I have a dataframe with 3 columns
$x -- at http://pastebin.com/SGrRUJcA
$y -- at http://pastebin.com/fhn7A1rj
$z -- at http://pastebin.com/VmVvdHEE
that I wish to use to generate a stacked barplot. All of these columns hold integer data. The stacked barplot should have the levels along the x-axis and the data for each level along the y-axis. The stacks should then correspond to each of $x, $y and $z.
UPDATE: I now have the following:
counted <- data.frame(table(myDf$x),variable='x')
counted <- rbind(counted,data.frame(table(myDf$y),variable='y'))
counted <- rbind(counted,data.frame(table(myDf$z),variable='z'))
counted <- counted[counted$Var1!=0,] # to get rid of 0th level??
stackedBp <- ggplot(counted,aes(x=Var1,y=Freq,fill=variable))
stackedBp <- stackedBp+geom_bar(stat='identity')+scale_x_discrete('Levels')+scale_y_continuous('Frequency')
stackedBp
which generates:
.
Two issues remain:
the x-axis labeling is not correct. For some reason, it goes: 46, 47, 53, 54, 38, 40.... How can I order it naturally?
I also wish to remove the 0th label.
I've tried using +scale_x_discrete(breaks = 0:50, labels = 1:50) but this doesn't work.
NB. axis labeling issue: Dataframe column appears incorrectly sorted
Not completely sure what you're wanting to see... but reading ?barplot says the first argument, height must be a vector or matrix. So to fix your initial error:
myDf <- data.frame(x=sample(1:10,100,replace=T),y=sample(11:20,100,replace=T),z=1:10)
barplot(as.matrix(myDf))
If you provide a reproducible example and a more specific description of your desired output you can get a better answer.
Or if I were to guess wildly (and use ggplot)...
myDf <- data.frame(x=sample(1:10,100,replace=T),y=sample(11:20,100,replace=T),z=1:10)
myDf.counted<- data.frame(table(myDf$x),variable='x')
myDf.counted <- rbind(myDf.counted,data.frame(table(myDf$y),variable='y'))
myDf.counted <- rbind(myDf.counted,data.frame(table(myDf$z),variable='z'))
ggplot(myDf.counted,aes(x=Var1,y=Freq,fill=variable))+geom_bar(stat='identity')
I'm surprised that didn't blow up in your face. Cross-classifying the joint occurrence of three different vectors each of length 35204 would often consume many gigabytes of RAM (and would possibly create lots of useless 0's as you found). Maybe you wanted to examine instead the results of sapply(myDf, table)? This then creates three separate tables of counts.
It's a rather irregular result and would need further work to get it into a matrix form but you might want to consider using densityplot to display the comparative distributions which I think is your goal.
$x
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
126 711 1059 2079 3070 2716 2745 3329 2916 2671 2349 2457 2055 1303 892 692
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
559 799 482 299 289 236 156 145 100 95 121 133 60 34 37 13
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
15 12 56 10 4 7 2 14 13 28 30 20 16 62 74 58
49 50
40 15
$y
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
3069 32 1422 1376 1780 1556 1937 1844 1967 1699 1910 1924 1047 894 975 865
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
635 1002 710 908 979 848 678 908 696 491 417 412 499 411 421 217
32 33 34 35 36 37 39 42 46 47 53 54
265 182 121 47 38 11 2 2 1 1 1 4
$z
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
31 202 368 655 825 1246 900 1136 1098 1570 1613 1144 1107 1037 1239 1372
16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
1306 1085 843 867 813 1057 1213 1020 1210 939 725 644 617 602 739 584
32 33 34 35 36 37 38 39 40 41 42 43
650 733 756 681 684 657 544 416 220 48 7 1
The density plot is really simple to create in lattice:
densityplot( ~x+y+z, myDf)
Related
What is the simplest way of turning a frequency data table into a prop table in R?
