Convert Matrix to Data Frame in R
I Have the Following Numeric Data Frame Dataset: X1 X2 X3. .. 1 2 3. .. I Did the Following Applying Shapiro Test to All Columns Lshap
I have the following numeric data frame dataset:
x1 x2 x3 ...
1 2 3
...
I did the following applying shapiro test to all columns
lshap <- lapply(dataset, shapiro.test)
lres <- t(sapply(lshap, `[`, c("statistic","p.value")))
The output of lres looks like this:
statistic p.value
Strong 0.8855107 6.884855e-14
Hardworking 0.9360735 8.031421e-10
Focused 0.9350827 6.421583e-10
Now, when I do:
class(lres)
It gives me "matrix" "array"
My question is how I convert lres to a data frame?
I want this output as a data frame:
variable statistic p.value
Strong 0.8855107 6.884855e-14
Hardworking 0.9360735 8.031421e-10
Focused 0.9350827 6.421583e-10
...
When I do to_df <- as.data.frame(lres) I get the following weird output:
statistic p.value
Strong <dbl [1]> <dbl [1]>
Hardworking <dbl [1]> <dbl [1]>
Focused <dbl [1]> <dbl [1]>
Gritty <dbl [1]> <dbl [1]>
Adaptable <dbl [1]> <dbl [1]>
...
What is wrong with this?
1 Answer
In base R, the issue with OP's 'lres' is that each element is a list element in the matrix. Instead of doing that, we could use
out <- do.call(rbind, lapply(mtcars, function(x)
as.data.frame(shapiro.test(x)[c('statistic', 'p.value')])))
out <- cbind(variable = row.names(out), out)
row.names(out) <- NULL
-output
out
# variable statistic p.value
#1 mpg 0.9475647 1.228814e-01
#2 cyl 0.7533100 6.058338e-06
#3 disp 0.9200127 2.080657e-02
#4 hp 0.9334193 4.880824e-02
#5 drat 0.9458839 1.100608e-01
#6 wt 0.9432577 9.265499e-02
#7 qsec 0.9732509 5.935176e-01
#8 vs 0.6322635 9.737376e-08
#9 am 0.6250744 7.836354e-08
#10 gear 0.7727856 1.306844e-05
#11 carb 0.8510972 4.382405e-04
Or we can use as_tibble
library(dplyr)
library(tidyr)
as_tibble(lres, rownames = 'variable') %>%
unnest(-variable)
-output
# A tibble: 11 x 3
# variable statistic p.value
# <chr> <dbl> <dbl>
# 1 mpg 0.948 0.123
# 2 cyl 0.753 0.00000606
# 3 disp 0.920 0.0208
# 4 hp 0.933 0.0488
# 5 drat 0.946 0.110
# 6 wt 0.943 0.0927
# 7 qsec 0.973 0.594
# 8 vs 0.632 0.0000000974
# 9 am 0.625 0.0000000784
#10 gear 0.773 0.0000131
#11 carb 0.851 0.000438
Or can be done in a single step
library(purrr)
library(broom)
imap_dfr(mtcars, ~ shapiro.test(.x) %>%
tidy %>%
select(-method), .id = 'variable')
-output
# A tibble: 11 x 3
# variable statistic p.value
# <chr> <dbl> <dbl>
# 1 mpg 0.948 0.123
# 2 cyl 0.753 0.00000606
# 3 disp 0.920 0.0208
# 4 hp 0.933 0.0488
# 5 drat 0.946 0.110
# 6 wt 0.943 0.0927
# 7 qsec 0.973 0.594
# 8 vs 0.632 0.0000000974
# 9 am 0.625 0.0000000784
#10 gear 0.773 0.0000131
#11 carb 0.851 0.000438
data
lshap <- lapply(mtcars, shapiro.test)
lres <- t(sapply(lshap, `[`, c("statistic","p.value")))
James Sterling reports on renewable energy developments, climate policy, ecological conservation, and green tech innovations around the globe.
