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rdataframedummy-variable

Creating Year/Region Dummy Variables with fastDummies

I have written code to create dummy variables for year (2014, 2015, 2016, 2017, or 2018) and region (coded as region 1, 2, 3, or 4) to add as new columns in my data frame NHIS1. But I am now trying to knit my work as an R Markdown file and getting the error that the object YEAR cannot be found. How could I rewrite this line of code to get the same output with my dummy columns but without the error message? Below is also a photo of what the resulting columns look like.

NHIS1 <- NHIS1 %>% mutate(YEAR = as.character(YEAR), REGION = as.character(REGION)) #turning data into integers to strings 
df_test <- NHIS1 %>% select(YEAR, REGION) #subset to include 2 variables
results <- fastDummies::dummy_cols(df_test) #154-156 adding dummy columns, transposing row to columns with 9 new columns 
results <- NHIS1 %>%
  fastDummies::dummy_cols(select_columns = c(YEAR, REGION))
results <- results %>% select(-c(YEAR, REGION)) 
NHIS1 <-NHIS1 %>% bind_cols(results) #merging 9 variables with old data frame

enter image description here

Solution

  • You need to use quotes, write c("YEAR", "REGION").

    library(dplyr)
results <- NHIS1.fake %>%
  fastDummies::dummy_cols(select_columns = c("YEAR", "REGION"))
results <- results %>% select(-c(YEAR, REGION)) 
NHIS1.fake <-NHIS1.fake %>% bind_cols(results) #merging 9 variables with old data frame

#   YEAR REGION           X          Y          X1         Y1 YEAR_2018 YEAR_2019 YEAR_2020
# 1 2018      a  1.37095845 -0.0627141  1.37095845 -0.0627141         1         0         0
# 2 2019      a -0.56469817  1.3048697 -0.56469817  1.3048697         0         1         0
# 3 2020      a  0.36312841  2.2866454  0.36312841  2.2866454         0         0         1
# 4 2018      b  0.63286260 -1.3888607  0.63286260 -1.3888607         1         0         0
# 5 2019      b  0.40426832 -0.2787888  0.40426832 -0.2787888         0         1         0
# 6 2020      b -0.10612452 -0.1333213 -0.10612452 -0.1333213         0         0         1
# 7 2018      c  1.51152200  0.6359504  1.51152200  0.6359504         1         0         0
# 8 2019      c -0.09465904 -0.2842529 -0.09465904 -0.2842529         0         1         0
# 9 2020      c  2.01842371 -2.6564554  2.01842371 -2.6564554         0         0         1
#   REGION_a REGION_b REGION_c
# 1        1        0        0
# 2        1        0        0
# 3        1        0        0
# 4        0        1        0
# 5        0        1        0
# 6        0        1        0
# 7        0        0        1
# 8        0        0        1
# 9        0        0        1

    Toy data

    NHIS1.fake <- structure(list(YEAR = c(2018L, 2019L, 2020L, 2018L, 2019L, 2020L, 
2018L, 2019L, 2020L), REGION = structure(c(1L, 1L, 1L, 2L, 2L, 
2L, 3L, 3L, 3L), .Label = c("a", "b", "c"), class = "factor"), 
    X = c(1.37095844714667, -0.564698171396089, 0.363128411337339, 
    0.63286260496104, 0.404268323140999, -0.106124516091484, 
    1.51152199743894, -0.0946590384130976, 2.01842371387704), 
    Y = c(-0.062714099052421, 1.30486965422349, 2.28664539270111, 
    -1.38886070111234, -0.278788766817371, -0.133321336393658, 
    0.635950398070074, -0.284252921416072, -2.65645542090478)), class = "data.frame", row.names = c(NA, 
-9L))