This is the data:
Time Total Blog News Social.Network Microblog Other Forums Pictures Video
1 15.KW 2022 1816 23 326 39 678 99 27 523 0
2 16.KW 2022 2535 32 690 42 815 135 26 644 1
3 17.KW 2022 2181 20 362 79 805 110 14 634 1
4 18.KW 2022 2583 19 895 25 692 127 6 658 0
5 19.KW 2022 2337 21 555 22 908 148 8 599 0
6 20.KW 2022 2091 23 392 18 851 119 5 554 0
7 21.KW 2022 1658 17 344 16 650 129 1 417 0
8 22.KW 2022 2476 24 798 24 937 150 7 443 0
9 23.KW 2022 1687 14 341 17 691 102 9 400 0
10 24.KW 2022 2476 21 521 29 984 110 19 509 0
11 25.KW 2022 2412 22 696 31 845 115 29 561 0
12 26.KW 2022 2197 22 715 13 709 128 59 445 0
13 27.KW 2022 2111 20 429 10 937 86 28 474 1
14 28.KW 2022 752 5 121 4 373 42 3 172 0
Your data frame df has a 2nd column called Total. It seems that you want to divide subsequent columns by this one.
df[-1] <- df[-1] / df$Total
After this, the 1st column Time does not change. 2nd column Total becomes 1. Other columns become proportions.
I am trying to run a time series analysis on the following data set:
Year 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780
Number 101 82 66 35 31 7 20 92 154 125
Year 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790
Number 85 68 38 23 10 24 83 132 131 118
Year 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800
Number 90 67 60 47 41 21 16 6 4 7
Year 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810
Number 14 34 45 43 48 42 28 10 8 2
Year 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820
Number 0 1 5 12 14 35 46 41 30 24
Year 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830
Number 16 7 4 2 8 17 36 50 62 67
Year 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
Number 71 48 28 8 13 57 122 138 103 86
Year 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850
Number 63 37 24 11 15 40 62 98 124 96
Year 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860
Number 66 64 54 39 21 7 4 23 55 94
Year 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870
Number 96 77 59 44 47 30 16 7 37 74
My problem is that the data is placed in multiple rows. I am trying to make two columns from the data. One for Year and one for Number, so that it is easily readable in R. I have tried
> library(tidyverse)
> sun.df = data.frame(sunspots)
> Year = filter(sun.df, sunspots == "Year")
to isolate the Year data, and it works, but I am unsure of how to then place it in a column.
Any suggestions?
Try this:
library(tidyverse)
df <- read_csv("test.csv",col_names = FALSE)
df
# A tibble: 6 x 4
# X1 X2 X3 X4
# <chr> <dbl> <dbl> <dbl>
# 1 Year 123 124 125
# 2 Number 1 2 3
# 3 Year 126 127 128
# 4 Number 4 5 6
# 5 Year 129 130 131
# 6 Number 7 8 9
# Removing first column and transpose it to get a dataframe of numbers
df_number <- as.data.frame(as.matrix(t(df[,-1])),row.names = FALSE)
df_number
# V1 V2 V3 V4 V5 V6
# 1 123 1 126 4 129 7
# 2 124 2 127 5 130 8
# 3 125 3 128 6 131 9
# Keep the first two column (V1,V2) and assign column names
df_new <- df_number[1:2]
colnames(df_new) <- c("Year","Number")
# Iterate and rbind with subsequent columns (2 by 2) to df_new
for(i in 1:((ncol(df_number) - 2 )/2)) {
df_mini <- df_number[(i*2+1):(i*2+2)]
colnames(df_mini) <- c("Year","Number")
df_new <- rbind(df_new,df_mini)
}
df_new
# Year Number
# 1 123 1
# 2 124 2
# 3 125 3
# 4 126 4
# 5 127 5
# 6 128 6
# 7 129 7
# 8 130 8
# 9 131 9
I have the following data:
Days Total cases
1 3
2 3
3 5
4 6
5 28
6 30
7 31
8 34
9 39
10 48
11 63
12 70
13 82
14 91
15 107
16 112
17 127
18 146
19 171
20 198
21 258
22 334
23 403
24 497
25 571
26 657
27 730
28 883
29 1024
30 1139
31 1329
32 1635
33 2059
34 2545
35 3105
36 3684
37 4289
38 4778
39 5351
40 5916
41 6729
42 7600
43 8452
44 9210
45 10453
46 11484
47 12370
48 13431
49 14353
50 15724
51 17304
52 18543
53 20080
54 21372
I defined days as 'days' and total cases as 'cases1'. I run the following code:
exp.mod <- lm(log(cases1)~days)
I get a good model with reasonable residuals and p-value.
