Instant Heat Maps in R How-to

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About This Book

• Learn something new in an Instant! A short, fast, focused guide delivering immediate results
• Create heat maps in R using different file formats
• Learn how to make choropleth maps and contour plots
• Generate your own customized heat maps and add interactivity for displaying on the web
  

Who This Book Is For

Heat Maps in R How-to is the book for you if you want to make use of this free and open source software to get the most out of your data analysis. You need to have at least some experience in using R and know how to run basic scripts from the command line. However, knowledge of other statistical scripting languages such as Octave, S-Plus, or MATLAB will suffice to follow along with the recipes. You need not be from a statistics background.

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关键词：Instant Heat maps STAN Ant displaying different something software source

1. ### loading packages
   
2. if (!require("gplots")) {
   
3. install.packages("gplots", dependencies = TRUE)
   
4. library(RColorBrewer)
   
5. }
   
6. if (!require("RColorBrewer")) {
   
7. install.packages("RColorBrewer", dependencies = TRUE)
   
8. library(RColorBrewer)
   
9. }
   
10. 
    
11. ### reading in data
    
12. gene_data <- read.csv("arabidopsis_genes.csv")
    
13. row_names <- gene_data[,1]
    
14. gene_data <- data.matrix(gene_data[,2:ncol(gene_data)])
    
15. rownames(gene_data) <- row_names
    
16. cat("\n\narabidopsis_genes.csv\n")
    
17. print(gene_data)
    
18. 
    
19. ### setting heatmap.2() default parameters
    
20. heat2 <- function(...) heatmap.2(gene_data,
    
21.         tracecol = "black",
    
22.         dendrogram = "column",
    
23.         Rowv = NA,
    
24.         trace = "none",
    
25.         margins = c(8,10),
    
26.         density.info = "density", ...)
    
27. 
    
28. pdf("custom_heatmaps.pdf")
    
29. 
    
30. ### 1) customizing colors
    
31. # 1.1) in-built color palettes
    
32. heat2(col = terrain.colors(n = 1000),
    
33.         main = "1.1) Terrain Colors")
    
34. 
    
35. # 1.2) RColorBrewer palettes
    
36. heat2(col = brewer.pal(n = 9, "YlOrRd"),
    
37.         main = "1.2) Brewer Palette")
    
38. 
    
39. # 1.3) creating own color palettes
    
40. my_colors <- c(y1 = "#F7F7D0",
    
41.         y2 = "#FCFC3A",
    
42.         y3 = "#D4D40D",
    
43.         b1 = "#40EDEA",
    
44.         b2 = "#18B3F0",
    
45.         b3 = "#186BF0",
    
46.         r1 = "#FA8E8E",
    
47.         r2 = "#F26666",
    
48.         r1 = "#C70404")
    
49. heat2(col = my_colors,
    
50.         main = "1.3) Own Color Palette")
    
51. my_palette <- colorRampPalette(c("blue", "yellow", "red"))(n = 1000)
    
52. heat2(col = my_palette, main = "1.3) ColorRampPalette")
    
53. 
    
54. # 1.4) gray scale
    
55. heat2(col = gray(level = (0:100)/100),
    
56.         main ="1.4) Gray Scale")
    
57. 
    
58. ### 2) adding cell notes
    
59. fold_change <- 2^gene_data
    
60. rounded_fold_changes  <- round(fold_change, 2)
    
61. heat2(cellnote = rounded_fold_changes,
    
62.         notecex = 0.5,
    
63.         notecol = "black",
    
64.         col = my_palette,
    
65.         main = "2) Cell Notes")
    
66. cat("\n\narabidopsis_genes.csv Fold changes\n")
    
67. 
    
68. ### 3) adding column side colors
    
69. heat2(ColSideColors = c("red", "gray", "red", rep("green", 13)),
    
70.         main = "3) ColSideColors")
    
71. 
    
72. dev.off()
    
73. cat("\n\nHeat maps saved as ", getwd(),
    
74.         "/custom_heatmaps.pdf", sep = "")

复制代码

1. ### loading packages
   
2. 
   
