I have a data frame with two factors (distance
) and years (years
). I would like to complete all years
values for every factor by 0.
i.e. from this:
distance years area
1 NPR 3 10
2 NPR 4 20
3 NPR 7 30
4 100 1 40
5 100 5 50
6 100 6 60
get this:
distance years area
1 NPR 1 0
2 NPR 2 0
3 NPR 3 10
4 NPR 4 20
5 NPR 5 0
6 NPR 6 0
7 NPR 7 30
8 100 1 40
9 100 2 0
10 100 3 0
11 100 4 0
12 100 5 50
13 100 6 60
14 100 7 0
I tried to apply expand
function:
library(tidyr)
library(dplyr, warn.conflicts = FALSE)
expand(df, years = 1:7)
but this just produces one column data frame and does not expand the original one:
# A tibble: 7 x 1
years
<int>
1 1
2 2
3 3
4 4
5 5
6 6
7 7
or expand.grid
does not working neither:
require(utils)
expand.grid(df, years = 1:7)
Error in match.names(clabs, names(xi)) :
names do not match previous names
In addition: Warning message:
In format.data.frame(x, digits = digits, na.encode = FALSE) :
corrupt data frame: columns will be truncated or padded with NAs
Is there a simple way to expand
my data frame? And how to expand it based on two categories: distance
and uniqueLoc
?
distance <- rep(c("NPR", "100"), each = 3)
years <-c(3,4,7, 1,5,6)
area <-seq(10,60,10)
uniqueLoc<-rep(c("a", "b"), 3)
df<-data.frame(uniqueLoc, distance, years, area)
> df
uniqueLoc distance years area
1 a NPR 3 10
2 b NPR 4 20
3 a NPR 7 30
4 b 100 1 40
5 a 100 5 50
6 b 100 6 60
Create a Dataframe So, in these cases where your data has more and more missing values, you can make use of the fill function in R to fill the corresponding values/neighbor values in place of missing data.
To find the unique pair combinations of an R data frame column values, we can use combn function along with unique function.
We can use complete. cases() to print a logical vector that indicates complete and missing rows (i.e. rows without NA). Rows 2 and 3 are complete; Rows 1, 4, and 5 have one or more missing values. We can also create a complete subset of our example data by using the complete.
You can use the tidyr::complete
function:
complete(df, distance, years = full_seq(years, period = 1), fill = list(area = 0))
# A tibble: 14 x 3
distance years area
<fct> <dbl> <dbl>
1 100 1. 40.
2 100 2. 0.
3 100 3. 0.
4 100 4. 0.
5 100 5. 50.
6 100 6. 60.
7 100 7. 0.
8 NPR 1. 0.
9 NPR 2. 0.
10 NPR 3. 10.
11 NPR 4. 20.
12 NPR 5. 0.
13 NPR 6. 0.
14 NPR 7. 30.
or slightly shorter:
complete(df, distance, years = 1:7, fill = list(area = 0))
Combining tidyr::pivot_wider()
and tidyr::pivot_longer()
also makes implicit missing values explicit.
# Load packages
library(tidyverse)
# Your data
df <- tibble(distance = c(rep("NPR",3), rep(100, 3)),
years = c(3,4,7,1,5,6),
area = seq(10, 60, by = 10))
# Solution
df %>%
pivot_wider(names_from = years,
values_from = area) %>% # pivot_wider() makes your implicit missing values explicit
pivot_longer(2:7, names_to = "years",
values_to = "area") %>% # Turn to your desired format (long)
mutate(area = replace_na(area, 0)) # Replace missing values (NA) with 0s
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