ggplot_line: label the top 2 peak with X-axis values - r
I am new to R programming. I am plotting a mass spectrum with ggplot and would like to label the top 2 peaks with their x-axis values (i.e. m). Does anyone know how to achieve that?
Thanks so much for your help!
Here is part of the raw data I used for the ggplot.
m Intensity
1 30001 2.964e+01
2 30002 3.336e+01
3 30003 3.968e+01
4 30004 5.015e+01
5 30005 6.838e+01
6 30006 1.016e+02
7 30007 1.464e+02
8 30008 2.130e+02
9 30009 3.115e+02
10 30010 3.951e+02
11 30011 5.134e+02
12 30012 5.316e+02
13 30013 6.377e+02
14 30014 8.813e+02
15 30015 1.071e+03
16 30016 1.119e+03
17 30017 1.202e+03
18 30018 1.299e+03
19 30019 1.112e+03
20 30020 1.205e+03
21 30021 1.422e+03
22 30022 1.653e+03
23 30023 1.726e+03
24 30024 2.423e+03
25 30025 3.059e+03
26 30026 3.267e+03
27 30027 3.993e+03
28 30028 5.172e+03
29 30029 5.278e+03
30 30030 2.794e+03
31 30031 1.459e+03
32 30032 2.512e+03
33 30033 6.590e+03
34 30034 1.245e+04
35 30035 1.144e+04
36 30036 5.197e+03
37 30037 6.012e+03
38 30038 1.453e+04
39 30039 1.513e+04
40 30040 5.802e+03
41 30041 9.226e+03
42 30042 5.809e+03
43 30043 3.074e+03
44 30044 3.882e+03
45 30045 9.941e+02
46 30046 8.170e+02
47 30047 1.149e+03
48 30048 3.567e+02
49 30049 3.805e+02
50 30050 3.654e+02
51 30051 4.724e+02
52 30052 7.819e+02
53 30053 8.634e+02
54 30054 5.235e+02
55 30055 1.712e+02
56 30056 9.232e+01
57 30057 9.434e+01
58 30058 7.191e+01
59 30059 8.036e+01
60 30060 4.456e+01
61 30061 9.428e+01
62 30062 9.392e+01
63 30063 8.413e+01
64 30064 5.671e+01
65 30065 2.639e+01
66 30066 2.027e+01
67 30067 4.584e+01
68 30068 6.956e+01
69 30069 6.181e+01
70 30070 6.450e+01
71 30071 2.826e+01
72 30072 3.610e+01
73 30073 6.325e+01
74 30074 3.509e+01
75 30075 3.478e+01
76 30076 1.120e+01
77 30077 6.993e+00
78 30078 9.936e+00
79 30079 7.738e+00
80 30080 9.771e+00
81 30081 1.762e+01
82 30082 3.060e+01
83 30083 2.175e+01
84 30084 2.816e+01
85 30085 2.700e+01
86 30086 2.114e+01
87 30087 4.378e+01
88 30088 5.824e+01
89 30089 6.193e+01
90 30090 4.146e+01
91 30091 9.697e+04
92 30092 9.458e+04
93 30093 9.216e+04
94 30094 8.972e+04
95 30095 8.723e+04
96 30096 8.468e+04
97 30097 8.211e+04
98 30098 7.959e+04
99 30099 7.726e+04
100 30100 7.527e+04
101 30101 7.379e+04
102 30102 7.298e+04
103 30103 7.301e+04
104 30104 7.399e+04
105 30105 7.602e+04
106 30106 7.916e+04
107 30107 8.340e+04
108 30108 8.862e+04
109 30109 9.460e+04
110 30110 1.010e+05
111 30111 1.074e+05
112 30112 1.133e+05
113 30113 1.180e+05
114 30114 1.211e+05
115 30115 1.222e+05
116 30116 1.213e+05
117 30117 1.186e+05
118 30118 1.146e+05
119 30119 1.100e+05
120 30120 1.054e+05
121 30121 1.014e+05
122 30122 9.838e+04
123 30123 9.637e+04
124 30124 9.535e+04
125 30125 9.508e+04
126 30126 9.520e+04
127 30127 9.527e+04
128 30128 9.484e+04
129 30129 9.355e+04
130 30130 9.128e+04
131 30131 8.809e+04
132 30132 8.425e+04
133 30133 8.012e+04
134 30134 7.603e+04
135 30135 7.225e+04
136 30136 6.895e+04
137 30137 6.617e+04
138 30138 6.392e+04
139 30139 6.214e+04
140 30140 6.078e+04
141 30141 5.980e+04
142 30142 5.922e+04
143 30143 5.905e+04
144 30144 5.934e+04
145 30145 6.013e+04
146 30146 6.143e+04
147 30147 6.324e+04
148 30148 6.552e+04
149 30149 6.816e+04
150 30150 7.100e+04
151 30151 7.384e+04
152 30152 7.655e+04
153 30153 7.904e+04
154 30154 8.132e+04
155 30155 8.353e+04
156 30156 8.595e+04
157 30157 8.896e+04
158 30158 9.302e+04
159 30159 9.864e+04
160 30160 1.063e+05
161 30161 1.165e+05
162 30162 1.293e+05
163 30163 1.443e+05
164 30164 1.605e+05
165 30165 1.759e+05
166 30166 1.883e+05
167 30167 1.957e+05
168 30168 1.969e+05
169 30169 1.921e+05
170 30170 1.824e+05
171 30171 1.693e+05
172 30172 1.544e+05
173 30173 1.390e+05
174 30174 1.241e+05
175 30175 1.102e+05
176 30176 9.755e+04
177 30177 8.644e+04
178 30178 7.692e+04
179 30179 6.900e+04
180 30180 6.262e+04
181 30181 5.766e+04
182 30182 5.397e+04
183 30183 5.137e+04
184 30184 4.972e+04
185 30185 4.889e+04
186 30186 4.881e+04
187 30187 4.940e+04
188 30188 5.059e+04
189 30189 5.230e+04
190 30190 5.444e+04
191 30191 5.690e+04
192 30192 5.960e+04
193 30193 6.244e+04
194 30194 6.539e+04
195 30195 6.842e+04
196 30196 7.153e+04
197 30197 7.471e+04
198 30198 7.795e+04
199 30199 8.118e+04
200 30200 8.430e+04
201 30201 8.719e+04
202 30202 8.976e+04
203 30203 9.193e+04
204 30204 9.364e+04
205 30205 9.480e+04
206 30206 9.531e+04
207 30207 9.504e+04
208 30208 9.391e+04
209 30209 9.189e+04
210 30210 8.912e+04
211 30211 8.587e+04
212 30212 8.251e+04
213 30213 7.939e+04
214 30214 7.680e+04
215 30215 7.492e+04
216 30216 7.381e+04
217 30217 7.349e+04
218 30218 7.394e+04
219 30219 7.510e+04
220 30220 7.690e+04
221 30221 7.919e+04
222 30222 8.174e+04
223 30223 8.425e+04
224 30224 8.637e+04
225 30225 8.776e+04
226 30226 8.826e+04
227 30227 8.788e+04
228 30228 8.690e+04
229 30229 8.569e+04
230 30230 8.465e+04
231 30231 8.405e+04
232 30232 8.398e+04
233 30233 8.434e+04
234 30234 8.494e+04
235 30235 8.554e+04
236 30236 8.598e+04
237 30237 8.623e+04
238 30238 8.638e+04
239 30239 8.665e+04
240 30240 8.736e+04
241 30241 8.884e+04
242 30242 9.147e+04
243 30243 9.559e+04
244 30244 1.016e+05
245 30245 1.097e+05
246 30246 1.200e+05
247 30247 1.321e+05
Here is my code for ggplot:
ggplot(data=raw.1) +
geom_line(mapping = aes(x=m, y=Intensity))
Below is the ggplot output:
I would do it this way. My solution requires the ggrepel package as well as some dplyr functions. The key to this working is that you can set data = for each geom_ layer in ggplot2. The geom_text_repel() layer from ggrepel ensures that the labels will not overlap your data from geom_line().
library(ggplot2)
library(dplyr)
library(ggrepel)
ggplot(mapping = aes(x = m, y = Intensity, label = m)) +
geom_line(data=raw.1) +
geom_text_repel(data = raw.1 %>%
arrange(desc(Intensity)) %>% # arranges in descending order
slice_head(n = 2)) # only keeps the top two intensities.
My plot does not look like yours since you only shared the first 247 data points. I suspect that this initial solution might not work for you because I am a chemist and have some idea what you hope to accomplish. This approach labels the top two highest intensities, not necessarily the top two peaks. We need to identify local all maxima and then select the two tallest.
