kslayerr kslayerr - 1 year ago 121
R Question

Calculate mean, standard deviation, n, etc. across columns and create new data frame

I am trying to calculate the number of samples, mean, standard deviation, coefficient of variation, lower and upper 95% confidence limits, and quartiles of this data set across each column and put it into a new data frame.

The numbers below are not necessarily all correct & I didn't fill them all in, just provides an example. These values will be used to create a box plot, hence the need for the quartiles. Rows and columns would be headers in the end. See example below.

Here is the structure:

B1 <- c(8, 6, 13, 6, 27, 104, 18, 3)
B2 <- c(2, 13, 1, 64, 127, 24, 4, 3)
B3 <- c(8, 16, 113, 680, 227, 310, 138, 30)
B4 <- c(238, 46, 613, 69, 7, 14, 4, 8)

x <- data.frame(B1, B2, B3, B4)

> head(x)
B1 B2 B3 B4
1 8 2 8 238
2 6 13 16 46
3 13 1 113 613
4 6 64 680 69
5 27 127 227 7
6 104 24 310 14


Desired output:

> y
B1 B2 B3 B4
n 8 8 8 8
mean 23 30 190 125
Stand dev 5 2 34 2
CoeffofVariation 0.3 0.4 0.7 1.3
LowerConfInterval 2 20 35 45
UpperConfInterval 50 120 122 120
LowerQuartile
Median
Upper Quantile
Inter Quartile Range
Minimum
Maximum
Regression equation

Answer Source

You could use something like this:

B1 <- c(8, 6, 13, 6, 27, 104, 18, 3)
B2 <- c(2, 13, 1, 64, 127, 24, 4, 3)
B3 <- c(8, 16, 113, 680, 227, 310, 138, 30)
B4 <- c(238, 46, 613, 69, 7, 14, 4, 8)

combDF <- data.frame(cbind(B1,B2,B3,B4))

data_long <- gather(combDF, factor_key=TRUE)

data_long%>% group_by(key)%>%
  summarise(mean= mean(value), sd= sd(value), max = max(value),min = min(value))

and the output would be:

    # A tibble: 4 x 5
     key    mean        sd   max   min
  <fctr>   <dbl>     <dbl> <dbl> <dbl>
1     B1  23.125  33.60458   104     3
2     B2  29.750  44.59260   127     1
3     B3 190.250 224.72253   680     8
4     B4 124.875 212.08653   613     4

You have not specified which confidence level you are looking but the code I posted can be adapted to your problem.

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