Removal of tbl_df from code per dplyr's new df[, 1] as a tbl_df appro…

…ach.

Revamped vignette to fix old errors and bugs.

R/chaining.R

@@ -104,5 +104,6 @@
 #' @param lhs The value to be piped.
 #' @param rhs A function or expression.
 #' @export
+#' @importFrom dplyr tbl_df
 #' @rdname chain
 `%>%` <- dplyr::`%>%`

R/random_sent.R

@@ -85,7 +85,8 @@ random_data <- function(n = 10, ..., n.people = 10, ages = 7:10,
     rownames(output) <- NULL
 
     text.var <- "dialogue"
-    output <- dplyr::tbl_df(output)
+    # removed on 10/1 per tbl_df no longer returning vector for df[, 1]
+    # output <- dplyr::tbl_df(output)
     class(output) <- unique(c("sent_split", "qdap_df", paste0("sent_split_text_var:", 
         text.var), class(output)))
     attributes(output)[["text.var"]] <- text.var

R/sentSplit.R

@@ -47,7 +47,6 @@
 #' \code{\link[qdap]{TOT}} 
 #' @keywords sentence, split, turn-of-talk
 #' @export
-#' @importFrom dplyr tbl_df
 #' @examples
 #' \dontrun{
 #' ## `sentSplit` EXAMPLE:
@@ -132,7 +131,6 @@ function(dataframe, text.var, rm.var = NULL, endmarks = c("?", ".", "!", "|"),
     if (!is.null(rm.var)) {
         rm.var <- paste0("rmvars_", paste(rm.var, collapse = ":"))
     } 
-    output <- tbl_df(output)
     class(output) <- unique(c("sent_split", "qdap_df", 
         paste0("sent_split_text_var:", text.var), rm.var, class(output)))
     attributes(output)[["text.var"]] <- text.var

R/tot_plot.R

@@ -87,7 +87,7 @@ tot_plot <- function(dataframe, text.var, grouping.var = NULL, facet.vars = NULL
             if (!"tot" %in% colnames(dataframe)) {
                 stop("supply valid tot argument")
             }
-            tot <- TOT(dataframe[, "tot"])
+            tot <- TOT(dataframe[["tot"]])
 
         } else {
 
@@ -96,7 +96,7 @@ tot_plot <- function(dataframe, text.var, grouping.var = NULL, facet.vars = NULL
                 DF[, "qdapIDqdap"] <- seq_len(nrow(DF))
 
                 ## Split, order and make tot
-                rmout <- lapply(split(DF, DF[, facet.vars]), function(x) {
+                rmout <- lapply(split(DF, DF[[facet.vars]]), function(x) {
                     x <- x[order(x[, "qdapIDqdap"]), ]
                     x[, "tot"] <- seq_len(nrow(x))
                     x

R/word_cor.R

@@ -65,13 +65,13 @@
 #' ## Throws multiple warning because small data set
 #' library(qdapTools)
 #' lapply(DATA3, function(x) {
-#'     word_cor(x[, "state"], id(x), qcv(computer, i, no, good), r = NULL)
+#'     word_cor(x[, "state"], qdapTools::id(x), qcv(computer, i, no, good), r = NULL)
 #' })
 #' 
 #' ## Find words correlated per turn of talk by person
 #' ## Throws multiple warning because small data set
 #' lapply(DATA3, function(x) {
-#'     word_cor(x[, "state"], id(x), qcv(computer, i, no, good))
+#'     word_cor(x[, "state"], qdapTools::id(x), qcv(computer, i, no, good))
 #' })
 #' 
 #' 
@@ -93,7 +93,7 @@
 #'     negative = qcv(no, dumb, distrust, not, stinks),
 #'     literacy = qcv(computer, talking, telling)
 #' )
-#' y <- wfdf(DATA$state, id(DATA, prefix = TRUE))
+#' y <- wfdf(DATA$state, qdapTools::id(DATA, prefix = TRUE))
 #' z <- wfm_combine(y, worlis)
 #' 
 #' out <- word_cor(t(z), word = c(names(worlis), "else.words"), r = NULL)
@@ -124,7 +124,7 @@ word_cor <- function(text.var, grouping.var = NULL, word, r = .7,
         WFM <- t(wfm(text.var = text.var, grouping.var = grouping.var, ...))
     }
 
-    WFM <- data.frame(WFM, check.names = F)
+    WFM <- data.frame(WFM, check.names = FALSE)
     wordlen <- length(word) == 1
 
     test1 <- word %in% colnames(WFM)

