864
21          1             1   96.85951
22          1             2  211.84366
23          1             3  274.00201
24          1             4  366.56450
25          1             1   55.07945
26          1             2  125.93130
27          1             3  166.65700
28          1             4  201.36331
29          1             1   41.46062
30          1             2   97.40787
31          1             3  128.97268
32          1             4  162.28454
33          1             1   45.39409
34          1             2   99.29807
35          1             3  128.08410
36          1             4  159.07788
37          1             1   53.68783
38          1             2  135.98586
39          1             3  185.15984
40          1             4  193.74419
41          1             1  104.37732
42          1             2  259.14564
43          1             3  353.49178
44          1             4  362.74578
45          1             1  133.39167
46          1             2  318.23507
47          1             3  396.05936
48          1             4  518.49257
49          1             1  136.77690
50          1             2  295.11977
51          1             3  364.56693
52          1             4  557.01677
53          1             1   97.06013
54          1             2  209.31152
55          1             3  271.39316
56          1             4  363.87054
57          1             1   54.97097
58          1             2  126.22992
59          1             3  164.20909
60          1             4  199.27397
61          1             1   40.66897
62          1             2  100.22716
63          1             3  127.49622
64          1             4  161.27193
65          1             1   40.48151
66          1             2   96.72501
67          1             3  122.17096
68          1             4  157.00873
69          1             1   53.25386
70          1             2  138.82691
71          1             3  187.73224
72          1             4  193.60715
73          1             1  102.39111
74          1             2  260.52388
75          1             3  354.29765
76          1             4  359.35956
77          1             1  135.75269
78          1             2  317.50676
79          1             3  397.83613
80          1             4  518.32311
81          1             1  138.79565
82          1             2  292.35749
83          1             3  363.76170
84          1             4  556.66708
85          1             1   96.48832
86          1             2  209.11592
87          1             3  275.51973
88          1             4  364.61600
89          1             1   53.60217
90          1             2  125.55428
91          1             3  165.84381
92          1             4  197.13480
93          1             1   43.46513
94          1             2  100.30294
95          1             3  130.08551
96          1             4  161.08577
97          1             1   41.16416
98          1             2   96.39678
99          1             3  125.34051
100         1             4  157.40893
101         1             1   52.06018
102         1             2  141.16304
103         1             3  189.18494
104         1             4  194.32384
105         1             1  101.72266
106         1             2  261.94612
107         1             3  353.23705
108         1             4  356.37074
109         1             1  132.90142
110         1             2  319.42037
111         1             3  395.95056
112         1             4  521.93098
113         1             1  142.26560
114         1             2  295.46460
115         1             3  359.92107
116         1             4  558.02587
117         1             1   92.80547
118         1             2  211.05424
119         1             3  271.66497
120         1             4  365.06408
121         1             1   55.66035
122         1             2  127.87672
123         1             3  162.13907
124         1             4  196.17583
125         1             1   44.29429
126         1             2   99.29476
127         1             3  127.79065
128         1             4  162.50479
129         1             1   39.48042
130         1             2   94.90886
131         1             3  127.86836
132         1             4  154.24029
133         2             1   99.63532
134         2             2  214.22823
135         2             3  285.28151
136         2             4  264.11613
137         2             1  195.08695
138         2             2  454.99094
139         2             3  616.23384
140         2             4  595.13052
141         2             1  257.95174
142         2             2  570.38794
143         2             3  706.58014
144         2             4  915.49010
145         2             1  266.41689
146         2             2  525.60002
147         2             3  632.40019
148         2             4  988.43878
149         2             1  179.38401
150         2             2  355.75182
151         2             3  452.65176
152         2             4  607.44097
153         2             1   97.57534
154         2             2  189.98896
155         2             3  235.54817
156         2             4  275.22657
157         2             1   75.50773
158         2             2  135.42960
159         2             3  164.10325
160         2             4  196.61668
161         2             1   70.11989
162         2             2  134.10857
163         2             3  159.37103
164         2             4  193.10484
165         2             1  100.84061
166         2             2  213.31136
167         2             3  284.00502
168         2             4  263.16784
169         2             1  194.57888
170         2             2  455.96174
171         2             3  612.78509
172         2             4  594.87396
173         2             1  257.22763
174         2             2  567.54302
175         2             3  702.92197
176         2             4  914.45759
177         2             1  269.51638
178         2             2  524.52510
179         2             3  633.74796
180         2             4  987.83587
181         2             1  185.32243
182         2             2  355.27109
183         2             3  452.32812
184         2             4  607.80851
185         2             1   96.34188
186         2             2  188.19638
187         2             3  236.25007
188         2             4  272.54817
189         2             1   77.28864
190         2             2  131.52547
191         2             3  165.93728
192         2             4  196.91406
193         2             1   73.59756
194         2             2  131.14389
195         2             3  158.90684
196         2             4  192.62900
197         2             1  103.03171
198         2             2  214.53572
199         2             3  282.99680
200         2             4  264.56310
201         2             1  194.42365
202         2             2  456.27639
203         2             3  616.79589
204         2             4  593.35686
205         2             1  259.72144
206         2             2  571.11460
207         2             3  703.98963
208         2             4  915.45568
209         2             1  266.31372
210         2             2  527.51316
211         2             3  634.24697
212         2             4  989.85690
213         2             1  182.59184
214         2             2  355.62031
215         2             3  452.44972
216         2             4  605.49352
217         2             1   98.19402
218         2             2  190.99681
219         2             3  237.36703
220         2             4  274.17848
221         2             1   76.41402
222         2             2  133.39942
223         2             3  168.17877
224         2             4  195.69723
225         2             1   71.84206
226         2             2  130.19928
227         2             3  156.25046
228         2             4  189.63300
229         2             1  100.22448
230         2             2  208.62470
231         2             3  283.44072
232         2             4  264.97796
233         2             1  193.69000
234         2             2  455.72932
235         2             3  614.28700
236         2             4  597.76957
237         2             1  261.56997
238         2             2  569.20471
239         2             3  702.47607
240         2             4  915.84054
241         2             1  267.42307
242         2             2  526.09681
243         2             3  632.13597
244         2             4  991.24672
245         2             1  179.28011
246         2             2  355.27136
247         2             3  451.85012
248         2             4  605.90014
249         2             1   96.51632
250         2             2  189.32969
251         2             3  236.13647
252         2             4  272.74155
253         2             1   76.33695
254         2             2  132.80480
255         2             3  161.79116
256         2             4  193.76308
257         2             1   73.47985
258         2             2  128.99312
259         2             3  160.50178
260         2             4  190.36815
261         2             1   99.28084
262         2             2  214.55484
263         2             3  285.69323
264         2             4  262.37863
265         3             1  424.12572
266         3             2  793.28245
267         3             3 1014.62094
268         3             4  965.36510
269         3             1  518.34040
270         3             2  958.95619
271         3             3 1144.44773
272         3             4 1443.53717
273         3             1  531.25886
274         3             2  896.36454
275         3             3 1041.98553
276         3             4 1557.53306
277         3             1  402.44745
278         3             2  639.35888
279         3             3  767.67767
280         3             4  983.39748
281         3             1  279.93615
282         3             2  386.21540
283         3             3  443.73595
284         3             4  485.66968
285         3             1  245.48521
286         3             2  303.31772
287         3             3  340.12829
288         3             4  368.79218
289         3             1  246.80704
290         3             2  298.97594
291         3             3  329.02825
292         3             4  361.05425
293         3             1  285.47676
294         3             2  426.24208
295         3             3  516.76006
296         3             4  468.12848
297         3             1  425.59781
298         3             2  791.27958
299         3             3 1013.61758
300         3             4  966.70559
301         3             1  522.98207
302         3             2  960.47516
303         3             3 1148.27515
304         3             4 1445.39310
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331         3             3 1011.45000
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524         4             4 1350.59773
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967         8             3  640.22305
968         8             4  457.85106
969         8             1  532.34520
970         8             2 1416.16609
971         8             3 1963.86215
972         8             4 1784.32967
973         8             1  790.79891
974         8             2 1870.90388
975         8             3 2317.22834
976         8             4 3064.58081
977         8             1  818.00323
978         8             2 1689.44779
979         8             3 2031.27967
980         8             4 3362.40569
981         8             1  481.86184
982         8             2 1014.55251
983         8             3 1305.23057
984         8             4 1832.03418
985         8             1  147.03253
986         8             2  341.76900
987         8             3  450.13362
988         8             4  499.45455
989         8             1   58.90307
990         8             2  120.04008
991         8             3  167.31141
992         8             4  191.51743
993         8             1   51.94716
994         8             2  108.99890
995         8             3  144.28486
996         8             4  165.11758
997         8             1  157.67123
998         8             2  448.15225
999         8             3  638.99854
1000        8             4  462.04973
1001        8             1  529.54983
1002        8             2 1417.15917
1003        8             3 1964.31863
1004        8             4 1785.35314
1005        8             1  790.77616
1006        8             2 1869.11930
1007        8             3 2317.23317
1008        8             4 3065.08609
1009        8             1  819.53471
1010        8             2 1694.05410
1011        8             3 2033.90536
1012        8             4 3362.39322
1013        8             1  482.07969
1014        8             2 1012.07499
1015        8             3 1309.51458
1016        8             4 1831.45565
1017        8             1  145.35858
1018        8             2  342.28707
1019        8             3  450.57685
1020        8             4  497.86819
1021        8             1   57.88865
1022        8             2  124.48843
1023        8             3  164.42759
1024        8             4  192.54192
1025        8             1   51.49858
1026        8             2  107.47559
1027        8             3  139.74594
1028        8             4  167.56370
1029        8             1  158.94537
1030        8             2  446.24743
1031        8             3  640.13889
1032        8             4  460.99905
1033        8             1  532.26148
1034        8             2 1416.44744
1035        8             3 1963.13444
1036        8             4 1783.68695
1037        8             1  792.66786
1038        8             2 1870.16751
1039        8             3 2313.94389
1040        8             4 3061.38200
1041        8             1  821.22900
1042        8             2 1695.92082
1043        8             3 2037.51015
1044        8             4 3362.11919
1045        8             1  480.65787
1046        8             2 1012.49957
1047        8             3 1311.81473
1048        8             4 1832.24639
1049        8             1  145.85255
1050        8             2  340.06444
1051        8             3  451.47432
1052        8             4  497.13624
1053        8             1   55.87291
1054        8             2  119.47002
1055        8             3  163.03373
1056        8             4  194.88801

