Assignment 4

This assignment builds on what you did in the previous assignment. It uses the dataframe you saved at the end.

It also requires you to submit your work by posting a document on RPubs. This will allow you to create interactive graphs.

Setup

library(tidyverse)

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library(plotly)

## Warning: package 'plotly' was built under R version 4.4.2

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## Attaching package: 'plotly'
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load("by_state_0323.Rdata")

Task 1

Make a graph showing the total number of births per year. Use plotly.

g <- by_state_0323 %>%
  group_by(Year) %>%
  summarise(Total_Births = sum(Births, na.rm = TRUE)) %>%
  ggplot(aes(x = Year, y = Total_Births)) +
  geom_line() +
  labs(title = "Total Number of Births per Year",
       x = "Year",
       y = "Total Births") +
  theme_minimal()

ggplotly(g)

Task 2

It is somewhat surprising that the total number of births in the US has declined. Has the number of women been declining in this time period?

Redo the previous dataframe and include the total number of women. Also compute the ratio of births to women. Print the head and tail of this dataframe so we can see all three variables.

data_with_ratios <- by_state_0323 %>%
  group_by(Year) %>%
  summarise(
    Total_Births = sum(Births, na.rm = TRUE),
    Total_Women = sum(Fpop, na.rm = TRUE)
  ) %>%
  mutate(Birth_to_Women_Ratio = Total_Births / Total_Women)

head(data_with_ratios)

## # A tibble: 6 × 4
##    Year Total_Births Total_Women Birth_to_Women_Ratio
##   <dbl>        <dbl>       <dbl>                <dbl>
## 1  2003      4069873    61745355               0.0659
## 2  2004      4091267    61826371               0.0662
## 3  2005      4117159    61926703               0.0665
## 4  2006      4243709    62042926               0.0684
## 5  2007      4293868    62142781               0.0691
## 6  2008      4225221    62207012               0.0679

tail(data_with_ratios)

## # A tibble: 6 × 4
##    Year Total_Births Total_Women Birth_to_Women_Ratio
##   <dbl>        <dbl>       <dbl>                <dbl>
## 1  2015      3957547    63425384               0.0624
## 2  2016      3924784    63429600               0.0619
## 3  2017      3834747    63771779               0.0601
## 4  2018      3771254    63982759               0.0589
## 5  2019      3727127    64134984               0.0581
## 6  2020      3593850    64351519               0.0558

Task 3

The total fertility rate (TFR) is the number of births for a woman during her lifetime. The rate data we have is the number of births per woman per year while she is in one of the 5-year age groups.

How do we use the rate information to construct the TFR? We add up all of the rates for the individual age groups and multiply the sum by 5. Do this for the State of Washington. Plot the time series using geom_point() and plotly.

tfr_washington <- by_state_0323 %>%
  filter(State == "Washington") %>%
  group_by(Year) %>%
  summarise(TFR = sum(Rate, na.rm = TRUE) * 5)

g <- tfr_washington %>%
  ggplot(aes(x = Year, y = TFR)) +
  geom_point() +
  labs(
    title = "TFR for Washington",
    x = "Year",
    y = "Total Fertility Rate (TFR)"
  ) +
  theme_minimal()

ggplotly(g)

Task 4

Repeat the exercise for all states. Again, use plotly so we will be able to identify states. In the aes(), add “group = State”. Also draw a red horizontal line at 2.1. Use the plotly framework I have set up.

Using the interactive graph, which states have the highest and lowest TFR values in 2003 and 2023?

tfr_all_states <- by_state_0323 %>%
  group_by(State, Year) %>%
  summarise(TFR = sum(Rate, na.rm = TRUE) * 5)

## `summarise()` has grouped output by 'State'. You can override using the
## `.groups` argument.

g <- tfr_all_states %>%
  ggplot(aes(x = Year, y = TFR, color = State, group = State)) +
  geom_line() +
  geom_hline(yintercept = 2.1, color = "red", linetype = "dashed") +
  labs(
    title = "TFR by State",
    x = "Year",
    y = "Total Fertility Rate (TFR)"
  ) +
  theme_minimal()

ggplotly(g)

Task 5

Create a graph showing the TFR for the US as a whole. We can’t use the rate data directly. We need to compute the total numbers of births and total numbers of women for each age group and year. Then compute the TFR by adding the calculated rates and multiplying by 5. Plot this using plotly.

us_tfr <- by_state_0323 %>%
  group_by(Year, Age) %>% # Use Age instead of Age_Group
  summarise(
    Total_Births = sum(Births, na.rm = TRUE),
    Total_Women = sum(Fpop, na.rm = TRUE)
  ) %>%
  group_by(Year) %>%
  summarise(TFR = sum(Total_Births / Total_Women, na.rm = TRUE) * 5)

## `summarise()` has grouped output by 'Year'. You can override using the
## `.groups` argument.

g <- us_tfr %>%
  ggplot(aes(x = Year, y = TFR)) +
  geom_line() +
  labs(
    title = "National TFR",
    x = "Year",
    y = "Total Fertility Rate (TFR)"
  ) +
  theme_minimal()

ggplotly(g)