Reading Data
Last Updated: 24, October, 2024 at 08:58
- Before we begin…
- Using the tidyverse vs built-in data reading functions
- Reading data from excel using readxl
- Reading data from a comma separated text file
- Tibbles
- Some interesting options when using
read_csv() - Reading files not seperated by commas
- Exploring data
- Addressing a single variable
- Creating new variables
- Creating new variables using
mutate - Exercises
- Possible solutions
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 1.0.2
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
Before we begin…
Download the following data files to your computer:
transit-data.xlsxpakistan_intellectual_capital.csvcars.txtwages1833.csv
Using the tidyverse vs built-in data reading functions
Default function
In base R, the default function for reading tabular data from a CSV file
is read.csv(). This function is widely used, but it has some quirks:
-
Automatic conversion of strings to factors (before R version 4.0.0): By default,
read.csv()automatically converts character columns to factors, which can cause issues if you’re not expecting categorical data. You can disable this behavior by settingstringsAsFactors = FALSE. -
Column name handling: Column names are automatically adjusted to be syntactically valid variable names. For example, spaces are replaced with periods (.), which can make column names harder to read.
-
Performance: While
read.csv()is perfectly fine for smaller datasets, it can be slower when working with larger files.
Example usage:
data <- read.csv("data/pakistan_intellectual_capital.csv")
The tidyverse way
The tidyverse provides its own functions to read data. For example, the
nearly-equivalent function to read.csv() is read_csv() (from the
readr package). Notice the small difference in the function name!
The tidyverse provides a more user-friendly function for reading CSV
files: read_csv() from the readr package. It addresses many of the
limitations of read.csv() and offers a more intuitive experience for
modern data analysis:
- Type guessing:
read_csv()automatically guesses the types of columns (e.g., numeric, character, date) but does not convert them to factors unless specified. - Handling of column names: Column names are left as-is, without modification. This keeps names readable, especially when they contain spaces or special characters.
- Faster performance:
read_csv()is optimized for speed and can handle larger datasets more efficiently thanread.csv(). This performance difference becomes noticeable for very large datasets (e.g., over 1 GB). - Tibble output: The result of
read_csv()is a tibble, which has advantages over a data.frame, such as better printing in the console and more intuitive column subsetting.
Example usage:
data <- read_csv("data/pakistan_intellectual_capital.csv")
## New names:
## Rows: 1142 Columns: 13
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (10): Teacher Name, University Currently Teaching, Department, Province ... dbl
## (3): ...1, S#, Year
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
Throughout this class, I will try to use the tidyverse functions for reading data.
Reading data from excel using readxl
Excel spreadsheets are often used and easy ways to store data.
Explore the data in transit-data.xlsx. (Please note the organization
of my project folder.)
Note that:
- The data data do not start in cell A1
- The data contain different date formats
- The data contain weird characters
Read the first sheet of data:
library(readxl)
data <-read_excel("data/transit-data.xlsx") #Be sure to use the correct relative path here.
Look at the help for the read_excel function:
?read_excel
Let’s specify the data range we want to read.
info <-read_excel("data/transit-data.xlsx", sheet = 'info', range = 'B1:C7')
info <-read_excel("data/transit-data.xlsx", sheet = 'info', range = cell_cols('B:C'))
Let’s now read the data in the second sheet:
more_data <- read_excel("data/transit-data.xlsx", sheet = 'transport data')
## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...8`
We need to skip the first line of the data.
more_data <- read_excel("data/transit-data.xlsx", sheet = 'transport data', skip=1)
The column names are human readable but not very handy for coding. There is a quick way to solve this.
Tip: Only use underscores or dots in column names. This will make your life easier when coding.
colnames(more_data) <- make.names(colnames(more_data))
Reading data from a comma separated text file
R has functions for reading in text files. However, the functions provided by tidyverse are often more powerful. When reading in data, R reports on the column types. Information about the different types and their labels can be found here: Column Types
Note: The default report on the column types is a bit disorganized in this view. It will look better in your console.
data <- read_csv('data/pakistan_intellectual_capital.csv')
## New names:
## Rows: 1142 Columns: 13
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (10): Teacher Name, University Currently Teaching, Department, Province ... dbl
## (3): ...1, S#, Year
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
Tibbles
Reading in data using tidyverse functions generates tibbles, instead
of R’s traditional data.frame. These are a newer version of the
classic data frame with features tweaked to make life a bit easier.
- Printing: Tibbles print more concisely than
data.frames. They show a preview of the data, while data.frames show the entire dataset in the console. - Subsetting: When you subset a data.frame with
mydata[1], the result is still a data.frame, whereas in a tibble, subsetting a column withmydata[[1]]ormydata$column_namereturns a vector directly. - Casting: Tibbles preserve the type of data you provide. A
data.frame, might coerce data into factors.
print(class(data))
## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"
Convert to an old school data frame.
old_school<-as.data.frame(data)
Some interesting options when using read_csv()
No column names? No problem!
If data has no column names, use col_names = FALSE
read_csv("1,2,3\n4,5,6", col_names = FALSE)
## Rows: 2 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): X1, X2, X3
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## # A tibble: 2 × 3
## X1 X2 X3
## <dbl> <dbl> <dbl>
## 1 1 2 3
## 2 4 5 6
You can also directly set the column names in this case.
read_csv("1,2,3\n4,5,6", col_names = c("x", "y", "z"))
## Rows: 2 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): x, y, z
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## # A tibble: 2 × 3
## x y z
## <dbl> <dbl> <dbl>
## 1 1 2 3
## 2 4 5 6
Specifying missing data
Another option that commonly needs tweaking is na: this specifies the
value (or values) that are used to represent missing values in your
file:
read_csv("a,b,c\n1,2,.", na = ".")
