Since President Trump has recently announced to end the DACA (Deferred Action for Childhood Arrivals) program, I have been wondering which part of the US will have more impact if this actually happens.
I have found this data set ‘Number of Form 1-812D, considerations of Deferred Action for Childhood Arrivals from FY2012 to the 1st quarter 2017’ at data.world, published by Caitlyn Kelleher. So I downloaded and quickly visualize the data to get the answers.
Here’s what I got.
Here, I’m comparing the number of the approved DACA requests by States. To make it easier to see, I’m filtering the data to show only the top 30 states.
We can see that California is the number one and far from the rest, then Texas as the distinct number two. Then, comes New York, Illinois, and Florida.
I’ve visualized this on Map as well.
You can clearly see which states will have more impacts than the others.
The populations among US States are varied, so it would be more interesting to compare the percentage of the approved DACA requests out of the population of each of the US States.
Here, we can see California still being at the top, but now States like Nevada, Arizona, New Mexico have risen to the top five. Nevada is actually the number two, greater than Texas. And New York and Florida are not even in the top 10 anymore.
By visualizing on Map, we can see some trends that the west and the south west part of the countries have higher percentage of the approved DACA requests than the other areas.
Looks like the states that are sharing the border with Mexico tend to have higher percentage of the DACA approved residents when compared to the total population.
Anyway, hope things will settle without anyone being forced to leave the county they were born in. I still have big hope for the American people to do the right thing in the end.
This analysis is done by Exploratory, the modern data science tool for non-programmers. If you are interested in, sign up from here for a free trial. If you are a student or teacher it’s free!
# Set libPaths.
.libPaths("/Users/---/.exploratory/R/3.4")
# Load required packages.
library(readr)
library(dplyr)
library(exploratory)
# Steps to produce the output
exploratory::read_delim_file("https://query.data.world/s/7LUzDzznZmC_svYRrCIMx7HOl4ja7C" , ",", quote = "\"", skip = 0 , col_names = TRUE , na = c("","NA") , locale=readr::locale(encoding = "UTF-8", decimal_mark = "."), trim_ws = FALSE , progress = FALSE) %>% readr::type_convert() %>% exploratory::clean_data_frame() %>%
filter(Residence %nin% c("Unknown", "Missing")) %>%
type_convert() %>%
mutate(`Approved Percent of Population` = `Approved Percent of Population`*100) %>%
top_n(30, `Accepted Initial Applications`)