accessing-data.qmd

# Automatically Accessing Online Data {#sec-accessing-data-chapter}

```{r}
i <- 1
chapter_number <- 11
source("_common.R")
```

```{r}
library(tidyverse)
library(tidycensus)
library(tigris)

options(tigris_use_cache = TRUE)

library(googlesheets4)
gs4_deauth()
```

So far, you’ve imported data into your projects from CSV files. Many online datasets allow you to export CSVs, but before you do so, you should look for packages to automate your data access. If you can eliminate the manual steps involved in fetching data, your analysis and reporting will be more accurate. You’ll also be able to efficiently update your report when the data changes.

R offers many ways to automate the process of accessing online data. In this chapter, I’ll discuss two such approaches. First, you will use the `googlesheets4` package to fetch data directly from Google Sheets. You’ll learn how to connect your R Markdown project to Google so you can automatically download data when a Google Sheet updates. Then, you’ll use the `tidycensus` package to access data from the US Census Bureau. You’ll work with two large census datasets, the Decennial Census and the American Community Survey, and practice visualizing them.

## Importing Data from Google Sheets with googlesheets4

By using the `googlesheets4` package to access data directly from Google Sheets, you avoid having to manually download data, copy it into your project, and adjust your code so it imports that new data every time you want to update a report. This package lets you write code that automatically fetches new data directly from Google Sheets. Whenever you need to update your report, you can simply run your code to refresh the data. In addition, if you work with Google Forms, you can pipe your data into Google Sheets, completely automating the workflow from data collection to data import.

Using the `googlesheets4` package can help you manage complex datasets that update frequently. For example, in her role at the Primary Care Research Institute at the University of Buffalo, Meghan Harris used it for a research project about people affected by opioid use disorder. The data came from a variety of surveys, all of which fed into a jumble of Google Sheets. Using `googlesheets4`, Harris was able to collect all of her data in one place and use R to put it to use. Data that had once been largely unused because accessing it was so complicated could now inform research on opioid use disorder.

This section demonstrates how the `googlesheets4` package works using a fake dataset about video game preferences that Harris created to replace her opioid survey data (which, for obvious reasons, is confidential).

### Connecting to Google

To begin, install the `googlesheets4` package by running `install.packages ("googlesheets4")`. Next, connect to your Google account by running the `gs4_auth()` function in the console. If you have more than one Google account, select the account that has access to the Google Sheet you want to work with.

Once you do so, a screen should appear. Check the box next to **See, Edit, Create, and Delete All Your Google Sheets Spreadsheets**. This will ensure that R can access data from your Google Sheets account. Click **Continue**, and you should see the message “Authentication complete. Please close this page and return to R.” The `googlesheets4` package will now save your credentials so that you can use them in the future without having to reauthenticate.

### Reading Data from a Sheet


Now that you’ve connected R to your Google account, you can import the fake data that Harris created about video game preferences (access it at <https://data.rfortherestofus.com/google-sheet>). @fig-video-game-survey-data shows what it looks like in Google Sheets.

```{r}
#| results: asis
print_nostarch_file_name(file_type_to_print = "png")
```

```{r}
#| label: fig-video-game-survey-data
#| fig-cap: "The video game data in Google Sheets"
knitr::include_graphics(here::here("assets/video-game-survey-data.png"))
```

```{r}
#| results: asis
save_image_for_nostarch(here::here("assets/video-game-survey-data.png"))
```

The `googlesheets4` package has a function called `read_sheet()` that allows you to pull in data directly from a Google Sheet. Import the data by passing the spreadsheet’s URL to the function like so:

```{r}
#| echo: true
library(googlesheets4)

survey_data_raw <- read_sheet("https://docs.google.com/spreadsheets/d/1AR0_RcFBg8wdiY4Cj-k8vRypp_txh27MyZuiRdqScog/edit?usp=sharing")
```

Take a look at the `survey_data_raw` object to confirm that the data was imported. Using the `glimpse()` function from the `dplyr` package makes it easier to read:

```{r}
#| echo: true
#| eval: false
library(tidyverse)

survey_data_raw %>%
  glimpse()
```

The `glimpse()` function, which creates one output row per variable, shows that you’ve successfully imported the data directly from Google Sheets:

```{r}
survey_data_raw %>%
  glimpse()
```

Once you have the data in R, you can use the same workflow you’ve been using to create reports with R Markdown.

