Overview

Overview
- ability
- airmiles
- AirPassengers
- airquality
- anscombe
- attenu
- attitude
- austres
- beavers
- BJsales
- BJsales.lead
- BOD
- cars
- ChickWeight
- chickwts
- co2
- CO2
- crimtab
- discoveries
- DNase
- esoph
- euro
- euro.cross
- eurodist
- EuStockMarkets
- faithful
- fdeaths
- Formaldehyde
- freeny freeny.x freeny.y
- HairEyeColor
- Harman23.cor
- Harman 74.cor
- Indometh
- infert
- InsectSprays
- iris
- iris3
- islands
- JohnsonJohnson
- LakeHuron
- ldeaths (mdeaths, fdeaths)
- lh
- LifeCycleSavings
- Loblolly
- longley
- lynx
- mdeaths
- morley
- mtcars
- nhtemp
- Nile
- nottem
- npk
- occupationalstatus
- Orange
- OrchardSprays
- PlantGrowth
- precip
- presidents
- pressure
- Puromycin
- quakes
- randu
- rivers
- rock
- Seatbelts
- sleep
- stackloss
- state
- sunspot.month
- sunspot.year
- sunspots
- swiss
- Theoph
- Titanic
- ToothGrowth
- treering
- trees
- UCBAdmissions
- UKDriverDeaths
- UKgas
- USArrests
- UScitiesD
- USJudgeRatings
- USPersonalExpenditures
- uspop
- VADeaths
- volcano
- warpbreaks
- women
- World Phones
- WWWusage

Overview

Tidy up all of the R builtin datasets.

library(tidyr)
library(dplyr)

knitr::opts_chunk$set(comment='#>')

format_strs <- function(strings, sep='.', ...) {
  strings %>%
    stringr::str_to_lower() %>%
    stringr::str_replace_all(' ', sep)
}

ability

ability.cov_tidy <- ability.cov %>%
  as.data.frame() %>%
  select(-n.obs, -center) %>%
  mutate(test = rownames(.)) %>%
  gather(cov, value, -test) %>%
  tbl_df()

ability.cov_tidy

#> Source: local data frame [36 x 3]
#> 
#>       test         cov  value
#>      (chr)       (chr)  (dbl)
#> 1  general cov.general 24.641
#> 2  picture cov.general  5.991
#> 3   blocks cov.general 33.520
#> 4     maze cov.general  6.023
#> 5  reading cov.general 20.755
#> 6    vocab cov.general 29.701
#> 7  general cov.picture  5.991
#> 8  picture cov.picture  6.700
#> 9   blocks cov.picture 18.137
#> 10    maze cov.picture  1.782
#> ..     ...         ...    ...

airmiles

airmiles_tidy <- airmiles %>%
  as.data.frame() %>%
  setNames('airmiles') %>%
  mutate(year = time(airmiles)) %>%
  tbl_df()

airmiles_tidy

#> Source: local data frame [24 x 2]
#> 
#>    airmiles  year
#>       (dbl) (dbl)
#> 1       412  1937
#> 2       480  1938
#> 3       683  1939
#> 4      1052  1940
#> 5      1385  1941
#> 6      1418  1942
#> 7      1634  1943
#> 8      2178  1944
#> 9      3362  1945
#> 10     5948  1946
#> ..      ...   ...

AirPassengers

# gawd time series are awful ...
# from http://stackoverflow.com/questions/5331901/transforming-a-ts-in-a-data-frame-and-back

dmn <- list(month.abb, unique(floor(time(AirPassengers))))
AirPassengers_df <- tbl_df(data.frame(matrix(AirPassengers, 12, dimnames = dmn)))

AirPassengers_tidy <- AirPassengers_df %>%
  mutate(month = rownames(.)) %>%
  gather(year, value, -month)

AirPassengers_tidy

#> Source: local data frame [144 x 3]
#> 
#>    month  year value
#>    (chr) (chr) (dbl)
#> 1    Jan X1949   112
#> 2    Feb X1949   118
#> 3    Mar X1949   132
#> 4    Apr X1949   129
#> 5    May X1949   121
#> 6    Jun X1949   135
#> 7    Jul X1949   148
#> 8    Aug X1949   148
#> 9    Sep X1949   136
#> 10   Oct X1949   119
#> ..   ...   ...   ...

airquality

# `airquality` already tidy

anscombe

anscombe %>% mutate(obs = seq_len(n())) %>%
  gather(key, value, -obs) %>%
  separate(key, into = c('var','set'), sep = 1) %>%
  spread(var, value) %>%
  arrange(set) -> anscombe_tidy

head(anscombe_tidy)

