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# Comparison with R / R libraries

Since `pandas` aims to provide a lot of the data manipulation and analysis functionality that people use R for, this page was started to provide a more detailed look at the R language and its many third party libraries as they relate to `pandas`. In comparisons with R and CRAN libraries, we care about the following things:

> - Functionality / flexibility: what can/cannot be done with each tool
> - Performance: how fast are operations. Hard numbers/benchmarks are preferable
> - Ease-of-use: Is one tool easier/harder to use (you may have to be the judge of this, given side-by-side code comparisons)

This page is also here to offer a bit of a translation guide for users of these R packages.

## Base R

### `aggregate`

In R you may want to split data into subsets and compute the mean for each. Using a data.frame called `df` and splitting it into groups `by1` and `by2`:

    df <- data.frame(
      v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9),
      v2 = c(11,33,55,77,88,33,55,NA,44,55,77,99),
      by1 = c("red", "blue", 1, 2, NA, "big", 1, 2, "red", 1, NA, 12),
      by2 = c("wet", "dry", 99, 95, NA, "damp", 95, 99, "red", 99, NA, NA))
    aggregate(x=df[, c("v1", "v2")], by=list(mydf2$by1, mydf2$by2), FUN = mean)

The `groupby()` method is similar to base R `aggregate` function.

    In [1]: from pandas import DataFrame

    In [2]: df = DataFrame({
       ...:   'v1': [1,3,5,7,8,3,5,np.nan,4,5,7,9],
       ...:   'v2': [11,33,55,77,88,33,55,np.nan,44,55,77,99],
       ...:   'by1': ["red", "blue", 1, 2, np.nan, "big", 1, 2, "red", 1, np.nan, 12],
       ...:   'by2': ["wet", "dry", 99, 95, np.nan, "damp", 95, 99, "red", 99, np.nan,
       ...:           np.nan]
       ...: })
       ...:

    In [3]: g = df.groupby(['by1','by2'])

    In [4]: g[['v1','v2']].mean()
    Out[4]:
               v1  v2
    by1  by2
    1    95     5  55
         99     5  55
    2    95     7  77
         99   NaN NaN
    big  damp   3  33
    blue dry    3  33
    red  red    4  44
         wet    1  11

    [8 rows x 2 columns]

For more details and examples see the groupby documentation.

### `match` / `%in%`

A common way to select data in R is using `%in%` which is defined using the function `match`. The operator `%in%` is used to return a logical vector indicating if there is a match or not:

    s <- 0:4
    s %in% c(2,4)

The `isin()` method is similar to R `%in%` operator:

    In [5]: s = pd.Series(np.arange(5),dtype=np.float32)

    In [6]: s.isin([2, 4])
    Out[6]:
    0    False
    1    False
    2     True
    3    False
    4     True
    dtype: bool

The `match` function returns a vector of the positions of matches of its first argument in its second:

    s <- 0:4
    match(s, c(2,4))

The `apply()` method can be used to replicate this:

    In [7]: s = pd.Series(np.arange(5),dtype=np.float32)

    In [8]: Series(pd.match(s,[2,4],np.nan))
    Out[8]:
    0   NaN
    1   NaN
    2     0
    3   NaN
    4     1
    dtype: float64

For more details and examples see the reshaping documentation.

### `tapply`

`tapply` is similar to `aggregate`, but data can be in a ragged array, since the subclass sizes are possibly irregular. Using a data.frame called `baseball`, and retrieving information based on the array `team`:

    baseball <-
      data.frame(team = gl(5, 5,
                 labels = paste("Team", LETTERS[1:5])),
                 player = sample(letters, 25),
                 batting.average = runif(25, .200, .400))

    tapply(baseball$batting.average, baseball.example$team,
           max)

In `pandas` we may use `pivot_table()` method to handle this:

    In [9]: import random

    In [10]: import string

    In [11]: baseball = DataFrame({
       ....:    'team': ["team %d" % (x+1) for x in range(5)]*5,
       ....:    'player': random.sample(list(string.ascii_lowercase),25),
       ....:    'batting avg': np.random.uniform(.200, .400, 25)
       ....:    })
       ....:

    In [12]: baseball.pivot_table(values='batting avg', cols='team', aggfunc=np.max)
    Out[12]:
    team
    team 1    0.321235
    team 2    0.399140
    team 3    0.386815
    team 4    0.387197
    team 5    0.392086
    Name: batting avg, dtype: float64

For more details and examples see the reshaping documentation.

### `subset`

New in version 0.13.

