Versions Compared

Version Old Version 4 New Version 5
Changes made by

Matt Bramble

Matt Bramble

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

Code Block
breakoutModewide
breakoutWidth760
dataf <- read.table("walrus_sounds.tsv", header = F, sep = "\t")
colnames(dataf) <- c('name', 'vocalization', 'min_sec')

sec1 <- as.numeric(str_split_i(dataf$min_sec, ':', 1))*60 
sec2 <- as.numeric(str_split_i(dataf$min_sec, ':', 2))

#Base R
dataf$seconds <- sec1+sec2
mean_by_group_baseR <- aggregate(seconds ~ name + vocalization, data = dataf, FUN = mean)
mean_by_group_baseR_jocko <- mean_by_group_baseR[mean_by_group_baseR$name == 'Jocko',]
jocko_seconds <- mean_by_group_baseR_jocko$seconds

#Dplyr
mean_by_group_dplyr_jocko <- dataf %>%
    mutate(seconds = sec1+sec2) %>%
    group_by(name,vocalization) %>%
    summarize(Mean_seconds = mean(seconds)) %>%
    filter(name=="Jocko") %>% 
    as.data.frame()

#if you want to extract a column into a vector, use pull()
jocko_seconds <- mean_by_group_dplyr_jocko %>% pull(Mean_seconds)

#option, left merge
Note

We sent the same dataframe into a series of base R manipulations, and into a dpyler pipe expression. What we ended up with were a dataframe and a tibble. You should know at this point that a tibble is different from a dataframe. A tibble will not behave like a dataframe, so use as.data.frame(tibble) to recover a dataframe.

...

Code Block
breakoutModewide
breakoutWidth760
mtcars_sub <- head(mtcars)
library(tidyverse)
mtcars_long <- mtcars_sub %>%
      rownames_to_column(var = "model") %>% # Convert row names (car models) to a column
      pivot_longer(
        cols = -c(mpg), # Select all columns except 'mpg, model' to pivot
        names_to = "variable", # New column for the original column names
        values_to = "value" # New column for the values from the original columns
      )

...

Code Block
breakoutModewide
breakoutWidth760
library(data.table)
mtcars_sub$model <- rownames(mtcars_sub)
mtcars_long <- melt(
  data.table(mtcars_sub),  
  id.vars = c("model", "mpg"), 
  variable.name = 'variable', 
  measure.name =  colnames(mtcars_sub)[colnames(mtcars_sub) != 'mpg'] #additional
methods   )
Info

ggplot2 is very powerful

...

Full (outer):

...

Inner:

...

Left:

...

Right:

...

Code Block

breakoutModewide
breakoutWidth760
dataf <- read.table("walrus_sounds.tsv", header = F, sep = "\t") colnames(dataf) <- c('name', 'vocalization', 'min_sec') sec1 <- as.numeric(str_split_i(dataf$min_sec, ':', 1))*60 sec2 <- as.numeric(str_split_i(dataf$min_sec, ':', 2)) dataf$seconds <- sec1+sec2 dataf result <- aggregate(seconds ~ name + vocalization, data = dataf, FUN = mean) df_wide <- reshape(result, idvar = "V1", # Column(s) that identify unique observations timevar = "V2", # Column whose values will become new column names v.names = "length", # Column(s) whose values will populate the new columns direction = "wide" # Specifies the reshaping direction ) mean_by_group_dplyr <- dataf %>% group_by(V1,V2) %>% summarize(Mean_Value = mean(length)) #instead, use dplyer to pivot wide library(tidyr) df_wide_tidy <- mean_by_group_dplyr %>% pivot_wider(names_from = V2, values_from = Mean_Value) as.data.frame(df_wide_tidy) Can combine them: but must be careful what type of objects you're working with: For example: #the following fails when run on mean_by_group_dplyr, because group_by outputs a 'tibble'. Must convert to data.frame for it to work properly in base R -- as.data.frame(), or conversely, as.tibble() df_wide <- reshape(as.data.frame(mean_by_group_dplyr), idvar = "V1", # Column(s) that identify unique observations timevar = "V2", # Column whose values will become new column names v.names = "Mean_Value", # Column(s) whose values will populate the new columns direction = "wide" # Specifies the reshaping direction )