Cleaning Data - Some Important Tricks (Part 2)

Replace NA values and Change values in a specific row

1. Replace NA values

a. Replace 0 for all NA values in table dat1

dat1[is.na(dat1)] <- 0

b. Replace NA with specific values for different columns

#install.packages("dplyr")
library(dplyr)
#install.packages("tidyr")
library(tidyr)

dat1 = data.frame(Name = c('Ha', NA, 'Ly'), Age = c(30,0,NA),stringsAsFactors = FALSE)
#Use: stringAsFactors for changing factor level in data frame into correct format (in this case: Name has chr type, Age has num type)
dat1

##   Name Age
## 1   Ha  30
## 2 <NA>   0
## 3   Ly  NA

dat1 <- dat1 %>% replace_na(list(Name = "Unknown", Age = 0))
dat1

##      Name Age
## 1      Ha  30
## 2 Unknown   0
## 3      Ly   0

2. Change values in a specific row

dat1$Name[dat1$Age == 30] <- "Harry"
dat1

##      Name Age
## 1   Harry  30
## 2 Unknown   0
## 3      Ly   0