#>   obs set  x    y
#> 1   1   1 10 8.04
#> 2   2   1  8 6.95
#> 3   3   1 13 7.58
#> 4   4   1  9 8.81
#> 5   5   1 11 8.33
#> 6   6   1 14 9.96

attenu

# `attenu` already tidy

attitude

# `attitude` already tidy

austres

dmn <- list(
  c("Qtr1","Qtr2","Qtr3","Qtr4"),
  unique(floor(time(austres)))
)

austres_tidy <- as.data.frame(t(matrix(c(NA,austres,NA,NA), 4, dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(qtr, value, -year) %>%
  arrange(year)

head(austres_tidy)

#>   year  qtr   value
#> 1 1971 Qtr1      NA
#> 2 1971 Qtr2 13067.3
#> 3 1971 Qtr3 13130.5
#> 4 1971 Qtr4 13198.4
#> 5 1972 Qtr1 13254.2
#> 6 1972 Qtr2 13303.7

beavers

# Combine the beaver1 and beaver2 datasets ...
beaver1_tidy <- beaver1 %>%
  mutate(obs = seq_len(n())) %>%
  gather(key, value, -obs) %>%
  mutate(beaver = '1')

beaver2_tidy <- beaver2 %>%
  mutate(obs = seq_len(n())) %>%
  gather(key, value, -obs) %>%
  mutate(beaver = '2')

beavers_tidy <- rbind_list(beaver1_tidy, beaver2_tidy)

#> Warning: `rbind_list()` is deprecated. Please use `bind_rows()` instead.

beavers_tidy

#> Source: local data frame [856 x 4]
#> 
#>      obs   key value beaver
#>    (int) (chr) (dbl)  (chr)
#> 1      1   day   346      1
#> 2      2   day   346      1
#> 3      3   day   346      1
#> 4      4   day   346      1
#> 5      5   day   346      1
#> 6      6   day   346      1
#> 7      7   day   346      1
#> 8      8   day   346      1
#> 9      9   day   346      1
#> 10    10   day   346      1
#> ..   ...   ...   ...    ...

BJsales

BJsales_tidy <- data.frame(
  time = as.numeric(time(BJsales)),
  val = as.numeric(BJsales)
)

head(BJsales_tidy)

#>   time   val
#> 1    1 200.1
#> 2    2 199.5
#> 3    3 199.4
#> 4    4 198.9
#> 5    5 199.0
#> 6    6 200.2

BJsales.lead

BJsales.lead_tidy <- data.frame(
  time = as.numeric(time(BJsales.lead)),
  val = as.numeric(BJsales.lead)
)

head(BJsales.lead_tidy)

#>   time   val
#> 1    1 10.01
#> 2    2 10.07
#> 3    3 10.32
#> 4    4  9.75
#> 5    5 10.33
#> 6    6 10.13

BOD

BOD_tidy <- BOD %>% tbl_df %>%
  mutate(obs = seq_len(n())) %>%
  gather(key, value, -obs)

BOD_tidy

#> Source: local data frame [12 x 3]
#> 
#>      obs    key value
#>    (int)  (chr) (dbl)
#> 1      1   Time   1.0
#> 2      2   Time   2.0
#> 3      3   Time   3.0
#> 4      4   Time   4.0
#> 5      5   Time   5.0
#> 6      6   Time   7.0
#> 7      1 demand   8.3
#> 8      2 demand  10.3
#> 9      3 demand  19.0
#> 10     4 demand  16.0
#> 11     5 demand  15.6
#> 12     6 demand  19.8

cars

cars_tidy <- cars %>% tbl_df %>%
  mutate(obs = seq_len(n())) %>%
  gather(key, value, -obs)

cars_tidy

#> Source: local data frame [100 x 3]
#> 
#>      obs   key value
#>    (int) (chr) (dbl)
#> 1      1 speed     4
#> 2      2 speed     4
#> 3      3 speed     7
#> 4      4 speed     7
#> 5      5 speed     8
#> 6      6 speed     9
#> 7      7 speed    10
#> 8      8 speed    10
#> 9      9 speed    10
#> 10    10 speed    11
#> ..   ...   ...   ...

ChickWeight

ChickWeight_tidy <- ChickWeight %>%
  gather(key, value, -Chick, convert = TRUE) %>%
  tbl_df

ChickWeight_tidy

#> Source: local data frame [1,734 x 3]
#> 
#>     Chick    key value
#>    (fctr)  (chr) (chr)
#> 1       1 weight    42
#> 2       1 weight    51
#> 3       1 weight    59
#> 4       1 weight    64
#> 5       1 weight    76
#> 6       1 weight    93
#> 7       1 weight   106
#> 8       1 weight   125
#> 9       1 weight   149
#> 10      1 weight   171
#> ..    ...    ...   ...

