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inst/doc/procs.R
In procs: Recreates Some 'SAS®' Procedures in 'R'
## ---- 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
# # Measure Value
# # 1 Chi-Square 1.382898e+02
# # 2 DF 9.000000e+00
# # 3 PR>ChiSq 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
# res <- proc_ttest(pressure, paired = "SBPbefore * SBPafter")
#
# # View results
# res
# # $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
# #
# # $TTests
# # VAR DF T PROBT
# # 1 SBPbefore-SBPafter 11 -1.089648 0.2991635
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# library(fmtr)
#
# # Filter and select using subset function
# res3 <- subset(res2, TYPE != 0, c(BY, CLASS, N, MEAN, STD, MIN, MAX))
#
# # Transpose statistics
# res4 <- proc_transpose(res3, id = CLASS, by = BY, name = Statistic)
#
# # View transformed data
# res4
# # BY Statistic Black Blond Brown Red
# # 1 Female N 4.00000 4.00000 4.00000 4.000000
# # 2 Female MEAN 13.00000 20.25000 35.75000 9.250000
# # 3 Female STD 15.59915 29.21615 21.88416 4.500000
# # 4 Female MIN 2.00000 4.00000 14.00000 7.000000
# # 5 Female MAX 36.00000 64.00000 66.00000 16.000000
# # 6 Male N 4.00000 4.00000 4.00000 4.000000
# # 7 Male MEAN 14.00000 11.50000 35.75000 8.500000
# # 8 Male STD 12.51666 12.50333 18.67931 1.732051
# # 9 Male MIN 3.00000 3.00000 15.00000 7.000000
# # 10 Male MAX 32.00000 30.00000 53.00000 10.000000
#
# # Assign factor to BY so we can sort
# res4$BY <- factor(res4$BY, levels = c("Male", "Female"))
#
# # Sort male to top
# res5 <- proc_sort(res4, by = BY)
# # BY Statistic Black Blond Brown Red
# # 6 Male N 4.00000 4.00000 4.00000 4.000000
# # 7 Male MEAN 14.00000 11.50000 35.75000 8.500000
# # 8 Male STD 12.51666 12.50333 18.67931 1.732051
# # 9 Male MIN 3.00000 3.00000 15.00000 7.000000
# # 10 Male MAX 32.00000 30.00000 53.00000 10.000000
# # 1 Female N 4.00000 4.00000 4.00000 4.000000
# # 2 Female MEAN 13.00000 20.25000 35.75000 9.250000
# # 3 Female STD 15.59915 29.21615 21.88416 4.500000
# # 4 Female MIN 2.00000 4.00000 14.00000 7.000000
# # 5 Female MAX 36.00000 64.00000 66.00000 16.000000
#
# # Create formatting list
# fmt <- flist(STD = "%.3f", type = "row", lookup = res5$Statistic)
#
# # Create vector lookup
# vf <- c(MEAN = "Mean", STD = "Std", MEDIAN = "Median",
# MIN = "Min", MAX = "Max")
#
# # Assign formats
# formats(res5) <- list(Statistic = vf,
# Black = fmt,
# Blond = fmt,
# Brown = fmt,
# Red = fmt)
#
# # Reassign first column name
# names(res5)[1] <- "stub"
#
# # Assign labels
# labels(res5) <- list(stub = "Sex")
#
# # Create list for reporting
# prnt <- list(res$`Hair * Eye`, res$Chisq, res5)
#
# # Print result
# proc_print(prnt,
# titles = "Analysis of Hair and Eyes Dataset",
# view = FALSE) # Set view = TRUE to see results
#
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procs documentation built on May 29, 2024, 2:12 a.m.
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## ---- 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
# # Measure Value
# # 1 Chi-Square 1.382898e+02
# # 2 DF 9.000000e+00
# # 3 PR>ChiSq 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
# res <- proc_ttest(pressure, paired = "SBPbefore * SBPafter")
#
# # View results
# res
# # $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
# #
# # $TTests
# # VAR DF T PROBT
# # 1 SBPbefore-SBPafter 11 -1.089648 0.2991635
#
## ----eval=FALSE, echo=TRUE----------------------------------------------------
# library(fmtr)
#
# # Filter and select using subset function
# res3 <- subset(res2, TYPE != 0, c(BY, CLASS, N, MEAN, STD, MIN, MAX))
#
# # Transpose statistics
# res4 <- proc_transpose(res3, id = CLASS, by = BY, name = Statistic)
#
# # View transformed data
# res4
# # BY Statistic Black Blond Brown Red
# # 1 Female N 4.00000 4.00000 4.00000 4.000000
# # 2 Female MEAN 13.00000 20.25000 35.75000 9.250000
# # 3 Female STD 15.59915 29.21615 21.88416 4.500000
# # 4 Female MIN 2.00000 4.00000 14.00000 7.000000
# # 5 Female MAX 36.00000 64.00000 66.00000 16.000000
# # 6 Male N 4.00000 4.00000 4.00000 4.000000
# # 7 Male MEAN 14.00000 11.50000 35.75000 8.500000
# # 8 Male STD 12.51666 12.50333 18.67931 1.732051
# # 9 Male MIN 3.00000 3.00000 15.00000 7.000000
# # 10 Male MAX 32.00000 30.00000 53.00000 10.000000
#
# # Assign factor to BY so we can sort
# res4$BY <- factor(res4$BY, levels = c("Male", "Female"))
#
# # Sort male to top
# res5 <- proc_sort(res4, by = BY)
# # BY Statistic Black Blond Brown Red
# # 6 Male N 4.00000 4.00000 4.00000 4.000000
# # 7 Male MEAN 14.00000 11.50000 35.75000 8.500000
# # 8 Male STD 12.51666 12.50333 18.67931 1.732051
# # 9 Male MIN 3.00000 3.00000 15.00000 7.000000
# # 10 Male MAX 32.00000 30.00000 53.00000 10.000000
# # 1 Female N 4.00000 4.00000 4.00000 4.000000
# # 2 Female MEAN 13.00000 20.25000 35.75000 9.250000
# # 3 Female STD 15.59915 29.21615 21.88416 4.500000
# # 4 Female MIN 2.00000 4.00000 14.00000 7.000000
# # 5 Female MAX 36.00000 64.00000 66.00000 16.000000
#
# # Create formatting list
# fmt <- flist(STD = "%.3f", type = "row", lookup = res5$Statistic)
#
# # Create vector lookup
# vf <- c(MEAN = "Mean", STD = "Std", MEDIAN = "Median",
# MIN = "Min", MAX = "Max")
#
# # Assign formats
# formats(res5) <- list(Statistic = vf,
# Black = fmt,
# Blond = fmt,
# Brown = fmt,
# Red = fmt)
#
# # Reassign first column name
# names(res5)[1] <- "stub"
#
# # Assign labels
# labels(res5) <- list(stub = "Sex")
#
# # Create list for reporting
# prnt <- list(res$`Hair * Eye`, res$Chisq, res5)
#
# # Print result
# proc_print(prnt,
# titles = "Analysis of Hair and Eyes Dataset",
# view = FALSE) # Set view = TRUE to see results
#
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R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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