I'm looking at some ecological data (diet) and trying to work out how to group by Predator. I would like to be able to extract the data so that I can look at the weights of each individual prey for each species for each predator, i.e work out the mean weight of each species eaten by e.g Predator 117. I've put a sample of my data below.
Predator PreySpecies PreyWeight
1 114 10 4.2035496
2 114 10 1.6307026
3 115 1 407.7279775
4 115 1 255.5430495
5 117 10 4.2503708
6 117 10 3.6268814
7 117 10 6.4342073
8 117 10 1.8590861
9 117 10 2.3181421
10 117 10 0.9749844
11 117 10 0.7424772
12 117 15 4.2803743
13 118 1 126.8559155
14 118 1 276.0256158
15 118 1 123.0529734
16 118 1 427.1129793
17 118 3 237.0437606
18 120 1 345.1957190
19 121 1 160.6688815
You can use the aggregate function as follows:
aggregate(formula = PreyWeight ~ Predator + PreySpecies, data = diet, FUN = mean)
# Predator PreySpecies PreyWeight
# 1 115 1 331.635514
# 2 118 1 238.261871
# 3 120 1 345.195719
# 4 121 1 160.668881
# 5 118 3 237.043761
# 6 114 10 2.917126
# 7 117 10 2.886593
# 8 117 15 4.280374
There are a few different ways of getting what you want:
The aggregate function. Probably what you are after.
aggregate(PreyWeight ~ Predator + PreySpecies, data=dd, FUN=mean)
tapply: Very useful, but only divides the variable by a single factor, hence, we need to create a need joint factor with the paste command:
tapply(dd$PreyWeight, paste(dd$Predator, dd$PreySpecies), mean)
ddply: Part of the plyr package. Very useful. Worth learning.
require(plyr)
ddply(dd, .(Predator, PreySpecies), summarise, mean(PreyWeight))
dcast: The output is in more of a table format. Part of the reshape2 package.
require(reshape2)
dcast(dd, PreyWeight ~ PreySpecies+ Predator, mean, fill=0)
mean(data$PreyWeight[data$Predator==117]);
Related
I am working on a problem set and absolutely cannot figure this one out. I think I've fried my brain to the point where it doesn't even make sense anymore.
Here is a look at the data ...
sex age chol tg ht wt sbp dbp vldl hdl ldl bmi
<chr> <int> <int> <int> <dbl> <dbl> <int> <int> <int> <int> <int> <dbl>
1 M 60 137 50 68.2 112. 110 70 10 53 74 2.40
2 M 26 154 202 82.8 185. 88 64 34 31 92 2.70
3 M 33 198 108 64.2 147 120 80 22 34 132 3.56
4 F 27 154 47 63.2 129 110 76 9 57 88 3.22
5 M 36 212 79 67.5 176. 130 100 16 37 159 3.87
6 F 31 197 90 64.5 121 122 78 18 58 111 2.91
7 M 28 178 163 66.5 167 118 68 19 30 135 3.78
8 F 28 146 60 63 105. 120 80 12 46 88 2.64
9 F 25 231 165 64 126 130 72 23 70 137 3.08
10 M 22 163 30 68.8 173 112 70 6 50 107 3.66
# … with 182 more rows
I must write a function, myTtest, to perform the following task:
Perform a two-sample t-tests to compare the differences of a series of numeric variables between each level of a classification variable
The first argument, dat, is a data frame
The second argument, classVar, is a character vector of length 1. It is the name of the classification variable, such as 'sex.'
The third argument, numVar, is a character vector that contains the name of the numeric variables, such as c("age", "chol", "tg"). This means I need to perform three t-tests to compare the difference of those between males and females.
The function should return a data frame with the following variables: Varname, F.mean, M.mean, t (for t-statistics), df (for degrees of freedom), and p (for p-value).
I should be able to run this ...
myTtest(dat = chol, classVar = "sex", numVar = c("age", "chol", "tg")
... and then get the data frame to appear.
Any help is greatly appreciated. I am pulling my hair out over this one! As well, as noted in my comment below, this has to be done without Tidyverse ... which is why I'm having so much trouble to begin with.
The intuition for this solution is that you can loop over your dependent variables, and call t.test() in each loop. Then save the results from each DV and stack them together in one big data frame.