## Rows: 1 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (2): a, b
## lgl (1): c
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## # A tibble: 1 × 3
## a b c
## <dbl> <dbl> <lgl>
## 1 1 2 NA
You can also specify multiple values that should be considered as missing data.
data <- read_csv("a,b,c\n1,2,.\n5,6,NA\n8,0,1", na = c("NA", "."))
## Rows: 3 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): a, b, c
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(data)
## # A tibble: 3 × 3
## a b c
## <dbl> <dbl> <dbl>
## 1 1 2 NA
## 2 5 6 NA
## 3 8 0 1
Reading from URL
You can read data directly from a URL as well.
url<-'https://raw.githubusercontent.com/dvanderelst-python-class/python-class/fall2022/11_Pandas_Statistics/data/pizzasize.csv'
data <- read_csv(url)
## Rows: 250 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Store, CrustDescription, Topping
## dbl (2): ID, Diameter
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Reading files not seperated by commas
read_csv() expects comma separated field. For data separated by other
characters use these:
- tab separated:
read_tsv() - all others:
read_delim(), specifying thedelimargument. read_csv2()uses;for the field separator and,for the decimal point. This format is common in some European countries.
Exploring data
After reading in the data, it’s a good idea to explore the data. Use either the environment tab in Rstudio or use functions that give you some info about your data.
“This is a critical step that should always be performed. It is simple but it is vital. You should make numerical summaries such as means, standard deviations (SDs), maximum and minimum, correlations and whatever else is appropriate to the specific dataset. Equally important are graphical summaries.”
(Faraway, J. J. (2004). Linear models with R. Chapman and Hall/CRC.).
One of the most simple things you can do is inspect your data using a text editor. When text files are large, it’s a good idea to get an editor that can handle large files. I suggest Sublime Text Editor. This editor can handle much larger files than you can open (as text) in Rstudio. Keep the file open while you’re cleaning it!
Let’s read in some data:
data <- read_csv('data/pakistan_intellectual_capital.csv') # n_max is the number of rows to read
## New names:
## Rows: 1142 Columns: 13
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (10): Teacher Name, University Currently Teaching, Department, Province ... dbl
## (3): ...1, S#, Year
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
Column names
colnames(data)
## [1] "...1"
## [2] "S#"
## [3] "Teacher Name"
## [4] "University Currently Teaching"
## [5] "Department"
## [6] "Province University Located"
## [7] "Designation"
## [8] "Terminal Degree"
## [9] "Graduated from"
## [10] "Country"
## [11] "Year"
## [12] "Area of Specialization/Research Interests"
## [13] "Other Information"
Head and tail
head(data)
## # A tibble: 6 × 13
## ...1 `S#` Teacher …¹ Unive…² Depar…³ Provi…⁴ Desig…⁵ Termi…⁶ Gradu…⁷ Country
## <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 2 3 Dr. Abdul… Univer… Comput… Baloch… Assist… PhD Asian … Thaila…
## 2 4 5 Dr. Wahee… Univer… Comput… Baloch… Assist… PhD Asian … Thaila…
## 3 5 6 Dr. Junai… Univer… Comput… Baloch… Assist… PhD Asian … Thaila…
## 4 6 7 Dr. Mahee… Univer… Comput… Baloch… Assist… PhD Asian … Thaila…
## 5 24 25 Samina Az… Sardar… Comput… Baloch… Lectur… BS Baloch… Pakist…
## 6 25 26 Nausheed … Sardar… Comput… Baloch… Lectur… MCS Univer… Pakist…
## # … with 3 more variables: Year <dbl>,
## # `Area of Specialization/Research Interests` <chr>,
## # `Other Information` <chr>, and abbreviated variable names ¹`Teacher Name`,
## # ²`University Currently Teaching`, ³Department,
## # ⁴`Province University Located`, ⁵Designation, ⁶`Terminal Degree`,
## # ⁷`Graduated from`
tail(data)
## # A tibble: 6 × 13
## ...1 `S#` Teacher …¹ Unive…² Depar…³ Provi…⁴ Desig…⁵ Termi…⁶ Gradu…⁷ Country
## <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1971 1972 Dr. Khali… Ghulam… Comput… KPK Profes… PhD Concor… Canada
## 2 1974 1975 Dr. Ahmar… Ghulam… Comput… KPK Associ… PhD JNU South …
## 3 1975 1976 Dr. Fawad… Ghulam… Comput… KPK Associ… PhD Grenob… France
## 4 1977 1978 Dr. Rasha… Ghulam… Comput… KPK Assist… PhD Florid… USA
## 5 1979 1980 Dr. Shaha… Ghulam… Comput… KPK Assist… PhD Ghulam… Pakist…
## 6 1980 1981 Dr. Sajid… Ghulam… Comput… KPK Assist… PhD Seoul … South …
## # … with 3 more variables: Year <dbl>,
## # `Area of Specialization/Research Interests` <chr>,
## # `Other Information` <chr>, and abbreviated variable names ¹`Teacher Name`,
## # ²`University Currently Teaching`, ³Department,
## # ⁴`Province University Located`, ⁵Designation, ⁶`Terminal Degree`,
## # ⁷`Graduated from`
Summary