### Using the Data in R Markdown

The following code is taken from an R Markdown report that Harris made to summarize the video games data. You can see the YAML, the `setup` code chunk, a code chunk that loads packages, and the code to import data from Google Sheets:

````{markdown}
#| echo: true
#| eval: false
---
title: "Video Game Survey"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  echo = FALSE,
  warning = FALSE,
  message = FALSE
)
```


```{r}
library(tidyverse)
library(janitor)
library(googlesheets4)
library(gt)
```

```{r}
# Import data from Google Sheets
survey_data_raw <- read_sheet("https://docs.google.com/spreadsheets/d/1AR0_RcFBg8wdiY4Cj-k8vRypp_txh27MyZuiRdqScog/edit?usp=sharing")
```
````

This R Markdown document resembles those discussed in previous chapters, except for the way you import the data. Because you’re bringing it in directly from Google Sheets, there’s no risk of, say, accidentally reading in the wrong CSV. Automating this step reduces the risk of error.

The next code chunk cleans the `survey_data_raw` object, saving the result as `survey_data_clean`:

````{markdown}
#| echo: true
#| eval: false
```{r}
# Clean data
survey_data_clean <- survey_data_raw %>%
  clean_names() %>%
  mutate(participant_id = as.character(row_number())) %>%
  rename(
    age = how_old_are_you,
    like_games = do_you_like_to_play_video_games,
    game_types = what_kind_of_games_do_you_like,
    favorite_game = whats_your_favorite_game
  ) %>%
  relocate(participant_id, .before = age) %>%
  mutate(age = factor(age, levels = c("Under 18", "18-24", "25-34", "35-44", "45-54", "55-64", "Over 65")))
```
````

Here, the `clean_names()` function from the janitor package makes the variable names easier to work with. Defining a `participant_id` variable using the `row_number()` function then adds a consecutively increasing number to each row, and the `as.character()` function makes the number a character. Next, the code changes several variable names with the `rename()` function. The `mutate()` function then transforms the age variable into a data structure known as a *factor*, which ensures that age will show up in the right order in your chart. Finally, the `relocate()` function positions `participant_id` before the `age` variable.

Now you can use the `glimpse()` function again to view your updated `survey_data_clean` data frame, which looks like this:

```{r}
survey_data_clean <- survey_data_raw %>%
  clean_names() %>%
  mutate(participant_id = as.character(row_number())) %>%
  rename(
    age = how_old_are_you,
    like_games = do_you_like_to_play_video_games,
    game_types = what_kind_of_games_do_you_like,
    favorite_game = whats_your_favorite_game
  ) %>%
  relocate(participant_id, .before = age) %>%
  mutate(age = factor(age, levels = c("Under 18", "18-24", "25-34", "35-44", "45-54", "55-64", "Over 65")))

survey_data_clean %>%
  glimpse()
```

The rest of the report uses this data to highlight various statistics:

````{markdown}
#| echo: true
#| eval: false
# Respondent Demographics

```{r}
# Calculate number of respondents
number_of_respondents <- nrow(survey_data_clean)
```

We received responses from `r number_of_respondents` respondents. Their ages are below.

```{r}
survey_data_clean %>%
  select(participant_id, age) %>%
  gt() %>%
  cols_label(
    participant_id = "Participant ID",
    age = "Age"
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  cols_align(
    align = "left",
    columns = everything()
  ) %>%
  cols_width(
    participant_id ~ px(200),
    age ~ px(700)
  )
```

# Video Games

We asked if respondents liked video games. Their responses are below.

```{r}
survey_data_clean %>%
  count(like_games) %>%
  ggplot(aes(
    x = like_games,
    y = n,
    fill = like_games
  )) +
  geom_col() +
  scale_fill_manual(values = c(
    "No" = "#6cabdd",
    "Yes" = "#ff7400"
  )) +
  labs(
    title = "How Many People Like Video Games?",
    x = NULL,
    y = "Number of Participants"
  ) +
  theme_minimal(base_size = 16) +
  theme(
    legend.position = "none",
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    axis.title.y = element_blank(),
    plot.title = element_text(
      face = "bold",
      hjust = 0.5
    )
  )
```
````

These sections calculate the number of survey respondents, then put this value in the text using inline R code; create a table that breaks down the respondents by age group; and generate a graph displaying how many respondents like video games. @fig-video-game-report shows the resulting report.

```{r}
#| results: asis
print_nostarch_file_name(file_type_to_print = "png")
```

```{r}
#| label: fig-video-game-report
#| fig-cap: "The rendered video game report"
knitr::include_graphics(here::here("assets/video-game-report.png"))
```

```{r}
#| results: asis
save_image_for_nostarch(here::here("assets/video-game-report.png"))
```

You can rerun the code at any point to fetch updated data. The survey had five responses today, but if you run it again tomorrow and it has additional responses, they will be included in the import. If you used Google Forms to run your survey and saved the results to a Google Sheet, you could produce this up-to-date report simply by clicking the Knit button in RStudio.

### Importing Only Certain Columns

In the previous sections, you read the data of the entire Google Sheet, but you also have the option to import only a section of a sheet. For example, the survey data includes a `timestamp` column. This variable is added automatically whenever someone submits a Google Form that pipes data into a Google Sheet, but you don’t use it in your analysis, so you could get rid of it.

To do so, use the `range` argument in the `read_sheet()` function when importing the data like so:

```{r}
#| echo: true
#| eval: false
read_sheet(
  "https://docs.google.com/spreadsheets/d/1AR0_RcFBg8wdiY4Cj-k8vRypp_txh27MyZuiRdqScog/edit?usp=sharing",
  range = "Sheet1!B:E"
) %>%
  glimpse()
```

This argument lets you specify a range of data to import. It uses the same syntax you may have used to select columns in Google Sheets. In this example, `range = "Sheet1!B:E"` imports columns B through E (but not A, which contains the timestamp). Adding `glimpse()` and then running this code produces output without the `timestamp` variable:

```{r}
read_sheet(
  "https://docs.google.com/spreadsheets/d/1AR0_RcFBg8wdiY4Cj-k8vRypp_txh27MyZuiRdqScog/edit?usp=sharing",
  range = "Sheet1!B:E"
) %>%
  glimpse()
```

There are a number of other useful functions in the `googlesheets4` package. For example, if you ever need to write your output back to a Google Sheet, the `write_sheet()` function is there to help. To explore other functions in the package, check out its documentation website at <https://googlesheets4.tidyverse.org/index.html>.

Now we’ll turn our attention to another R package that allows you to automatically fetch data, this time from the US Census Bureau.

## Accessing Census Data with tidycensus

If you’ve ever worked with data from the US Census Bureau, you know what a hassle it can be. Usually, the process involves visiting the Census Bureau website, searching for the data you need, downloading it, and then analyzing it in your tool of choice. This pointing and clicking gets very tedious after a while.

Kyle Walker, a geographer at Texas Christian University, and Matt Herman (creator of the Westchester COVID-19 website discussed in @sec-websites-chapter) developed the `tidycensus` package to automate the process of importing Census Bureau data into R. With `tidycensus`, you can write just a few lines of code to get data about, say, the median income in all counties in the United States.

In this section, I’ll show you how the `tidycensus` package works using examples from two datasets to which it provides access: the Decennial Census, administered every 10 years, and the annual American Community Survey. I’ll also show you how to use the data from these two sources to perform additional analysis and make maps by accessing geospatial and demographic data simultaneously.

### Connecting to the Census Bureau with an API Key

Begin by installing `tidycensus` using `install.packages("tidycensus")`. To use `tidycensus`, you must get an application programming interface (API) key from the Census Bureau. API keys are like passwords that online services use to determine whether you’re authorized to access data.

To obtain this key, which is free, go to <https://api.census.gov/data/key_signup.html> and enter your details. Once you receive the key by email, you need to put it in a place where `tidycensus` can find it. The `census_api_key()` function does this for you, so after loading the tidycensus package, run the function as follows, replacing 123456789 with your actual API key:

```{r}
#| echo: true
#| eval: false
library(tidycensus)

census_api_key("123456789", install = TRUE)
```

The `install = TRUE` argument saves your API key in your *.Renviron* file, which is designed for storing confidential information. The package will look for your API key there in the future so that you don’t have to reenter it every time you use the package.

Now you can use tidycensus to access Census Bureau datasets. While the Decennial Census and the American Community Survey are the most common, Chapter 2 of Kyle Walker’s book *Analyzing US Census Data: Methods, Maps, and Models in R* discusses others you can access.

### Working with Decennial Census Data

The `tidycensus` packages includes several functions dedicated to specific Census Bureau datasets, such as `get_decennial()` for Decennial Census data. To access data from the 2020 Decennial Census about the Asian population in each state, use the `get_decennial()` function with three arguments as follows:

```{r}
#| echo: true
#| eval: false
get_decennial(
  geography = "state",
  variables = "P1_006N",
  year = 2020
)
```

Setting the `geography` argument to "state" tells `get_decennial()` to access data at the state level. In addition to the 50 states, it will return data for the District of Columbia and Puerto Rico. The `variables` argument specifies the variable or variables you want to access. Here, `P1_006N` is the variable name for the total Asian population. I’ll discuss how to identify other variables you may want to use in the next section. Finally, `year` specifies the year for which you want to access data — in this case, 2020.

Running this code returns the following:

```{r}
get_decennial(
  geography = "state",
  variables = "P1_006N",
  year = 2020
)
```

The resulting data frame has four variables. `GEOID` is the geographic identifier assigned to the state by the Census Bureau. Each state has a geographic identifier, as do all counties, census tracts, and other geographies. `NAME` is the name of each state, and variable is the name of the variable you passed to the `get_decennial()` function. Finally, `value` is the numeric value for the state and variable in each row. In this case, it represents the total Asian population in each state.

#### Identifying Census Variable Values

You’ve just seen how to retrieve the total number of Asian residents of each state, but say you want to calculate that number instead as a percentage of all the state’s residents. To do that, first you need to retrieve the variable for the state’s total population.

The `tidycensus` package has a function called `load_variables()` that shows all of the variables from a Decennial Census. Run it with the `year` argument set to `2020` and `dataset` set to `pl` as follows:

```{r}
#| echo: true
#| eval: false
load_variables(
  year = 2020,
  dataset = "pl"
)
```

Running this code pulls data from so-called redistricting summary data files (which Public Law 94-171 requires the Census Bureau to produce every 10 years) and returns the name, label (description), and concept (category) of all available variables:

```{r}
load_variables(
  year = 2020,
  dataset = "pl"
)
```

By looking at this list, you can see that the variable `P1_001N` returns the total population.

### Using Multiple Census Variables

Now that you know which variables you need, you can use the `get_decennial()` function again with two variables at once:

```{r}
#| echo: true
#| eval: false
get_decennial(
  geography = "state",
  variables = c("P1_001N", "P1_006N"),
  year = 2020
) %>%
  arrange(NAME)
```

Adding `arrange(NAME)` after `get_decennial()` sorts the results by state name, allowing you to easily see that the output includes both variables for each state:

```{r}
get_decennial(
  geography = "state",
  variables = c("P1_001N", "P1_006N"),
  year = 2020
) %>%
  arrange(NAME)
```

When you’re working with multiple census variables like this, you might have trouble remembering what names like `P1_001N` and `P1_006N` mean. Fortunately, you can adjust the code in the call to `get_decennial()` to give these variables more meaningful names using the following syntax:

```{r}
#| echo: true
#| eval: false
get_decennial(
  geography = "state",
  variables = c(
    total_population = "P1_001N",
    asian_population = "P1_006N"
  ),
  year = 2020
) %>%
  arrange(NAME)
```

Within the `variables` argument, this code specifies the new names for the variables, followed by the equal sign and the original variable names. The `c()` function allows you to rename multiple variables at one time.

Now it’s much easier to see which variables you’re working with:

```{r}
get_decennial(
  geography = "state",
  variables = c(
    total_population = "P1_001N",
    asian_population = "P1_006N"
  ),
  year = 2020
) %>%
  arrange(NAME)
```

Instead of `P1_001N` and `P1_006N`, the variables appear as `total_population` and `asian_population`. Much better!

### Analyzing Census Data

Now you have the data you need to calculate the Asian population in each state as a percentage of the total. There are just a few functions to add to the code from the previous section:

```{r}
#| echo: true
#| eval: false
get_decennial(
  geography = "state",
  variables = c(
    total_population = "P1_001N",
    asian_population = "P1_006N"
  ),
  year = 2020
) %>%
  arrange(NAME) %>%
  group_by(NAME) %>%
  mutate(pct = value / sum(value)) %>%
  ungroup() %>%
  filter(variable == "asian_population")
```

The `group_by(NAME)` function creates one group for each state because you want to calculate the Asian population percentage in each state (not for the entire United States). Then `mutate()` calculates each percentage, taking the value in each row and dividing it by the `total_population` and `asian_population` rows for each state. The `ungroup()` function removes the state-level grouping, and `filter()` shows only the Asian population percentage.

When you run this code, you should see both the total Asian population and the Asian population as a percentage of the total population in each state:

```{r}
get_decennial(
  geography = "state",
  variables = c(
    total_population = "P1_001N",
    asian_population = "P1_006N"
  ),
  year = 2020
) %>%
  arrange(NAME) %>%
  group_by(NAME) %>%
  mutate(pct = value / sum(value)) %>%
  ungroup() %>%
  filter(variable == "asian_population")
```

This is a reasonable way to calculate the Asian population as a percentage of the total population in each state — but it’s not the only way.

### Using a Summary Variable

Kyle Walker knew that calculating summaries like you’ve just done would be a common use case for `tidycensus`. To calculate, say, the Asian population as a percentage of the whole, you need to have a numerator (the Asian population) and denominator (the total population). So, to simplify things, Walker included the `summary_var` argument, which can be used within `get_decennial()` to import the total population as a separate variable. Instead of putting `P1_001N` (total population) in the variables argument and renaming it, you can assign it to the `summary_var` argument as follows:

```{r}
#| echo: true
#| eval: false
get_decennial(
  geography = "state",
  variables = c(asian_population = "P1_006N"),
  summary_var = "P1_001N",
  year = 2020
) %>%
  arrange(NAME)
```

This returns a nearly identical data frame to what you just got, except that the total population is now a separate variable, rather than additional rows for each state:

```{r}
get_decennial(
  geography = "state",
  variables = c(asian_population = "P1_006N"),
  summary_var = "P1_001N",
  year = 2020
) %>%
  arrange(NAME)
```

With the data in this new format, now you can calculate the Asian population as a percentage of the whole by dividing the value variable by the `summary_value` variable. Then you drop the `summary_value` variable because you no longer need it after doing this calculation:

```{r}
#| echo: true
#| eval: false
get_decennial(
  geography = "state",
  variables = c(asian_population = "P1_006N"),
  summary_var = "P1_001N",
  year = 2020
) %>%
  arrange(NAME) %>%
  mutate(pct = value / summary_value) %>%
  select(-summary_value)
```

The resulting output is identical to the output of the previous section:

```{r}
get_decennial(
  geography = "state",
  variables = c(asian_population = "P1_006N"),
  summary_var = "P1_001N",
  year = 2020
) %>%
  arrange(NAME) %>%
  mutate(pct = value / summary_value) %>%
  select(-summary_value)
```

How you choose to calculate summary statistics is up to you; `tidycensus` makes it easy to do either way.

## Visualizing American Community Survey Data

Once you’ve accessed data using the `tidycensus` package, you can do whatever you want with it. In this section, you’ll practice analyzing and visualizing survey data using the American Community Survey. This survey, which is conducted every year, differs from the Decennial Census in two major ways: it is given to a sample of people rather than the entire population, and it includes a wider range of questions.

Despite these differences, you can access data from the American Community Survey nearly identically to how you access Decennial Census data. Instead of `get_decennial()`, you use the `get_acs()` function, but the arguments you pass to it are the same:

```{r}
#| echo: true
#| eval: false
get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2020
)
```

This code uses the `B01002_001` variable to get median age data from 2020 for each state. Here’s what the output looks like:

```{r}
get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2020
)
```

You should notice two differences in the output from `get_acs()` compared to that from `get_decennial()`. First, instead of the value column, `get_acs()` produces a column called estimate. Second, it adds a column called `moe`, for the margin of error. These changes are the result of American Community Survey being given only to a sample of the population, since extrapolating values from that sample to produce an estimate for the population as a whole introduces a margin of error.

In the state-level data, the margins of error are relatively low, but in smaller geographies, they tend to be higher. Cases in which your margins of error are high relative to your estimates indicate a greater level of uncertainty about how well the data represents the population as a whole, so you should interpret such results with caution.

### Making Charts

To pipe your data on median age into ggplot to create a bar chart, add the following lines:

```{r}
#| echo: true
#| eval: false
get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2020
) %>%
  ggplot(aes(
    x = estimate,
    y = NAME
  )) +
  geom_col()
```

After importing the data with the `get_acs()` function, the `ggplot()` function pipes it directly into ggplot. States (which use the variable `NAME`) will go on the y-axis, and median age (`estimate`) will go on the x-axis. A simple `geom_col()` creates the bar chart shown in @fig-median-age-chart.

```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-median-age-chart
#| fig-cap: "A bar chart generated using data acquired with the get_asc() function"
#| fig-height: 8
get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2020
) %>%
  ggplot(aes(
    x = estimate,
    y = NAME
  )) +
  geom_col()
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 6)
```

This chart is nothing special, but the fact that it takes just six lines of
code to create most definitely is!

### Making Population Maps with the geometry Argument

In addition to co-creating `tidycensus`, Kyle Walker created the `tigris` package for working with geospatial data. As a result, these packages are tightly integrated. Within the `get_acs()` function, you can set the geometry argument to `TRUE` to receive both demographic data from the Census Bureau and geospatial data from `tigris`:

```{r}
#| echo: true
#| eval: false
get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2020,
  geometry = TRUE
)
```

In the resulting data, you can see that it has the metadata and `geometry`
column of the simple features objects that you saw in @sec-maps-chapter:

```{r}
get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2020,
  geometry = TRUE,
  progress_bar = FALSE
)
```

The geometry type is `MULTIPOLYGON`, which you learned about in @sec-maps-chapter. To pipe this data into ggplot to make a map, add the following code:

```{r}
#| echo: true
#| eval: false
get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2020,
  geometry = TRUE
) %>%
  ggplot(aes(fill = estimate)) +
  geom_sf() +
  scale_fill_viridis_c()
```

After importing the data with `get_acs()` and piping it into the `ggplot()` function, this code sets the `estimate` variable to use for the fill aesthetic property; that is, the fill color of each state will vary depending on the median age of its residents. Then `geom_sf()` draws the map, and the `scale_fill_viridis_c()` function gives it a colorblind-friendly palette.

The resulting map, shown in @fig-median-age-map-bad, is less than ideal because the Aleutian Islands in Alaska cross the 180-degree line of longitude, or the International Date Line. As a result, most of Alaska appears on one side
of the map and a small part appears on the other side. What’s more, both Hawaii and Puerto Rico are hard to see.

```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-median-age-map-bad
#| fig-cap: "A hard-to-read map showing median age by state"
get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2020,
  geometry = TRUE,
  progress_bar = FALSE
) %>%
  ggplot(aes(fill = estimate)) +
  geom_sf() +
  scale_fill_viridis_c()
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

To fix these problems, load the tigris package, then use the `shift_geometry()` To fix these problems, load the `tigris` package, then use the `shift _geometry()` function to move Alaska, Hawaii, and Puerto Rico into places where they’ll be more easily visible:

```{r}
#| echo: true
#| eval: false
library(tigris)

get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2020,
  geometry = TRUE
) %>%
  shift_geometry(preserve_area = FALSE) %>%
  ggplot(aes(fill = estimate)) +
  geom_sf() +
  scale_fill_viridis_c()
```

Setting the `preserve_area` argument to `FALSE` shrinks the giant state of Alaska and makes Hawaii and Puerto Rico larger. Although the state sizes in the map won’t be precise, the map will be easier to read, as you can see in @fig-median-age-map-good.

```{r}
#| results: asis
print_nostarch_file_name()
```

```{r}
#| label: fig-median-age-map-good
#| fig-cap: "An easier-to-read map tweaked using tigris functions"
get_acs(
  geography = "state",
  variables = "B01002_001",
  year = 2020,
  geometry = TRUE,
  progress_bar = FALSE
) %>%
  shift_geometry(preserve_area = FALSE) %>%
  ggplot(aes(fill = estimate)) +
  geom_sf() +
  scale_fill_viridis_c()
```

```{r}
#| results: asis
save_figure_for_nostarch(figure_height = 5)
```

Now try making the same map for all 3,000 counties by changing the `geography` argument to "county". Other geographies include region, tract (for census tracts), place (for census-designated places, more commonly known as towns and cities), and congressional district. There are also many more arguments in both the `get_decennial()` and `get_acs()` functions; I’ve shown you only a few of the most common. If you want to learn more, Walker’s book *Analyzing US Census Data: Methods, Maps, and Models in R* is a great resource.

## Summary

This chapter explored two packages that use APIs to access data directly from its source. The `googlesheets4` package lets you import data from a Google Sheet. It’s particularly useful when you’re working with survey data, as it makes it easy to update your reports when new results come in. If you don’t work with Google Sheets, you could use similar packages to fetch data from Excel365 (`Microsoft365R`), Qualtrics (`qualtRics`), Survey Monkey (`svmkrR`), and other sources.

If you work with US Census Bureau data, the `tidycensus` package is a huge time-saver. Rather than having to manually download data from the Census Bureau website, you can use `tidycensus` to write R code that accesses the data automatically, making it ready for analysis and reporting. Because of the package’s integration with `tigris`, you can also easily map this demographic data.

If you’re looking for census data from other countries, there are also R packages to bring data from Canada (`cancensus`), Kenya (`rKenyaCensus`), Mexico (`mxmaps` and `inegiR`), Europe (`eurostat`), and other regions. Before hitting that download button in your data collection tool to get a CSV file, it’s worth looking for a package that can import that data directly into R.

## Additional Resources

- Isabella Velásquez and Curtis Kephart, “Automated Survey Reporting with googlesheets4, pins, and R Markdown,” Posit, June 15, 2022, <https://posit.co/blog/automated-survey-reporting/>.

- Kyle Walker, *Analyzing US Census Data: Methods, Maps, and Models in R* (Boca Raton, FL: CRC Press, 2023), <https://walker-data.com/census-r/>.