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sfirke / gist:e9bd0c41f5105bc540d5
Created Oct 9, 2015
StackOverflow SEDE query for set.seed() values
View gist:e9bd0c41f5105bc540d5
SELECT Id, Body, Tags
FROM Posts
WHERE Body LIKE '%set.seed(%' AND Tags LIKE '%r%'
@sfirke
sfirke / date_to_sy.R
Created Jul 5, 2016
Function for turning Date into school year string
View date_to_sy.R
## Date to SY function
## Year of 2nd argument does not matter
## Turns 2015-10-02 into "2015-16", and 2016-04-05 into "2015-16", with cutoff day = 2010-07-01
date_to_sy <- function(date_var, last_day_of_sy){
if(!(is.Date(date_var) & is.Date(last_day_of_sy))){stop("`date_var` and `last_day_of_sy` must both be class Date")}
cutoff_day <- day(last_day_of_sy)
cutoff_month <- month(last_day_of_sy)
case_when(
is.na(date_var) ~ as.character(NA),
month(date_var) > cutoff_month ~ paste0(year(date_var), " - ", year(date_var) + 1), # if past cutoff, X - X+!
@sfirke
sfirke / email_split.R
Created Jul 5, 2016
separating first and last names in email
View email_split.R
library(stringr)
get_part_before_dot <- function(email){
x <- str_split(email, "[.]")
lapply(x, `[[`, 1) %>%
unlist
}
dat <- data.frame(email = c("robert.rosen@tntp.org", "Sam.firke@tntp.org"))
@sfirke
sfirke / gist:c0bd2b9c4d4e044b040966841e19a73b
Last active Oct 19, 2016
quick hack at get_fuzzy_dupes() function
View gist:c0bd2b9c4d4e044b040966841e19a73b
library(pacman)
p_load(fuzzyjoin, dplyr)
# returns clusters of records that almost match
get_fuzzy_dupes <- function(x, max_dist = 2){
result <- stringdist_inner_join(x, x, max_dist = max_dist, distance_col = "distance")
result <- result[result[[1]] != result[[2]], ] # remove actual 100% accurate duplicates
result <- t(apply(result, 1, sort)) # these two lines treat A, B as a duplicate of B, A and remove it. From http://stackoverflow.com/a/9028416
result <- result[!duplicated(result), ]
as_data_frame(result) %>%
@sfirke
sfirke / final_predictions.R
Created Mar 16, 2017
making final Kaggle March Mania predictions
View final_predictions.R
final_blank <- read_csv("data/kaggle/SampleSubmission.csv") %>%
separate(Id, into = c("year", "lower_team", "higher_team"), sep = "_", convert = TRUE, remove = FALSE) %>%
dplyr::select(-Pred)
final_blank_with_data <- final_blank %>%
add_kp_data %>%
create_vars_for_prediction %>%
mutate(lower_team_court_adv = as.factor("N")) %>%
dplyr::select(contains("diff"), lower_team_court_adv, contains("rank")) %>%
dplyr::select(-lower_pre_seas_rank_all, -higher_pre_seas_rank_all)
View file28dc3223345c.R
Package: janitor
Title: Simple Tools for Examining and Cleaning Dirty Data
Version: 0.3.0.9000
Authors@R: c(person("Sam", "Firke", email = "samuel.firke@gmail.com", role = c("aut", "cre")),
person("Chris", "Haid", email = "chrishaid@gmail.com", role = "ctb"),
person("Ryan", "Knight", email = "ryangknight@gmail.com", role = "ctb"))
Description: The main janitor functions can: perfectly format data.frame column
names; provide quick one- and two-variable tabulations (i.e., frequency
tables and crosstabs); and isolate duplicate records. Other janitor functions
nicely format the tabulation results. These tabulate-and-report functions
@sfirke
sfirke / add_centered_title.R
Last active Sep 21, 2017
Center all of your ggplot2 titles over the whole plot using a function
View add_centered_title.R
library(ggplot2)
library(dplyr)
library(grid)
library(gridExtra)
add_centered_title <- function(p, text, font_size){
title.grob <- textGrob(
label = text,
gp = gpar(fontsize = font_size,
@sfirke
sfirke / split_tinker_combine_tidyverse.R
Created Mar 7, 2018
Using split with magrittr's $%$ to reference the names of the listed data.frames
View split_tinker_combine_tidyverse.R
# I want to remove duplicate mpg rows where cylinder is 4
# Split, tinker with the data.frames by name, bind_rows
library(magrittr)
library(dplyr)
mtcars %>%
split(., .$cyl == 4) %$%
bind_rows(`FALSE`,
`TRUE` %>%
distinct(mpg, .keep_all = TRUE))
@sfirke
sfirke / tidytext_wordclouds.R
Created Mar 9, 2018
Make wordclouds from a text column in R
View tidytext_wordclouds.R
library(pacman)
p_load(tidytext, wordcloud, janeaustenr, dplyr)
data("stop_words")
ppdf <- data.frame(prideprejudice, stringsAsFactors = FALSE)
# create a word cloud
create_word_cloud <- function(dat, col_name, exclude = "", max.words = 50, colors = "#034772", ...){
col <- deparse(substitute(col_name))
dat %>%
@sfirke
sfirke / clean_names.R
Created Jan 29, 2016
Cleaning data.frame names with dplyr
View clean_names.R
clean_names <- function(dat){
# Takes a data.frame, returns the same data frame with cleaned names
old_names <- names(dat)
new_names <- old_names %>%
gsub("%", "percent", .) %>%
make.names(.) %>%
gsub("[.]+", "_", .) %>%
tolower(.) %>%
gsub("_$", "", .)
setNames(dat, new_names)