chickwts

# `chickwts` already tidy

co2

dmn <- list(month.abb, unique(floor(time(co2))))

co2_tidy <-  data.frame(t(matrix(co2, 12, dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(month, value, -year) %>%
  arrange(year)

head(co2_tidy)

#>   year month  value
#> 1 1959   Jan 315.42
#> 2 1959   Feb 316.31
#> 3 1959   Mar 316.50
#> 4 1959   Apr 317.56
#> 5 1959   May 318.13
#> 6 1959   Jun 318.00

CO2

# `CO2` already tidy

crimtab

crimtab_tidy <- as.data.frame(crimtab)

head(crimtab_tidy)

#>   Var1   Var2 Freq
#> 1  9.4 142.24    0
#> 2  9.5 142.24    0
#> 3  9.6 142.24    0
#> 4  9.7 142.24    0
#> 5  9.8 142.24    0
#> 6  9.9 142.24    0

discoveries

discoveries_tidy <- data.frame(
  year = as.numeric(time(discoveries)),
  count = as.numeric(discoveries))

head(discoveries_tidy)

#>   year count
#> 1 1860     5
#> 2 1861     3
#> 3 1862     0
#> 4 1863     2
#> 5 1864     0
#> 6 1865     3

DNase

# `DNase` already tidy

esoph

# `esoph` already tidy

euro

euro_tidy <- data.frame(
  Country = names(euro),
  val = as.numeric(euro))

head(euro_tidy)

#>   Country       val
#> 1     ATS  13.76030
#> 2     BEF  40.33990
#> 3     DEM   1.95583
#> 4     ESP 166.38600
#> 5     FIM   5.94573
#> 6     FRF   6.55957

euro.cross

euro.cross_tidy <- as.data.frame(euro.cross) %>%
  mutate(Euro_curr = rownames(euro.cross)) %>%
  gather(Euro_curr2, value, -Euro_curr) %>%
  arrange(Euro_curr)

head(euro.cross_tidy)

#>   Euro_curr Euro_curr2      value
#> 1       ATS        ATS  1.0000000
#> 2       ATS        BEF  2.9316149
#> 3       ATS        DEM  0.1421357
#> 4       ATS        ESP 12.0917422
#> 5       ATS        FIM  0.4320931
#> 6       ATS        FRF  0.4767025

eurodist

# `eurodist` is a dist object (and I don't feel like figuring it out)

EuStockMarkets

dmn <- list(colnames(EuStockMarkets), time(EuStockMarkets))

EuStockMarkets_tidy <- data.frame(t(matrix(EuStockMarkets, 4, dimnames = dmn))) %>%
  mutate(time_ind = rownames(.)) %>%
  gather(key, val, -time_ind) %>%
  separate(time_ind, into = c("year","time"), sep = 4) %>%
  arrange(year)

head(EuStockMarkets_tidy)

#>   year         time key     val
#> 1 1991 .49615384615 DAX 1628.75
#> 2 1991           .5 DAX 1618.16
#> 3 1991 .50384615385 DAX 1635.47
#> 4 1991 .50769230769 DAX 1629.93
#> 5 1991 .51153846154 DAX 1631.99
#> 6 1991 .51538461538 DAX 1605.75

faithful

# `faithful` already tidy

fdeaths

dmn <- list(month.abb, unique(floor(time(fdeaths))))

fdeaths_tidy <- data.frame(t(matrix(fdeaths, 12, dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(month, deaths, -year) %>%
  arrange(year)

head(fdeaths_tidy)

#>   year month deaths
#> 1 1974   Jan    901
#> 2 1974   Feb    689
#> 3 1974   Mar    827
#> 4 1974   Apr    677
#> 5 1974   May    522
#> 6 1974   Jun    406

Formaldehyde

# `Formaldehyde` already tidy

freeny freeny.x freeny.y

freeny_y_df <- as.data.frame(t(matrix(c(NA,freeny.y), 4)))

colnames(freeny_y_df) <- c("Qtr1","Qtr2","Qtr3","Qtr4")
rownames(freeny_y_df) <- 1962:1971

freeny_y_tmp <- freeny_y_df %>% mutate(year = rownames(freeny_y_df)) %>%
  gather(Qtr, value, -year) %>%
  arrange(year)

freeny_y_tidy <- freeny_y_tmp[2:40,]

freeny_tidy <- freeny %>% mutate(year = freeny_y_tidy$year, Qtr = freeny_y_tidy$Qtr) %>%
  gather(exp_var, value, -year, -Qtr)

#> Warning: attributes are not identical across measure variables; they will
#> be dropped

head(freeny_tidy)

#>   year  Qtr exp_var   value
#> 1 1962 Qtr2       y 8.79236
#> 2 1962 Qtr3       y 8.79137
#> 3 1962 Qtr4       y 8.81486
#> 4 1963 Qtr1       y 8.81301
#> 5 1963 Qtr2       y 8.90751
#> 6 1963 Qtr3       y 8.93673