The `query()` method is similar to the base R `subset` function. In R you might want to get the rows of a `data.frame` where one column’s values are less than another column’s values:

    df <- data.frame(a=rnorm(10), b=rnorm(10))
    subset(df, a <= b)
    df[df$a <= df$b,]  # note the comma

In `pandas`, there are a few ways to perform subsetting. You can use `query()` or pass an expression as if it were an index/slice as well as standard boolean indexing:

    In [13]: df = DataFrame({'a': np.random.randn(10), 'b': np.random.randn(10)})

    In [14]: df.query('a <= b')
    Out[14]:
              a         b
    2 -0.838260  0.980077
    6 -0.017685  0.027505
    7 -0.182877  0.703105
    9 -1.717420 -0.986426

    [4 rows x 2 columns]

    In [15]: df[df.a <= df.b]
    Out[15]:
              a         b
    2 -0.838260  0.980077
    6 -0.017685  0.027505
    7 -0.182877  0.703105
    9 -1.717420 -0.986426

    [4 rows x 2 columns]

    In [16]: df.loc[df.a <= df.b]
    Out[16]:
              a         b
    2 -0.838260  0.980077
    6 -0.017685  0.027505
    7 -0.182877  0.703105
    9 -1.717420 -0.986426

    [4 rows x 2 columns]

For more details and examples see the query documentation.

### `with`

New in version 0.13.

An expression using a data.frame called `df` in R with the columns `a` and `b` would be evaluated using `with` like so:

    df <- data.frame(a=rnorm(10), b=rnorm(10))
    with(df, a + b)
    df$a + df$b  # same as the previous expression

In `pandas` the equivalent expression, using the `eval()` method, would be:

    In [17]: df = DataFrame({'a': np.random.randn(10), 'b': np.random.randn(10)})

    In [18]: df.eval('a + b')
    Out[18]:
    0   -0.163194
    1    0.985872
    2    2.864538
    3    0.782622
    4    0.962818
    5    1.974849
    6    0.258445
    7   -2.288045
    8   -0.800437
    9    2.667426
    dtype: float64

    In [19]: df.a + df.b  # same as the previous expression
    Out[19]:
    0   -0.163194
    1    0.985872
    2    2.864538
    3    0.782622
    4    0.962818
    5    1.974849
    6    0.258445
    7   -2.288045
    8   -0.800437
    9    2.667426
    dtype: float64

In certain cases `eval()` will be much faster than evaluation in pure Python. For more details and examples see the eval documentation.

## zoo

## xts

## plyr

`plyr` is an R library for the split-apply-combine strategy for data analysis. The functions revolve around three data structures in R, `a` for `arrays`, `l` for `lists`, and `d` for `data.frame`. The table below shows how these data structures could be mapped in Python.

R | Python
--- | ---
array | list
lists | dictionary or list of objects
data.frame | dataframe

### `ddply`

An expression using a data.frame called `df` in R where you want to summarize `x` by `month`:

    require(plyr)
    df <- data.frame(
      x = runif(120, 1, 168),
      y = runif(120, 7, 334),
      z = runif(120, 1.7, 20.7),
      month = rep(c(5,6,7,8),30),
      week = sample(1:4, 120, TRUE)
    )

    ddply(df, .(month, week), summarize,
          mean = round(mean(x), 2),
          sd = round(sd(x), 2))

In `pandas` the equivalent expression, using the `groupby()` method, would be:

    In [20]: df = DataFrame({
       ....:     'x': np.random.uniform(1., 168., 120),
       ....:     'y': np.random.uniform(7., 334., 120),
       ....:     'z': np.random.uniform(1.7, 20.7, 120),
       ....:     'month': [5,6,7,8]*30,
       ....:     'week': np.random.randint(1,4, 120)
       ....: })
       ....:

    In [21]: grouped = df.groupby(['month','week'])

    In [22]: print grouped['x'].agg([np.mean, np.std])
                      mean        std
    month week
    5     1      74.750543  37.602035
          2      91.420601  56.817107
          3      80.270102  55.994654
    6     1      81.840060  50.966643
          2      97.434542  59.919288
          3      79.867371  47.377914
    7     1      83.997435  39.391772
          2      86.244632  41.066830
          3     108.811608  45.048738
    8     1      81.647843  50.264539
          2      94.056653  47.677568
          3      76.004631  47.048914

    [12 rows x 2 columns]

For more details and examples see the groupby documentation.

## reshape / reshape2

### `melt.array`

An expression using a 3 dimensional array called `a` in R where you want to melt it into a data.frame:

    a <- array(c(1:23, NA), c(2,3,4))
    data.frame(melt(a))

In Python, since `a` is a list, you can simply use list comprehension.

    In [23]: a = np.array(range(1,24)+[np.NAN]).reshape(2,3,4)

    In [24]: DataFrame([tuple(list(x)+[val]) for x, val in np.ndenumerate(a)])
    Out[24]:
        0  1  2   3
    0   0  0  0   1
    1   0  0  1   2
    2   0  0  2   3
    3   0  0  3   4
    4   0  1  0   5
    5   0  1  1   6
    6   0  1  2   7
    7   0  1  3   8
    8   0  2  0   9
    9   0  2  1  10
    10  0  2  2  11
    11  0  2  3  12
    12  1  0  0  13
    13  1  0  1  14
    14  1  0  2  15
       .. .. .. ...

