How to calculate standard deviation with R for a file with a single numeric column? - statistics

I have a file with the following data:
12341231
1231312
1233123
1231313
523454
6567
73525
I would like to read the file into an R object and calculate STD on the data.


I'd probably use scan for that file. You don't need to construct a data frame to calculate standard deviation on a vector. scan reads the data and gives a vector and it is faster than read.table for what you're doing here.
## put your data into a file, "new.txt"
> txt <- '12341231
1231312
1233123
1231313
523454
6567
73525'
> writeLines(txt, "new.txt")
## read and calculate standard deviation
> x <- scan("new.txt", what = integer())
> x
# [1] 12341231 1231312 1233123 1231313 523454 6567 73525
> sd(x)
# [1] 4426815

Related

detect highest peaks automatically from noisy data python

Is there any way to detect the highest peaks using a python library without setting any parameter?. I'm developing a user interface and I want the algorithm to be able to detect highest peaks automatically...
I want it to be able to detect these peaks in picture below:
graph here
Data looks like this:
8.60291e-07
-1.5491e-06
5.64568e-07
-9.51195e-07
1.07203e-06
4.6521e-07
6.43967e-07
-9.86092e-07
-9.82323e-07
6.38977e-07
-1.93884e-06
-2.98309e-08
1.33543e-06
1.05064e-06
1.17332e-06
-1.53549e-07
-8.9357e-07
1.59176e-06
-2.17331e-06
1.46756e-06
5.63301e-07
-8.77556e-07
7.47681e-09
-8.30101e-07
-3.6647e-07
5.27046e-07
-1.94983e-06
1.89018e-07
1.22533e-06
8.00735e-07
-8.51166e-07
1.13437e-06
-2.75787e-07
1.79601e-06
-1.67875e-06
1.13529e-06
-1.29865e-06
9.9688e-07
-9.34486e-07
8.89931e-07
-3.88634e-07
1.15124e-06
-4.23569e-07
-1.8029e-07
1.20537e-07
4.10736e-07
-9.99077e-07
-3.62984e-07
2.97916e-06
-1.95828e-06
-1.07398e-06
2.422e-06
-6.33202e-07
-1.36953e-06
1.6694e-06
-4.71764e-07
3.98849e-07
-1.0071e-06
-9.72984e-07
8.13553e-07
2.64193e-06
-3.12365e-06
1.34049e-06
-1.30419e-06
1.48369e-07
1.26033e-06
-2.59872e-07
4.28284e-07
-6.44356e-07
2.99934e-07
8.34335e-07
3.53226e-07
-7.08252e-07
4.1243e-07
2.41525e-06
-8.92159e-07
8.82339e-08
4.31945e-06
3.75152e-06
1.091e-06
3.8204e-06
-1.21356e-06
3.35564e-06
-1.06234e-06
-5.99808e-07
2.18155e-06
5.90652e-07
-1.36728e-06
-4.97017e-07
-7.77283e-08
8.68263e-07
4.37645e-07
-1.26514e-06
2.26413e-06
-8.52966e-07
-7.35596e-07
4.11911e-07
1.7585e-06
-inf
1.10779e-08
-1.49507e-06
9.87305e-07
-3.85296e-06
4.31265e-06
-9.89227e-07
-1.33537e-06
4.1713e-07
1.89362e-07
3.21968e-07
6.80237e-08
2.31636e-07
-2.98523e-07
7.99133e-07
7.36305e-07
6.39862e-07
-1.11932e-06
-1.57262e-06
1.86305e-06
-3.63716e-07
3.83865e-07
-5.23293e-07
1.31812e-06
-1.23608e-06
2.54684e-06
-3.99796e-06
2.90441e-06
-5.20203e-07
1.36295e-06
-1.89317e-06
1.22366e-06
-1.10373e-06
2.71276e-06
9.48181e-07
7.70881e-06
5.17066e-06
6.21254e-06
1.3513e-05
1.47878e-05
8.78543e-06
1.61819e-05
1.68438e-05
1.16082e-05
5.74059e-06
4.92458e-06
1.11884e-06
-1.07419e-06
-1.28517e-06
-2.70949e-06
1.65662e-06
1.42964e-06
3.40604e-06
-5.82825e-07
1.98288e-06
1.42819e-06
1.65517e-06
4.42749e-07
-1.95609e-06
-2.1756e-07
1.69164e-06
8.7204e-08
-5.35324e-07
7.43546e-07
-1.08687e-06
2.07289e-06
2.18529e-06
-2.8161e-06
1.88821e-06
4.07272e-07
1.063e-06
8.47244e-07
1.53879e-06
-9.0799e-07
-1.26709e-07
2.40044e-06
-9.48166e-07
1.41788e-06
3.67615e-07
-1.29199e-06
3.868e-06
9.54654e-06
2.51951e-05
2.2769e-05
7.21716e-06
1.36545e-06
-1.32681e-06