Now I have to convert the integer variables race and age back to a factor variables. I will overwrite the integer values be rerunning the code assigning the factor variables.

syn_cdcdata$AgeYears <- rep(c("10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-45", "45+"), each = n_mothers/8) #Make the ageyear factor levels as specified by with an equal number of entries for each level
syn_cdcdata$RaceEthnicity <- rep(c("All races and origins", "Hispanic", "Non-Hispanic Black", "Non-Hispanic White"), length.out = n_mothers) #Make the raceethnicity factor levels, I kept length.out instead of each so that there were different age levels within each race level
syn_cdcdata$AgeYears <- factor(syn_cdcdata$AgeYears) #Set ageyears to a factor
syn_cdcdata$RaceEthnicity <- factor(syn_cdcdata$RaceEthnicity) #set raceethnicity to a factor
print(syn_cdcdata) #Check the data altogether

     AgeYears         RaceEthnicity       Rate
1       10-14 All races and origins   40.34811
2       10-14              Hispanic   99.57421
3       10-14    Non-Hispanic Black  123.56909
4       10-14    Non-Hispanic White  157.62264
5       10-14 All races and origins   58.18016
6       10-14              Hispanic  138.29324
7       10-14    Non-Hispanic Black  187.28966
8       10-14    Non-Hispanic White  192.93064
9       10-14 All races and origins  101.34635
10      10-14              Hispanic  260.69845
11      10-14    Non-Hispanic Black  355.64172
12      10-14    Non-Hispanic White  358.29230
13      10-14 All races and origins  132.36174
14      10-14              Hispanic  317.04675
15      10-14    Non-Hispanic Black  395.75433
16      10-14    Non-Hispanic White  519.02297
17      10-14 All races and origins  136.98660
18      10-14              Hispanic  291.62831
19      10-14    Non-Hispanic Black  362.54870
20      10-14    Non-Hispanic White  556.27864
21      10-14 All races and origins   96.85951
22      10-14              Hispanic  211.84366
23      10-14    Non-Hispanic Black  274.00201
24      10-14    Non-Hispanic White  366.56450
25      10-14 All races and origins   55.07945
26      10-14              Hispanic  125.93130
27      10-14    Non-Hispanic Black  166.65700
28      10-14    Non-Hispanic White  201.36331
29      10-14 All races and origins   41.46062
30      10-14              Hispanic   97.40787
31      10-14    Non-Hispanic Black  128.97268
32      10-14    Non-Hispanic White  162.28454
33      10-14 All races and origins   45.39409
34      10-14              Hispanic   99.29807
35      10-14    Non-Hispanic Black  128.08410
36      10-14    Non-Hispanic White  159.07788
37      10-14 All races and origins   53.68783
38      10-14              Hispanic  135.98586
39      10-14    Non-Hispanic Black  185.15984
40      10-14    Non-Hispanic White  193.74419
41      10-14 All races and origins  104.37732
42      10-14              Hispanic  259.14564
43      10-14    Non-Hispanic Black  353.49178
44      10-14    Non-Hispanic White  362.74578
45      10-14 All races and origins  133.39167
46      10-14              Hispanic  318.23507
47      10-14    Non-Hispanic Black  396.05936
48      10-14    Non-Hispanic White  518.49257
49      10-14 All races and origins  136.77690
50      10-14              Hispanic  295.11977
51      10-14    Non-Hispanic Black  364.56693
52      10-14    Non-Hispanic White  557.01677
53      10-14 All races and origins   97.06013
54      10-14              Hispanic  209.31152
55      10-14    Non-Hispanic Black  271.39316
56      10-14    Non-Hispanic White  363.87054
57      10-14 All races and origins   54.97097
58      10-14              Hispanic  126.22992
59      10-14    Non-Hispanic Black  164.20909
60      10-14    Non-Hispanic White  199.27397
61      10-14 All races and origins   40.66897
62      10-14              Hispanic  100.22716
63      10-14    Non-Hispanic Black  127.49622
64      10-14    Non-Hispanic White  161.27193
65      10-14 All races and origins   40.48151
66      10-14              Hispanic   96.72501
67      10-14    Non-Hispanic Black  122.17096
68      10-14    Non-Hispanic White  157.00873
69      10-14 All races and origins   53.25386
70      10-14              Hispanic  138.82691
71      10-14    Non-Hispanic Black  187.73224
72      10-14    Non-Hispanic White  193.60715
73      10-14 All races and origins  102.39111
74      10-14              Hispanic  260.52388
75      10-14    Non-Hispanic Black  354.29765
76      10-14    Non-Hispanic White  359.35956
77      10-14 All races and origins  135.75269
78      10-14              Hispanic  317.50676
79      10-14    Non-Hispanic Black  397.83613
80      10-14    Non-Hispanic White  518.32311
81      10-14 All races and origins  138.79565
82      10-14              Hispanic  292.35749
83      10-14    Non-Hispanic Black  363.76170
84      10-14    Non-Hispanic White  556.66708
85      10-14 All races and origins   96.48832
86      10-14              Hispanic  209.11592
87      10-14    Non-Hispanic Black  275.51973
88      10-14    Non-Hispanic White  364.61600
89      10-14 All races and origins   53.60217
90      10-14              Hispanic  125.55428
91      10-14    Non-Hispanic Black  165.84381
92      10-14    Non-Hispanic White  197.13480
93      10-14 All races and origins   43.46513
94      10-14              Hispanic  100.30294
95      10-14    Non-Hispanic Black  130.08551
96      10-14    Non-Hispanic White  161.08577
97      10-14 All races and origins   41.16416
98      10-14              Hispanic   96.39678
99      10-14    Non-Hispanic Black  125.34051
100     10-14    Non-Hispanic White  157.40893
101     10-14 All races and origins   52.06018
102     10-14              Hispanic  141.16304
103     10-14    Non-Hispanic Black  189.18494
104     10-14    Non-Hispanic White  194.32384
105     10-14 All races and origins  101.72266
106     10-14              Hispanic  261.94612
107     10-14    Non-Hispanic Black  353.23705
108     10-14    Non-Hispanic White  356.37074
109     10-14 All races and origins  132.90142
110     10-14              Hispanic  319.42037
111     10-14    Non-Hispanic Black  395.95056
112     10-14    Non-Hispanic White  521.93098
113     10-14 All races and origins  142.26560
114     10-14              Hispanic  295.46460
115     10-14    Non-Hispanic Black  359.92107
116     10-14    Non-Hispanic White  558.02587
117     10-14 All races and origins   92.80547
118     10-14              Hispanic  211.05424
119     10-14    Non-Hispanic Black  271.66497
120     10-14    Non-Hispanic White  365.06408
121     10-14 All races and origins   55.66035
122     10-14              Hispanic  127.87672
123     10-14    Non-Hispanic Black  162.13907
124     10-14    Non-Hispanic White  196.17583
125     10-14 All races and origins   44.29429
126     10-14              Hispanic   99.29476
127     10-14    Non-Hispanic Black  127.79065
128     10-14    Non-Hispanic White  162.50479
129     10-14 All races and origins   39.48042
130     10-14              Hispanic   94.90886
131     10-14    Non-Hispanic Black  127.86836
132     10-14    Non-Hispanic White  154.24029
133     15-19 All races and origins   99.63532
134     15-19              Hispanic  214.22823
135     15-19    Non-Hispanic Black  285.28151
136     15-19    Non-Hispanic White  264.11613
137     15-19 All races and origins  195.08695
138     15-19              Hispanic  454.99094
139     15-19    Non-Hispanic Black  616.23384
140     15-19    Non-Hispanic White  595.13052
141     15-19 All races and origins  257.95174
142     15-19              Hispanic  570.38794
143     15-19    Non-Hispanic Black  706.58014
144     15-19    Non-Hispanic White  915.49010
145     15-19 All races and origins  266.41689
146     15-19              Hispanic  525.60002
147     15-19    Non-Hispanic Black  632.40019
148     15-19    Non-Hispanic White  988.43878
149     15-19 All races and origins  179.38401
150     15-19              Hispanic  355.75182
151     15-19    Non-Hispanic Black  452.65176
152     15-19    Non-Hispanic White  607.44097
153     15-19 All races and origins   97.57534
154     15-19              Hispanic  189.98896
155     15-19    Non-Hispanic Black  235.54817
156     15-19    Non-Hispanic White  275.22657
157     15-19 All races and origins   75.50773
158     15-19              Hispanic  135.42960
159     15-19    Non-Hispanic Black  164.10325
