## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Create sample data
#  dat <- read.table(header = TRUE,
#                    text = 'x y z   g
#                            6 A 60  P
#                            6 A 70  P
#                            2 A 100 P
#                            2 B 10  P
#                            3 B 67  Q
#                            2 C 81  Q
#                            3 C 63  Q
#                            5 C 55  Q')
#  
#  # View sample data
#  dat
#    x y   z g
#  1 6 A  60 P
#  2 6 A  70 P
#  3 2 A 100 P
#  4 2 B  10 P
#  5 3 B  67 Q
#  6 2 C  81 Q
#  7 3 C  63 Q
#  8 5 C  55 Q
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Turn off printing for CRAN checks
#  options("procs.print" = FALSE)
#  
#  # No parameters
#  proc_means(dat)
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Specific variable
#  proc_means(dat, var = x)

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Custom statistics options
#  proc_means(dat, stats = v(median, sum, q1, q3))
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Output dataset using "report" option
#  res1 <- proc_means(dat,
#                     stats = v(median, sum, q1, q3),
#                     output = report)
#  
#  # View results
#  res1
#  #   VAR MEDIAN SUM   Q1   Q3
#  # 1   x      3  29  2.0  5.5
#  # 2   z     65 506 57.5 75.5
#  
#  
#  # Output dataset using "out" option
#  res2 <- proc_means(dat,
#                     stats = v(median, sum, q1, q3),
#                     output = out)
#  
#  # View results
#  res2
#  #   TYPE FREQ VAR MEDIAN SUM   Q1   Q3
#  # 1    0    8   x      3  29  2.0  5.5
#  # 2    0    8   z     65 506 57.5 75.5
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  
#  # Turn off TYPE and FREQ variables
#  res3 <- proc_means(dat,
#                     stats = v(median, sum, q1, q3),
#                     output = out,
#                     options = v(notype, nofreq))
#  
#  # View results
#  res3
#  #   VAR MEDIAN SUM   Q1   Q3
#  # 1   x      3  29  2.0  5.5
#  # 2   z     65 506 57.5 75.5

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Class grouping
#  res1 <- proc_means(dat, stats = v(median, sum, q1, q3),
#                     class = y, options = v(maxdec = 4))

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # View results - class
#  res1
#  #   CLASS TYPE FREQ VAR MEDIAN SUM   Q1    Q3
#  # 1  <NA>    0    8   x    3.0  29  2.0   5.5
#  # 2  <NA>    0    8   z   65.0 506 57.5  75.5
#  # 3     A    1    3   x    6.0  14  2.0   6.0
#  # 4     A    1    3   z   70.0 230 60.0 100.0
#  # 5     B    1    2   x    2.5   5  2.0   3.0
#  # 6     B    1    2   z   38.5  77 10.0  67.0
#  # 7     C    1    3   x    3.0  10  2.0   5.0
#  # 8     C    1    3   z   63.0 199 55.0  81.0
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # By grouping
#  res2 <- proc_means(dat, stats = v(median, sum, q1, q3),
#                     by = y, options = v(maxdec = 4))
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # View results - by
#  res2
#  #   BY TYPE FREQ VAR MEDIAN SUM Q1  Q3
#  # 1  A    0    3   x    6.0  14  2   6
#  # 2  A    0    3   z   70.0 230 60 100
#  # 3  B    0    2   x    2.5   5  2   3
#  # 4  B    0    2   z   38.5  77 10  67
#  # 5  C    0    3   x    3.0  10  2   5
#  # 6  C    0    3   z   63.0 199 55  81

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Class grouping - two variables
#  res1 <- proc_means(dat, stats = v(median, sum, q1, q3),
#                     class = v(g, y), options = v(maxdec = 0))