-3.09641e-06
4.90417e-07
2.99335e-06
1.578e-06
6.0025e-07
2.90656e-06
-2.08258e-06
-1.54214e-06
2.19757e-07
3.74982e-06
-1.76944e-06
2.15018e-06
-1.01935e-06
4.37469e-07
1.39078e-06
6.39587e-07
-1.7807e-06
-6.16455e-09
1.61557e-06
1.59644e-06
-2.35217e-06
5.29449e-07
1.9169e-06
-7.54822e-07
2.00342e-06
-3.28452e-06
3.91663e-06
1.66016e-08
-2.65897e-06
-1.4064e-06
4.67987e-07
1.67786e-06
4.69543e-07
-8.90106e-07
-1.4584e-06
1.37915e-06
1.98483e-06
-2.3735e-06
4.45618e-07
1.91504e-06
1.09653e-06
-8.00873e-07
1.32321e-06
2.04846e-06
-1.50656e-06
7.23816e-07
2.06049e-06
-2.43918e-06
1.64417e-06
2.65411e-07
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-8.01788e-07
2.05121e-06
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1.83594e-06
-8.14026e-07
-2.69342e-06
1.81152e-06
1.11664e-07
-4.21863e-06
-7.20551e-06
-5.92407e-07
-1.44629e-06
-2.08136e-06
2.86105e-06
3.77911e-06
-1.91898e-06
1.41742e-06
2.67914e-07
-8.55835e-07
-9.8584e-07
-2.74115e-06
3.39044e-06
1.39639e-06
-2.4964e-06
8.2486e-07
2.02432e-06
1.65793e-06
-1.43094e-06
-3.36807e-06
-8.96515e-07
5.31323e-06
-8.27209e-07
-1.39221e-06
-3.3754e-06
2.12372e-06
3.08218e-06
-1.42947e-06
-2.36777e-06
3.86218e-06
2.29327e-06
-3.3941e-06
-1.67291e-06
2.63828e-06
2.21008e-07
7.07794e-07
1.8172e-06
-2.00082e-06
1.80664e-06
6.69739e-07
-3.95395e-06
1.92148e-06
-1.07187e-06
-4.04938e-07
-1.76553e-06
2.7099e-06
1.30768e-06
1.41812e-06
-1.55518e-07
-3.78302e-06
4.00137e-06
-8.38623e-07
4.54651e-07
1.00027e-06
1.32196e-06
-2.62717e-06
1.67865e-06
-6.99249e-07
2.8837e-06
-1.00516e-06
-3.68011e-06
1.61847e-06
1.90887e-06
1.59641e-06
4.16779e-07
-1.35245e-06
1.65717e-06
-2.92667e-06
3.6203e-07
2.53528e-06
-2.0578e-07
-3.41919e-07
-1.42154e-06
-2.33322e-06
3.07175e-06
-2.69165e-08
-8.21045e-07
2.3175e-06
-7.22992e-07
1.49069e-06
8.75488e-07
-2.02676e-06
-2.81158e-07
3.6004e-06
-3.94708e-06
4.72983e-06
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-1.4678e-06
1.04251e-06
-2.06625e-06
3.10406e-06
-8.13873e-07
7.23694e-07
-9.78912e-07
-8.65967e-07
7.37335e-07
1.52563e-06
-2.33591e-06
1.78265e-06
9.58435e-07
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1.14789e-06
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2.12241e-06
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1.76086e-07
-3.77194e-06
-2.71326e-06
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1.70807e-07
-2.495e-06
4.22324e-06
-3.62476e-06
-9.56055e-07
7.16583e-07
3.01447e-06
-1.41229e-06
-1.67694e-06
7.61627e-07
3.55881e-06
2.31015e-06
-9.50378e-07
4.45251e-08
-1.94791e-06
2.27081e-06
-3.34717e-06
3.05688e-06
4.57062e-07
3.87326e-06
-2.39215e-06
-3.52682e-06
-2.05212e-06
5.26495e-06
-3.28613e-07
-5.76569e-07
-7.46338e-07
5.98795e-06
8.80493e-07
-4.82965e-06
2.56839e-06
-1.58792e-06
-2.2294e-06
1.83841e-06
2.65482e-06
-3.10474e-06
-3.46741e-07
2.45557e-06
2.01328e-06
-3.92606e-06
inf
-8.11737e-07
5.72174e-07
1.57245e-06
8.02612e-09
-2.901e-06
1.22079e-06
-6.31714e-07
3.06241e-06
1.20059e-06
-1.80344e-06
4.90784e-07
3.74243e-06
-2.94342e-07
-3.45764e-08
-3.42099e-06
-1.43695e-06
5.91064e-07
3.47308e-06
3.78232e-06
4.01093e-07
-1.58435e-06
-3.47375e-06
1.34943e-06
1.11768e-06
1.95212e-06
-8.28033e-07
1.53705e-06
6.38031e-07
-1.84702e-06
1.34689e-06
-6.98669e-07
1.81653e-06
-2.42355e-06
-1.35257e-06
3.04367e-06
-1.21976e-06
1.61896e-06
-2.69528e-06
1.84601e-06
6.45447e-08
-4.94263e-07
3.47568e-06
-2.00531e-06
3.56693e-06
-3.19446e-06
