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

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  library(procs)
#  
#  # Turn off printing for CRAN checks
#  options("procs.print" = FALSE)
#  
#  # Prepare sample data
#  dt <- as.data.frame(HairEyeColor, stringsAsFactors = FALSE)
#  
#  # Assign labels
#  labels(dt) <- list(Hair = "Hair Color",
#                     Eye = "Eye Color")
#  
#  # Produce frequency statistics
#  res <- proc_freq(dt, tables = v(Hair, Eye, Hair * Eye),
#                   weight = Freq,
#                   output = report,
#                   options = chisq,
#                   titles = "Hair and Eye Frequency Statistics")
#  

## ----eval=FALSE, echo = TRUE--------------------------------------------------
#  # View output datasets
#  res
#  # $Hair
#  #     CAT   N CNT      PCT CUMSUM    CUMPCT
#  # 1 Black 592 108 18.24324    108  18.24324
#  # 2 Blond 592 127 21.45270    235  39.69595
#  # 3 Brown 592 286 48.31081    521  88.00676
#  # 4   Red 592  71 11.99324    592 100.00000
#  #
#  # $Eye
#  #     CAT   N CNT      PCT CUMSUM    CUMPCT
#  # 1  Blue 592 215 36.31757    215  36.31757
#  # 2 Brown 592 220 37.16216    435  73.47973
#  # 3 Green 592  64 10.81081    499  84.29054
#  # 4 Hazel 592  93 15.70946    592 100.00000
#  #
#  # $`Hair * Eye`
#  #      CAT Statistic       Blue      Brown      Green     Hazel     Total
#  # 1  Black Frequency  20.000000  68.000000  5.0000000 15.000000 108.00000
#  # 2  Black   Percent   3.378378  11.486486  0.8445946  2.533784  18.24324
#  # 3  Black   Row Pct  18.518519  62.962963  4.6296296 13.888889        NA
#  # 4  Black   Col Pct   9.302326  30.909091  7.8125000 16.129032        NA
#  # 5  Blond Frequency  94.000000   7.000000 16.0000000 10.000000 127.00000
#  # 6  Blond   Percent  15.878378   1.182432  2.7027027  1.689189  21.45270
#  # 7  Blond   Row Pct  74.015748   5.511811 12.5984252  7.874016        NA
#  # 8  Blond   Col Pct  43.720930   3.181818 25.0000000 10.752688        NA
#  # 9  Brown Frequency  84.000000 119.000000 29.0000000 54.000000 286.00000
#  # 10 Brown   Percent  14.189189  20.101351  4.8986486  9.121622  48.31081
#  # 11 Brown   Row Pct  29.370629  41.608392 10.1398601 18.881119        NA
#  # 12 Brown   Col Pct  39.069767  54.090909 45.3125000 58.064516        NA
#  # 13   Red Frequency  17.000000  26.000000 14.0000000 14.000000  71.00000
#  # 14   Red   Percent   2.871622   4.391892  2.3648649  2.364865  11.99324
#  # 15   Red   Row Pct  23.943662  36.619718 19.7183099 19.718310        NA
#  # 16   Red   Col Pct   7.906977  11.818182 21.8750000 15.053763        NA
#  # 17 Total Frequency 215.000000 220.000000 64.0000000 93.000000 592.00000
#  # 18 Total   Percent  36.317568  37.162162 10.8108108 15.709459 100.00000
#  #
#  # $Chisq
#  #                         STAT DF      VAL         PROB
#  # 1                 Chi-Square  9 138.2898 2.325287e-25
#  # 2 Continuity Adj. Chi-Square  9 138.2898 2.325287e-25
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Perform calculations
#  res2 <- proc_means(dt, var = Freq,
#                     class = Hair,
#                     by = Sex,
#                     options = c(maxdec = 4),
#                     titles = "Hair and Eye Summary Statistics by Sex")
#  

## ----eval=FALSE, echo = TRUE--------------------------------------------------
#  # View the summary statistics
#  res2
#  #        BY CLASS TYPE FREQ  VAR  N    MEAN       STD MIN MAX
#  # 1  Female  <NA>    0   16 Freq 16 19.5625 20.713824   2  66
#  # 2  Female Black    1    4 Freq  4 13.0000 15.599145   2  36
#  # 3  Female Blond    1    4 Freq  4 20.2500 29.216149   4  64
#  # 4  Female Brown    1    4 Freq  4 35.7500 21.884165  14  66
#  # 5  Female   Red    1    4 Freq  4  9.2500  4.500000   7  16
#  # 6    Male  <NA>    0   16 Freq 16 17.4375 16.008201   3  53
#  # 7    Male Black    1    4 Freq  4 14.0000 12.516656   3  32
#  # 8    Male Blond    1    4 Freq  4 11.5000 12.503333   3  30
#  # 9    Male Brown    1    4 Freq  4 35.7500 18.679311  15  53
#  # 10   Male   Red    1    4 Freq  4  8.5000  1.732051   7  10

