Keiku

# パッケージをインストールする
pkgs <- c("dplyr", "rpart", "rpart.plot", "rattle", "mlr", "evtree")
install.packages(pkgs, quiet = TRUE)

# パッケージを読み込む
library("dplyr")
library("rattle")
library("mlr")
library("evtree")

Keiku

library(dplyr)
library(lazyeval)

df <- data_frame(group = c(1, 2, 2, 3, 3, 3))

g <- "group"

df %>%
  group_by_(g) %>%
  summarise_(

Keiku

import pandas as pd

df = pd.DataFrame({'A':['A1', 'A2', 'A3'], 'B':[None, 'B2', None]})
df
# Out[51]:
#     A     B
# 0  A1  None
# 1  A2    B2
# 2  A3  None

Keiku

import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc

import seaborn as sns
sns.set('talk', 'whitegrid', 'dark', font_scale=1.5, font='Ricty',
        rc={"lines.linewidth": 2, 'grid.linestyle': '--'})

fpr, tpr, _ = roc_curve([1, 0, 1, 0, 1, 0, 0], [0.9, 0.8, 0.7, 0.7, 0.6, 0.5, 0.4])
roc_auc = auc(fpr, tpr)

Keiku

library(dplyr)

iris_df <- as_data_frame(iris)
iris_df %>% rename_(.dots = setNames(names(.), toupper(names(.)))) %>% head(2)
# A tibble: 2 × 5
# SEPAL.LENGTH SEPAL.WIDTH PETAL.LENGTH PETAL.WIDTH SPECIES
# <dbl>       <dbl>        <dbl>       <dbl>  <fctr>
# 1          5.1         3.5          1.4         0.2  setosa
# 2          4.9         3.0          1.4         0.2  setosa

Keiku

a <- c(1, 3, 5, 7, 9)
b <- c(3, 6, 8, 9, 10)
c <- c(2, 3, 4, 5, 7, 9)

intersect_all <- function(...) Reduce(intersect, list(...))
union_all <- function(...) Reduce(union, list(...))

intersect_all(a, b, c)
# [1] 3 9
union_all(a, b, c)

Keiku

import time
from tqdm import tqdm

pbar = tqdm(["1", "2", "3", "4", "5"])
for char in pbar:
    pbar.set_description("Processing %s" % char)
    time.sleep(1)
#   0%|                                                     | 0/5 [00:00<?, ?it/s]
# Processing 1:  20%|██████▏                        | 1/5 [00:01<00:04,  1.00s/it]
# Processing 2:  40%|████████████▍                  | 2/5 [00:02<00:03,  1.00s/it]

Keiku

library(gplots)
library(dplyr)
library(magrittr)

check_id_sets <- function(ids){
  ids_venn <- gplots::venn(ids, show.plot=FALSE)
  ids_list <- unlist(as.list(ids_venn))
  mat_dim <- c((length(ids_list) / (length(ids)+1)), length(ids)+1)
  id_sets <- ids_list %>%
    matrix(., mat_dim) %>%

Keiku

# compress/decompress zip file. 
zip file.csv.zip file.csv
unzip file.csv.zip

# compress/decompress gz file. 
gzip file.csv
gzip -d file.csv.gz

# compress/decompress bz2 file. 
bzip2 file.csv

Keiku

library(dplyr)
library(purrr)
library(broom)

df <- data_frame(
  group = rep(letters[1:2], each = 50),
  cat1 = letters[round(runif(100) * 5) + 1],
  cat2 = letters[round(runif(100) * 3) + 1]
)