modified bp with option for adding stats and boxplplots. Moved old one to redundant
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8 changed files with 443 additions and 102 deletions
193
scripts/functions/lf_bp.R
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193
scripts/functions/lf_bp.R
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#############################
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# Barplots: ggplot
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# stats +/-
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# violin +/-
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# barplot +/
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# beeswarm
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#############################
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lf_bp <- function(lf_df
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, p_title = ""
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, colour_categ = ""
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, x_grp = "mutation_info"
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, y_var = "param_value"
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, facet_var = "param_type"
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, n_facet_row = 1
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, y_scales = "free_y"
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, colour_bp_strip = "khaki2"
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, dot_size = 3
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, dot_transparency = 0.3
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, violin_quantiles = c(0.25, 0.5, 0.75) # can be NULL
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, my_ats = 22 # axis text size
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, my_als = 20 # axis label size
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, my_fls = 20 # facet label size
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, my_pts = 22 # plot title size)
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, make_boxplot = FALSE
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, bp_width = c("auto", 0.5)
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, add_stats = FALSE
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, stat_grp_comp = c("DM", "OM")
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, stat_method = "wilcox.test"
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, my_paired = FALSE
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, stat_label = c("p.format", "p.signif") ){
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p1 <- ggplot(lf_df, aes(x = eval(parse(text = x_grp))
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, y = eval(parse(text = y_var)) )) +
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facet_wrap(~ eval(parse(text = facet_var))
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, nrow = n_facet_row
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, scales = y_scales) +
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geom_violin(trim = T
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, scale = "width"
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#, position = position_dodge(width = 0.9)
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, draw_quantiles = violin_quantiles)
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if (make_boxplot){
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if (bp_width == "auto"){
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bp_width = 0.5/length(unique(lf_df[[x_grp]]))
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cat("\nAutomatically calculated boxplot width, using bp_width:\n", bp_width, "\n")
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}else{
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cat("\nBoxplot width value provided, using:", bp_width, "\n")
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bp_width = bp_width}
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p2 = p1 + geom_boxplot(fill = "white"
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, outlier.colour = NA
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#, position = position_dodge(width = 0.9)
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, width = bp_width) +
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geom_beeswarm(priority = "density"
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#, shape = 21
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, size = dot_size
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, alpha = dot_transparency
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, show.legend = FALSE
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, cex = 0.8
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, aes(colour = factor(eval(parse(text = colour_categ))) ))
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} else {
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# ggbeeswarm (better than geom_point)
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p2 = p1 + geom_beeswarm(priority = "density"
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#, shape = 21
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, size = dot_size
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, alpha = dot_transparency
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, show.legend = FALSE
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, cex = 0.8
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, aes(colour = factor(eval(parse(text = colour_categ))) ))
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}
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# Add foramtting to graph
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OutPlot = p2 + theme(axis.text.x = element_text(size = my_ats)
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, axis.text.y = element_text(size = my_ats
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = my_ats)
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, axis.title.y = element_text(size = my_ats)
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, plot.title = element_text(size = my_pts
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, hjust = 0.5
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, colour = "black"
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, face = "bold")
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, strip.background = element_rect(fill = colour_bp_strip)
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, strip.text.x = element_text(size = my_fls
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, colour = "black")
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, legend.title = element_text(color = "black"
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, size = my_als)
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, legend.text = element_text(size = my_ats)
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, legend.direction = "vertical") +
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labs(title = p_title
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, x = ""
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, y = "")
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if (add_stats){
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my_comparisonsL <- list( stat_grp_comp )
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OutPlot = OutPlot + stat_compare_means(comparisons = my_comparisonsL
