I have a dataframe where one column shows hour type and another shows number of hours, but I want it to be a dataframe where each hour type has its own column.
Like from this:
name hourtype hours
Amy A 3
Amy B 2
Bob B 1
Bob C 4
Cam A 5
Cam B 1
Cam C 1
Dan A 2
To this:
name A B C
Amy 3 2 0
Bob 0 1 4
Cam 5 1 1
Dan 2 0 0
Try this. Your issue is mainly to the format of data. You have data in long format so you want now in wide format. The function pivot_wider() from tidyr, a tidyverse package can help with that. After this, you can set NA values to zero with replace. Here the code:
library(tidyverse)
#Code
df %>% pivot_wider(names_from = hourtype,values_from=hours) %>%
replace(is.na(.),0)
The output:
# A tibble: 4 x 4
name A B C
<chr> <int> <int> <int>
1 Amy 3 2 0
2 Bob 0 1 4
3 Cam 5 1 1
4 Dan 2 0 0
Some data used:
#Data
df <- structure(list(name = c("Amy", "Amy", "Bob", "Bob", "Cam", "Cam",
"Cam", "Dan"), hourtype = c("A", "B", "B", "C", "A", "B", "C",
"A"), hours = c(3L, 2L, 1L, 4L, 5L, 1L, 1L, 2L)), class = "data.frame", row.names = c(NA,
-8L))