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Create sample data
#  pressure <- read.table(header = TRUE, text = '
#    SBPbefore SBPafter
#    120 128
#    124 131
#    130 131
#    118 127
#    140 132
#    128 125
#    140 141
#    135 137
#    126 118
#    130 132
#    126 129
#    127 135
#    ')
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Perform T-Test
#  res3 <- proc_ttest(pressure, paired = "SBPbefore * SBPafter")
#  
#  # View results
#  res3
#  # $Statistics
#  #                  VAR  N      MEAN      STD   STDERR MIN MAX
#  # 1 SBPbefore-SBPafter 12 -1.833333 5.828353 1.682501  -9   8
#  #
#  # $ConfLimits
#  #                  VAR      MEAN      LCLM     UCLM      STD
#  # 1 SBPbefore-SBPafter -1.833333 -5.536492 1.869825 5.828353
#  #
#  # $TTests
#  #                  VAR DF         T     PROBT
#  # 1 SBPbefore-SBPafter 11 -1.089648 0.2991635
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  library(fmtr)
#  
#  # Filter and select using subset function
#  res4 <- subset(res2, TYPE != 0, c(BY, CLASS, N, MEAN, STD, MIN, MAX))
#  
#  # Transpose statistics
#  res5 <- proc_transpose(res3, id = CLASS, by = BY, name = Statistic)
#  
#  # View transformed data
#  res5
#  #        BY Statistic    Black    Blond    Brown       Red
#  # 1  Female         N  4.00000  4.00000  4.00000  4.000000
#  # 2  Female      MEAN 13.00000 20.25000 35.75000  9.250000
#  # 3  Female       STD 15.59915 29.21615 21.88416  4.500000
#  # 4  Female       MIN  2.00000  4.00000 14.00000  7.000000
#  # 5  Female       MAX 36.00000 64.00000 66.00000 16.000000
#  # 6    Male         N  4.00000  4.00000  4.00000  4.000000
#  # 7    Male      MEAN 14.00000 11.50000 35.75000  8.500000
#  # 8    Male       STD 12.51666 12.50333 18.67931  1.732051
#  # 9    Male       MIN  3.00000  3.00000 15.00000  7.000000
#  # 10   Male       MAX 32.00000 30.00000 53.00000 10.000000
#  
#  # Assign factor to BY so we can sort
#  res5$BY <- factor(res5$BY, levels = c("Male", "Female"))
#  
#  # Sort male to top
#  res6 <- proc_sort(res5, by = BY)
#  #        BY Statistic    Black    Blond    Brown       Red
#  # 6    Male         N  4.00000  4.00000  4.00000  4.000000
#  # 7    Male      MEAN 14.00000 11.50000 35.75000  8.500000
#  # 8    Male       STD 12.51666 12.50333 18.67931  1.732051
#  # 9    Male       MIN  3.00000  3.00000 15.00000  7.000000
#  # 10   Male       MAX 32.00000 30.00000 53.00000 10.000000
#  # 1  Female         N  4.00000  4.00000  4.00000  4.000000
#  # 2  Female      MEAN 13.00000 20.25000 35.75000  9.250000
#  # 3  Female       STD 15.59915 29.21615 21.88416  4.500000
#  # 4  Female       MIN  2.00000  4.00000 14.00000  7.000000
#  # 5  Female       MAX 36.00000 64.00000 66.00000 16.000000
#  
#  # Create formatting list
#  fmt <- flist(STD = "%.3f", type = "row", lookup = res6$Statistic)
#  
#  # Create vector lookup
#  vf <- c(MEAN = "Mean", STD = "Std", MEDIAN = "Median",
#          MIN = "Min", MAX = "Max")
#  
#  # Assign formats
#  formats(res6) <- list(Statistic = vf,
#                        Black = fmt,
#                        Blond = fmt,
#                        Brown = fmt,
#                        Red = fmt)
#  
#  # Reassign first column name
#  names(res6)[1] <- "stub"
#  
#  # Assign labels
#  labels(res6) <- list(stub = "Sex")
#  
#  # Create list for reporting
#  prnt <- list(res$`Hair * Eye`, res$Chisq, res6)
#  
#  # Print result
#  proc_print(prnt,
#             titles = "Analysis of Hair and Eyes Dataset",
#             view = FALSE) # Set view = TRUE to see results
#