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, method = stat_method
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, paired = my_paired
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, label = stat_label[1])
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}
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return(OutPlot)
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}
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#############################
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# Barplot NO stats: plotly
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# violin +/-
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# barplot +/
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# beeswarm
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# TODO: plot_ly()
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#############################
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lf_bp_plotly <- function(lf_df
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, p_title = ""
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, colour_categ = ""
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, x_grp = mutation_info
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, y_var = param_value
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, facet_var = param_type
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, n_facet_row = 1
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, y_scales = "free_y"
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, colour_bp_strip = "khaki2"
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, dot_size = 3
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, dot_transparency = 0.3
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, violin_quantiles = c(0.25, 0.5, 0.75) # can be NULL
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, my_ats = 20 # axis text size
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, my_als = 18 # axis label size
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, my_fls = 18 # facet label size
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, my_pts = 22 # plot title size)
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#, make_boxplot = FALSE
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, bp_width = c("auto", 0.5)
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#, add_stats = FALSE
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#, stat_grp_comp = c("DM", "OM")
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#, stat_method = "wilcox.test"
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#, my_paired = FALSE
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#, stat_label = c("p.format", "p.signif")
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){
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OutPlotly = ggplot(lf_df, aes(x = eval(parse(text = x_grp))
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, y = eval(parse(text = y_var))
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, label1 = x_grp
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, label2 = y_var
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, lable3 = colour_categ) ) +
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facet_wrap(~ eval(parse(text = facet_var))
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, nrow = n_facet_row
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, scales = y_scales) +
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geom_violin(trim = T
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, scale = "width"
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, draw_quantiles = violin_quantiles) +
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geom_beeswarm(priority = "density"
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, size = dot_size
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, alpha = dot_transparency
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, show.legend = FALSE
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, cex = 0.8
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, aes(colour = factor(eval(parse(text = colour_categ) ) ) ) ) +
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theme(axis.text.x = element_text(size = my_ats)
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, axis.text.y = element_text(size = my_ats
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, angle = 0
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, hjust = 1
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, vjust = 0)
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, axis.title.x = element_text(size = my_ats)
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, axis.title.y = element_text(size = my_ats)
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, plot.title = element_text(size = my_pts
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, hjust = 0.5
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, colour = "black"
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, face = "bold")
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, strip.background = element_rect(fill = colour_bp_strip)
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, strip.text.x = element_text(size = my_fls
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, colour = "black")
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, legend.title = element_text(color = "black"
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, size = my_als)
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, legend.text = element_text(size = my_ats)
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, legend.position = "none")+
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labs(title = p_title
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, x = ""
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, y = "")
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OutPlotly = ggplotly(OutPlotly
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#, tooltip = c("label")
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)
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return(OutPlotly)
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}
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@ -14,13 +14,23 @@ lf_bp_with_stats <- function(lf_df
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, stat_grp_comp = c("DM", "OM")
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, stat_method = "wilcox.test"
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, my_paired = FALSE
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#, stat_label = "p.format")
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, bp_width = c("auto", 0.5)
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, dot_size = 3
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, dot_transparency = 0.3
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, stat_label = c("p.format", "p.signif")
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, my_ats = 22 # axis text size
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, my_als = 20 # axis label size
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, my_fls = 20 # facet label size
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, my_pts = 22 # plot title size
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) {
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if (bp_width == "auto"){
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bp_width = 0.5/length(unique(lf_df[[x_grp]]))