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # View results - two class variables
#  res1
#  #    CLASS1 CLASS2 TYPE FREQ VAR MEDIAN SUM   Q1    Q3
#  # 1    <NA>   <NA>    0    8   x    3.0  29  2.0   5.5
#  # 2    <NA>   <NA>    0    8   z   65.0 506 57.5  75.5
#  # 3    <NA>      A    1    3   x    6.0  14  2.0   6.0
#  # 4    <NA>      A    1    3   z   70.0 230 60.0 100.0
#  # 5    <NA>      B    1    2   x    2.5   5  2.0   3.0
#  # 6    <NA>      B    1    2   z   38.5  77 10.0  67.0
#  # 7    <NA>      C    1    3   x    3.0  10  2.0   5.0
#  # 8    <NA>      C    1    3   z   63.0 199 55.0  81.0
#  # 9       P   <NA>    2    4   x    4.0  16  2.0   6.0
#  # 10      P   <NA>    2    4   z   65.0 240 35.0  85.0
#  # 11      Q   <NA>    2    4   x    3.0  13  2.5   4.0
#  # 12      Q   <NA>    2    4   z   65.0 266 59.0  74.0
#  # 13      P      A    3    3   x    6.0  14  2.0   6.0
#  # 14      P      A    3    3   z   70.0 230 60.0 100.0
#  # 15      P      B    3    1   x    2.0   2  2.0   2.0
#  # 16      P      B    3    1   z   10.0  10 10.0  10.0
#  # 17      Q      B    3    1   x    3.0   3  3.0   3.0
#  # 18      Q      B    3    1   z   67.0  67 67.0  67.0
#  # 19      Q      C    3    3   x    3.0  10  2.0   5.0
#  # 20      Q      C    3    3   z   63.0 199 55.0  81.0

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # By grouping - two variables
#  res2 <- proc_means(dat, stats = v(median, sum, q1, q3),
#                     by = v(g, y), options = v(maxdec = 0))

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # View results - two by variables
#  res2
#  #   BY1 BY2 TYPE FREQ VAR MEDIAN SUM Q1  Q3
#  # 1   P   A    0    3   x      6  14  2   6
#  # 2   P   A    0    3   z     70 230 60 100
#  # 3   P   B    0    1   x      2   2  2   2
#  # 4   P   B    0    1   z     10  10 10  10
#  # 5   Q   B    0    1   x      3   3  3   3
#  # 6   Q   B    0    1   z     67  67 67  67
#  # 7   Q   C    0    3   x      3  10  2   5
#  # 8   Q   C    0    3   z     63 199 55  81

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # By grouping - by and class
#  res3 <- proc_means(dat, stats = v(median, sum, q1, q3),
#                     by = g,
#                     class = y,
#                     options = v(maxdec = 0))

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # View results - by and class
#  res3
#  #    BY CLASS TYPE FREQ VAR MEDIAN SUM   Q1  Q3
#  # 1   P  <NA>    0    4   x      4  16  2.0   6
#  # 2   P  <NA>    0    4   z     65 240 35.0  85
#  # 3   P     A    1    3   x      6  14  2.0   6
#  # 4   P     A    1    3   z     70 230 60.0 100
#  # 5   P     B    1    1   x      2   2  2.0   2
#  # 6   P     B    1    1   z     10  10 10.0  10
#  # 7   Q  <NA>    0    4   x      3  13  2.5   4
#  # 8   Q  <NA>    0    4   z     65 266 59.0  74
#  # 9   Q     B    1    1   x      3   3  3.0   3
#  # 10  Q     B    1    1   z     67  67 67.0  67
#  # 11  Q     C    1    3   x      3  10  2.0   5
#  # 12  Q     C    1    3   z     63 199 55.0  81
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  dat2 <- read.table(header = TRUE, text = '
#                  Name      Assessment     Score   Weight
#                  Smith     Quiz1          84      0.05
#                  Smith     Quiz2          25      0.05
#                  Smith     Midterm        85      0.35
#                  Smith     Quiz3           0      0.05
#                  Smith     Quiz4          62      0.05
#                  Smith     Final          93      0.45
#                  Wang      Quiz1         100      0.05
#                  Wang      Quiz2          95      0.05
#                  Wang      Midterm        98      0.35
#                  Wang      Quiz3         105      0.05
#                  Wang      Quiz4          87      0.05
#                  Wang      Final          96      0.45')
#  
#  # View data
#  dat2
#  #     Name Assessment Score Weight
#  # 1  Smith      Quiz1    84   0.05
#  # 2  Smith      Quiz2    25   0.05
#  # 3  Smith    Midterm    85   0.35
#  # 4  Smith      Quiz3     0   0.05
#  # 5  Smith      Quiz4    62   0.05
#  # 6  Smith      Final    93   0.45
#  # 7   Wang      Quiz1   100   0.05
#  # 8   Wang      Quiz2    95   0.05
#  # 9   Wang    Midterm    98   0.35
#  # 10  Wang      Quiz3   105   0.05
#  # 11  Wang      Quiz4    87   0.05
#  # 12  Wang      Final    96   0.45
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  res <- proc_means(dat2, var = Score,
#                    class = Name,
#                    options = "nway",
#                    stats = v(n, mean, median, std, min, max, vari))
#  
#  # View results
#  res
#  #   CLASS TYPE FREQ   VAR N     MEAN MEDIAN       STD MIN MAX       VARI
#  # 1 Smith    1    6 Score 6 58.16667     73 37.679791   0  93 1419.76667
#  # 2  Wang    1    6 Score 6 96.83333     97  5.980524  87 105   35.76667

