Sample Test 1 Solution Key Question 1: False Question 2: DF2 %>% mutate(date = dmy(date)) Question 3: sales<- read.csv("sales_data.csv") sales_long <- sales %>% pivot_longer(-firm_id, names_to= "year", values_to = "sales", names_prefix = "sales_") sales_long %>% filter(firm_id == 200) %>% summarise(mean_sales = mean(sales, na.rm=T)) Question 4: quakes %>% filter(stations == 14) %>% summarise(sd_mag = sd(mag)) quakes %>% filter(stations == 12) %>% summarise(sd_mag = sd(mag)) quakes %>% filter(stations == 21) %>% summarise(sd_mag = sd(mag)) Question 5: crime<- read.csv("crime.csv") police<- read.csv("police.csv") df<- inner_join(crime, police, by="county_year") df %>% filter(county.x == 25) %>% summarise(average_crime = mean(crime_rate, na.rm=T) df %>% ggplot(aes(x=crime_rate, y= police_per_capita)) + geom_point() + labs(x = "Crime rate", y = "Police per capita") Question 6: df %>% group_by(state) %>% summarise(sum_spending = sum(spending)) %>% arrange(desc(sum_spending)) %>% head(5) df %>% filter(name == "Ada") %>% group_by(state) %>% summarise(mean_spending = mean(spending)) %>% arrange(desc(mean_spending)) %>% head(1) IQR <- quantile(df$spending, 0.75) - quantile(df$spending, 0.25) upper_bound <- 1.5*IQR + quantile(df$spending, 0.75) lower_bound <- quantile(df$spending, 0.25) - 1.5*IQR df %>% filter(spending > upper_bound | spending < lower_bound) 51作业君版权所有