HairEyeColor

males <- as.data.frame(HairEyeColor[1:4,1:4,1])

males_tidy <- males %>% 
  mutate(hair_color = rownames(males), sex = rep("male",4)) %>%
  gather(eye_color, value, -sex, -hair_color)

females <- as.data.frame(HairEyeColor[1:4,1:4,2])

females_tidy <- females %>% 
  mutate(hair_color = rownames(females), sex = rep("female",4)) %>%
  gather(eye_color, value, -sex, -hair_color)

HairEyeColor_tidy <- rbind(males_tidy,females_tidy)
HairEyeColor_tidy <- HairEyeColor_tidy[,c("sex","eye_color","hair_color","value")]

head(HairEyeColor_tidy)

#>    sex eye_color hair_color value
#> 1 male     Brown      Black    32
#> 2 male     Brown      Brown    53
#> 3 male     Brown        Red    10
#> 4 male     Brown      Blond     3
#> 5 male      Blue      Black    11
#> 6 male      Blue      Brown    50

Harman23.cor

Harman 74.cor

Indometh

# `Indometh` already tidy

infert

# `infert` already tidy

InsectSprays

# `InsectSprays` already tidy

iris

# `iris` already tidy

iris3

iris3_tidy <- as.data.frame(rbind(iris3[,,1],iris3[,,2],iris3[,,3])) %>%
  mutate(species = c(
    rep(dimnames(iris3)[[3]][1], length(iris3[,1,1])),
    rep(dimnames(iris3)[[3]][2], length(iris3[,1,2])),
    rep(dimnames(iris3)[[3]][3], length(iris3[,1,3]))
  ))

head(iris3_tidy)

#>   Sepal L. Sepal W. Petal L. Petal W. species
#> 1      5.1      3.5      1.4      0.2  Setosa
#> 2      4.9      3.0      1.4      0.2  Setosa
#> 3      4.7      3.2      1.3      0.2  Setosa
#> 4      4.6      3.1      1.5      0.2  Setosa
#> 5      5.0      3.6      1.4      0.2  Setosa
#> 6      5.4      3.9      1.7      0.4  Setosa

islands

islands_tidy <- data.frame(
  land = names(islands),
  area = as.numeric(islands)
)

head(islands_tidy)

#>           land  area
#> 1       Africa 11506
#> 2   Antarctica  5500
#> 3         Asia 16988
#> 4    Australia  2968
#> 5 Axel Heiberg    16
#> 6       Baffin   184

JohnsonJohnson

dmn <- list(
  c("Qtr1","Qtr2","Qtr3","Qtr4"),
  unique(floor(time(JohnsonJohnson)))
)

JohnsonJohnson_tidy <- as.data.frame(t(matrix(JohnsonJohnson, 4, dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(Qtr, val, -year) %>%
  arrange(year)

head(JohnsonJohnson_tidy)

#>   year  Qtr  val
#> 1 1960 Qtr1 0.71
#> 2 1960 Qtr2 0.63
#> 3 1960 Qtr3 0.85
#> 4 1960 Qtr4 0.44
#> 5 1961 Qtr1 0.61
#> 6 1961 Qtr2 0.69

LakeHuron

LakeHuron_tidy <- data.frame(
  year = unique(time(LakeHuron)),
  level = as.numeric((LakeHuron))
)

head(LakeHuron_tidy)

#>   year  level
#> 1 1875 580.38
#> 2 1876 581.86
#> 3 1877 580.97
#> 4 1878 580.80
#> 5 1879 579.79
#> 6 1880 580.39

ldeaths (mdeaths, fdeaths)

dmn <- list(month.abb, unique(floor(time(ldeaths))))

ldeaths_tidy <- data.frame(t(matrix(ldeaths, 12, dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(month, deaths, -year) %>%
  arrange(year)

head(ldeaths_tidy)

#>   year month deaths
#> 1 1974   Jan   3035
#> 2 1974   Feb   2552
#> 3 1974   Mar   2704
#> 4 1974   Apr   2554
#> 5 1974   May   2014
#> 6 1974   Jun   1655

lh

lh_tidy <- data.frame(
  time_int = as.numeric(time(lh)),
  lh = as.numeric(lh)
)

head(lh_tidy)

#>   time_int  lh
#> 1        1 2.4
#> 2        2 2.4
#> 3        3 2.4
#> 4        4 2.2
#> 5        5 2.1
#> 6        6 1.5

LifeCycleSavings

LifeCycleSavings_tidy <- LifeCycleSavings %>%
  mutate(country = rownames((LifeCycleSavings))) %>%
  gather(stat, val, -country) %>%
  arrange(country)

head(LifeCycleSavings_tidy)