Get a quick summary
summary(data)
## ...1 S# Teacher Name
## Min. : 2.0 Min. : 3.0 Length:1142
## 1st Qu.: 689.2 1st Qu.: 690.2 Class :character
## Median :1087.5 Median :1088.5 Mode :character
## Mean :1054.4 Mean :1055.4
## 3rd Qu.:1476.8 3rd Qu.:1477.8
## Max. :1980.0 Max. :1981.0
##
## University Currently Teaching Department Province University Located
## Length:1142 Length:1142 Length:1142
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Designation Terminal Degree Graduated from Country
## Length:1142 Length:1142 Length:1142 Length:1142
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Year Area of Specialization/Research Interests Other Information
## Min. :1983 Length:1142 Length:1142
## 1st Qu.:2008 Class :character Class :character
## Median :2012 Mode :character Mode :character
## Mean :2010
## 3rd Qu.:2014
## Max. :2018
## NA's :653
Glimpse
The function glimpse gives you also a quick overview:
glimpse(data)
## Rows: 1,142
## Columns: 13
## $ ...1 <dbl> 2, 4, 5, 6, 24, 25, 26, 27…
## $ `S#` <dbl> 3, 5, 6, 7, 25, 26, 27, 28…
## $ `Teacher Name` <chr> "Dr. Abdul Basit", "Dr. Wa…
## $ `University Currently Teaching` <chr> "University of Balochistan…
## $ Department <chr> "Computer Science & IT", "…
## $ `Province University Located` <chr> "Balochistan", "Balochista…
## $ Designation <chr> "Assistant Professor", "As…
## $ `Terminal Degree` <chr> "PhD", "PhD", "PhD", "PhD"…
## $ `Graduated from` <chr> "Asian Institute of Techno…
## $ Country <chr> "Thailand", "Thailand", "T…
## $ Year <dbl> NA, NA, NA, NA, 2005, 2008…
## $ `Area of Specialization/Research Interests` <chr> "Software Engineering & DB…
## $ `Other Information` <chr> NA, NA, NA, NA, NA, NA, NA…
Skim
And what about this beautiful exploratory tool?
library(skimr)
skim(data)
| Name | data |
| Number of rows | 1142 |
| Number of columns | 13 |
| _______________________ | |
| Column type frequency: | |
| character | 10 |
| numeric | 3 |
| ________________________ | |
| Group variables | None |
Data summary
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Teacher Name | 0 | 1.00 | 5 | 40 | 0 | 1133 | 0 |
| University Currently Teaching | 0 | 1.00 | 7 | 71 | 0 | 63 | 0 |
| Department | 0 | 1.00 | 7 | 43 | 0 | 17 | 0 |
| Province University Located | 0 | 1.00 | 3 | 11 | 0 | 5 | 0 |
| Designation | 19 | 0.98 | 4 | 39 | 0 | 46 | 0 |
| Terminal Degree | 4 | 1.00 | 2 | 30 | 0 | 41 | 0 |
| Graduated from | 0 | 1.00 | 3 | 88 | 0 | 347 | 0 |
| Country | 0 | 1.00 | 2 | 18 | 0 | 35 | 0 |
| Area of Specialization/Research Interests | 519 | 0.55 | 3 | 477 | 0 | 570 | 0 |
| Other Information | 1018 | 0.11 | 8 | 132 | 0 | 51 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1.00 | 1054.35 | 520.20 | 2 | 689.25 | 1087.5 | 1476.75 | 1980 | ▅▅▆▇▅ |
| S# | 0 | 1.00 | 1055.35 | 520.20 | 3 | 690.25 | 1088.5 | 1477.75 | 1981 | ▅▅▆▇▅ |
| Year | 653 | 0.43 | 2010.46 | 5.58 | 1983 | 2008.00 | 2012.0 | 2014.00 | 2018 | ▁▁▁▆▇ |
skim_tee(data)
## ── Data Summary ────────────────────────
## Values
## Name data
## Number of rows 1142
## Number of columns 13
## _______________________
## Column type frequency:
## character 10
## numeric 3
## ________________________
## Group variables None
##
## ── Variable type: character ────────────────────────────────────────────────────
## skim_variable n_missing complete_rate min max
## 1 Teacher Name 0 1 5 40
## 2 University Currently Teaching 0 1 7 71
## 3 Department 0 1 7 43
## 4 Province University Located 0 1 3 11
## 5 Designation 19 0.983 4 39
## 6 Terminal Degree 4 0.996 2 30
## 7 Graduated from 0 1 3 88
## 8 Country 0 1 2 18
## 9 Area of Specialization/Research Interests 519 0.546 3 477
## 10 Other Information 1018 0.109 8 132
## empty n_unique whitespace
## 1 0 1133 0
## 2 0 63 0
## 3 0 17 0
## 4 0 5 0
## 5 0 46 0
## 6 0 41 0
## 7 0 347 0
## 8 0 35 0
## 9 0 570 0
## 10 0 51 0
##
## ── Variable type: numeric ──────────────────────────────────────────────────────
## skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100
## 1 ...1 0 1 1054. 520. 2 689. 1088. 1477. 1980
## 2 S# 0 1 1055. 520. 3 690. 1088. 1478. 1981
## 3 Year 653 0.428 2010. 5.58 1983 2008 2012 2014 2018
## hist
## 1 ▅▅▆▇▅
## 2 ▅▅▆▇▅
## 3 ▁▁▁▆▇
Tabulating
You can use the janitor package to get a quick overview of categorical
variables, i.e., tabulating categorical variables. It’s somewhat
confusing that the percent column is not a percentage but a
proportion.
