How would flash filling based on observation data that is tied to another column look for R?
Example
df <- data.frame(A = c(1,1,1,1,2,2,2,2),
B = c('my initials1', NA, NA, NA,NA,'my initials2',NA,NA))
Is there a way to have my initials (which are tied to 1) fill down?
I've tried
df |> fill(B)
But what happens is it fills down to the next observation in B. Where I'd like the fill to stop at the end of 1 based on column A instead.
I was just thinking of copying the data to a separate data frame and joining it on A to achieve that.
We may use
library(dplyr)
df %>%
group_by(A) %>%
mutate(B = first(B[!is.na(B)])) %>%
ungroup
# A tibble: 8 × 2
A B
<dbl> <chr>
1 1 my initials1
2 1 my initials1
3 1 my initials1
4 1 my initials1
5 2 my initials2
6 2 my initials2
7 2 my initials2
8 2 my initials2
You can group by the first column, then it will only fill down within the group:
library(tidyverse)
df %>%
group_by(A) %>%
fill(B, .direction = "down")
Output
A B
<dbl> <chr>
1 1 my initials1
2 1 my initials1
3 1 my initials1
4 1 my initials1
5 2 NA
6 2 my initials2
7 2 my initials2
8 2 my initials2
Or if you want to fill in for every group, then you can change the .direction argument:
df %>%
group_by(A) %>%
fill(B, .direction = "updown")
Output
A B
<dbl> <chr>
1 1 my initials1
2 1 my initials1
3 1 my initials1
4 1 my initials1
5 2 my initials2
6 2 my initials2
7 2 my initials2
8 2 my initials2
Related
I am trying to fill NA values of my dataframe. However, I would like to fill them based on the first value of each group.
#> df = data.frame(
group = c(rep("A", 4), rep("B", 4)),
val = c(1, 2, NA, NA, 4, 3, NA, NA)
)
#> df
group val
1 A 1
2 A 2
3 A NA
4 A NA
5 B 4
6 B 3
7 B NA
8 B NA
#> fill(df, val, .direction = "down")
group val
1 A 1
2 A 2
3 A 2 # -> should be 1
4 A 2 # -> should be 1
5 B 4
6 B 3
7 B 3 # -> should be 4
8 B 3 # -> should be 4
Can I do this with tidyr::fill()? Or is there another (more or less elegant) way how to do this? I need to use this in a longer chain (%>%) operation.
Thank you very much!
Use tidyr::replace_na() and dplyr::first() (or val[[1]]) inside a grouped mutate():
library(dplyr)
library(tidyr)
df %>%
group_by(group) %>%
mutate(val = replace_na(val, first(val))) %>%
ungroup()
#> # A tibble: 8 × 2
#> group val
#> <chr> <dbl>
#> 1 A 1
#> 2 A 2
#> 3 A 1
#> 4 A 1
#> 5 B 4
#> 6 B 3
#> 7 B 4
#> 8 B 4
PS - #richarddmorey points out the case where the first value for a group is NA. The above code would keep all NA values as NA. If you'd like to instead replace with the first non-missing value per group, you could subset the vector using !is.na():
df %>%
group_by(group) %>%
mutate(val = replace_na(val, first(val[!is.na(val)]))) %>%
ungroup()
Created on 2022-11-17 with reprex v2.0.2
This should work, which uses dplyr's case_when
library(dplyr)
df %>%
group_by(group) %>%
mutate(val = case_when(
is.na(val) ~ val[1],
TRUE ~ val
))
Output:
group val
<chr> <dbl>
1 A 1
2 A 2
3 A 1
4 A 1
5 B 4
6 B 3
7 B 4
8 B 4
I have a data frame like this:
Team
GF
A
3
B
5
A
2
A
3
B
1
B
6
Looking for output like this (just an additional column):
Team
x
avg(X)
A
3
0
B
5
0
A
2
3
A
3
2.5
B
1
5
B
6
3
avg(x) is the average of all previous instances of x where Team is the same. I have the following R code which gets the overall average, however I'm looking for the "step-wise" average.
new_df <- df %>% group_by(Team) %>% summarise(avg_x = mean(x))
Is there a way to vectorize this while only evaluating the previous rows on each "iteration"?
You want the cummean() function from dplyr, combined with lag():
df %>% group_by(Team) %>% mutate(avg_x = replace_na(lag(cummean(x)), 0))
Producing the following:
# A tibble: 6 × 3
# Groups: Team [2]
Team x avg_x
<chr> <dbl> <dbl>
1 A 3 0
2 B 5 0
3 A 2 3
4 A 3 2.5
5 B 1 5
6 B 6 3
As required.
