Create a unique identifier row for each name and then use pivot_wider library(dplyr) d %>% group_by(name) %>% mutate(row = row_number()) %>% tidyr::pivot_wider(names_from = name, values_from = val) %>% select(-row) # A tibble: 51 x 4 # time x1 `C Farolillo` `Plaza Eliptica` # <date> <dbl> <dbl> <dbl> # 1 2016-04-20 51.5 7 32 # 2 … Read more
You can use the tidyr::complete function: complete(df, distance, years = full_seq(years, period = 1), fill = list(area = 0)) # A tibble: 14 x 3 distance years area <fct> <dbl> <dbl> 1 100 1. 40. 2 100 2. 0. 3 100 3. 0. 4 100 4. 0. 5 100 5. 50. 6 100 6. 60. … Read more
You can try : tidyr::pivot_longer(df, cols = -ID_IE, names_to = c(‘.value’, ‘grade’), names_pattern = ‘(.*)(\\d+)’) # A tibble: 8 x 4 # ID_IE grade BLS_tchrG ELS_tchrG # <dbl> <chr> <dbl> <dbl> #1 2135 2 1 1 #2 2135 7 1 1 #3 2101 2 0 0 #4 2101 7 2 0 #5 2103 2 0 … Read more
This approach seems pretty natural to me: df %>% gather(key, value, -id, -time) %>% extract(key, c(“question”, “loop_number”), “(Q.\\..)\\.(.)”) %>% spread(question, value) First gather all question columns, use extract() to separate into question and loop_number, then spread() question back into the columns. #> id time loop_number Q3.2 Q3.3 #> 1 1 2009-01-01 1 0.142259203 -0.35842736 #> … Read more
reshape(dat, idvar=”ID”, direction=”long”, varying=list(Start=c(2,5,8), End=c(3,6,9), Value=c(4,7,10)), v.names = c(“DateRangeStart”, “DateRangeEnd”, “Value”) ) #————- ID time DateRangeStart DateRangeEnd Value 1.1 1 1 1/1/90 3/1/90 4.4 1.2 1 2 4/5/91 6/7/91 6.2 1.3 1 3 5/5/95 6/6/96 3.3 (Added the v.names per Josh’s suggestion.)