Reshaping Data.Frame from Wide to Long Format

Reshaping data.frame from wide to long format

reshape() takes a while to get used to, just as melt/cast. Here is a solution with reshape, assuming your data frame is called d:

reshape(d, 
        direction = "long",
        varying = list(names(d)[3:7]),
        v.names = "Value",
        idvar = c("Code", "Country"),
        timevar = "Year",
        times = 1950:1954)

reshape dataframe from wide to long in R

Using data.table:

library(data.table)
setDT(mydata)
result <- melt(mydata, id=c('id', 'name'), 
                 measure.vars = patterns(fixed='fixed_', current='current_'), 
                 variable.name = 'year')
years <- as.numeric(gsub('.+_(\\d+)', '\\1', grep('fixed', names(mydata), value = TRUE)))
result[, year:=years[year]]
result[, id:=seq(.N), by=.(name)]
result
##    id name year fixed current
## 1:  1    A 2020  2300    3000
## 2:  2    A 2019  2100    3100
## 3:  3    A 2018  2600    3200
## 4:  4    A 2017  2600    3300
## 5:  5    A 2016  1900    3400

This should be very fast but your data-set is not very big tbh.

Note that this assumes the fixed and current columns are in the same order and associated with the same year(s). So if there is a fixed_2020 as the first fixed_* column, there is also a current_2020 as the first current_* column, and so on. Otherwise, the year column will correctly associate with fixed but not current

Reshaping long format dataframe to wide format according to the value of the elements in columns

Reshape the dataframe using pivot then subtract 5 from all the values and add prefix of n to column names:

df.pivot('group', 'ID', 'rank').rsub(5).add_prefix('n')

---

ID      n1   n2   n3   n4
group                    
1      3.0  2.0  4.0  1.0
2      3.0  4.0  NaN  NaN
3      4.0  1.0  2.0  3.0
4      4.0  NaN  NaN  NaN
5      2.0  3.0  4.0  NaN
6      4.0  NaN  NaN  NaN

Reshaping wide dataframe to long format

Does this work for you?

library(dplyr)
library(tidyr)

structure(list(name = structure(1:3, .Label = c("A", "B", "C"
), class = "factor"), other_info = structure(1:3, .Label = c("Info1", 
"Info2", "Info3"), class = "factor"), revenues_2015 = structure(c(1L, 
3L, 2L), .Label = c("1", "11", "6"), class = "factor"), ebitda_2015 = structure(c(2L, 
3L, 1L), .Label = c("12", "2", "7"), class = "factor"), ebitda_2016 = structure(c(2L, 
3L, 1L), .Label = c("13", "3", "8"), class = "factor"), revenues_2015 = structure(c(2L, 
3L, 1L), .Label = c("14", "4", "9"), class = "factor"), other_2017 = structure(c(3L, 
1L, 2L), .Label = c("10", "15", "5"), class = "factor")), class = "data.frame", row.names = c(NA, 
-3L)) %>% 
  pivot_longer(revenues_2015:other_2017, names_pattern = "(.+)_(\\d{4})", names_to = c("metric", "year"))

Reshape wide dataframe of unequal size to long format

You could use

library(dplyr)
library(tidyr)

df %>% 
  pivot_longer(-value, values_to = "word") %>% 
  drop_na(word) %>% 
  select(word, value)

This returns

# A tibble: 5 x 2
  word             value
  <chr>            <dbl>
1 abmachen           0.4
2 abgemacht          0.4
3 abmachst           0.4
4 Aktualisierung     0.2
5 Aktualisierungen   0.2

---

Related Topics