I have a CSV dataset (call it data) as follow:
CLASS CoverageT1 CoverageT2 CoverageT3
Gamma 90 80 75
Gamma 89 72 79
Gamma 92 86 75
Alpha 50 80 67
Alpha 53 78 60
Alpha 58 81 75
I would like to retrieve the unique classes and calculate the average for each coverage column.
What I've done so far is the following:
classes <- subset(data, select = c(CLASS))
unique_classes <- unique(classes)
for(x in unique_classes){
cove <- subset(data, CLASS == x , select=c(CoverageT1:CoverageT3))
average <- colMeans(cove)
print(cove)
}
As a result, I got the following results:
CoverageT1 CoverageT2 CoverageT3
1 90 80 75
3 92 86 75
4 50 80 67
6 58 81 75
I want to retrieve the coverage values based on each class and then calculate the average. When I print the retrieved coverage values, I get some rows and the other are missing!
Can someone help me solving this issue
Thanks
Your code isn't working because, amongst other things, you are assigning to average on each iteration and the previous is lost
There are several ways to do what you are trying to do. This would be my approach:
library(dplyr)
data %>% group_by(CLASS) %>% summarise_all(mean)
Another option using aggregate
aggregate(data, . ~ CLASS , mean)
Taking your idea and wrapping it in by.
xy <- read.table(text = "CLASS CoverageT1 CoverageT2 CoverageT3
Gamma 90 80 75
Gamma 89 72 79
Gamma 92 86 75
Alpha 50 80 67
Alpha 53 78 60
Alpha 58 81 75", header = TRUE)
out <- by(data = xy[, -1], INDICES = list(xy$CLASS), FUN = colMeans)
out <- do.call(rbind, out)
out
CoverageT1 CoverageT2 CoverageT3
Alpha 53.66667 79.66667 67.33333
Gamma 90.33333 79.33333 76.33333
This is how I solved it:
coverage_all <- aggregate(coverage , list(class=data$CLASS), mean)
Related
For my project, I have taken a data set which have 1296765 observations of 23 columns, I want to take just 10% of this data randomly. How can I do that in R.
I tried the below code but it only sampled out just 10 rows. But, I wanted to select randomly 10% of the data. I am a beginner so please help.
library(dplyr)
x <- sample_n(train, 10)
Here is a function from dplyr that select rows at random by a specific proportion:
dplyr::slice_sample(train,prop = .1)
In base R, you can subset by sampling a proportion of nrow():
set.seed(13)
train <- data.frame(id = 1:101, x = rnorm(101))
train[sample(nrow(train), nrow(train) / 10), ]
id x
69 69 1.14382456
101 101 -0.36917269
60 60 0.69967564
58 58 0.82651036
59 59 1.48369123
72 72 -0.06144699
12 12 0.46187091
89 89 1.60212039
8 8 0.23667967
49 49 0.27714729
I have some dataframe. Here is a small expample:
a <- rnorm(100, 5, 2)
b <- rnorm(100, 10, 3)
c <- rnorm(100, 15, 4)
df <- data.frame(a, b, c)
And I have a character variable vect <- "c('a','b')"
When I try to calculate sum of vars using command
df$d <- df[vect]
which must be an equivalent of
df$d <- df[c('a','b')]
But, as a reslut I have got an error
[.data.frame(df, vect) :undefined columns selected
You're assumption that
vect <- "c('a','b')"
df$d <- df[vect]
is equivalent to
df$d <- df[c('a','b')]
is incorrect.
As #Karthik points out, you should remove the quotation marks in the assignment to vect
However, from your question it sounds like you want to then sum the elements specified in vect and then assign to d. To do this you need to slightly change your code
vect <- c('a','b')
df$d <- apply(X = df[vect], MARGIN = 1, FUN = sum)
This does elementwise sum on the columns in df specified by vect. The MARGIN = 1 specifies that we want to apply the sum rowise rather than columnwise.
EDIT:
As #ThomasIsCoding points out below, if for some reason vect has to be a string, you can parse a string to an R expression using str2lang
vect <- "c('a','b')"
parsed_vect <- eval(str2lang(vect))
df$d <- apply(X = df[parsed_vect], MARGIN = 1, FUN = sum)
Perhaps you can try
> df[eval(str2lang(vect))]
a b
1 8.1588519 9.0617818
2 3.9361214 13.2752377
3 5.5370983 8.8739725
4 8.4542050 8.5704234
5 3.9044461 13.2642793
6 5.6679639 12.9529061
7 4.0183808 6.4746806
8 3.6415608 11.0308990
9 4.5237453 7.3255129
10 6.9379168 9.4594150
11 5.1557935 11.6776181
12 2.3829337 3.5170335
13 4.3556430 7.9706624
14 7.3274615 8.1852829
15 -0.5650641 2.8109197
16 7.1742283 6.8161200
17 3.3412044 11.6298940
18 2.5388981 10.1289533
19 3.8845686 14.1517643
20 2.4431608 6.8374837
21 4.8731053 12.7258259
22 6.9534912 6.5069513
23 4.4394807 14.5320225
24 2.0427553 12.1786148
25 7.1563978 11.9671603
26 2.4231207 6.1801862
27 6.5830372 0.9814878
28 2.5443326 9.8774632
29 1.1260322 9.4804636
30 4.0078436 12.9909014
31 9.3599808 12.2178596
32 3.5362245 8.6758910
33 4.6462337 8.6647953
34 2.0698037 7.2750532
35 7.0727970 8.9386798
36 4.8465248 8.0565347
37 5.6084462 7.5676308
38 6.7617479 9.5357666
39 5.2138482 13.6822924
40 3.6259103 13.8659939
41 5.8586547 6.5087016
