I have a dataset like this (in the actual dataset, I have more columns like subj01):
# A tibble: 10 x 4
item subj01 subj02 subj03
<int> <dbl> <dbl> <dbl>
1 1 1 1 1
2 2 2 2 6
3 3 5 5 9
4 4 9 6 NA
5 5 10 8 NA
6 6 NA 9 NA
7 7 NA 10 NA
8 8 NA NA NA
9 9 NA NA NA
10 10 NA NA NA
I created the dataset using the code below.
data = tibble(item = 1:10, subj01 = c(1,2,5,9,10,NA,NA,NA,NA,NA), subj02 = c(1,2,5,6,8,9,10,NA,NA,NA), subj03 = c(1,6,9,NA,NA,NA,NA,NA,NA,NA))
I would like to reorder all the columns beginning with "subj" so that the position of the values match that in the item column.
That is, for this example dataset, I would like to end up with this:
# A tibble: 10 x 4
item subj01 subj02 subj03
<int> <dbl> <dbl> <dbl>
1 1 1 1 1
2 2 2 2 NA
3 3 NA NA NA
4 4 NA NA NA
5 5 5 5 NA
6 6 NA 6 6
7 7 NA NA NA
8 8 NA 8 NA
9 9 9 9 9
10 10 10 10 NA
I've figured that I can match and re-order one column by running this:
data$subj01[match(data$item,data$subj01)]
[1] 1 2 NA NA 5 NA NA NA 9 10
But I am struggling to apply this across multiple columns (ideally I'd like to embed the command in a dplyr pipe).
I tried the command below, but this gave me an error "Error in mutate(x. = x.[match(item, x.)]) : object 'x.' not found".
data = data %>% across(mutate(x.=x.[match(item,x.)]))
I'd appreciate any suggestions! Thank you.
library(tidyverse)
data %>%
pivot_longer(-item) %>%
filter(!is.na(value)) %>%
mutate(item = value) %>%
complete(item = 1:10, name) %>%
pivot_wider(names_from = name, values_from = value)
# A tibble: 10 × 4
item subj01 subj02 subj03
<dbl> <dbl> <dbl> <dbl>
1 1 1 1 1
2 2 2 2 NA
3 3 NA NA NA
4 4 NA NA NA
5 5 5 5 NA
6 6 NA 6 6
7 7 NA NA NA
8 8 NA 8 NA
9 9 9 9 9
10 10 10 10 NA
Related
I have a data frame grouped by 'id' and a variable 'age' which contains missing values, NA.
Within each 'id', I want to replace missing values of 'age', but only "fill up" before the first non-NA value.
data <- data.frame(id=c(1,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3),
age=c(NA,6,NA,8,NA,NA,NA,NA,3,8,NA,NA,NA,7,NA,9))
id age
1 1 NA
2 1 6 # first non-NA in id = 1. Fill up from here
3 1 NA
4 1 8
5 1 NA
6 1 NA
7 2 NA
8 2 NA
9 2 3 # first non-NA in id = 2. Fill up from here
10 2 8
11 2 NA
12 3 NA
13 3 NA
14 3 7 # first non-NA in id = 3. Fill up from here
15 3 NA
16 3 9
Expected output:
1 1 6
2 1 6
3 1 NA
4 1 8
5 1 NA
6 1 NA
7 2 3
8 2 3
9 2 3
10 2 8
11 2 NA
12 3 7
13 3 7
14 3 7
15 3 NA
16 3 9
I tried using fill with .direction = "up" like this:
library(dplyr)
library(tidyr)
data1 <- data %>% group_by(id) %>%
fill(!is.na(age[1]), .direction = "up")
You could use cumall(is.na(age)) to find the positions before the first non-NA value.