#>     country  stat     val
#> 1 Australia    sr   11.43
#> 2 Australia pop15   29.35
#> 3 Australia pop75    2.87
#> 4 Australia   dpi 2329.68
#> 5 Australia  ddpi    2.87
#> 6   Austria    sr   12.07

Loblolly

Loblolly_tidy <- as.data.frame(Loblolly) %>%
  mutate(index = as.numeric(rownames(Loblolly))) %>%
  arrange(index)

head(Loblolly_tidy)

#>   height age Seed index
#> 1   4.51   3  301     1
#> 2   4.55   3  303     2
#> 3   4.79   3  305     3
#> 4   3.91   3  307     4
#> 5   4.81   3  309     5
#> 6   3.88   3  311     6

longley

# `longley` already tidy

lynx

lynx_tidy <- data.frame(
  year = as.numeric(time(lynx)),
  val = as.numeric(lynx)
)

head(lynx_tidy)

#>   year  val
#> 1 1821  269
#> 2 1822  321
#> 3 1823  585
#> 4 1824  871
#> 5 1825 1475
#> 6 1826 2821

mdeaths

dmn <- list(month.abb, unique(floor(time(mdeaths))))

mdeaths_tidy <- data.frame(t(matrix(mdeaths, 12, dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(month, deaths, -year) %>%
  arrange(year)

head(mdeaths_tidy)

#>   year month deaths
#> 1 1974   Jan   2134
#> 2 1974   Feb   1863
#> 3 1974   Mar   1877
#> 4 1974   Apr   1877
#> 5 1974   May   1492
#> 6 1974   Jun   1249

morley

# `morley` already tidy

mtcars

mtcars_tidy <- mtcars %>%
  mutate(car = format_strs(rownames(.))) %>% 
  gather(key, value, -car) %>% tbl_df

mtcars_tidy

#> Source: local data frame [352 x 3]
#> 
#>                  car   key value
#>                (chr) (chr) (dbl)
#> 1          mazda.rx4   mpg  21.0
#> 2      mazda.rx4.wag   mpg  21.0
#> 3         datsun.710   mpg  22.8
#> 4     hornet.4.drive   mpg  21.4
#> 5  hornet.sportabout   mpg  18.7
#> 6            valiant   mpg  18.1
#> 7         duster.360   mpg  14.3
#> 8          merc.240d   mpg  24.4
#> 9           merc.230   mpg  22.8
#> 10          merc.280   mpg  19.2
#> ..               ...   ...   ...

nhtemp

nhtemp_tidy <- data.frame(
  year = as.numeric(time(nhtemp)),
  val = as.numeric(nhtemp)
)

head(nhtemp_tidy)

#>   year  val
#> 1 1912 49.9
#> 2 1913 52.3
#> 3 1914 49.4
#> 4 1915 51.1
#> 5 1916 49.4
#> 6 1917 47.9

Nile

Nile_tidy <- data.frame(
  year = as.numeric(time(Nile)),
  val = as.numeric(Nile)
)

head(Nile_tidy)

#>   year  val
#> 1 1871 1120
#> 2 1872 1160
#> 3 1873  963
#> 4 1874 1210
#> 5 1875 1160
#> 6 1876 1160

nottem

dmn <- list(month.abb, unique(floor(time(nottem))))

nottem_tidy <- data.frame(t(matrix(nottem,12,dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(month, val, -year) %>%
  arrange(year)

head(nottem_tidy)

#>   year month  val
#> 1 1920   Jan 40.6
#> 2 1920   Feb 40.8
#> 3 1920   Mar 44.4
#> 4 1920   Apr 46.7
#> 5 1920   May 54.1
#> 6 1920   Jun 58.5

npk

# `npk` already tidy

occupationalstatus

occupationalStatus_tidy <- as.data.frame(occupationalStatus) %>%
  arrange(origin)

head(occupationalStatus_tidy)

#>   origin destination Freq
#> 1      1           1   50
#> 2      1           2   19
#> 3      1           3   26
#> 4      1           4    8
#> 5      1           5    7
#> 6      1           6   11

Orange

# `Orange` already tidy

OrchardSprays

# `OrchardSprays` already tidy

PlantGrowth

# `PlantGrowth` already tidy

precip

precip_tidy <- data.frame(
  City = names(precip),
  val = as.numeric(precip)
) %>% arrange(City)

head(precip_tidy)

#>            City  val
#> 1        Albany 33.4
#> 2   Albuquerque  7.8
#> 3       Atlanta 48.3
#> 4 Atlantic City 45.5
#> 5     Baltimore 41.8
#> 6       Bismark 16.2

presidents

dmn <- list(
  c("Qtr1","Qtr2","Qtr3","Qtr4"),
  unique(floor(time(presidents)))
)

presidents_tidy <- data.frame(t(matrix(presidents,4,dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(Qtr, val, -year) %>%
  arrange(year)

head(presidents_tidy)