160     15-19    Non-Hispanic White  196.61668
161     15-19 All races and origins   70.11989
162     15-19              Hispanic  134.10857
163     15-19    Non-Hispanic Black  159.37103
164     15-19    Non-Hispanic White  193.10484
165     15-19 All races and origins  100.84061
166     15-19              Hispanic  213.31136
167     15-19    Non-Hispanic Black  284.00502
168     15-19    Non-Hispanic White  263.16784
169     15-19 All races and origins  194.57888
170     15-19              Hispanic  455.96174
171     15-19    Non-Hispanic Black  612.78509
172     15-19    Non-Hispanic White  594.87396
173     15-19 All races and origins  257.22763
174     15-19              Hispanic  567.54302
175     15-19    Non-Hispanic Black  702.92197
176     15-19    Non-Hispanic White  914.45759
177     15-19 All races and origins  269.51638
178     15-19              Hispanic  524.52510
179     15-19    Non-Hispanic Black  633.74796
180     15-19    Non-Hispanic White  987.83587
181     15-19 All races and origins  185.32243
182     15-19              Hispanic  355.27109
183     15-19    Non-Hispanic Black  452.32812
184     15-19    Non-Hispanic White  607.80851
185     15-19 All races and origins   96.34188
186     15-19              Hispanic  188.19638
187     15-19    Non-Hispanic Black  236.25007
188     15-19    Non-Hispanic White  272.54817
189     15-19 All races and origins   77.28864
190     15-19              Hispanic  131.52547
191     15-19    Non-Hispanic Black  165.93728
192     15-19    Non-Hispanic White  196.91406
193     15-19 All races and origins   73.59756
194     15-19              Hispanic  131.14389
195     15-19    Non-Hispanic Black  158.90684
196     15-19    Non-Hispanic White  192.62900
197     15-19 All races and origins  103.03171
198     15-19              Hispanic  214.53572
199     15-19    Non-Hispanic Black  282.99680
200     15-19    Non-Hispanic White  264.56310
201     15-19 All races and origins  194.42365
202     15-19              Hispanic  456.27639
203     15-19    Non-Hispanic Black  616.79589
204     15-19    Non-Hispanic White  593.35686
205     15-19 All races and origins  259.72144
206     15-19              Hispanic  571.11460
207     15-19    Non-Hispanic Black  703.98963
208     15-19    Non-Hispanic White  915.45568
209     15-19 All races and origins  266.31372
210     15-19              Hispanic  527.51316
211     15-19    Non-Hispanic Black  634.24697
212     15-19    Non-Hispanic White  989.85690
213     15-19 All races and origins  182.59184
214     15-19              Hispanic  355.62031
215     15-19    Non-Hispanic Black  452.44972
216     15-19    Non-Hispanic White  605.49352
217     15-19 All races and origins   98.19402
218     15-19              Hispanic  190.99681
219     15-19    Non-Hispanic Black  237.36703
220     15-19    Non-Hispanic White  274.17848
221     15-19 All races and origins   76.41402
222     15-19              Hispanic  133.39942
223     15-19    Non-Hispanic Black  168.17877
224     15-19    Non-Hispanic White  195.69723
225     15-19 All races and origins   71.84206
226     15-19              Hispanic  130.19928
227     15-19    Non-Hispanic Black  156.25046
228     15-19    Non-Hispanic White  189.63300
229     15-19 All races and origins  100.22448
230     15-19              Hispanic  208.62470
231     15-19    Non-Hispanic Black  283.44072
232     15-19    Non-Hispanic White  264.97796
233     15-19 All races and origins  193.69000
234     15-19              Hispanic  455.72932
235     15-19    Non-Hispanic Black  614.28700
236     15-19    Non-Hispanic White  597.76957
237     15-19 All races and origins  261.56997
238     15-19              Hispanic  569.20471
239     15-19    Non-Hispanic Black  702.47607
240     15-19    Non-Hispanic White  915.84054
241     15-19 All races and origins  267.42307
242     15-19              Hispanic  526.09681
243     15-19    Non-Hispanic Black  632.13597
244     15-19    Non-Hispanic White  991.24672
245     15-19 All races and origins  179.28011
246     15-19              Hispanic  355.27136
247     15-19    Non-Hispanic Black  451.85012
248     15-19    Non-Hispanic White  605.90014
249     15-19 All races and origins   96.51632
250     15-19              Hispanic  189.32969
251     15-19    Non-Hispanic Black  236.13647
252     15-19    Non-Hispanic White  272.74155
253     15-19 All races and origins   76.33695
254     15-19              Hispanic  132.80480
255     15-19    Non-Hispanic Black  161.79116
256     15-19    Non-Hispanic White  193.76308
257     15-19 All races and origins   73.47985
258     15-19              Hispanic  128.99312
259     15-19    Non-Hispanic Black  160.50178
260     15-19    Non-Hispanic White  190.36815
261     15-19 All races and origins   99.28084
262     15-19              Hispanic  214.55484
263     15-19    Non-Hispanic Black  285.69323
264     15-19    Non-Hispanic White  262.37863
265     20-24 All races and origins  424.12572
266     20-24              Hispanic  793.28245
267     20-24    Non-Hispanic Black 1014.62094
268     20-24    Non-Hispanic White  965.36510
269     20-24 All races and origins  518.34040
270     20-24              Hispanic  958.95619
271     20-24    Non-Hispanic Black 1144.44773
272     20-24    Non-Hispanic White 1443.53717
273     20-24 All races and origins  531.25886
274     20-24              Hispanic  896.36454
275     20-24    Non-Hispanic Black 1041.98553
276     20-24    Non-Hispanic White 1557.53306
277     20-24 All races and origins  402.44745
278     20-24              Hispanic  639.35888
279     20-24    Non-Hispanic Black  767.67767
280     20-24    Non-Hispanic White  983.39748
281     20-24 All races and origins  279.93615
282     20-24              Hispanic  386.21540
283     20-24    Non-Hispanic Black  443.73595
284     20-24    Non-Hispanic White  485.66968
285     20-24 All races and origins  245.48521
286     20-24              Hispanic  303.31772
287     20-24    Non-Hispanic Black  340.12829
288     20-24    Non-Hispanic White  368.79218
289     20-24 All races and origins  246.80704
290     20-24              Hispanic  298.97594
291     20-24    Non-Hispanic Black  329.02825
292     20-24    Non-Hispanic White  361.05425
293     20-24 All races and origins  285.47676
294     20-24              Hispanic  426.24208
295     20-24    Non-Hispanic Black  516.76006
296     20-24    Non-Hispanic White  468.12848
297     20-24 All races and origins  425.59781
298     20-24              Hispanic  791.27958
299     20-24    Non-Hispanic Black 1013.61758
300     20-24    Non-Hispanic White  966.70559
301     20-24 All races and origins  522.98207
302     20-24              Hispanic  960.47516
303     20-24    Non-Hispanic Black 1148.27515
304     20-24    Non-Hispanic White 1445.39310
305     20-24 All races and origins  529.85971
306     20-24              Hispanic  892.62941
307     20-24    Non-Hispanic Black 1040.93837
308     20-24    Non-Hispanic White 1556.92413
309     20-24 All races and origins  404.39758
310     20-24              Hispanic  640.03444
311     20-24    Non-Hispanic Black  765.77780
312     20-24    Non-Hispanic White  985.64468
313     20-24 All races and origins  280.99656
314     20-24              Hispanic  385.69538
315     20-24    Non-Hispanic Black  445.51981
316     20-24    Non-Hispanic White  486.72338
317     20-24 All races and origins  248.21853
318     20-24              Hispanic  304.06477
319     20-24    Non-Hispanic Black  335.58833
320     20-24    Non-Hispanic White  369.36657
321     20-24 All races and origins  243.15653
322     20-24              Hispanic  302.37864
323     20-24    Non-Hispanic Black  329.87128
324     20-24    Non-Hispanic White  361.10048
325     20-24 All races and origins  283.06318
326     20-24              Hispanic  424.26977
327     20-24    Non-Hispanic Black  517.71861
328     20-24    Non-Hispanic White  468.96399
329     20-24 All races and origins  421.74333
330     20-24              Hispanic  788.40814
331     20-24    Non-Hispanic Black 1011.45000
332     20-24    Non-Hispanic White  965.89273
333     20-24 All races and origins  520.76238
334     20-24              Hispanic  958.81314
335     20-24    Non-Hispanic Black 1145.81488
336     20-24    Non-Hispanic White 1443.72895
337     20-24 All races and origins  530.26641
338     20-24              Hispanic  894.06179
339     20-24    Non-Hispanic Black 1039.68318