library(janitor)
##
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test
tabyl(data, `Province University Located`)
## Province University Located n percent
## Balochistan 16 0.01401051
## Capital 247 0.21628722
## KPK 86 0.07530648
## Punjab 449 0.39316988
## Sindh 344 0.30122592
https://chatgpt.com/c/6707c7ce-00c0-800a-b35a-a647ab7503be The
janitor package is also useful for other things, like cleaning up
column names. This is a link to an online article that describes some of
the uses of the package on
medium.com.
Addressing a single variable
A simple way to get variables from a tibble is using the $ operator.
my_variable <- data$Year
summary(my_variable)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1983 2008 2012 2010 2014 2018 653
However, this does not work if your variables names have names with a space (or other weird characters). This does work, however,
my_variable <- data['Terminal Degree']
Creating new variables
Often, you will want to create new variables based on existing ones to make subsequent processing easier.
data['very_simple'] <- 1
data['very_simple']
## # A tibble: 1,142 × 1
## very_simple
## <dbl>
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 1
## # … with 1,132 more rows
However, we can do more interesting things.
data['has_phd'] <- data['Terminal Degree'] == 'PhD'
data['has_phd']
## # A tibble: 1,142 × 1
## has_phd
## <lgl>
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 FALSE
## 6 FALSE
## 7 FALSE
## 8 FALSE
## 9 FALSE
## 10 FALSE
## # … with 1,132 more rows
We can calculate new variables.
data['years_since_graduation'] <- 2022 - data$Year
Creating new variables using mutate
The function mutate is the Tidyverse-approach to creating new
variables. Let’s use the pizza data for this demo.
url<-'https://raw.githubusercontent.com/dvanderelst-python-class/python-class/fall2022/11_Pandas_Statistics/data/pizzasize.csv'
data <- read_csv(url)
## Rows: 250 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Store, CrustDescription, Topping
## dbl (2): ID, Diameter
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Mathematical Operations on Existing Columns
Here are number of slides (not by me) that also explain this: https://tinyurl.com/4a6hek9f
Let’s create a new column giving the surface area of each column.
data <- mutate(data, area = pi * (Diameter/2)^2)
data
## # A tibble: 250 × 6
## ID Store CrustDescription Topping Diameter area
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 1 Dominos ThinNCrispy Supreme 29.4 679.
## 2 2 Dominos ThinNCrispy BBQMeatlovers 29.6 690.
## 3 3 Dominos DeepPan Hawaiian 27.1 575.
## 4 4 Dominos ThinNCrispy Supreme 27.4 592.
## 5 5 Dominos ClassicCrust Hawaiian 26.6 555.
## 6 6 Dominos DeepPan BBQMeatlovers 27.2 579.
## 7 7 EagleBoys MidCrust SuperSupremo 29.2 667.
## 8 8 EagleBoys DeepPan Hawaiian 28.8 651.
## 9 9 EagleBoys ThinCrust BBQMeatlovers 30.0 709.
## 10 10 EagleBoys DeepPan BBQMeatlovers 29.4 678.
## # … with 240 more rows
For fun: transmute only keeps the new variable.
data <- transmute(data, area = pi * (Diameter/2)^2)
data
## # A tibble: 250 × 1
## area
## <dbl>
## 1 679.
## 2 690.
## 3 575.
## 4 592.
## 5 555.
## 6 579.
## 7 667.
## 8 651.
## 9 709.
## 10 678.
## # … with 240 more rows
Logical/Conditional Variables using case_when
Tip: You can use mutate to created categories in your data.