library(dplyr)
data %>%
group_by(id) %>%
mutate(age2 = replace(age, cumall(is.na(age)), age[!is.na(age)][1])) %>%
ungroup()
# A tibble: 16 × 3
id age age2
<dbl> <dbl> <dbl>
1 1 NA 6
2 1 6 6
3 1 NA NA
4 1 8 8
5 1 NA NA
6 1 NA NA
7 2 NA 3
8 2 NA 3
9 2 3 3
10 2 8 8
11 2 NA NA
12 3 NA 7
13 3 NA 7
14 3 7 7
15 3 NA NA
16 3 9 9
Another option (agnostic about where the missing and non-missing values start) could be:
data %>%
group_by(id) %>%
mutate(rleid = with(rle(is.na(age)), rep(seq_along(lengths), lengths)),
age2 = ifelse(rleid == min(rleid[is.na(age)]),
age[rleid == (min(rleid[is.na(age)]) + 1)][1],
age))
id age rleid age2
<dbl> <dbl> <int> <dbl>
1 1 NA 1 6
2 1 6 2 6
3 1 NA 3 NA
4 1 8 4 8
5 1 NA 5 NA
6 1 NA 5 NA
7 2 NA 1 3
8 2 NA 1 3
9 2 3 2 3
10 2 8 2 8
11 2 NA 3 NA
12 3 NA 1 7
13 3 NA 1 7
14 3 7 2 7
15 3 NA 3 NA
16 3 9 4 9
I have a list of dataframes with either 2 or 4 columns.
a <- data.frame(a=1:10,
b=1:10,
c=1:10,
d=1:10)
b <- data.frame(a=1:10,
b=1:10)
list_of_df <- list(a,b)
I want to add 2 empty columns to each dataframe with only 2 columns.
I've tried this lapply approach:
lapply(list_of_df, function(x) ifelse(ncol(x) < 4,x%>%add_column(empty=NA),x <- x))
Which does not work unfortunately. How can I fix this?
I came up with something similar:
add_col <- function(x){
col_to_add <- 4 - ncol(x)
if(col_to_add == 0) return(x)
z <- rep(NA, nrow(x))
for (i in 1:col_to_add){
x <- cbind(x, z)
}
x
}
lapply(list_of_df, add_col)
I would use a for loop to avoid copying the whole list:
for (i in seq_along(list_of_df)) {
n_columns = ncol(list_of_df[[i]])
if (n_columns == 2L) {
list_of_df[[i]][c('empty1', 'empty2')] <- NA
}
}
Result:
> list_of_df
[[1]]
a b c d
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
5 5 5 5 5
6 6 6 6 6
7 7 7 7 7
8 8 8 8 8
9 9 9 9 9
10 10 10 10 10
[[2]]
a b empty1 empty2
1 1 1 NA NA
2 2 2 NA NA
3 3 3 NA NA
4 4 4 NA NA
5 5 5 NA NA
6 6 6 NA NA
7 7 7 NA NA
8 8 8 NA NA
9 9 9 NA NA
10 10 10 NA NA
We could use bind_rows and then group_split and map from purrr to remove the id_Group column:
library(dplyr)
library(purrr)
bind_rows(list_of_df) %>%
group_split(id_Group =cumsum(a==1)) %>%
map(., ~ (.x %>% ungroup() %>%
select(-id_Group)))
[[1]]
# A tibble: 10 x 4
a b c d
<int> <int> <int> <int>
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
5 5 5 5 5
6 6 6 6 6
7 7 7 7 7
8 8 8 8 8
9 9 9 9 9
10 10 10 10 10
[[2]]
# A tibble: 10 x 4
a b c d
<int> <int> <int> <int>
1 1 1 NA NA
2 2 2 NA NA
3 3 3 NA NA
4 4 4 NA NA
5 5 5 NA NA
6 6 6 NA NA
7 7 7 NA NA
8 8 8 NA NA
9 9 9 NA NA
10 10 10 NA NA
This question already has answers here:
How can I automatically create n lags in a timeseries?
(3 answers)
Closed 1 year ago.
Given this tibble:
tibble(x = c(1:9))
I want to add a column x_lag_1 = c(NA,1:8), a column x_lag_2 = c(NA,NA,1:7), etc.
Up to x_lag_n.