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cat("\nAutomatically calculated boxplot width, using bp_width:\n", bp_width, "\n")
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}else{
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cat("\nBoxplot width value provided, using:", bp_width, "\n")
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bp_width = bp_width
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}
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my_comparisonsL <- list( stat_grp_comp )
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bp_statP <- ggplot(lf_df, aes(x = eval(parse(text = x_grp))
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@ -30,13 +40,28 @@ lf_bp_with_stats <- function(lf_df
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, nrow = n_facet_row
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, scales = y_scales) +
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geom_boxplot(fill = "white", outlier.colour = NA
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geom_violin(trim = T
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, scale = "width"
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#, position = position_dodge(width = 0.9)
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, width = 0.2) +
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, draw_quantiles = c(0.25, 0.5, 0.75)) +
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geom_point(position = position_jitterdodge(dodge.width = 0.01)
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, alpha = 0.5
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# geom_boxplot(fill = "white"
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# , outlier.colour = NA
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# #, position = position_dodge(width = 0.9)
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# , width = bp_width) +
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# geom_point(position = position_jitterdodge(dodge.width = 0.5)
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# , alpha = 0.5
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# , show.legend = FALSE
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# , aes(colour = factor(eval(parse(text = colour_categ))) )) +
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# ggbeeswarm (better than geom_point)
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geom_beeswarm(priority = "density"
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#, shape = 21
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, size = dot_size
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, alpha = dot_transparency
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, show.legend = FALSE
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, cex = 0.8
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, aes(colour = factor(eval(parse(text = colour_categ))) )) +
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theme(axis.text.x = element_text(size = my_ats)
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@ -46,10 +71,15 @@ lf_bp_with_stats <- function(lf_df
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, vjust = 0)
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, axis.title.x = element_text(size = my_ats)
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, axis.title.y = element_text(size = my_ats)
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, plot.title = element_text(size = my_pts , hjust = 0.5, colour = "black", face = "bold")
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, plot.title = element_text(size = my_pts
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, hjust = 0.5
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, colour = "black"
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, face = "bold")
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, strip.background = element_rect(fill = colour_bp_strip)
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, strip.text.x = element_text(size = my_fls, colour = "black")
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, legend.title = element_text(color = "black", size = my_als)
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, strip.text.x = element_text(size = my_fls
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, colour = "black")
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, legend.title = element_text(color = "black"
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, size = my_als)
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, legend.text = element_text(size = my_ats)
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, legend.direction = "vertical") +
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@ -60,7 +90,6 @@ lf_bp_with_stats <- function(lf_df
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stat_compare_means(comparisons = my_comparisonsL
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, method = stat_method
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, paired = my_paired
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#, label = "p.format")
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, label = stat_label[1])
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return(bp_statP)
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83
scripts/functions/redundant/test_lf_bp_with_stats.R
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83
scripts/functions/redundant/test_lf_bp_with_stats.R
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@ -0,0 +1,83 @@
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setwd("~/git/LSHTM_analysis/scripts/plotting/")
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source("../functions/lf_bp_with_stats.R")
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source("../functions/lf_bp.R")
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######################
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# Make plot
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######################
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# Note: Data
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# run other_plots_data.R
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# to get the long format data to test this function
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lf_bp(lf_df = lf_dynamut2
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, p_title = "Dynamut2"
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, colour_categ = "ddg_dynamut2_outcome"
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, x_grp = "mutation_info"
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, y_var = "param_value"
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, facet_var = "param_type"
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, n_facet_row = 1
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, y_scales = "free_y"
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, colour_bp_strip = "khaki2"
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, dot_size = 3
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, dot_transparency = 0.3
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, violin_quantiles = c(0.25, 0.5, 0.75)
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, my_ats = 22 # axis text size
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, my_als = 20 # axis label size
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, my_fls = 20 # facet label size
