How to Spread Repeated Measures of Multiple Variables into Wide Format

How can I spread repeated measures of multiple variables into wide format?

Edit: I'm updating this answer since pivot_wider has been around for a while now and addresses the issue in this question and comments. You can now do

pivot_wider(
    dat, 
    id_cols = 'Person', 
    names_from = 'Time', 
    values_from = c('Score1', 'Score2', 'Score3'), 
    names_glue = '{Time}.{.value}'
)

to get the desired result.

---

The original answer was

dat %>% 
  gather(temp, score, starts_with("Score")) %>% 
  unite(temp1, Time, temp, sep = ".") %>% 
  spread(temp1, score)

Converting many variables from long to wide in R

You can create a unique identifier row for every family, id and time variable and then use pivot_wider.

library(dplyr)

D %>% 
   group_by(family, id, time) %>% 
   mutate(row = row_number()) %>% 
   tidyr::pivot_wider(names_from = time, values_from = c(x, y))

Convert wide data to long format for repeated measures/mixed models

dplyr

tidyr::pivot_longer(wide, -Participant, names_to = "Item", values_to = "Accuracy")
# # A tibble: 9 x 3
#   Participant Item   Accuracy
#   <chr>       <chr>     <int>
# 1 P1          Banana        0
# 2 P1          Apple         0
# 3 P1          Orange        0
# 4 P2          Banana        1
# 5 P2          Apple         1
# 6 P2          Orange        1
# 7 P3          Banana        0
# 8 P3          Apple         0
# 9 P3          Orange        0

NA values and extra rows when spreading repeated measures of multiple variables into wide format?

You are almost there. The lat and long go into different rows because their IndYear is different. As you only keep the first value of IndYear for each IndiDII in the final data.frame, add IndYear = first(IndYear) will give you the desired result.

Dat %>%  
    group_by(IndIDII) %>%
    mutate(YearNum = row_number(), IndYear = first(IndYear)) %>% 
    gather(Group, LatLong, c(WintLat,  WintLong)) %>% 
    unite(GroupNew, YearNum, Group, sep = "-") %>% 
    spread(GroupNew, LatLong) %>% 
    as.data.frame()

#   IndIDII      IndYear 1-WintLat 1-WintLong 2-WintLat 2-WintLong 3-WintLat 3-WintLong 4-WintLat 4-WintLong
# 1 BHS_265 BHS_265-2015  47.61025  -112.7210  47.59884  -112.7089        NA         NA        NA         NA
# 2 BHS_377 BHS_377-2015  43.34744  -109.4821  43.35559  -109.4445  43.35195  -109.4566  43.34765  -109.4892
# 3 BHS_770 BHS_770-2016  42.97379  -109.0400  42.97129  -109.0367  42.97244  -109.0509        NA         NA

wide to long multiple measures each time

This is pretty close and changing the names of columns should be within your skillset:

reshape(DF, 
       varying=c(work= c(3, 7), play= c(4,8), talk= c(5,9), total= c(6,10) ), 
       direction="long")

EDIT: Adding a version that is almost an exact solution:

reshape(DF, varying=list(work= c(3, 7), play= c(4,8), talk= c(5,9), total= c(6,10) ), 
        v.names=c("Work", "Play", "Talk", "Total"), 
          # that was needed after changed 'varying' arg to a list to allow 'times' 
        direction="long",  
        times=1:2,        # substitutes number for T1 and T2
        timevar="times")  # to name the time col

Reshaping by ID number into wide format

Not so hard with tidyverse...

df<-data.frame(ID=c(100,101,101,101,102,103),
           DEGREE=c("BA","BA","MS","PHD","BA","BA"),
             YEAR=c(1980,1990, 1992, 1996, 2000, 2004),
             stringsAsFactors=FALSE)

df1 <- df %>% select(-3) %>% group_by(ID) %>% mutate(i=row_number()) %>%
       as.data.frame() %>%
       reshape(direction="wide",idvar="ID",v.names="DEGREE",timevar="i",sep="_")
df1[is.na(df1)] <- ""

df2 <- df %>% select(-2) %>% group_by(ID) %>% mutate(i=row_number()) %>%
       as.data.frame() %>%
       reshape(direction="wide",idvar="ID",v.names="YEAR",timevar="i",sep="_")
df2[is.na(df2)] <- ""

inner_join(df1,df2,"ID")
#   ID DEGREE_1 DEGREE_2 DEGREE_3 YEAR_1 YEAR_2 YEAR_3
#1 100       BA                     1980              
#2 101       BA       MS      PHD   1990   1992   1996
#3 102       BA                     2000              
#4 103       BA                     2004              

wide to long, multiple variables R

You can use pivot_longer :

tidyr::pivot_longer(wide, 
                     cols = large_firm:large_hi, 
                     names_to = c('firm_size', '.value'), 
                     names_sep = '_')

#     id   sex   age group      firm_size  firm    hi
#  <dbl> <dbl> <dbl> <chr>      <chr>     <dbl> <dbl>
#1     1     2    25 non_unique large         1     1
#2     1     2    25 non_unique small         3     6
#3     2     1    33 unique     large         6     2
#4     2     1    33 unique     small         1     4

---

To get your exact output shown you can use :

library(dplyr)

tidyr::pivot_longer(wide, 
                    cols = large_firm:large_hi, 
                     names_to = c('firm_size', '.value'), 
                     names_sep = '_') %>%
    mutate(hi_group = firm_size, 
           firm_size = paste(firm_size, 'firm', sep = '_')) %>%
    rename(firm_preference_score = firm, hi_score = hi)

grouping table by multiple factors and spreading it from long format to wide - the data.table way in R

In data.table, we can group by multiple columns and to reshape we can use dcast.

library(data.table)
dcast(mtcars[, .N, .(carb, cyl, gear)], carb+cyl~gear, value.var = "N")

#   carb cyl  3  4  5
#1:    1   4  1  4 NA
#2:    1   6  2 NA NA
#3:    2   4 NA  4  2
#4:    2   8  4 NA NA
#5:    3   8  3 NA NA
#6:    4   6 NA  4 NA
#7:    4   8  5 NA  1
#8:    6   6 NA NA  1
#9:    8   8 NA NA  1

You may use fill argument in dcast to replace NAs with 0 or any other number.

---

Related Topics