ayoskovich

df %>% 
  ggplot(aes(x, y, color=foo))+
  geom_count()+
  
  # Remove legend for only the size aesthetic
  scale_size(guide = 'none')+
  
  # Increase legend point size
  guides(colour = guide_legend(override.aes = list(size=5)))

ayoskovich

  theme_bw()+
  theme(
    axis.ticks = element_blank(),
    axis.text = element_text(color='black'),
    legend.position = 'none',
    strip.text.y.left = element_text(angle = 0, face = 'bold'),
    strip.text.x = element_text(face='bold'),
    strip.placement = 'outside',
    strip.background = element_rect(fill='white', linetype = 'blank')
  )

ayoskovich

snippet qq
	dbGetQuery(con, read_file(here('${0}')))
	
snippet gl	
	${0} %>% glimpse()
	
snippet vv
	${0} %>% View()
	
snippet pw

ayoskovich

df %>% 
  select(
    colA,
    colB,
    colC
  ) %>% 
  mutate_all(~as.character(.x)) %>% 
  map_df(
    ~count(data.frame(x=.x), x),
    .id="var"

ayoskovich

snippet qq
	dbGetQuery(con, read_file(here('${0}')))
	
snippet vv
	${0} %>% View()

ayoskovich

howMany <- function(x){ length(unique(x))}
allSame <- function(x){ length(unique(x)) == 1}

df %>% 

  # Keep rows that have values for all columns that contain '-'
  filter(if_all(matches('-'), ~ !is.na(.))) %>% 
  rowwise() %>% 
  
  # Use c_across to work with rowwise

ayoskovich

ref_doc <- function(fname, prog){
  system(paste0("TASKKILL /F /IM ", prog))
  browseURL(fname)
}

ref_doc('foo.docx',
        'winword.exe')

ayoskovich

import pyodbc
import pandas as pd

def rs(fname, conn):
    """ Returns a pandas dataframe from external file. """
    with open(fname, 'r') as f:
        return pd.read_sql_query(f.read(), conn)
      
conn = pyodbc.connect('Driver={SQL Server};'
                      'Server=servername;'

ayoskovich

Use the value in D2 to create a red bar chart relative to the max value in column d

=SPARKLINE(D2,{"charttype","bar"; "color1", "red"; "max",max($D$2:D)})

ayoskovich

df = pd.DataFrame({"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]})

Crit1 = df.AAA <= 5.5
Crit2 = df.BBB == 10.0
Crit3 = df.CCC > -40.0

# Could do this
# AllCrit = Crit1 & Crit2 & Crit3

# But this is better