This can be quick with data.table:
library(data.table)
n <- seq(4)
setDT(df)[, paste0('x_lag_', n) := shift(x, n)]
df
x x_lag_1 x_lag_2 x_lag_3 x_lag_4
1: 1 NA NA NA NA
2: 2 1 NA NA NA
3: 3 2 1 NA NA
4: 4 3 2 1 NA
5: 5 4 3 2 1
6: 6 5 4 3 2
7: 7 6 5 4 3
8: 8 7 6 5 4
9: 9 8 7 6 5
You may use map_dfc to add n new columns.
library(dplyr)
library(purrr)
df <- tibble(x = c(1:9))
n <- 3
bind_cols(df, map_dfc(seq_len(n), ~df %>%
transmute(!!paste0('x_lag', .x) := lag(x, .x))))
# x x_lag1 x_lag2 x_lag3
# <int> <int> <int> <int>
#1 1 NA NA NA
#2 2 1 NA NA
#3 3 2 1 NA
#4 4 3 2 1
#5 5 4 3 2
#6 6 5 4 3
#7 7 6 5 4
#8 8 7 6 5
#9 9 8 7 6
Edit 2: Reworked the answer to contemplate the case of a grouped df.
library(tidyverse)
set.seed(123)
df <- tibble(group = sample(letters[1:3], 30, replace = TRUE), x = c(1:30))
formulas <- seq(3, 12, 3) %>%
map(~ as.formula(str_glue("~lag(.,n={.x})"))) %>%
set_names(str_c("lag", seq(3, 12, 3)))
df %>%
summarise(x, across(x, lst(!!!formulas)))
#> # A tibble: 30 × 5
#> x x_lag3 x_lag6 x_lag9 x_lag12
#> <int> <int> <int> <int> <int>
#> 1 1 NA NA NA NA
#> 2 2 NA NA NA NA
#> 3 3 NA NA NA NA
#> 4 4 1 NA NA NA
#> 5 5 2 NA NA NA
#> 6 6 3 NA NA NA
#> 7 7 4 1 NA NA
#> 8 8 5 2 NA NA
#> 9 9 6 3 NA NA
#> 10 10 7 4 1 NA
#> # … with 20 more rows
df %>%
group_by(group) %>%
summarise(x, across(x, lst(!!!formulas)), .groups = "drop")
#> # A tibble: 30 × 6
#> group x x_lag3 x_lag6 x_lag9 x_lag12
#> <chr> <int> <int> <int> <int> <int>
#> 1 a 10 NA NA NA NA
#> 2 a 13 NA NA NA NA
#> 3 a 16 NA NA NA NA
#> 4 a 19 10 NA NA NA
#> 5 a 20 13 NA NA NA
#> 6 a 21 16 NA NA NA
#> 7 a 22 19 10 NA NA
#> 8 a 27 20 13 NA NA
#> 9 b 4 NA NA NA NA
#> 10 b 6 NA NA NA NA
#> # … with 20 more rows
Created on 2021-12-30 by the reprex package (v2.0.1)
I want to group my data in different chunks when the data is continuous. Trying to get the group column from dummy data like this:
a b group
<dbl> <dbl> <dbl>
1 1 1 1
2 2 2 1
3 3 3 1
4 4 NA NA
5 5 NA NA
6 6 NA NA
7 7 12 2
8 8 15 2
9 9 NA NA
10 10 25 3
I tried using
test %>% mutate(test = complete.cases(.)) %>%
group_by(group = cumsum(test == TRUE)) %>%
select(group, everything())
But it doesn't work as expected:
group a b test
<int> <dbl> <dbl> <lgl>
1 1 1 1 TRUE
2 2 2 2 TRUE
3 3 3 3 TRUE
4 3 4 NA FALSE
5 3 5 NA FALSE
6 3 6 NA FALSE
7 4 7 12 TRUE
8 5 8 15 TRUE
9 5 9 NA FALSE
10 6 10 25 TRUE
Any advice?
