Zelos Zhu 11/3/2018
library(tidyverse)
library(purrr)
library(knitr)
library(broom)
library(ggthemes)setwd("./data/")
patient_df <- tibble(filenames = list.files()) %>%
mutate(weekly_data = map(filenames, read_csv)) %>%
unnest() %>%
mutate(arm = substring(filenames, 1, 3),
subject_id = substring(filenames, 5, 6)) %>%
gather(week, measure, 2:9) %>%
mutate(week = as.numeric(str_replace(week, "week_","")))
patient_df## # A tibble: 160 x 5
## filenames arm subject_id week measure
## <chr> <chr> <chr> <dbl> <dbl>
## 1 con_01.csv con 01 1 0.2
## 2 con_02.csv con 02 1 1.13
## 3 con_03.csv con 03 1 1.77
## 4 con_04.csv con 04 1 1.04
## 5 con_05.csv con 05 1 0.47
## 6 con_06.csv con 06 1 2.37
## 7 con_07.csv con 07 1 0.03
## 8 con_08.csv con 08 1 -0.08
## 9 con_09.csv con 09 1 0.08
## 10 con_10.csv con 10 1 2.14
## # ... with 150 more rows
setwd("..") #move back to original repo, don't want to continue working in the /data folder
ggplot(patient_df, aes(x = week, y = measure, group = filenames, color = arm)) +
geom_line(alpha = 0.5) +
scale_x_discrete(name ="Week", limits=1:8) +
ylab("Measure") +
ggtitle("Patient Measurements by Week") +
geom_smooth(aes(group = arm))
The trend for control group patients' measurements seems stagnant and doesn't seem to change over time. I would suspect this is a placebo of some sort. On the other hand, patients in the experimental arm, their measurements generally increase over time. There is not enough information provided to interpret whether this increase is a good or bad thing. I would also suspect this difference in trends would be statistically significant just from a visual standpoint.
homicide_df <- read_csv("https://raw.githubusercontent.com/washingtonpost/data-homicides/master/homicide-data.csv")
homicide_df <- homicide_df %>%
mutate(state = ifelse(city == "Tulsa" & state == "AL", "OK", state), #There is a typo in the data set based on lat/long
city_state = str_c(city, ", ", state),
homicide_status = ifelse(disposition == "Closed by arrest", "Solved", "Unsolved"))
#Homicide Case Counts
homicide_df %>%
group_by(city_state) %>%
count(homicide_status) %>%
spread(homicide_status, n) %>%
kable()| city_state | Solved | Unsolved |
|---|---|---|
| Albuquerque, NM | 232 | 146 |
| Atlanta, GA | 600 | 373 |
| Baltimore, MD | 1002 | 1825 |
| Baton Rouge, LA | 228 | 196 |
| Birmingham, AL | 453 | 347 |
| Boston, MA | 304 | 310 |
| Buffalo, NY | 202 | 319 |
| Charlotte, NC | 481 | 206 |
| Chicago, IL | 1462 | 4073 |
| Cincinnati, OH | 385 | 309 |
| Columbus, OH | 509 | 575 |
| Dallas, TX | 813 | 754 |
| Denver, CO | 143 | 169 |
| Detroit, MI | 1037 | 1482 |
| Durham, NC | 175 | 101 |
| Fort Worth, TX | 294 | 255 |
| Fresno, CA | 318 | 169 |
| Houston, TX | 1449 | 1493 |
| Indianapolis, IN | 728 | 594 |
| Jacksonville, FL | 571 | 597 |
| Kansas City, MO | 704 | 486 |
| Las Vegas, NV | 809 | 572 |
| Long Beach, CA | 222 | 156 |
| Los Angeles, CA | 1151 | 1106 |
| Louisville, KY | 315 | 261 |
| Memphis, TN | 1031 | 483 |
| Miami, FL | 294 | 450 |
| Milwaukee, wI | 712 | 403 |
| Minneapolis, MN | 179 | 187 |
| Nashville, TN | 489 | 278 |
| New Orleans, LA | 504 | 930 |
| New York, NY | 384 | 243 |
| Oakland, CA | 439 | 508 |
| Oklahoma City, OK | 346 | 326 |
| Omaha, NE | 240 | 169 |
| Philadelphia, PA | 1677 | 1360 |
| Phoenix, AZ | 410 | 504 |
| Pittsburgh, PA | 294 | 337 |
| Richmond, VA | 316 | 113 |
| Sacramento, CA | 237 | 139 |
| San Antonio, TX | 476 | 357 |
| San Bernardino, CA | 105 | 170 |
| San Diego, CA | 286 | 175 |
| San Francisco, CA | 327 | 336 |
| Savannah, GA | 131 | 115 |
| St. Louis, MO | 772 | 905 |
| Stockton, CA | 178 | 266 |
| Tampa, FL | 113 | 95 |
| Tulsa, OK | 391 | 193 |
| Washington, DC | 756 | 589 |
#Just Baltimore
baltimore_prop_df <- filter(homicide_df, city_state == "Baltimore, MD") %>%
mutate(homicide_status = factor(homicide_status, levels = c("Unsolved", "Solved")))
