Public Libraries Survey (PLS)

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

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'
)