epi_clean_merge_nested_dfs: Recursively merge data frames that are stored as lists within...

In AntonioJBT/episcout: Quickly Clean, Explore and Visualise Large Epidemiological Datasets

Recursively merge data frames that are stored as lists within a list

Description

Recursively merge data frames that are stored as lists within a list. Flattens with purrr::flatten() if there is more than one level. Assumes:

• there are are 3 or more data frames to merge

• there are no duplicates in any of the data frames The function performs a full outer join with base R merge(df1, df2, by = id_col, all = TRUE)

Usage

epi_clean_merge_nested_dfs(
  nested_list_dfs = NULL,
  id_col = "",
  all.x = TRUE,
  ...
)

Arguments

nested_list_dfs	A nested list of dataframes to merge, such as the output from epi_clean_spread_repeated.
id_col	A string to identify the column to merge by. This is passed to the by parameter in merge(). Requires all dataframes to have the same column name.
all.x	corresponds to merge() all.x parameter. TRUE by default.
...	any further arguments that merge.data.frame or merge.data.table can take.

Value

A data.table in wide format with each sub-dataframe contained as a sub-list

Note

This function helps with spreading and gathering long and wide dataframes. You may want to see gather, spread as well as similar base functions and other packages such as data.table depending on your problem. See example below in case you have a messier dataframe which doesn't easily yield to existing workflows and functions. Note that merge.data.table is dispatched (as opposed to merge.data.frame). To get all = TRUE, pass all.x = TRUE and all.y = TRUE.

Author(s)

Antonio Berlanga-Taylor <\url{https://github.com/AntonioJBT/episcout}>

See Also

epi_clean_add_colname_suffix, epi_clean_spread_repeated, epi_clean_transpose, merge.

Examples

## Not run: 
# Generate some data:
n <- 20
df <- data.frame(
var_id = rep(1:(n / 2), each = 2),
var_to_rep = rep(c('Pre', 'Post'), n / 2),
x = rnorm(n),
y = rbinom(n, 1, 0.50),
z = rpois(n, 2)
)
df
# Create a nested list of dataframes using the repeated measurements variable:
df_spread <- epi_clean_spread_repeated(df, 'var_to_rep', 1)
# Returns a nested list:
df_spread

# Run an example with epi_clean_merge_nested_dfs()
# to create a single dataframe with repeated observations spread and
# no duplicate IDs (create a wide instead of a long dataframe):
library(purrr)
library(tibble)
nested_list_dfs <- purrr::flatten(list(df_spread, df_spread, df_spread))
id_col <- 'var_id'
epi_list_head(nested_list_dfs, 2, 3)
epi_list_tail(nested_list_dfs, 2, 3)
all_merged <- epi_clean_merge_nested_dfs(nested_list_dfs, id_col)
dim(all_merged)
as.tibble(all_merged)
names(all_merged)

# The above with epi_clean_merge_nested_dfs() would be equivalent to
# iteratively doing the following:
library(dplyr)

# Create sets with distinct observations:
var_id <- 'var_id'
var_to_rep <- 'var_to_rep'
reps <- epi_clean_add_rep_num(df, 'var_id', 'var_to_rep')
reps
identical(as.character(reps[[var_id]]),
          as.character(df[[var_id]])) # should be TRUE
# Bind:
df2 <- as.tibble(cbind(df, 'rep_num' = reps$rep_num))
# merge() adds all rows from both data frames as there are duplicates
# so use cbind after making sure order is exact
epi_head_and_tail(df2, rows = 3)
epi_head_and_tail(df2, rows = 3, last_cols = TRUE)

# See how many replicates there are:
df2 %>%
  transmute(as.factor(rep_num)) %>%
  summary()

# Generate a data frame for each:
baseline <- df2 %>% filter(rep_num == 1)
baseline
# Sanity check, should be empty:
epi_clean_get_dups(baseline, 'var_id', 1)
# Change col names to baseline, time_1, time_2, etc.:
new_colnames <- epi_clean_add_colname_suffix(baseline, 1, '.0')
names(baseline)[2:ncol(baseline)] <- new_colnames
names(baseline)

# First set of repeated observations:
time_1 <- df2 %>% filter(rep_num == 2)
time_1
epi_clean_get_dups(time_1, 'var_id', 1)
# Change col names:
new_colnames <- epi_clean_add_colname_suffix(time_1, 1, '.1')
names(time_1)[2:ncol(time_1)] <- new_colnames
names(time_1)

# Nothing left:
df2 %>% filter(rep_num == 3)

# Merge the data frames into one:
all_merged <- merge(baseline, time_1, by = 'var_id', all = TRUE)
dim(all_merged)
as.tibble(all_merged)
names(all_merged)
epi_head_and_tail(all_merged)
epi_head_and_tail(all_merged, last_cols = TRUE)
View(all_merged)

## End(Not run)

AntonioJBT/episcout documentation built on June 8, 2024, 7:47 a.m.