340     20-24    Non-Hispanic White 1558.00469
341     20-24 All races and origins  399.90298
342     20-24              Hispanic  635.25451
343     20-24    Non-Hispanic Black  764.34116
344     20-24    Non-Hispanic White  984.96178
345     20-24 All races and origins  278.00416
346     20-24              Hispanic  386.04244
347     20-24    Non-Hispanic Black  444.74732
348     20-24    Non-Hispanic White  481.97951
349     20-24 All races and origins  246.41293
350     20-24              Hispanic  306.64477
351     20-24    Non-Hispanic Black  334.33243
352     20-24    Non-Hispanic White  369.19447
353     20-24 All races and origins  245.31795
354     20-24              Hispanic  299.08554
355     20-24    Non-Hispanic Black  329.37434
356     20-24    Non-Hispanic White  360.78618
357     20-24 All races and origins  287.59355
358     20-24              Hispanic  422.95152
359     20-24    Non-Hispanic Black  517.00028
360     20-24    Non-Hispanic White  469.80217
361     20-24 All races and origins  422.62153
362     20-24              Hispanic  793.00536
363     20-24    Non-Hispanic Black 1014.01681
364     20-24    Non-Hispanic White  968.01147
365     20-24 All races and origins  519.98391
366     20-24              Hispanic  961.93748
367     20-24    Non-Hispanic Black 1143.85897
368     20-24    Non-Hispanic White 1444.86879
369     20-24 All races and origins  533.29112
370     20-24              Hispanic  893.61580
371     20-24    Non-Hispanic Black 1039.06538
372     20-24    Non-Hispanic White 1559.18640
373     20-24 All races and origins  401.57379
374     20-24              Hispanic  639.97551
375     20-24    Non-Hispanic Black  766.34921
376     20-24    Non-Hispanic White  985.59894
377     20-24 All races and origins  278.88290
378     20-24              Hispanic  389.29131
379     20-24    Non-Hispanic Black  446.22040
380     20-24    Non-Hispanic White  483.67271
381     20-24 All races and origins  249.67771
382     20-24              Hispanic  306.65313
383     20-24    Non-Hispanic Black  339.29504
384     20-24    Non-Hispanic White  370.19805
385     20-24 All races and origins  246.49117
386     20-24              Hispanic  302.49584
387     20-24    Non-Hispanic Black  330.61870
388     20-24    Non-Hispanic White  358.78779
389     20-24 All races and origins  283.30480
390     20-24              Hispanic  425.67639
391     20-24    Non-Hispanic Black  515.99779
392     20-24    Non-Hispanic White  469.67806
393     20-24 All races and origins  422.79378
394     20-24              Hispanic  790.85829
395     20-24    Non-Hispanic Black 1013.84766
396     20-24    Non-Hispanic White  961.78418
397     25-29 All races and origins  795.19287
398     25-29              Hispanic 1364.19217
399     25-29    Non-Hispanic Black 1598.04376
400     25-29    Non-Hispanic White 1989.29446
401     25-29 All races and origins  810.70351
402     25-29              Hispanic 1275.84281
403     25-29    Non-Hispanic Black 1456.63115
404     25-29    Non-Hispanic White 2138.19232
405     25-29 All races and origins  641.83581
406     25-29              Hispanic  934.11002
407     25-29    Non-Hispanic Black 1096.15133
408     25-29    Non-Hispanic White 1377.78109
409     25-29 All races and origins  473.58937
410     25-29              Hispanic  596.52489
411     25-29    Non-Hispanic Black  669.43026
412     25-29    Non-Hispanic White  707.03773
413     25-29 All races and origins  428.09151
414     25-29              Hispanic  490.50919
415     25-29    Non-Hispanic Black  524.21208
416     25-29    Non-Hispanic White  558.39427
417     25-29 All races and origins  425.78430
418     25-29              Hispanic  483.60810
419     25-29    Non-Hispanic Black  513.08214
420     25-29    Non-Hispanic White  539.95038
421     25-29 All races and origins  480.89051
422     25-29              Hispanic  654.26500
423     25-29    Non-Hispanic Black  763.47343
424     25-29    Non-Hispanic White  686.82952
425     25-29 All races and origins  667.27112
426     25-29              Hispanic 1135.81713
427     25-29    Non-Hispanic Black 1426.05635
428     25-29    Non-Hispanic White 1349.01712
429     25-29 All races and origins  797.12035
430     25-29              Hispanic 1364.41201
431     25-29    Non-Hispanic Black 1598.99238
432     25-29    Non-Hispanic White 1987.38482
433     25-29 All races and origins  811.91975
434     25-29              Hispanic 1275.54928
435     25-29    Non-Hispanic Black 1461.88836
436     25-29    Non-Hispanic White 2140.49160
437     25-29 All races and origins  643.23846
438     25-29              Hispanic  938.26809
439     25-29    Non-Hispanic Black 1098.48556
440     25-29    Non-Hispanic White 1375.32054
441     25-29 All races and origins  473.84703
442     25-29              Hispanic  597.30176
443     25-29    Non-Hispanic Black  667.73202
444     25-29    Non-Hispanic White  709.84418
445     25-29 All races and origins  430.97548
446     25-29              Hispanic  489.97096
447     25-29    Non-Hispanic Black  526.51455
448     25-29    Non-Hispanic White  554.21038
449     25-29 All races and origins  429.65185
450     25-29              Hispanic  483.25768
451     25-29    Non-Hispanic Black  513.92346
452     25-29    Non-Hispanic White  542.01296
453     25-29 All races and origins  480.87341
454     25-29              Hispanic  651.23744
455     25-29    Non-Hispanic Black  762.49953
456     25-29    Non-Hispanic White  688.77783
457     25-29 All races and origins  664.58503
458     25-29              Hispanic 1137.12227
459     25-29    Non-Hispanic Black 1423.01654
460     25-29    Non-Hispanic White 1352.03907
461     25-29 All races and origins  795.53157
462     25-29              Hispanic 1361.31106
463     25-29    Non-Hispanic Black 1601.31218
464     25-29    Non-Hispanic White 1987.55774
465     25-29 All races and origins  814.67257
466     25-29              Hispanic 1276.00831
467     25-29    Non-Hispanic Black 1460.60695
468     25-29    Non-Hispanic White 2137.15320
469     25-29 All races and origins  642.07297
470     25-29              Hispanic  933.71665
471     25-29    Non-Hispanic Black 1099.02210
472     25-29    Non-Hispanic White 1374.02773
473     25-29 All races and origins  469.48700
474     25-29              Hispanic  599.40087
475     25-29    Non-Hispanic Black  667.27275
476     25-29    Non-Hispanic White  710.39791
477     25-29 All races and origins  429.85502
478     25-29              Hispanic  489.94172
479     25-29    Non-Hispanic Black  525.14464
480     25-29    Non-Hispanic White  555.70073
481     25-29 All races and origins  426.66075
482     25-29              Hispanic  482.85565
483     25-29    Non-Hispanic Black  511.50813
484     25-29    Non-Hispanic White  543.09873
485     25-29 All races and origins  477.47638
486     25-29              Hispanic  650.75900
487     25-29    Non-Hispanic Black  761.51645
488     25-29    Non-Hispanic White  687.26728
489     25-29 All races and origins  668.11264
490     25-29              Hispanic 1136.60720
491     25-29    Non-Hispanic Black 1423.55307
492     25-29    Non-Hispanic White 1350.60543
493     25-29 All races and origins  795.06282
494     25-29              Hispanic 1362.03685
495     25-29    Non-Hispanic Black 1603.21741
496     25-29    Non-Hispanic White 1989.91754
497     25-29 All races and origins  809.69100
498     25-29              Hispanic 1277.19685
499     25-29    Non-Hispanic Black 1458.82933
500     25-29    Non-Hispanic White 2135.92706
501     25-29 All races and origins  640.93983
502     25-29              Hispanic  935.61501
503     25-29    Non-Hispanic Black 1098.80658
504     25-29    Non-Hispanic White 1374.47528
505     25-29 All races and origins  472.36030
506     25-29              Hispanic  599.13945
507     25-29    Non-Hispanic Black  670.59402
508     25-29    Non-Hispanic White  710.12157
509     25-29 All races and origins  428.89948
510     25-29              Hispanic  490.79190
511     25-29    Non-Hispanic Black  525.62331
512     25-29    Non-Hispanic White  552.48161
513     25-29 All races and origins  428.25628
514     25-29              Hispanic  481.43231
515     25-29    Non-Hispanic Black  514.17085
516     25-29    Non-Hispanic White  543.51313
517     25-29 All races and origins  482.07759
518     25-29              Hispanic  650.56020