data <- read_delim("data/cars.txt", delim = " ")
## Rows: 93 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: " "
## chr (6): make, model, type, cylinders, rearseat, luggage
## dbl (20): min_price, mid_price, max_price, mpg_city, mpg_hgw, airbag, drive,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
data <- mutate(data, mpg_difference = mpg_hgw - mpg_city,
fuel_efficiency_category = case_when(
mpg_difference < 5 ~ "Efficient",
mpg_difference >= 5 & mpg_difference <= 10 ~ "Moderate",
mpg_difference > 10 ~ "Inefficient"
))
head(data)
## # A tibble: 6 × 28
## make model type min_p…¹ mid_p…² max_p…³ mpg_c…⁴ mpg_hgw airbag drive cylin…⁵
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Acura Inte… Small 12.9 15.9 18.8 25 31 0 1 4
## 2 Acura Lege… Mids… 29.2 33.9 38.7 18 25 2 1 6
## 3 Audi 90 Comp… 25.9 29.1 32.3 20 26 1 1 6
## 4 Audi 100 Mids… 30.8 37.7 44.6 19 26 2 1 6
## 5 BMW 535i Mids… 23.7 30 36.2 22 30 1 0 4
## 6 Buick Cent… Mids… 14.2 15.7 17.3 22 31 1 1 4
## # … with 17 more variables: engine <dbl>, horsepower <dbl>, rpm <dbl>,
## # rpmile <dbl>, manual <dbl>, tank <dbl>, passengers <dbl>, length <dbl>,
## # wheelbase <dbl>, width <dbl>, uturn <dbl>, rearseat <chr>, luggage <chr>,
## # weight <dbl>, domestic <dbl>, mpg_difference <dbl>,
## # fuel_efficiency_category <chr>, and abbreviated variable names ¹min_price,
## # ²mid_price, ³max_price, ⁴mpg_city, ⁵cylinders
Logical/Conditional Variables using if_else
data <- mutate(data, small_engine = if_else(engine < 2, 1, 0))
names(data)
## [1] "make" "model"
## [3] "type" "min_price"
## [5] "mid_price" "max_price"
## [7] "mpg_city" "mpg_hgw"
## [9] "airbag" "drive"
## [11] "cylinders" "engine"
## [13] "horsepower" "rpm"
## [15] "rpmile" "manual"
## [17] "tank" "passengers"
## [19] "length" "wheelbase"
## [21] "width" "uturn"
## [23] "rearseat" "luggage"
## [25] "weight" "domestic"
## [27] "mpg_difference" "fuel_efficiency_category"
## [29] "small_engine"
data <- mutate(data, `small tank` = if_else(tank < 15, 1, 0))
names(data)
## [1] "make" "model"
## [3] "type" "min_price"
## [5] "mid_price" "max_price"
## [7] "mpg_city" "mpg_hgw"
## [9] "airbag" "drive"
## [11] "cylinders" "engine"
## [13] "horsepower" "rpm"
## [15] "rpmile" "manual"
## [17] "tank" "passengers"
## [19] "length" "wheelbase"
## [21] "width" "uturn"
## [23] "rearseat" "luggage"
## [25] "weight" "domestic"
## [27] "mpg_difference" "fuel_efficiency_category"
## [29] "small_engine" "small tank"
Logical/Conditional Variables using cut
The cut function is useful for creating categories based on continuous
variables. You can find more information about the cut() function
here.
data <- mutate(data, category = cut(min_price,
breaks = c(0, 10, 20, 30, 40),
labels = c("Low", "Medium", "High", "Very High")))
glimpse(data)
## Rows: 93
## Columns: 31
## $ make <chr> "Acura", "Acura", "Audi", "Audi", "BMW", "Bui…
## $ model <chr> "Integra", "Legend", "90", "100", "535i", "Ce…
## $ type <chr> "Small", "Midsize", "Compact", "Midsize", "Mi…
## $ min_price <dbl> 12.9, 29.2, 25.9, 30.8, 23.7, 14.2, 19.9, 22.…
## $ mid_price <dbl> 15.9, 33.9, 29.1, 37.7, 30.0, 15.7, 20.8, 23.…
## $ max_price <dbl> 18.8, 38.7, 32.3, 44.6, 36.2, 17.3, 21.7, 24.…
## $ mpg_city <dbl> 25, 18, 20, 19, 22, 22, 19, 16, 19, 16, 16, 2…
## $ mpg_hgw <dbl> 31, 25, 26, 26, 30, 31, 28, 25, 27, 25, 25, 3…
## $ airbag <dbl> 0, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 0, 1, 2, 0, …
## $ drive <dbl> 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, …
## $ cylinders <chr> "4", "6", "6", "6", "4", "4", "6", "6", "6", …
## $ engine <dbl> 1.8, 3.2, 2.8, 2.8, 3.5, 2.2, 3.8, 5.7, 3.8, …
## $ horsepower <dbl> 140, 200, 172, 172, 208, 110, 170, 180, 170, …
## $ rpm <dbl> 6300, 5500, 5500, 5500, 5700, 5200, 4800, 400…
## $ rpmile <dbl> 2890, 2335, 2280, 2535, 2545, 2565, 1570, 132…
## $ manual <dbl> 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, …
## $ tank <dbl> 13.2, 18.0, 16.9, 21.1, 21.1, 16.4, 18.0, 23.…
## $ passengers <dbl> 5, 5, 5, 6, 4, 6, 6, 6, 5, 6, 5, 5, 5, 4, 6, …
## $ length <dbl> 177, 195, 180, 193, 186, 189, 200, 216, 198, …
## $ wheelbase <dbl> 102, 115, 102, 106, 109, 105, 111, 116, 108, …
## $ width <dbl> 68, 71, 67, 70, 69, 69, 74, 78, 73, 73, 74, 6…
## $ uturn <dbl> 37, 38, 37, 37, 39, 41, 42, 45, 41, 43, 44, 3…
## $ rearseat <chr> "26.5", "30.0", "28.0", "31.0", "27.0", "28.0…
## $ luggage <chr> "11", "15", "14", "17", "13", "16", "17", "21…
## $ weight <dbl> 2705, 3560, 3375, 3405, 3640, 2880, 3470, 410…
## $ domestic <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ mpg_difference <dbl> 6, 7, 6, 7, 8, 9, 9, 9, 8, 9, 9, 11, 9, 9, 8,…
## $ fuel_efficiency_category <chr> "Moderate", "Moderate", "Moderate", "Moderate…
## $ small_engine <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `small tank` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category <fct> Medium, High, High, Very High, High, Medium, …
Recode values
recode() is a method to change the observations within a variable. It
can be used together with mutate().