2.72141e-06
-1.39059e-06
2.20032e-06
-1.76819e-06
2.32727e-07
-3.47382e-07
2.11823e-07
-5.22614e-07
2.69846e-06
-1.47983e-06
2.14554e-06
-6.27594e-07
-8.8501e-10
7.89124e-07
-2.8653e-07
8.30902e-07
-2.12857e-06
-1.90887e-07
1.07593e-06
1.40781e-06
2.41641e-06
-4.52689e-06
2.37207e-06
-2.19479e-06
1.65131e-06
1.2706e-06
-2.18387e-06
-1.72821e-07
5.41687e-07
7.2879e-07
7.56927e-07
1.57739e-06
-3.79395e-07
-1.02887e-06
-1.20987e-06
1.43066e-06
8.96301e-08
5.09766e-07
-2.8812e-06
-2.35944e-06
2.25912e-06
-2.78967e-06
-4.69913e-06
1.60822e-06
6.9342e-07
4.6225e-07
-1.33276e-06
-3.59033e-06
1.11206e-06
1.83521e-06
2.39163e-06
2.3468e-08
5.91431e-07
-8.80249e-07
-2.77405e-08
-1.13184e-06
-1.28036e-06
1.66229e-06
2.81784e-06
-2.97589e-06
8.73413e-08
1.06439e-06
2.39075e-06
-2.76974e-06
1.20862e-06
-5.12817e-07
-5.19104e-07
4.51324e-07
-4.7168e-07
2.35608e-06
5.46906e-07
-1.66748e-06
5.85236e-07
6.42944e-07
2.43164e-07
4.01031e-07
-1.93646e-06
2.07416e-06
-1.16116e-06
4.27155e-07
5.2951e-07
9.09149e-07
-8.71887e-08
-1.5564e-09
1.07266e-06
-9.49402e-08
2.04016e-06
-6.38123e-07
-1.94241e-06
-5.17294e-06
-2.18622e-06
-8.26703e-06
2.54364e-06
4.32614e-06
8.3847e-07
-2.85309e-06
2.72345e-06
-3.42752e-06
-1.36871e-07
2.23346e-06
5.26825e-07
1.3566e-06
-2.17111e-06
2.1463e-07
2.06479e-06
1.76929e-06
-1.2655e-06
-1.3797e-06
3.10706e-06
-4.72189e-06
4.38138e-06
6.41815e-07
-3.25623e-08
-4.93707e-06
5.05743e-06
5.17578e-07
-5.30524e-06
3.62463e-06
5.68909e-07
1.16226e-06
1.10843e-06
-5.00854e-07
9.48761e-07
-2.18701e-06
-3.57635e-07
4.26709e-06
-1.50836e-06
-5.84412e-06
3.5054e-06
3.94019e-06
-4.7623e-06
2.05856e-06
-2.22992e-07
1.64969e-06
2.64694e-06
-8.49487e-07
-3.63562e-06
1.0386e-06
1.69461e-06
-2.05798e-06
3.60349e-06
3.42651e-07
-1.46686e-06
1.19949e-06
-1.60519e-06
2.37793e-07
6.12366e-07
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1.43668e-06
1.87009e-06
-2.22626e-06
2.15155e-06
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2.05188e-06
-4.40002e-07
2.06683e-06
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5.96924e-07
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2.4892e-06
1.13083e-06
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5.10651e-07
2.73499e-07
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1.40564e-06
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1.45947e-06
3.70544e-07
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1.72098e-06
8.19031e-07
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1.10107e-06
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1.57654e-07
3.30716e-06
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1.73126e-07
1.30447e-06
7.64771e-08
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-1.4346e-06
5.03171e-06
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2.3212e-06
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2.16675e-08
2.24026e-06
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1.78775e-06
1.28174e-08
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1.39478e-06
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2.16751e-06
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3.95905e-07
5.74371e-07
1.33575e-07
-3.98315e-07
4.93927e-07
-5.23987e-07
-1.74713e-07
6.49384e-07
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2.35733e-06
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1.74722e-06
4.03503e-07
3.5965e-07
1.44836e-07]

