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Remove underscored functions #7

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3 changes: 1 addition & 2 deletions DESCRIPTION
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,7 @@ Depends:
Imports:
ggplot2 (>= 1.0.0),
dplyr (>= 0.4.3),
lazyeval (>= 0.1.10),
tibble
rlang
URL: https://github.com/tinyheero/cofeatureR
BugReports: https://github.com/tinyheero/cofeatureR/issues
License: GPL-3
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1 change: 1 addition & 0 deletions NAMESPACE
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@@ -1,4 +1,5 @@
# Generated by roxygen2: do not edit by hand

export(plot_cofeature_mat)
importFrom(rlang,.data)
importFrom(stats,setNames)
86 changes: 33 additions & 53 deletions R/plot_cofeature_mat.R
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Expand Up @@ -144,63 +144,49 @@ plot_cofeature_mat <- function(

if (type.display.mode == "single") {
message("Using type.display.mode single")
in.df <- dplyr::distinct_(in.df)
in.df <- dplyr::distinct(in.df)

mutate.call <- lazyeval::interp(~ factor(type, levels = rev(type.order)),
type = as.name("type"),
type.order = as.name("type.order"))
in.df <- dplyr::mutate(in.df,
type = factor(.data$type, levels = rev(type.order)))

in.df <- dplyr::mutate_(in.df,
.dots = setNames(list(mutate.call), "type"))

in.df <- dplyr::group_by_(in.df, .dots = c("sampleID", "feature"))
in.df <- dplyr::arrange_(in.df, .dots = c("type"))
in.df <- dplyr::group_by(in.df, .data$sampleID, .data$feature)
in.df <- dplyr::arrange(in.df, .data$type)
in.df <- dplyr::top_n(in.df, n = 1)
in.df <- dplyr::ungroup(in.df)
}

message("Setting feature order")
mutate.call <- lazyeval::interp(~ as.numeric(
factor(feature,
levels = feature.order)),
feature = as.name("feature"))
in.df <- dplyr::mutate_(in.df,
.dots = setNames(list(mutate.call), "feature"))
in.df <- dplyr::mutate(in.df,
feature = as.numeric(factor(.data$feature,
levels = feature.order)))

# Set sample order
message("Setting sample order")
mutate.call <- lazyeval::interp(~ factor(sampleID,
levels = sample.id.order),
sampleID = as.name("sampleID"))
in.df <- dplyr::mutate_(in.df,
.dots = setNames(list(mutate.call), "sampleID"))
in.df <- dplyr::mutate(in.df,
sampleID = factor(.data$sampleID,
levels = sample.id.order))

# Calculate shift
in.df <- dplyr::group_by_(in.df, .dots = c("feature", "sampleID"))
mutate.call <- lazyeval::interp(~ (1:n())/n() -
1/(2 * n()) - 1/2)
in.df <- dplyr::mutate_(in.df,
.dots = setNames(list(mutate.call), "shift"))
in.df <- dplyr::group_by(in.df, .data$feature, .data$sampleID)
in.df <- dplyr::mutate(in.df,
shift = (1:dplyr::n()) / dplyr::n() -
1 / (2 * dplyr::n()) - 1/2)

# Calculate height
mutate.call <- lazyeval::interp(~ 1/n())
in.df <- dplyr::mutate_(in.df,
.dots = setNames(list(mutate.call), "height"))
in.df <- dplyr::mutate(in.df,
height = 1 / dplyr::n())

# Calculate feature_shift
mutate.call <- lazyeval::interp(~ feature + shift,
feature = as.name("feature"),
shift = as.name("shift"))
in.df <- dplyr::mutate_(in.df,
.dots = setNames(list(mutate.call), "feature_shift"))

p1 <- ggplot2::ggplot(in.df,
ggplot2::aes_string(x = "sampleID",
y = "feature_shift",
height = "height",
fill = "type")) +
in.df <- dplyr::mutate(in.df,
feature_shift = .data$feature + .data$shift)

p1 <- ggplot2::ggplot(in.df,
ggplot2::aes(x = .data$sampleID,
y = .data$feature_shift,
height = .data$height,
fill = .data$type)) +
ggplot2::scale_x_discrete(drop = drop.x) +
ggplot2::scale_y_discrete(limits = 1:length(feature.order),
ggplot2::scale_y_discrete(limits = 1:length(feature.order),
labels = feature.order) +
ggplot2::ylab("Feature") +
ggplot2::xlab("Sample ID")
Expand All @@ -213,7 +199,7 @@ plot_cofeature_mat <- function(
if (missing(missing.fill.col)) {
if (tile.flag) {
p1 <- p1 +
ggplot2::geom_tile(color = tile.col, size = 1)
ggplot2::geom_tile(color = tile.col, linewidth = 1)
}
} else {
p1 <- add_tiles(p1, in.df, tile.col, missing.fill.col, tile.border.size)
Expand All @@ -234,7 +220,7 @@ plot_cofeature_mat <- function(
if (dot.flag) {
if (!missing(dot.size)) {
p1 <- p1 +
ggplot2::geom_point(ggplot2::aes_string(size = dot.size))
ggplot2::geom_point(ggplot2::aes(size = .data[[dot.size]]))
} else {
p1 <- p1 +
ggplot2::geom_point()
Expand All @@ -243,27 +229,21 @@ plot_cofeature_mat <- function(
p1
}

#' Add tiles to the ggplot2
#' Add tiles to the ggplot2
#'
#' @param p1 Existing ggplot2
#' @inheritParams plot_cofeature_mat
add_tiles <- function(p1, in.df, tile.col, missing.fill.col, tile.border.size) {

# Plot two geom_tile. 1 for data present and 1 for data missing
filter.crit.1 <- lazyeval::interp(~ !is.na(type),
.values = list(type = as.name("type")))
filter.crit.2 <- lazyeval::interp(~ is.na(type),
.values = list(type = as.name("type")))

# No borders for missing data.
p1 <-
p1 <-
p1 +
ggplot2::geom_tile(
data = dplyr::filter_(in.df, filter.crit.1),
color = tile.col, size = tile.border.size
data = dplyr::filter(in.df, !is.na(.data$type)),
color = tile.col, linewidth = tile.border.size
) +
ggplot2::geom_tile(
data = dplyr::filter_(in.df, filter.crit.2),
data = dplyr::filter(in.df, is.na(.data$type)),
fill = missing.fill.col, color = tile.col
)

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