Revert "API changes"

DESCRIPTION

@@ -11,12 +11,11 @@ Depends:
 License: MIT + file LICENSE
 URL: https://rsquaredacademy.github.io/inferr/, https://github.com/rsquaredacademy/inferr
 BugReports: https://github.com/rsquaredacademy/inferr/issues
-Imports: 
+Imports:
     dplyr,
     magrittr,
     purrr,
     Rcpp,
-    rlang,
     shiny,
     tibble,
     tidyr

NAMESPACE

@@ -76,24 +76,15 @@ export(var_test)
 export(var_test_shiny)
 importFrom(Rcpp,sourceCpp)
 importFrom(dplyr,funs)
-importFrom(dplyr,group_by)
 importFrom(dplyr,group_by_)
 importFrom(dplyr,mutate)
-importFrom(dplyr,pull)
 importFrom(dplyr,select)
 importFrom(dplyr,select_)
 importFrom(dplyr,summarise_all)
 importFrom(magrittr,"%>%")
-importFrom(magrittr,subtract)
-importFrom(magrittr,use_series)
 importFrom(purrr,map)
 importFrom(purrr,map_dbl)
-importFrom(purrr,map_df)
 importFrom(purrr,map_int)
-importFrom(rlang,"!!!")
-importFrom(rlang,"!!")
-importFrom(rlang,enquo)
-importFrom(rlang,quos)
 importFrom(shiny,runApp)
 importFrom(stats,anova)
 importFrom(stats,as.formula)

R/RcppExports.R

@@ -2,10 +2,10 @@
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 nsignC <- function(x) {
-    .Call('_inferr_nsignC', PACKAGE = 'inferr', x)
+    .Call(`_inferr_nsignC`, x)
 }
 
 gvar <- function(ln, ly) {
-    .Call('_inferr_gvar', PACKAGE = 'inferr', ln, ly)
+    .Call(`_inferr_gvar`, ln, ly)
 }
 

R/infer-anova.R

@@ -1,10 +1,9 @@
 #' @importFrom stats as.formula lm pf
-#' @importFrom rlang enquo !!
 #' @title One Way ANOVA
 #' @description One way analysis of variance
-#' @param data a \code{data.frame} or a \code{tibble}
-#' @param x numeric; column in \code{data}
-#' @param y factor; column in \code{data}
+#' @param data a data frame
+#' @param x character vector; name of a continuous variable from \code{data}
+#' @param y character vector; name of a categorical variable from \code{data}
 #' @param ... additional arguments passed to or from other methods
 #' @return \code{owanova} returns an object of class \code{"owanova"}.
 #' An object of class \code{"owanova"} is a list containing the
@@ -32,34 +31,37 @@
 #'
 #' @seealso \code{\link[stats]{anova}}
 #' @examples
-#' infer_oneway_anova(mtcars, mpg, cyl)
-#' infer_oneway_anova(hsb, write, prog)
+#' infer_oneway_anova(mtcars, 'mpg', 'cyl')
+#' infer_oneway_anova(hsb, 'write', 'prog')
 #' @export
 #'
 infer_oneway_anova <- function(data, x, y, ...) UseMethod('infer_oneway_anova')
 
 #' @export
 infer_oneway_anova.default <- function(data, x, y, ...) {
 
-  x1 <- enquo(x)
-  y1 <- enquo(y)
+		if (!is.data.frame(data)) {
+      stop('data must be a data frame')
+    }
 
-  fdata <-
-      data %>%
-      select(!! x1, !! y1)
+    if (!x %in% colnames(data)) {
+      stop('x must be a column in data')
+    }
 
-  sample_mean <- anova_avg(fdata, !! x1)
-  sample_stats <- anova_split(fdata, !! x1, !! y1, sample_mean)
-  k <- anova_calc(fdata, sample_stats, !! x1, !! y1)
+    if (!y %in% colnames(data)) {
+      stop('y must be a column in data')
+    }
 
+	   sample_mean <- anova_avg(data, x)
+	  sample_stats <- anova_split(data, x, y, sample_mean)
+	  					 k <- anova_calc(data, sample_stats, x, y)
 
-  result <- list(between = k$sstr, within = k$ssee, total = k$total,
-                 df_btw = k$df_sstr, df_within = k$df_sse,
-	             df_total = k$df_sst, ms_btw = k$mstr, ms_within = k$mse,
-	             f = k$f, p = k$sig, r2 = round(k$reg$r.squared, 4),
-	             ar2 = round(k$reg$adj.r.squared, 4),
-	             sigma = round(k$reg$sigma, 4), obs = k$obs,
-	             tab = sample_stats[, c(1, 2, 3, 5)])
+
+	 result <- list( between = k$sstr, within = k$ssee, total = k$total, df_btw = k$df_sstr,
+								 df_within = k$df_sse, df_total = k$df_sst, ms_btw = k$mstr,
+								 ms_within = k$mse, f = k$f, p = k$sig, r2 = round(k$reg$r.squared, 4),
+								 ar2 = round(k$reg$adj.r.squared, 4), sigma = round(k$reg$sigma, 4),
+								 obs = k$obs, tab = round(sample_stats[, c(1, 2, 3, 5)], 3))
 
 	class(result) <- 'infer_oneway_anova'
 	return(result)