Here is how we do that. The following code calculates the slope between each point, and then looks for points where a positive slope changes to a negative slope (local maximum), then it sorts and selects the top two by intensity.
top_two <- raw.1 %>%
mutate(deriv = Intensity - lag(Intensity) ,
max = case_when(deriv >=0 & lead(deriv) <0 ~ T,
T ~ F)) %>%
filter(max) %>%
arrange(desc(Intensity)) %>%
slice_head(n = 2)
Let's modify the original plot code to put this in.
ggplot(mapping = aes(x = m, y = Intensity, label = m)) +
geom_line(data = raw.1) +
geom_text_repel(data = top_two, nudge_y = 1e4)
Data:
raw.1 <- structure(list(m = c(30001, 30002, 30003, 30004, 30005, 30006,
30007, 30008, 30009, 30010, 30011, 30012, 30013, 30014, 30015,
30016, 30017, 30018, 30019, 30020, 30021, 30022, 30023, 30024,
30025, 30026, 30027, 30028, 30029, 30030, 30031, 30032, 30033,
30034, 30035, 30036, 30037, 30038, 30039, 30040, 30041, 30042,
30043, 30044, 30045, 30046, 30047, 30048, 30049, 30050, 30051,
30052, 30053, 30054, 30055, 30056, 30057, 30058, 30059, 30060,
30061, 30062, 30063, 30064, 30065, 30066, 30067, 30068, 30069,
30070, 30071, 30072, 30073, 30074, 30075, 30076, 30077, 30078,
30079, 30080, 30081, 30082, 30083, 30084, 30085, 30086, 30087,
30088, 30089, 30090, 30091, 30092, 30093, 30094, 30095, 30096,
30097, 30098, 30099, 30100, 30101, 30102, 30103, 30104, 30105,
30106, 30107, 30108, 30109, 30110, 30111, 30112, 30113, 30114,
30115, 30116, 30117, 30118, 30119, 30120, 30121, 30122, 30123,
30124, 30125, 30126, 30127, 30128, 30129, 30130, 30131, 30132,
30133, 30134, 30135, 30136, 30137, 30138, 30139, 30140, 30141,
30142, 30143, 30144, 30145, 30146, 30147, 30148, 30149, 30150,
30151, 30152, 30153, 30154, 30155, 30156, 30157, 30158, 30159,
30160, 30161, 30162, 30163, 30164, 30165, 30166, 30167, 30168,
30169, 30170, 30171, 30172, 30173, 30174, 30175, 30176, 30177,
30178, 30179, 30180, 30181, 30182, 30183, 30184, 30185, 30186,
30187, 30188, 30189, 30190, 30191, 30192, 30193, 30194, 30195,
30196, 30197, 30198, 30199, 30200, 30201, 30202, 30203, 30204,
30205, 30206, 30207, 30208, 30209, 30210, 30211, 30212, 30213,
30214, 30215, 30216, 30217, 30218, 30219, 30220, 30221, 30222,
30223, 30224, 30225, 30226, 30227, 30228, 30229, 30230, 30231,
30232, 30233, 30234, 30235, 30236, 30237, 30238, 30239, 30240,
30241, 30242, 30243, 30244, 30245, 30246, 30247), Intensity = c(29.64,
33.36, 39.68, 50.15, 68.38, 101.6, 146.4, 213, 311.5, 395.1,
513.4, 531.6, 637.7, 881.3, 1071, 1119, 1202, 1299, 1112, 1205,
1422, 1653, 1726, 2423, 3059, 3267, 3993, 5172, 5278, 2794, 1459,
2512, 6590, 12450, 11440, 5197, 6012, 14530, 15130, 5802, 9226,
5809, 3074, 3882, 994.1, 817, 1149, 356.7, 380.5, 365.4, 472.4,
781.9, 863.4, 523.5, 171.2, 92.32, 94.34, 71.91, 80.36, 44.56,
94.28, 93.92, 84.13, 56.71, 26.39, 20.27, 45.84, 69.56, 61.81,
64.5, 28.26, 36.1, 63.25, 35.09, 34.78, 11.2, 6.993, 9.936, 7.738,
9.771, 17.62, 30.6, 21.75, 28.16, 27, 21.14, 43.78, 58.24, 61.93,
41.46, 96970, 94580, 92160, 89720, 87230, 84680, 82110, 79590,
77260, 75270, 73790, 72980, 73010, 73990, 76020, 79160, 83400,
88620, 94600, 101000, 107400, 113300, 118000, 121100, 122200,
121300, 118600, 114600, 110000, 105400, 101400, 98380, 96370,
95350, 95080, 95200, 95270, 94840, 93550, 91280, 88090, 84250,
80120, 76030, 72250, 68950, 66170, 63920, 62140, 60780, 59800,
59220, 59050, 59340, 60130, 61430, 63240, 65520, 68160, 71000,
73840, 76550, 79040, 81320, 83530, 85950, 88960, 93020, 98640,
106300, 116500, 129300, 144300, 160500, 175900, 188300, 195700,
196900, 192100, 182400, 169300, 154400, 139000, 124100, 110200,
97550, 86440, 76920, 69000, 62620, 57660, 53970, 51370, 49720,
48890, 48810, 49400, 50590, 52300, 54440, 56900, 59600, 62440,
65390, 68420, 71530, 74710, 77950, 81180, 84300, 87190, 89760,
91930, 93640, 94800, 95310, 95040, 93910, 91890, 89120, 85870,
82510, 79390, 76800, 74920, 73810, 73490, 73940, 75100, 76900,
79190, 81740, 84250, 86370, 87760, 88260, 87880, 86900, 85690,
84650, 84050, 83980, 84340, 84940, 85540, 85980, 86230, 86380,
86650, 87360, 88840, 91470, 95590, 101600, 109700, 120000, 132100
)), row.names = c(NA, -247L), class = c("tbl_df", "tbl", "data.frame"
))
This approach assumes or treats your x-axis as discrete values of a continuous variable and finds the local maxima based on 2nd derivative using code from Finding local maxima and minima
Rest of the plotting is similar to Ben Norris's answer using geom_text_repel() to label the points of interest.
Also as noted, the data your provided are different vs. the figure in your question.
library(ggplot2)
library(ggrepel)
# find local maxima aka peaks
local_maximas <- raw.1[which(diff(sign(diff(raw.1$Intensity)))==-2)+1,]
top2 <- tail(local_maximas[order(local_maximas$Intensity),],2) #subset of top 2 highest peaks
raw.1$label <- ifelse(raw.1$m %in% top2$m, raw.1$m, NA) #make labels for plot
ggplot(data = raw.1) +
geom_line(aes(x=m, y=Intensity)) +
geom_text_repel(aes(x = m, y = Intensity, label = label))
Related
Barplot using ggplot2 for 4 variables
I have data like this: ID height S1 S2 S3 1 927 0.90695438 0.28872194 0.67114294 2 777 0.20981677 0.71783084 0.74498220 3 1659 0.35813799 0.92339744 0.44001698 4 174 0.44829914 0.67493949 0.11503942 5 1408 0.90642643 0.18593999 0.67564278 6 1454 0.38943930 0.34806716 0.73155952 7 2438 0.51745975 0.12351953 0.48398490 8 1114 0.12523909 0.10811622 0.17104804 9 1642 0.03014575 0.29795320 0.67584853 10 515 0.77180549 0.83819990 0.26298995 11 1877 0.32741508 0.99277109 0.34148083 12 2647 0.38947869 0.43713441 0.21024554 13 845 0.04105275 0.20256457 0.01631959 14 1198 0.36139663 0.96387150 0.37676288 15 2289 0.57097808 0.66038711 0.56230740 16 2009 0.68488024 0.29811683 0.67998461 17 618 0.97111675 0.11926219 0.74538877 18 1076 0.70195881 0.59975160 0.95007272 19 1082 0.01154550 0.12019055 0.16309071 20 2072 0.53553213 0.78843202 0.32475690 21 1610 0.83657146 0.36959607 0.13271604 22 2134 0.80686674 0.95632284 0.63729744 23 1617 0.08093264 0.91357666 0.33092961 24 2248 0.23890930 0.82333634 0.64907957 25 1263 0.96598986 0.31948216 0.30288836 26 518 0.03767233 0.87770033 0.07123327 27 2312 0.91640643 0.80035100 0.66239047 28 2646 0.72622658 0.61135664 0.75960356 29 1650 0.20077621 0.07242114 0.55336017 30 837 0.84020075 0.42158771 0.53927210 31 1467 0.39666235 0.34446560 0.84959232 32 2786 0.39270226 0.75173569 0.65322596 33 1049 0.47255689 0.21875132 0.95088576 34 2863 0.58365691 0.29213397 0.61722305 35 2087 0.35238717 0.35595337 0.49284063 36 2669 0.02847401 0.63196192 0.97600657 37 545 0.99508793 0.89253107 0.49034522 38 1890 0.95755846 0.74403278 0.65517230 39 2969 0.55165118 0.45722242 0.59880179 40 395 0.10195396 0.03609544 0.94756902 41 995 0.23791515 0.56851452 0.36801151 42 2596 0.86009766 0.43901589 0.87818701 43 2334 0.73826129 0.60048445 0.45487507 44 2483 0.49731226 0.95138276 0.49646702 45 1812 0.57992109 0.26943131 0.46061562 46 1476 0.01618339 0.65883839 0.61790820 47 2342 0.47212988 0.07647121 0.60414349 48 2653 0.04238973 0.07128521 0.78587960 49 627 0.46315442 0.37033152 0.55526847 50 925 0.62999477 0.29710220 0.76897834 51 995 0.67324929 0.55107827 0.40428567 52 600 0.08703467 0.36989059 0.51071981 53 711 0.14358380 0.84568953 0.52353644 54 828 0.90847850 0.62079070 0.99279921 55 1776 0.12253259 0.39914002 0.42964742 56 764 0.72886279 0.29966153 0.99601125 57 375 0.95037718 0.38111984 0.78660025 58 694 0.04335591 0.70113494 0.51591063 59 1795 0.01959930 0.94686529 0.50268797 60 638 0.19907246 0.77282832 0.91163748 61 1394 0.50508626 0.21955016 0.26441590 62 1943 0.92638876 0.71611036 0.17385687 63 2882 0.13840169 0.66421796 0.40033126 64 2031 0.16919458 0.70625020 0.53835738 65 1338 0.60662738 0.27962799 0.24496437 66 1077 0.81587669 0.71225050 0.37585096 67 1370 0.84338121 0.66094211 0.58025355 68 1339 0.78807719 0.04101269 0.20895531 69 739 0.01902087 0.06114149 0.80133001 70 2085 0.69808750 0.27976169 0.63880242 71 1240 0.81509312 0.30196772 0.73633076 72 987 0.56840006 0.95661083 0.43881241 73 1720 0.48006288 0.38981872 0.57981238 74 2901 0.16137012 0.37178879 0.25604401 75 1987 0.08925623 0.84314249 0.46371823 76 1876 0.16268237 0.84723500 0.16861486 77 2571 0.02672845 0.31933115 0.61389453 78 2325 0.70962948 0.13250605 0.95810262 79 2503 0.76101818 0.61710912 0.47819473 80 279 0.85747478 0.79130451 0.75115933 81 1381 0.43726582 0.33804871 0.02058322 82 1800 0.41713645 0.90544760 0.17096903 83 2760 0.58564949 0.19755671 0.63996650 84 2949 0.82496758 0.79408518 0.16497848 85 118 0.79313923 0.75460289 0.35472278 86 1736 0.32615257 0.91139485 0.18642647 87 2201 0.95793194 0.32268770 0.89765616 88 750 0.65301961 0.08616947 0.23778386 89 906 0.45867582 0.91120045 0.98494348 90 2202 0.60602188 0.95517383 0.02133074 I want to make a barplot using ggplot2 like this: In the above-mentioned dataset height should be on the y-axis and S1, S2, S3 should be representing colors of each sample. I have tried the base R function barplot which gave me the following output. Please give me any suggestion. barplot(t(as.matrix(examp[,3:5])),col=rainbow(3))
It's not clear to me exactly what you want to plot. You say you want height on the y axis, but the examples you show are all 'filled to the top', implying the same height for each ID. Also, it is not clear what the numbers associated with each sample represent. I am guessing they should be relative weightings for the bar heights.
