reshape a mutation dataframe for plot of mutation distribution

Question

Hi I am struggling with the following data frame ( that i generate form a larger one using:

dcast(subset_mydata, ID.name ~ Canonical_Hugo_Symbol) 

I would like to get a plot of this table similar to the one in figure. I have tried with the heatmap function but i could not get the same result. does anybody know with which function is could i get a similar figure? Thanks!

dput(head(heat_ddat))
structure(list(ID.name = structure(2:7, .Label = c("", "1075", 
"1104", "1108", "1120", "1121", "1137", "1258", "1264", "1280", 
"1286", "1310", "1317", "1338", "1392", "1401", "1435", "1477", 
"1480", "1494", "1519", "1574", "1588", "1595", "1607", "1611", 
"1644", "1645", "1651", "1653", "1654", "1673", "1687", "1702", 
"1714", "1740", "1776", "1781", "1812", "1835", "1838", "1857", 
"1874", "1890", "1899", "1911", "1933", "1936", "1999", "2006", 
"2046", "2063", "2079", "2081", "2088", "2116", "2135", "2144", 
"2147", "2155", "2166", "2167", "2176", "2183", "2200", "2209", 
"2223", "2253", "2256", "2442", "2444", "2453", "2456", "2462", 
"2467", "2472", "2482", "2497", "2504", "2507", "2513", "2518", 
"2523", "2567", "2568", "2576", "2578", "2598", "2600", "2619", 
"2623", "2625", "2632", "2636", "2646", "2652", "2659", "2660", 
"2676", "2680", "2682", "2705", "2711", "2756", "2765", "2772", 
"2793", "2803", "2854", "2856", "2882", "2912", "2916", "2919", 
"3058", "3063", "3114", "3116", "3117", "3125", "3132", "3140", 
"3145", "3175", "3181", "3248", "3383", "3431", "3436", "3442", 
"3472", "3576", "3639", "4093", "FL1_01215", "FL10_01501", "FL12_01593", 
"FL13_01598", "FL16_01738", "FL17_01752", "FL18_01763", "FL19_01881", 
"FL2_01222", "FL22_02025", "FL23_02032", "FL24_02085", "FL25_02175", 
"FL26_02242", "FL28_02459", "FL3_01235", "FL30_02558", "FL35_02726", 
"FL37_02808", "FL41_02865", "FL43_02926", "FL44_02994", "FL45_03018", 
"FL47_03119", "FL48_03128", "FL55_03303", "FL62_03406", "FL64_03418", 
"FL65_03421", "FL69_03484", "FL7_01306", "FL70_03517", "FL71_03534", 
"FL76_03644", "FL77_03651", "FL8_01425"), class = "factor"), 
ACTB = c(0L, 0L, 0L, 0L, 0L, 0L), ALMS1 = c(0L, 0L, 0L, 0L, 
0L, 0L), ARID1A = c(0L, 1L, 0L, 0L, 0L, 1L), ARID1B = c(0L, 
0L, 0L, 1L, 0L, 0L), ARID2 = c(0L, 0L, 0L, 1L, 0L, 0L), ARID3A = c(1L, 
0L, 0L, 0L, 0L, 0L), ASXL1 = c(0L, 0L, 0L, 0L, 0L, 0L), ATM = c(0L, 
0L, 0L, 0L, 0L, 0L), B2M = c(0L, 0L, 0L, 0L, 0L, 0L), BCL2 = c(1L, 
1L, 1L, 0L, 1L, 2L), BCL7A = c(0L, 0L, 0L, 0L, 0L, 0L), BCORL1 = c(0L, 
0L, 0L, 0L, 0L, 0L), BCR = c(0L, 0L, 0L, 0L, 0L, 0L), BRD2 = c(0L, 
