Most of the tools we examined in Data Wrangling I were general purpose things – what tidy data is, using dplyr and tidyr for manipulation of data tables. Two variable types, strings and factors, present enough challenges to examine in some detail. Now might also be a good time to read up on the history of strings and factors in R!

This is the second module in the Data Wrangling II topic.

Overview

Learning Objectives

Edit/manipulate strings and take control of factors.

Video Lecture


Example

I’ll write code for today’s content in a new R Markdown document called strings_and_factors.Rmd, and put it in the same directory / GitHub repo as reading_data.Rmd. Since we’ll revisit some scraped examples, I’ll load rvest now; we’ll also use some datasets in p8105.datasets so I’ll load that as well. And finally, we’ll use some of these examples, so I’ll make sure I have them in a data subdirectory..

library(rvest)
## 
## Attaching package: 'rvest'
## The following object is masked from 'package:readr':
## 
##     guess_encoding
library(p8105.datasets)

Strings and regex

Lots of the examples below are drawn from materials by Jeff Leek.

The most frequent operation involving strings is to search for an exact match. You can use str_detect to find cases where the match exists (often useful in conjunction with filter), and you can use str_replace to replace an instance of a match with something else (often useful in conjunction with mutate). In the following examples we’ll mostly focus on vectors to avoid the complication of data frames, but we’ll see those shortly.

string_vec = c("my", "name", "is", "jeff")

str_detect(string_vec, "jeff")
## [1] FALSE FALSE FALSE  TRUE
str_replace(string_vec, "jeff", "Jeff")
## [1] "my"   "name" "is"   "Jeff"

For exact matches, you can designate matches at the beginning or end of a line.

string_vec = c(
  "i think we all rule for participating",
  "i think i have been caught",
  "i think this will be quite fun actually",
  "it will be fun, i think"
  )

str_detect(string_vec, "^i think")
## [1]  TRUE  TRUE  TRUE FALSE
str_detect(string_vec, "i think$")
## [1] FALSE FALSE FALSE  TRUE

You can designate a list of characters that will count as a match.

string_vec = c(
  "Y'all remember Pres. HW Bush?",
  "I saw a green bush",
  "BBQ and Bushwalking at Molonglo Gorge",
  "BUSH -- LIVE IN CONCERT!!"
  )

str_detect(string_vec,"[Bb]ush")
## [1]  TRUE  TRUE  TRUE FALSE

You don’t have to list these; instead, you can provide a range of letters or numbers that count as a match.

string_vec = c(
  '7th inning stretch',
  '1st half soon to begin. Texas won the toss.',
  'she is 5 feet 4 inches tall',
  '3AM - cant sleep :('
  )

str_detect(string_vec, "^[0-9][a-zA-Z]")
## [1]  TRUE  TRUE FALSE  TRUE

The character . matches anything.

string_vec = c(
  'Its 7:11 in the evening',
  'want to go to 7-11?',
  'my flight is AA711',
  'NetBios: scanning ip 203.167.114.66'
  )

str_detect(string_vec, "7.11")
## [1]  TRUE  TRUE FALSE  TRUE

Some characters are “special”. These include [ and ], ( and ), and .. If you want to search for these, you have to indicate they’re special using \. Unfortunately, \ is also special, so things get weird.

string_vec = c(
  'The CI is [2, 5]',
  ':-]',
  ':-[',
  'I found the answer on pages [6-7]'
  )

str_detect(string_vec, "\\[")
## [1]  TRUE FALSE  TRUE  TRUE

There are a lot of other regular expression resources; we’re really only scratching the surface. The stuff we have already will be useful and it’s probably not worth going down the regex rabbit hole. That said, it’s helpful to know what other functions exist in stringr – you should at least know they exist even if you don’t know exactly how they work right now!

Thoughts on factors

Factors are maybe the least intuitive of R’s data types. They are very useful, but they also do things you don’t expect; this is especially bad when you have factors but think you have characters – which happens more than you’d expect, because R uses factors a lot (mostly for historical reasons). Folks get pretty riled up about factors.

Factors are the way to store categorical variables in R. They can take on specific levels (e.g. male and female) which are usually presented as characters but are, in fact, stored by R as integers. These integer values are used by functions throughout R – in making plots, in organizing tables, in determining the “reference” category – but most of the time are hidden by easier-to-read character string labels. This close relationship to strings, when in fact there is a lot of added structure, is why factors can be so confusing.

This is the kind of thing that can get you in trouble.

vec_sex = factor(c("male", "male", "female", "female"))
vec_sex
## [1] male   male   female female
## Levels: female male
as.numeric(vec_sex)
## [1] 2 2 1 1
vec_sex = fct_relevel(vec_sex, "male")
vec_sex
## [1] male   male   female female
## Levels: male female
as.numeric(vec_sex)
## [1] 1 1 2 2

The previous code also illustrates coersion: forcing a variable from one type (e.g. factor) to another (e.g. numeric). Understanding how R coerces variables is important, because it sometimes happens unintentionally and can break your code or impact your analyses.