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, my_pts = 22 # plot title size
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, make_boxplot = F
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, bp_width = "auto"
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, add_stats = T
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, stat_grp_comp = c("DM", "OM")
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, stat_method = "wilcox.test"
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, my_paired = FALSE
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, stat_label = c("p.format", "p.signif") )
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# foo = lf_dynamut2 %>%
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# group_by(mutation_info, param_type) %>%
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# summarise( Mean = mean(param_value, na.rm = T)
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# , SD = sd(param_value, na.rm = T)
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# , Median = median(param_value, na.rm = T)
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# , IQR = IQR(param_value, na.rm = T) )
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# Quick tests
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plotdata_sel = subset(lf_dynamut2
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, lf_dynamut2$param_type == "ASA")
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plot_sum = plotdata_sel %>%
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group_by(mutation_info, param_type) %>%
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summarise(n = n()
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, Mean = mean(param_value, na.rm = T)
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, SD = sd(param_value, na.rm = T)
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, Min = min(param_value, na.rm = T)
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, Q1 = quantile(param_value, na.rm = T, 0.25)
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, Median = median(param_value, na.rm = T)
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, Q3 = quantile(param_value, na.rm = T, 0.75)
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, Max = max(param_value, na.rm = T) ) %>%
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rename('Mutation Class' = mutation_info
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, Parameter = param_type)
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plot_sum = as.data.frame(plot_sum, row.names = NULL)
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plot_sum
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bar = compare_means(param_value ~ mutation_info
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, group.by = "param_type"
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, data = plotdata_sel
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, paired = FALSE
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, p.adjust.method = "BH")
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bar2 = bar[c("param_type"
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, "group1"
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, "group2"
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, "p.format"
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, "p.signif"
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, "p.adj")] %>%
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rename(Parameter = param_type
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, Group1 = group1
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, Group2 = group2
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, "P-value" = p.format
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, "P-sig" = p.signif
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, "P-adj" = p.adj)
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bar2 = data.frame(bar2); bar2
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library(Hmisc)
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describe(lf_dynamut2)
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55
scripts/functions/test_lf_bp.R
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55
scripts/functions/test_lf_bp.R
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setwd("~/git/LSHTM_analysis/scripts/plotting/")
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source("Header_TT.R")
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source("../functions/lf_bp.R")
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# ================================================
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# Data: run get_plotting_data.R
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# to get the long format data to test this function
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# drug = "streptomycin"
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# gene = "gid"
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# source("get_plotting_dfs.R")
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# ==================================================
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######################
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# Make plot: ggplot
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######################
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lf_bp(lf_df = lf_dynamut2
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, p_title = "Dynamut2"
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, colour_categ = "ddg_dynamut2_outcome"
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, x_grp = "mutation_info"
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, y_var = "param_value"
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, facet_var = "param_type"
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, n_facet_row = 1
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, y_scales = "free_y"
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, colour_bp_strip = "khaki2"
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, dot_size = 3
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, dot_transparency = 0.3
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, violin_quantiles = c(0.25, 0.5, 0.75)
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, my_ats = 22 # axis text size
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, my_als = 20 # axis label size
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, my_fls = 20 # facet label size
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, my_pts = 22 # plot title size
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, make_boxplot = F
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, bp_width = "auto"
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, add_stats = T
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, stat_grp_comp = c("DM", "OM")
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, stat_method = "wilcox.test"
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, my_paired = FALSE
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, stat_label = c("p.format", "p.signif") )
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######################
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# Make plot: plotly
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######################
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# FIXME: This labels are not working as I want!