I'll leave out some bits for you to fill in, but here's the gist:
First, some example data:
set.seed(123)
n <- 20
grp <- sample(c("m", "f"), n, replace = TRUE)
df <- data.frame(grp = grp, age = rnorm(n), chol = rnorm(n), tg = rnorm(n))
df
grp age chol tg
1 m 1.2240818 0.42646422 0.25331851
2 m 0.3598138 -0.29507148 -0.02854676
3 m 0.4007715 0.89512566 -0.04287046
4 f 0.1106827 0.87813349 1.36860228
5 m -0.5558411 0.82158108 -0.22577099
6 f 1.7869131 0.68864025 1.51647060
7 f 0.4978505 0.55391765 -1.54875280
8 f -1.9666172 -0.06191171 0.58461375
9 m 0.7013559 -0.30596266 0.12385424
10 m -0.4727914 -0.38047100 0.21594157
Now make a container that each of the model outputs will go into:
fits_df <- data.frame()
Loop over each DV and append the model output to fits_df each time with rbind:
for (dv in c("age", "chol", "tg")) {
frml <- as.formula(paste0(dv, " ~ grp")) # make a model formula: dv ~ grp
fit <- t.test(frml, two.sided = TRUE, data = df) # perform the t-test
# hint: use str(fit) to figure out how to pull out each value you care about
fit_df <- data.frame(
dv = col,
f_mean = xxx,
m_mean = xxx,
t = xxx,
df = xxx,
p = xxx
)
fits_df <- rbind(fits_df, fit_df)
}
Your output will look like this:
fits_df
dv f_mean m_mean t df p
1 age -0.18558068 -0.04446755 -0.297 15.679 0.7704954
2 chol 0.07731514 0.22158672 -0.375 17.828 0.7119400
3 tg 0.09349567 0.23693052 -0.345 14.284 0.7352112
One note: When you're pulling out values from fit, you may get odd row names in your output data frame. This is due to the names property of the various fit attributes. You can get rid of these by using as.numeric() or as.character() wrappers around the values you pull from fit (for example, fit$statistic can be cleaned up with as.character(round(fit$statistic, 3))).
Here's my data. It shows the amount of fish I found at three different sites.
Selidor.Bay Enlades.Bay Cumphrey.Bay
1 39 29 187
2 70 370 50
3 13 44 52
4 0 65 20
5 43 110 220
6 0 30 266
What I would like to do is create a script to calculate basic statistics for each site.
If I re-arrange the data by stacking it. I.e :
values site
1 29 Selidor.Bay
2 370 Selidor.Bay
3 44 Selidor.Bay
4 65 Enlades.Bay
I'm able to use the following:
data <- ddply(df, c("site"), summarise,
N = length(values),
mean = mean(values),
sd = sd(values),
se = sd / sqrt(N),
sum = sum(values)
)
data.
My question is how can I use the script without having to stack my dataframe?
Thanks.
A slight variation on #docendodiscimus' comment:
library(reshape2)
library(dplyr)
DF %>%
melt(variable.name="site") %>%
group_by(site) %>%
summarise_each(funs( n(), mean, sd, se=sd(.)/sqrt(n()), sum ), value)
# site n mean sd se sum
# 1 Selidor.Bay 6 27.5 27.93385 11.40395 165
# 2 Enlades.Bay 6 108.0 131.84688 53.82626 648
# 3 Cumphrey.Bay 6 132.5 104.29909 42.57992 795
melt does what the OP referred to as "stacking" the data.frame. There is likely some analogous function in the tidyr package.
I have a data frame, which i want to reorder based on column mean. I want to reorder it by decreasing column mean
SNR SignalIntensity ID
1 1.0035798 6.817374 109
2 11.9438978 11.545993 110
4 3.2894878 9.780420 112
5 4.0170266 9.871984 113
6 1.6310523 9.078186 114
7 1.6405415 8.228931 116
8 1.6625413 8.043536 117
9 0.8489116 6.179346 118
10 7.5312260 10.558180 119
11 7.2832911 10.474533 120
12 0.5732577 4.157294 121
14 0.8149754 6.045174 124
I use the following code
means <- colMeans(df) ## to get mean
df <- df[,order(means)] ## to reorder
to get the mean of columns and the order, but i get the column in increasing mean, opposite of my interest. what should i do to reorder in decreasing column mean
expected output
ID SignalIntensity SNR
1 109 6.817374 1.0035798
2 110 11.545993 11.9438978
4 112 9.780420 3.2894878
5 113 9.871984 4.0170266
6 114 9.078186 1.6310523
7 116 8.228931 1.6405415
8 117 8.043536 1.6625413
9 118 6.179346 0.8489116
10 119 10.558180 7.5312260
11 120 10.474533 7.2832911
12 121 4.157294 0.5732577
14 124 6.045174 0.8149754
The default settings in order is decreasing=FALSE. We can change that to TRUE
df[order(means, decreasing=TRUE)]
Or get the order of negative values of 'means'
df[order(-means)]
I am trying to find the most efficient way to split a list of numbers into bins by value and then calculate a cumulative sum for each successive category.