Assuming you actually want a filled bar plot as in the examples, with the relative sizes of the bars dictated by the sample values, you can do:
library(tidyr)
library(dplyr)
library(ggplot2)
df %>%
mutate(ID = reorder(ID, S3/(S3 + S2 + S1))) %>%
pivot_longer(3:5, names_to = "Sample", values_to = "Value") %>%
ggplot(aes(ID, Value * height, fill = Sample)) +
geom_col(position = "fill", color = NA) +
labs(y = "Height") +
theme_classic() +
scale_fill_manual(values = c("red", "green", "blue"))
Alternative
df %>%
arrange(order(height)) %>%
group_by(height) %>%
summarize(across(everything(), mean)) %>%
pivot_longer(3:5, names_to = "Sample", values_to = "Value") %>%
ggplot(aes(height, Value, fill = Sample, colour = Sample)) +
geom_smooth(method = loess, formula = y ~ x, linetype = 2, alpha = 0.2) +
theme_bw()
New column with percentage change in R
How do I make a new column in DF with the percentage change in share price over the year?
DF <- data.frame(name = c("EQU", "YAR", "MOWI", "AUSS", "GJF", "KOG", "SUBC"),
price20 = c(183, 343, 189, 88, 179, 169, 62),
price21 = c(221, 453, 183, 85, 198, 232, 67))
Here's a line that will do it for you. I've also added a round function to it so the table is more readable. DF$percent_change <- round((DF$price21 - DF$price20) / DF$price20 * 100, 2) name price20 price21 percent_change 1 EQU 183 221 20.77 2 YAR 343 453 32.07 3 MOWI 189 183 -3.17 4 AUSS 88 85 -3.41 5 GJF 179 198 10.61 6 KOG 169 232 37.28 7 SUBC 62 67 8.06
This line should do it: DF$change <- (DF$price21/DF$price20*100) - 100
We can use a simple division and scales::percent: library(dplyr) DF %>% mutate(percent_change = scales::percent((price21-price20)/price20)) name price20 price21 percent_change 1 EQU 183 221 20.77% 2 YAR 343 453 32.07% 3 MOWI 189 183 -3.17% 4 AUSS 88 85 -3.41% 5 GJF 179 198 10.61% 6 KOG 169 232 37.28% 7 SUBC 62 67 8.06%
Why are my 95% confidence intervals of my multivariate regression being plotted as a loess line?
I've been trying to plot a 95% prediction interval for a multivariate regression line in ggplot2. The graph is a regression of three independent variables ("x", "y", and "z") being used to predict a dependent variable ("a") on the y-axis. However, when I actually try to plot the results in ggplot2, I get a rather unusual result where the regression line is straightforward but the 95% prediction interval bands are very squiggly and do not resemble a straight line at all. They look like loess lines more than anything. Here is a picture showing the result I get:
Does anyone know why I am getting a result where the 95% confidence intervals aren't smooth lines? The only thing I can think of is that this is related to the fact that this is a multivariate regression rather than a univariate one, but checking the actual variables all three show a strong correlation with the dependent variable (r2 > 0.95). I looked up the results of a plot of a multivariate regression with a 95% confidence interval, but none of them seemed similar to my result, they all seemed to have pretty smooth lines.
I tried fitting a method="lm" into the predict() call of my code following this question, but that did not work either.
Below is a dataset and code that replicates this result.
x y z a
1 2.366153239 5.420534999 2.328204243 10.55858156
2 1.431094272 2.975529566 1.724972338 2.533696814
3 2.60453538 5.75827066 2.399639694 11.48783737
4 2.483771412 5.470167623 2.338838948 10.74706177
5 1.971210737 4.287715955 2.070680071 7.334766592
6 2.5596573 5.558000525 2.357541203 11.6127708
7 2.177892158 4.730480377 2.174966753 8.631949429
8 1.49665751 3.203559121 1.78984891 3.020424886
9 2.728865195 6.376658918 2.525204728 12.51412704
10 1.908668224 4.025351691 2.006327912 6.593044534
11 1.978895443 4.24563401 2.060493633 7.402451521
12 1.627855104 3.344274234 1.828735693 3.731699451
13 1.53436705 3.350605596 1.83046595 3.170525564
14 2.448831586 5.585936937 2.363458681 10.76866329
15 2.443160968 5.331752143 2.309058714 10.58310613
16 2.156078216 4.417635062 2.101817086 8.109576771
17 1.931534652 4.249610334 2.061458303 6.790693233
18 1.452715015 3.225752129 1.796037897 3.356200016
19 1.729354145 3.683866912 1.919340228 5.420225217
20 1.861239059 3.912023005 1.977883466 6.267750682
21 1.822955174 3.804437795 1.950496807 5.991464547
22 2.113126565 4.492001488 2.119434238 8.114076324
23 2.171856126 4.662613282 2.159308519 7.806138626
24 1.391215895 3.010620886 1.735114084 2.461296784
25 1.319165859 2.895911938 1.701737917 2.055404964
26 2.034006688 4.322608316 2.079088338 6.977001452
27 2.85574569 6.160996329 2.482135437 14.34613881
28 1.411579618 3.097385927 1.759939183 2.613006652
29 2.576957482 6.029643051 2.45553315 11.91628836
30 1.796913834 3.923259637 1.980721999 5.911392672
31 2.024389004 4.345833727 2.084666335 8.022132643
32 1.63435577 3.493472658 1.869083374 3.515715835
33 1.584595569 3.453157121 1.858267236 3.397523976
34 1.881578895 4.030076005 2.00750492 6.011267174
35 1.728309802 3.752101123 1.937034105 5.225370259
36 1.414715557 3.140049044 1.772018353 2.736961545
37 1.488730081 3.116621591 1.76539559 2.902519892
38 1.522138034 3.257327011 1.804806641 2.890371758
39 1.800033345 3.987130478 1.996780027 5.640594153
40 1.794222122 4.143928062 2.035664035 6.206575927
41 2.676710091 6.289901082 2.50796752 13.49805633
42 2.328582719 5.13691546 2.266476442 9.430961545
43 2.484723966 5.458712793 2.336388836 10.7561993
44 2.287108375 4.856940066 2.203846652 9.917240545
45 2.417128932 5.582744146 2.362783136 10.54534144
46 2.328332495 5.105945474 2.259633925 9.840475333
47 2.362264634 5.293304825 2.300718328 9.848820151
48 2.28292536 5.018934097 2.24029777 9.269934816
49 1.449825221 3.006177531 1.73383319 3.121042465
50 2.211679876 4.692264893 2.166163635 8.631218063
51 2.704614597 6.072756474 2.464296345 12.31992499
52 2.48097622 5.43590303 2.331502312 11.2245765
53 1.497529983 3.380994674 1.838748127 3.752088968
54 2.696365396 5.825540285 2.413615604 12.36222133
55 2.165729837 4.666265285 2.160153996 8.455875079
56 2.410978268 5.417499423 2.327552239 10.08813972
57 2.185447829 4.991792206 2.234231905 9.215327913
58 2.041898307 4.22566518 2.055642279 7.418180823
59 2.099077244 4.375757022 2.091831021 7.696212639
60 2.000032635 4.234467391 2.057782153 7.110696123
61 2.025963678 4.260852439 2.064183238 6.851163763
62 2.083395224 4.351567427 2.08604109 7.884576511
63 1.981523362 4.318820559 2.07817722 7.43543802
64 2.033235038 4.336636932 2.082459347 7.313220387
65 1.423999144 3.206803244 1.790754937 2.564949357
66 2.217982257 4.825910853 2.196795587 8.920558764
67 1.240285111 2.808498672 1.675857593 1.568615918
68 2.215837149 5.041487758 2.245325758 8.802372134
69 2.134859238 4.731890939 2.175291001 8.132101136
70 2.306998207 5.059171458 2.249260202 9.336074756
71 1.896404791 4.104681782 2.026001427 6.445449942
72 1.922935417 4.151905673 2.037622554 6.818169682
73 2.111422924 4.716264233 2.171696165 8.366370302