but when i run the following:
predict(exp.mod, data.frame(days=60))
I get the value of 11.66476, which doesnt seem to be correct.
I need to get the value and also include the predictive plot in the exponential model.
Hope that clarifies the issue.
you should consider the EST models from the forecast package.
Below an example.
library(dplyr)
library(forecast)
ausair %>% ets() %>% forecast() %>% autoplot()
I suggest you to check the free book of the Prof. Rob J Hyndman and Prof George Athanasopoulos wrote (are the authors of the forecast package).
I have this data from an r package, where X is the dataset with all the data
library(ISLR)
data("Hitters")
X=Hitters
head(X)
here is one part of the data:
AtBat Hits HmRun Runs RBI Walks Years CAtBat CHits CHmRun CRuns CRBI CWalks League Division PutOuts Assists Errors Salary NewLeague
-Andy Allanson 293 66 1 30 29 14 1 293 66 1 30 29 14 A E 446 33 20 NA A
-Alan Ashby 315 81 7 24 38 39 14 3449 835 69 321 414 375 N W 632 43 10 475.0 N
-Alvin Davis 479 130 18 66 72 76 3 1624 457 63 224 266 263 A W 880 82 14 480.0 A
-Andre Dawson 496 141 20 65 78 37 11 5628 1575 225 828 838 354 N E 200 11 3 500.0 N
-Andres Galarraga 321 87 10 39 42 30 2 396 101 12 48 46 33 N E 805 40 4 91.5 N
-Alfredo Griffin 594 169 4 74 51 35 11 4408 1133 19 501 336 194 A W 282 421 25 750.0 A
I want to convert all the columns and the rows with non numeric values to zero, is there any simple way to do this.
I found here an example how to remove the rows for one column just but for more I have to do it for every column manually.
Is in r any function that does this for all columns and rows?
To remove non-numeric columns, perhaps something like this?
df %>%
select(which(sapply(., is.numeric)))
# AtBat Hits HmRun Runs RBI Walks Years CAtBat CHits CHmRun
#-Andy Allanson 293 66 1 30 29 14 1 293 66 1
#-Alan Ashby 315 81 7 24 38 39 14 3449 835 69
#-Alvin Davis 479 130 18 66 72 76 3 1624 457 63
#-Andre Dawson 496 141 20 65 78 37 11 5628 1575 225
#-Andres Galarraga 321 87 10 39 42 30 2 396 101 12
#-Alfredo Griffin 594 169 4 74 51 35 11 4408 1133 19
# CRuns CRBI CWalks PutOuts Assists Errors Salary
#-Andy Allanson 30 29 14 446 33 20 NA
#-Alan Ashby 321 414 375 632 43 10 475.0
#-Alvin Davis 224 266 263 880 82 14 480.0
#-Andre Dawson 828 838 354 200 11 3 500.0
#-Andres Galarraga 48 46 33 805 40 4 91.5
#-Alfredo Griffin 501 336 194 282 421 25 750.0
or
df %>%
select(-which(sapply(., function(x) is.character(x) | is.factor(x))))
Or much neater (thanks to #AntoniosK):
df %>% select_if(is.numeric)
Update
To additionally replace NAs with 0, you can do
df %>% select_if(is.numeric) %>% replace(is.na(.), 0)
# AtBat Hits HmRun Runs RBI Walks Years CAtBat CHits CHmRun
#-Andy Allanson 293 66 1 30 29 14 1 293 66 1
#-Alan Ashby 315 81 7 24 38 39 14 3449 835 69
#-Alvin Davis 479 130 18 66 72 76 3 1624 457 63
