We do a normal nesting grouping by rows. Mine is different.
I want to create a nested tibble grouping by column prefixes (before the first '_'), preserving the original column names in the nested tibbles.
The current approach works but looks overcomplicated.
tibble(a_1=1:3, a_2=2:4, b_1=3:5) %>%
print() %>%
# A tibble: 3 x 3
# a_1 a_2 b_1
# <int> <int> <int>
# 1 1 2 3
# 2 2 3 4
# 3 3 4 5
pivot_longer(everything()) %>%
nest(data=-name) %>%
mutate(data=map2(data, name, ~rename(.x, '{.y}' := value))) %>%
mutate(gr=str_extract(name, '^[^_]+'), .keep='unused') %>%
nest(data=-gr) %>%
mutate(data=map(data, ~bind_cols(.[[1]]))) %>%
print() %>%
# A tibble: 2 x 2
# gr data
# <chr> <list>
# 1 a <tibble [3 x 2]>
# 2 b <tibble [3 x 1]>
{ .$data[[1]] }
# A tibble: 3 x 2
# a_1 a_2
# <int> <int>
# 1 1 2
# 2 2 3
# 3 3 4
UPD: if possible, tidyverse solution
Using a neat little trick I learned lately you could do:
library(tidyr)
library(dplyr, warn = FALSE)
tibble(a_1 = 1:3, a_2 = 2:4, b_1 = 3:5) %>%
split.default(., gsub("_[0-9]", "", names(.))) %>%
lapply(nest, data = everything()) %>%
bind_rows(.id = "gr")
#> # A tibble: 2 × 2
#> gr data
#> <chr> <list>
#> 1 a <tibble [3 × 2]>
#> 2 b <tibble [3 × 1]>
Another possible solution, based on purrr::map_dfr:
library(tidyverse)
map_dfr(unique(str_remove(names(df), "_\\d+")),
~ tibble(gr = .x, nest(select(df, which(str_detect(names(df), .x))),
data = everything())))
#> # A tibble: 2 × 2
#> gr data
#> <chr> <list>
#> 1 a <tibble [3 × 2]>
#> 2 b <tibble [3 × 1]>
my version, a little more modified, tidyversed version of stepan's answer
tibble(a_1 = 1:3, a_2 = 2:4, b_1 = 3:5) %>%
split.default(str_extract(names(.), "^[^_]+")) %>%
map(nest, data = everything()) %>%
bind_rows(.id = "gr")
Couldn't find an alternative to split.default()
Related
I need to accumulate a value over nested tibbles.
Here is my simplified example:
tab <- data.frame(A = c("A","B","A","B"),
ID = c(1,1,2,2),
V1 = c(20,40,25,30),
V2 = c(0.2,0.8,0.3,0.7)
) %>%
group_by(ID) %>%
nest()
In my solution, the accumulate function use as initial value always 1000 instead of the accumulated "1000".
tab %>%
mutate(
G_i = purrr::accumulate(data,
function(G_i,data){
out <- data %>%
group_by(A) %>%
mutate(G_i = G_i+G_i*V2/V1)
sum(out$G_i)
},
.init = 1000)%>%
tail(-1)
)
A tibble: 2 × 3
# Groups: ID [2]
ID data G_i
<dbl> <list> <dbl>
1 1 <tibble [2 × 3]> 2030
2 2 <tibble [2 × 3]> 2035.
The desired output should deliver:
A tibble: 2 × 3
# Groups: ID [2]
ID data G_i
<dbl> <list> <dbl>
1 1 <tibble [2 × 3]> 2030
2 2 <tibble [2 × 3]> 4131.727
Thank you for your help.
As noted by #MikkoMarttila, all you need need to do is ungroup prior to using accumulate.
library(tidyverse)
tab %>%
ungroup %>%
mutate(
G_i = purrr::accumulate(data,
function(G_i,data){
out <- data %>%
group_by(A) %>%
mutate(G_i = G_i+G_i*V2/V1)
sum(out$G_i)
},
.init = 1000)%>%
tail(-1)
)
Output
ID data G_i
<dbl> <list> <dbl>
1 1 <tibble [2 × 3]> 2030.000
2 2 <tibble [2 × 3]> 4131.727
Simple example:
mydf <- data.frame(
x = 1:3,
y = c(1, 0, 1),
z = 1:3
) %>% group_by(x) %>% nest
mydf %>% mutate(blah = map_dbl(.x = data, ~ .x$z * 2))
Returns:
# A tibble: 3 x 3
# Groups: x [3]
x data blah
<int> <list> <dbl>
1 1 <tibble [1 × 2]> 2
2 2 <tibble [1 × 2]> 4
3 3 <tibble [1 × 2]> 6
I would like to mutate or map conditional on y. If y=1, then process with .x * 2 else (y = 0) then just use NA.