#>   year  Qtr val
#> 1 1945 Qtr1  NA
#> 2 1945 Qtr2  87
#> 3 1945 Qtr3  82
#> 4 1945 Qtr4  75
#> 5 1946 Qtr1  63
#> 6 1946 Qtr2  50

pressure

# `pressure` already tidy

Puromycin

# `Puromycin` already tidy

quakes

quakes_tidy <- quakes %>% mutate(event = rownames(.))

head(quakes_tidy)

#>      lat   long depth mag stations event
#> 1 -20.42 181.62   562 4.8       41     1
#> 2 -20.62 181.03   650 4.2       15     2
#> 3 -26.00 184.10    42 5.4       43     3
#> 4 -17.97 181.66   626 4.1       19     4
#> 5 -20.42 181.96   649 4.0       11     5
#> 6 -19.68 184.31   195 4.0       12     6

randu

# `randu` already tidy

rivers

rivers_tidy <- data.frame(index = 1:length(rivers), length = rivers)

head(rivers_tidy)

#>   index length
#> 1     1    735
#> 2     2    320
#> 3     3    325
#> 4     4    392
#> 5     5    524
#> 6     6    450

rock

# `rock` already tidy

Seatbelts

Seatbelts_tidy <- data.frame(Seatbelts) %>%
  mutate(year = as.numeric(floor(time(Seatbelts))),
         month = rep(month.abb,(length(as.numeric(floor(time(Seatbelts))))/12))) %>%
  gather(key, val, -year,-month) %>%
  arrange(year)

head(Seatbelts_tidy)

#>   year month           key val
#> 1 1969   Jan DriversKilled 107
#> 2 1969   Feb DriversKilled  97
#> 3 1969   Mar DriversKilled 102
#> 4 1969   Apr DriversKilled  87
#> 5 1969   May DriversKilled 119
#> 6 1969   Jun DriversKilled 106

sleep

# `sleep` already tidy

stackloss

# `stackloss` already tidy

state

state_tidy <- data.frame(state = state.name, state_abb = state.abb, area = state.area,
                         center_x = state.center$x, center_y = state.center$y, division = state.division)

head(state_tidy)

#>        state state_abb   area  center_x center_y           division
#> 1    Alabama        AL  51609  -86.7509  32.5901 East South Central
#> 2     Alaska        AK 589757 -127.2500  49.2500            Pacific
#> 3    Arizona        AZ 113909 -111.6250  34.2192           Mountain
#> 4   Arkansas        AR  53104  -92.2992  34.7336 West South Central
#> 5 California        CA 158693 -119.7730  36.5341            Pacific
#> 6   Colorado        CO 104247 -105.5130  38.6777           Mountain

sunspot.month

dmn <- list(month.abb, unique(floor(time(sunspot.month))))

sunspot.month_tidy <- data.frame(t(matrix(c(sunspot.month,NA,NA,NA),12,dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(month, val, -year) %>%
  arrange(year)

head(sunspot.month_tidy)

#>   year month  val
#> 1 1749   Jan 58.0
#> 2 1749   Feb 62.6
#> 3 1749   Mar 70.0
#> 4 1749   Apr 55.7
#> 5 1749   May 85.0
#> 6 1749   Jun 83.5

sunspot.year

sunspot.year_tidy <- data.frame(
  year = time(sunspot.year),
  val = as.numeric(sunspot.year)
)

head(sunspot.year_tidy)

#>   year val
#> 1 1700   5
#> 2 1701  11
#> 3 1702  16
#> 4 1703  23
#> 5 1704  36
#> 6 1705  58

sunspots

dmn <- list(month.abb, unique(floor(time(sunspots))))

sunspots_tidy <- data.frame(t(matrix(sunspots,12,dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(month, val, -year) %>%
  arrange(year)

head(sunspots_tidy)

#>   year month  val
#> 1 1749   Jan 58.0
#> 2 1749   Feb 62.6
#> 3 1749   Mar 70.0
#> 4 1749   Apr 55.7
#> 5 1749   May 85.0
#> 6 1749   Jun 83.5

swiss

swiss_tidy <- swiss %>% mutate(province = rownames(.)) %>%
  gather(key, val, -province) %>%
  arrange(province)

head(swiss_tidy)

#>   province              key   val
#> 1    Aigle        Fertility 64.10
#> 2    Aigle      Agriculture 62.00
#> 3    Aigle      Examination 21.00
#> 4    Aigle        Education 12.00
#> 5    Aigle         Catholic  8.52
#> 6    Aigle Infant.Mortality 16.50

