/tidyrdatasets

tidiers for the R builtin datasets

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
#> ..   ...   ...   ...

austres

# Figure out how to get the 'Qtr1' ... colnames

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
#> ..   ...   ...   ...    ...

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

# `CO2` already tidy

DNase

# `DNase` already tidy

esoph

# `esoph` already tidy

faithful

# `faithful` already tidy

Formaldehyde

# `Formaldehyde` 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
#> ..               ...   ...   ...

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
#> ..   ...       ...   ...