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R4DS Study Group - Week 25

Pierrette Lo 9/25/2020

This week’s assignment

  • Chapter 11
library(tidyverse)

Ch 11:3 Parsing a Vector

Notes

  • Off-topic #1: If “Redo” (Ctrl/Cmd-Y) doesn’t work on your RStudio, go to Tools > Modify Keyboard Shortcuts, search for “Paste Last Yank”, and delete the shortcut if it is also set to Ctrl-Y.

  • Off-topic #2: these are my main uses for date format codes:

# get today's date

Sys.Date()
## [1] "2020-09-24"

# today's date in different formats

format(Sys.Date(), "%B %d, %Y")
## [1] "September 24, 2020"

format(Sys.Date(), "%m/%d/%y")
## [1] "09/24/20"

# write out a CSV file with today's date in the filename
# paste0 collapses a series of strings/variables with no space between

head(mtcars) %>% 
  write_csv(paste0("mtcars_subset_", Sys.Date(), ".csv"))

Exercises

  1. What are the most important arguments to locale()?

locale captures the defaults for date format, decimals, grouping marks, etc. that are specific to different countries. You then feed the locale object into your read or parse function. See ?locale() for details about arguments.

  1. What happens if you try and set decimal_mark and grouping_mark to the same character? What happens to the default value of grouping_mark when you set decimal_mark to “,”? What happens to the default value of decimal_mark when you set the grouping_mark to “.”?
locale(decimal_mark = ",", grouping_mark = ",")
## Error: `decimal_mark` and `grouping_mark` must be different

locale(grouping_mark = ".")
## <locale>
## Numbers:  123.456,78
## Formats:  %AD / %AT
## Timezone: UTC
## Encoding: UTF-8
## <date_names>
## Days:   Sunday (Sun), Monday (Mon), Tuesday (Tue), Wednesday (Wed), Thursday
##         (Thu), Friday (Fri), Saturday (Sat)
## Months: January (Jan), February (Feb), March (Mar), April (Apr), May (May),
##         June (Jun), July (Jul), August (Aug), September (Sep), October
##         (Oct), November (Nov), December (Dec)
## AM/PM:  AM/PM
  1. I didn’t discuss the date_format and time_format options to locale(). What do they do? Construct an example that shows when they might be useful.

Defaults:

  • %AD = flexible YMD parser
  • %AT = flexible HMS parser

These should be sufficient for most dates/times - you would only need to specify if your data is in an unusual format.

Example from the solutions manual: date given as “Day 01 Month 02 Year 20”)

locale_custom <- locale(date_format = "Day %d Month %M Year %y")

locale_custom
## <locale>
## Numbers:  123,456.78
## Formats:  Day %d Month %M Year %y / %AT
## Timezone: UTC
## Encoding: UTF-8
## <date_names>
## Days:   Sunday (Sun), Monday (Mon), Tuesday (Tue), Wednesday (Wed), Thursday
##         (Thu), Friday (Fri), Saturday (Sat)
## Months: January (Jan), February (Feb), March (Mar), April (Apr), May (May),
##         June (Jun), July (Jul), August (Aug), September (Sep), October
##         (Oct), November (Nov), December (Dec)
## AM/PM:  AM/PM
  1. If you live outside the US, create a new locale object that encapsulates the settings for the types of file you read most commonly.
locale_quebec <- locale(date_names = "fr",
                        decimal_mark = ",",
                        grouping_mark = " ")

locale_quebec
## <locale>
## Numbers:  123 456,78
## Formats:  %AD / %AT
## Timezone: UTC
## Encoding: UTF-8
## <date_names>
## Days:   dimanche (dim.), lundi (lun.), mardi (mar.), mercredi (mer.), jeudi
##         (jeu.), vendredi (ven.), samedi (sam.)
## Months: janvier (janv.), février (févr.), mars (mars), avril (avr.), mai (mai),
##         juin (juin), juillet (juil.), août (août), septembre (sept.),
##         octobre (oct.), novembre (nov.), décembre (déc.)
## AM/PM:  AM/PM
  1. What’s the difference between read_csv() and read_csv2()?

read_csv = comma separator (most common in English-speaking locales) read_csv2 = semicolon separator (common in European locales)

  1. What are the most common encodings used in Europe? What are the most common encodings used in Asia? Do some googling to find out.

See the wikipedia page for a list of encodings in different languages.

  1. Generate the correct format string to parse each of the following dates and times:

Formats are listed/described in the help (?strptime)

First create the objects in the text:

d1 <- "January 1, 2010"
d2 <- "2015-Mar-07"
d3 <- "06-Jun-2017"
d4 <- c("August 19 (2015)", "July 1 (2015)")
d5 <- "12/30/14" # Dec 30, 2014
t1 <- "1705"
t2 <- "11:15:10.12 PM"

Then parse each with the appropriate format. If the parsing works, you will get an object of class “Date”. Otherwise you will get a “parsing failure” error.

d1_parsed <- parse_date(d1, format = "%B %d, %Y")

class(d1)
## [1] "character"

class(d1_parsed)
## [1] "Date"

The other examples:

# d1 <- "January 1, 2010"
parse_date(d1, format = "%B %d, %Y")
## [1] "2010-01-01"

# d2 <- "2015-Mar-07"
parse_date(d2, format = "%Y-%b-%d")
## [1] "2015-03-07"

# d3 <- "06-Jun-2017"
parse_date(d3, format = "%d-%b-%Y")
## [1] "2017-06-06"

# d4 <- c("August 19 (2015)", "July 1 (2015)")
parse_date(d4, format = "%B %d (%Y)")
## [1] "2015-08-19" "2015-07-01"

# d5 <- "12/30/14"
parse_date(d5, format = "%m/%d/%y")
## [1] "2014-12-30"

# t1 <- "1705"
parse_time(t1, format = "%H%M")
## 17:05:00

# t2 <- "11:15:10.12 PM"
parse_time(t2, format = "%I:%M:%S %p")
## 23:15:10