data <- mutate(data,
category = recode(cylinders,
'3' = "Low Power",
'4' = "Low Power",
'6' = "Moderate Power",
'8' = "High Power"))
glimpse(data)
## Rows: 93
## Columns: 31
## $ make <chr> "Acura", "Acura", "Audi", "Audi", "BMW", "Bui…
## $ model <chr> "Integra", "Legend", "90", "100", "535i", "Ce…
## $ type <chr> "Small", "Midsize", "Compact", "Midsize", "Mi…
## $ min_price <dbl> 12.9, 29.2, 25.9, 30.8, 23.7, 14.2, 19.9, 22.…
## $ mid_price <dbl> 15.9, 33.9, 29.1, 37.7, 30.0, 15.7, 20.8, 23.…
## $ max_price <dbl> 18.8, 38.7, 32.3, 44.6, 36.2, 17.3, 21.7, 24.…
## $ mpg_city <dbl> 25, 18, 20, 19, 22, 22, 19, 16, 19, 16, 16, 2…
## $ mpg_hgw <dbl> 31, 25, 26, 26, 30, 31, 28, 25, 27, 25, 25, 3…
## $ airbag <dbl> 0, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 0, 1, 2, 0, …
## $ drive <dbl> 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, …
## $ cylinders <chr> "4", "6", "6", "6", "4", "4", "6", "6", "6", …
## $ engine <dbl> 1.8, 3.2, 2.8, 2.8, 3.5, 2.2, 3.8, 5.7, 3.8, …
## $ horsepower <dbl> 140, 200, 172, 172, 208, 110, 170, 180, 170, …
## $ rpm <dbl> 6300, 5500, 5500, 5500, 5700, 5200, 4800, 400…
## $ rpmile <dbl> 2890, 2335, 2280, 2535, 2545, 2565, 1570, 132…
## $ manual <dbl> 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, …
## $ tank <dbl> 13.2, 18.0, 16.9, 21.1, 21.1, 16.4, 18.0, 23.…
## $ passengers <dbl> 5, 5, 5, 6, 4, 6, 6, 6, 5, 6, 5, 5, 5, 4, 6, …
## $ length <dbl> 177, 195, 180, 193, 186, 189, 200, 216, 198, …
## $ wheelbase <dbl> 102, 115, 102, 106, 109, 105, 111, 116, 108, …
## $ width <dbl> 68, 71, 67, 70, 69, 69, 74, 78, 73, 73, 74, 6…
## $ uturn <dbl> 37, 38, 37, 37, 39, 41, 42, 45, 41, 43, 44, 3…
## $ rearseat <chr> "26.5", "30.0", "28.0", "31.0", "27.0", "28.0…
## $ luggage <chr> "11", "15", "14", "17", "13", "16", "17", "21…
## $ weight <dbl> 2705, 3560, 3375, 3405, 3640, 2880, 3470, 410…
## $ domestic <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ mpg_difference <dbl> 6, 7, 6, 7, 8, 9, 9, 9, 8, 9, 9, 11, 9, 9, 8,…
## $ fuel_efficiency_category <chr> "Moderate", "Moderate", "Moderate", "Moderate…
## $ small_engine <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ `small tank` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ category <chr> "Low Power", "Moderate Power", "Moderate Powe…
Exercises
It might be a good idea to start your first R script while working on these exercises.
Exercise 1
-
Read in the
cars.txtfile -
Print the first lines of the data
-
Print the column names
-
Calculate the difference in mpg on the highway and the city, add this difference as a new variable to the data.
Exercise 2
Download the wages1833.csv data file (from the data folder) and save
it to your computer. This file contains data on the wages of Lancashire
cotton factory workers in 1833. For each age category, the file lists
the following:
-
age: age in years -
mnum:number of male workers of the corresponding age -
mwage: average wage of male workers of the corresponding age -
fnum: number of female workers of the corresponding age -
fwage: average wage of female workers of the corresponding age
More info on the data can be found in this paper: Boot, H.M. 1995. How Skilled Were the Lancashire Cotton Factory Workers in 1833? Economic History Review 48: 283-303.
Write a script that does the following:
-
Reads in the data
-
Adds a new variable that lists the difference between the number of male and female workers
-
Adds a new variable
\[diff_{pct} = 100 \times \frac{mwage - fwage}{fwage}\]diff_pctthat gives the difference in average wage between the male and female workers expressed as a percentage of the female wage. Thediff_pctwill give a positive number if males earn more on average, and a negative number if females earn more.
Exercise 3
Read in the wages1833.csv data file using the read_csv() function.
Create a new column called age_group, which categorizes workers into the
following groups based on their age:
- “Child”: Age < 14
- “Young”: 14 <= Age <= 20
- “Adult”: Age > 20
Exercise 4
Read in the wages1833.csv file using read_csv(). Create a new column
called wage_difference, which gives the absolute difference between
the average male wage (mwage) and the average female wage (fwage).
Create a second new column called higher_wage, which indicates whether
males or females earned more on average in each row (“male” or
“female”). Print the first few rows of the data.