519     25-29    Non-Hispanic Black  761.30887
520     25-29    Non-Hispanic White  690.22437
521     25-29 All races and origins  666.01959
522     25-29              Hispanic 1134.76082
523     25-29    Non-Hispanic Black 1430.53862
524     25-29    Non-Hispanic White 1350.59773
525     25-29 All races and origins  799.02540
526     25-29              Hispanic 1362.17294
527     25-29    Non-Hispanic Black 1601.43874
528     25-29    Non-Hispanic White 1989.77108
529     30-34 All races and origins  989.41975
530     30-34              Hispanic 1558.12843
531     30-34    Non-Hispanic Black 1783.81579
532     30-34    Non-Hispanic White 2624.76032
533     30-34 All races and origins  779.53205
534     30-34              Hispanic 1132.98366
535     30-34    Non-Hispanic Black 1326.17673
536     30-34    Non-Hispanic White 1667.50356
537     30-34 All races and origins  569.48586
538     30-34              Hispanic  715.11316
539     30-34    Non-Hispanic Black  792.03268
540     30-34    Non-Hispanic White  837.52546
541     30-34 All races and origins  513.29536
542     30-34              Hispanic  576.28020
543     30-34    Non-Hispanic Black  611.31305
544     30-34    Non-Hispanic White  643.53025
545     30-34 All races and origins  512.69664
546     30-34              Hispanic  567.97922
547     30-34    Non-Hispanic Black  596.62540
548     30-34    Non-Hispanic White  626.53324
549     30-34 All races and origins  580.91242
550     30-34              Hispanic  779.94833
551     30-34    Non-Hispanic Black  907.48992
552     30-34    Non-Hispanic White  809.88854
553     30-34 All races and origins  812.46502
554     30-34              Hispanic 1387.76725
555     30-34    Non-Hispanic Black 1737.95012
556     30-34    Non-Hispanic White 1638.02498
557     30-34 All races and origins  973.09963
558     30-34              Hispanic 1667.16123
559     30-34    Non-Hispanic Black 1954.54713
560     30-34    Non-Hispanic White 2437.08772
561     30-34 All races and origins  992.12063
562     30-34              Hispanic 1557.02226
563     30-34    Non-Hispanic Black 1778.03292
564     30-34    Non-Hispanic White 2623.33038
565     30-34 All races and origins  779.65050
566     30-34              Hispanic 1133.79043
567     30-34    Non-Hispanic Black 1330.45936
568     30-34    Non-Hispanic White 1668.54966
569     30-34 All races and origins  569.02208
570     30-34              Hispanic  712.28072
571     30-34    Non-Hispanic Black  792.29378
572     30-34    Non-Hispanic White  835.19257
573     30-34 All races and origins  513.71893
574     30-34              Hispanic  570.97152
575     30-34    Non-Hispanic Black  610.41262
576     30-34    Non-Hispanic White  641.01133
577     30-34 All races and origins  509.19743
578     30-34              Hispanic  568.63147
579     30-34    Non-Hispanic Black  596.64593
580     30-34    Non-Hispanic White  626.62803
581     30-34 All races and origins  577.57040
582     30-34              Hispanic  776.27046
583     30-34    Non-Hispanic Black  911.96952
584     30-34    Non-Hispanic White  808.65656
585     30-34 All races and origins  811.26982
586     30-34              Hispanic 1384.16291
587     30-34    Non-Hispanic Black 1738.76806
588     30-34    Non-Hispanic White 1638.42251
589     30-34 All races and origins  973.31353
590     30-34              Hispanic 1669.13817
591     30-34    Non-Hispanic Black 1957.02033
592     30-34    Non-Hispanic White 2438.55883
593     30-34 All races and origins  992.26385
594     30-34              Hispanic 1557.99261
595     30-34    Non-Hispanic Black 1780.24163
596     30-34    Non-Hispanic White 2624.64776
597     30-34 All races and origins  778.42525
598     30-34              Hispanic 1132.93262
599     30-34    Non-Hispanic Black 1327.61006
600     30-34    Non-Hispanic White 1666.73393
601     30-34 All races and origins  572.94723
602     30-34              Hispanic  715.22011
603     30-34    Non-Hispanic Black  794.27047
604     30-34    Non-Hispanic White  830.77827
605     30-34 All races and origins  515.07681
606     30-34              Hispanic  575.29244
607     30-34    Non-Hispanic Black  612.69508
608     30-34    Non-Hispanic White  643.97740
609     30-34 All races and origins  511.00697
610     30-34              Hispanic  567.14770
611     30-34    Non-Hispanic Black  596.49994
612     30-34    Non-Hispanic White  630.31392
613     30-34 All races and origins  577.45707
614     30-34              Hispanic  776.34758
615     30-34    Non-Hispanic Black  908.30782
616     30-34    Non-Hispanic White  809.12577
617     30-34 All races and origins  811.22947
618     30-34              Hispanic 1386.03847
619     30-34    Non-Hispanic Black 1738.83006
620     30-34    Non-Hispanic White 1635.92907
621     30-34 All races and origins  971.40099
622     30-34              Hispanic 1668.22584
623     30-34    Non-Hispanic Black 1960.06677
624     30-34    Non-Hispanic White 2436.71790
625     30-34 All races and origins  994.12154
626     30-34              Hispanic 1561.90745
627     30-34    Non-Hispanic Black 1779.04893
628     30-34    Non-Hispanic White 2624.69010
629     30-34 All races and origins  777.26931
630     30-34              Hispanic 1135.20506
631     30-34    Non-Hispanic Black 1327.46133
632     30-34    Non-Hispanic White 1666.38004
633     30-34 All races and origins  571.36919
634     30-34              Hispanic  715.20486
635     30-34    Non-Hispanic Black  795.28868
636     30-34    Non-Hispanic White  835.15707
637     30-34 All races and origins  515.66680
638     30-34              Hispanic  575.84042
639     30-34    Non-Hispanic Black  612.33993
640     30-34    Non-Hispanic White  639.47775
641     30-34 All races and origins  508.27243
642     30-34              Hispanic  570.79658
643     30-34    Non-Hispanic Black  598.49501
644     30-34    Non-Hispanic White  629.41182
645     30-34 All races and origins  579.28986
646     30-34              Hispanic  779.62336
647     30-34    Non-Hispanic Black  912.41116
648     30-34    Non-Hispanic White  810.20968
649     30-34 All races and origins  807.23824
650     30-34              Hispanic 1387.60445
651     30-34    Non-Hispanic Black 1737.01344
652     30-34    Non-Hispanic White 1637.14899
653     30-34 All races and origins  970.39692
654     30-34              Hispanic 1667.53886
655     30-34    Non-Hispanic Black 1958.08397
656     30-34    Non-Hispanic White 2435.65865
657     30-34 All races and origins  989.73381
658     30-34              Hispanic 1558.32816
659     30-34    Non-Hispanic Black 1781.59269
660     30-34    Non-Hispanic White 2626.89687
661     35-39 All races and origins  682.22202
662     35-39              Hispanic 1093.50590
663     35-39    Non-Hispanic Black 1323.05204
664     35-39    Non-Hispanic White 1724.04638
665     35-39 All races and origins  433.06068
666     35-39              Hispanic  593.71898
667     35-39    Non-Hispanic Black  678.84776
668     35-39    Non-Hispanic White  724.46080
669     35-39 All races and origins  366.27660
670     35-39              Hispanic  424.62391
671     35-39    Non-Hispanic Black  466.58137
672     35-39    Non-Hispanic White  495.64414
673     35-39 All races and origins  358.65241
674     35-39              Hispanic  418.69367
675     35-39    Non-Hispanic Black  450.59582
676     35-39    Non-Hispanic White  478.48685
677     35-39 All races and origins  436.74804
678     35-39              Hispanic  669.21364
679     35-39    Non-Hispanic Black  825.90554
680     35-39    Non-Hispanic White  692.90027
681     35-39 All races and origins  719.51531
682     35-39              Hispanic 1397.12269
683     35-39    Non-Hispanic Black 1816.08082
684     35-39    Non-Hispanic White 1689.17893
685     35-39 All races and origins  914.13066
686     35-39              Hispanic 1742.22050
687     35-39    Non-Hispanic Black 2074.96524
688     35-39    Non-Hispanic White 2650.03193
689     35-39 All races and origins  941.78813
690     35-39              Hispanic 1604.29008
691     35-39    Non-Hispanic Black 1868.01723
692     35-39    Non-Hispanic White 2872.94297
693     35-39 All races and origins  678.37610
694     35-39              Hispanic 1095.42559
695     35-39    Non-Hispanic Black 1323.45157
696     35-39    Non-Hispanic White 1724.52067
697     35-39 All races and origins  435.91116