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# lf_bp_plotly(lf_df = lf_deepddg
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# , p_title = "DeepDDG"
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# , colour_categ = "deepddg_outcome"
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# , x_grp = "mutation_info"
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# , y_var = "param_value"
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# , facet_var = "param_type"
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# , n_facet_row = 1
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# , y_scales = "free_y"
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# , colour_bp_strip = "khaki2"
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# , dot_size = 3
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# , dot_transparency = 0.3
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# , violin_quantiles = c(0.25, 0.5, 0.75)
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# )
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@ -1,28 +0,0 @@
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setwd("~/git/LSHTM_analysis/scripts/plotting/")
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source("../functions/lf_bp_with_stats.R")
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######################
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# call function
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######################
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# Note: Data
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# run other_plots_data.R
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# to get the long format data to test this function
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lf_bp_with_stats(lf_df = lf_dynamut2
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, x_grp = "mutation_info"
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, y_var = "param_value"
|
||||
, facet_var = "param_type"
|
||||
, n_facet_row = 1
|
||||
, y_scales = "free_y"
|
||||
, p_title = "Dynamut2"
|
||||
, colour_categ = "ddg_dynamut2_outcome"
|
||||
, stat_grp_comp = c("DM", "OM")
|
||||
, stat_method = "wilcox.test"
|
||||
, my_paired = FALSE
|
||||
#, stat_label = "p.format")
|
||||
, stat_label = c("p.format", "p.signif")
|
||||
, my_ats = 22 # axis text size
|
||||
, my_als = 20 # axis label size
|
||||
, my_fls = 20 # facet label size
|
||||
, my_pts = 22 )# plot title size
|
||||
|
|
@ -3,12 +3,6 @@
|
|||
#########################################################
|
||||
#lib_loc = "/usr/local/lib/R/site-library")
|
||||
|
||||
#if (!require("gplots")) {
|
||||
# install.packages("gplots", dependencies = TRUE)
|
||||
# library(gplots)
|
||||
#}
|
||||
require(extrafont)
|
||||
|
||||
require("getopt", quietly = TRUE) # cmd parse arguments
|
||||
|
||||
if (!require("tidyverse")) {
|
||||
|
|
@ -16,9 +10,23 @@ if (!require("tidyverse")) {
|
|||
library(tidyverse)
|
||||
}
|
||||
|
||||
if (!require("ggplot2")) {
|
||||
install.packages("ggplot2", dependencies = TRUE)
|
||||
library(ggplot2)
|
||||
# if (!require("ggplot2")) {
|
||||
# install.packages("ggplot2", dependencies = TRUE)
|
||||
# library(ggplot2)
|
||||
# }
|
||||
|
||||
# if (!require ("dplyr")){
|
||||
# install.packages("dplyr")
|
||||
# library(dplyr)
|
||||
# }
|
||||
|
||||
# Install
|
||||
#if(!require(devtools)) install.packages("devtools")
|
||||
#devtools::install_github("kassambara/ggcorrplot")
|
||||
|
||||
if (!require ("ggbeeswarm")){
|
||||
install.packages("ggbeeswarm")
|
||||
library(ggbeeswarm)
|
||||
}
|
||||
|