For this reason, it’s important to be deliberate factors, particularly by using functions in forcats.

NSDUH

We’ll revisit the table scraped from the National Survey on Drug Use and Health data on this page. In reading data from the web, we loaded this data using the code below, but noted it wasn’t tidy.

nsduh_url = "http://samhda.s3-us-gov-west-1.amazonaws.com/s3fs-public/field-uploads/2k15StateFiles/NSDUHsaeShortTermCHG2015.htm"

table_marj = 
  read_html(nsduh_url) %>% 
  html_table() %>% 
  first() %>%
  slice(-1)

There are a few steps we need to implement to tidy these data. For now I’m not interested in the p-values (I’d rather just see the data); we also have age groups and year ranges in column titles, both of which are, in fact, variables. Lastly, the table includes letters as superscripts next to table entries; if we only want the percents we’ll need to strip these out.

data_marj = 
  table_marj %>%
  select(-contains("P Value")) %>%
  pivot_longer(
    -State,
    names_to = "age_year", 
    values_to = "percent") %>%
  separate(age_year, into = c("age", "year"), sep = "\\(") %>%
  mutate(
    year = str_replace(year, "\\)", ""),
    percent = str_replace(percent, "[a-c]$", ""),
    percent = as.numeric(percent)) %>%
  filter(!(State %in% c("Total U.S.", "Northeast", "Midwest", "South", "West")))

We used stringr and regular expressions a couple of times above:

  • in separate, we split age and year at the open parentheses using "\\("
  • we stripped out the close parenthesis in mutate
  • to remove character superscripts, we replaced any character using "[a-c]$"

Let’s quickly visualize these data for the 12-17 age group; to make the plot readable, we’ll treat State as a factor are reorder according to the median percent value.

data_marj %>%
  filter(age == "12-17") %>% 
  mutate(State = fct_reorder(State, percent)) %>% 
  ggplot(aes(x = State, y = percent, color = year)) + 
    geom_point() + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1))

Restaurant inspections

As a kind of involved example of strings and factors, we’ll take a look at the NYC Restuarant Inspections data. Although we won’t talk about this in detail now, it’s worth mentioning that these data were collected using the NYC Open Data API; code is available with the data description.

First we’ll import the data and take a look.

data("rest_inspec")

rest_inspec %>% 
  group_by(boro, grade) %>% 
  summarize(n = n()) %>% 
  pivot_wider(names_from = grade, values_from = n)
## `summarise()` has grouped output by 'boro'. You can override using the `.groups` argument.
## # A tibble: 6 × 8
## # Groups:   boro [6]
##   boro              A     B     C `Not Yet Graded`     P     Z  `NA`
##   <chr>         <int> <int> <int>            <int> <int> <int> <int>
## 1 BRONX         13688  2801   701              200   163   351 16833
## 2 BROOKLYN      37449  6651  1684              702   416   977 51930
## 3 MANHATTAN     61608 10532  2689              765   508  1237 80615
## 4 Missing           4    NA    NA               NA    NA    NA    13
## 5 QUEENS        35952  6492  1593              604   331   913 45816
## 6 STATEN ISLAND  5215   933   207               85    47   149  6730

To simplify things, I’ll remove inspections with scores other than A, B, or C, and also remove the restaurants with missing boro information. I’ll also clean up boro names a bit.

rest_inspec =
  rest_inspec %>%
  filter(grade %in% c("A", "B", "C"), boro != "Missing") %>% 
  mutate(boro = str_to_title(boro))

Let’s focus only on pizza places for now, and re-examine grades by borough.

rest_inspec %>% 
  filter(str_detect(dba, "Pizza")) %>% 
  group_by(boro, grade) %>% 
  summarize(n = n()) %>% 
  pivot_wider(names_from = grade, values_from = n)
## `summarise()` has grouped output by 'boro'. You can override using the `.groups` argument.
## # A tibble: 5 × 3
## # Groups:   boro [5]
##   boro              A     B
##   <chr>         <int> <int>
## 1 Bronx             9     3
## 2 Brooklyn          6    NA
## 3 Manhattan        26     8
## 4 Queens           17    NA
## 5 Staten Island     5    NA