baltimore_proptest <- prop.test(table(baltimore_prop_df$homicide_status))
tidy(baltimore_proptest) %>%
select(estimate, conf.low, conf.high) %>%
mutate(city_state = "Baltimore, MD") %>%
kable()| estimate | conf.low | conf.high | city_state |
|---|---|---|---|
| 0.6455607 | 0.6275625 | 0.6631599 | Baltimore, MD |
#doing it for all cities
city_props <- homicide_df %>%
group_by(city_state) %>%
count(homicide_status) %>%
spread(homicide_status, n) %>% #stopping here would get us the case counts from earlier
mutate(total = Unsolved + Solved,
test = map(map2(.x = Unsolved, .y = total, ~prop.test(x = .x, n = .y)), tidy)) %>% #make a total variable to make map easier
unnest() %>%
select(city_state, estimate, conf.low, conf.high)
kable(city_props)| city_state | estimate | conf.low | conf.high |
|---|---|---|---|
| Albuquerque, NM | 0.3862434 | 0.3372604 | 0.4375766 |
| Atlanta, GA | 0.3833505 | 0.3528119 | 0.4148219 |
| Baltimore, MD | 0.6455607 | 0.6275625 | 0.6631599 |
| Baton Rouge, LA | 0.4622642 | 0.4141987 | 0.5110240 |
| Birmingham, AL | 0.4337500 | 0.3991889 | 0.4689557 |
| Boston, MA | 0.5048860 | 0.4646219 | 0.5450881 |
| Buffalo, NY | 0.6122841 | 0.5687990 | 0.6540879 |
| Charlotte, NC | 0.2998544 | 0.2660820 | 0.3358999 |
| Chicago, IL | 0.7358627 | 0.7239959 | 0.7473998 |
| Cincinnati, OH | 0.4452450 | 0.4079606 | 0.4831439 |
| Columbus, OH | 0.5304428 | 0.5002167 | 0.5604506 |
| Dallas, TX | 0.4811742 | 0.4561942 | 0.5062475 |
| Denver, CO | 0.5416667 | 0.4846098 | 0.5976807 |
| Detroit, MI | 0.5883287 | 0.5687903 | 0.6075953 |
| Durham, NC | 0.3659420 | 0.3095874 | 0.4260936 |
| Fort Worth, TX | 0.4644809 | 0.4222542 | 0.5072119 |
| Fresno, CA | 0.3470226 | 0.3051013 | 0.3913963 |
| Houston, TX | 0.5074779 | 0.4892447 | 0.5256914 |
| Indianapolis, IN | 0.4493192 | 0.4223156 | 0.4766207 |
| Jacksonville, FL | 0.5111301 | 0.4820460 | 0.5401402 |
| Kansas City, MO | 0.4084034 | 0.3803996 | 0.4370054 |
| Las Vegas, NV | 0.4141926 | 0.3881284 | 0.4407395 |
| Long Beach, CA | 0.4126984 | 0.3629026 | 0.4642973 |
| Los Angeles, CA | 0.4900310 | 0.4692208 | 0.5108754 |
| Louisville, KY | 0.4531250 | 0.4120609 | 0.4948235 |
| Memphis, TN | 0.3190225 | 0.2957047 | 0.3432691 |
| Miami, FL | 0.6048387 | 0.5685783 | 0.6400015 |
| Milwaukee, wI | 0.3614350 | 0.3333172 | 0.3905194 |
| Minneapolis, MN | 0.5109290 | 0.4585150 | 0.5631099 |
| Nashville, TN | 0.3624511 | 0.3285592 | 0.3977401 |
| New Orleans, LA | 0.6485356 | 0.6231048 | 0.6731615 |
| New York, NY | 0.3875598 | 0.3494421 | 0.4270755 |
| Oakland, CA | 0.5364308 | 0.5040588 | 0.5685037 |
| Oklahoma City, OK | 0.4851190 | 0.4467861 | 0.5236245 |
| Omaha, NE | 0.4132029 | 0.3653146 | 0.4627477 |
| Philadelphia, PA | 0.4478103 | 0.4300380 | 0.4657157 |
| Phoenix, AZ | 0.5514223 | 0.5184825 | 0.5839244 |
| Pittsburgh, PA | 0.5340729 | 0.4942706 | 0.5734545 |
| Richmond, VA | 0.2634033 | 0.2228571 | 0.3082658 |
| Sacramento, CA | 0.3696809 | 0.3211559 | 0.4209131 |
| San Antonio, TX | 0.4285714 | 0.3947772 | 0.4630331 |
| San Bernardino, CA | 0.6181818 | 0.5576628 | 0.6753422 |
| San Diego, CA | 0.3796095 | 0.3354259 | 0.4258315 |
| San Francisco, CA | 0.5067873 | 0.4680516 | 0.5454433 |
| Savannah, GA | 0.4674797 | 0.4041252 | 0.5318665 |
| St. Louis, MO | 0.5396541 | 0.5154369 | 0.5636879 |
| Stockton, CA | 0.5990991 | 0.5517145 | 0.6447418 |
| Tampa, FL | 0.4567308 | 0.3881009 | 0.5269851 |
| Tulsa, OK | 0.3304795 | 0.2927201 | 0.3705039 |
| Washington, DC | 0.4379182 | 0.4112495 | 0.4649455 |
city_props %>%
ungroup()%>%
arrange(estimate) %>%
mutate(city_state = factor(city_state, levels = city_state)) %>%
ggplot(aes(x = city_state, y = estimate)) +
geom_point() +
geom_errorbar(aes(x = city_state, ymin = conf.low, ymax = conf.high), width=0.2, size=1, color="blue") +
coord_flip() +
ylab("Estimated Proportion of Unsolved Homicides") +
xlab("City, State") +
ggtitle("Estimated Proportion of Unsolved Homicides by City/State") +
theme_few()