5.8 Data Frame Operations

Basic operations:

  • colSums(x) returns sum for each column
  • rowSums(x) returns sum for each row
  • setNames() updates the column names without having to write another replacement function.

magrittr package provides a series of aliases which can be more pleasant to use when composing chains using the %>%operator.

Alias Cmd
set_colnames colnames<-
set_rownames rownames<-
set_names names<-

subset(x, subset, select, drop = FALSE, …) Subsetting Vectors, Matrices And Data Frames

  • x object to be subsetted.

  • subset logical expression indicating elements or rows to keep: missing values are taken as false.

  • select columns to select from a data frame.

    subset(airquality, Temp > 80, select = c(Ozone, Temp))
subset(airquality, Day == 1, select = -Temp)
subset(airquality, select = Ozone:Wind)

with(airquality, subset(Ozone, Temp > 80))

Column/Row-wise Operations

sweep(data, MARGIN, STATS, FUN='-') Return an array obtained from an input array by sweeping out a summary statistic; useful in standardizing data, eg., center or scale columns.

  • data: matrix/dataframe

  • MARGIN: 1 row-wise, 2 column-wise..

  • STATS: a vector with the same length as row \[MARGIN=1\]/ column \[MARGIN=2\].

  • FUN: the function to be used to carry out the sweep.

    Example:

    sweep(z, 2, colMeans(z),'-') substract column mean from each column;

Alternatively, can use mutate_at. You can perform the operation on selected cols without changing the data structure. By contrast, with sweep, you need to subset the relevant cols, apply the operation, and concatenate them back to the remainder of the columns afterwards.

Takeaway: Use sweep if the operation is to all cols; use mutate_at if to selected cols.

center_col <- function(data, cols){
    ## Mean center columns in a table
    # @data: table or data frame
    # @col: a vector of selected columns to center
    # @return A data frame with the selected columns mean-centered 
    #   (i.e., each value minus its column mean).
    
    data %>% 
        mutate_at(cols, ~.-mean(., na.rm=TRUE))
}

Remove duplicate columns, regardless of column names:

# remove duplicate columns, regardless column names
df[!duplicated(lapply(df, summary))]

5.8.1 Process NA values

NaN not a number. 0/0 is an example of a calculation that will produce a NaN. NaNs print as NaN, but generally act like NAs. Use is.nan to check if NaN.

Inf is infinite numbers. is.infinite find infinite numbers (Inf, -Inf).

na.omit(x) remove NA values in x;

  • x could be vectors, matrices, and data frames;
    • if x is a data frame, remove rows with NA values;

Find NA values

complete.cases(x) return a logical vector indicating which cases/rows are complete.

# count number of rows with NA values
data %>% negate(complete.cases)() %>% sum()

purrr::negate() works similar to base::Negate()

  • R is case-sensitive.

(!is.na(x)) %>% colSums() %>% sort() returns a vector of the number of non-NA values per column, column names as vector name.

When you have a long list of columns, vector is hard to read, use as_tibble_row() %>% t() to convert to a tibble column.

# when you have a long list of columns, you can convert to a tibble column for best visualization.
miss_per_col <- data %>% 
    filter(date > ymd("2013-01-01")) %>% 
    is.na() %>% 
        colSums()
miss_per_col <- miss_per_col %>% as_tibble_row() %>% t()
miss_per_col %>% dim()
miss_per_col[1:40, ] %>% t() %>% t()
miss_per_col[41:77, ] %>% t() %>% t()

(!is.na(x)) %>% rowSums() %>% sort() calculate the number of non-NA values per row

tidyr::drop_na(x, any_of(vars)) allow you to specify which columns you want to eliminate NA values from; it doesn’t have to be the whole columns;

  • x must be a data frame.

which(is.na(data)) returns positions of omitted missing values

A custom function for handling common missing/invalid values:

cast_na <- function(x){
    # Remove records with nonvalid values, such as NA, Inf, NaN 
    # remain x when mask==TRUE
    if (is.null(dim(x))){
                # `x` is a vector
        mask <-  !(x %in% c(NA, NaN, Inf, -Inf) )
        x <- x[mask]
    }
    else {
        # `x` is a data.frame
        mask <- apply(x, 1, function(x) sum(x %in% c(NA, NaN, Inf, -Inf)))
        mask <- (mask == 0)
        x <- x[mask,]}
    return (x)
}

Keep rows with least NA’s for duplicated rows

User_Table %>%
  arrange(rowSums(is.na(.))) %>%        # sort rows by number of NAs
  distinct(User_ID, .keep_all = TRUE)   # keep first row per User_ID only

Fill missing values

Forward/Backward filling

tidyr::fill(data, ..., .direction = c("down", "up", "downup", "updown")) Fill missing values in selected columns using the next or previous entry. This is useful in the common output format where values are not repeated, and are only recorded when they change.

  • ... Columns to fill.
  • .direction Direction in which to fill missing values. Default: “down”.

Alternatively, you can use zoo::na.locf

# Last obs. carried forward
na.locf(x)                

# Next obs. carried backward
na.locf(x, fromLast = TRUE)

fromLast defaults to FALSE, carry forward. If set to TRUE, carry backward.

Replace with specific values

dplyr::na_if(x, y) that replaces any values in x that are equal to y with NA. It is useful if you want to convert an annoying value to NA.

> na_if(1:5, 5:1)
# [1]  1  2 NA  4  5
> y <- c("abc", "def", "", "ghi")
> na_if(y, "")
# [1] "abc" "def"  NA  "ghi"

tidyr::replace_na(data, replace) Replace NAs with specified values

  • replace

    • If data is a data frame, replace takes a list of values, with one value for each column that has NA values to be replaced.

      # Replace NAs in a data frame
df <- tibble(x = c(1, 2, NA), y = c("a", NA, "b"))
df %>% replace_na(list(x = 0, y = "unknown"))

    • If data is a vector, replace takes a single value. This single value replaces all of the NA values in the vector.

      # Replace NAs in a vector
df %>% dplyr::mutate(x = replace_na(x, 0))
# OR
df$x %>% replace_na(0)
df$y %>% replace_na("unknown")