inst/Rmd_vignette/qdap_vignette.Rmd

@@ -3229,7 +3229,7 @@ termco(DATA$state, DATA$person, syns)
 ```
 
 ```{r echo = FALSE}
-termco(DATA$state, DATA$person, syns)$rnp[, c(1:4, 9:10)]
+termco(DATA[["state"]], DATA[["person"]], syns)$rnp[, c(1:4, 7:8)]
 ```
 
 `r FUN("termco")` also has a plot method that plots a heat map of the `r FUN("termco")` output based on the percent usage by grouping variable.  This allows for rapid visualizations of patterns and enables fast spotting of extreme values.  Here are some plots from the `r HR("#rajex", "Romeo and Juliet Act 1 Example")` above.
@@ -4195,7 +4195,7 @@ qheat(scores(poldat), high="blue", low="yellow", grid=NULL, order.b="ave.polarit
 
 <pre><code class="r">poldat4 <- with(rajSPLIT, polarity(dialogue, act, constrain = TRUE))
 
-polcount <- counts(poldat4)$polarity
+polcount <- na.omit(counts(poldat4)$polarity)
 len <- length(polcount)
 
 cummean <- function(x){cumsum(x)/seq_along(x)}
@@ -4205,30 +4205,30 @@ cumpolarity <- data.frame(cum_mean = cummean(polcount), Time=1:len)
 ## Calculate background rectangles
 ends <- cumsum(rle(counts(poldat4)$act)$lengths)
 starts <- c(1, head(ends + 1, -1))
-rects <- data.frame(xstart = starts, xend = ends + 1, 
+rects <- data.frame(xstart = starts, xend = ends + 1,
     Act = c("I", "II", "III", "IV", "V"))
 
 library(ggplot2)
 ggplot() + theme_bw() +
-    geom_rect(data = rects, aes(xmin = xstart, xmax = xend, 
-        ymin = -Inf, ymax = Inf, fill = Act), alpha = 0.2) +
+    geom_rect(data = rects, aes(xmin = xstart, xmax = xend,
+        ymin = -Inf, ymax = Inf, fill = Act), alpha = 0.17) +
     geom_smooth(data = cumpolarity, aes(y=cum_mean, x = Time)) +
-    geom_hline(y=mean(polcount), color="grey30", size=1, alpha=.3, linetype=2) + 
-    annotate("text", x = mean(ends[1:2]), y = mean(polcount), color="grey30", 
+    geom_hline(y=mean(polcount), color="grey30", size=1, alpha=.3, linetype=2) +
+    annotate("text", x = mean(ends[1:2]), y = mean(polcount), color="grey30",
         label = "Average Polarity", vjust = .3, size=3) +
     geom_line(data = cumpolarity, aes(y=cum_mean, x = Time), size=1) +
     ylab("Cumulative Average Polarity") + xlab("Duration") +
     scale_x_continuous(expand = c(0,0)) +
-    geom_text(data=rects, aes(x=(xstart + xend)/2, y=-.04, 
-        label=paste("Act", Act)), size=3) + 
+    geom_text(data=rects, aes(x=(xstart + xend)/2, y=-.04,
+        label=paste("Act", Act)), size=3) +
     guides(fill=FALSE) +
     scale_fill_brewer(palette="Set1")
 </code></pre>
 
 ```{r, echo = FALSE, fig.width=14.5, fig.height=6, message=FALSE, warning=FALSE}
 poldat4 <- readRDS("data/poldat4.rds")
 
-polcount <- counts(poldat4)$polarity
+polcount <- na.omit(counts(poldat4)$polarity)
 len <- length(polcount)
 
 cummean <- function(x){cumsum(x)/seq_along(x)}
@@ -4238,22 +4238,22 @@ cumpolarity <- data.frame(cum_mean = cummean(polcount), Time=1:len)
 ## Calculate background rectangles
 ends <- cumsum(rle(counts(poldat4)$act)$lengths)
 starts <- c(1, head(ends + 1, -1))
-rects <- data.frame(xstart = starts, xend = ends + 1, 
+rects <- data.frame(xstart = starts, xend = ends + 1,
     Act = c("I", "II", "III", "IV", "V"))
 
 library(ggplot2)
 ggplot() + theme_bw() +
-    geom_rect(data = rects, aes(xmin = xstart, xmax = xend, 
-        ymin = -Inf, ymax = Inf, fill = Act), alpha = 0.2) +
+    geom_rect(data = rects, aes(xmin = xstart, xmax = xend,
+        ymin = -Inf, ymax = Inf, fill = Act), alpha = 0.17) +
     geom_smooth(data = cumpolarity, aes(y=cum_mean, x = Time)) +
-    geom_hline(y=mean(polcount), color="grey30", size=1, alpha=.3, linetype=2) + 
-    annotate("text", x = mean(ends[1:2]), y = mean(polcount), color="grey30", 
+    geom_hline(y=mean(polcount), color="grey30", size=1, alpha=.3, linetype=2) +
+    ggplot2::annotate("text", x = mean(ends[1:2]), y = mean(polcount), color="grey30",
         label = "Average Polarity", vjust = .3, size=3) +
     geom_line(data = cumpolarity, aes(y=cum_mean, x = Time), size=1) +
     ylab("Cumulative Average Polarity") + xlab("Duration") +
     scale_x_continuous(expand = c(0,0)) +
-    geom_text(data=rects, aes(x=(xstart + xend)/2, y=-.04, 
-        label=paste("Act", Act)), size=3) + 
+    geom_text(data=rects, aes(x=(xstart + xend)/2, y=-.04,
+        label=paste("Act", Act)), size=3) +
     guides(fill=FALSE) +
     scale_fill_brewer(palette="Set1")
 ```