The task you are describing could be treated like anomaly/outlier detection.
One possible solution is to use a Z-score transformation and treat every value with a z score above a certain threshold as an outlier. Because there is no clear definition of an outlier it won't be able to detect such peaks without setting any parameters (threshold).
One possible solution could be:
import numpy as np
def detect_outliers(data):
outliers = []
d_mean = np.mean(data)
d_std = np.std(data)
threshold = 3 # this defines what you would consider a peak (outlier)
for point in data:
z_score = (point - d_mean)/d_std
if np.abs(z_score) > threshold:
outliers.append(point)
return outliers
# create normal data
data = np.random.normal(size=100)
# create outliers
outliers = np.random.normal(100, size=3)
# combine normal data and outliers
full_data = data.tolist() + outliers.tolist()
# print outliers
print(detect_outliers(full_data))
If you only want to detect peaks, remove the np.abs function call from the code.
This code snippet is based on a Medium Post, which also provides another way of detecting outliers.

Calculate the average of Spearman correlation

I have 2 columns A and B which contain the Spearman's correlation values as follows:
0.127272727 -0.260606061
-0.090909091 -0.224242424
0.345454545 0.745454545
0.478787879 0.660606061
-0.345454545 -0.333333333
0.151515152 -0.127272727
0.478787879 0.660606061
-0.321212121 -0.284848485
0.284848485 0.515151515
0.36969697 -0.139393939
-0.284848485 0.272727273
How can I calculate the average of those correlation values in these 2 columns in Excel or Matlab ? I found a close answer in this link : https://stats.stackexchange.com/questions/8019/averaging-correlation-values
The main point is we can not use mean or average in this case, as explained in the link. They proposed a nice way to do that, but I dont know how to implement it in Excel or Matlab.

Following the second answer of the link you provided, which is the most general case, you can calculate the average Spearman's rho in Matlab as follows:
M = [0.127272727 -0.260606061;
-0.090909091 -0.224242424;
0.345454545 0.745454545;
0.478787879 0.660606061;
-0.345454545 -0.333333333;
0.151515152 -0.127272727;
0.478787879 0.660606061;
-0.321212121 -0.284848485;
0.284848485 0.515151515;
0.36969697 -0.139393939;
-0.284848485 0.272727273];
z = atanh(M);
meanRho = tanh(mean(z));
As you can see it gives mean values of
meanRho =
0.1165 0.1796
whereas the simple mean is quite close:
mean(M)
ans =
0.1085 0.1350
Edit: more information on Fisher's transformation here.

In MATLAB, define a matrix with these values and use mean function as follows:
%define a matrix M
M = [0.127272727 -0.260606061;
-0.090909091 -0.224242424;
0.345454545 0.745454545;
0.478787879 0.660606061;
-0.345454545 -0.333333333;
0.151515152 -0.127272727;
0.478787879 0.660606061;
-0.321212121 -0.284848485;
0.284848485 0.515151515;
0.36969697 -0.139393939;
-0.284848485 0.272727273];
%calculates the mean of each column
meanVals = mean(M);
Result
meanVals =
0.1085 0.1350
It is also possible to calculate the total meanm and the mean of each row as follows:
meanVals = mean(M); %total mean
meanVals = mean(M,2); %mean of each row

