R: Replace All Values in a Dataframe Lower Than a Threshold with Na

R: replace all values in a dataframe lower than a threshold with NA

Try this:

 df[df<minval]=NA

df < minval creates a boolean matrix, which is used to select the values you want to replace with NA.

Replace all values lower than threshold in R

pmax is a good candidate for this

  > pmax(x, 1)
    [1] 1 1 1 2 3

Use dplyr to change all values above threshold to NA

We can assign the output to the original object to make those changes as the %>% will not do the output printed on the console.

df <- df %>% 
       mutate(across(everything(), ~ ifelse(. > 8, NA, .)))

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Or another option is %<>% operator from magrittr

library(magrittr)
df %<>%
    mutate(across(everything(), ~ ifelse(. > 8, NA, .)))

Replace columns less than a threshold to 0

dplyr:

df <- mutate_all(df, funs(ifelse(. < 0.5, 0, .)))

base R:

df[df < 0.05] <- 0

Replacing values bigger than threshold with 0 in specified range of columns in R dataframe

Dplyr (use the latest version) has a nice "across()" function to be used with mutate. Just be sure to update your dplyr package as it is quite recent

library(dplyr)

df1 %>% mutate(across(where(is.numeric), function(x) ifelse(x >= 10, 0, x)))

   ID string1 S_2018_p S_2019_p S_2020_p S_2021_p string2
1: a1      x2        3        3        0        4      si
2: a2      g3        5        5        4        0      q2
3: a3      n2        0        6        0        3      oq
4: a4      m3        3        0        9        8      mx
5: a5      2w        9        1        0        5      ix
6: a6     ps2        0        4        7        4      p2
7: a7     kg2        6        0        9        6      2q

Replace values in a dataframe column that are below a certain threshold with NaN

np.where

df['A'] = np.where(df['A']<=cutoff , np.nan, df['A'])

Using rollmean filtering out NA with threshold

1) Define a function which returns NaN if there are thresh or more NA's in its input and returns mean of the non-NA's otherwise. Then use it with rollapply. Convert that to a data frame if desired using as.data.frame but since the data is entirely numeric leaving it as a matrix may be sufficient.

w <- 5
thresh <- w/2

Mean <- function(x, thresh) if (sum(is.na(x)) > thresh) NaN else mean(x,na.rm=TRUE)
rollapply(df, w, Mean, thresh = thresh, fill = NA)

2) Another possibility is to check if there are more than thresh NA's in each cell and if so return NaN and otherwise return the rolling mean. Again use as.data.frame on the result if a data frame is needed. (1) has the advantage over this one that it only calls roll* once instead of twice.

w <- 5
thresh <- w/2

ifelse(rollsum(is.na(df), w, fill = NA) > thresh, NaN, 
    rollmean(df, w, na.rm = TRUE, fill = NA))

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