Possible solutions
Exercise 1
data <- read_delim("data/cars.txt", delim = " ")
## Rows: 93 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: " "
## chr (6): make, model, type, cylinders, rearseat, luggage
## dbl (20): min_price, mid_price, max_price, mpg_city, mpg_hgw, airbag, drive,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(data)
## # A tibble: 6 × 26
## make model type min_p…¹ mid_p…² max_p…³ mpg_c…⁴ mpg_hgw airbag drive cylin…⁵
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Acura Inte… Small 12.9 15.9 18.8 25 31 0 1 4
## 2 Acura Lege… Mids… 29.2 33.9 38.7 18 25 2 1 6
## 3 Audi 90 Comp… 25.9 29.1 32.3 20 26 1 1 6
## 4 Audi 100 Mids… 30.8 37.7 44.6 19 26 2 1 6
## 5 BMW 535i Mids… 23.7 30 36.2 22 30 1 0 4
## 6 Buick Cent… Mids… 14.2 15.7 17.3 22 31 1 1 4
## # … with 15 more variables: engine <dbl>, horsepower <dbl>, rpm <dbl>,
## # rpmile <dbl>, manual <dbl>, tank <dbl>, passengers <dbl>, length <dbl>,
## # wheelbase <dbl>, width <dbl>, uturn <dbl>, rearseat <chr>, luggage <chr>,
## # weight <dbl>, domestic <dbl>, and abbreviated variable names ¹min_price,
## # ²mid_price, ³max_price, ⁴mpg_city, ⁵cylinders
colnames(data)
## [1] "make" "model" "type" "min_price" "mid_price"
## [6] "max_price" "mpg_city" "mpg_hgw" "airbag" "drive"
## [11] "cylinders" "engine" "horsepower" "rpm" "rpmile"
## [16] "manual" "tank" "passengers" "length" "wheelbase"
## [21] "width" "uturn" "rearseat" "luggage" "weight"
## [26] "domestic"
data <- mutate(data, mpg_difference = mpg_hgw - mpg_city)
head(data)
## # A tibble: 6 × 27
## make model type min_p…¹ mid_p…² max_p…³ mpg_c…⁴ mpg_hgw airbag drive cylin…⁵
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Acura Inte… Small 12.9 15.9 18.8 25 31 0 1 4
## 2 Acura Lege… Mids… 29.2 33.9 38.7 18 25 2 1 6
## 3 Audi 90 Comp… 25.9 29.1 32.3 20 26 1 1 6
## 4 Audi 100 Mids… 30.8 37.7 44.6 19 26 2 1 6
## 5 BMW 535i Mids… 23.7 30 36.2 22 30 1 0 4
## 6 Buick Cent… Mids… 14.2 15.7 17.3 22 31 1 1 4
## # … with 16 more variables: engine <dbl>, horsepower <dbl>, rpm <dbl>,
## # rpmile <dbl>, manual <dbl>, tank <dbl>, passengers <dbl>, length <dbl>,
## # wheelbase <dbl>, width <dbl>, uturn <dbl>, rearseat <chr>, luggage <chr>,
## # weight <dbl>, domestic <dbl>, mpg_difference <dbl>, and abbreviated
## # variable names ¹min_price, ²mid_price, ³max_price, ⁴mpg_city, ⁵cylinders
glimpse(data)
## Rows: 93
## Columns: 27
## $ make <chr> "Acura", "Acura", "Audi", "Audi", "BMW", "Buick", "Buic…
## $ model <chr> "Integra", "Legend", "90", "100", "535i", "Century", "L…
## $ type <chr> "Small", "Midsize", "Compact", "Midsize", "Midsize", "M…
## $ min_price <dbl> 12.9, 29.2, 25.9, 30.8, 23.7, 14.2, 19.9, 22.6, 26.3, 3…
## $ mid_price <dbl> 15.9, 33.9, 29.1, 37.7, 30.0, 15.7, 20.8, 23.7, 26.3, 3…
## $ max_price <dbl> 18.8, 38.7, 32.3, 44.6, 36.2, 17.3, 21.7, 24.9, 26.3, 3…
## $ mpg_city <dbl> 25, 18, 20, 19, 22, 22, 19, 16, 19, 16, 16, 25, 25, 19,…
## $ mpg_hgw <dbl> 31, 25, 26, 26, 30, 31, 28, 25, 27, 25, 25, 36, 34, 28,…
## $ airbag <dbl> 0, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 0, 1, 2, 0, 0, 0, 1, 1…
## $ drive <dbl> 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 2, 0, 0…
## $ cylinders <chr> "4", "6", "6", "6", "4", "4", "6", "6", "6", "8", "8", …
## $ engine <dbl> 1.8, 3.2, 2.8, 2.8, 3.5, 2.2, 3.8, 5.7, 3.8, 4.9, 4.6, …
## $ horsepower <dbl> 140, 200, 172, 172, 208, 110, 170, 180, 170, 200, 295, …
## $ rpm <dbl> 6300, 5500, 5500, 5500, 5700, 5200, 4800, 4000, 4800, 4…
## $ rpmile <dbl> 2890, 2335, 2280, 2535, 2545, 2565, 1570, 1320, 1690, 1…
## $ manual <dbl> 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1…
## $ tank <dbl> 13.2, 18.0, 16.9, 21.1, 21.1, 16.4, 18.0, 23.0, 18.8, 1…
## $ passengers <dbl> 5, 5, 5, 6, 4, 6, 6, 6, 5, 6, 5, 5, 5, 4, 6, 7, 8, 6, 2…
## $ length <dbl> 177, 195, 180, 193, 186, 189, 200, 216, 198, 206, 204, …
## $ wheelbase <dbl> 102, 115, 102, 106, 109, 105, 111, 116, 108, 114, 111, …
## $ width <dbl> 68, 71, 67, 70, 69, 69, 74, 78, 73, 73, 74, 66, 68, 74,…
## $ uturn <dbl> 37, 38, 37, 37, 39, 41, 42, 45, 41, 43, 44, 38, 39, 43,…
## $ rearseat <chr> "26.5", "30.0", "28.0", "31.0", "27.0", "28.0", "30.5",…
## $ luggage <chr> "11", "15", "14", "17", "13", "16", "17", "21", "14", "…
## $ weight <dbl> 2705, 3560, 3375, 3405, 3640, 2880, 3470, 4105, 3495, 3…
## $ domestic <dbl> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ mpg_difference <dbl> 6, 7, 6, 7, 8, 9, 9, 9, 8, 9, 9, 11, 9, 9, 8, 5, 5, 9, …
Exercise 2