0L, 0L, 0L, 0L, 0L), BRWD3 = c(0L, 0L, 0L, 0L, 0L, 0L), BTG1 = c(0L, 
0L, 0L, 0L, 0L, 0L), BTG2 = c(0L, 0L, 0L, 0L, 0L, 0L), CARD11 = c(0L, 
0L, 1L, 0L, 0L, 0L), CCDC80 = c(0L, 0L, 0L, 0L, 0L, 0L), 
CCND1 = c(0L, 0L, 0L, 0L, 0L, 0L), CCND3 = c(0L, 0L, 0L, 
0L, 0L, 0L), CD40 = c(0L, 0L, 0L, 0L, 0L, 0L), CD58 = c(0L, 
0L, 0L, 0L, 0L, 0L), CD79A = c(1L, 0L, 0L, 0L, 0L, 0L), CD79B = c(0L, 
0L, 0L, 0L, 0L, 0L), CDH23 = c(0L, 0L, 0L, 0L, 0L, 0L), CDK6 = c(0L, 
0L, 0L, 0L, 0L, 0L), CDKN2B = c(0L, 0L, 0L, 0L, 0L, 0L), 
CHD2 = c(0L, 0L, 0L, 0L, 0L, 0L), CIITA = c(0L, 0L, 0L, 0L, 
0L, 0L), CREBBP = c(0L, 1L, 1L, 2L, 1L, 2L), CTSS = c(0L, 
0L, 0L, 0L, 0L, 0L), CXCR4 = c(0L, 0L, 0L, 0L, 0L, 0L), DDX3X = c(0L, 
0L, 0L, 0L, 0L, 0L), DIRAS3 = c(0L, 0L, 0L, 0L, 0L, 0L), 
DMD = c(0L, 0L, 0L, 0L, 0L, 0L), DNMT3A = c(0L, 0L, 0L, 0L, 
0L, 0L), DST = c(0L, 0L, 0L, 0L, 0L, 0L), DTX1 = c(0L, 0L, 
0L, 0L, 0L, 1L), EP300 = c(1L, 0L, 0L, 1L, 0L, 0L), EPHA6 = c(0L, 
0L, 0L, 0L, 1L, 0L), EPHA7 = c(0L, 0L, 0L, 0L, 0L, 0L), ETS1 = c(0L, 
0L, 0L, 0L, 0L, 0L), EZH2 = c(0L, 0L, 0L, 1L, 0L, 0L), FAT2 = c(0L, 
0L, 0L, 0L, 0L, 0L), FBXO11 = c(0L, 0L, 0L, 0L, 0L, 0L), 
FOXO1 = c(0L, 0L, 0L, 0L, 0L, 0L), GNA13 = c(0L, 0L, 0L, 
0L, 0L, 0L), GNB1 = c(0L, 0L, 0L, 0L, 0L, 0L), HUWE1 = c(0L, 
0L, 0L, 0L, 0L, 0L), IKBKE = c(0L, 0L, 0L, 0L, 0L, 0L), IKZF1 = c(0L, 
0L, 0L, 0L, 0L, 0L), IKZF2 = c(0L, 0L, 0L, 0L, 0L, 0L), IKZF3 = c(0L, 
0L, 0L, 0L, 0L, 0L), IRF4 = c(0L, 0L, 0L, 0L, 0L, 0L), IRF8 = c(1L, 
0L, 0L, 0L, 0L, 0L), KAT2A = c(0L, 0L, 0L, 0L, 0L, 0L), KAT2B = c(0L, 
0L, 0L, 0L, 0L, 0L), KAT5 = c(0L, 0L, 0L, 0L, 0L, 0L), KDM6A = c(0L, 
0L, 0L, 0L, 1L, 0L), KIF20B = c(0L, 0L, 0L, 0L, 0L, 0L), 
KLHL6 = c(0L, 0L, 0L, 0L, 0L, 1L), MALT1 = c(0L, 0L, 0L, 
0L, 0L, 0L), MAP3K14 = c(0L, 0L, 0L, 0L, 0L, 0L), MAP4K1 = c(0L, 
0L, 0L, 0L, 0L, 0L), MCL1 = c(0L, 0L, 0L, 0L, 0L, 0L), MEF2B = c(0L, 
0L, 0L, 1L, 0L, 0L), MLL2 = c(2L, 2L, 2L, 0L, 1L, 1L), MUC16 = c(0L, 
0L, 0L, 0L, 0L, 0L), MUM1 = c(0L, 0L, 0L, 0L, 1L, 0L), MYC = c(0L, 
0L, 0L, 0L, 0L, 0L), NF1 = c(0L, 0L, 0L, 0L, 0L, 0L), NFKB2 = c(0L, 
0L, 0L, 0L, 0L, 0L), NOTCH1 = c(0L, 0L, 0L, 1L, 1L, 0L), 
NOTCH2 = c(0L, 0L, 0L, 0L, 0L, 0L), NPM1 = c(0L, 0L, 0L, 
0L, 0L, 0L), P2RY8 = c(0L, 0L, 0L, 0L, 0L, 0L), PASD1 = c(0L, 
0L, 0L, 0L, 0L, 0L), PAX5 = c(0L, 0L, 0L, 0L, 0L, 0L), PCLO = c(0L, 
0L, 0L, 0L, 0L, 0L), PDGFRA = c(0L, 0L, 0L, 0L, 0L, 0L), 
PDGFRB = c(0L, 0L, 0L, 0L, 0L, 1L), PHF6 = c(0L, 0L, 0L, 
0L, 0L, 0L), PIK3CA = c(0L, 0L, 0L, 0L, 0L, 0L), PIK3CD = c(0L, 
0L, 0L, 0L, 1L, 0L), PIK3R1 = c(0L, 0L, 0L, 0L, 0L, 0L), 
PIM1 = c(0L, 0L, 0L, 0L, 0L, 0L), PTEN = c(0L, 0L, 0L, 0L, 