74 2.28264494 4.852811209 2.202909714 9.210340372
75 2.190760504 4.574710979 2.1388574 8.447427164
76 2.037589062 4.275276265 2.06767412 6.989197008
77 1.717192759 3.810543836 1.952061433 4.610157727
78 1.876769266 4.043051268 2.010734012 6.306275287
79 2.030134158 4.579339426 2.139939117 7.715792425
80 1.93577016 4.356708827 2.08727306 6.788521191
81 2.056518774 4.445588116 2.108456335 7.636510887
82 2.120080841 4.615120517 2.148283156 7.916807491
83 2.232689054 4.861361591 2.204849562 8.694167142
84 2.181147406 4.782479201 2.186888017 8.854567878
85 2.92779884 6.305666829 2.511108685 13.9593635
86 1.860080456 4.459637473 2.111785376 6.163314804
87 1.913818428 4.602767301 2.145406092 7.174915716
88 1.877883958 4.594104966 2.143386332 6.335054251
89 1.994987686 4.632100752 2.152231575 7.707952547
90 2.14756511 5.023880521 2.241401464 9.161721393
91 1.503591471 3.687628672 1.92031994 4.280824129
92 1.4536743 3.579343567 1.891915317 3.761200116
93 1.50872427 3.584888833 1.893380266 4.106767082
94 1.537573733 3.649466946 1.910357806 4.126327608
95 1.934796461 4.373238129 2.091228856 7.584097036
96 1.526250724 3.248434627 1.802341429 3.228826156
97 1.606399474 3.500439216 1.870946075 4.939855112
98 1.943162189 4.329208633 2.080675043 6.460498957
99 1.963384107 4.353112625 2.086411423 6.649308332
100 2.183124049 4.711248626 2.170541091 8.474527832
101 1.640763809 3.543853682 1.882512598 3.832330237
102 1.659456682 3.523415014 1.877076188 3.997282849
103 1.436096958 3.166318574 1.779415234 2.839078464
104 2.428955194 4.91133048 2.216152179 10.44793169
105 2.668500746 6.154858094 2.480898646 12.73883098
106 2.676812229 6.178980921 2.485755604 12.64109656
107 2.126920019 4.640923356 2.154280241 8.600833727
108 1.878254881 4.025530246 2.00637241 6.253828812
109 2.242102174 4.726797674 2.174119977 8.29404964
110 1.676813632 3.822754538 1.955186574 5.370638028
111 1.874531192 4.17438727 2.043131731 7.265087007
112 1.998637301 4.2363594 2.058241822 6.722389092
113 1.944116978 4.159527009 2.039491851 6.038562805
114 2.308184503 5.192956851 2.278806014 9.36048303
115 2.042370888 4.49535532 2.120225299 7.320526962
116 2.015621187 4.318820559 2.07817722 7.081078135
117 1.81401665 4.146304301 2.036247603 6.492542819
118 1.676813632 3.87937827 1.969613736 5.221868194
119 2.807346477 6.428545769 2.535457704 13.72308897
120 1.621259207 3.543853682 1.882512598 4.162470391
121 1.50100345 3.321793359 1.822578766 3.106378794
122 1.582428764 3.464319806 1.861268333 4.143134726
123 1.654547625 3.591817741 1.895209155 4.509649984
124 2.332936461 4.937777822 2.222111118 9.398917323
125 2.498105588 5.513601542 2.348105948 11.29414737
126 1.890319403 3.887730313 1.97173282 5.847161058
127 1.804890841 3.940999114 1.985194981 6.17864926
128 2.096209309 4.6042388 2.145749007 7.788418833
129 2.047658751 4.337290741 2.082616321 7.612336837
130 2.680572077 5.989462544 2.447337848 12.15745472
131 2.333554566 5.407171771 2.325332615 10.44467195
132 2.212180997 4.932817886 2.220994797 8.881836305
133 1.478852439 3.063390922 1.750254531 2.890371758
134 1.648334702 3.518387649 1.875736562 4.141546164
135 2.307921185 4.90823336 2.215453308 9.305650552
136 2.13384989 4.645130271 2.155256428 8.018790088
137 1.728309802 3.555348061 1.885563062 4.941642423
138 1.691821236 3.556775613 1.885941572 4.886582645
139 1.746238611 3.891820298 1.972769702 5.363543151
140 1.679155631 3.642966397 1.908655652 4.754882459
141 1.94348069 4.156536582 2.038758589 6.277601677
142 1.549402462 3.250374492 1.8028795 3.342508385
143 1.856975574 4.232023463 2.057188242 6.413458957
144 2.529503815 5.684310793 2.38417927 11.22830537
145 2.035545742 4.643428898 2.154861689 7.244227516
146 2.467132416 5.697093487 2.386858497 11.50287513
147 2.298324686 4.870031331 2.206814748 9.286469586
148 1.937388065 4.34601078 2.0847088 7.322972679
149 1.956955486 4.536730733 2.129960266 7.739019572
150 2.036823984 4.518958489 2.125784206 8.594154233
151 1.972996546 4.529692045 2.128307319 7.967481199
152 1.58746864 3.283839256 1.812136655 3.314186005
153 1.521311054 3.464922216 1.861430153 3.681603045
154 2.44446969 5.445011746 2.333454895 10.3609124
155 2.294121109 4.731979033 2.17531125 9.105210941
156 3.126345733 6.927557906 2.632025438 15.6772624
157 1.867746396 4.253056253 2.06229393 6.32459191
158 1.839082858 4.029806041 2.00743768 5.382980154
159 2.127330896 4.844974178 2.201130205 7.863266724
160 2.404523583 5.236441963 2.288327329 10.04902409
161 2.262955985 4.845642719 2.201282063 9.034969801
162 2.253418218 4.727387819 2.174255693 9.130463484
163 2.302083991 5.167955549 2.273313781 10.06411762
164 2.192165626 4.835011259 2.198865903 9.262695602
165 1.672685332 3.734489965 1.93248285 4.565493369
166 1.568460311 3.539508997 1.881358285 3.52282487
167 1.609819887 3.523868735 1.877197042 3.920784511
168 1.616583967 3.587676949 1.894116403 4.394572604
169 1.643301653 3.654700957 1.911727218 3.912023005
170 1.621923158 3.581532841 1.892493815 3.891820298
171 2.090637708 4.527208645 2.127723818 8.536995819
172 2.109497906 4.585222548 2.141313277 8.203668045
173 2.03091153 4.429625613 2.104667578 7.785783239
174 2.09487893 4.582924577 2.140776629 8.204589814
175 2.040382454 4.335786342 2.08225511 6.632541816
176 2.312894869 5.342334252 2.311349011 9.798127037
177 1.430087263 3.148453361 1.774388165 2.939161922
178 2.293711966 4.871098263 2.20705647 9.392661929
179 2.391075023 4.894101478 2.212261621 9.375295332
180 2.517077345 5.718436483 2.391325257 11.47221284
181 1.989024673 4.154969184 2.038374152 6.872128101
182 2.02016078 4.294014757 2.072200463 7.403304815
183 1.797360845 4.076689627 2.019081382 5.90560705
184 1.705239225 3.931825633 1.982883162 5.697965589
185 1.471533812 3.312439025 1.820010721 3.529590596
186 1.438083095 3.346917175 1.829458164 3.533978493
187 1.619261465 3.559624618 1.886696748 4.109233175
188 1.609819887 3.6558396 1.912025 4.166355098
189 2.346796539 5.146965796 2.26869253 9.872567414
190 1.784208279 3.519720884 1.876091918 4.879539029
191 1.832126365 3.811539467 1.952316436 5.259368616
192 1.677986168 3.452840615 1.858182073 3.885884348
193 1.966109701 4.163870625 2.04055645 6.526348436
194 1.701367309 3.828641396 1.956691441 4.605170186
195 1.931534652 4.279440046 2.06868075 6.927802974
196 1.36183801 3.102342009 1.761346646 2.645465326
197 2.432819556 5.883322388 2.425556099 10.46486408
198 2.078341803 4.564943223 2.136572775 7.650468513
199 1.432099112 3.171155089 1.780773733 2.931193752
200 2.174427741 4.839451482 2.199875333 8.482392615
201 2.16404302 4.710430697 2.170352666 8.620246046
202 1.738643812 3.737669618 1.933305361 5.834810737
203 2.303817478 5.000921602 2.236274044 9.718344619
204 1.741189967 3.731819205 1.931791708 5.090062428
205 1.794671893 3.904293207 1.975928442 5.247024072
206 1.757635562 3.857777991 1.964122703 5.006560336
207 1.676226207 3.66137978 1.913473224 4.566637236
208 1.77911412 3.86388263 1.965676125 5.669260041