Desired result:
# A tibble: 3 x 3
# Groups: x [3]
x data blah
<int> <list> <dbl>
1 1 <tibble [1 × 2]> 2
2 2 <tibble [1 × 2]> NA
3 3 <tibble [1 × 2]> 6
Should I use mutate_if, mutate_across, map_if? How can I get this result?
In case the OP needs to retain the map model in their real use case, map2() is one possibility...
mydf %>% mutate(blah = map2(x, y, ~ifelse(.y == 1, .x * 2, NA)))
x y blah
1 1 1 2
2 2 0 NA
3 3 1 6
I have a dataframe which contains duplicate values in a list column and I want to keep only the first appearence of each unique value.
Let's say I have the following tibble:
df <- tribble(
~x, ~y,
1, tibble(a = 1:2, b = 2:3),
2, tibble(a = 1:2, b = 2:3),
3, tibble(a = 0:1, b = 0:1)
)
df
#> # A tibble: 3 x 2
#> x y
#> <dbl> <list>
#> 1 1 <tibble [2 x 2]>
#> 2 2 <tibble [2 x 2]>
#> 3 3 <tibble [2 x 2]>
The desired outcome is:
desired_df
#> # A tibble: 2 x 2
#> x y
#> <dbl> <list>
#> 1 1 <tibble [2 x 2]>
#> 2 3 <tibble [2 x 2]>
Wasn't y a list column I'd be able to use distinct(df, y, .keep_all = TRUE), but the fuction doesn't support list columns properly, as shown:
distinct(df, y, .keep_all = TRUE)
#> Warning: distinct() does not fully support columns of type `list`.
#> List elements are compared by reference, see ?distinct for details.
#> This affects the following columns:
#> - `y`
#> # A tibble: 3 x 2
#> x y
#> <dbl> <list>
#> 1 1 <tibble [2 x 2]>
#> 2 2 <tibble [2 x 2]>
#> 3 3 <tibble [2 x 2]>
Is there any "clean" way to achieve what I want?
One option is to use filter with duplicated
library(dplyr)
df %>%
filter(!duplicated(y))
I have come to an answer, but I think it's quite "wordy" (and I suspect it might be slow as well):
df <- df %>%
mutate(unique_list_id = match(y, unique(y))) %>%
group_by(unique_list_id) %>%
slice(1) %>%
ungroup() %>%
select(-unique_list_id)
df
#> # A tibble: 2 x 2
#> x y
#> <dbl> <list>
#> 1 1 <tibble [2 x 2]>
#> 2 3 <tibble [2 x 2]>
I would like to perform calculations on a nested data frame (stored as a list-column), and add the calculated variable back to each dataframe using purrr functions. I'll use this result to join to other data, and keeping it compact helps me to organize and examine it better. I can do this in a couple of steps, but it seems like there may be a solution I haven't come across. If there is a solution out there, I haven't been able to find it easily.
Load libraries. example requires the following packages (available on CRAN):
library(dplyr)
library(purrr)
library(RcppRoll) # to calculate rolling mean
Example data with 3 subjects, and repeated measurements over time:
test <- data_frame(
id= rep(1:3, each=20),
time = rep(1:20, 3),
var1 = rnorm(60, mean=10, sd=3),
var2 = rnorm(60, mean=95, sd=5)
)
Store the data as nested dataframe:
t_nest <- test %>% nest(-id)
id data
<int> <list>
1 1 <tibble [20 x 3]>
2 2 <tibble [20 x 3]>
3 3 <tibble [20 x 3]>
Perform calculations. I will calculate multiple new variables based on the data, although a solution for just one could be expanded later. The result of each calculation will be a numeric vector, same length as the input (n=20):
t1 <- t_nest %>%
mutate(var1_rollmean4 = map(data, ~RcppRoll::roll_mean(.$var1, n=4, align="right", fill=NA)),
var2_delta4 = map(data, ~(.$var2 - lag(.$var2, 3))*0.095),
var3 = map2(var1_rollmean4, var2_delta4, ~.x -.y))
id data var1_rollmean4 var2_delta4 var3
<int> <list> <list> <list> <list>
1 1 <tibble [20 x 3]> <dbl [20]> <dbl [20]> <dbl [20]>
2 2 <tibble [20 x 3]> <dbl [20]> <dbl [20]> <dbl [20]>
3 3 <tibble [20 x 3]> <dbl [20]> <dbl [20]> <dbl [20]>
my solution is to unnest this data, and then nest again. There doesn't seem to be anything wrong with this, but seems like a better solution may exist.
t1 %>% unnest %>%
nest(-id)
id data
<int> <list>
1 1 <tibble [20 x 6]>
2 2 <tibble [20 x 6]>
3 3 <tibble [20 x 6]>
This other solution (from SO 42028710) is close, but not quite because it is a list rather than nested dataframes:
map_df(t_nest$data, ~ mutate(.x, var1calc = .$var1*100))
I've found quite a bit of helpful information using the purrr Cheatsheet but can't quite find the answer.