Theoph

# `Theoph` already tidy

Titanic

Titanic_tidy <- data.frame(NULL)

for(i in 1:2){
  for(j in 1:2){
    x <- as.data.frame(Titanic[,,i,j]) %>%
      mutate(class = rownames(.),
             age = rep(dimnames(Titanic)$Age[i],length(Titanic[,1,i,j])),
             survived = rep(dimnames(Titanic)$Survived[j],length(Titanic[,1,i,j])))
    Titanic_tidy <- rbind(Titanic_tidy,x)
  }
}

head(Titanic_tidy)

#>   Male Female class   age survived
#> 1    0      0   1st Child       No
#> 2    0      0   2nd Child       No
#> 3   35     17   3rd Child       No
#> 4    0      0  Crew Child       No
#> 5    5      1   1st Child      Yes
#> 6   11     13   2nd Child      Yes

ToothGrowth

ToothGrowth_tidy <- ToothGrowth %>%
  mutate(index = as.numeric(rownames(.))) %>%
  gather(key,val,-index) %>%
  arrange(index)

#> Warning: attributes are not identical across measure variables; they will
#> be dropped

head(ToothGrowth_tidy)

#>   index  key  val
#> 1     1  len  4.2
#> 2     1 supp   VC
#> 3     1 dose  0.5
#> 4     2  len 11.5
#> 5     2 supp   VC
#> 6     2 dose  0.5

treering

treering_tidy <- data.frame(
  time = time(treering),
  val = as.numeric(treering)
)

head(treering_tidy)

#>    time   val
#> 1 -6000 1.345
#> 2 -5999 1.077
#> 3 -5998 1.545
#> 4 -5997 1.319
#> 5 -5996 1.413
#> 6 -5995 1.069

trees

trees_tidy <- trees %>% mutate(index = as.numeric(rownames(.))) %>%
  gather(key, val, -index) %>%
  arrange(index)

head(trees_tidy)

#>   index    key  val
#> 1     1  Girth  8.3
#> 2     1 Height 70.0
#> 3     1 Volume 10.3
#> 4     2  Girth  8.6
#> 5     2 Height 65.0
#> 6     2 Volume 10.3

UCBAdmissions

UCBAdmissions_tidy <- data.frame(NULL)

for(i in 1:length(UCBAdmissions[1,1,])){
  x <- as.data.frame(UCBAdmissions[,,i]) %>%
    mutate(admit = rownames(.),
           dept = rep(dimnames(UCBAdmissions)$Dept[i],length(UCBAdmissions[,1,i]))) %>%
    gather(sex,val,-admit,-dept)
  
  UCBAdmissions_tidy <- rbind(UCBAdmissions_tidy,x)
}

head(UCBAdmissions_tidy)

#>      admit dept    sex val
#> 1 Admitted    A   Male 512
#> 2 Rejected    A   Male 313
#> 3 Admitted    A Female  89
#> 4 Rejected    A Female  19
#> 5 Admitted    B   Male 353
#> 6 Rejected    B   Male 207

UKDriverDeaths

dmn <- list(month.abb, unique(floor(time(UKDriverDeaths))))

UKDriverDeaths_tidy <- data.frame(t(matrix(UKDriverDeaths,12,dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(month,val,-year) %>%
  arrange(year)

head(UKDriverDeaths_tidy)

#>   year month  val
#> 1 1969   Jan 1687
#> 2 1969   Feb 1508
#> 3 1969   Mar 1507
#> 4 1969   Apr 1385
#> 5 1969   May 1632
#> 6 1969   Jun 1511

UKgas

dmn <- list(
  c("Qtr1","Qtr2","Qtr3","Qtr4"),
  unique(floor(time(UKgas)))
)

UKgas_tidy <- data.frame(t(matrix(UKgas, 4, dimnames = dmn))) %>%
  mutate(year = rownames(.)) %>%
  gather(Qtr,val,-year) %>%
  arrange(year)

head(UKgas_tidy)

#>   year  Qtr   val
#> 1 1960 Qtr1 160.1
#> 2 1960 Qtr2 129.7
#> 3 1960 Qtr3  84.8
#> 4 1960 Qtr4 120.1
#> 5 1961 Qtr1 160.1
#> 6 1961 Qtr2 124.9

USArrests

USArrests_tidy <- as.data.frame(USArrests) %>%
  mutate(state = rownames(.)) %>%
  gather(type, val, -state) %>%
  arrange(state)

head(USArrests_tidy)

#>     state     type   val
#> 1 Alabama   Murder  13.2
#> 2 Alabama  Assault 236.0
#> 3 Alabama UrbanPop  58.0
#> 4 Alabama     Rape  21.2
#> 5  Alaska   Murder  10.0
#> 6  Alaska  Assault 263.0