    [24 rows x 4 columns]

### `melt.list`

An expression using a list called `a` in R where you want to melt it into a data.frame:

    a <- as.list(c(1:4, NA))
    data.frame(melt(a))

In Python, this list would be a list of tuples, so `DataFrame()` method would convert it to a dataframe as required.

    In [25]: a = list(enumerate(range(1,5)+[np.NAN]))

    In [26]: DataFrame(a)
    Out[26]:
       0   1
    0  0   1
    1  1   2
    2  2   3
    3  3   4
    4  4 NaN

    [5 rows x 2 columns]

For more details and examples see the Into to Data Structures documentation.

### `melt.data.frame`

An expression using a data.frame called `cheese` in R where you want to reshape the data.frame:

    cheese <- data.frame(
      first = c('John', 'Mary'),
      last = c('Doe', 'Bo'),
      height = c(5.5, 6.0),
      weight = c(130, 150)
    )
    melt(cheese, id=c("first", "last"))

In Python, the `melt()` method is the R equivalent:

    In [27]: cheese = DataFrame({'first' : ['John', 'Mary'],
       ....:                     'last' : ['Doe', 'Bo'],
       ....:                     'height' : [5.5, 6.0],
       ....:                     'weight' : [130, 150]})
       ....:

    In [28]: pd.melt(cheese, id_vars=['first', 'last'])
    Out[28]:
      first last variable  value
    0  John  Doe   height    5.5
    1  Mary   Bo   height    6.0
    2  John  Doe   weight  130.0
    3  Mary   Bo   weight  150.0

    [4 rows x 4 columns]

    In [29]: cheese.set_index(['first', 'last']).stack() # alternative way
    Out[29]:
    first  last
    John   Doe   height      5.5
                 weight    130.0
    Mary   Bo    height      6.0
                 weight    150.0
    dtype: float64

For more details and examples see the reshaping documentation.

### `cast`

In R `acast` is an expression using a data.frame called `df` in R to cast into a higher dimensional array:

    df <- data.frame(
      x = runif(12, 1, 168),
      y = runif(12, 7, 334),
      z = runif(12, 1.7, 20.7),
      month = rep(c(5,6,7),4),
      week = rep(c(1,2), 6)
    )

    mdf <- melt(df, id=c("month", "week"))
    acast(mdf, week ~ month ~ variable, mean)

In Python the best way is to make use of `pivot_table()`:

    In [30]: df = DataFrame({
       ....:      'x': np.random.uniform(1., 168., 12),
       ....:      'y': np.random.uniform(7., 334., 12),
       ....:      'z': np.random.uniform(1.7, 20.7, 12),
       ....:      'month': [5,6,7]*4,
       ....:      'week': [1,2]*6
       ....: })
       ....:

    In [31]: mdf = pd.melt(df, id_vars=['month', 'week'])

    In [32]: pd.pivot_table(mdf, values='value', rows=['variable','week'],
       ....:                  cols=['month'], aggfunc=np.mean)
       ....:
    Out[32]:
    month                   5           6           7
    variable week
    x        1      89.863679   78.824388   50.832050
             2     132.209447   36.715123   75.566345
    y        1     216.526257  110.507591   11.484571
             2     153.506838  239.965235  160.223954
    z        1      15.536152    8.826941    7.015962
             2      14.646656   17.064267   11.806954

    [6 rows x 3 columns]

Similarly for `dcast` which uses a data.frame called `df` in R to aggregate information based on `Animal` and `FeedType`:

    df <- data.frame(
      Animal = c('Animal1', 'Animal2', 'Animal3', 'Animal2', 'Animal1',
                 'Animal2', 'Animal3'),
      FeedType = c('A', 'B', 'A', 'A', 'B', 'B', 'A'),
      Amount = c(10, 7, 4, 2, 5, 6, 2)
    )

    dcast(df, Animal ~ FeedType, sum, fill=NaN)
    # Alternative method using base R
    with(df, tapply(Amount, list(Animal, FeedType), sum))

Python can approach this in two different ways. Firstly, similar to above using `pivot_table()`:

    In [33]: df = DataFrame({
       ....:     'Animal': ['Animal1', 'Animal2', 'Animal3', 'Animal2', 'Animal1',
       ....:                'Animal2', 'Animal3'],
       ....:     'FeedType': ['A', 'B', 'A', 'A', 'B', 'B', 'A'],
       ....:     'Amount': [10, 7, 4, 2, 5, 6, 2],
       ....: })
       ....:

    In [34]: df.pivot_table(values='Amount', rows='Animal', cols='FeedType', aggfunc='sum')
    Out[34]:
    FeedType   A   B
    Animal
    Animal1   10   5
    Animal2    2  13
    Animal3    6 NaN

    [3 rows x 2 columns]

The second approach is to use the `groupby()` method:

    In [35]: df.groupby(['Animal','FeedType'])['Amount'].sum()
    Out[35]:
    Animal   FeedType
    Animal1  A           10
             B            5
    Animal2  A            2
             B           13
    Animal3  A            6
    Name: Amount, dtype: int64

For more details and examples see the reshaping documentation or the groupby documentation.

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