Correct the columns' order

This function is part of a group of functions intended to solve a scenario where there is equivalent data that is potentially stored heterogeneously (e.g., different column names and datatypes). In particular, this function relocate the columns to follow the order of selected_columns.

Usage

relocate_columns(df, selected_columns)

Arguments

df

Data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). Its columns should be a subset of selected_columns.

selected_columns

Atomic vector that is a subset of the uninames dict's column. In other words, is a set of desired columns that individually, should be in at least one file of the group of files.

Value

An object of the same type as df that potentially changes the columns's location to replicate the selected_columns' order.

See also

For a full example, see the vignette process_data_with_partial_dict in the website or with the command vignette('process_data_with_partial_dict', package = dataRC').

Examples

if (FALSE) {
# Parameters
folder <- 'my_folder'
files <- list.files(folder)
dict <- readxl::read_excel('my_dict.xlsx')
selected_columns <- dict$uniname[1L:3L]

# Make a unique database using lazy evaluation.
df <- NULL
for (file in files) {
  df0 <- arrow::open_dataset(file.path(folder, file)) %>%
    unify_colnames(dict, file, selected_columns) %>%
    unify_classes(dict, file, selected_columns) %>% collect
 df <- dplyr::bind_rows(df, df0)
}
df %>% relocate_columns(selected_columns) %>%
  write_parquet('unified_data.parquet')
}
if (FALSE) {
# Parameters
folder <- 'my_folder'
files <- list.files(folder)
dict <- readxl::read_excel('my_dict.xlsx')
selected_columns <- c('ID', 'YEAR', 'MONTH')

# Count number of people per month using lazy evaluation.
df <- NULL
for (file in files) {
  df0 <- arrow::read_parquet(file.path(folder, file)) %>%
    unify_colnames(dict, file, selected_columns) %>%
    unify_classes(dict, file, selected_columns) %>%
    dplyr::distinct() %>%
    dplyr::group_by(YEAR, MONTH) %>%
    dplyr::summarise(n_ = n()) %>% collect
 df <- rbind(df, df0)
}
df %>% relocate_columns(selected_columns) %>%
  dplyr::group_by(YEAR, MONTH) %>%
  dplyr::summarise(n_ = sum(n_)) %>%
  write_parquet('people_per_month.parquet')
}