698     35-39              Hispanic  595.02762
699     35-39    Non-Hispanic Black  683.05298
700     35-39    Non-Hispanic White  728.77960
701     35-39 All races and origins  364.18768
702     35-39              Hispanic  427.70691
703     35-39    Non-Hispanic Black  467.08793
704     35-39    Non-Hispanic White  492.96378
705     35-39 All races and origins  360.02503
706     35-39              Hispanic  417.22855
707     35-39    Non-Hispanic Black  446.26334
708     35-39    Non-Hispanic White  477.76352
709     35-39 All races and origins  442.95618
710     35-39              Hispanic  668.33908
711     35-39    Non-Hispanic Black  823.56513
712     35-39    Non-Hispanic White  696.15760
713     35-39 All races and origins  718.94798
714     35-39              Hispanic 1395.64631
715     35-39    Non-Hispanic Black 1817.64615
716     35-39    Non-Hispanic White 1687.69357
717     35-39 All races and origins  915.27558
718     35-39              Hispanic 1737.36267
719     35-39    Non-Hispanic Black 2081.76010
720     35-39    Non-Hispanic White 2647.77647
721     35-39 All races and origins  936.00299
722     35-39              Hispanic 1605.25671
723     35-39    Non-Hispanic Black 1868.03683
724     35-39    Non-Hispanic White 2872.23552
725     35-39 All races and origins  679.14162
726     35-39              Hispanic 1095.40253
727     35-39    Non-Hispanic Black 1322.43306
728     35-39    Non-Hispanic White 1725.65756
729     35-39 All races and origins  434.23715
730     35-39              Hispanic  590.67292
731     35-39    Non-Hispanic Black  681.17045
732     35-39    Non-Hispanic White  725.68470
733     35-39 All races and origins  364.47992
734     35-39              Hispanic  427.51170
735     35-39    Non-Hispanic Black  467.39821
736     35-39    Non-Hispanic White  493.14912
737     35-39 All races and origins  360.37723
738     35-39              Hispanic  420.00233
739     35-39    Non-Hispanic Black  446.62775
740     35-39    Non-Hispanic White  477.84782
741     35-39 All races and origins  442.17438
742     35-39              Hispanic  668.44910
743     35-39    Non-Hispanic Black  820.27063
744     35-39    Non-Hispanic White  694.41765
745     35-39 All races and origins  723.09817
746     35-39              Hispanic 1396.59767
747     35-39    Non-Hispanic Black 1814.52658
748     35-39    Non-Hispanic White 1690.10759
749     35-39 All races and origins  911.17961
750     35-39              Hispanic 1738.12699
751     35-39    Non-Hispanic Black 2079.61602
752     35-39    Non-Hispanic White 2645.75777
753     35-39 All races and origins  937.49607
754     35-39              Hispanic 1603.27356
755     35-39    Non-Hispanic Black 1868.47983
756     35-39    Non-Hispanic White 2872.79296
757     35-39 All races and origins  682.81401
758     35-39              Hispanic 1095.54369
759     35-39    Non-Hispanic Black 1324.71981
760     35-39    Non-Hispanic White 1722.90011
761     35-39 All races and origins  434.61677
762     35-39              Hispanic  589.36349
763     35-39    Non-Hispanic Black  680.86208
764     35-39    Non-Hispanic White  724.66042
765     35-39 All races and origins  366.39872
766     35-39              Hispanic  425.71768
767     35-39    Non-Hispanic Black  465.37273
768     35-39    Non-Hispanic White  491.77384
769     35-39 All races and origins  356.78458
770     35-39              Hispanic  416.76448
771     35-39    Non-Hispanic Black  445.04303
772     35-39    Non-Hispanic White  477.33237
773     35-39 All races and origins  442.78389
774     35-39              Hispanic  670.84970
775     35-39    Non-Hispanic Black  822.80571
776     35-39    Non-Hispanic White  696.85232
777     35-39 All races and origins  722.33379
778     35-39              Hispanic 1396.50037
779     35-39    Non-Hispanic Black 1817.32704
780     35-39    Non-Hispanic White 1689.83366
781     35-39 All races and origins  916.14602
782     35-39              Hispanic 1735.21920
783     35-39    Non-Hispanic Black 2079.54549
784     35-39    Non-Hispanic White 2646.33237
785     35-39 All races and origins  937.82029
786     35-39              Hispanic 1606.03252
787     35-39    Non-Hispanic Black 1868.43926
788     35-39    Non-Hispanic White 2871.16180
789     35-39 All races and origins  681.92125
790     35-39              Hispanic 1096.52498
791     35-39    Non-Hispanic Black 1324.58858
792     35-39    Non-Hispanic White 1724.76113
793     40-45 All races and origins  214.17622
794     40-45              Hispanic  392.50442
795     40-45    Non-Hispanic Black  488.33225
796     40-45    Non-Hispanic White  537.65150
797     40-45 All races and origins  136.42266
798     40-45              Hispanic  201.67436
799     40-45    Non-Hispanic Black  237.93960
800     40-45    Non-Hispanic White  271.18224
801     40-45 All races and origins  131.11005
802     40-45              Hispanic  187.94503
803     40-45    Non-Hispanic Black  222.97181
804     40-45    Non-Hispanic White  248.45012
805     40-45 All races and origins  225.95399
806     40-45              Hispanic  484.79152
807     40-45    Non-Hispanic Black  657.02646
808     40-45    Non-Hispanic White  501.40632
809     40-45 All races and origins  555.14787
810     40-45              Hispanic 1331.81495
811     40-45    Non-Hispanic Black 1816.47966
812     40-45    Non-Hispanic White 1661.09694
813     40-45 All races and origins  776.59610
814     40-45              Hispanic 1729.54805
815     40-45    Non-Hispanic Black 2122.59240
816     40-45    Non-Hispanic White 2782.38248
817     40-45 All races and origins  806.94404
818     40-45              Hispanic 1575.98827
819     40-45    Non-Hispanic Black 1878.77299
820     40-45    Non-Hispanic White 3043.30012
821     40-45 All races and origins  509.04591
822     40-45              Hispanic  982.36185
823     40-45    Non-Hispanic Black 1244.90715
824     40-45    Non-Hispanic White 1704.74791
825     40-45 All races and origins  213.49957
826     40-45              Hispanic  394.66397
827     40-45    Non-Hispanic Black  493.29746
828     40-45    Non-Hispanic White  537.24719
829     40-45 All races and origins  139.11884
830     40-45              Hispanic  199.22671
831     40-45    Non-Hispanic Black  241.42550
832     40-45    Non-Hispanic White  272.51675
833     40-45 All races and origins  131.36578
834     40-45              Hispanic  187.48748
835     40-45    Non-Hispanic Black  216.28830
836     40-45    Non-Hispanic White  249.16268
837     40-45 All races and origins  227.04756
838     40-45              Hispanic  484.27676
839     40-45    Non-Hispanic Black  657.93827
840     40-45    Non-Hispanic White  503.87102
841     40-45 All races and origins  551.96991
842     40-45              Hispanic 1330.51817
843     40-45    Non-Hispanic Black 1818.87641
844     40-45    Non-Hispanic White 1662.73717
845     40-45 All races and origins  779.77531
846     40-45              Hispanic 1728.91866
847     40-45    Non-Hispanic Black 2122.58398
848     40-45    Non-Hispanic White 2777.17299
849     40-45 All races and origins  806.05736
850     40-45              Hispanic 1575.43134
851     40-45    Non-Hispanic Black 1876.40944
852     40-45    Non-Hispanic White 3044.93573
853     40-45 All races and origins  507.77743
854     40-45              Hispanic  979.94426
855     40-45    Non-Hispanic Black 1241.23754
856     40-45    Non-Hispanic White 1704.29866
857     40-45 All races and origins  217.22725
858     40-45              Hispanic  394.73367
859     40-45    Non-Hispanic Black  490.86316
860     40-45    Non-Hispanic White  536.75206
861     40-45 All races and origins  136.46642
862     40-45              Hispanic  200.58675
863     40-45    Non-Hispanic Black  240.77128
864     40-45    Non-Hispanic White  270.69081
865     40-45 All races and origins  130.64237
866     40-45              Hispanic  189.44506
867     40-45    Non-Hispanic Black  222.16204
868     40-45    Non-Hispanic White  249.65451
869     40-45 All races and origins  225.96871
870     40-45              Hispanic  482.88123
871     40-45    Non-Hispanic Black  655.60926
872     40-45    Non-Hispanic White  504.62925
873     40-45 All races and origins  552.14114
874     40-45              Hispanic 1332.87415
875     40-45    Non-Hispanic Black 1817.78707
876     40-45    Non-Hispanic White 1662.15567
877     40-45 All races and origins  779.62524
