How to create dataframe using results (output) of sens.slope function? - r

Question

I have an Excel data with multiple sheets. I imported them into R and applied Mann-Kendall trend test with the function sens.slope(). The results of this function are in htest class, but I want to put them in a table.
I installed packages needed and imported each sheets of dataset.
require(readxl)
require(trend)
tmin1 <- read_excel("C:/TEZ/ANALİZ/future_projection/2051-2100/model 3-3/average_tmin_3_3_end.xlsx", sheet = "acipayam")
tmin2 <- read_excel("C:/TEZ/ANALİZ/future_projection/2051-2100/model 3-3/average_tmin_3_3_end.xlsx", sheet = "adana")
...
tmin57 <- read_excel("C:/TEZ/ANALİZ/future_projection/2051-2100/model 3-3/average_tmin_3_3_end.xlsx", sheet = "yumurtalik")
Then, specified the columns for trend test.
x1<-tmin1$`13`
x2<-tmin1$`14`
x3<-tmin1$`15`
x4<-tmin1$`16`
x5<-tmin1$`17`
...
x281<-tmin57$`13`
x282<-tmin57$`14`
x283<-tmin57$`15`
x284<-tmin57$`16`
x285<-tmin57$`17`
And appplied the function.
sens.slope(x1)
sens.slope(x2)
sens.slope(x3)
....
sens.slope(x285)
The result is looking like this.
> sens.slope(x1)
Sen's slope
data: x1
z = 4.6116, n = 49, p-value = 3.996e-06
alternative hypothesis: true z is not equal to 0
95 percent confidence interval:
0.03241168 0.08101651
sample estimates:
Sen's slope
0.05689083
> sens.slope(x2)
Sen's slope
data: x2
z = 6.8011, n = 49, p-value = 1.039e-11
alternative hypothesis: true z is not equal to 0
95 percent confidence interval:
0.05632911 0.08373755
sample estimates:
Sen's slope
0.07032428
...
How can I put these values in a single table and write them to an Excel file? (names of needed values are statistic and estimates in the function.)

Answer 1

There is a package broom precisely for this:
library(tidyverse)
library(trend)
sens.slope(runif(1000)) %>%
broom::tidy()
# A tibble: 1 x 7
statistic p.value parameter conf.low conf.high method alternative
<dbl> <dbl> <int> <dbl> <dbl> <chr> <chr>
1 0.548 0.584 1000 -0.0000442 0.0000801 Sen's slope two.sided
And if you have many data frames, bind them all into one list and loop it over with map_df:
A = tibble(Value = runif(1000))
B = tibble(Value = runif(1000))
C = tibble(Value = runif(1000))
D = tibble(Value = runif(1000))
list(A,B,C,D) %>%
map_df(~.x %>%
pull(1) %>%
sens.slope() %>%
broom::tidy())
# A tibble: 4 x 7
statistic p.value parameter conf.low conf.high method alternative
<dbl> <dbl> <int> <dbl> <dbl> <chr> <chr>
1 -0.376 0.707 1000 -0.0000732 0.0000502 Sen's slope two.sided
2 -2.30 0.0215 1000 -0.000138 -0.0000110 Sen's slope two.sided
3 -1.30 0.194 1000 -0.000104 0.0000209 Sen's slope two.sided
4 0.674 0.500 1000 -0.0000410 0.0000848 Sen's slope two.sided
Edit: Just realised that broom::tidy in this case doesn't provide the estimate (haven't encountered this before), here is the solution without using broom:
A = tibble(Value = runif(1000))
B = tibble(Value = runif(1000))
C = tibble(Value = runif(1000))
D = tibble(Value = runif(1000))
list(A,B,C,D) %>%
purrr::map_df(.,~{
Test = sens.slope(.x %>% pull(1))
Test = tibble(Estimate = Test["estimates"] %>% unlist,
Statistic = Test["statistic"] %>% unlist)
}
)
# A tibble: 4 x 2
Estimate Statistic
<dbl> <dbl>
1 -0.0000495 -1.55
2 -0.00000491 -0.155
3 0.0000242 0.755
4 -0.0000301 -0.921

Answer 2

Try using lists instead of having so many objects in global environment.
Now since you already have them, you can combine them in a list, apply sens.slope on each one, extract statistic and estimates from them an get the dataframe.
library(trend)
output <- data.frame(t(sapply(mget(paste0('x', 1:285)), function(y)
{temp <- sens.slope(y);c(temp$statistic, temp$estimates)})))
You can now write this dataframe as csv using write.csv.
write.csv(output, 'output.csv', row.names = FALSE)