209 2.059914227 4.564348191 2.136433521 7.695152987
210 1.32424147 3.104586678 1.761983734 2.182674796
211 1.604334732 3.751518852 1.936883799 4.85787254
212 1.662497734 3.79739748 1.948691222 5.073109185
213 1.44885795 3.04690056 1.745537327 2.907447359
214 2.487551021 5.598973005 2.366214911 10.97673998
215 2.438166592 5.528436532 2.351262753 10.75773968
216 1.892477044 4.164647686 2.040746845 7.15334893
217 1.520482581 3.272335343 1.80895974 3.424588334
218 2.488969385 5.681996883 2.383693957 10.74868607
219 2.215837149 4.53044664 2.128484588 7.620705087
220 2.442786243 5.526780079 2.350910479 10.69919132
221 2.570602875 5.907702431 2.430576563 11.59161344
222 2.608344119 6.053264948 2.460338381 12.33182385
223 2.524368131 5.738731256 2.395564914 11.20612853
224 1.539964086 3.38269391 1.839210132 3.571221411
225 1.541550744 3.476614021 1.864568052 3.523119986
226 2.111209474 4.695924549 2.167008202 8.126284621
227 1.910391851 4.139955073 2.034687955 6.467590025
228 2.801971864 6.015864434 2.452725919 13.0280527
229 2.616209119 5.780126041 2.404189269 11.53329656
230 2.570130461 5.673975975 2.38201091 10.97701107
231 2.545595117 5.629669374 2.372692431 11.14107887
232 2.618299253 5.800606659 2.408444863 11.97035031
233 2.443348195 5.385412073 2.320649063 10.85417971
234 2.385152788 5.279188197 2.297648406 10.67131308
235 2.512400994 5.685007319 2.384325338 11.58593194
236 2.39352554 5.12693575 2.26427378 10.4590302
237 1.823796962 3.992680908 1.998169389 6.109247583
238 1.768267491 3.745968421 1.935450444 5.260096154
239 2.376820756 5.302583255 2.302733865 10.4487146
240 2.042402374 4.477336814 2.115971837 7.810068783
241 2.159700495 4.673996377 2.161942732 8.189916149
242 1.948229832 4.378018613 2.092371528 6.932447892
243 1.330510703 3.059880093 1.749251295 2.083184528
244 1.464097665 3.342685111 1.828301154 3.072693315
245 1.446917352 3.196630216 1.787912251 2.829087196
246 2.082252099 4.60990894 2.14706985 8.075582637
247 1.933494729 4.136126096 2.033746812 7.003065459
248 1.840298976 3.949126093 1.987240824 7.056175284
249 1.649584193 3.645188765 1.909237745 4.51129897
250 1.778648064 3.883623531 1.97069113 5.09681299
251 2.526339825 5.903056741 2.429620699 11.66907415
252 2.512244141 5.734958092 2.394777253 10.93748043
253 1.947599667 4.356708827 2.08727306 6.514712691
254 2.181687439 4.946274535 2.224022153 8.799405331
255 2.109497906 4.510859507 2.123878411 8.132101136
256 1.831713667 4.188138442 2.046494183 6.109247583
257 1.5517319 3.446807893 1.856558077 3.765840495
258 2.47549747 5.727881894 2.393299374 10.78967984
259 1.96580772 4.156693187 2.038796995 6.229496711
260 1.978602442 4.21508618 2.053067505 7.258412151
261 2.064000486 4.339901708 2.083243075 7.670717659
262 2.117775721 4.510639702 2.123826665 7.731676304
263 2.221912965 4.838923916 2.199755422 8.877208949
264 1.940925986 4.266896327 2.065646709 6.450865289
265 2.040382454 4.579852378 2.140058966 7.857666456
266 2.173143952 4.666735542 2.160262841 8.561717125
267 2.240859653 4.901564199 2.21394765 8.808442394
268 1.888874933 4.080921542 2.02012909 6.163314804
269 1.845529749 4.082609306 2.020546784 6.885284696
270 2.238519604 4.984229093 2.23253871 8.987910316
271 2.393206767 5.29338648 2.300736073 10.70491521
272 2.702044102 5.884714177 2.425842983 12.3883942
273 2.219296721 4.854631045 2.203322728 9.263449766
274 1.96161829 4.090838423 2.022582118 6.993932975
275 2.00561407 4.171305603 2.042377439 7.324270223
276 2.467836387 5.578051269 2.361789844 10.8016414
277 1.390119244 3.100092289 1.760707894 2.379546134
278 1.365322726 3.044760505 1.744924212 2.401525041
279 1.598782218 3.516726026 1.875293584 4.234975692
280 1.94538671 4.131961426 2.032722663 6.199494461
281 2.172592522 4.89858579 2.213274902 8.804952261
282 1.908668224 4.102312732 2.025416681 6.374172668
283 1.944434766 4.112266337 2.027872367 6.54672802
284 1.58387445 3.505557397 1.872313381 3.941581808
285 1.743721514 3.832670536 1.95772075 5.11349268
286 1.592453126 3.549329989 1.883966557 4.871143315
287 1.283414418 2.79971739 1.673235605 1.54329811
288 1.320439849 2.90690106 1.704963654 2.070653036
289 1.194572818 2.708716646 1.645817926 1.184789985
290 1.231175294 2.681021529 1.637382524 1.115141591
291 1.365322726 3.074795481 1.753509476 2.254444718
292 1.408422528 3.000719815 1.732258588 2.422144328
293 2.184734225 4.886582645 2.210561613 8.803574418
294 2.030652566 4.649187071 2.156197364 7.901007052
295 1.890679763 4.02356438 2.005882444 6.212726329
296 1.855414729 4.027135813 2.006772486 5.858647185
297 1.819146836 3.737669618 1.933305361 5.340274716
298 1.51380043 3.337192052 1.826798306 3.514823642
299 1.923936518 4.162158962 2.040136996 6.485993092
300 2.54480266 5.875913394 2.42402834 11.44989333
301 2.015083881 4.471638793 2.114624977 7.725330038
302 1.902054478 4.30514559 2.074884476 7.141300544
303 1.932189012 4.149463861 2.037023284 7.112433389
304 1.357151358 2.977059008 1.725415605 2.054123734
305 2.172040349 4.677490848 2.162750759 7.584422406
306 2.12856108 4.80073697 2.191058413 8.165269798
307 1.597383378 3.38269391 1.839210132 3.948162052
308 1.571436916 3.451890496 1.857926397 3.970291914
309 1.669116161 3.728100167 1.930828881 4.514479321
310 1.792870023 3.818920387 1.95420582 5.395716273
311 2.701422654 6.042632834 2.45817673 12.51412704
312 2.724885462 6.056784013 2.461053436 12.63817968
313 2.649668658 5.96870756 2.44309385 12.34583459
314 1.328012928 3.19047635 1.786190457 2.231089091
315 2.290836238 4.827072968 2.197060074 8.67484801
316 2.375600157 5.495650681 2.344280419 10.05803872
317 1.625886455 3.693369359 1.92181408 5.110178924
318 2.329332455 5.313205979 2.305039258 9.613803477
319 1.515480102 3.456316681 1.859117178 3.185525845
320 1.472454994 3.284663565 1.812364082 3.025291076
321 1.506165026 3.349904087 1.83027432 3.054001182
322 1.473374347 3.306520335 1.81838399 3.25617161
323 1.527068855 3.325036021 1.82346813 3.36729583
324 2.110354575 4.662495253 2.159281189 8.165363632
325 1.523787537 3.514526067 1.874706928 3.062923523
326 2.023599447 4.094344562 2.02344868 6.938769333
327 2.753898938 5.917548864 2.432601255 12.66032792
328 2.617941755 5.678362097 2.382931408 11.46939146
329 2.119034653 4.483002552 2.117310216 8.240121298
330 2.066147705 4.476199805 2.115703147 7.14397299
331 2.101925481 4.630837933 2.151938181 7.659327016
332 2.508777239 5.407171771 2.325332615 11.8987611
333 2.005568244 4.463030419 2.112588559 7.397665697
334 1.726738648 3.759687344 1.938991321 4.430816799
335 1.774901671 3.812975852 1.952684268 4.937562683
336 1.648959883 3.423610976 1.85030024 3.988984047
337 1.777714463 3.797733859 1.948777529 5.403847868
338 1.704136403 3.63758616 1.9072457 5.272486607
339 1.844729114 3.968445871 1.992095849 6.429622699
340 1.768267491 3.797733859 1.948777529 5.523339153
341 2.159320704 4.744410253 2.178166718 8.301035184
342 2.109497906 4.580877493 2.140298459 7.726636028