svm train output file has less lines than that of the input file

I am currently building a binary classification model and have created an input file for svm-train (svm_input.txt). This input file has 453 lines, 4 No. features and 2 No. classes [0,1].
i.e
0 1:15.0 2:40.0 3:30.0 4:15.0
1 1:22.73 2:40.91 3:36.36 4:0.0
1 1:31.82 2:27.27 3:22.73 4:18.18
0 1:22.73 2:13.64 3:36.36 4:27.27
1 1:30.43 2:39.13 3:13.04 4:17.39 ......................
My problem is that when I count the number of lines in the output model generated by svm-train (svm_train_model.txt), this has 12 fewer lines than that of the input file. The line count here shows 450, although there are obviously also 9 lines at the beginning showing the various parameters generated
i.e.
svm_type c_svc
kernel_type rbf
gamma 1
nr_class 2
total_sv 441
rho -0.156449
label 0 1
nr_sv 228 213
SV
Therefore 12 lines in total from the original input of 453 have gone. I am new to svm and was hoping that someone could shed some light on why this might have happened?
Thanks in advance
Updated.........
I now believe that in generating the model, it has removed lines whereby the labels and all the parameters are exactly the same.
To explain............... My input is a set of miRNAs which have been classified as 1 and 0 depending on their involvement in a particular process or not (i.e 1=Yes & 0=No). The input file looks something like.......
0 1:22 2:30 3:14 4:16
1 1:26 2:15 3:17 4:25
0 1:22 2:30 3:14 4:16
Whereby, lines one and three are exactly the same and as a result will be removed from the output model. My question is then both why the output model would do this and how I can get around this (whilst using the same features)?
Whilst both SOME OF the labels and their corresponding feature values are identical within the input file, these are still different miRNAs.
NOTE: The Input file does not have a feature for miRNA name (and this would clearly show the differences in each line) however, in terms of the features used (i.e Nucleotide Percentage Content), some of the miRNAs do have exactly the same percentage content of A,U,G & C and as a result are viewed as duplicates and then removed from the output model as it obviously views them as duplicates even though they are not (hence there are less lines in the output model).
the format of the input file is:
Where:
Column 0 - label (i.e 1 or 0): 1=Yes & 0=No
Column 1 - Feature 1 = Percentage Content "A"
Column 2 - Feature 2 = Percentage Content "U"
Column 3 - Feature 3 = Percentage Content "G"
Column 4 - Feature 4 = Percentage Content "C"
The input file actually looks something like (See the very first two lines below), as they appear identical, however each line represents a different miRNA):
1 1:23 2:36 3:23 4:18
1 1:23 2:36 3:23 4:18
0 1:36 2:32 3:5 4:27
1 1:14 2:41 3:36 4:9
1 1:18 2:50 3:18 4:14
0 1:36 2:23 3:23 4:18
0 1:15 2:40 3:30 4:15
In terms of software, I am using libsvm-3.22 and python 2.7.5

Align your input file properly, is my first observation. The code for libsvm doesnt look for exactly 4 features. I identifies by the string literals you have provided separating the features from the labels. I suggest manually converting your input file to create the desired input argument.
Try the following code in python to run
Requirements - h5py, if your input is from matlab. (.mat file)
pip install h5py
import h5py
f = h5py.File('traininglabel.mat', 'r')# give label.mat file for training
variables = f.items()
labels = []
c = []
import numpy as np
for var in variables:
data = var[1]
lables = (data.value[0])
trainlabels= []
for i in lables:
trainlabels.append(str(i))
finaltrain = []
trainlabels = np.array(trainlabels)
for i in range(0,len(trainlabels)):
if trainlabels[i] == '0.0':
trainlabels[i] = '0'
if trainlabels[i] == '1.0':
trainlabels[i] = '1'
print trainlabels[i]
f = h5py.File('training_features.mat', 'r') #give features here
variables = f.items()
lables = []
file = open('traindata.txt', 'w+')
for var in variables:
data = var[1]
lables = data.value
for i in range(0,1000): #no of training samples in file features.mat
file.write(str(trainlabels[i]))
file.write(' ')
for j in range(0,49):
file.write(str(lables[j][i]))
file.write(' ')
file.write('\n')

Spatially Subsetting Images in batch mode using IDL and ENVI

I would like to spatially subset LANDSAT photos in ENVI using an IDL program. I have over 150 images that I would like to subset, so I'd like to run the program in batch mode (with no interaction). I know how to do it manually, but what command would I use to spatially subset the image via lat/long coordinates in IDL code?