Using rle in base R -
transform(df, group1 = with(rle(!is.na(b)), rep(cumsum(values), lengths))) |>
transform(group1 = replace(group1, is.na(b), NA))
# a b group group1
#1 1 1 1 1
#2 2 2 1 1
#3 3 3 1 1
#4 4 NA NA NA
#5 5 NA NA NA
#6 6 NA NA NA
#7 7 12 2 2
#8 8 15 2 2
#9 9 NA NA NA
#10 10 25 3 3
A couple of approaches to consider if you wish to use dplyr for this.
First, you could look at transition from non-complete cases (using lag) to complete cases.
library(dplyr)
test %>%
mutate(test = complete.cases(.)) %>%
group_by(group = cumsum(test & !lag(test, default = F))) %>%
mutate(group = replace(group, !test, NA))
Alternatively, you could add row numbers to your data.frame. Then, you could filter to include only complete cases, and group_by enumerating with cumsum based on gaps in row numbers. Then, join back to original data.
test$rn <- seq.int(nrow(test))
test %>%
filter(complete.cases(.)) %>%
group_by(group = c(0, cumsum(diff(rn) > 1)) + 1) %>%
right_join(test) %>%
arrange(rn) %>%
dplyr::select(-rn)
Output
a b group
<int> <int> <dbl>
1 1 1 1
2 2 2 1
3 3 3 1
4 4 NA NA
5 5 NA NA
6 6 NA NA
7 7 12 2
8 8 15 2
9 9 NA NA
10 10 25 3
Using data.table, get rleid then remove group IDs for NAs, then fix the sequence with factor to integer conversion:
library(data.table)
setDT(test)[, group1 := {
x <- complete.cases(test)
grp <- rleid(x)
grp[ !x ] <- NA
as.integer(factor(grp))
}]
# a b group group1
# 1: 1 1 1 1
# 2: 2 2 1 1
# 3: 3 3 1 1
# 4: 4 NA NA NA
# 5: 5 NA NA NA
# 6: 6 NA NA NA
# 7: 7 12 2 2
# 8: 8 15 2 2
# 9: 9 NA NA NA
# 10: 10 25 3 3
I have a dataframe with three columns Time, observed value (Obs.Value), and an interpolated value (Interp.Value). If the value of Obs.Value is NA then the value of Interp.Value should also be NA. I can make the whole row NA but I need to keep the Time value.
Here is the repex:
dat <- data.frame(matrix(ncol = 3, nrow = 10))
x <- c("Time", "Obs.Value", "Interp.Value")
colnames(dat) <- x
dat$Time <- seq(1,10,1)
dat$Obs.Value <- c(5,6,7,NA,NA,5,4,3,NA,2)
interp <- approx(dat$Time,dat$Obs.Value,dat$Time)
dat$Interp.Value <- round(interp$y,1)
Here is the code that makes the whole row NA
dat[with(dat, is.na(Obs.Value)|is.na("Interp.Value")),] <- NA
Here is what the output should look like:
Time Obs.Value Interp.Value
1 1 5 5
2 2 6 6
3 3 7 7
4 4 NA NA
5 5 NA NA
6 6 5 5
7 7 4 4
8 8 3 3
9 9 NA NA
10 10 2 2
dat$Interp.Value[is.na(dat$Obs.Value)] <- NA
dat
# Time Obs.Value Interp.Value
# 1 1 5 5
# 2 2 6 6
# 3 3 7 7
# 4 4 NA NA
# 5 5 NA NA
# 6 6 5 5
# 7 7 4 4
# 8 8 3 3
# 9 9 NA NA
# 10 10 2 2
Or if either column being NA is sufficient, then
dat[!complete.cases(dat[,-1]),-1] <- NA
If there is only one column to change #r2evans' answer is pretty straightforward and way to go. If there are more than one column that you want to change you can use across in dplyr.
library(dplyr)
dat %>%
mutate(across(-c(Time,Obs.Value), ~replace(., is.na(Obs.Value), NA)))
# Time Obs.Value Interp.Value
#1 1 5 5
#2 2 6 6
#3 3 7 7
#4 4 NA NA
#5 5 NA NA
#6 6 5 5
#7 7 4 4
#8 8 3 3
#9 9 NA NA
#10 10 2 2