R/infer-binom-test.R

@@ -5,8 +5,7 @@
 #' @param n number of observations
 #' @param success number of successes
 #' @param prob assumed probability of success on a trial
-#' @param data a \code{data.frame} or a \code{tibble}
-#' @param variable factor; column in \code{data}
+#' @param data binary/dichotomous factor
 #' @param ... additional arguments passed to or from other methods
 #' @return \code{binom_test} returns an object of class \code{"binom_test"}.
 #' An object of class \code{"binom_test"} is a list containing the
@@ -31,7 +30,7 @@
 #' infer_binom_calc(32, 13, prob = 0.5)
 #'
 #' # using data set
-#' infer_binom_test(hsb, female, prob = 0.5)
+#' infer_binom_test(as.factor(hsb$female), prob = 0.5)
 #' @export
 #'
 infer_binom_calc <- function(n, success, prob = 0.5, ...) UseMethod('infer_binom_calc')
@@ -82,19 +81,13 @@ print.infer_binom_calc <- function(x, ...) {
 
 #' @export
 #' @rdname infer_binom_calc
-infer_binom_test <- function(data, variable, prob = 0.5) {
+infer_binom_test <- function(data, prob = 0.5) {
 
-    varyable <- enquo(variable)
-
-    fdata <-
-        data %>%
-        pull(!! varyable)
-
-    if (!is.factor(fdata)) {
-      stop('variable must be of type factor', call. = FALSE)
+    if (!is.factor(data)) {
+      stop('data must be of type factor', call. = FALSE)
     }
 
-    if (nlevels(fdata) > 2) {
+    if (nlevels(data) > 2) {
       stop('Binomial test is applicable only to binary data i.e. categorical data with 2 levels.', call. = FALSE)
     }
 
@@ -106,8 +99,8 @@ infer_binom_test <- function(data, variable, prob = 0.5) {
       stop('prob must be between 0 and 1', call. = FALSE)
     }
 
-    n <- length(fdata)
-    k <- table(fdata)[[2]]
+    n <- length(data)
+    k <- table(data)[[2]]
     infer_binom_calc.default(n, k, prob)
 }
 
@@ -118,6 +111,6 @@ infer_binom_test <- function(data, variable, prob = 0.5) {
 binom_test <- function(data, prob = 0.5) {
 
     .Deprecated("infer_binom_test()")
-
+    infer_binom_test(data, prob = 0.5)
 
 }

R/infer-chisq-assoc-test.R

@@ -1,11 +1,9 @@
 #' @importFrom stats pchisq
-#' @importFrom dplyr pull
 #' @title Chi Square Test of Association
 #' @description Chi Square test of association to examine if there is a
 #' relationship between two categorical variables.
-#' @param data a \code{data.frame} or \code{tibble}
-#' @param x factor; column in \code{data}
-#' @param y factor; column in \code{data}
+#' @param x a categorical variable
+#' @param y a categorical variable
 #' @return \code{infer_chisq_assoc_test} returns an object of class
 #' \code{"infer_chisq_assoc_test"}. An object of class
 #' \code{"infer_chisq_assoc_test"} is a list containing the
@@ -32,37 +30,26 @@
 #' @references Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric
 #' Statistical Procedures, 4th edition. : Chapman & Hall/CRC.
 #' @examples
-#' infer_chisq_assoc_test(hsb, female, schtyp)
+#' infer_chisq_assoc_test(as.factor(hsb$female), as.factor(hsb$schtyp))
 #'
-#' infer_chisq_assoc_test(hsb, female, ses)
+#' infer_chisq_assoc_test(as.factor(hsb$female), as.factor(hsb$ses))
 #' @export
 #'
-infer_chisq_assoc_test <- function(data, x, y) UseMethod('infer_chisq_assoc_test')
+infer_chisq_assoc_test <- function(x, y) UseMethod('infer_chisq_assoc_test')
 
 #' @export
-infer_chisq_assoc_test.default <- function(data, x, y) {
+infer_chisq_assoc_test.default <- function(x, y) {
 
-    x1 <- enquo(x)
-    y1 <- enquo(y)
-
-    xone <-
-        data %>%
-        pull(!! x1)
-
-    yone <-
-        data %>%
-        pull(!! y1)
-
-    if (!is.factor(xone)) {
+    if (!is.factor(x)) {
       stop('x must be a categorical variable')
     }
 
-    if (!is.factor(yone)) {
+    if (!is.factor(y)) {
       stop('y must be a categorical variable')
     }
 
     # dimensions
-    k <- table(xone, yone)
+    k <- table(x, y)
     dk <- dim(k)
     ds <- prod(dk)
     nr <- dk[1]
@@ -71,7 +58,7 @@ infer_chisq_assoc_test.default <- function(data, x, y) {
 
     if (ds == 4) {
 
-        twoway <- matrix(table(xone, yone), nrow = 2)
+        twoway <- matrix(table(x, y), nrow = 2)
             df <- df_chi(twoway)
             ef <- efmat(twoway)
              k <- pear_chsq(twoway, df, ef)
@@ -81,15 +68,15 @@ infer_chisq_assoc_test.default <- function(data, x, y) {
 