42 4.3490281 9.5367522
43 7.5130701 8.1699117
44 3.7933813 9.3241308
45 4.9466813 9.4432584
46 -0.3730035 6.4695187
47 2.0646458 10.6511916
48 4.6027309 4.9207746
49 5.9919348 7.1946723
50 6.0148330 13.4702419
51 5.5354452 9.0193366
52 5.2621651 12.8856488
53 6.8580210 6.3526151
54 8.0812166 14.4659778
55 3.6039030 5.9857886
56 9.8548553 15.9081336
57 3.3675037 14.7207681
58 3.9935336 14.3186175
59 3.4308085 10.6024579
60 3.9609624 6.6595521
61 4.2358603 10.6600581
62 5.1791856 9.3241118
63 4.6976289 13.2833055
64 5.1868906 7.1323826
65 3.1810915 12.8402472
66 6.0258287 9.3805249
67 5.3768112 6.3805096
68 5.7072092 7.1130150
69 6.5789349 8.0092541
70 5.3175820 17.3377234
71 9.7706112 10.8648956
72 5.2332127 12.3418373
73 4.7626124 13.8816910
74 3.9395911 6.5270785
75 6.4394724 10.6344965
76 2.6803695 10.4501753
77 3.5577834 8.2323369
78 5.8431140 7.7932460
79 2.8596818 8.9581837
80 2.7365174 10.2902512
81 4.7560973 6.4555758
82 4.6519084 8.9786777
83 4.9467471 11.2818536
84 5.6167284 5.2641380
85 9.4700525 2.9904731
86 4.7392906 11.3572521
87 3.1221908 6.3881556
88 5.6949432 7.4518023
89 5.1435241 10.8912283
90 2.1628966 10.5080671
91 3.6380837 15.0594135
92 5.3434709 7.4034042
93 -0.1298439 0.4832707
94 7.8759390 2.7411723
95 2.0898649 9.7687250
96 4.2131549 9.3175228
97 5.0648105 11.3943350
98 7.7225193 11.4180456
99 3.1018895 12.8890257
100 4.4166832 10.4901303
I have 2880 observations in my data.frame. I have to create a new data.frame in which, I have to select rows from 25-77 from every 96 selected rows.
df.new = df[seq(25, nrow(df), 77), ] # extract from 25 to 77
The above code extracts only row number 25 to 77 but I want every row from 25 to 77 in every 96 rows.
One option is to create a vector of indeces with which subset the dataframe.
idx <- rep(25:77, times = nrow(df)/96) + 96*rep(0:29, each = 77-25+1)
df[idx, ]
You can use recycling technique to extract these rows :
from = 25
to = 77
n = 96
df.new <- df[rep(c(FALSE, TRUE, FALSE), c(from - 1, to - from + 1, n - to))), ]
To explain for this example it will work as :
length(rep(c(FALSE, TRUE, FALSE), c(24, 53, 19))) #returns
#[1] 96
In these 96 values, value 25-77 are TRUE and rest of them are FALSE which we can verify by :
which(rep(c(FALSE, TRUE, FALSE), c(24, 53, 19)))
# [1] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
#[23] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68
#[45] 69 70 71 72 73 74 75 76 77
Now this vector is recycled for all the remaining rows in the dataframe.
First, define a Group variable, with values 1 to 30, each value repeating 96 times. Then define RowWithinGroup and filter as required. Finally, undo the changes introduced to do the filtering.
df <- tibble(X=rnorm(2880)) %>%
add_column(Group=rep(1:96, each=30)) %>%
group_by(Group) %>%
mutate(RowWithinGroup=row_number()) %>%
filter(RowWithinGroup >= 25 & RowWithinGroup <= 77) %>%
select(-Group, -RowWithinGroup) %>%
ungroup()
Welcome to SO. This question may not have been asked in this exact form before, but the proinciples required have been rerefenced in many, many questions and answers,
A one-liner base solution.
lapply(split(df, cut(1:nrow(df), nrow(df)/96, F)), `[`, 25:77, )
Note: Nothing after the last comma
The code above returns a list. To combine all data together, just pass the result above into
do.call(rbind, ...)
Assume i have a dataset like:
df<-data.frame(data=(1:100))
how can i select the nth 20% of my data?
let's say, i need to access the third 20%, which contains numbers between 40-60
Using the function ntile from the dplyr package. We divide the data frame into 5 buckets and take the third one.
library(dplyr)
# One line
df[ntile(df$data, 5) == 3, ]
# Using pipes
df %>%
mutate(n = ntile(data, 5)) %>%
filter(n == 3) %>%
select(data)
Output:
[1] 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Here's a quick function to call for the specific rows based on percentage of rows of data
rowNumbs <- function(i, perc, df){
((i - 1)*ceiling(perc*nrow(df)) + 1) : (i*ceiling(perc*nrow(df)))
}
where i is the nth set, perc is the percentage and df is the data.frame.
To call the third 20% of your data.frame:
df[rowNumbs(3, .2, df), ]
[1] 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
So i have this data
10 21 22 23 23 43
20 12 26 43 23 65
21 54 64 73 25 75
My expected outcome is:
142
189
312
I tried to use:
df = data.matrix(df)
df = colSums(df)
df = as.data.frame(df)
However, the sum of values are wrong. I would like to know how to improve or correct this solution?
We can use rowSums
rowSums(df)
#[1] 142 189 312
Your data is stored as factors. You must convert it to numeric using as.numeric(as.character()).
In your situation I suggest to do:
for(i in 1:nrow(df)){
df[i,]<-as.numeric(as.character(df[i,]))
}
rowSums(df)