343 2.521315024 5.573617308 2.360850971 11.60597801
344 2.576758408 5.79269513 2.406801847 11.8427421
345 2.669803365 5.872117789 2.423245301 12.427118
346 2.441001399 5.430134791 2.330264962 11.48863277
347 2.117775721 4.750395438 2.17954019 8.556413905
348 2.023599447 4.553734634 2.133948133 7.34601021
349 2.394268344 5.066826574 2.250961255 9.954988325
350 2.106053393 4.696472344 2.167134593 7.892825526
351 2.100394247 4.736330019 2.176311103 7.72870183
352 2.160269524 4.922204729 2.21860423 8.255482913
353 2.188997276 4.774912961 2.185157422 9.409191231
354 1.874905013 3.86388263 1.965676125 6.003887067
355 2.061842158 4.182126476 2.045024811 6.745236349
356 1.418864374 3.119939077 1.766334928 2.7631695
data<-read.csv(data.csv,header=T)
fit.all<-lm(a~x+y+z,data=data)
b<-data.frame(data,predict(fit.all,interval="prediction"))
ggplot(data,aes(x=x+y+z,y=a))+
geom_point(size=3,shape=1,col="black")+
geom_smooth(method="lm")+
geom_line(aes(y=lwr), color = "red", linetype = "dashed")+
geom_line(aes(y=upr), color = "red", linetype = "dashed")+
theme_classic()
That approach is not going to produce a sensible graphical display as Ben suggested. What you could do is examine the relationship between each predictor and the outcome separately while holding the other predictors not under immediate consideration constant at some chosen level. Here I use the means as those chosen levels. data_x.yz <- data.frame( x = seq(min(data$x), max(data$x), 0.1), y = mean(data$y), z = mean(data$z) ) data_x.yz <- cbind( data_x.yz, predict(fit.all, newdata = data_x.yz, interval = "prediction") ) ggplot(data_x.yz, aes(x, fit, ymin = lwr, ymax = upr)) + geom_line(color = "blue") + geom_ribbon(fill = NA, color = "red", linetype = "dashed") data_y.xz <- data.frame( x = mean(data$x), y = seq(min(data$y), max(data$y), 0.1), z = mean(data$z) ) data_y.xz <- cbind( data_y.xz, predict(fit.all, newdata = data_y.xz, interval = "prediction") ) ggplot(data_y.xz, aes(y, fit, ymin = lwr, ymax = upr)) + geom_line(color = "blue") + geom_ribbon(fill = NA, color = "red", linetype = "dashed") data_z.yx <- data.frame( x = mean(data$x), y = mean(data$y), z = seq(1.6, 2.6, 0.1) ) data_z.yx <- cbind( data_z.yx, predict(fit.all, newdata = data_z.yx, interval = "prediction") ) ggplot(data_z.yx, aes(z, fit, ymin = lwr, ymax = upr)) + geom_line(color = "blue") + geom_ribbon(fill = NA, color = "red", linetype = "dashed")
Calculate mean value for each row with interval
i need to calculate the mean value for each row (mean of interval). Here is a basic example (maybe anyone has even better idea to do it): M_1_mb <- (15 : -15)#creating a vector value --> small M_31 <- cut(M_31_mb,128)# getting 128 groups from the small vector #M_1_mb <- (1500 : -1500)#creating a vector value #M_1 <- cut(M_1_mb,128)# getting 128 groups from the vector I do need to get the mean value for each row/group out of 128 intervals created in M_1 (actually i do not need even those intervals, i just need the mean of them) and i cannot figure out how to do it... I had a look at the cut2 function from Hmisc library but unfortunatelly there is no option to set up number of intervals into which vector is to be cut (-> but there is an option to get the mean value of created intervals: levels.mean...) I would appreciate any help! Thanks! Additional Info: cut2 function is working well for bigger vectors (M_1_mb), however when my vector is small (M_31_mb), then i am getting a Warning message: Warning message: In min(xx[xx > upper]) : no non-missing arguments to min; returning Inf and only 31 groups are created: M_31_mb <- (15 : -15) # smaller vector M_31 <- table(cut2(M_31_mb,g=128,levels.mean = TRUE)) whereas g = number of quantile groups
like this? aggregate(M_1_mb,by=list(M_1),mean) EDIT: Result Group.1 x 1 (-1.5e+03,-1.48e+03] -1488.5 2 (-1.48e+03,-1.45e+03] -1465.0 3 (-1.45e+03,-1.43e+03] -1441.5 4 (-1.43e+03,-1.41e+03] -1418.0 5 (-1.41e+03,-1.38e+03] -1394.5 6 (-1.38e+03,-1.36e+03] -1371.0 7 (-1.36e+03,-1.34e+03] -1347.5 8 (-1.34e+03,-1.31e+03] -1324.0 9 (-1.31e+03,-1.29e+03] -1301.0 10 (-1.29e+03,-1.27e+03] -1277.5 11 (-1.27e+03,-1.24e+03] -1254.0 12 (-1.24e+03,-1.22e+03] -1230.5 13 (-1.22e+03,-1.2e+03] -1207.0 14 (-1.2e+03,-1.17e+03] -1183.5 15 (-1.17e+03,-1.15e+03] -1160.0 16 (-1.15e+03,-1.12e+03] -1136.5 17 (-1.12e+03,-1.1e+03] -1113.0 18 (-1.1e+03,-1.08e+03] -1090.0 19 (-1.08e+03,-1.05e+03] -1066.5 20 (-1.05e+03,-1.03e+03] -1043.0 21 (-1.03e+03,-1.01e+03] -1019.5 22 (-1.01e+03,-984] -996.0 23 (-984,-961] -972.5 24 (-961,-938] -949.0 25 (-938,-914] -926.0 26 (-914,-891] -902.5 27 (-891,-867] -879.0 28 (-867,-844] -855.5 29 (-844,-820] -832.0 30 (-820,-797] -808.5 31 (-797,-773] -785.0 32 (-773,-750] -761.5 33 (-750,-727] -738.0 34 (-727,-703] -715.0 35 (-703,-680] -691.5 36 (-680,-656] -668.0 37 (-656,-633] -644.5 38 (-633,-609] -621.0 39 (-609,-586] -597.5 40 (-586,-562] -574.0 41 (-562,-539] -551.0 42 (-539,-516] -527.5 43 (-516,-492] -504.0 44 (-492,-469] -480.5 45 (-469,-445] -457.0 46 (-445,-422] -433.5 47 (-422,-398] -410.0 48 (-398,-375] -386.5 49 (-375,-352] -363.0 50 (-352,-328] -340.0 51 (-328,-305] -316.5 52 (-305,-281] -293.0 53 (-281,-258] -269.5 54 (-258,-234] -246.0 55 (-234,-211] -222.5 56 (-211,-188] -199.0 57 (-188,-164] -176.0 58 (-164,-141] -152.5 59 (-141,-117] -129.0 60 (-117,-93.8] -105.5 61 (-93.8,-70.3] -82.0 62 (-70.3,-46.9] -58.5 63 (-46.9,-23.4] -35.0 64 (-23.4,0] -11.5 65 (0,23.4] 12.0 66 (23.4,46.9] 35.0 67 (46.9,70.3] 58.5 68 (70.3,93.8] 82.0 69 (93.8,117] 105.5 70 (117,141] 129.0 71 (141,164] 152.5 72 (164,188] 176.0 73 (188,211] 199.0 74 (211,234] 222.5 75 (234,258] 246.0 76 (258,281] 269.5 77 (281,305] 293.0 78 (305,328] 316.5 79 (328,352] 340.0 80 (352,375] 363.5 81 (375,398] 387.0 82 (398,422] 410.0 83 (422,445] 433.5 84 (445,469] 457.0 85 (469,492] 480.5 86 (492,516] 504.0 87 (516,539] 527.5 88 (539,562] 551.0 89 (562,586] 574.0 90 (586,609] 597.5 91 (609,633] 621.0 92 (633,656] 644.5 93 (656,680] 668.0 94 (680,703] 691.5 95 (703,727] 715.0 96 (727,750] 738.5 97 (750,773] 762.0 98 (773,797] 785.0 99 (797,820] 808.5 100 (820,844] 832.0 101 (844,867] 855.5 102 (867,891] 879.0 103 (891,914] 902.5 104 (914,938] 926.0 105 (938,961] 949.0 106 (961,984] 972.5 107 (984,1.01e+03] 996.0 108 (1.01e+03,1.03e+03] 1019.5 109 (1.03e+03,1.05e+03] 1043.0 110 (1.05e+03,1.08e+03] 1066.5 111 (1.08e+03,1.1e+03] 1090.0 112 (1.1e+03,1.12e+03] 1113.5 113 (1.12e+03,1.15e+03] 1137.0 114 (1.15e+03,1.17e+03] 1160.0 115 (1.17e+03,1.2e+03] 1183.5 116 (1.2e+03,1.22e+03] 1207.0 117 (1.22e+03,1.24e+03] 1230.5 118 (1.24e+03,1.27e+03] 1254.0 119 (1.27e+03,1.29e+03] 1277.5 120 (1.29e+03,1.31e+03] 1301.0 121 (1.31e+03,1.34e+03] 1324.0 122 (1.34e+03,1.36e+03] 1347.5 123 (1.36e+03,1.38e+03] 1371.0 124 (1.38e+03,1.41e+03] 1394.5 125 (1.41e+03,1.43e+03] 1418.0 126 (1.43e+03,1.45e+03] 1441.5 127 (1.45e+03,1.48e+03] 1465.0 128 (1.48e+03,1.5e+03] 1488.5
R generate 2D histogram from raw data
