基于r中另一个数据帧中的列向数据帧添加值

Question

Hej!

I have a dataset with three columns: Value, Country and Species. For example a data frame that can be created like this:

Value <- c(1,2,3,4,2,6,3,5)
Country <- c("Country A", "Country A", "Country A", "Country B", "Country B", "Country B", "Country B", "Country B")
Species <- c("Species A", "Species B", "Species C", "Species A", "Species B", "Species C", "Species D", "Species E")
p <- data.frame(Value, Country, Species)

Then I also have another data frame that is empty, except for the first column. Created like this:

Species2 <- levels(p$Species)
Country2 <- levels(p$Country)
x <- data.frame(Country2)
x[Species2] <- NA

And now I'm looking for a way to combine these two datasets, so that I can put the values from dataset p into the empty cells from dataset x, based on the names from the Country and the Species. So for example the Value with Country A and Species A was 1 (in dataframe p) so I want a 1 in dataframe x in the cell from the column from Species A and where the first column says Country A.

I hope this question makes sense, and that somebody could help me! Thank you!

Answer 1

If your datasets are different you can reshape to long x and then merge with p using left_join() . After that, reshape to wide. Next the code with tidyverse functions:

library(tidyverse)
#Code
newx <- x %>% pivot_longer(-Country2) %>%
  rename(Country=Country2,Species=name) %>%
  left_join(p) %>%
  mutate(value=Value) %>% select(-Value) %>% 
  pivot_wider(names_from = Species,values_from=value) %>%
  rename(Country2=Country)
 

Output:

# A tibble: 2 x 6
  Country2  `Species A` `Species B` `Species C` `Species D` `Species E`
  <fct>           <dbl>       <dbl>       <dbl>       <dbl>       <dbl>
1 Country A           1           2           3          NA          NA
2 Country B           4           2           6           3           5

Answer 2

In base R , we can do this easily

xtabs(Value ~ Country + Species, p)

-output

#           Species
#Country     Species A Species B Species C Species D Species E
#  Country A         1         2         3         0         0
#  Country B         4         2         6         3         5

Answer 3

Maybe you can try reshape which transform long data frame to a wide one

> reshape(p,direction = "wide",idvar = "Country",timevar = "Species")
    Country Value.Species A Value.Species B Value.Species C Value.Species D
1 Country A               1               2               3              NA
4 Country B               4               2               6               3
  Value.Species E
1              NA
4               5