Public Libraries Survey (PLS)

A comprehensive compilation of administrative information on all public libraries in the United States.

Two tables, with one record per library system and one record per library building or bookmobile.
Released annually since 1992.
Conducted by the Institute of Museum and Library Services (IMLS), collected by the Census Bureau.

Please skim before you begin:

Data File Documentation and User’s Guide
README FY #### PLS PUD.txt included in each zipped file
A haiku regarding this microdata:

# census, not survey.
# dewey decimal index
# finger to lips shush

Download, Import, Preparation

Download and import the most recent administrative entity csv file:

this_tf <- tempfile()

csv_url <- "https://www.imls.gov/sites/default/files/2023-06/pls_fy2021_csv.zip"

download.file( csv_url , this_tf, mode = 'wb' )

unzipped_files <- unzip( this_tf , exdir = tempdir() )
        
administrative_entity_csv_fn <-
    unzipped_files[ grepl( 'AE(.*)csv$' , basename( unzipped_files ) ) ]

pls_df <- read.csv( administrative_entity_csv_fn )

names( pls_df ) <- tolower( names( pls_df ) )

pls_df[ , 'one' ] <- 1

Recode missing values as described in the readme included with each zipped file:

for( this_col in names( pls_df ) ){

    if( class( pls_df[ , this_col ] ) == 'character' ){
    
        pls_df[ pls_df[ , this_col ] %in% 'M' , this_col ] <- NA
        
    }
    
    if( 
        ( class( pls_df[ , this_col ] ) == 'numeric' ) | 
        ( this_col %in% c( 'phone' , 'startdat' , 'enddate' ) ) 
    ){
    
        pls_df[ pls_df[ , this_col ] %in% c( -1 , -3 , -4 , -9 ) , this_col ] <- NA
        
    }
    
}

Save Locally

Save the object at any point:

# pls_fn <- file.path( path.expand( "~" ) , "PLS" , "this_file.rds" )
# saveRDS( pls_df , file = pls_fn , compress = FALSE )

Load the same object:

# pls_df <- readRDS( pls_fn )

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 )
                
    )

Analysis Examples with base R

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" ] , na.rm = TRUE )

tapply(
    pls_df[ , "popu_lsa" ] ,
    pls_df[ , "stabr" ] ,
    mean ,
    na.rm = TRUE 
)

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" ] , na.rm = TRUE )

tapply(
    pls_df[ , "popu_lsa" ] ,
    pls_df[ , "stabr" ] ,
    sum ,
    na.rm = TRUE 
)

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

quantile( pls_df[ , "popu_lsa" ] , 0.5 , na.rm = TRUE )

tapply(
    pls_df[ , "popu_lsa" ] ,
    pls_df[ , "stabr" ] ,
    quantile ,
    0.5 ,
    na.rm = TRUE 
)

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" ] , na.rm = TRUE )

Measures of Uncertainty

Calculate the variance, overall and by groups:

var( pls_df[ , "popu_lsa" ] , na.rm = TRUE )

tapply(
    pls_df[ , "popu_lsa" ] ,
    pls_df[ , "stabr" ] ,
    var ,
    na.rm = TRUE 
)

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 )

Replication Example

This example matches Interlibrary Relationship Frequencies on PDF page 169 of the User’s Guide:

# remove closed and temporarily closed libraries
results <- table( pls_df[ !( pls_df[ , 'statstru' ] %in% c( 3 , 23 ) ) , 'c_relatn' ] )

stopifnot( results[ "HQ-Headquarters of a federation or cooperative" ] == 112 )
stopifnot( results[ "ME-Member of a federation or cooperative" ] == 6859 )
stopifnot( results[ "NO-Not a member of a federation or cooperative" ] == 2236 )

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 <- as_tibble( pls_df )

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

pls_tbl %>%
    summarize( mean = mean( popu_lsa , na.rm = TRUE ) )

pls_tbl %>%
    group_by( stabr ) %>%
    summarize( mean = mean( popu_lsa , na.rm = TRUE ) )

Analysis Examples with `data.table`

The R data.table library provides a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. data.table offers concise syntax: fast to type, fast to read, fast speed, memory efficiency, a careful API lifecycle management, an active community, and a rich set of features. This vignette details the available features. As a starting point for PLS users, this code replicates previously-presented examples:

library(data.table)
pls_dt <- data.table( pls_df )

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

pls_dt[ , mean( popu_lsa , na.rm = TRUE ) ]

pls_dt[ , mean( popu_lsa , na.rm = TRUE ) , by = stabr ]

Analysis Examples with `duckdb`

The R duckdb library provides an embedded analytical data management system with support for the Structured Query Language (SQL). duckdb offers a simple, feature-rich, fast, and free SQL OLAP management system. This vignette details the available features. As a starting point for PLS users, this code replicates previously-presented examples:

library(duckdb)
con <- dbConnect( duckdb::duckdb() , dbdir = 'my-db.duckdb' )
dbWriteTable( con , 'pls' , pls_df )

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

dbGetQuery( con , 'SELECT AVG( popu_lsa ) FROM pls' )

dbGetQuery(
    con ,
    'SELECT
        stabr ,
        AVG( popu_lsa )
    FROM
        pls
    GROUP BY
        stabr'
)

Public Libraries Survey (PLS)

Download, Import, Preparation

Save Locally

Variable Recoding

Analysis Examples with base R

Unweighted Counts

Descriptive Statistics

Subsetting

Measures of Uncertainty

Regression Models and Tests of Association

Replication Example

Analysis Examples with dplyr

Analysis Examples with data.table

Analysis Examples with duckdb

Analysis Examples with `dplyr`

Analysis Examples with `data.table`

Analysis Examples with `duckdb`