#########################################################################################
# This gist contains a quick walk-through of several ways to produce scales capturing
# the average value of one or more variables. Each row (observation) gets its own
# value. We'll assume your data are not fully "tidy." What I mean by this is that you
# have an observation for each row and you want to calculate that observation's value
# on the scale, but each variable that should go into your scale is in its own column.
#########################################################################################

#########################################################################################
# Set-up (packages, fake data, etc.)
#########################################################################################

# Load dplyr
library(dplyr)

# Set seed
set.seed(9)

# Create some fake data with variables x, y, z, w
# for example purposes
mydata <- data.frame(x = rnorm(10),
                     y = runif(10),
                     z = seq(1:10),
                     w = c(1,rep(NA,9)))

# Let's take a look at our raw data. Note
# that we have missing values in variable w.
mydata

#########################################################################################
# Scale creation
#########################################################################################

# We'll now create 2 scales. Each scale will
# capture the average of two variable in our data.

# Which variables? Let's choose two variables
# for each of the scales: scale1 is going
# to reflect the mean of variables named x and y,
# scale2 will reflect the average of variables z and y.

scale1_vars <- c("x","y")
scale2_vars <- c("y","z")

# Now let's generate our scales. If a row is missing a
# value for one of the variables, the average will be
# computed from the other variable if it is not also
# missing. If both values are missing, the scale value
# will be NA.

# The code below tells the mutate() function from the dplyr package
# that we want to generate new variables (scale1 and scale2) by
# calculating the average of the values observed in each row for
# only those columns containing the variables we previously included
# in scale1_vars or scale2_vars. A different scale is generated for
# each group of variables.
fulldf <- mydata %>% dplyr::mutate(scale1 = rowMeans(mydata[ ,scale1_vars], na.rm = TRUE),
                                   scale2 = rowMeans(mydata[ ,scale2_vars], na.rm = TRUE))

# A benefit of the above approach is that you can put any variables you
# want into scales1_vars or scales2_vars and you don't need to know how
# many variables you put in - the code will simply calculate the average
# across all of the columns.

# Now let's calculate the averages manually. We can do this by
# adding up the variables we want in our scale, then dividing by the
# number of variables to get the average of the variables.
fulldf <- fulldf %>% dplyr::mutate(scale1_manual = (x + y) / 2,
                                   scale2_manual = (y + z) / 2)

# Let's make sure the manual approach and the other approach produce
# the same result. We can use the identical() function to tell us if the
# two variables are identical! TRUE if so, FALSE if not.
identical(fulldf$scale1_manual, fulldf$scale1) # TRUE
identical(fulldf$scale2_manual, fulldf$scale2) # TRUE

# Based on the above, our approach works! The former approach is nice because
# (1) you don't need to type out the # of variables you are including in the scale
# and so don't run the risk of forgetting to change the number you're dividing
# by if/when you change the number of items in your scale and (2) it calculates
# averages for rows using the available variables where there is no missingness
# rather than simply returning NA if ANY variable is missing. On the other hand,
# (a) it is more lines of code because you first say which sets of variables you
# want to include in your scales in some lines of code then actually generate
# the scales in some more code, (b) it's not so obvious mathematically what you
# are doing unless you immdiately see "rowMeans" and know that means it is
# calculating the average of the rows for each scale, and (c) you might want
# to drop any observation with missing data rather than simply use the available
# data to calculate an average. For example, if we make a scale using the variable
# with missing data (w) then we will find the two approaches produce different
# results:

# Approach 1
scale3_vars <- c("w","z")
fulldf <- fulldf %>% dplyr::mutate(scale3 = rowMeans(fulldf[ ,scale3_vars], na.rm = TRUE))

# Approach 2
fulldf <- fulldf %>% dplyr::mutate(scale3_manual = (w + z) / 2)

# Test if identical
identical(fulldf$scale3, fulldf$scale3_manual)

# To see why, here are the computed scale values for the two different approaches:
fulldf$scale3
fulldf$scale3_manual

# Note: there are more efficient ways of doing this as well, such as writing a
# function to implement the second approach simply by giving the function the
# variable names and scale name, while also building in functionality to let
# the user choose to include or omit variables with missing values. The
# examples above are reasonable ways of doing this for those just learning R
# and/or the Tidyverse.