3. if (!require("ggplot2")) {
   
4. install.packages("ggplot2", dependencies = TRUE)
   
5. library(ggplot2)
   
6. }
   
7. if (!require("maps")) {
   
8. install.packages("maps", dependencies = TRUE)
   
9. library(maps)
   
10. }
    
11. if (!require("mapdata")) {
    
12. install.packages("mapdata", dependencies = TRUE)
    
13. library(mapdata)
    
14. }
    
15. if (!require("fields")) {
    
16. install.packages("fields", dependencies = TRUE)
    
17. library(fields)
    
18. }
    
19. 
    
20. pdf("chloropleth_maps.pdf", height = 7,width = 10)
    
21. 
    
22. ### 1) average temperature USA
    
23. 
    
24. # 1.1) reading in and processing data
    
25. usa_map <- map_data(map = "state")
    
26. 
    
27. usa_temp <- read.csv("usa_temp.csv", comment.char = "#")
    
28. 
    
29. usa_data <- merge(usa_temp, usa_map,
    
30. by.x ="state", by.y = "region") # case sensitive
    
31. 
    
32. usa_sorted <- usa_data[order(usa_data["order"]),]
    
33. 
    
34. # 1.2) plotting USA chloropleth maps
    
35. usa_map1 <- ggplot(data = usa_sorted) +
    
36.         geom_polygon(aes(x = long, y = lat,
    
37. group = group, fill = fahrenheit)) +
    
38.         ggtitle("USA Map 1")
    
39. print(usa_map1)
    
40. 
    
41. usa_map2 <- usa_map1 + coord_map("polyconic") +
    
42.         ggtitle("USA Map 2 - polyconic")
    
43. print(usa_map2)
    
44. 
    
45. usa_map3 <- usa_map2 +
    
46.         geom_path(aes(x = long, y = lat, group = group),
    
47. color = "black") +
    
48.         ggtitle("USA Map 3 - black contours")
    
49. print(usa_map3)
    
50. 
    
51. usa_map4 <- usa_map3 +
    
52.         scale_fill_gradient(low = "yellow", high = "red") +
    
53.         ggtitle("USA Map 4 - gradient 1")
    
54. print(usa_map4)
    
55. 
    
56. usa_map5 <- usa_map3 +
    
57. scale_fill_gradient2(low = "steelblue", mid = "yellow",
    
58.         high = "red",  midpoint = colMeans(usa_sorted["fahrenheit"])) +
    
59.         ggtitle("USA Map 5 - gradient 2")
    
60. print(usa_map5)
    
61. 
    
62. 
    
63. ### 2) South American population count
    
64. 
    
65. # 2.1) reading in and processing data
    
66. south_am_map <- map_data("worldHires",
    
67. region = c("Argentina", "Bolivia", "Brazil",
    
68.         "Chile", "Colombia", "Ecuador", "Falkland Islands",
    
69. "French Guiana", "Guyana", "Paraguay", "Peru",
    
70. "Suriname", "Uruguay", "Venezuela"))
    
71. 
    
72. south_am_pop <- read.csv("south_america_pop.csv",
    
73. comment.char = "#")
    
74. 
    
75. south_am_data <- merge(south_am_pop, south_am_map,  
    
76.         by.x = "country", by.y = "region")
    
77. 
    
78. south_am_sorted <- south_am_data[order(
    
79. south_am_data["order"]),]
    
80. 
    
81. # 2.2) creating chloropleth maps
    
82. south_am_map1 <- ggplot(data = south_am_sorted) +
    
83.         geom_polygon(aes(x = long, y = lat,
    
84. group = group, fill = population)) +
    
85.         geom_path(aes(x = long, y = lat, group = group),
    
86. color = "black") +
    
87.         coord_map("polyconic") +
    
88. scale_fill_gradient(low = "lightyellow",
    
89.         high = "red", guide = "legend")
    
90. print(south_am_map1)
    
91. 
    
92. south_am_map2 = south_am_map1 +
    
93.         theme(panel.background = element_blank(),
    
94.         axis.text = element_blank(),
    
95.         axis.title = element_blank(),
    
96.         axis.ticks = element_blank())
    
97. print(south_am_map2)
    
98. 
    
