Iterate through columns' suffixes in a for loop. R - r

Question

I am trying to modify my dataset with a for loop. I want to modify certain cells of some columns depending on the value of its "paired" column. My dataset could be:
data1989 <- data.frame("date" = c("1987-01-01", "1987-01-03", "1987-01-19"),
"NDVI_1" = c(NA, 0.589, 0.120),
"NDVI_3" = c(NA, 0.447, NA),
"NDVI_4" = c(NA, NA, NA),
"pixelQA_1" = c(NA, 66.897,90.599),
"pixelQA_3" = c(NA, 66.097,NA),
"pixelQA_4" = c(NA, NA, NA),
stringsAsFactors = FALSE)
> data1989
date NDVI_1 NDVI_3 NDVI_4 pixelQA_1 pixelQA_3 pixelQA_4
1 1987-01-01 NA NA NA NA NA NA
2 1987-01-03 0.589 0.447 NA 66.897 66.097 NA
3 1987-01-19 0.120 NA NA 90.599 NA NA
Columns are "paired" by the suffix of each column, so NDVI_1 is paired with pixelQA_1, and so on. I want to modify the values under NDVI's columns depending on it's "paired" values on pixelQA column, following:
if PixelQa is NA -> then NDVI should be also NA.
if Pixel Qa is 66±0.5 OR 130±0.5 -> then NDVI remains the same value.
if Pixel Qa is different to 66±0.5 OR 130±0.5 -> then NDVI value is set to NA (this is bad quality data which needs to be ignored).
Applying these very simple rules my data should look like:
data1989clean <- data.frame("date" = c("1987-01-01", "1987-01-03", "1987-01-19"),
"NDVI_1" = c(NA, NA, NA),
"NDVI_3" = c(NA, 0.447, NA),
"NDVI_4" = c(NA, NA, NA),
"pixelQA_1" = c(NA, 66.897,90.599),
"pixelQA_3" = c(NA, 66.097,NA),
"pixelQA_4" = c(NA, NA, NA),
stringsAsFactors = FALSE)
> data1989clean
date NDVI_1 NDVI_3 NDVI_4 pixelQA_1 pixelQA_3 pixelQA_4
1 1987-01-01 NA NA NA NA NA NA
2 1987-01-03 NA 0.447 NA 66.897 66.097 NA
3 1987-01-19 NA NA NA 90.599 NA NA
To reach my goal I am trying the following for loop:
for(i in 1:4){
data1989$NDVI_[i] <- ifelse(data1989$pixelQA_[i] < 66.5 & data1989$pixelQA_[i] > 65.5 |
data1989$pixelQA_[i] < 130.5 & data1989$pixelQA_[i] > 129.5,
data1989$NDVI_[i], NA)
}
But so far it is not working, as the dataset output looks exactly the same as the original one. Any suggestion will be welcomed.

Answer 1

As suggested by #George Savva, you can achieve this by pivoting longer, correcting the data, and pivoting back wider. So, using the tidyverse, that gives:
library(tidyverse)
newdd1 <-
#
data1989 %>%
#
pivot_longer(cols = -date,
names_to = c(".value", "set"),
names_sep = "_") %>%
#
mutate(NDVI = case_when(is.na(pixelQA) ~ NA_real_,
between(pixelQA, 65.5, 66.5) ~ NDVI,
between(pixelQA, 129.5, 130.5) ~ NDVI,
TRUE ~ NA_real_)) %>%
#
pivot_wider(names_from = set,
values_from = c(NDVI, pixelQA))