#-Andre Dawson 496 141 20 65 78 37 11 5628 1575 225
#-Andres Galarraga 321 87 10 39 42 30 2 396 101 12
#-Alfredo Griffin 594 169 4 74 51 35 11 4408 1133 19
# CRuns CRBI CWalks PutOuts Assists Errors Salary
#-Andy Allanson 30 29 14 446 33 20 0.0
#-Alan Ashby 321 414 375 632 43 10 475.0
#-Alvin Davis 224 266 263 880 82 14 480.0
#-Andre Dawson 828 838 354 200 11 3 500.0
#-Andres Galarraga 48 46 33 805 40 4 91.5
#-Alfredo Griffin 501 336 194 282 421 25 750.0
library(ISLR)
data("Hitters")
d = head(Hitters)
library(dplyr)
d %>%
mutate_if(function(x) !is.numeric(x), function(x) 0) %>% # if column is non numeric add zeros
mutate_all(function(x) ifelse(is.na(x), 0, x)) # if there is an NA element replace it with 0
# AtBat Hits HmRun Runs RBI Walks Years CAtBat CHits CHmRun CRuns CRBI CWalks League Division PutOuts Assists Errors Salary NewLeague
# 1 293 66 1 30 29 14 1 293 66 1 30 29 14 0 0 446 33 20 0.0 0
# 2 315 81 7 24 38 39 14 3449 835 69 321 414 375 0 0 632 43 10 475.0 0
# 3 479 130 18 66 72 76 3 1624 457 63 224 266 263 0 0 880 82 14 480.0 0
# 4 496 141 20 65 78 37 11 5628 1575 225 828 838 354 0 0 200 11 3 500.0 0
# 5 321 87 10 39 42 30 2 396 101 12 48 46 33 0 0 805 40 4 91.5 0
# 6 594 169 4 74 51 35 11 4408 1133 19 501 336 194 0 0 282 421 25 750.0 0
If you want to avoid function(x) you can use this
d %>%
mutate_if(Negate(is.numeric), ~0) %>%
mutate_all(~ifelse(is.na(.), 0, .))
You can get the numeric columns with sapply/inherits.
X <- Hitters
inx <- sapply(X, inherits, c("integer", "numeric"))
Y <- X[inx]
Then, it wouldn't make much sense to remove the rows with non-numeric entries, they were already removed, but you could do
inx <- apply(Y, 1, function(y) all(inherits(y, c("integer", "numeric"))))
Y[inx, ]
Was wondering if you could help me with the following. I am trying to calculate the amount of points that fall within each polygon US state. There are 52 states total. The point data and the polygon data are both in the same transformation.
I can run the function:
over(Transformed.States, clip.points)
Which returns:
0 1 2 3 4 5 6 7 8 9 10
4718 NA 488 2688 4454 3762 2041 NA 5 NA 3620
11 12 13 14 15 16 17 18 19 20 21
412 3042 2028 3390 2755 4250 3275 2484 466 4255 1
22 23 24 25 26 27 28 29 30 31 32
3238 744 4125 2926 927 495 3541 4640 3039 895 620
33 34 35 36 37 38 39 40 41 42 43
4069 4671 3801 1012 4023 626 1158 4627 217 13 4055
44 45 46 47 48 49 50 51
573 3456 NA 4670 4505 903 4172 4641
However, I want to write this function so that each polygon is given a value based on the amount of points in the polygon that can then be plotted such as:
plot(points.in.state)
What would be the best function to go about this? So that I still have polygon data but with the new point in polygons data attached?
The end goal of this is to make a graduated symbol map for each state based on the values for points in each state.
Thanks!
Jim