878     40-45              Hispanic 1731.55896
879     40-45    Non-Hispanic Black 2120.16622
880     40-45    Non-Hispanic White 2780.44577
881     40-45 All races and origins  805.56163
882     40-45              Hispanic 1574.72399
883     40-45    Non-Hispanic Black 1876.65938
884     40-45    Non-Hispanic White 3043.23063
885     40-45 All races and origins  505.85632
886     40-45              Hispanic  978.86340
887     40-45    Non-Hispanic Black 1240.01038
888     40-45    Non-Hispanic White 1704.84763
889     40-45 All races and origins  219.06634
890     40-45              Hispanic  393.18106
891     40-45    Non-Hispanic Black  490.35375
892     40-45    Non-Hispanic White  536.69909
893     40-45 All races and origins  138.99450
894     40-45              Hispanic  198.63693
895     40-45    Non-Hispanic Black  242.27670
896     40-45    Non-Hispanic White  267.18797
897     40-45 All races and origins  130.17803
898     40-45              Hispanic  187.30619
899     40-45    Non-Hispanic Black  219.69036
900     40-45    Non-Hispanic White  249.89673
901     40-45 All races and origins  228.10466
902     40-45              Hispanic  481.81259
903     40-45    Non-Hispanic Black  658.14181
904     40-45    Non-Hispanic White  503.06145
905     40-45 All races and origins  552.39691
906     40-45              Hispanic 1331.59305
907     40-45    Non-Hispanic Black 1818.71824
908     40-45    Non-Hispanic White 1664.32915
909     40-45 All races and origins  779.24085
910     40-45              Hispanic 1732.32589
911     40-45    Non-Hispanic Black 2123.16360
912     40-45    Non-Hispanic White 2778.31263
913     40-45 All races and origins  807.86237
914     40-45              Hispanic 1571.89687
915     40-45    Non-Hispanic Black 1876.66462
916     40-45    Non-Hispanic White 3044.24109
917     40-45 All races and origins  504.54333
918     40-45              Hispanic  980.28374
919     40-45    Non-Hispanic Black 1244.01316
920     40-45    Non-Hispanic White 1702.70851
921     40-45 All races and origins  214.68905
922     40-45              Hispanic  393.18379
923     40-45    Non-Hispanic Black  490.74331
924     40-45    Non-Hispanic White  538.55982
925       45+ All races and origins   58.96766
926       45+              Hispanic  119.08742
927       45+    Non-Hispanic Black  162.47100
928       45+    Non-Hispanic White  192.07299
929       45+ All races and origins   48.10944
930       45+              Hispanic  110.19724
931       45+    Non-Hispanic Black  140.62692
932       45+    Non-Hispanic White  169.68321
933       45+ All races and origins  158.86761
934       45+              Hispanic  444.85731
935       45+    Non-Hispanic Black  636.69284
936       45+    Non-Hispanic White  460.93229
937       45+ All races and origins  530.01320
938       45+              Hispanic 1415.33675
939       45+    Non-Hispanic Black 1963.44296
940       45+    Non-Hispanic White 1784.50939
941       45+ All races and origins  791.39814
942       45+              Hispanic 1871.49616
943       45+    Non-Hispanic Black 2315.01813
944       45+    Non-Hispanic White 3063.43252
945       45+ All races and origins  819.20905
946       45+              Hispanic 1690.63969
947       45+    Non-Hispanic Black 2033.85851
948       45+    Non-Hispanic White 3362.96445
949       45+ All races and origins  480.89153
950       45+              Hispanic 1012.52113
951       45+    Non-Hispanic Black 1308.90603
952       45+    Non-Hispanic White 1833.00503
953       45+ All races and origins  146.59529
954       45+              Hispanic  341.71795
955       45+    Non-Hispanic Black  449.11952
956       45+    Non-Hispanic White  498.36398
957       45+ All races and origins   58.31749
958       45+              Hispanic  124.38599
959       45+    Non-Hispanic Black  162.51755
960       45+    Non-Hispanic White  195.72425
961       45+ All races and origins   52.08008
962       45+              Hispanic  112.31477
963       45+    Non-Hispanic Black  141.61949
964       45+    Non-Hispanic White  167.19318
965       45+ All races and origins  160.57979
966       45+              Hispanic  447.46184
967       45+    Non-Hispanic Black  640.22305
968       45+    Non-Hispanic White  457.85106
969       45+ All races and origins  532.34520
970       45+              Hispanic 1416.16609
971       45+    Non-Hispanic Black 1963.86215
972       45+    Non-Hispanic White 1784.32967
973       45+ All races and origins  790.79891
974       45+              Hispanic 1870.90388
975       45+    Non-Hispanic Black 2317.22834
976       45+    Non-Hispanic White 3064.58081
977       45+ All races and origins  818.00323
978       45+              Hispanic 1689.44779
979       45+    Non-Hispanic Black 2031.27967
980       45+    Non-Hispanic White 3362.40569
981       45+ All races and origins  481.86184
982       45+              Hispanic 1014.55251
983       45+    Non-Hispanic Black 1305.23057
984       45+    Non-Hispanic White 1832.03418
985       45+ All races and origins  147.03253
986       45+              Hispanic  341.76900
987       45+    Non-Hispanic Black  450.13362
988       45+    Non-Hispanic White  499.45455
989       45+ All races and origins   58.90307
990       45+              Hispanic  120.04008
991       45+    Non-Hispanic Black  167.31141
992       45+    Non-Hispanic White  191.51743
993       45+ All races and origins   51.94716
994       45+              Hispanic  108.99890
995       45+    Non-Hispanic Black  144.28486
996       45+    Non-Hispanic White  165.11758
997       45+ All races and origins  157.67123
998       45+              Hispanic  448.15225
999       45+    Non-Hispanic Black  638.99854
1000      45+    Non-Hispanic White  462.04973
1001      45+ All races and origins  529.54983
1002      45+              Hispanic 1417.15917
1003      45+    Non-Hispanic Black 1964.31863
1004      45+    Non-Hispanic White 1785.35314
1005      45+ All races and origins  790.77616
1006      45+              Hispanic 1869.11930
1007      45+    Non-Hispanic Black 2317.23317
1008      45+    Non-Hispanic White 3065.08609
1009      45+ All races and origins  819.53471
1010      45+              Hispanic 1694.05410
1011      45+    Non-Hispanic Black 2033.90536
1012      45+    Non-Hispanic White 3362.39322
1013      45+ All races and origins  482.07969
1014      45+              Hispanic 1012.07499
1015      45+    Non-Hispanic Black 1309.51458
1016      45+    Non-Hispanic White 1831.45565
1017      45+ All races and origins  145.35858
1018      45+              Hispanic  342.28707
1019      45+    Non-Hispanic Black  450.57685
1020      45+    Non-Hispanic White  497.86819
1021      45+ All races and origins   57.88865
1022      45+              Hispanic  124.48843
1023      45+    Non-Hispanic Black  164.42759
1024      45+    Non-Hispanic White  192.54192
1025      45+ All races and origins   51.49858
1026      45+              Hispanic  107.47559
1027      45+    Non-Hispanic Black  139.74594
1028      45+    Non-Hispanic White  167.56370
1029      45+ All races and origins  158.94537
1030      45+              Hispanic  446.24743
1031      45+    Non-Hispanic Black  640.13889
1032      45+    Non-Hispanic White  460.99905
1033      45+ All races and origins  532.26148
1034      45+              Hispanic 1416.44744
1035      45+    Non-Hispanic Black 1963.13444
1036      45+    Non-Hispanic White 1783.68695
1037      45+ All races and origins  792.66786
1038      45+              Hispanic 1870.16751
1039      45+    Non-Hispanic Black 2313.94389
1040      45+    Non-Hispanic White 3061.38200
1041      45+ All races and origins  821.22900
1042      45+              Hispanic 1695.92082
1043      45+    Non-Hispanic Black 2037.51015
1044      45+    Non-Hispanic White 3362.11919
1045      45+ All races and origins  480.65787
1046      45+              Hispanic 1012.49957
1047      45+    Non-Hispanic Black 1311.81473
1048      45+    Non-Hispanic White 1832.24639
1049      45+ All races and origins  145.85255
1050      45+              Hispanic  340.06444
1051      45+    Non-Hispanic Black  451.47432
1052      45+    Non-Hispanic White  497.13624
1053      45+ All races and origins   55.87291
1054      45+              Hispanic  119.47002
1055      45+    Non-Hispanic Black  163.03373
1056      45+    Non-Hispanic White  194.88801

