Recoding Variables

The recodes() functions makes it very easy to recode one or more variables in the your data frame. The format is

Original dataset

Consider the following data set (below). Lets make the following changes.

sex	race	outcome	Q1	Q2	age	rating
1	b	better	20	15	12	1
2	w	worse	30	23	20	2
1	a	same	44	18	33	5
2	b	same	15	86	55	3
2	w	better	50	99	30	4
2	h	worse	99	35	100	5

sex

For sex, set 1 to “Male” and 2 to “Female”.

df <- recodes(data=df, vars="sex", 
               from=c(1,2), to=c("Male", "Female"))

sex	race	outcome	Q1	Q2	age	rating
Male	b	better	20	15	12	1
Female	w	worse	30	23	20	2
Male	a	same	44	18	33	5
Female	b	same	15	86	55	3
Female	w	better	50	99	30	4
Female	h	worse	99	35	100	5

race

Recode race to “White” vs. “Other”.

df <- recodes(data=df, vars="race", 
              from=c("w", "b", "a", "h"), 
              to=c("White", "Other", "Other", "Other"))

sex	race	outcome	Q1	Q2	age	rating
Male	Other	better	20	15	12	1
Female	White	worse	30	23	20	2
Male	Other	same	44	18	33	5
Female	Other	same	15	86	55	3
Female	White	better	50	99	30	4
Female	Other	worse	99	35	100	5

outcome

Recode outcome to 1 (better) vs. 0 (not better).

df <- recodes(data=df, vars="outcome", 
              from=c("better", "same", "worse"), 
              to=c(1, 0, 0))

sex	race	outcome	Q1	Q2	age	rating
Male	Other	1	20	15	12	1
Female	White	0	30	23	20	2
Male	Other	0	44	18	33	5
Female	Other	0	15	86	55	3
Female	White	1	50	99	30	4
Female	Other	0	99	35	100	5

Q1 and Q2

For Q1 and Q2 set values of 86 and 99 to missing.

df <- recodes(data=df, vars=c("Q1", "Q2"), 
              from=c(86, 99), to=NA)
#> Note: 'from' is longer than 'to', so 'to' was recycled.

sex	race	outcome	Q1	Q2	age	rating
Male	Other	1	20	15	12	1
Female	White	0	30	23	20	2
Male	Other	0	44	18	33	5
Female	Other	0	15	NA	55	3
Female	White	1	50	NA	30	4
Female	Other	0	NA	35	100	5

age

For age, set values

less than 20 or greater than 90 to missing,
20 <= age <= 30 to “Younger”,
30 < age <= 50 to “Middle Aged”, and
50 < age <= 90 to “Older”.

You can use expressions in your from fields. When they are TRUE, the corresponding to values will be applied. We will use the dollar sign ($) to represent the variable (age in this case). The symbols ( |, & ) mean OR and AND respectively.

df <- recodes(data=df, vars="age", 
              from=c("$ <   20 | $ >  90", 
                     "$ >=  20 & $ <= 30",
                     "$ >   30 & $ <= 50",
                     "$ >   50 & $ <= 90"), 
              to=c(NA, "Younger", "Middle Aged", "Older"))

We can also write this as

df <- recodes(data=df, vars="age", 
              from=c("$ < 20", "$ <= 30", "$ <= 50", "$ <= 90", "$ > 90"), 
              to=  c(NA, "Younger", "Middle Aged", "Older", "NA"))

This works because once the age value for an observations meets a criteria that is TRUE (working left to right), it is recoded. It isn’t changed again by later criteria in the same recodes statement.

sex	race	outcome	Q1	Q2	age	rating
Male	Other	1	20	15	NA	1
Female	White	0	30	23	Younger	2
Male	Other	0	44	18	Middle Aged	5
Female	Other	0	15	NA	Older	3
Female	White	1	50	NA	Younger	4
Female	Other	0	NA	35	NA	5

rating

Finally, for the rating variable, reverse the scoring so that 1 to 5 becomes 5 to 1.

df <- recodes(data=df, vars="rating", from=1:5, to=5:1)

sex	race	outcome	Q1	Q2	age	rating
Male	Other	1	20	15	NA	5
Female	White	0	30	23	Younger	4
Male	Other	0	44	18	Middle Aged	1
Female	Other	0	15	NA	Older	3
Female	White	1	50	NA	Younger	2
Female	Other	0	NA	35	NA	1