I have some raw data in 2D, x, y as given below. I want to generate a 2D histogram from the data. Typically, dividing the x,y values into bins of size 0.5, and count the number of occurrences in each bin (for both x and y at the same time). Is there any way to do that? > df x y 1 4.2179611 5.7588577 2 5.3901279 5.8219784 3 4.1933089 6.4317645 4 5.8076411 5.8999598 5 5.5781166 5.9382342 6 4.5569735 6.7833469 7 4.4024492 5.8019719 8 4.1734975 6.0896355 9 5.1707871 5.5640962 10 5.6380258 6.9112775 11 4.6405353 5.2251746 12 4.1809004 6.1127144 13 4.2764079 5.4598799 14 5.4466446 6.0130047 15 5.2443804 5.5421851 16 5.7521515 5.4115965 17 4.9667564 5.3519795 18 4.5007141 6.8669231 19 5.0268273 5.7681888 20 4.4738948 6.4241168 21 4.4116357 5.9819519 22 4.5741988 6.4595129 23 4.0839075 6.8105259 24 4.7154364 6.5054761 25 4.8986785 5.5511226 26 5.6262397 6.8996480 27 4.9034275 5.6716375 28 4.1872928 5.8387641 29 4.0444855 5.2554446 30 4.8911393 5.8449165 31 5.7268887 6.7100432 32 5.9136374 6.5059128 33 4.9481286 6.4679917 34 4.6198987 5.7462047 35 5.7306916 6.0613158 36 5.5818586 6.4533566 37 5.9240267 6.7748290 38 4.8160926 6.4942865 39 5.5456258 5.7911897 40 4.3075173 6.8165520 41 4.9654533 5.8904734 42 5.9581820 5.7692468 43 4.2417172 5.7990554 44 5.3670112 5.8252479 45 5.2932098 5.3983672 46 5.7456521 6.2563828 47 4.9398795 5.2879065 48 4.8526884 6.9827555 49 5.6135753 6.5219431 50 4.0727956 5.2647714 51 6.9418969 5.2584325 52 5.4189039 5.9936456 53 3.9193741 6.7099562 54 5.5885252 5.9680734 55 5.9581279 5.1843804 56 4.5724421 6.6774004 57 4.7700303 6.6083613 58 5.5490254 6.2431170 59 4.1668548 5.1017475 60 5.8948947 6.7646917 61 6.5501872 5.2803433 62 5.6011444 4.2733087 63 5.1337226 6.5225780 64 5.3153358 6.6164809 65 3.3815056 6.4077659 66 3.8405670 5.3677008 67 6.7036350 4.3090214 68 3.2446588 4.0965275 69 4.6563593 7.6868628 70 5.2382914 7.0020874 71 6.0771605 6.6232541 72 3.5672511 6.9333691 73 5.0865233 4.0778233 74 5.6743559 5.5177734 75 4.5759146 7.2210012 76 5.8203140 4.9787148 77 3.1106176 6.3937707 78 4.6310679 4.4731806 79 6.8237641 6.2679791 80 3.7653803 5.9188107 81 5.6139040 5.8586176 82 6.2016662 5.3514293 83 3.9362048 5.3217560 84 6.8005236 7.9247371 85 5.8030101 7.7492432 86 6.0143418 6.0709249 87 6.5734089 7.6112815 88 4.0569383 5.8440535 89 4.6825752 7.7926235 90 4.8204027 6.3106798 91 3.5001675 6.3156079 92 3.6521280 7.5155810 93 5.0945236 4.8206873 94 3.8732946 5.6771599 95 6.4812309 5.6082170 96 5.0308355 7.6877289 97 5.2193389 7.7133717 98 6.2239631 5.5387684 99 4.6501488 7.8559335 100 3.5389389 5.4594034 101 5.7139486 4.5008182 102 3.5425132 7.3562487 103 6.9950663 6.1036549 104 5.3801845 5.8903123 105 4.7629191 5.3394552 106 4.4102815 7.2312852 107 5.8723641 4.1410996 108 3.4691208 4.6383708 109 4.6479362 5.8562699 110 3.0315732 6.8614265 111 5.9456145 4.7497545 112 4.8461189 4.4730002 113 4.9606723 5.1099093 114 4.7802659 7.8147864 115 5.0189229 6.9308301 116 6.4738074 5.0539666 117 5.3725075 5.3282273 118 6.5374505 7.0508875 119 4.0907139 5.0855075 120 5.0557532 5.6449829 121 6.5483249 7.5800015 122 3.1083616 7.3697234 123 3.6119548 7.7639486 124 6.5157691 7.7152933 125 4.0305622 7.0521419 126 3.2197769 6.5881246 127 4.7570419 6.4564400 128 4.0063007 6.3981942 129 4.4412649 7.6576221 130 5.7348769 6.7601804 131 3.1312551 5.6295996 132 3.8627964 7.5817083 133 5.2008281 5.1082509 134 6.4229161 6.2816475 135 2.5241894 6.0802138 136 7.3759753 5.1090478 137 3.7284166 5.2045976 138 3.4404286 6.9708127 139 6.4237399 5.1363851 140 4.1829368 5.1612791 141 5.9500285 5.4765621 142 3.3555182 6.2627360 143 7.7691356 5.1877095 144 4.0684189 7.1663495 145 7.3929140 7.3819058 146 2.1659981 7.9796005 147 4.8539955 7.3108966 148 5.3932658 4.7116979 149 3.5610560 4.6096759 150 5.1883331 6.8068501 151 6.4233558 7.2955388 152 7.3308739 6.1761356 153 3.0710449 4.5296235 154 7.5400128 5.1559900 155 3.5776389 5.2057676 156 4.0402288 7.1487121 157 2.3107258 6.9816127 158 7.2065591 7.7307439 159 5.7577620 5.6652052 160 2.0595554 7.4373547 161 7.5994468 4.6216856 162 4.8053745 3.9113634 163 7.5769460 7.6019067 164 5.5362034 8.9270974 165 3.6713241 3.9060205 166 6.0612046 7.3862080 167 6.9205755 7.0792392 168 6.0892821 6.3248315 169 2.0532905 4.1545875 170 3.4086310 3.5510909 171 5.2148895 5.3266145 172 4.7638780 7.9240988 173 6.4717329 5.1350172 174 7.8287022 4.3457324 175 6.0299681 3.0952274 176 3.2760103 5.2730464 177 2.5729991 7.6594251 178 3.9403251 7.8928014 179 6.0021556 7.5313493 180 7.8561727 4.5092728 181 3.5818174 4.1140876 182 7.4972295 5.5313987 183 6.0138287 6.9369784 184 3.9257191 7.6395296 185 3.0462106 3.1347680 186 6.0630447 4.1847229 187 7.4878528 5.1004141 188 4.5145570 4.6389011 189 6.2777996 4.2647980 190 3.0166336 7.5755042 191 2.8791041 6.4471746 192 7.1029767 7.0061048 193 2.4526181 6.3373793 194 5.8762775 7.0746223 195 7.0609100 8.1256569 196 4.7252400 8.4829780 197 3.3695501 8.8786640 198 3.8505741 6.8260398 199 5.3573846 6.3864944 200 3.7039072 8.9951078 201 4.6216933 6.7890198 202 7.0390643 5.9458624 203 5.7172605 6.9083246 204 2.3814644 8.3856125 205 2.4432566 3.2618192 206 4.3881965 6.7022219 207 5.2583749 7.2432485 208 5.8540367 8.5154705 209 6.4267791 4.9593757 210 5.0668461 3.1358129 211 2.6845736 8.9880143 212 7.3094761 5.4049133 213 4.2176252 5.5062193 214 5.2025716 4.0798478 215 6.5592571 8.1852765 216 2.0417939 7.0843906 217 7.6045374 7.4870940 218 6.5971789 8.8641329 219 5.3541694 7.2176914 220 2.8314803 6.4831720 221 2.4252467 4.0918736 222 6.6804732 6.3624739 223 6.0325285 6.2057468 224 2.2751047 5.1275412 225 5.5397481 5.9890834 226 4.6420585 4.6013327 227 7.6385642 5.1722194 228 6.7378078 5.8246169 229 5.0647686 7.9219705 230 2.8672731 6.6371082 231 7.5487359 4.5727898 232 1.0837662 7.1788146 233 5.4483746 6.8955122 234 9.3085746 4.8330044 235 3.8484225 6.0133789 236 2.8034987 3.0023096 237 2.8952626 8.2623788 238 5.7666136 3.2158710 239 6.4978214 5.7866574 240 1.5184268 5.9791716 241 2.3836147 8.2897188 242 4.7318649 6.1174515 243 5.8544588 7.5056688 244 9.6776416 6.5151695 245 0.4319531 4.2470331 246 0.9810053 8.6452087 247 7.0819634 3.2488110 248 1.9084265 6.1122130 249 7.5096342 3.3495096 250 8.9564496 3.4960564 251 5.7603943 6.9091760 252 0.8801204 7.2744429 253 1.2183581 6.4264214 254 1.7761613 7.1199729 255 3.2490662 7.9935963 256 3.5420375 8.4801333 257 8.7709382 3.8011487 258 8.4770868 3.4749692 259 0.9965042 6.7509705 260 7.5049457 5.4313474 261 9.7261151 6.5909553 262 5.3893371 4.0194548 263 9.6154510 7.3117416 264 1.0327841 6.2376586 265 4.0064715 3.7333634 266 6.6941050 3.9452152 267 4.1317951 9.3322756 268 9.6481471 7.5330023 269 7.3474233 1.0310166 270 3.7343864 4.9808341 271 9.1412231 2.6655861 272 5.8414100 0.1329439 273 2.4837309 7.4956203 274 2.7983337 1.3563719 275 0.6335727 7.9273816 276 7.5566740 0.4321263 277 8.6182079 0.6038505 278 0.8928523 8.0131172 279 5.7375090 8.5275545 280 0.7864533 3.3954255 281 8.7808839 