UScitiesD

# `UScitiesD` is a dist file

USJudgeRatings

USJudgeRatings_tidy <- as.data.frame(USJudgeRatings) %>%
  mutate(judge = rownames(.)) %>%
  gather(var, val, -judge) %>%
  arrange(judge)

head(USJudgeRatings_tidy)

#>           judge  var val
#> 1 AARONSON,L.H. CONT 5.7
#> 2 AARONSON,L.H. INTG 7.9
#> 3 AARONSON,L.H. DMNR 7.7
#> 4 AARONSON,L.H. DILG 7.3
#> 5 AARONSON,L.H. CFMG 7.1
#> 6 AARONSON,L.H. DECI 7.4

USPersonalExpenditures

USPersonalExpenditure_tidy <- as.data.frame(USPersonalExpenditure) %>%
  mutate(type = rownames(.)) %>%
  gather(year, val, -type) %>%
  arrange(year)

head(USPersonalExpenditure_tidy)

#>                  type year    val
#> 1    Food and Tobacco 1940 22.200
#> 2 Household Operation 1940 10.500
#> 3  Medical and Health 1940  3.530
#> 4       Personal Care 1940  1.040
#> 5   Private Education 1940  0.341
#> 6    Food and Tobacco 1945 44.500

uspop

uspop_tidy <- data.frame(
  time = time(uspop),
  val = as.numeric(uspop)
)

head(uspop_tidy)

#>   time   val
#> 1 1790  3.93
#> 2 1800  5.31
#> 3 1810  7.24
#> 4 1820  9.64
#> 5 1830 12.90
#> 6 1840 17.10

VADeaths

VADeaths_tidy <- as.data.frame(VADeaths) %>%
  mutate(age = rownames(.)) %>%
  gather(key, val, -age) %>%
  arrange(age)

head(VADeaths_tidy)

#>     age          key  val
#> 1 50-54   Rural Male 11.7
#> 2 50-54 Rural Female  8.7
#> 3 50-54   Urban Male 15.4
#> 4 50-54 Urban Female  8.4
#> 5 55-59   Rural Male 18.1
#> 6 55-59 Rural Female 11.7

volcano

# `volcano` is a topographic map

warpbreaks

# `warpbreaks` already tidy

women

# `women` already tidy

World Phones

WorldPhones_tidy <- WorldPhones %>%
  as.data.frame() %>%
  mutate(year = rownames(.)) %>%
  gather(continent, value, -year) %>% tbl_df

WorldPhones_tidy

#> Source: local data frame [49 x 3]
#> 
#>     year continent value
#>    (chr)     (chr) (dbl)
#> 1   1951    N.Amer 45939
#> 2   1956    N.Amer 60423
#> 3   1957    N.Amer 64721
#> 4   1958    N.Amer 68484
#> 5   1959    N.Amer 71799
#> 6   1960    N.Amer 76036
#> 7   1961    N.Amer 79831
#> 8   1951    Europe 21574
#> 9   1956    Europe 29990
#> 10  1957    Europe 32510
#> ..   ...       ...   ...

WWWusage

WWWusage_tidy <- data.frame(
  time = time(WWWusage),
  val = as.numeric(WWWusage)
)

head(WWWusage_tidy)

#>   time val
#> 1    1  88
#> 2    2  84
#> 3    3  85
#> 4    4  85
#> 5    5  84
#> 6    6  85

Name		Name	Last commit message	Last commit date
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.gitignore		.gitignore
README.Rmd		README.Rmd
README.md		README.md
Thumbs.db		Thumbs.db
tidyrdatasets.Rproj		tidyrdatasets.Rproj

mackie90125/tidyrdatasets

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Overview

ability

airmiles

AirPassengers

airquality

anscombe

attenu

attitude

austres

beavers

BJsales

BJsales.lead

BOD

cars

ChickWeight

chickwts

co2

CO2

crimtab

discoveries

DNase

esoph

euro

euro.cross

eurodist

EuStockMarkets

faithful

fdeaths

Formaldehyde

freeny freeny.x freeny.y

HairEyeColor

Harman23.cor

Harman 74.cor

Indometh

infert

InsectSprays

iris

iris3

islands

JohnsonJohnson

LakeHuron

ldeaths (mdeaths, fdeaths)

lh

LifeCycleSavings

Loblolly

longley

lynx

mdeaths

morley

mtcars

nhtemp

Nile

nottem

npk

occupationalstatus

Orange

OrchardSprays

PlantGrowth

precip

presidents

pressure

Puromycin

quakes

randu

rivers

rock

Seatbelts

sleep

stackloss

state

sunspot.month

sunspot.year

sunspots

swiss

Theoph

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