Creating a new dataframes based on two conditions from two other dataframes - r

Question

I'm fairly new to coding languages and have been asked to create a new dataframe based on two existing dataframes. Dataframe 1 is the original and dataframe 2 is a subset of the original. The new data frame needs to be a copy of the original with certain scores removed if they meet certain conditions from
df2, ie identify the task type in df2 and remove the corresponding value from df1, if the sample ID matches.
E.g.Dataframe1:
sample_id Low Mid High
13420 NA 2.4 3.7
43905 7.5 NA NA
52078 5.6 3.2 5.6
43292 10 NA 1.9
79327 5.7 3.2 NA
Dataframe2:
Sample Task type
13420 High
52078 Low
52078 Mid
43292 High
79327 Low
New dataframe:
13420 NA 2.4 NA
43905 7.5 NA NA
52078 NA NA 5.6
43292 10 NA NA
79327 NA 3.2 NA
Can anyone help, please? I've tried a few conditional statements, but have had no luck.

Answer 1

sample data
df1 <- data.table::fread("sample_id Low Mid High
13420 NA 2.4 3.7
43905 7.5 NA NA
52078 5.6 3.2 5.6
43292 10 NA 1.9
79327 5.7 3.2 NA")
df2 <- data.table::fread("Sample Tasktype
13420 High
52078 Low
52078 Mid
43292 High
79327 Low")
code
library( data.table )
#or make data.frames a data.table
data.table::setDT(df1);data.table::setDT(df2)
#melt df1 to long format
df1.melt <- melt( df1, id.vars = "sample_id" )
#update join the molten dataset with df2, updating the value with NA
df1.melt[ df2, value := NA, on = .(sample_id = Sample, variable = Tasktype) ]
#and cast df1 wit the new values back to wide format
dcast( df1.melt, sample_id ~ variable, value.var = "value" )
output
# sample_id Low Mid High
# 1: 13420 NA 2.4 NA
# 2: 43292 10.0 NA NA
# 3: 43905 7.5 NA NA
# 4: 52078 NA NA 5.6
# 5: 79327 NA 3.2 NA

Answer 2

Here is an approach with dplyr and tidyr:
library(dplyr)
library(tidyr)
df1 %>%
pivot_longer(-sample_id) %>%
left_join(df2, by = c("sample_id" = "Sample",
"name" = "Task.type"),
keep = TRUE) %>%
mutate(value = ifelse(!is.na(Task.type) & Task.type == name,
NA_real_, value)) %>%
dplyr::select(-c(Sample,Task.type)) %>%
pivot_wider(id_cols = c("sample_id"))
## A tibble: 5 x 4
# sample_id Low Mid High
# <int> <dbl> <dbl> <dbl>
#1 13420 NA 2.4 NA
#2 43905 7.5 NA NA
#3 52078 NA NA 5.6
#4 43292 10 NA NA
#5 79327 NA 3.2 NA
Sample Data
df1<-structure(list(sample_id = c(13420L, 43905L, 52078L, 43292L,
79327L), Low = c(NA, 7.5, 5.6, 10, 5.7), Mid = c(2.4, NA, 3.2,
NA, 3.2), High = c(3.7, NA, 5.6, 1.9, NA)), class = "data.frame", row.names = c(NA,
-5L))
df2 <- structure(list(Sample = c(13420L, 52078L, 52078L, 43292L, 79327L
), Task.type = structure(c(1L, 2L, 3L, 1L, 2L), .Label = c("High",
"Low", "Mid"), class = "factor")), class = "data.frame", row.names = c(NA,
-5L))