CODEWITHSUNDEEP
rdplyrtidyverse
user2597079
user2597079

Reputation: 47

r - Gather multiple keys with a single pipe in tidyverse

I am trying to compute observed, minimum and maximum values by group in order to plot with ggplot. I was able to do that in the following way:

library(tidyverse)
df <- cbind.data.frame("V1"=rnorm(100,10,2),
                       "V1_std"=rgamma(100,2),
                       "V2"=rnorm(100,10,2),
                       "V2_std"=rgamma(100,2))
df_obs <- df %>%
  mutate("id"=1:nrow(.)) %>%
  select(-c(V1_std,V2_std)) %>%
  gather(...=1:2)

df_min <- df %>%  mutate("V1_min"= V1 - V1_std) %>%
  mutate("V2_min"= V2 - V2_std) %>% 
  select(V1_min,V2_min) %>%
  gather(...=1:2)

df_max <- df %>%  mutate("V1_max"= V1 + V1_std) %>%
  mutate("V2_max"= V2 + V2_std) %>% 
  select(V1_max,V2_max) %>%
  gather(...=1:2)

df_final <- cbind.data.frame(df_obs,'min'=df_min$value,'max'=df_max$value)

which gives me:

    key     value      min       max
1    V1 11.411261  7.2531585 15.569364
2    V1 10.804986 10.3518400 11.258132
3    V1  9.809049  8.5992110 11.018887
4    V1 11.225030 10.3028566 12.147204
5    V1 10.532991 10.2911703 10.774812
6    V1  9.993123  8.8835294 11.102717
7    V1  6.891480  4.3241776  9.458782
8    V1 12.492458 11.6869964 13.297920
9    V1  8.986359  5.0335530 12.939165
10   V1  8.667382  8.3316045  9.003160

Is there a simpler way to do that, using a single pipe?

Thanks

Upvotes: 2

Views: 1711

Answers (3)

eipi10
eipi10

Reputation: 93771

And yet another version:

bind_rows(df %>% select(v=V1,std=V1_std) %>% mutate(key="V1"),
          df %>% select(v=V2,std=V2_std) %>% mutate(key="V2")) %>% 
  mutate(max = v + std,
         min = v - std) %>% 
  select(key, value=v, min, max)

    key     value       min       max
1    V1  8.747092  7.910670  9.583515
2    V1 10.367287  9.526856 11.207717
3    V1  8.328743  6.628955 10.028531
4    V1 13.190562 12.385141 13.995983
5    V1 10.659016 10.317527 11.000504
6    V1  8.359063  5.537228 11.180899

Reproducible data:

set.seed(1)
df <- data.frame(V1=rnorm(100,10,2),
                 V1_std=rgamma(100,2),
                 V2=rnorm(100,10,2),
                 V2_std=rgamma(100,2))

Upvotes: 2

thelatemail
thelatemail

Reputation: 93813

Here's my attempt. The real issue was the naming of your columns, which made it difficult to clearly split into separate variables which could be used to create the intermediate long dataset:

df %>% 
  set_names(paste(rep(c("V1","V2"),each=2),c("val","std"),sep="_")) %>% 
  mutate(id = row_number()) %>% 
  gather(key,value,-id) %>%
  separate(key, c("key","group")) %>%
  spread(group, value) %>%
  mutate(min=val - std, max=val + std) %>%
  select(-std)

#     id key       val        min       max
#1     1  V1  7.342068  5.7006317  8.983503
#2     1  V2  7.372698  5.0818045  9.663591
#3     2  V1 13.397766 11.1738412 15.621691
# ...

And for fun, here is the base reshape version:

names(df) <- paste(c("val","std"),rep(c("Vl","V2"),each=2),sep="_")
df_final <- reshape(df, direction="long", sep="_", varying=TRUE, timevar="key")
df_final <- transform(df_final, min = val-std, max=val+std)[c("id","key","val","min","max")]

Upvotes: 2

Christoph Wolk
Christoph Wolk

Reputation: 1758

Here's my version. It's a bit messy, but it should continue to work if you add more columns with the same pattern.

df %>% mutate(ID=1:n()) %>% gather("key", "value", -ID) %>% 
  separate(key, c("var", "SD"), sep=2) %>% 
  mutate(SD=replace(SD, SD == "", "value")) %>% spread("SD", "value") %>% 
  mutate(min = value - `_std`, max=value - `_std`) %>% 
  select(-`_std`, -ID) %>% arrange(var)

Upvotes: 2

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