pivot_wider does not keep all the variables
I would like to keep the variable cat (category) in the output of my function. However, I am not able to keep it.
The idea is to apply a similar function to m <- 1 - (1 - se * p2)^df$n based on the category. But in order to perform that step, I need to keep the variable category.
Here's the code:
#script3
suppressPackageStartupMessages({
library(mc2d)
library(tidyverse)
})
sim_one <- function() {
df<-data.frame(id=c(1:30),cat=c(rep("a",12),rep("b",18)),month=c(1:6,1,6,4,1,5,2,3,2,5,4,6,3:6,4:6,1:5,5),n=rpois(30,5))
nr <- nrow(df)
df$n[df$n == "0"] <- 3
se <- rbeta(nr, 96, 6)
epi.a <- rpert(nr, min = 1.5, mode = 2, max = 3)
p <- 0.2
p2 <- epi.a*p
m <- 1 - (1 - se * p2)^df$n
results <- data.frame(month = df$month, m, df$cat)
results %>%
arrange(month) %>%
group_by(month) %>%
mutate(n = row_number(), .groups = "drop") %>%
pivot_wider(
id_cols = n,
names_from = month,
names_glue = "m_{.name}",
values_from =m
)
}
set.seed(99)
iters <- 1000
sim_list <- replicate(iters, sim_one(), simplify = FALSE)
sim_list[[1]]
#> # A tibble: 7 x 7
#> n m_1 m_2 m_3 m_4 m_5 m_6
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0.970 0.623 0.905 0.998 0.929 0.980
#> 2 2 0.912 0.892 0.736 0.830 0.890 0.862
#> 3 3 0.795 0.932 0.553 0.958 0.931 0.798
#> 4 4 0.950 0.892 0.732 0.649 0.777 0.743
#> 5 5 NA NA NA 0.657 0.980 0.945
#> 6 6 NA NA NA 0.976 0.836 NA
#> 7 7 NA NA NA NA 0.740 NA
Created on 2022-05-07 by the reprex package (v2.0.1)
2 answers
-
answered 2022-05-07 06:31
AndrewGB
If you also want to keep
df.catas a row, then you can add it to theid_colswhen youpivot_wider, so that it is not dropped.suppressPackageStartupMessages({ library(mc2d) library(tidyverse) }) sim_one <- function() { df <- data.frame( id = c(1:30), cat = c(rep("a", 12), rep("b", 18)), month = c(1:6, 1, 6, 4, 1, 5, 2, 3, 2, 5, 4, 6, 3:6, 4:6, 1:5, 5), n = rpois(30, 5) ) nr <- nrow(df) df$n[df$n == "0"] <- 3 se <- rbeta(nr, 96, 6) epi.a <- rpert(nr, min = 1.5, mode = 2, max = 3) p <- 0.2 p2 <- epi.a * p m <- 1 - (1 - se * p2) ^ df$n results <- data.frame(month = df$month, m, df$cat) results %>% arrange(month) %>% group_by(month) %>% mutate(n = row_number(), .groups = "drop") %>% pivot_wider( id_cols = c(n, df.cat), names_from = month, names_glue = "m_{.name}", values_from = m ) } set.seed(99) iters <- 100 sim_list <- replicate(iters, sim_one(), simplify = FALSE) sim_list[[1]]Output
n df.cat m_1 m_2 m_3 m_4 m_5 m_6 <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 1 a 0.970 0.623 0.905 0.998 0.929 0.980 2 2 a 0.912 0.892 NA 0.830 0.890 0.862 3 3 a 0.795 NA NA NA NA NA 4 4 b 0.950 0.892 0.732 0.649 0.777 0.743 5 3 b NA 0.932 0.553 0.958 0.931 0.798 6 2 b NA NA 0.736 NA NA NA 7 5 b NA NA NA 0.657 0.980 0.945 8 6 b NA NA NA 0.976 0.836 NA 9 7 b NA NA NA NA 0.740 NA -
answered 2022-05-07 07:02
Ronak Shah
If you drop
idcolsargument frompivot_widerit should keep thedf.catcolumn.Also
mutatedoes not have.groupsargument, so here it actually creates a new column with the name.groups..groupsargument is present insummarise.library(mc2d) library(tidyverse) sim_one <- function() { df<-data.frame(id=c(1:30),cat=c(rep("a",12),rep("b",18)),month=c(1:6,1,6,4,1,5,2,3,2,5,4,6,3:6,4:6,1:5,5),n=rpois(30,5)) nr <- nrow(df) df$n[df$n == "0"] <- 3 se <- rbeta(nr, 96, 6) epi.a <- rpert(nr, min = 1.5, mode = 2, max = 3) p <- 0.2 p2 <- epi.a*p m <- 1 - (1 - se * p2)^df$n results <- data.frame(month = df$month, m, df$cat) results %>% arrange(month) %>% group_by(month) %>% mutate(n = row_number()) %>% pivot_wider( names_from = month, names_glue = "m_{.name}", values_from =m ) } set.seed(99) iters <- 1000 sim_list <- replicate(iters, sim_one(), simplify = FALSE) sim_list[[1]] # df.cat n m_1 m_2 m_3 m_4 m_5 m_6 # <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #1 a 1 0.970 0.623 0.905 0.998 0.929 0.980 #2 a 2 0.912 0.892 NA 0.830 0.890 0.862 #3 a 3 0.795 NA NA NA NA NA #4 b 4 0.950 0.892 0.732 0.649 0.777 0.743 #5 b 3 NA 0.932 0.553 0.958 0.931 0.798 #6 b 2 NA NA 0.736 NA NA NA #7 b 5 NA NA NA 0.657 0.980 0.945 #8 b 6 NA NA NA 0.976 0.836 NA #9 b 7 NA NA NA NA 0.740 NA
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