I have these four data frames of different lengths that I am trying to match and merge into a single data frame.
Basically, each dataset consists of algal species and their respective densities at different depths in the water column. Each depth has different species and what I am trying to do is find the species that are present between data frames and return their respective densities. If there is not a match in any data frame, then I want the resulting value to be 0.
For example, if Microcystis aeruginosa is present in df0 anf df5, but not df10 or df20, I want the densities to be reported for df0 and df5, but 0 to be reported for df10 and df20. I also need for the species to be lined up with each other. So that it would look like, for example, in a single column "Microcystis aeruginosa 456, 887, 0, 0."
I know this is a lot and I don't even know if it's possible, but any help would be so greatly appreciated!
It is possible however it is a bit problematic due to the format of the data. The data needs a lot of wrangling and it would have been better to have it in a tidy format. You can check more about tidy format in the following article. It will help you in any situation in which you work with data, not just this one.
On the other hand, you might need the data in this specific format so I have posted below the code, that will transform your data in a tidy format, will bring it in one dataset, replace the missing values with 0's and transform the data back in the original format.
The code might not be the best elegant, however it will do what you need.
library(tidyverse)
# We will create some additional datasets to help in better transforming them
df0_longer <- df0 %>% pivot_longer(colnames(df0), names_to = "Species", values_to = "Density_0")
df10_longer <- df10 %>% pivot_longer(colnames(df10), names_to = "Species", values_to = "Density_10")
df20_longer <- df20 %>% pivot_longer(colnames(df20), names_to = "Species", values_to = "Density_20")
df5_longer <- df5 %>% pivot_longer(colnames(df5), names_to = "Species", values_to = "Density_5")
# We will create a list of all the species in all the tables so we can take them all into accout
df <- df0_longer["Species"] %>%
rbind(df10_longer["Species"]) %>%
rbind(df20_longer["Species"]) %>%
rbind(df5_longer["Species"]) %>%
distinct()
# Now that we have all the species in one column, we can bring the density information using left_join()
df <- df %>%
left_join(df0_longer, by = "Species") %>%
left_join(df10_longer, by = "Species") %>%
left_join(df20_longer, by = "Species") %>%
left_join(df5_longer, by = "Species") %>%
# And to replace missing values with 0's
mutate(across(everything(), ~ ifelse(is.na(.), 0, .)))
# Now we need to transform the data back and bind it to each other
final_df <- df %>% select(Species, Density_0) %>% pivot_wider(names_from = "Species", values_from = "Density_0") %>%
rbind(df %>% select(Species, Density_10) %>% pivot_wider(names_from = "Species", values_from = "Density_10")) %>%
rbind(df %>% select(Species, Density_20) %>% pivot_wider(names_from = "Species", values_from = "Density_20")) %>%
rbind(df %>% select(Species, Density_5) %>% pivot_wider(names_from = "Species", values_from = "Density_5"))