CODEWITHSUNDEEP
rdplyrreshape2sqldf
Sharath
Sharath

Reputation: 2267

Update a column in df2 by matching patterns in columns in df1 & df2 using R

I have 2 data frames like this 

TEAM <- c("PE","PE","MPI","TDT","HPT","ATD")
CODE <- c(NA,"F","A","H","G","D")
df1 <- data.frame(TEAM,CODE)

CODE <- c(NA,"F100","A234","D664","H435","G123","A666","D345","G324",NA)
TEAM <- c(NA,NA,NA,NA,NA,NA,NA,NA,NA,NA)
df2 <- data.frame(CODE,TEAM)

I am trying to update the TEAM in df2 by matching the first letter in code column in df1 with the code column in df2

My desired output for df2

   CODE TEAM
1    NA   PE
2  F100   PE
3  A234  MPI
4  D664  ATD
5  H435  TDT
6  G123  HPT
7  A666  MPI
8  D345  ATD
9  G324  HPT
10   NA   PE

I am trying this way with sqldf but it is not right 

library(sqldf)
df2 <- sqldf(c("update df2 set TEAM = 
                  case
                    when CODE like '%F%' then 'PE'
                    when CODE like '%A%' then 'MPI'
                    when CODE like '%D%' then 'ATD'
                    when CODE like '%G%' then 'HPT'
                    when CODE like '%H%' then 'TDT'
                    else 'NA'
                  end"))

Can someone help me provide some directions on achieving this without sqldf?

Upvotes: 0

Views: 76

Answers (2)

G. Grothendieck
G. Grothendieck

Reputation: 269694

Assuming you are looking for an sqldf solution try this:

sqldf("select CODE, 
              case
                 when CODE like 'F%' then 'PE'
                 when CODE like 'A%' then 'MPI'
                 when CODE like 'D%' then 'ATD'
                 when CODE like 'G%' then 'HPT'
                 when CODE like 'H%' then 'TDT'
                 else 'PE'
              end TEAM from df2", method = "raw")

or this:

sqldf("select df2.CODE, coalesce(df1.TEAM, 'PE') TEAM 
       from df2 
       left join df1 on substr(df2.CODE, 1, 1) = df1.CODE")

Upvotes: 0

Gregor Thomas
Gregor Thomas

Reputation: 145805

Using match and substr (both in base R):

df2$TEAM = df1$TEAM[match(substr(df2$CODE, 1, 1), df1$CODE)]

df2
#    CODE TEAM
# 1  <NA>   PE
# 2  F100   PE
# 3  A234  MPI
# 4  D664  ATD
# 5  H435  TDT
# 6  G123  HPT
# 7  A666  MPI
# 8  D345  ATD
# 9  G324  HPT
# 10 <NA>   PE

This is expedient for a single case - if you're doing things like this frequently I would encourage you to just extract the first letter of code into its own column, CODE_1, and then do a regular merge or join.

Upvotes: 2

Related Questions