||||
if (!require("plotly")) {
|
||||
|
|
@ -101,11 +109,6 @@ if (!require ("psych")){
|
|||
library(psych)
|
||||
}
|
||||
|
||||
if (!require ("dplyr")){
|
||||
install.packages("dplyr")
|
||||
library(dplyr)
|
||||
}
|
||||
|
||||
if (!require ("compare")){
|
||||
install.packages("compare")
|
||||
library(compare)
|
||||
|
|
@ -116,31 +119,25 @@ if (!require ("arsenal")){
|
|||
library(arsenal)
|
||||
}
|
||||
|
||||
if(!require(ggseqlogo)){
|
||||
install.packages("ggseqlogo")
|
||||
library(ggseqlogo)
|
||||
}
|
||||
|
||||
# for PDB files
|
||||
if(!require(bio3d)){
|
||||
install.packages("bio3d")
|
||||
library(bio3d)
|
||||
}
|
||||
|
||||
library(protr)
|
||||
if(!require(protr)){
|
||||
install.packages("protr")
|
||||
library(protr)
|
||||
}
|
||||
|
||||
#if (!requireNamespace("BiocManager", quietly = TRUE))
|
||||
# install.packages("BiocManager")
|
||||
|
||||
#BiocManager::install("Logolas")
|
||||
library("Logolas")
|
||||
|
||||
#install.packages("ggseqlogo")
|
||||
library(ggseqlogo)
|
||||
|
||||
|
||||
####TIDYVERSE
|
||||
# Install
|
||||
#if(!require(devtools)) install.packages("devtools")
|
||||
#devtools::install_github("kassambara/ggcorrplot")
|
||||
|
||||
library(ggcorrplot)
|
||||
|
||||
|
||||
###for PDB files
|
||||
#install.packages("bio3d")
|
||||
if(!require(bio3d)){
|
||||
install.packages("bio3d")
|
||||
library(bio3d)
|
||||
}
|
||||
|
||||
#install.packages("protr")
|
||||
library(protr)
|
||||
|
|
|
|||
|
|
@ -88,6 +88,8 @@ all_plot_dfs = combining_dfs_plotting(my_df_u
|
|||
|
||||
merged_df2 = all_plot_dfs[[1]]
|
||||
merged_df3 = all_plot_dfs[[2]]
|
||||
merged_df2_comp = all_plot_dfs[[3]]
|
||||
merged_df3_comp = all_plot_dfs[[4]]
|
||||
#======================================================================
|
||||
# read other files
|
||||
infilename_dynamut = paste0("~/git/Data/", drug, "/output/dynamut_results/", gene
|
||||
|
|
@ -99,9 +101,14 @@ infilename_dynamut2 = paste0("~/git/Data/", drug, "/output/dynamut_results/dyna
|
|||
infilename_mcsm_na = paste0("~/git/Data/", drug, "/output/mcsm_na_results/", gene
|
||||
, "_complex_mcsm_na_norm.csv")
|
||||
|
||||
infilename_mcsm_f_snps <- paste0("~/git/Data/", drug, "/output/", gene
|
||||
, "_mcsm_formatted_snps.csv")
|
||||
|
||||
dynamut_df = read.csv(infilename_dynamut)
|
||||
dynamut2_df = read.csv(infilename_dynamut2)
|
||||
mcsm_na_df = read.csv(infilename_mcsm_na)
|
||||
mcsm_f_snps = read.csv(infilename_mcsm_f_snps, header = F)
|
||||
names(mcsm_f_snps) = "mutationinformation"
|
||||
|
||||
####################################################################
|
||||
# Data for subcols barplot (~heatmpa)
|
||||
|
|
@ -430,11 +437,17 @@ if (nrow(corr_ps_df3) == nrow(merged_df3) && nrow(merged_df3_comp) == check1) {
|
|||
, "\nGot: ", check1)
|
||||
}
|
||||
|
||||
|
||||
rm(foo)
|
||||
####################################################################
|
||||
# Data for DM OM Plots: Long format dfs
|
||||
####################################################################
|
||||
source("other_plots_data.R")
|
||||
|
||||
########################################################################
|
||||
# End of script
|
||||
########################################################################
|
||||
rm(foo)
|
||||
|
||||
cat("\n===================================================\n"
|
||||
cat("\n######################################################\n"
|
||||
, "\nSuccessful: get_plotting_dfs.R worked!"
|
||||
, "\n====================================================")
|
||||
, "\n###################################################\n")
|
||||
|
|
|
|||
|
|
@ -3,10 +3,9 @@
|
|||
