R Megre Data Frame Column and Recode - r

Question

I have 2 R data frames that looks like this:
DATA FRAME 1:
identifier
ef_posterior
position_no
classification
11111
0.260
1
yes
11111
0.0822
2
yes
11111
0.00797
3
yes
11111
0.04
4
no
11111
0.245
5
yes
11111
0.432
6
yes
11112
0.342
1
maybe
11112
0.453
2
yes
11112
0.0032
3
yes
11112
0.241
5
no
11112
0.0422
6
yes
11112
0.311
4
no
DATAFRAME 2:
study_identifier
%LVEF
11111
62
11112
76
I want to merge and rearrange these two data frames into something like this:
Study_identifier and identifier are the same thing (just different column names). Additionally, I would like to recode the classification so that yes = 0, no = 1, maybe = 2
identifier
pos_1
pos_1_class
pos_2
pos_2_class
pos_3
pos_3_class
pos_4
pos_4_class
pos_5
pos_5_class
pos_6
pos_6_class
%LVEF
11111
0.260
0
0.0822
0
0.00797
0
0.04
1
0.245
0
0.432
0
62
11112
0.342
2
0.453
0
0.0032
0
0.311
1
0.241
1
0.0422
0
76
df1 %>% mutate(position_no = paste0("position_", position_no)) %>%
pivot_wider(id_cols = identifier, names_from = position_no, values_from = ef_posterior) %>%
left_join(df2 %>% mutate(study_identifier = as.numeric(as.character(study_identifier))), by = c("identifier" = "study_identifier"))
This is the code I have right now, but I can't figure out where to put in the code for the classification column
How would I go about doing this?
Any help would be very much appreciated!

Answer 1

You can recode quite easily with dplyr and case_when:
df1 %>% mutate(
classification =
case_when( classification == "yes" ~ 1,
classification == "no" ~ 0,
classification == "maybe" ~ 2)
)
I would solve it the following way:
library(tidyverse)
df1 <- data.frame(
stringsAsFactors = FALSE,
identifier = c(11111L,11111L,11111L,11111L,
11111L,11111L,11112L,11112L,11112L,11112L,11112L,
11112L),
ef_posterior = c(0.26,0.0822,0.00797,0.04,
0.245,0.432,0.342,0.453,0.0032,0.241,0.0422,0.311),
position_no = c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 5L, 6L, 4L),
classification = c("yes","yes","yes","no",
"yes","yes","maybe","yes","yes","no","yes","no")
)
df2 <- data.frame(
check.names = FALSE,
study_identifier = c(11111L, 11112L),
`%LVEF` = c(62L, 76L)
)
df1 %>% mutate(
classification =
case_when( classification == "yes" ~ 1,
classification == "no" ~ 0,
classification == "maybe" ~ 2)
) %>%
pivot_wider(
id_cols = c(identifier), names_from = c(position_no), values_from = c(classification,ef_posterior)) %>%
left_join(df2, by = c("identifier" = "study_identifier"))
#> # A tibble: 2 x 14
#> identifier classification_1 classification_2 classification_3 classification_4
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 11111 1 1 1 0
#> 2 11112 2 1 1 0
#> # … with 9 more variables: classification_5 <dbl>, classification_6 <dbl>,
#> # ef_posterior_1 <dbl>, ef_posterior_2 <dbl>, ef_posterior_3 <dbl>,
#> # ef_posterior_4 <dbl>, ef_posterior_5 <dbl>, ef_posterior_6 <dbl>,
#> # `%LVEF` <int>
Created on 2021-04-12 by the reprex package (v0.3.0)