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  res <- proc_means(dat2, var = Score,
#                    class = Name,
#                    weight = Weight,
#                    options = c("nway", vardef = "wgt"),
#                    stats = v(n, mean, median, std, min, max, vari))
#  
#  # View results
#  res
#  #   CLASS TYPE FREQ   VAR N  MEAN MEDIAN       STD MIN MAX     VARI
#  # 1 Smith    1    6 Score 6 80.15     85 23.937993   0  93 573.0275
#  # 2  Wang    1    6 Score 6 96.85     96  3.102821  87 105   9.6275

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  res <- proc_means(dat2, var = Score,
#                    class = Name,
#                    weight = Weight,
#                    options = c("nway", vardef = "wgt"),
#                    stats = v(n, mean, median, std, min, max, vari),
#                    where = expression(Score <= 100)
#                    )
#  
#  # View results
#  res
#  #   CLASS TYPE FREQ   VAR N     MEAN MEDIAN      STD MIN MAX       VARI
#  # 1 Smith    1    6 Score 6 80.15000     85 23.93799   0  93 573.027500
#  # 2  Wang    1    5 Score 5 96.42105     96  2.54053  87 100   6.454294

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Shape wide
#  res1 <- proc_means(dat, stats = v(median, sum, q1, q3),
#                     output = wide)
#  
#  # Wide results
#  res1
#  #   TYPE FREQ VAR MEDIAN SUM   Q1   Q3
#  # 1    0    8   x      3  29  2.0  5.5
#  # 2    0    8   z     65 506 57.5 75.5
#  
#  # Shape long
#  res2 <- proc_means(dat, stats = v(median, sum, q1, q3),
#                     output = long)
#  
#  # Long results
#  res2
#  #   TYPE FREQ   STAT    x     z
#  # 1    0    8 MEDIAN  3.0  65.0
#  # 2    0    8    SUM 29.0 506.0
#  # 3    0    8     Q1  2.0  57.5
#  # 4    0    8     Q3  5.5  75.5
#  
#  # Shape stacked
#  res3 <- proc_means(dat, stats = v(median, sum, q1, q3),
#                     output = stacked)
#  
#  # Stacked results
#  res3
#  #   TYPE FREQ VAR   STAT VALUES
#  # 1    0    8   x MEDIAN    3.0
#  # 2    0    8   x    SUM   29.0
#  # 3    0    8   x     Q1    2.0
#  # 4    0    8   x     Q3    5.5
#  # 5    0    8   z MEDIAN   65.0
#  # 6    0    8   z    SUM  506.0
#  # 7    0    8   z     Q1   57.5
#  # 8    0    8   z     Q3   75.5
#