0L, 0L), RB1 = c(0L, 0L, 0L, 0L, 0L, 0L), RELN = c(0L, 0L, 
0L, 0L, 0L, 0L), RET = c(0L, 0L, 0L, 0L, 0L, 0L), RFX7 = c(0L, 
0L, 0L, 0L, 0L, 0L), RHOA = c(0L, 0L, 0L, 0L, 0L, 0L), ROS1 = c(0L, 
0L, 0L, 0L, 0L, 0L), SAMD9 = c(0L, 0L, 0L, 0L, 0L, 0L), SBF1 = c(0L, 
0L, 0L, 2L, 0L, 0L), SF3B1 = c(0L, 0L, 0L, 0L, 0L, 0L), SGK1 = c(1L, 
0L, 0L, 0L, 0L, 0L), SIN3A = c(0L, 0L, 0L, 0L, 0L, 0L), SLITRK6 = c(0L, 
0L, 0L, 0L, 0L, 0L), SMARCA2 = c(0L, 0L, 0L, 0L, 0L, 0L), 
SMARCA4 = c(0L, 0L, 0L, 0L, 0L, 1L), SMARCB1 = c(0L, 0L, 
0L, 0L, 0L, 0L), SOCS1 = c(0L, 0L, 0L, 0L, 0L, 0L), STAT6 = c(0L, 
0L, 0L, 1L, 0L, 0L), SWAP70 = c(0L, 0L, 0L, 0L, 0L, 0L), 
TBL1XR1 = c(0L, 0L, 0L, 0L, 0L, 0L), TET2 = c(0L, 0L, 0L, 
0L, 0L, 0L), TNFAIP3 = c(0L, 0L, 0L, 0L, 0L, 0L), TNFRSF14 = c(1L, 
1L, 0L, 1L, 0L, 0L), TP53 = c(1L, 0L, 0L, 1L, 0L, 0L), TRAF6 = c(0L, 
0L, 0L, 0L, 0L, 0L), TYK2 = c(0L, 0L, 0L, 0L, 0L, 0L), UBR5 = c(0L, 
0L, 0L, 0L, 0L, 0L), ULK4 = c(0L, 0L, 0L, 0L, 0L, 0L), UNC5C = c(0L, 
0L, 0L, 0L, 0L, 0L), USP6 = c(0L, 0L, 0L, 0L, 0L, 0L), VPS13A = c(0L, 
0L, 0L, 0L, 0L, 0L), ZNF608 = c(0L, 0L, 0L, 0L, 0L, 0L), 
ZNF708 = c(0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("ID.name", 
"ACTB", "ALMS1", "ARID1A", "ARID1B", "ARID2", "ARID3A", "ASXL1", 
"ATM", "B2M", "BCL2", "BCL7A", "BCORL1", "BCR", "BRD2", "BRWD3", 
"BTG1", "BTG2", "CARD11", "CCDC80", "CCND1", "CCND3", "CD40", 
"CD58", "CD79A", "CD79B", "CDH23", "CDK6", "CDKN2B", "CHD2", 
"CIITA", "CREBBP", "CTSS", "CXCR4", "DDX3X", "DIRAS3", "DMD", 
"DNMT3A", "DST", "DTX1", "EP300", "EPHA6", "EPHA7", "ETS1", "EZH2", 
"FAT2", "FBXO11", "FOXO1", "GNA13", "GNB1", "HUWE1", "IKBKE", 
"IKZF1", "IKZF2", "IKZF3", "IRF4", "IRF8", "KAT2A", "KAT2B", 
"KAT5", "KDM6A", "KIF20B", "KLHL6", "MALT1", "MAP3K14", "MAP4K1", 
"MCL1", "MEF2B", "MLL2", "MUC16", "MUM1", "MYC", "NF1", "NFKB2", 
"NOTCH1", "NOTCH2", "NPM1", "P2RY8", "PASD1", "PAX5", "PCLO", 
"PDGFRA", "PDGFRB", "PHF6", "PIK3CA", "PIK3CD", "PIK3R1", "PIM1", 
"PTEN", "RB1", "RELN", "RET", "RFX7", "RHOA", "ROS1", "SAMD9", 
"SBF1", "SF3B1", "SGK1", "SIN3A", "SLITRK6", "SMARCA2", "SMARCA4", 
"SMARCB1", "SOCS1", "STAT6", "SWAP70", "TBL1XR1", "TET2", "TNFAIP3", 
"TNFRSF14", "TP53", "TRAF6", "TYK2", "UBR5", "ULK4", "UNC5C", 
"USP6", "VPS13A", "ZNF608", "ZNF708"), row.names = c(NA, 6L), class = "data.frame")

Answer 1

First, convert your data frame from wide format to long format.

library(reshape2)
heat.df<-melt(heat_ddat)

Then you can use geom_tile() to get similar heatmap. If you need dicrete colors then use value as factor.

library(ggplot2)
ggplot(heat.df,aes(ID.name,variable,fill=as.factor(value)))+geom_tile()