In msperlin/afedR: Data and Functions for Book "Analyzing Financial and Economical Data with R"

set.seed(1)

my_df <- tibble::tibble(x = -100:100, 
                        y = x + 5)

# solution

Question

If you have not already done so, repeat exercises 1, 2 and 3 using the functions of the tidyverse universe and the pipeline operator.

Solution

library(tidyverse)

set.seed(1)

my_df <- tibble(x = -100:100,
                y = x + 5) %>%
  mutate(cumsum_x = cumsum(x))

# solution 01
sol_01 <- my_df %>%
  filter(x > 10, 
         x < 25) %>%
  nrow()

# solution 02
sol_02 <- my_df %>%
  mutate(cumsum_x = cumsum(x)) %>%
  filter(cumsum_x > -3500) %>%
  nrow()

# solution 03
sol_03 <- my_df %>%
  filter(y > 0) %>%
  nrow()

message(str_glue(
  'Solution 01 = {sol_01} \n',
  'Solution 02 = {sol_02} \n',
  'Solution 03 = {sol_03}'
  )
)

Meta-information

extype: string exsolution: r mchoice2string(c(TRUE, FALSE, FALSE, FALSE, FALSE), single = TRUE) exname: "dataframe basic 04" exshuffle: TRUE

msperlin/afedR documentation built on Sept. 11, 2022, 9:49 a.m.