I can't seem to get the value categories from this for the plot.
> scores
[1] 115 119 119 134 121 128 128 152 97 108 98 130 108 110 111 122 106 142 143 140 141 151 125 126
> table(cut(scores,breaks=10))
(96.9,102] (102,108] (108,113] (113,119] (119,124] (124,130] (130,136] (136,141] (141,147] (147,152]
2 1 4 1 4 5 1 2 2 2
> cumsum(table(cut(scores,breaks=10)))
(96.9,102] (102,108] (108,113] (113,119] (119,124] (124,130] (130,136] (136,141] (141,147] (147,152]
2 3 7 8 12 17 18 20 22 24
> plot(100*cumsum(table(cut(scores,breaks=10)))/length(scores),ylab="percent of scores")
> lines(100*cumsum(table(cut(scores,breaks=10)))/length(scores))
This produces an acceptable plot, which contains index values (2,4,6...). How can I get the values 96.9, 102, etc... Is there a better way to do this?
You need to set xaxt = "n" to force the plot not to display the x axis labels, and display them by yourself using axis while retrieving them using names
plot(100*cumsum(table(cut(scores,breaks=10)))/length(scores),ylab="percent of scores", xaxt = "n")
lines(100*cumsum(table(cut(scores,breaks=10)))/length(scores))
axis(1, 1:10, names(table(cut(scores,breaks=10))))
I have a data frame having 20 columns. I need to filter / remove noise from one column. After filtering using convolve function I get a new vector of values. Many values in the original column become NA due to filtering process. The problem is that I need the whole table (for later analysis) with only those rows where the filtered column has values but I can't bind the filtered column to original table as the number of rows for both are different. Let me illustrate using the 'age' column in 'Orange' data set in R:
> head(Orange)
Tree age circumference
1 1 118 30
2 1 484 58
3 1 664 87
4 1 1004 115
5 1 1231 120
6 1 1372 142
Convolve filter used
smooth <- function (x, D, delta){
z <- exp(-abs(-D:D/delta))
r <- convolve (x, z, type='filter')/convolve(rep(1, length(x)),z,type='filter')
r <- head(tail(r, -D), -D)
r
}
Filtering the 'age' column
age2 <- smooth(Orange$age, 5,10)
data.frame(age2)
The number of rows for age column and age2 column are 35 and 15 respectively. The original dataset has 2 more columns and I like to work with them also. Now, I only need 15 rows of each column corresponding to the 15 rows of age2 column. The filter here removed first and last ten values from age column. How can I apply the filter in a way that I get truncated dataset with all columns and filtered rows?
You would need to figure out how the variables line up. If you can add NA's to age2 and then do Orange$age2 <- age2 followed by na.omit(Orange) you should have what you want. Or, equivalently, perhaps this is what you are looking for?
df <- tail(head(Orange, -10), -10) # chop off the first and last 10 observations
df$age2 <- age2
df
Tree age circumference age2
11 2 1004 156 915.1678
12 2 1231 172 876.1048
13 2 1372 203 841.3156
14 2 1582 203 911.0914
15 3 118 30 948.2045
16 3 484 51 1008.0198
17 3 664 75 955.0961
18 3 1004 108 915.1678
19 3 1231 115 876.1048
20 3 1372 139 841.3156
21 3 1582 140 911.0914
22 4 118 32 948.2045
23 4 484 62 1008.0198
24 4 664 112 955.0961
25 4 1004 167 915.1678
Edit: If you know the first and last x observations will be removed then the following works:
x <- 2
df <- tail(head(Orange, -x), -x) # chop off the first and last x observations
df$age2 <- age2