如何将一组值添加到现有数据框中？

Question

I have a data frame containing three columns: ID, year, growth . The last one contains data of growth in milimeters for each year.

Example:

df <- data.frame(ID=rep(c("CHC01", "CHC02", "CHC03"), each=4), 
                 year=rep(2015:2018, 3), 
                 growth=c(NA, 2.3, 2.1, 3.0, NA, NA, NA, 3.2, NA, NA, 2.1, 1.2))

In another data frame, I have other three columns: ID, missing_length, missing_years . Missing length relates to the estimated length missed in the measurements. Missing years relates to the number of missing years in df

estimate <- data.frame(ID=c("CHC01", "CHC02", "CHC03"), 
                       missing_length=c(1.0, 4.4, 3.5), 
                       missing_years=c(1,3,2))

For calculating the growth for each missing year, I tried:

missing <- rep(estimate$missing_length / estimate$missing_years, estimate$missing_years)

Does anyone have any idea of how to deal with this problem?

Thank you very much!

Answer 1

We can do a join and then replace the NA with the calculated value

library(dplyr)
df %>% 
   left_join(estimate) %>% 
   group_by(ID) %>% 
   transmute(year, growth  = replace(growth, is.na(growth), 
                 missing_length[1]/missing_years[1]))
# A tibble: 12 x 3
# Groups:   ID [3]
#   ID     year growth
#   <fct> <int>  <dbl>
# 1 CHC01  2015   1   
# 2 CHC01  2016   2.3 
# 3 CHC01  2017   2.1 
# 4 CHC01  2018   3   
# 5 CHC02  2015   1.47
# 6 CHC02  2016   1.47
# 7 CHC02  2017   1.47
# 8 CHC02  2018   3.2 
# 9 CHC03  2015   1.75
#10 CHC03  2016   1.75
#11 CHC03  2017   2.1 
#12 CHC03  2018   1.2 

Or with coalesce

df %>%
   mutate(growth = coalesce(growth,  with(estimate, 
        setNames(missing_length/missing_years, ID))[as.character(ID)])) %>%
   as_tibble
# A tibble: 12 x 3
#   ID     year growth
#   <fct> <int>  <dbl>
# 1 CHC01  2015   1   
# 2 CHC01  2016   2.3 
# 3 CHC01  2017   2.1 
# 4 CHC01  2018   3   
# 5 CHC02  2015   1.47
# 6 CHC02  2016   1.47
# 7 CHC02  2017   1.47
# 8 CHC02  2018   3.2 
# 9 CHC03  2015   1.75
#10 CHC03  2016   1.75
#11 CHC03  2017   2.1 
#12 CHC03  2018   1.2 

---

Or similar option in data.table

library(data.table)
setDT(df)[estimate, growth := fcoalesce(growth, 
           missing_length/missing_years), on = .(ID)]

Answer 2

Base R solution. Supposing tables "df" and "estimate" are sorted by id (ascending CHC) and we keep your "missing" object, this should work:

df$growth=replace(df$growth,which(is.na(df$growth)),missing)

Output:

      ID year   growth
1  CHC01 2015 1.000000
2  CHC01 2016 2.300000
3  CHC01 2017 2.100000
4  CHC01 2018 3.000000
5  CHC02 2015 1.466667
6  CHC02 2016 1.466667
7  CHC02 2017 1.466667
8  CHC02 2018 3.200000
9  CHC03 2015 1.750000
10 CHC03 2016 1.750000
11 CHC03 2017 2.100000
12 CHC03 2018 1.200000