That doesn’t look right – for sure there are more pizza place ratings than that! The problem is that the match in str_detect is case-sensitive until we tell it not to be:

rest_inspec %>% 
  filter(str_detect(dba, "[Pp][Ii][Zz][Zz][Aa]")) %>% 
  group_by(boro, grade) %>% 
  summarize(n = n()) %>% 
  pivot_wider(names_from = grade, values_from = n)
## `summarise()` has grouped output by 'boro'. You can override using the `.groups` argument.
## # A tibble: 5 × 4
## # Groups:   boro [5]
##   boro              A     B     C
##   <chr>         <int> <int> <int>
## 1 Bronx          1170   305    56
## 2 Brooklyn       1948   296    61
## 3 Manhattan      1983   420    76
## 4 Queens         1647   259    48
## 5 Staten Island   323   127    21

The table is okay but I’d like to visualize this.

rest_inspec %>% 
  filter(str_detect(dba, "[Pp][Ii][Zz][Zz][Aa]")) %>%
  ggplot(aes(x = boro, fill = grade)) + 
  geom_bar() 

Might help to have things in a different order – maybe number of pizza places? Fortunately this can be done using fct_infreq.

rest_inspec %>% 
  filter(str_detect(dba, "[Pp][Ii][Zz][Zz][Aa]")) %>%
  mutate(boro = fct_infreq(boro)) %>%
  ggplot(aes(x = boro, fill = grade)) + 
  geom_bar() 

Suppose I want to rename a borough. I could try using str_replace.

rest_inspec %>% 
  filter(str_detect(dba, "[Pp][Ii][Zz][Zz][Aa]")) %>%
  mutate(
    boro = fct_infreq(boro),
    boro = str_replace(boro, "Manhattan", "The City")) %>%
  ggplot(aes(x = boro, fill = grade)) + 
  geom_bar() 

This renamed the borough, but then converted the result back to a string – which, when plotted, was implicitly made a factor and ordered alphabetically. I could switch the order in which I rename and encode the factor order I want, but that might not always work.

If I tried base R (maybe because I found some code online), I might try to do this using replace.

rest_inspec %>% 
  filter(str_detect(dba, "[Pp][Ii][Zz][Zz][Aa]")) %>%
  mutate(
    boro = fct_infreq(boro),
    boro = replace(boro, which(boro == "Manhattan"), "The City")) %>%
  ggplot(aes(x = boro, fill = grade)) + 
  geom_bar()
## Warning in `[<-.factor`(`*tmp*`, list, value = "The City"): invalid factor
## level, NA generated

That didn’t work at all! Factors have very specific values, trying to use a value that is not an existing factor level won’t work.

Fortunately there is a dedicated function for renaming factor levels:

rest_inspec %>% 
  filter(str_detect(dba, regex("pizza", ignore_case = TRUE))) %>%
  mutate(
    boro = fct_infreq(boro),
    boro = fct_recode(boro, "The City" = "Manhattan")) %>%
  ggplot(aes(x = boro, fill = grade)) + 
  geom_bar()

Success!

Weather data

We saw factors in in Viz Pt 2. In that case, we were interested in reordering a factor variable in order to produce clearer plots – ggplot uses factor levels to determine the order in which categories appear. Let’s revisit those examples now.

Our first step is to load the data we used.

weather_df = 
  rnoaa::meteo_pull_monitors(
    c("USW00094728", "USC00519397", "USS0023B17S"),
    var = c("PRCP", "TMIN", "TMAX"), 
    date_min = "2017-01-01",
    date_max = "2017-12-31") %>%
  mutate(
    name = recode(
      id, 
      USW00094728 = "CentralPark_NY", 
      USC00519397 = "Waikiki_HA",
      USS0023B17S = "Waterhole_WA"),
    tmin = tmin / 10,
    tmax = tmax / 10) %>%
  select(name, id, everything())
## Registered S3 method overwritten by 'hoardr':
##   method           from
##   print.cache_info httr
## using cached file: /Users/jeffgoldsmith/Library/Caches/R/noaa_ghcnd/USW00094728.dly
## date created (size, mb): 2020-09-25 14:56:47 (7.519)
## file min/max dates: 1869-01-01 / 2020-09-30
## using cached file: /Users/jeffgoldsmith/Library/Caches/R/noaa_ghcnd/USC00519397.dly
## date created (size, mb): 2020-09-25 14:56:52 (1.699)
## file min/max dates: 1965-01-01 / 2020-03-31
## using cached file: /Users/jeffgoldsmith/Library/Caches/R/noaa_ghcnd/USS0023B17S.dly
## date created (size, mb): 2020-09-25 14:56:54 (0.877)
## file min/max dates: 1999-09-01 / 2020-09-30
weather_df
## # A tibble: 1,095 x 6
##    name           id          date        prcp  tmax  tmin
##    <chr>          <chr>       <date>     <dbl> <dbl> <dbl>
##  1 CentralPark_NY USW00094728 2017-01-01     0   8.9   4.4
##  2 CentralPark_NY USW00094728 2017-01-02    53   5     2.8
##  3 CentralPark_NY USW00094728 2017-01-03   147   6.1   3.9
##  4 CentralPark_NY USW00094728 2017-01-04     0  11.1   1.1
##  5 CentralPark_NY USW00094728 2017-01-05     0   1.1  -2.7
##  6 CentralPark_NY USW00094728 2017-01-06    13   0.6  -3.8
##  7 CentralPark_NY USW00094728 2017-01-07    81  -3.2  -6.6
##  8 CentralPark_NY USW00094728 2017-01-08     0  -3.8  -8.8
##  9 CentralPark_NY USW00094728 2017-01-09     0  -4.9  -9.9
## 10 CentralPark_NY USW00094728 2017-01-10     0   7.8  -6  
## # … with 1,085 more rows

