One sample T-TEST IN R within groups

Question

I have been doing one sample t-tests in R but today I have got one big challenge. I have data grouped in by one variable and I want to perform a one sample t-test per group. I can do this perfectly well in SPSS but it's now a headache in R, whoever knows how to do this to assist.Sample scenario

Location=rep(c("Area_A","Area_B"),4) 
temp=rnorm(length(Location),34,5) 
sample_data=data.frame(Location,ph)
sample_data
Location       temp
1   Area_A 32.73782
2   Area_B 26.29996
3   Area_A 40.75101
4   Area_B 26.68309
5   Area_A 33.94259
6   Area_B 26.48326
7   Area_A 37.92506
8   Area_B 29.22532

Say the hypothesised mean in the above example is 35 ,the one sample t test would be,

t.test(sample_data$temp,mu=35)

which gives me

 One Sample t-test

data:  sample_data$ph
t = -1.6578, df = 7, p-value = 0.1413
alternative hypothesis: true mean is not equal to 35
95 percent confidence interval:
 27.12898 36.38304
sample estimates:
mean of x 
 31.75601

But this is for all the groups combined. I can do it in SPSS. Is there any way to do this in R with a line of code or if not possible with a single line of code, who can do this for me. Thanks in advance.

No data provided and no info about the value you want to compare with. Can't help you much.... However, here you'll find some very useful info and examples: https://sebastiansauer.github.io/multiple-t-tests-with-dplyr/ — AntoniosK, Aug 07 '18 at 10:01
I think you need to update `data.frame(Location,ph)` to `data.frame(Location,temp)` — AntoniosK, Aug 07 '18 at 10:59

AntoniosK · Accepted Answer · 2018-08-07T10:57:33.673

One solution is to save t.test results per group as a list:

# reproducible results
set.seed(8)

# example data
Location=rep(c("Area_A","Area_B"),4) 
temp=rnorm(length(Location),34,5) 
sample_data=data.frame(Location,temp)

library(dplyr)

dt_res = sample_data %>%
  group_by(Location) %>%                       # for each group
  summarise(res = list(t.test(temp, mu=35)))   # run t.test and save results as a list

# see the list of results
dt_res$res  

# [[1]]
# 
# One Sample t-test
# 
# data:  temp
# t = -0.76098, df = 3, p-value = 0.502
# alternative hypothesis: true mean is not equal to 35
# 95 percent confidence interval:
#   29.93251 38.11170
# sample estimates:
#   mean of x 
# 34.0221 
# 
# 
# [[2]]
# 
# One Sample t-test
# 
# data:  temp
# t = -1.045, df = 3, p-value = 0.3728
# alternative hypothesis: true mean is not equal to 35
# 95 percent confidence interval:
#   26.37007 39.36331
# sample estimates:
#   mean of x 
# 32.86669

Another solution is to save t.test results per group as a dataframe:

library(dplyr)
library(tidyr)
library(broom)

sample_data %>%
  group_by(Location) %>%                       
  summarise(res = list(tidy(t.test(temp, mu=35)))) %>%
  unnest()

# # A tibble: 2 x 9
#   Location estimate statistic p.value parameter conf.low conf.high method            alternative
#    <fct>       <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>             <chr>      
# 1 Area_A       34.0    -0.761   0.502         3     29.9      38.1 One Sample t-test two.sided  
# 2 Area_B       32.9    -1.05    0.373         3     26.4      39.4 One Sample t-test two.sided

The philosophy in both approaches is the same. You group by Location and you perform a t.test for each group. It's all about what kind of output you prefer to have.

One sample T-TEST IN R within groups

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