# TASK: producing boxplots for dr and other muts
|
||||
|
||||
#########################################################
|
||||
#=======================================================================
|
||||
# working dir and loading libraries
|
||||
# getwd()
|
||||
setwd("~/git/LSHTM_analysis/scripts/plotting")
|
||||
# setwd("~/git/LSHTM_analysis/scripts/plotting")
|
||||
# getwd()
|
||||
|
||||
# make cmd
|
||||
|
|
@ -14,21 +13,21 @@ setwd("~/git/LSHTM_analysis/scripts/plotting")
|
|||
# drug = "streptomycin"
|
||||
# gene = "gid"
|
||||
|
||||
source("get_plotting_dfs.R")
|
||||
# source("get_plotting_dfs.R")
|
||||
#=======================================================================
|
||||
# MOVE TO COMBINE or singular file for deepddg
|
||||
#
|
||||
# cols_to_select = c("mutation", "mutationinformation"
|
||||
# , "wild_type", "position", "mutant_type"
|
||||
# , "mutation_info")
|
||||
#
|
||||
# merged_df3_short = merged_df3[, cols_to_select]
|
||||
|
||||
cols_to_select = c("mutation", "mutationinformation"
|
||||
, "wild_type", "position", "mutant_type"
|
||||
, "mutation_info")
|
||||
|
||||
merged_df3_short = merged_df3[, cols_to_select]
|
||||
|
||||
infilename_mcsm_f_snps <- paste0("~/git/Data/", drug, "/output/", gene
|
||||
, "_mcsm_formatted_snps.csv")
|
||||
|
||||
mcsm_f_snps<- read.csv(infilename_mcsm_f_snps, header = F)
|
||||
names(mcsm_f_snps) <- "mutationinformation"
|
||||
# infilename_mcsm_f_snps <- paste0("~/git/Data/", drug, "/output/", gene
|
||||
# , "_mcsm_formatted_snps.csv")
|
||||
#
|
||||
# mcsm_f_snps<- read.csv(infilename_mcsm_f_snps, header = F)
|
||||
# names(mcsm_f_snps) <- "mutationinformation"
|
||||
|
||||
# write merged_df3 to generate structural figure on chimera
|
||||
#write.csv(merged_df3_short, "merged_df3_short.csv")
|
||||
|
|
@ -52,11 +51,11 @@ my_min = min(merged_df3$deepddg_scaled); my_min
|
|||
my_max = max(merged_df3$deepddg_scaled); my_max
|
||||
|
||||
if (my_min == -1 && my_max == 1){
|
||||
cat("PASS: DeepDDG successfully scaled b/w -1 and 1"
|
||||
cat("\nPASS: DeepDDG successfully scaled b/w -1 and 1"
|
||||
#, "\nProceeding with assigning deep outcome category")
|
||||
, "\n")
|
||||
}else{
|
||||
cat("FAIL: could not scale DeepDDG ddg values"
|
||||
cat("\nFAIL: could not scale DeepDDG ddg values"
|
||||
, "Aborting!")
|
||||
}
|
||||
|
||||
|
|
@ -100,7 +99,7 @@ if (merging_cols == "mutationinformation") {
|
|||
cols_check <- c(c1, c2, c3, c4)
|
||||
expected_cols = n_comb_cols - ( length(cols_check) - 1)
|
||||
if (all(cols_check)){
|
||||
cat("\nStage 2:Proceeding with merging dfs:\n")
|
||||
cat("\nStage 2: Proceeding with merging dfs:\n")
|
||||
comb_df <- Reduce(inner_join, list(cols_mcsm_df
|
||||
, cols_mcsm_na_df
|
||||
, dynamut_df
|
||||
|
|
@ -115,12 +114,13 @@ if (merging_cols == "mutationinformation") {
|
|||
|
||||
}
|
||||
}
|
||||
names(comb_df_s)
|
||||
#names(comb_df_s)
|
||||
cat("\n!!!IT GOT TO HERE!!!!")
|
||||
#=======================================================================
|
||||
fact_cols = colnames(comb_df_s)[grepl( "_outcome|_info", colnames(comb_df_s) )]
|
||||
fact_cols
|
||||
lapply(comb_df_s[, fact_cols], class)
|
||||
comb_df_s[,fact_cols] <- lapply(comb_df_s[,cols],as.factor)
|
||||
comb_df_s[, fact_cols] <- lapply(comb_df_s[, fact_cols], as.factor)
|
||||
|
||||
if (any(lapply(comb_df_s[, fact_cols], class) == "character")){
|
||||
cat("\nChanging cols to factor")
|
||||
|
|
@ -512,7 +512,6 @@ rm(all_plot_dfs
|
|||
, my_data_snp
|
||||
, my_df
|
||||
, my_df_u
|
||||
, ols_mcsm_df
|
||||
, other_muts
|
||||
, pd_df
|
||||
, subcols_df_ps
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue