From 15daa6dfc134423824bbec53069d6f5b96ecca31 Mon Sep 17 00:00:00 2001
From: Tanushree Tunstall <tanu@tunstall.in>
Date: Fri, 31 Jan 2020 15:32:32 +0000
Subject: [PATCH] remove .Rhistory

---
 mcsm_analysis/pyrazinamide/scripts/.Rhistory | 512 -------------------
 1 file changed, 512 deletions(-)
 delete mode 100644 mcsm_analysis/pyrazinamide/scripts/.Rhistory

diff --git a/mcsm_analysis/pyrazinamide/scripts/.Rhistory b/mcsm_analysis/pyrazinamide/scripts/.Rhistory
deleted file mode 100644
index efb7b8e..0000000
--- a/mcsm_analysis/pyrazinamide/scripts/.Rhistory
+++ /dev/null
@@ -1,512 +0,0 @@
-max(my_df2$Dis_lig_Ang) #9.847
-min(my_df2$Dis_lig_Ang) #3.047
-# count no of unique positions
-length(unique(my_df2$Position))#47
-#count no of unique mutations
-length(unique(my_df2$Mutationinformation)) #110
-#clear variable to avoid confusion
-rm(my_df)
-#%%%%%%%%%%
-# Reassign
-#%%%%%%%%%%
-my_df = my_df2
-# Stacked Barplot with colours: Lig_outcome @ position coloured by
-# stability scores. This is a barplot where each bar corresponds
-# to a SNP and is coloured by its corresponding PredAff stability value.
-# Normalised values (range between -1 and 1 ) to aid visualisation
-# NOTE: since barplot plots discrete values, colour = score, so number of
-# colours will be equal to the no. of unique normalised scores
-# rather than a continuous scale
-# will require generating the colour scale separately.
-#============================
-# sanity checks
-upos = unique(my_df$Position)
-str(my_df$Lig_outcome)
-colnames(my_df)
-#===========================
-# Data preparation for plots
-#===========================
-#!!!!!!!!!!!!!!!!!
-# REASSIGNMENT
-df <- my_df
-rm(my_df)
-# sanity checks
-# should be a factor
-is.factor(df$Lig_outcome)
-table(df$Lig_outcome)
-# sanity checks
-# should be a factor
-is.factor(df$Lig_outcome); as.factor(df$Lig_outcome)
-# sanity checks
-# should be a factor
-is.factor(df$Lig_outcome); as.factor(df$Lig_outcome)
-#===========================
-# Data preparation for plots
-#===========================
-#!!!!!!!!!!!!!!!!!
-# REASSIGNMENT
-df <- my_df
-getwd()
-setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
-getwd()
-############################################################
-# 1: Installing and loading required packages and functions
-############################################################
-source("../Header_TT.R")
-source("../barplot_colour_function.R")
-############################################################
-# Output dir for plots
-############################################################
-out_dir = "~/git/Data/pyrazinamide/output/plots"
-############################################################
-# 2: call script the prepares the data with columns containing
-# colours for axis labels
-############################################################
-source("subcols_axis.R")
-# this should return
-#mut_pos_cols: 130, 4
-#my_df: 335, 39
-# clear excess variable
-# "mut_pos_cols" is just for inspection in case you need to cross check
-# position numbers and colours
-# open file from deskptop ("sample_axis_cols") for cross checking
-table(mut_pos_cols$lab_bg)
-sum( table(mut_pos_cols$lab_bg) ) == nrow(mut_pos_cols) # should  be True
-table(mut_pos_cols$lab_bg2)
-sum( table(mut_pos_cols$lab_bg2) ) ==  nrow(mut_pos_cols) # should  be True
-table(mut_pos_cols$lab_fg)
-sum( table(mut_pos_cols$lab_fg) ) ==  nrow(mut_pos_cols) # should  be True
-# very important!
-my_axis_colours = mut_pos_cols$lab_fg
-# now clear mut_pos_cols
-rm(mut_pos_cols)
-########################### !!! only for mcsm_lig
-# 4: Filter/subset data
-# Lig plots < 10Ang
-# Filter the lig plots for Dis_to_lig < 10Ang
-###########################
-# check range of distances
-max(my_df$Dis_lig_Ang)
-min(my_df$Dis_lig_Ang)
-# subset data to have only values less than 10 Ang
-my_df2 = subset(my_df, my_df$Dis_lig_Ang < 10)
-# sanity checks
-table(my_df2$Dis_lig_Ang<10)
-table(my_df2$Dis_lig_Ang>10)
-max(my_df2$Dis_lig_Ang)
-min(my_df2$Dis_lig_Ang)
-# count no of unique positions
-length(unique(my_df2$Position))
-#count no of unique mutations
-length(unique(my_df2$Mutationinformation))
-# clear variable to avoid confusion
-rm(my_df)
-#%%%%%%%%%%
-# Reassign to keep code below consistent
-#%%%%%%%%%%
-my_df = my_df2
-###########################
-# 2: Plot: Lig scores
-###########################
-#==========================
-# Plot 2: Barplot with scores (unordered)
-# corresponds to Lig_outcome
-# Stacked Barplot with colours: Lig_outcome @ position coloured by
-# stability scores. This is a barplot where each bar corresponds
-# to a SNP and is coloured by its corresponding PredAff stability value.
-# Normalised values (range between -1 and 1 ) to aid visualisation
-# NOTE: since barplot plots discrete values, colour = score, so number of
-# colours will be equal to the no. of unique normalised scores
-# rather than a continuous scale
-# will require generating the colour scale separately.
-#============================
-# sanity checks
-upos = unique(my_df$Position)
-str(my_df$Lig_outcome)
-colnames(my_df)
-#===========================
-# Data preparation for plots
-#===========================
-#!!!!!!!!!!!!!!!!!
-# REASSIGNMENT
-df <- my_df
-rm(my_df)
-# sanity checks
-# should be a factor
-is.factor(df$Lig_outcome); as.factor(df$Lig_outcome)
-df$Lig_outcome = as.factor(df$Lig_outcome)
-is.factor(df$Lig_outcome);
-table(df$Lig_outcome)
-# should be -1 and 1
-min(df$ratioPredAff)
-max(df$ratioPredAff)
-# sanity checks
-# very important!!!!
-tapply(df$ratioPredAff, df$Lig_outcome, min)
-tapply(df$ratioPredAff, df$Lig_outcome, max)
-# sanity checks
-# very important!!!!
-tapply(df$ratioPredAff, df$Lig_outcome, min)
-tapply(df$ratioPredAff, df$Lig_outcome, max)
-# check unique values in normalised data
-u = unique(df$ratioPredAff)
-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-# Run this section if rounding is to be used
-# specify number for rounding
-n = 3
-df$ratioPredAffR = round(df$ratioPredAff, n)
-u = unique(df$ratioPredAffR)
-# create an extra column called group which contains the "gp name and score"
-# so colours can be generated for each unique values in this column
-my_grp = df$ratioPredAffR
-df$group <- paste0(df$Lig_outcome, "_", my_grp, sep = "")
-# Call the function to create the palette based on the group defined above
-colours <- ColourPalleteMulti(df, "Lig_outcome", "my_grp")
-my_title = "Protein stability (PredAff)"
-library(ggplot2)
-# axis label size
-my_xaxls = 13
-my_title = "Ligand Affinity"
-# axis label size
-my_xaxls = 13
-my_yaxls = 15
-# axes text size
-my_xaxts = 15
-my_yaxts = 15
-# no ordering of x-axis according to frequency
-g = ggplot(df, aes(factor(Position, ordered = T)))
-g +
-geom_bar(aes(fill = group), colour = "grey") +
-scale_fill_manual( values = colours
-, guide = 'none') +
-theme( axis.text.x = element_text(size = my_xaxls
-, angle = 90
-, hjust = 1
-, vjust = 0.4)
-, axis.text.y = element_text(size = my_yaxls
-, angle = 0
-, hjust = 1
-, vjust = 0)
-, axis.title.x = element_text(size = my_xaxts)
-, axis.title.y = element_text(size = my_yaxts ) ) +
-labs(title = my_title
-, x = "Position"
-, y = "Frequency")
-class(df$lab_bg)
-# make this a named vector
-# define cartesian coord
-my_xlim = length(unique(df$Position)); my_xlim
-# axis label size
-my_xals = 15
-my_yals = 15
-# axes text size
-my_xats = 15
-my_yats = 18
-# using geom_tile
-g = ggplot(df, aes(factor(Position, ordered = T)))
-g +
-coord_cartesian(xlim = c(1, my_xlim)
-, ylim = c(0, 6)
-, clip = "off") +
-geom_bar(aes(fill = group), colour = "grey") +
-scale_fill_manual( values = colours
-, guide = 'none') +
-geom_tile(aes(,-0.8, width = 0.95, height = 0.85)
-, fill = df$lab_bg) +
-geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
-, fill = df$lab_bg2) +
-# Here it's important to specify that your axis goes from 1 to max number of levels
-theme( axis.text.x = element_text(size = my_xats
-, angle = 90
-, hjust = 1
-, vjust = 0.4
-, colour = my_axis_colours)
-, axis.text.y = element_text(size = my_yats
-, angle = 0
-, hjust = 1
-, vjust = 0)
-, axis.title.x = element_text(size = my_xals)
-, axis.title.y = element_text(size = my_yals )
-, axis.ticks.x = element_blank()
-) +
-labs(title = my_title
-, x = "Position"
-, y = "Frequency")
-#========================
-# output plot as svg/png
-#========================
-class(df$lab_bg)
-# make this a named vector
-# define cartesian coord
-my_xlim = length(unique(df$Position)); my_xlim
-# axis label size
-my_xals = 18
-my_yals = 18
-# axes text size
-my_xats = 14
-my_yats = 18
-# set output dir for plots
-#getwd()
-#setwd("~/git/Data/pyrazinamide/output/plots")
-#getwd()
-getwd()
-setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
-getwd()
-############################################################
-# 1: Installing and loading required packages and functions
-############################################################
-source("../Header_TT.R")
-source("../barplot_colour_function.R")
-############################################################
-# Output dir for plots
-############################################################
-out_dir = "~/git/Data/pyrazinamide/output/plots"
-############################################################
-# 2: call script the prepares the data with columns containing
-# colours for axis labels
-############################################################
-source("subcols_axis.R")
-# this should return
-#mut_pos_cols: 130, 4
-#my_df: 335, 39
-# clear excess variable
-# "mut_pos_cols" is just for inspection in case you need to cross check
-# position numbers and colours
-# open file from deskptop ("sample_axis_cols") for cross checking
-table(mut_pos_cols$lab_bg)
-sum( table(mut_pos_cols$lab_bg) ) == nrow(mut_pos_cols) # should  be True
-table(mut_pos_cols$lab_bg2)
-sum( table(mut_pos_cols$lab_bg2) ) ==  nrow(mut_pos_cols) # should  be True
-table(mut_pos_cols$lab_fg)
-sum( table(mut_pos_cols$lab_fg) ) ==  nrow(mut_pos_cols) # should  be True
-# very important!