     } else {
 
-        twoway <- matrix(table(xone, yone), nrow = dk[1])
+        twoway <- matrix(table(x, y), nrow = dk[1])
             ef <- efm(twoway, dk)
             df <- df_chi(twoway)
              k <- pear_chi(twoway, df, ef)
              m <- lr_chsq2(twoway, df, ef, ds)
 
     }
 
-    j <- chigf(xone, yone, k$chi)
+    j <- chigf(x, y, k$chi)
 
     result <- if (ds == 4) {
       list(chi = k$chi, chilr = m$chilr, chimh = p$chimh, chiy = n$chi_y,
@@ -112,7 +99,7 @@ infer_chisq_assoc_test.default <- function(data, x, y) {
 chisq_test <- function(x, y) {
 
     .Deprecated("infer_chisq_assoc_test()")
-
+    infer_chisq_assoc_test(x, y)
 
 }
 

R/infer-chisq-gof-test.R

@@ -1,8 +1,7 @@
 #' @title Chi Square Goodness of Fit Test
 #' @description Test whether the observed proportions for a categorical variable
 #' differ from hypothesized proportions
-#' @param data a \code{data.frame} or \code{tibble}
-#' @param x factor; column in \code{data}
+#' @param x categorical variable
 #' @param y expected proportions
 #' @param correct logical; if TRUE continuity correction is applied
 #' @return \code{infer_chisq_gof_test} returns an object of class
@@ -28,35 +27,18 @@
 #' @references Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric
 #' Statistical Procedures, 4th edition. : Chapman & Hall/CRC.
 #' @examples
-#' infer_chisq_gof_test(hsb, race, c(20, 20, 20, 140))
+#' infer_chisq_gof_test(as.factor(hsb$race), c(20, 20, 20, 140))
 #'
 #' # apply continuity correction
-#' infer_chisq_gof_test(hsb, race, c(20, 20, 20, 140), correct = TRUE)
+#' infer_chisq_gof_test(as.factor(hsb$race), c(20, 20, 20, 140), correct = TRUE)
 #' @export
 #'
-infer_chisq_gof_test <- function(data, x, y, correct = FALSE) UseMethod('infer_chisq_gof_test')
+infer_chisq_gof_test <- function(x, y, correct = FALSE) UseMethod('infer_chisq_gof_test')
 
 #' @export
-infer_chisq_gof_test.default <- function(data, x, y, correct = FALSE) {
+infer_chisq_gof_test.default <- function(x, y, correct = FALSE) {
 
-    x1 <- enquo(x)
-
-    xcheck <-
-        data %>%
-        pull(!! x1)
-
-    xlen <-
-        data %>%
-        pull(!! x1) %>%
-        length
-
-    xone <-
-        data %>%
-        pull(!! x1) %>%
-        table %>%
-        as.vector
-
-	if (!is.factor(xcheck)) {
+	if (!is.factor(x)) {
 		stop('x must be an object of class factor')
 	}
 
@@ -68,13 +50,10 @@ infer_chisq_gof_test.default <- function(data, x, y, correct = FALSE) {
 		stop('correct must be either TRUE or FALSE')
 	}
 
-
-	varname <-
-	    data %>%
-	    select(!! x1) %>%
-	    names
-
-    n <- length(xone)
+		 x1 <- x
+	varname <- l(deparse(substitute(x)))
+          x <- as.vector(table(x))
+          n <- length(x)
 
 	if (length(y) != n) {
 		stop('Length of y must be equal to the number of categories in x')
@@ -83,19 +62,19 @@ infer_chisq_gof_test.default <- function(data, x, y, correct = FALSE) {
     df <- n - 1
 
     if (sum(y) == 1) {
-        y <- xlen * y
+        y <- length(x1) * y
     }
 
     if ((df == 1) || (correct == TRUE)) {
-        k <- chi_cort(xone, y)
+        k <- chi_cort(x, y)
     } else {
-        k <- chigof(xone, y)
+        k <- chigof(x, y)
     }
 
 	sig <- round(pchisq(k$chi, df, lower.tail = FALSE), 4)
 
-	result <- list(chisquare = k$chi, pvalue = sig, df = df, ssize = length(xcheck),
-    	    names = levels(xcheck), level = nlevels(xcheck), obs = xone, exp = y,
+	result <- list(chisquare = k$chi, pvalue = sig, df = df, ssize = length(x1),
+    	    names = levels(x1), level = nlevels(x1), obs = x, exp = y,
     	deviation = format(k$dev, nsmall = 2), std = format(k$std, nsmall = 2),
     	  varname = varname)
 
@@ -110,6 +89,7 @@ infer_chisq_gof_test.default <- function(data, x, y, correct = FALSE) {
 chisq_gof <- function(x, y, correct = FALSE) {
 
     .Deprecated("infer_chisq_gof_test()")
+    infer_chisq_gof_test(x, y, correct = FALSE)
 
 }