str(syn_cdcdata) #check the structure

'data.frame':   1056 obs. of  3 variables:
 $ AgeYears     : Factor w/ 8 levels "10-14","15-19",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ RaceEthnicity: Factor w/ 4 levels "All races and origins",..: 1 2 3 4 1 2 3 4 1 2 ...
 $ Rate         : num  40.3 99.6 123.6 157.6 58.2 ...

Plotting the synthetic data

Lastly I will make a chart to check that the trends in rate match those in the ageyears and raceethnicity factors shown in the original data set. For this, I will use code similar to Cassia’s to produce a line graph.

First I will collapse the ageyears and raceethnicity factor levels to more easily display ont he graph, similar to what is above.

syn_dataagg <- syn_cdcdata %>%
  group_by(AgeYears, RaceEthnicity) %>% #use groupby() to group the two factor levels in question
  summarise_all(list(mean = mean, sd = sd)) %>% #find the summary stats of mean and sd
  ungroup() #ungroup before plotting the means and sds

# Checking to see if the means and sds are similar in my data to the original
knitr::kable(syn_dataagg, caption = "Mean and Standard Deviation of Birth Rates by Age")

Mean and Standard Deviation of Birth Rates by Age

AgeYears	RaceEthnicity	mean	sd
10-14	All races and origins	81.68289	38.14852
10-14	Hispanic	190.13380	85.28455
10-14	Non-Hispanic Black	245.31032	106.48088
10-14	Non-Hispanic White	309.20305	153.09566
15-19	All races and origins	154.44624	75.68713
15-19	Hispanic	318.43119	168.15329
15-19	Non-Hispanic Black	402.41333	209.95592
15-19	Non-Hispanic White	496.63699	304.35074
20-24	All races and origins	368.50830	111.35657
20-24	Hispanic	593.88822	253.91557
20-24	Non-Hispanic Black	708.83954	317.84401
20-24	Non-Hispanic White	833.64958	452.27530
25-29	All races and origins	597.02338	153.54691
25-29	Hispanic	881.70593	346.08521
25-29	Non-Hispanic Black	1024.68475	429.60676
25-29	Non-Hispanic White	1193.31565	618.63673
30-34	All races and origins	724.24048	192.24529
30-34	Hispanic	1063.29927	428.42423
30-34	Non-Hispanic Black	1231.69122	530.52612
30-34	Non-Hispanic White	1446.92300	782.53867
35-39	All races and origins	609.02667	223.91717
35-39	Hispanic	995.69502	502.78484
35-39	Non-Hispanic Black	1192.24655	625.87870
35-39	Non-Hispanic White	1425.04867	905.36169
40-45	All races and origins	413.35072	263.59136
40-45	Hispanic	846.15100	592.09293
40-45	Non-Hispanic Black	1065.60223	737.16642
40-45	Non-Hispanic White	1319.38068	1063.48675
45+	All races and origins	369.93229	303.33212
45+	Hispanic	853.56129	682.90524
45+	Non-Hispanic Black	1097.98780	850.65417
45+	Non-Hispanic White	1383.08914	1224.05509

print(syn_dataagg) #printing the data to check

# A tibble: 32 × 4
   AgeYears RaceEthnicity          mean    sd
   <fct>    <fct>                 <dbl> <dbl>
 1 10-14    All races and origins  81.7  38.1
 2 10-14    Hispanic              190.   85.3
 3 10-14    Non-Hispanic Black    245.  106. 
 4 10-14    Non-Hispanic White    309.  153. 
 5 15-19    All races and origins 154.   75.7
 6 15-19    Hispanic              318.  168. 
 7 15-19    Non-Hispanic Black    402.  210. 
 8 15-19    Non-Hispanic White    497.  304. 
 9 20-24    All races and origins 369.  111. 
10 20-24    Hispanic              594.  254. 
# ℹ 22 more rows

#Plotting a bar graph
syn_plot1 <- ggplot(syn_dataagg, aes(x = AgeYears, y = mean, fill = RaceEthnicity)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Mean Rate by Age Group and Race/Ethnicity",
       x = "Age Group",
       y = "Mean Rate",
       fill = "Race/Ethnicity") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_fill_brewer(palette = "Set2")  # Choose a color palette if needed
plot(syn_plot1)

#Saving figure
figure_file <- here("cdcdata-exercise", "mean_rate_bar_synthetic.png")
ggsave(filename = figure_file, plot=syn_plot1)

#Plotting a line graph
p2 <- ggplot(cdcdata2_agg, aes(x = Age_Years, y = mean, color = Race_Ethnicity, group = Race_Ethnicity)) +
  geom_line() +
  labs(title = "Mean Rate by Age Group and Race/Ethnicity",
       x = "Age Group",
       y = "Mean Rate",
       color = "Race/Ethnicity") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_color_brewer(palette = "Set2")  # Choose a color palette if needed
plot(p2)

#Saving figure
figure_file <- here("cdcdata-exercise", "mean_rate_line.png")
ggsave(filename = figure_file, plot=p2)

This is NOT the structure that I want. Assuming this is because of the previous integer values being multiplied by the means (b1, b2, and b12) to derive the rate values, For race: 1= All 2= Hispanic 3= Black 4= White For Age: 1= 10-14 2= 15-19 3= 20-24 4= 25-19 5= 30-34 6= 35-39 7= 40-45 8= 45+

This has created a skewed chart with the largest integer values for both race and age corresponding the highest rates. I honestly do not know how to fix this.

I will turn this in for now, as I have spent a cumulative 8+ hours on part 2 of this exercise. However, please let me know if you can help me fix this.