You can wrap another mutate when mapping through the data column and add the columns in each nested tibble:
t11 <- t_nest %>%
mutate(data = map(data,
~ mutate(.x,
var1_rollmean4 = RcppRoll::roll_mean(var1, n=4, align="right", fill=NA),
var2_delta4 = (var2 - lag(var2, 3))*0.095,
var3 = var1_rollmean4 - var2_delta4
)
))
t11
# A tibble: 3 x 2
# id data
# <int> <list>
#1 1 <tibble [20 x 6]>
#2 2 <tibble [20 x 6]>
#3 3 <tibble [20 x 6]>
unnest-nest method, and then reorder the columns inside:
nest_unnest <- t1 %>%
unnest %>% nest(-id) %>%
mutate(data = map(data, ~ select(.x, time, var1, var2, var1_rollmean4, var2_delta4, var3)))
identical(nest_unnest, t11)
# [1] TRUE
It seems like for what you're trying to do, nesting is not necessary
library(tidyverse)
library(zoo)
test %>%
group_by(id) %>%
mutate(var1_rollmean4 = rollapplyr(var1, 4, mean, fill=NA),
var2_delta4 = (var2 - lag(var2, 3))*0.095,
var3 = (var1_rollmean4 - var2_delta4))
# A tibble: 60 x 7
# Groups: id [3]
# id time var1 var2 var1_rollmean4 var2_delta4 var3
# <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 1 1 9.865199 96.45723 NA NA NA
# 2 1 2 9.951429 92.78354 NA NA NA
# 3 1 3 12.831509 95.00553 NA NA NA
# 4 1 4 12.463664 95.37171 11.277950 -0.10312483 11.381075
# 5 1 5 11.781704 92.05240 11.757076 -0.06945881 11.826535
# 6 1 6 12.756932 92.15666 12.458452 -0.27064269 12.729095
# 7 1 7 12.346409 94.32411 12.337177 -0.09952197 12.436699
# 8 1 8 10.223695 100.89043 11.777185 0.83961377 10.937571
# 9 1 9 4.031945 87.38217 9.839745 -0.45357658 10.293322
# 10 1 10 11.859477 97.96973 9.615382 0.34633428 9.269047
# ... with 50 more rows
Edit You could nest the result with %>% nest(-id) still
If you still prefer to nest or are nesting for other reasons, it would go like
t1 <- t_nest %>%
mutate(data = map(data, ~.x %>% mutate(...)))
That is, you mutate on .x within the map statement. This will treat data as a data.frame and mutate will column-bind results to it.
Here's a dumb example dataframe:
df <- data_frame(A = c(rep(1, 5), rep(2, 4)), B = 1:9) %>%
group_by(A) %>%
nest()
which looks like this:
> df
# A tibble: 2 × 2
A data
<dbl> <list>
1 1 <tibble [5 × 1]>
2 2 <tibble [4 × 1]>
I would like to add a third column called N with entries equal to the number of rows in each nested data_frame in data. I figured this would work:
> df %>%
+ mutate(N = nrow(data))
Error: Unsupported type NILSXP for column "N"
What's going wrong?
Combining dplyr and purrr you could do:
library(tidyverse)
df %>%
mutate(n = map_dbl(data, nrow))
#> # A tibble: 2 × 3
#> A data n
#> <dbl> <list> <dbl>
#> 1 1 <tibble [5 × 1]> 5
#> 2 2 <tibble [4 × 1]> 4
I like this approach, because you stay within your usual workflow, creating a new column within mutate, but leveraging the map_*-family, since you need to operate on a list.
You could do:
df %>%
rowwise() %>%
mutate(N = nrow(data))
Which gives:
#Source: local data frame [2 x 3]
#Groups: <by row>
#
## A tibble: 2 × 3
# A data N
# <dbl> <list> <int>
#1 1 <tibble [5 × 1]> 5
#2 2 <tibble [4 × 1]> 4
With dplyr:
df %>%
group_by(A) %>%
mutate(N = nrow(data.frame(data)))
A data N
<dbl> <list> <int>
1 1 <tibble [5 × 1]> 5
2 2 <tibble [4 × 1]> 4