Edit 1:
As #Ritchie Sacramento pointed out, the following is cleaner and clearer:
df %>% group_by(Team) %>% mutate(avg_x = lag(cummean(x), default = 0))
I want to subset the first date per observation per id. For example, just get the rows for the first date in which observations A and B appeared. If we have the following dataset:
df =
id date Observation
1 3 A
1 2 B
1 8 B
2 5 B
2 3 A
2 9 A
the outcome should look like this:
df =
id date Observation
1 3 A
1 2 B
2 5 B
2 3 A
thanks
If you don't mind the order being different, it can be accomplished using dplyr by grouping then slicing:
library(tidyverse)
df <- read_table("id date Observation
1 3 A
1 2 B
1 8 B
2 5 B
2 3 A
2 9 A")
df %>%
group_by(id, Observation) %>%
slice(1)
#> # A tibble: 4 x 3
#> # Groups: id, Observation [4]
#> id date Observation
#> <dbl> <dbl> <chr>
#> 1 1 3 A
#> 2 1 2 B
#> 3 2 3 A
#> 4 2 5 B
Created on 2021-04-12 by the reprex package (v1.0.0)
library(dplyr)
df %>%
group_by(id, Observation) %>%
slice(1) %>%
ungroup()
# OR
df %>%
group_by(id, Observation) %>%
filter(row_number() == 1) %>%
ungroup()
For the dataframe below I want to add the original values for Var_x after a group_by on ID and event and a max() on quest, but I cannot get my code right. Any suggestions? By the way, in my original dataframe more than 1 column needs to be added.
df <- data.frame(ID = c(1,1,1,1,1,1,2,2,2,3,3,3),
quest = c(1,1,2,2,3,3,1,2,3,1,2,3),
event = c("A","B","A","B","A",NA,"C","D","C","D","D",NA),
VAR_X = c(2,4,3,6,3,NA,6,4,5,7,5,NA))
Code:
df %>%
group_by(ID,event) %>%
summarise(quest = max(quest))
Desired output:
ID quest event VAR_X
1 1 2 B 6
2 1 3 A 3
3 2 2 D 4
4 2 3 C 5
5 3 2 D 5
Start by omiting the na values and in the end do an inner_join with the original data set.
df %>%
na.omit() %>%
group_by(ID, event) %>%
summarise(quest = max(quest)) %>%
inner_join(df, by = c("ID", "event", "quest"))
## A tibble: 5 x 4
## Groups: ID [3]
# ID event quest VAR_X
# <dbl> <fct> <dbl> <dbl>
#1 1 A 3 3
#2 1 B 2 6
#3 2 C 3 5
#4 2 D 2 4
#5 3 D 2 5
df %>%
drop_na() %>% # remove if necessary ..
group_by(ID, event) %>%
filter(quest == max(quest)) %>%
ungroup()
# A tibble: 5 x 4
# ID quest event VAR_X
#<dbl> <dbl> <chr> <dbl>
# 1 1 2 B 6
# 2 1 3 A 3
# 3 2 2 D 4
# 4 2 3 C 5
# 5 3 2 D 5
I have a grouped df and I would like to add additional rows to the top of the groups that match with a variable (item_code) from the df.
The additional rows do not have an id column. The additional rows should not be duplicated within the groups of df.
Example data:
df <- as.tibble(data.frame(id=rep(1:3,each=2),
item_code=c("A","A","B","B","B","Z"),
score=rep(1,6)))
additional_rows <- as.tibble(data.frame(item_code=c("A","Z"),
score=c(6,6)))
What I tried
I found this post and tried to apply it:
Add row in each group using dplyr and add_row()
df %>% group_by(id) %>% do(add_row(additional_rows %>%
filter(item_code %in% .$item_code)))
What I get:
# A tibble: 9 x 3
# Groups: id [3]
id item_code score
<int> <fct> <dbl>
1 1 A 6
2 1 Z 6
3 1 NA NA
4 2 A 6
5 2 Z 6
6 2 NA NA
7 3 A 6
8 3 Z 6
9 3 NA NA
What I am looking for:
# A tibble: 6 x 3
id item_code score
<int> <fct> <dbl>
1 1 A 6
2 1 A 1
3 1 A 1
4 2 B 1
5 2 B 1
6 3 B 1
7 3 Z 6
8 3 Z 1
This should do the trick:
library(plyr)
df %>%
join(subset(df, item_code %in% additional_rows$item_code, select = c(id, item_code)) %>%
join(additional_rows) %>%
subset(!duplicated(.)), type = "full") %>%
arrange(id, item_code, -score)
Not sure if its the best way, but it works
Edit: to get the score in the same order added the other arrange terms
Edit 2: alright, there should now be no duplicated rows added from the additional rows as per your comment