README.md

In LEPSNES/createdictionary: Create One-document Dictionary

createdictionary

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This package of createdictionary was developed at the Neuroepidemiology Section (NES) of Laboratory of Epidemiology & Population Science (LEPS), NIA. The goal is to build a one-document dictionary from multiple datasets; the key values are extracted for each variable.

Installation

You can install the released version of createdictionary from CRAN with (This installation option is currently unavailable yet):

install.packages("createdictionary")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("LEPSNES/createdictionary")

Example

This is a basic example which shows you how to solve a common problem:

## get folder path
df <- system.file(package = "haven")
## search all the files under the folder for sas dataset
dse <- flat2file(df, "*.sas7bdat")
## dic_value_extract_one_dataset_path uses file path as input
dse %>%
  dic_value_extract_one_dataset_path()
#> # A tibble: 5 x 21
#>   var_name label value_distinct mean  sd    largest_1 largest_2 largest_3
#>   <chr>    <chr> <chr>          <chr> <chr> <chr>     <chr>     <chr>    
#> 1 Sepal_L… <NA>  35             5.84… 0.82… 7.9       7.7       7.6      
#> 2 Sepal_W… <NA>  23             3.05… 0.43… 4.4       4.2       4.1      
#> 3 Petal_L… <NA>  43             3.758 1.76… 6.9       6.7       6.6      
#> 4 Petal_W… <NA>  22             1.19… 0.76… 2.5       2.4       2.3      
#> 5 Species  <NA>  3              <NA>  <NA>  virgin    versic    setosa   
#> # … with 13 more variables: smallest_1 <chr>, smallest_2 <chr>,
#> #   smallest_3 <chr>, top1_value <chr>, top1_freq <chr>, top2_value <chr>,
#> #   top2_freq <chr>, top3_value <chr>, top3_freq <chr>, num_NA <chr>,
#> #   total_row <int>, file_name <chr>, dir_name <chr>

## dic_value_extract_one_dataset uses a data frame as input
dse[1] %>% 
  read_sas() %>% 
  dic_value_extract_one_dataset()
#> # A tibble: 5 x 18
#>   var_name label value_distinct mean  sd    largest_1 largest_2 largest_3
#>   <chr>    <chr> <chr>          <chr> <chr> <chr>     <chr>     <chr>    
#> 1 Sepal_L… <NA>  35             5.84… 0.82… 7.9       7.7       7.6      
#> 2 Sepal_W… <NA>  23             3.05… 0.43… 4.4       4.2       4.1      
#> 3 Petal_L… <NA>  43             3.758 1.76… 6.9       6.7       6.6      
#> 4 Petal_W… <NA>  22             1.19… 0.76… 2.5       2.4       2.3      
#> 5 Species  <NA>  3              <NA>  <NA>  virgin    versic    setosa   
#> # … with 10 more variables: smallest_1 <chr>, smallest_2 <chr>,
#> #   smallest_3 <chr>, top1_value <chr>, top1_freq <chr>, top2_value <chr>,
#> #   top2_freq <chr>, top3_value <chr>, top3_freq <chr>, num_NA <chr>

## dic_value_extract_one_var process a variable each time
dse %>% 
  read_sas() %>% 
  imap_dfr( ~ dic_value_extract_one_var(.x, .y))
#> # A tibble: 5 x 18
#>   var_name label value_distinct mean  sd    largest_1 largest_2 largest_3
#>   <chr>    <chr> <chr>          <chr> <chr> <chr>     <chr>     <chr>    
#> 1 Sepal_L… <NA>  35             5.84… 0.82… 7.9       7.7       7.6      
#> 2 Sepal_W… <NA>  23             3.05… 0.43… 4.4       4.2       4.1      
#> 3 Petal_L… <NA>  43             3.758 1.76… 6.9       6.7       6.6      
#> 4 Petal_W… <NA>  22             1.19… 0.76… 2.5       2.4       2.3      
#> 5 Species  <NA>  3              <NA>  <NA>  virgin    versic    setosa   
#> # … with 10 more variables: smallest_1 <chr>, smallest_2 <chr>,
#> #   smallest_3 <chr>, top1_value <chr>, top1_freq <chr>, top2_value <chr>,
#> #   top2_freq <chr>, top3_value <chr>, top3_freq <chr>, num_NA <chr>

df <- "/LSC/NES/study/CARDIA/core datasets/Y25/DATA"
## on windows, use this
## df <- "T:/LEPS/NES/study/CARDIA/core datasets/Y25/DATA"
dse <- flat2file(df, "*.sas7bdat")
dse[3] %>%
  dic_value_extract_one_dataset_path()
#> # A tibble: 9 x 21
#>   var_name label value_distinct mean  sd    largest_1 largest_2 largest_3
#>   <chr>    <chr> <chr>          <chr> <chr> <chr>     <chr>     <chr>    
#> 1 ID       SUBJ… 3480           2653… 1132… 41681722… 41679622… 41678331…
#> 2 CENTER   YEAR… 4              2.57… 1.12… 4         3         2        
#> 3 HL7CRPS… C-RE… 2              <NA>  <NA>  F         C         <NA>     
#> 4 HL7CRPT… C-RE… 3              <NA>  <NA>  S         N         C        
#> 5 HL6CRPBN C-RE… 828            3.26… 6.26… 199       103       66.4     
#> 6 HL7CRPC… C-RE… 1              1960… 0     1960-01-… <NA>      <NA>     
#> 7 HL7CRPR… C-RE… 1              1960… 0     1960-01-… <NA>      <NA>     
#> 8 HL7CRPA… C-RE… 1              1960… 0     1960-01-… <NA>      <NA>     
#> 9 HL6CRPF  FLAG… 2              2     0     2         <NA>      <NA>     
#> # … with 13 more variables: smallest_1 <chr>, smallest_2 <chr>,
#> #   smallest_3 <chr>, top1_value <chr>, top1_freq <chr>, top2_value <chr>,
#> #   top2_freq <chr>, top3_value <chr>, top3_freq <chr>, num_NA <chr>,
#> #   total_row <int>, file_name <chr>, dir_name <chr>

dse[1:3] %>%
  map_dfr(dic_value_extract_one_dataset_path) %>% 
  slice_head(n=7)
#> # A tibble: 7 x 21
#>   var_name label value_distinct mean  sd    largest_1 largest_2 largest_3
#>   <chr>    <chr> <chr>          <chr> <chr> <chr>     <chr>     <chr>    
#> 1 short_ID <NA>  5114           2678… 1130… 41681     41679     41678    
#> 2 Y15cact… <NA>  218            8.15… 97.2… 4426.505… 2292.617… 615.9132…
#> 3 Y15cact… <NA>  220            8.44… 86.7… 3530.067… 2114.105… 909.8505…
#> 4 Y15CABG  <NA>  2              0     0     0         <NA>      <NA>     
#> 5 Y15pacer <NA>  2              0     0     0         <NA>      <NA>     
#> 6 Y15stent <NA>  3              0.00… 0.02… 1         0         <NA>     
#> 7 Y15valve <NA>  3              0.00… 0.01… 1         0         <NA>     
#> # … with 13 more variables: smallest_1 <chr>, smallest_2 <chr>,
#> #   smallest_3 <chr>, top1_value <chr>, top1_freq <chr>, top2_value <chr>,
#> #   top2_freq <chr>, top3_value <chr>, top3_freq <chr>, num_NA <chr>,
#> #   total_row <int>, file_name <chr>, dir_name <chr>

Please check the help document for each function to see how to use them.

?flat2file
?dic_value_extract_one_dataset_path
?dic_value_extract_one_dataset
?dic_value_extract_one_var

future direction.

1. other dataset types except sas7bdat

   • SPSS
   • Stata
   • CSV
   • database table

2. handle possible file opening errors

3. handle possible value extraction errors

Hope this tool can be of somewhat help with your research.

any comments or suggestions are welcome!

LEPSNES/createdictionary documentation built on Dec. 13, 2021, 1:51 a.m.