fill missing values with value from previous column

Question

I have a data.frame whit some columns with missing values, and I want that the missing values are filled in with data from a previous column. For example:

country <- c('a','b','c')
yr01 <- c(15,16,7)
yr02 <- c(NA,18,NA)
yr03 <- c(20,22,NA)
yr04 <- c(15,18,19)

tab <- data.frame(country,yr01,yr02,yr03,yr04)
tab

  country yr01 yr02 yr03 yr04
1       a   15   NA   20   15
2       b   16   18   22   18
3       c    7   NA   NA   19

How can I make it so that the NA are replaced by the previous value? For example, in country a column yr02 will be equals to 15, and in country c columns year02 and yr03 will be 7. Thanks!

Gregor Thomas · Accepted Answer · 2018-01-18T16:49:09.043

2

It's usually easier to work with columns, but we can apply to rows the standard answer from the R-FAQ Replace NAs with latest non-NA value.

tab[-1] = t(apply(tab[-1], 1, zoo::na.locf))
tab
#   country yr01 yr02 yr03 yr04
# 1       a   15   15   20   15
# 2       b   16   18   22   18
# 3       c    7    7    7   19

edited Jan 18 '18 at 16:49

answered Jan 18 '18 at 16:42

Gregor Thomas

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Thanks Gregor, but it seems that the values changed. Look at country c for example. – Bruno Guarita Jan 18 '18 at 16:47
Sorry, needed to transpose the result. Fixed now. – Gregor Thomas Jan 18 '18 at 16:49
By the way Gregor, if I had the data in columns, like column country, year and data. How would I fill the missing values, BY country? Thanks! – Bruno Guarita Jan 18 '18 at 17:36
Generally, you can use `dplyr` or `data.table` to easily do anything by group. Here's a complete question on filling NA by group: https://stackoverflow.com/q/23340150/903061 – Gregor Thomas Jan 18 '18 at 17:40

fill missing values with value from previous column

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