Here is some inspiration, for a single file.
You can do the same for a large number of files by building up
a list of filenames and looping over it.
; define the image to be opened (could be in a loop), I believe it can also be a tif, img...
img_file='path/to/image.hdr'
envi_open_file,img_file,r_fid=fid
if (fid eq -1) then begin
print, 'Error when opening file ',img_file
return
endif
; let's define some coordinates
XMap=[-70.0580916, -70.5006694]
YMap=[-32.6030694, -32.9797194]
; now convert coordinates into pixel position:
; the transformation function uses the image geographic information:
ENVI_CONVERT_FILE_COORDINATES, FID, XF, YF, XMap, YMap
; we must consider integer. Think twice here, maybe you need to floor() or ceil()
XF=ROUND(XF)
YF=ROUND(YF)
; read the image
envi_file_query, fid, DIMS=DIMS, NB=NB, NL=NL, NS=NS
pos = lindgen(nb)
; and store it in an array
image=fltarr(NS, NL, NB)
; read each band sequentially
FOR i=0, NB-1 DO BEGIN
image[*,*,i]= envi_get_data(fid=fid, dims=dims, pos=pos[i])
endfor
; simply crop the data with array-indexing function
imagen= image[XF[0]:XF[1],YF[0]:YF[1]]
nl2=YF[1]-YF[0]
ns2=XF[1]-XF[0]
; read mapinfo to save it in the final file
map_info=envi_get_map_info(fid=fid)
envi_write_envi_file, imagen, data_type=4, $
descrip = 'cropped', $
map_info = map_info, $
nl=nl2, ns=ns2, nb=nb, r_fid=r_fid, $
OUT_NAME = 'path/to/cropped.hdr'

plotting 3D bar graph in matlab or excel

I need to plot a 3D bar graph in matlab or excel. I am going to use some dates in x-axis, time in y-axis and some amount on the z-axis. Each record in csv file looks like ...
18-Apr, 21, 139.45
I am not sure how to do this right. can anyone help me please. I tried using pivort chart of excel. however, i could not manipulate the axis and use appropriate space between each tick.
thanks
kaisar

Since the question is lacking details, let me illustrate with an example.
Consider the following code:
%# read file contents: date,time,value
fid = fopen('data.csv','rt');
C = textscan(fid, '%s %s %f', 'Delimiter',',');
fclose(fid);
%# correctly reshape the data, and extract x/y labels
num = 5;
d = reshape(C{1},num,[]); d = d(1,:);
t = reshape(C{2},num,[]); t = t(:,1);
Z = reshape(C{3},num,[]);
%# plot 3D bars
bar3(Z)
xlabel('date'), ylabel('time'), zlabel('value')
set(gca, 'XTickLabel',d, 'YTickLabel',t)
I ran on the following data file:
data.csv
18-Apr,00:00,0.85535
18-Apr,03:00,0.38287
18-Apr,06:00,0.084649
18-Apr,09:00,0.73387
18-Apr,12:00,0.33199
19-Apr,00:00,0.83975
19-Apr,03:00,0.37172
19-Apr,06:00,0.82822
19-Apr,09:00,0.17652
19-Apr,12:00,0.12952
20-Apr,00:00,0.87988
20-Apr,03:00,0.044079
20-Apr,06:00,0.68672
20-Apr,09:00,0.73377
20-Apr,12:00,0.43717
21-Apr,00:00,0.37984
21-Apr,03:00,0.97966
21-Apr,06:00,0.39899
21-Apr,09:00,0.44019
21-Apr,12:00,0.15681
22-Apr,00:00,0.32603
22-Apr,03:00,0.31406
22-Apr,06:00,0.8945
22-Apr,09:00,0.24702
22-Apr,12:00,0.31068
23-Apr,00:00,0.40887
23-Apr,03:00,0.70801
23-Apr,06:00,0.14364
23-Apr,09:00,0.87132
23-Apr,12:00,0.083156
24-Apr,00:00,0.46174
24-Apr,03:00,0.030389
24-Apr,06:00,0.7532
24-Apr,09:00,0.70004
24-Apr,12:00,0.21451
25-Apr,00:00,0.6799
25-Apr,03:00,0.55729
25-Apr,06:00,0.85068
25-Apr,09:00,0.55857
25-Apr,12:00,0.90177
26-Apr,00:00,0.41952
26-Apr,03:00,0.35813
26-Apr,06:00,0.48899
26-Apr,09:00,0.25596
26-Apr,12:00,0.92917
27-Apr,00:00,0.46676
27-Apr,03:00,0.25401
27-Apr,06:00,0.43122
27-Apr,09:00,0.70253
27-Apr,12:00,0.40233

Use MATLAB's CSV reading functions (or write your own) and then use bar3 to display the data.

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