-my_axis_colours = mut_pos_cols$lab_fg
-# now clear mut_pos_cols
-rm(mut_pos_cols)
-########################### !!! only for mcsm_lig
-# 4: Filter/subset data
-# Lig plots < 10Ang
-# Filter the lig plots for Dis_to_lig < 10Ang
-###########################
-# check range of distances
-max(my_df$Dis_lig_Ang)
-min(my_df$Dis_lig_Ang)
-# subset data to have only values less than 10 Ang
-my_df2 = subset(my_df, my_df$Dis_lig_Ang < 10)
-# sanity checks
-table(my_df2$Dis_lig_Ang<10)
-table(my_df2$Dis_lig_Ang>10)
-max(my_df2$Dis_lig_Ang)
-min(my_df2$Dis_lig_Ang)
-# count no of unique positions
-length(unique(my_df2$Position))
-#count no of unique mutations
-length(unique(my_df2$Mutationinformation))
-# clear variable to avoid confusion
-rm(my_df)
-#%%%%%%%%%%
-# Reassign to keep code below consistent
-#%%%%%%%%%%
-my_df = my_df2
-###########################
-# 2: Plot: Lig scores
-###########################
-#==========================
-# Plot 2: Barplot with scores (unordered)
-# corresponds to Lig_outcome
-# Stacked Barplot with colours: Lig_outcome @ position coloured by
-# stability scores. This is a barplot where each bar corresponds
-# to a SNP and is coloured by its corresponding PredAff stability value.
-# Normalised values (range between -1 and 1 ) to aid visualisation
-# NOTE: since barplot plots discrete values, colour = score, so number of
-# colours will be equal to the no. of unique normalised scores
-# rather than a continuous scale
-# will require generating the colour scale separately.
-#============================
-# sanity checks
-upos = unique(my_df$Position)
-str(my_df$Lig_outcome)
-colnames(my_df)
-#===========================
-# Data preparation for plots
-#===========================
-#!!!!!!!!!!!!!!!!!
-# REASSIGNMENT
-df <- my_df
-#!!!!!!!!!!!!!!!!!
-rm(my_df)
-# sanity checks
-# should be a factor
-is.factor(df$Lig_outcome);
-#FALSE
-df$Lig_outcome = as.factor(df$Lig_outcome)
-is.factor(df$Lig_outcome);
-#TRUE
-table(df$Lig_outcome)
-# check the range
-min(df$ratioPredAff)
-max(df$ratioPredAff)
-# sanity checks
-# very important!!!!
-tapply(df$ratioPredAff, df$Lig_outcome, min)
-tapply(df$ratioPredAff, df$Lig_outcome, max)
-# My colour FUNCTION: based on group and subgroup
-# in my case;
-# df = df
-# group = Lig_outcome
-# subgroup = normalised score i.e ratioPredAff
-# Prepare data: round off ratioPredAff scores
-# round off to 3 significant digits:
-# 323 if no rounding is performed: used to generate the original graph
-# 287 if rounded to 3 places
-# FIXME: check if reducing precicion creates any ML prob
-# check unique values in normalised data
-u = unique(df$ratioPredAff)
-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-# Run this section if rounding is to be used
-# specify number for rounding
-n = 3
-df$ratioPredAffR = round(df$ratioPredAff, n)
-u = unique(df$ratioPredAffR)
-# create an extra column called group which contains the "gp name and score"
-# so colours can be generated for each unique values in this column
-my_grp = df$ratioPredAffR
-df$group <- paste0(df$Lig_outcome, "_", my_grp, sep = "")
-# ELSE
-# uncomment the below if rounding is not required
-#my_grp = df$ratioPredAff
-#df$group <- paste0(df$Lig_outcome, "_", my_grp, sep = "")
-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#******************
-# generate plot
-#******************
-# Call the function to create the palette based on the group defined above
-colours <- ColourPalleteMulti(df, "Lig_outcome", "my_grp")
-my_title = "Ligand Affinity"
