I'm new to R.
I have a data frame that each column contains 1 and 0 only.
I need to sample 10,000 times each column in the data frame, and then create new row that contains the probability of choosing 1 (I guess it's mean), and then binding it to the date frame.
I know how to create another row and binding it to the the original data frame.
But how to itearate over columns and sample (including doing mean) seperatly and then modify the new row accordingly? The syntax is little bit tricky for me.
iterating over columns and sample
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2 Answers
Since all of your columns need to be sampled, you do not need to iterate. You can randomly sample n number of rows from a data frame in two different ways:
- Using Base R
- Using dplyr package
# Using Base R with built in mtcars data
> library(datasets) # comes by default in R
> cars_df <- mtcars
> set.seed(123) # allows us to reproduce the data
> cars_df_15 <- cars_df[sample(nrow(cars_df), 15),]
> cars_df_15
mpg cyl disp hp drat wt qsec vs am gear carb
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# Using dplyr package
> library(dplyr)
> cars_df <- mtcars
> set.seed(123)
> cars_df_15 <- sample_n(cars_df, 15)
> cars_df_15
mpg cyl disp hp drat wt qsec vs am gear carb
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# Then you can use sapply() and colmeans() functions to get the means
# since all of your columns have the same numeric data. In the
# cars_df_15 I created above I would use
# Get Mean of the multiple columns
row_with_means <- colMeans(cars_df_15[sapply(cars_df_15, is.numeric())])
# is.numeric checks that a given column has numeric data
# do not confuse it with is.Numeric
# is.Numeric differs from is.numeric in that data.frames with all columns numeric are accepted as numeric.
# now use row bind function rbind() to add this new row to the dataset
# with 10,000 rows (in my example 15)
cars_df_15_with_means <- rbind(cars_df_15, row_with_means)
If you have only 0/1 in your columns then you can simply calculate the column means with colMeans. Then simply do a sim on all columns using rbinom and rbind to your orgininal data frame. Like this:
x <- sample(0:1, 1000, replace = TRUE)
m <- matrix(x, nrow = 100)
df <- as.data.frame(m) # Example data frame
means <- colMeans(df)
simmeans <- sapply(means, function(x) mean(rbinom(10000, 1, x)))
df2 <- rbind(df, simmeans)
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