99. dev.off()
    
100. cat("Heat maps saved as ", getwd(),
     
101. "/chloropleth_maps.pdf", sep = "")
     
102. 
     
103. ### 3) Volcano contour plot
     
104. 
     
105. pdf("contour_plot.pdf", height = 7,width = 10)
     
106. 
     
107. data(volcano)
     
108. image.plot(volcano)
     
109. contour(volcano, add = TRUE)
     
110. 
     
111. dev.off()
     
112. 
     
113. cat("Heat maps and contour plot saved as ", getwd(),
     
114. "/chloropleth_maps.pdf and", getwd(), "/contour_plot.pdf" , sep = "")

复制代码

1. if (!require("gplots")) {
   
2. install.packages("gplots", dependencies = TRUE)
   
3. library(gplots)
   
4. }
   
5. if (!require("RColorBrewer")) {
   
6. install.packages("RColorBrewer", dependencies = TRUE)
   
7. library(gplots)
   
8. }
   
9. 
   
10. ### converting data
    
11. data(co2)
    
12. rowcolNames <- list(as.character(1959:1997), month.abb)
    
13. co2_data <- matrix(co2,
    
14.         ncol = 12,
    
15.         byrow = TRUE,
    
16.         dimnames = rowcolNames)
    
17. 
    
18. heat2 <- function(...)
    
19.         heatmap.2(co2_data,trace = "none",
    
20.         density.info = "none",
    
21.         dendrogram = "none",
    
22.         Colv = FALSE,
    
23.         Rowv = FALSE,
    
24.         col = colorRampPalette(c("blue", "yellow", "red"))(n = 100),
    
25.         margin = c(5,8),
    
26.         lhei = c(0.25,1.25),
    
27.          ...)
    
28. 
    
29. 
    
30. png("1_PNG_default.png")
    
31. 
    
32. heat2(main = "PNG default")
    
33. dev.off()
    
34. 
    
35. png("2_PNG_highres.png",
    
36.         width = 5*300,
    
37.         height = 5*300,
    
38.         res = 300,
    
39.         pointsize = 8)
    
40. heat2(main = "PNG High Resolution")
    
41. dev.off()
    
42. 
    
43. jpeg("3_JPEG_highres.png",
    
44.         width = 5*300,
    
45.         height = 5*300,
    
46.         res = 300,
    
47.         pointsize = 8)
    
48. heat2(main = "JPEG default")
    
49. dev.off()
    
50. 
    
51. bmp("4_BMP_default.bmp",
    
52. width = 5*300,
    
53.         height = 5*300,
    
54.         res = 300,
    
55.         pointsize = 8)
    
56. heat2(main = "BMP default")
    
57. dev.off()
    
58. 
    
59. pdf("5_PDF_default.pdf",
    
60.         width = 5,
    
61.         height = 5,
    
62.         pointsize = 8)
    
63. heat2(main = "PDF default")
    
64. dev.off()
    
65. 
    
66. svg("6_SVG_default.svg",
    
67.         width = 5,
    
68.         height = 5,
    
69.         pointsize = 8)
    
70. heat2(main = "SVG default")
    
71. dev.off()
    
72. 
    
73. postscript("7_PostScript_default.ps",
    
74.         width = 5,
    
75.         height = 5,
    
76.         pointsize = 8)
    
77. heat2(main = "PostScript default")
    
78. dev.off()
    
79. 
    
80. png("8_PNG_transp.png",
    
81.         width = 5*300,
    
82.         height = 5*300,
    
83.         res = 300,
    
84.         pointsize = 8,
    
85.         bg = "transparent")
    
86. heat2(main = "PNG Transparent Background")
    
87. dev.off()
    
88. 
    
89. pdf("9_PDF_mono.pdf",
    
90.         family = "Courier",
    
91.         paper = "USr")
    
92. heat2(main = "PDF Monospace Font")
    
93. dev.off()
    
94. 
    
95. cat("Heat maps saved in ", getwd(),
    
96.         sep = "")

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謝謝分享

新书吧

2013年的，不新了

look, thanks

thanks for sharing

看看！