data <- read_csv("data/wages1833.csv")
## New names:
## Rows: 51 Columns: 6
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (6): ...1, age, mnum, mwage, fnum, fwage
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
data <- mutate(data, diff = mnum - fnum, diff_pct = 100 * (mwage - fwage) / fwage)
head(data)
## # A tibble: 6 × 8
## ...1 age mnum mwage fnum fwage diff diff_pct
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 10 204 30.5 122 35 82 -12.9
## 2 2 11 195 37.8 198 38 -3 -0.526
## 3 3 12 245 43 241 44 4 -2.27
## 4 4 13 233 50.5 233 46 0 9.78
## 5 5 14 256 56.5 236 59 20 -4.24
## 6 6 15 240 63 215 68 25 -7.35
glimpse(data)
## Rows: 51
## Columns: 8
## $ ...1 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
## $ age <dbl> 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 2…
## $ mnum <dbl> 204, 195, 245, 233, 256, 240, 204, 141, 164, 135, 92, 180, 14…
## $ mwage <dbl> 30.5, 37.8, 43.0, 50.5, 56.5, 63.0, 83.5, 88.5, 141.0, 138.3,…
## $ fnum <dbl> 122, 198, 241, 233, 236, 215, 256, 245, 279, 251, 209, 251, 1…
## $ fwage <dbl> 35, 38, 44, 46, 59, 68, 72, 78, 90, 98, 97, 96, 102, 100, 101…
## $ diff <dbl> 82, -3, 4, 0, 20, 25, -52, -104, -115, -116, -117, -71, -47, …
## $ diff_pct <dbl> -12.8571429, -0.5263158, -2.2727273, 9.7826087, -4.2372881, -…
Exercise 3
data <- read_csv("data/wages1833.csv")
## New names:
## Rows: 51 Columns: 6
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (6): ...1, age, mnum, mwage, fnum, fwage
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
data <- mutate(data, age_group = case_when(
age < 14 ~ "child",
age >= 14 & age <= 20 ~ "young",
age > 20 ~ "adult"
))
head(data)
## # A tibble: 6 × 7
## ...1 age mnum mwage fnum fwage age_group
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 1 10 204 30.5 122 35 child
## 2 2 11 195 37.8 198 38 child
## 3 3 12 245 43 241 44 child
## 4 4 13 233 50.5 233 46 child
## 5 5 14 256 56.5 236 59 young
## 6 6 15 240 63 215 68 young
glimpse(data)
## Rows: 51
## Columns: 7
## $ ...1 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1…
## $ age <dbl> 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, …
## $ mnum <dbl> 204, 195, 245, 233, 256, 240, 204, 141, 164, 135, 92, 180, 1…
## $ mwage <dbl> 30.5, 37.8, 43.0, 50.5, 56.5, 63.0, 83.5, 88.5, 141.0, 138.3…
## $ fnum <dbl> 122, 198, 241, 233, 236, 215, 256, 245, 279, 251, 209, 251, …
## $ fwage <dbl> 35, 38, 44, 46, 59, 68, 72, 78, 90, 98, 97, 96, 102, 100, 10…
## $ age_group <chr> "child", "child", "child", "child", "young", "young", "young…
Exercise 4
data <- read_csv("data/wages1833.csv")
## New names:
## Rows: 51 Columns: 6
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (6): ...1, age, mnum, mwage, fnum, fwage
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
data <- mutate(data, wage_difference = abs(mwage - fwage), higher_wage = ifelse(mwage > fwage, 'male', 'female'))
head(data)
## # A tibble: 6 × 8
## ...1 age mnum mwage fnum fwage wage_difference higher_wage
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 1 10 204 30.5 122 35 4.5 female
## 2 2 11 195 37.8 198 38 0.200 female
## 3 3 12 245 43 241 44 1 female
## 4 4 13 233 50.5 233 46 4.5 male
## 5 5 14 256 56.5 236 59 2.5 female
## 6 6 15 240 63 215 68 5 female
glimpse(data)
## Rows: 51
## Columns: 8
## $ ...1 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…
## $ age <dbl> 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23…
## $ mnum <dbl> 204, 195, 245, 233, 256, 240, 204, 141, 164, 135, 92, …
## $ mwage <dbl> 30.5, 37.8, 43.0, 50.5, 56.5, 63.0, 83.5, 88.5, 141.0,…
## $ fnum <dbl> 122, 198, 241, 233, 236, 215, 256, 245, 279, 251, 209,…
## $ fwage <dbl> 35, 38, 44, 46, 59, 68, 72, 78, 90, 98, 97, 96, 102, 1…
## $ wage_difference <dbl> 4.5, 0.2, 1.0, 4.5, 2.5, 5.0, 11.5, 10.5, 51.0, 40.3, …
## $ higher_wage <chr> "female", "female", "female", "male", "female", "femal…