1.7059789 282 9.6621659 0.9215045 283 8.4894688 8.7667948 284 1.0358920 7.2505891 285 0.7378660 0.1173287 286 9.5485481 3.3186128 287 6.8987508 9.5480887 288 7.4105831 5.8809522 289 6.6984457 5.9509037 290 1.7878216 9.1932955 291 0.8443295 5.1662902 292 0.4498266 8.9636923 293 2.5068754 5.3692908 294 9.2509052 2.4204235 295 4.1333742 6.2581851 296 6.5510938 7.2923688 297 4.3412873 3.5514825 298 4.2349765 9.3207514 299 2.8730785 7.2752405 300 2.0425362 6.6513146 301 6.4498432 7.2949259 302 5.7453188 6.3263712 303 7.0501276 8.2238207 304 4.1915008 1.5325379 305 8.1307954 7.7681944 306 7.3156552 6.3031412 307 4.0302052 0.3039900 308 3.3740358 2.1386235 309 8.2055657 2.9112215 310 1.8817856 7.0503046 311 7.0820523 6.8739097 312 5.0725238 6.9951556 313 1.6246224 5.4126084 314 3.8865553 7.6398192 315 6.6727672 8.9677947 316 9.6048687 7.6757966 317 2.2006018 9.6385351 318 9.6403802 7.6438900 319 0.1267512 0.9048408 320 1.8160829 7.3193066 321 9.9318386 9.6068456 322 2.1275892 7.8034724 323 1.2232242 1.0695030 324 3.0198057 3.8964732 325 3.3265773 8.5865587 326 5.1519605 7.5068253 327 0.4137485 5.9223826 328 1.6896445 0.6071874 329 1.8534083 2.3554291 330 1.7182264 9.3488597 331 6.4165456 9.8670765 332 7.6270001 2.1839607 333 8.9867227 5.9565743 334 6.9185079 0.2440980 335 6.7359209 7.1072908 336 3.8034763 5.8466404 337 3.4583027 6.9041502 338 1.7983897 1.7108336 339 6.9184406 6.3632716 340 1.3538600 6.8484462 341 3.6731748 4.9846946 342 5.6139620 8.0637827 343 9.0991782 2.3051189 344 1.1220448 8.9624365 345 2.5925265 8.3673795 346 9.9977377 8.5423564 347 5.1761187 5.1240824 348 5.9330451 9.4141322 349 6.3337224 6.8055697 350 2.7287418 5.7100024 351 6.1022411 2.9733360 352 2.7331869 3.7135612 353 6.7394034 8.2721572 354 2.1757932 9.0574057 355 5.5011486 6.0124142 356 4.5301911 2.5865048 357 5.3137001 0.7062267 358 0.6959286 3.2395043 359 5.3494169 6.5742589 360 7.1472046 6.3821916 361 0.1749855 0.3954287 362 6.7709760 6.5212015 363 7.2983482 3.0086604 364 0.6147726 9.3336870 365 7.4417342 2.6836695 366 1.2769881 4.0591093 367 9.5342317 5.3443613 368 0.9368862 1.1391497 369 8.4271193 8.6641296 370 6.2000851 8.2987486 371 2.1768279 6.0684896 372 5.2021222 6.9222675 373 0.6095874 8.4759464 374 2.0217473 9.5844241 375 4.8080163 6.5052801 376 3.6099334 0.3272768 377 6.0132712 7.9920535 378 4.0495344 8.8153621 379 6.9646704 7.0375214 380 3.9211171 2.5994333 381 4.4749268 1.0517360 382 1.1683429 3.8710614 383 1.7618115 0.3513996 384 1.1257639 5.7446745 385 3.7351688 8.7376011 386 4.9234662 7.1975462 387 7.4899861 7.3846309 388 7.4170082 2.2885060 389 0.8526702 3.8160722 390 4.5907512 8.9315418 391 7.6996179 9.8409051 392 0.2340987 4.2906009 393 2.2502736 1.7819172 394 3.5679969 1.7419479 395 5.4214908 5.6001803 396 3.9965213 9.2021549 397 3.8610336 2.0462740 398 5.9490575 4.4422382 399 9.8897791 5.6402915 400 6.1153192 4.1236797 401 5.8906384 2.6153750 402 8.0582664 2.7137804 403 7.2969209 2.9362187 404 3.8673527 1.0837191 405 3.5647339 6.2338014 406 9.6490210 0.8373270 407 0.8133243 6.3393130 408 2.8760565 9.9462423 409 3.3836457 7.4451869 410 4.7772609 2.9141127 411 8.6635971 5.7812494 412 5.6192160 1.4764255 413 9.1334625 8.9822399 414 0.4662385 6.6440937 415 3.4503559 4.2064800 416 0.6704780 2.8508758 417 0.5211872 4.3109175 418 7.5615411 9.2851454 419 7.5081906 4.0019450 420 8.8851669 9.7323717 421 7.3856288 8.6152906 422 9.5926351 0.3993818 423 1.4478981 1.4845263 424 5.0425560 1.3501638 425 0.8952120 7.9407680 426 6.4732584 7.1493210 427 9.6595225 5.2377876 428 7.2204625 2.0300222 429 3.5410601 7.3117738 430 6.7991771 3.6368291 Just for clarification, I want to get something like this plot below (this plot doesn't have to do anything with my raw data, I am just showing it to explain the problem more clearly! If I use hist(df$x) it will show the distribution of x only.)
The ggplot is elegant and fast and pretty, as usual. But if you want to use base graphics (image, contour, persp) and display your actual frequencies (instead of the smoothing 2D kernel), you have to first obtain the binnings yourself and create a matrix of frequencies. Here's some code (not necessarily elegant, but pretty robust) that does 2D binning and generates plots somewhat similar to the ones above: require(mvtnorm) xy <- rmvnorm(1000,c(5,10),sigma=rbind(c(3,-2),c(-2,3))) nbins <- 20 x.bin <- seq(floor(min(xy[,1])), ceiling(max(xy[,1])), length=nbins) y.bin <- seq(floor(min(xy[,2])), ceiling(max(xy[,2])), length=nbins) freq <- as.data.frame(table(findInterval(xy[,1], x.bin),findInterval(xy[,2], y.bin))) freq[,1] <- as.numeric(freq[,1]) freq[,2] <- as.numeric(freq[,2]) freq2D <- diag(nbins)*0 freq2D[cbind(freq[,1], freq[,2])] <- freq[,3] par(mfrow=c(1,2)) image(x.bin, y.bin, freq2D, col=topo.colors(max(freq2D))) contour(x.bin, y.bin, freq2D, add=TRUE, col=rgb(1,1,1,.7)) palette(rainbow(max(freq2D))) cols <- (freq2D[-1,-1] + freq2D[-1,-(nbins-1)] + freq2D[-(nbins-1),-(nbins-1)] + freq2D[-(nbins-1),-1])/4 persp(freq2D, col=cols) For a really fun time, try making an interactive, zoomable, 3D surface: require(rgl) surface3d(x.bin,y.bin,freq2D/10, col="red")
Bivariate density estimates can be done with MASS::kde2d, or KernSmooth::bkde2D (both supplied with the base R distribution). The latter uses an algorithm based on the fast Fourier transform over a grid of points, and is very fast. The result can be plotted with contour or persp or similar functions in other graphing packages. Using your data: require(KernSmooth) z <- bkde2D(df, .5) persp(z$fhat)
If you want it with a 2d contour, you can also use the package ggplot2. Some example code is shown in this question: gradient breaks in a ggplot stat_bin2d plot Adjusted slightly: x <- rnorm(10000)+5 y <- rnorm(10000)+5 df <- data.frame(x,y) require(ggplot2) p <- ggplot(df, aes(x, y)) p <- p + stat_bin2d(bins = 20) p Here's the output of the code above:
For completeness, you can also use the hist2d{gplots} function. It seems to be the most straightforward for a 2D plot:
library(gplots)
# data is in variable df
# define bin sizes
bin_size <- 0.5
xbins <- (max(df$x) - min(df$x))/bin_size
ybins <- (max(df$y) - min(df$y))/bin_size
# create plot
hist2d(df, same.scale=TRUE, nbins=c(xbins, ybins))
# if you want to retrieve the data for other purposes
df.hist2d <- hist2d(df, same.scale=TRUE, nbins=c(xbins, ybins), show=FALSE)
df.hist2d$counts
i came to this page from http://www.r-bloggers.com/5-ways-to-do-2d-histograms-in-r/ which lists one of the answers above. It provides code samples for a total of 5 methods: hist2d from the library gplots hexbin,hexbinplot from the library hexbin stat_bin2d from the library ggplot2 kde2d from the library MASS the "hard way" solution listed above.
freq <- as.data.frame(table(findInterval(xy[,1], x.bin),findInterval(xy[,2], y.bin))) freq[,1] <- as.numeric(freq[,1]) freq[,2] <- as.numeric(freq[,2]) This is probably wrong since it destroys the original indices.