statgeek

%macro drop_missing_pct(input_dsn = , output_dsn=, pct = , id_vars=);

*input_dsn = input data set name;
*output_dsn = output data set name;
*pct = missing percent, variables with a percentage of missing above this value are dropped;
*id_vars = space delimited list of variables that you do not want to include in the analysis such as ID variables;

statgeek

*https://communities.sas.com/t5/SAS-Programming/combining-multiple-columns/m-p/705769

/*This example illustrates how to combine variables from labels or variable names 
from a list of indicator variables

Author: F. Khurshed
Date: 2020-12-14
*/

data have;

statgeek

/*Modified from this question on SO: https://stackoverflow.com/questions/65219061/sas-loop-create-columns-from-the-records-which-are-having-a-value
Initial answer from Richard D.
*/

*sample data;
data have;
input id found  code $;
datalines;            
1   1      001
2   0      v58

statgeek

/*This program exports all files from a library into CSV files. 
Author: F. Khurshed
Date: 2020-09-25
*/

options dlcreatedir;

proc options option=dlcreatedir;
run;

statgeek

/*This example goes over how to create a data set with all values. In this case item_id should have values 1 through 4 for every record
Missing values are included for the missing resp

Question originally from here: https://communities.sas.com/t5/SAS-Programming/How-to-format-this-data/m-p/665054

*/
*fake data;
data have;
input item_id $ resp;
datalines;

statgeek

month <- c(12,3,6,2,3,7)
month.abb[month]
#[1] "Dec" "Mar" "Jun" "Feb" "Mar" "Jul"


#To apply as label in graphs:

factor(month.abb[serviceMonth], levels=month.abb)

statgeek

# This program illustrates how to save an RDS file with the date in the name

#file name
myfileName <- "exampleData"

#create full file name including date and extension
myfilePath <- paste0(getwd(), "/", myfileName, "_" ,Sys.Date(), ".RDS")

#pass variable to save function
saveRDS(mpg, myfilePath)

statgeek

#This shows how to :
#1. customize the order of bars in a bar plot
#2. format legend
#3. Add labels to axis, graph title, legend
#4. Create clustered bar chart
#F.Khurshed
#2020-05-11

#create fake data to play with
demo <- dput(structure(list(days_stay = c("<=30", "<=30", "120+", "31 to 60", 

statgeek

/*This demonstrates how to ensure all levels are in your report
using the preloadfmt option*/


data raw;
  input person firsttest $ secondtest $;
  datalines;
  1 Good Good
  2 Poor Good
  3 Good Bad

statgeek

*Create ranks for variables value;
proc rank data=sashelp.class out=class_ranked 
/*      (where= (rank_weight < 4)) */
     ;
var weight;
ranks rank_weight;
run;

*display output;
title 'Unsorted but ranked';