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Fetching Frequencies

Lesson Description
-
  • Sometimes, we want to work with the concept of "Fetching Frequencies" in a clear, repeatable way.
  • This lesson walks through a simple example and shows the key steps.
  • We will see one approach on how we can do it in SAS and R.
data CLASS;
 infile datalines dlm='|' dsd missover;
input Name : $8. Sex : $1. Age : best32. Height : best32. Weight : best32.;
label ;
 format ;
 datalines4;
Alfred|M|14|69|112.5
Alice|F|13|56.5|84
Barbara|F|13|65.3|98
;;;;
 run;

data CLASS1;
 infile datalines dlm='|' dsd missover;
input Name : $8. Sex : $1. Age : best32. Height : best32. Weight : best32.;
label ;
 format ;
 datalines4;
Alfred|M|14|69|112.5
Alice|F|13|56.5|84
Barbara|F|13|65.3|98
Henry|M|14|63.5|102.5
James|M|12|57.3|83
;;;;
 run;

proc freq data=class;
     tables sex/out=counts01;
 run;


proc freq data=class1;
     tables sex*age/out=counts02;
 run;

 
  • These SAS code snippets demonstrate how to perform frequency analysis on variables in a dataset named "class" using the proc freq procedure. The results of the frequency analysis are stored in separate output datasets named "counts01" and "counts02" for different table configurations.
  • In the first code snippet:
  • The proc freq procedure is used to compute frequencies of the variable "sex" in the "class" dataset.
  • The tables statement specifies the variable "sex" to be analyzed.
  • The out option is used to store the frequency counts in an output dataset named "counts01".
  • After executing the first code snippet, the output dataset "counts01" will contain the frequency counts for the variable "sex" in the "class" dataset.
  • In the second code snippet:
  • The proc freq procedure is used to compute frequencies of the combination of variables "sex" and "age" in a different dataset named "class1".
  • The tables statement specifies a cross-tabulation of the variables "sex" and "age".
  • The out option is used to store the frequency counts in an output dataset named "counts02".
  • After executing the second code snippet, the output dataset "counts02" will contain the frequency counts for the combinations of "sex" and "age" in the "class1" dataset.
class<-tribble(
~Name,~Sex,~Age,~Height,~Weight,
"Alfred","M",14,69,112.5,
"Alice","F",13,56.5,84,
"Barbara","F",13,65.3,98,
)

class1<-tribble(
~Name,~Sex,~Age,~Height,~Weight,
"Alfred","M",14,69,112.5,
"Alice","F",13,56.5,84,
"Barbara","F",13,65.3,98,
"Henry","M",14,63.5,102.5,
"James","M",12,57.3,83,
)


counts01<-count(class,Sex)


counts02<-count(class1,Sex,Age)
  • These R Tidyverse code snippets demonstrate how to perform frequency analysis on variables in a data frame named "class" using the count function from the dplyr package. The results of the frequency analysis are stored in separate data frames named "counts01" and "counts02" for different variable configurations.
  • In the first code snippet:
  • The count function is used to compute frequencies of the variable "Sex" in the "class" data frame.
  • The first argument specifies the input data frame, which is "class" in this case.
  • The second argument specifies the variable "Sex" to be analyzed.
  • After executing the first code snippet, the "counts01" data frame will contain the frequency counts for the variable "Sex" in the "class" data frame.
  • In the second code snippet:
  • The count function is used to compute frequencies of the combination of variables "Sex" and "Age" in a different data frame named "class1".
  • The first argument specifies the input data frame, which is "class1" in this case.
  • The subsequent arguments specify the variables "Sex" and "Age" for the cross-tabulation.
  • After executing the second code snippet, the "counts02" data frame will contain the frequency counts for the combinations of "Sex" and "Age" in the "class1" data frame.
class <- data.frame(
  Name = c("Alfred", "Alice", "Barbara"),
  Sex = c("M", "F", "F"),
  Age = c(14, 13, 13),
  Height = c(69, 56.5, 65.3),
  Weight = c(112.5, 84, 98)
  , stringsAsFactors = FALSE
)

class1 <- data.frame(
  Name = c("Alfred", "Alice", "Barbara", "Henry", "James"),
  Sex = c("M", "F", "F", "M", "M"),
  Age = c(14, 13, 13, 14, 12),
  Height = c(69, 56.5, 65.3, 63.5, 57.3),
  Weight = c(112.5, 84, 98, 102.5, 83)
  , stringsAsFactors = FALSE
)

counts01 <- as.data.frame(table(class$Sex))

counts02 <- as.data.frame(table(class1$Sex, class1$Age))
  • table(x) counts values; table(x, y) creates a cross-tab.
  • as.data.frame() converts the table to a data frame.