Our first example reordered name “by hand” using fct_relevel:

weather_df %>%
  mutate(name = forcats::fct_relevel(name, c("Waikiki_HA", "CentralPark_NY", "Waterhole_WA"))) %>% 
  ggplot(aes(x = name, y = tmax)) + 
  geom_violin(aes(fill = name), color = "blue", alpha = .5) + 
  theme(legend.position = "bottom")

We could instead reorder name according to tmax values in each name using fct_reorder:

weather_df %>%
  mutate(name = forcats::fct_reorder(name, tmax)) %>% 
  ggplot(aes(x = name, y = tmax)) + 
  geom_violin(aes(fill = name), color = "blue", alpha = .5) + 
  theme(legend.position = "bottom")

Although you may not have seen linear regression previously, the ordering of factor variables play an important in this case as well. Specifically, the ordering determines the “reference” category, and is something that can be adjusted as needed.

weather_df %>%
  lm(tmax ~ name, data = .)
## 
## Call:
## lm(formula = tmax ~ name, data = .)
## 
## Coefficients:
##      (Intercept)    nameWaikiki_HA  nameWaterhole_WA  
##           17.366            12.291            -9.884
weather_df %>%
  mutate(name = forcats::fct_relevel(name, c("Waikiki_HA", "CentralPark_NY", "Waterhole_WA"))) %>% 
  lm(tmax ~ name, data = .)
## 
## Call:
## lm(formula = tmax ~ name, data = .)
## 
## Coefficients:
##        (Intercept)  nameCentralPark_NY    nameWaterhole_WA  
##              29.66              -12.29              -22.18

In this example, we’re also using a feature of %>% that allows us to specify where the tibble goes: . is a placeholder for the result of the preceding call.

PULSE data

Let’s revisit a dataset we’ve seen a few times now. In tidy data we spent some time tidying the PULSE dataset. As part of that we used tools available to us at the time, but now we have some better tools.

The code below updates the data processing pipeline for the PULSE data using stringr and forcats. The result is the same, and the differences are pretty small, but this is a bit cleaner.

pulse_data = 
  haven::read_sas("./data/public_pulse_data.sas7bdat") %>%
  janitor::clean_names() %>%
  pivot_longer(
    bdi_score_bl:bdi_score_12m,
    names_to = "visit", 
    names_prefix = "bdi_score_",
    values_to = "bdi") %>%
  select(id, visit, everything()) %>%
  mutate(
    visit = str_replace(visit, "bl", "00m"),
    visit = factor(visit)) %>%
  arrange(id, visit)

print(pulse_data, n = 12)
## # A tibble: 4,348 × 5
##       id visit   age sex     bdi
##    <dbl> <fct> <dbl> <chr> <dbl>
##  1 10003 00m    48.0 male      7
##  2 10003 01m    48.0 male      1
##  3 10003 06m    48.0 male      2
##  4 10003 12m    48.0 male      0
##  5 10015 00m    72.5 male      6
##  6 10015 01m    72.5 male     NA
##  7 10015 06m    72.5 male     NA
##  8 10015 12m    72.5 male     NA
##  9 10022 00m    58.5 male     14
## 10 10022 01m    58.5 male      3
## 11 10022 06m    58.5 male      8
## 12 10022 12m    58.5 male     NA
## # … with 4,336 more rows

Airbnb

We could also use factors in an exploratory analysis of the Airbnb data, for example when looking at the distribution of prices in various neighborhoods. Ordering these according to the median price makes for clearer plots than ordering neighborhoods alphabetically.

data("nyc_airbnb")

nyc_airbnb %>%
  filter(neighbourhood_group == "Manhattan") %>% 
  mutate(
    neighbourhood = fct_reorder(neighbourhood, price)) %>% 
  ggplot(aes(x = neighbourhood, y = price)) +
  geom_boxplot() +
  coord_flip() + 
  ylim(0, 1000)

Other materials

The code that I produced working examples in lecture is here.