-library(ggplot2)
-# axis label size
-my_xaxls = 13
-my_yaxls = 15
-# axes text size
-my_xaxts = 15
-my_yaxts = 15
-# no ordering of x-axis according to frequency
-g = ggplot(df, aes(factor(Position, ordered = T)))
-g +
-geom_bar(aes(fill = group), colour = "grey") +
-scale_fill_manual( values = colours
-, guide = 'none') +
-theme( axis.text.x = element_text(size = my_xaxls
-, angle = 90
-, hjust = 1
-, vjust = 0.4)
-, axis.text.y = element_text(size = my_yaxls
-, angle = 0
-, hjust = 1
-, vjust = 0)
-, axis.title.x = element_text(size = my_xaxts)
-, axis.title.y = element_text(size = my_yaxts ) ) +
-labs(title = my_title
-, x = "Position"
-, y = "Frequency")
-#========================
-# plot with axis colours
-#========================
-class(df$lab_bg)
-# make this a named vector
-# define cartesian coord
-my_xlim = length(unique(df$Position)); my_xlim
-# axis label size
-my_xals = 15
-my_yals = 15
-# axes text size
-my_xats = 15
-my_yats = 18
-# using geom_tile
-g = ggplot(df, aes(factor(Position, ordered = T)))
-g +
-coord_cartesian(xlim = c(1, my_xlim)
-, ylim = c(0, 6)
-, clip = "off") +
-geom_bar(aes(fill = group), colour = "grey") +
-scale_fill_manual( values = colours
-, guide = 'none') +
-geom_tile(aes(,-0.8, width = 0.95, height = 0.85)
-, fill = df$lab_bg) +
-geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
-, fill = df$lab_bg2) +
-# Here it's important to specify that your axis goes from 1 to max number of levels
-theme( axis.text.x = element_text(size = my_xats
-, angle = 90
-, hjust = 1
-, vjust = 0.4
-, colour = my_axis_colours)
-, axis.text.y = element_text(size = my_yats
-, angle = 0
-, hjust = 1
-, vjust = 0)
-, axis.title.x = element_text(size = my_xals)
-, axis.title.y = element_text(size = my_yals )
-, axis.ticks.x = element_blank()
-) +
-labs(title = my_title
-, x = "Position"
-, y = "Frequency")
-#========================
-# output plot as svg/png
-#========================
-class(df$lab_bg)
-# make this a named vector
-# define cartesian coord
-my_xlim = length(unique(df$Position)); my_xlim
-# axis label size
-my_xals = 18
-my_yals = 18
-# axes text size
-my_xats = 14
-my_yats = 18
-# set output dir for plots
-#getwd()
-#setwd("~/git/Data/pyrazinamide/output/plots")
-#getwd()
-plot_name = "barplot_LIG_acoloured.svg"
-my_plot_name = paste0(out_dir, "/", plot_name); my_plot_name
-svg(my_plot_name, width = 26, height = 4)
-g = ggplot(df, aes(factor(Position, ordered = T)))
-outFile = g +
-coord_cartesian(xlim = c(1, my_xlim)
-, ylim = c(0, 6)
-, clip = "off"
-) +
-geom_bar(aes(fill = group), colour = "grey") +
-scale_fill_manual( values = colours
-, guide = 'none') +
-#  geom_tile(aes(,-0.6, width = 0.9, height = 0.7)
-#            , fill = df$lab_bg) +
-#  geom_tile(aes(,-1, width = 0.9, height = 0.3)
-#            , fill = df$lab_bg2) +
-geom_tile(aes(,-0.8, width = 0.95, height = 0.85)
-, fill = df$lab_bg) +
-geom_tile(aes(,-1.2, width = 0.95, height = -0.2)
-, fill = df$lab_bg2) +
-# Here it's important to specify that your axis goes from 1 to max number of levels
-theme( axis.text.x = element_text(size = my_xats
-, angle = 90
-, hjust = 1
-, vjust = 0.4
-, colour = my_axis_colours)
-, axis.text.y = element_text(size = my_yats
-, angle = 0
-, hjust = 1
-, vjust = 0)
-, axis.title.x = element_text(size = my_xals)
-, axis.title.y = element_text(size = my_yals )
-, axis.ticks.x = element_blank()
-) +
-labs(title = ""
-, x = "Position"
-, y = "Frequency")
-print(outFile)
-dev.off()
-# for sanity and good practice
-#rm(df)