# R package to compute statistics from the American Community Survey (ACS) and Decennial US Census

The `acsr`

package helps extracting variables and computing statistics using the America Community Survey and Decennial US Census. It was created for the Applied Population Laboratory (APL) at the University of Wisconsin-Madison.

## Installation

The functions depend on the `acs`

and `data.table`

packages, so it is necessary to install then before using `acsr`

. The `acsr`

package is hosted on a github repository and can be installed using `devtools`

:

Remember to set the ACS API key, to check the help documentation and the default values of the `acsr`

functions.

The default dataset is `acs`

, the level is `state`

(Wisconsin, `state = "WI"`

), the `endyear`

is 2014, and the confidence level to compute margins of error (MOEs) is 90%.

## Levels

The `acsr`

functions can extract all the levels available in the `acs`

package. The table below shows the summary and required levels when using the `acsdata`

and `sumacs`

functions:

summary number | levels |
---|---|

010 | us |

020 | region |

030 | division |

040 | state |

050 | state, county |

060 | state, county, county.subdivision |

140 | state, county, tract |

150 | state, county, tract, block.group |

160 | state, place |

250 | american.indian.area |

320 | state, msa |

340 | state, csa |

350 | necta |

400 | urban.area |

500 | state, congressional.district |

610 | state, state.legislative.district.upper |

620 | state, state.legislative.district.lower |

795 | state, puma |

860 | zip.code |

950 | state, school.district.elementary |

960 | state, school.district.secondary |

970 | state, school.district.unified |

## Getting variables and statistics

We can use the `sumacs`

function to extract variable and statistics. We have to specify the corresponding method (e.g., *proportion* or just *variable*), and the name of the statistic or variable to be included in the output.

To download the data can be slow, especially when many levels are being used (e.g., blockgroup). A better approach in those cases is, first, download the data using the function `acsdata`

, and then use them as input.

## Standard errors

When computing statistics there are two ways to define the standard errors:

- Including all standard errors of the variables used to compute a statistic (
`one.zero = FALSE`

) - Include all standard errors except those of variables that are equal to zero. Only the maximum standard error of the variables equal to zero is included (
`one.zero = TRUE`

) - The default value is
`one.zero = TRUE`

For more details about how standard errors are computed for proportions, ratios and aggregations look at A Compass for Understanding and Using American Community Survey Data.

Below an example when estimating proportions and using `one.zero = FALSE`

:

When `one.zero = TRUE`

:

When the square root value in the standard error formula doesn’t exist (e.g., the square root of a negative number), the ratio formula is instead used. The ratio adjustment is done **variable by variable** .

It can also be that the `one.zero`

option makes the square root undefinable. In those cases, the function uses again the **ratio** formula to compute standard errors. There is also a possibility that the standard error estimates using the **ratio** formula are higher than the **proportion** estimates without the `one.zero`

option.

## Decennial Data from the US Census

Let’s get the African American and Hispanic population by state. In this case, we don’t have any estimation of margin of error.

## Output

The output can be formatted using a wide or long format:

And it can also be exported to a csv file:

## Combining geographic levels

We can combine geographic levels using two methods: (1) `sumacs`

and (2) `combine.output`

. The first one allows only single combinations, the second multiple ones.

If I want to combine two states (e.g., Wisconsin and Minnesota) I can use:

If I want to put together multiple combinations (e.g., groups of states):

## A map?

Let’s color a map using poverty by county:

In sum, the `acsr`

package:

- Reads formulas directly and extracts any ACS/Census variable
- Provides an automatized and tailored way to obtain indicators and MOEs
- Allows different outputs’ formats (wide and long, csv)
- Provides an easy way to adjust MOEs to different confidence levels
- Includes a variable-by-variable ratio adjustment of standard errors
- Includes the zero-option when computing standard errors for proportions, ratios, and aggregations
- Combines geographic levels flexibly

*Last Update: 02/07/2016*