R gt table make NA values not appear

Question

I have a tgt table that has like 20 numerical columns that I color based on their value. Additionally, I have each row alternating between grey and white. I would like to make the table not show NA values.

Here is an example table:

library(gt)
library(magrittr)
id <- 1:10
res1 <- sample(1:20, 10)
nm2 <-
  c("red",
    "purple",
    "green",
    "turtle",
    "name",
    "dog",
    "cat",
    "horse",
    "space",
    "planet")
res3 <- sample(1:20, 10)
nm4 <- nm2

# Add NAs
res1[6] <- NA
nm2[6] <- "NA"
res3[4] <- NA
nm4[4] <- "NA"
df <- data.frame(id, res1, nm2, res3, nm4)
pal <-
  RColorBrewer::brewer.pal(8, "RdYlGn") %>% gt::adjust_luminance(-1.0)

plot <-df %>%
  gt() %>%
   tab_style(style = cell_fill("lightgrey"),
             locations = cells_body(rows = seq(1, nrow(df), by = 2))) %>%
  data_color(
    columns = colnames(df)[grep("res", colnames(df))],
    colors = scales::col_numeric(palette = pal,
                                 domain = c(0, 20))
  )

The only way I currently know how to do that is to manually change the text and color of each column based on whether or not there is a NA, but doing this:

  tab_style(
    style = list(cell_text("white"),
                 cell_fill("white")),
    locations = cells_body(
      columns = c("res1", "nm2"),
      rows = is.na(df$res1)  & row_number(df$res1) ==seq(0, nrow(df), by = 2)
    )
  ) %>%
  tab_style(
    style = list(cell_text("lightgrey"),
                 cell_fill("lightgrey")),
    locations = cells_body(
      columns = c("res1", "nm2"),
      rows = is.na(df$res1)  & row_number(df$res1) ==seq(1, nrow(df), by = 2)
    )
  )

I want all 10 IDs to show regardless of whether or not any of the values in that row are NA. Is there anyway to do this more efficiently?

Answer 1

library(dplyr)
library(gt)
library(magrittr)
id <- 1:10
res1 <- sample(1:20, 10)
nm2 <-
  c("red",
    "purple",
    "green",
    "turtle",
    "name",
    "dog",
    "cat",
    "horse",
    "space",
    "planet")
res3 <- sample(1:20, 10)
nm4 <- nm2

# Add NAs
res1[6] <- NA
nm2[6] <- "NA"
res3[4] <- NA
nm4[4] <- "NA"
df <- data.frame(id, res1, nm2, res3, nm4)
pal <-
  RColorBrewer::brewer.pal(8, "RdYlGn") %>% gt::adjust_luminance(-1.0)
## omit NAs here
#df <- na.omit(df)
plot <-df %>%
  gt() %>%
  tab_style(style = cell_fill("lightgrey"),
            locations = cells_body(rows = seq(1, nrow(df), by = 2))) %>%
  data_color(
    columns = colnames(df)[grep("res", colnames(df))],
    colors = scales::col_numeric(palette = pal,
                                 domain = c(0, 20), 
                                 na.color = "transparent")
  )

Answer 2

TBMK your approach to do this manually is the way to go. However, IMHO you could simplify your code a bit by setting the na.color used by data_color to "white" so that you only have to take care of NA values in rows with a grey background. Additionally, you could simplify even further by using a custom function instead of duplicating the code for each column.

Note 1: I use seq_along(...) %% 2 == 1 to apply tab_style to only uneven rows.

Note 2: I slightly adjusted your example data to include an NA in an uneven row, i.e. row 5.

library(gt)
library(magrittr)

tab_style_na <- function(data, col) {
  tab_style(
    data,
    style = list(cell_fill("lightgrey")),
    locations = cells_body(
      columns = all_of(col),
      rows = is.na(!!sym(col)) & seq_along(!!sym(col)) %% 2 == 1
    )
  )
}

df %>%
  gt() %>%
  tab_style(
    style = cell_fill("lightgrey"),
    locations = cells_body(rows = seq(1, nrow(df), by = 2))
  ) %>%
  data_color(
    columns = colnames(df)[grep("res", colnames(df))],
    colors = scales::col_numeric(
      palette = pal,
      domain = c(0, 20),
      na.color = "transparent"
    )
  ) %>%
  tab_style_na("res1") %>%
  tab_style_na("res3")

DATA

set.seed(123)

id <- 1:10
res1 <- sample(1:20, 10)
nm2 <-
  c(
    "red",
    "purple",
    "green",
    "turtle",
    "name",
    "dog",
    "cat",
    "horse",
    "space",
    "planet"
  )
res3 <- sample(1:20, 10)
nm4 <- nm2

# Add NAs
res1[6] <- NA
nm2[6] <- "NA"
res3[5] <- NA
nm4[5] <- "NA"
df <- data.frame(id, res1, nm2, res3, nm4)