Grab and parse data

df_rdata <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-12-17/dog_moves.csv")

df_rdata <- df_rdata %>% 
  filter(inUS == 'TRUE') %>%
  select(location, total)

df_rdata %>% head()

## # A tibble: 6 x 2
##   location       total
##   <chr>          <dbl>
## 1 Texas            566
## 2 Alabama         1428
## 3 North Carolina  2627
## 4 South Carolina  1618
## 5 Georgia         3479
## 6 California      1664

Plot data

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# note the r. before the df_rdata value
fig = sns.barplot(x="total", y="location", data=r.df_rdata, orient="h")

plt.xlabel("Adoptable Dogs Available")
plt.ylabel("")
plt.figtext(0.9, 0.03, "by: @eeysirhc", horizontalalignment="right")
plt.figtext(0.9, 0.01, "source: The Pudding", horizontalalignment="right")
plt.show(fig)

Grab and parse data

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df_pydata = pd.read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-12-17/dog_moves.csv", sep=',', error_bad_lines=False, index_col=False, dtype='unicode')

df_pydata = df_pydata[df_pydata['inUS']=='TRUE']
df_pydata = df_pydata[['location', 'total']]
df_pydata.total = pd.to_numeric(df_pydata.total)

df_pydata.head()

##          location  total
## 0           Texas    566
## 1         Alabama   1428
## 2  North Carolina   2627
## 3  South Carolina   1618
## 4         Georgia   3479

Plot data

# note the py$ before the df_pydata
py$df_pydata %>% 
  ggplot(aes(location, total, fill = location)) +
  geom_col() +
  coord_flip() +
  scale_y_continuous(labels = scales::comma_format()) +
  labs(x = NULL, y = "Adoptable Dogs Available", caption = "by: @eeysirhc\nsource: The Pudding") +
  theme_minimal() +
  theme(legend.position = 'none')