data-raw/05_tidy3-age.md

In csiu/candyplay: 2015 Hallowe'een Candy Hierarchy survey data

05_tidy2-age.R

csiu November 6, 2015

At a glance

• clean up the "How old are you?" column to age

Code

suppressPackageStartupMessages(library(dplyr))
library(readr)

raw <- read_csv("02_candy-survey.csv")

After loading the data, we will rename the columns.

## HELPER FUNCTION:
Rename_columns <- function(raw, old.name, new.name){
  cnames <- colnames(raw)
  colnames(raw)[grep(old.name, cnames)] <- new.name
  raw
}

## RENAME COLUMNS:
raw <- Rename_columns(raw,
                      "How old are you?",
                      "age")

good_data <- raw %>%
  select(user, age) %>%
  mutate(age = plyr::mapvalues(age,
                               from = grep("taking", age, value = TRUE),
                               to = grep("taking", age, value = TRUE) %>%
                                 substr(0, 2),
                               warn_missing = FALSE),
         age = plyr::mapvalues(age,
                               from = grep("^\\d+[,:]$", age, value = TRUE),
                               to = grep("^\\d+[,:]$", age, value = TRUE) %>%
                                 sub("[,:]$", "", .),
                               warn_missing = FALSE),
         age = plyr::mapvalues(age,
                               from = grep("43!", age, value = TRUE),
                               to = 43,
                               warn_missing = FALSE),
         age = plyr::mapvalues(age,
                               from = grep("^40. deal with it.|^45, but the|^50 \\(despair\\)",
                                           age, value = TRUE),
                               to = grep("^40. deal with it.|^45, but the|^50 \\(despair\\)",
                                         age, value = TRUE) %>%
                                 substr(0, 2),
                               warn_missing = FALSE),
         age = suppressWarnings(as.integer(age)),
         age = ifelse(age > 116 | age < 1, NA, age)
  )

Here we also cleanup the age range.

range(na.omit(good_data$age))

## [1]   5 115

According to wikipedia's list of verified oldest people, the oldest person alive is Susannah Mushatt Jones (116 years old). This means that any age about this value, I will set as NA.

Here is the resulting list of oldest surveyees

good_data %>%
  arrange(desc(age)) %>%
  head() %>%
  knitr::kable()

user age

ID-1809 115 ID-3502 108 ID-1408 100 ID-1423 100 ID-2640 100 ID-1257 99

I am also going to set any age less than 1 as NA -- this is to get rid of negative ages and/or babies.

Here is the resulting list of youngest surveyees

good_data %>%
  arrange(age) %>%
  head() %>%
  knitr::kable()

user age

ID-0978 5 ID-0371 6 ID-0374 6 ID-2173 6 ID-2366 6 ID-1355 7

what might also be interesting is to analyze surveyees who commented their ages as "old"

raw %>%
  select(user, age) %>%
  filter(grepl("old", age, ignore.case = TRUE),
         !grepl("year[ -]old", age, ignore.case = TRUE)) %>%
  arrange(age) %>%
  knitr::kable()

user age

ID-0426 old ID-2128 old ID-2823 old ID-3117 old ID-3978 old ID-4108 old ID-4453 old ID-4873 old ID-2951 Old ID-3040 Old ID-3379 Old ID-3666 Old ID-5484 Old ID-0041 old but still know joy from despair ID-0352 old enough ID-0393 old enough ID-0006 old enough ID-4849 old enough ID-4616 old enough to know better ID-1704 old enough to party ID-5513 Old, very old ID-1502 older than dirt ID-1506 older than dirt ID-1059 Older than you ID-5360 So old ID-2520 too old ID-2717 too old ID-2788 too old ID-3039 too old ID-4332 too old ID-4841 too old ID-1565 Too old ID-1566 Too old ID-1567 Too old ID-1568 Too old ID-1569 Too old ID-1965 Too old ID-1248 too old for this

# good_data %>%
#   filter(!grepl('^\\d+$', age)) %>%
#   mutate(age = tolower(age)) %>%
#   group_by(age) %>%
#   summarise(count = length(age)) %>%
#   mutate(age = plyr::mapvalues(age,
#                                from = grep("taking", age, value = TRUE),
#                                to = grep("taking", age, value = TRUE) %>%
#                                  substr(0, 2)),
#          age = plyr::mapvalues(age,
#                                from = grep("^\\d+[,:]$", age, value = TRUE),
#                                to = grep("^\\d+[,:]$", age, value = TRUE) %>%
#                                  sub("[,:]$", "", .)),
#          age = plyr::mapvalues(age,
#                                from = grep("43!", age, value = TRUE),
#                                to = 43),
#          age = plyr::mapvalues(age,
#                                from = grep("^40. deal with it.|^45, but the|^50 \\(despair\\)", age, value = TRUE),
#                                to = grep("^40. deal with it.|^45, but the|^50 \\(despair\\)", age, value = TRUE) %>%
#                                  substr(0, 2))
#          ) %>%
#   filter(!grepl('^\\d+$', age)) %>%
#   knitr::kable()

title: "05_tidy3-age.R" author: "csiu" date: "Sun Nov 8 13:34:54 2015"

csiu/candyplay documentation built on May 14, 2019, 12:24 p.m.