# Public Libraries Survey (PLS)

An annual census of public libraries in the United States.

One table with one row per state, a second table with one row per library system, and a third table with one row per library building or bookmobile.

Released annually since 1992.

Conducted by the Institute of Museum and Library Services (IMLS) and collected by the US Census Bureau.

## Simplified Download and Importation

The R `lodown`

package easily downloads and imports all available PLS microdata by simply specifying `"pls"`

with an `output_dir =`

parameter in the `lodown()`

function. Depending on your internet connection and computer processing speed, you might prefer to run this step overnight.

```
library(lodown)
lodown( "pls" , output_dir = file.path( path.expand( "~" ) , "PLS" ) )
```

## Analysis Examples with base R

Load a data frame:

`pls_df <- readRDS( file.path( path.expand( "~" ) , "PLS" , "2014/pls_fy_ae_puplda.rds" ) )`

### Variable Recoding

Add new columns to the data set:

```
pls_df <-
transform(
pls_df ,
c_relatn =
factor( c_relatn , levels = c( "HQ" , "ME" , "NO" ) ,
c( "HQ-Headquarters of a federation or cooperative" ,
"ME-Member of a federation or cooperative" ,
"NO-Not a member of a federation or cooperative" )
) ,
more_than_one_librarian = as.numeric( libraria > 1 )
)
```

### Unweighted Counts

Count the unweighted number of records in the table, overall and by groups:

```
nrow( pls_df )
table( pls_df[ , "stabr" ] , useNA = "always" )
```

### Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

```
mean( pls_df[ , "popu_lsa" ] )
tapply(
pls_df[ , "popu_lsa" ] ,
pls_df[ , "stabr" ] ,
mean
)
```

Calculate the distribution of a categorical variable, overall and by groups:

```
prop.table( table( pls_df[ , "c_relatn" ] ) )
prop.table(
table( pls_df[ , c( "c_relatn" , "stabr" ) ] ) ,
margin = 2
)
```

Calculate the sum of a linear variable, overall and by groups:

```
sum( pls_df[ , "popu_lsa" ] )
tapply(
pls_df[ , "popu_lsa" ] ,
pls_df[ , "stabr" ] ,
sum
)
```

Calculate the median (50th percentile) of a linear variable, overall and by groups:

```
quantile( pls_df[ , "popu_lsa" ] , 0.5 )
tapply(
pls_df[ , "popu_lsa" ] ,
pls_df[ , "stabr" ] ,
quantile ,
0.5
)
```

### Subsetting

Limit your `data.frame`

to more than one million annual visits:

`sub_pls_df <- subset( pls_df , visits > 1000000 )`

Calculate the mean (average) of this subset:

`mean( sub_pls_df[ , "popu_lsa" ] )`

### Measures of Uncertainty

Calculate the variance, overall and by groups:

```
var( pls_df[ , "popu_lsa" ] )
tapply(
pls_df[ , "popu_lsa" ] ,
pls_df[ , "stabr" ] ,
var
)
```

### Regression Models and Tests of Association

Perform a t-test:

`t.test( popu_lsa ~ more_than_one_librarian , pls_df )`

Perform a chi-squared test of association:

```
this_table <- table( pls_df[ , c( "more_than_one_librarian" , "c_relatn" ) ] )
chisq.test( this_table )
```

Perform a generalized linear model:

```
glm_result <-
glm(
popu_lsa ~ more_than_one_librarian + c_relatn ,
data = pls_df
)
summary( glm_result )
```

## Analysis Examples with `dplyr`

The R `dplyr`

library offers an alternative grammar of data manipulation to base R and SQL syntax. dplyr offers many verbs, such as `summarize`

, `group_by`

, and `mutate`

, the convenience of pipe-able functions, and the `tidyverse`

style of non-standard evaluation. This vignette details the available features. As a starting point for PLS users, this code replicates previously-presented examples:

```
library(dplyr)
pls_tbl <- tbl_df( pls_df )
```

Calculate the mean (average) of a linear variable, overall and by groups:

```
pls_tbl %>%
summarize( mean = mean( popu_lsa ) )
pls_tbl %>%
group_by( stabr ) %>%
summarize( mean = mean( popu_lsa ) )
```