Coading Goals for the Week

Getting Github and understanding the pull mechanism to be able to work with my group members
To continue on with the experiment one plots from last week and get it looking closer to the paper’s plots regarding colour and proportions.
To get a start on tidying up the experiment data for experiment 2 and getting the demographics
This was something I took up on the side which was re-completing all the exercises from Dani’s video tutorials just to help me catch up and get more fluent with code usage.

How I went about achieving my goals

Successes

One of my goals for last week was increasing my workflow and communication with my group and I really think I came through with this one, I spent almost every single day (whether it was a couple of hours or longer) doing exercises and trying to increase my fluency with R code.
I made a github account, joined the tutorial for github from Jenny and was able to learn how to use the pull mechanism and how to upload documents of our own. I even implemented in our script analysis task during the workshop this week!
I actually ended up skipping improving upon the plots and went straight to analyzing the demographic descriptives and mean/sd. The reason for that is due to all the software (onedrive) issues last week and a poor workflow, most of the work I inputted in the learning log wasn’t truthfully my own. A lot of it was due to the support and work of my team mates - which I am so grateful for. However, this meant that I wasn’t actually sure of applying R code for the descriptives myself but after some time I managed to be able to do so!!
I ended up re-doing all the exercises from Dani’s video tutorials to increase my coding fluency, and this really ended up helping with my workflow for making the descriptives.

Challenges

One of the main challenges regarding GitHub specifically for my pc is SURPRISE yet another software issue 😠😠. For some reason, the push function on my pc takes an extraordinarily long amount of time, one time it worked after 10 minutes but another push failed to go after an hour. After mentioning it too Jenny, she suggested it may be due to file size changes or internet but I checked both and neither were issues. I searched up the issue and it was suggested it may be due to an overload of file pathways and I’d need to reset my files for that. I’ve decided its too much effort as of right now, since I have so many files regarding my assignment that I don’t even want to risk losing.
The other is just a challenge regarding coding for graphs, I’ve still yet to grapple with coding for more complex graphs however aim to do so next week
Overall, even though there were some challenges, I think this is the first week where my successes outdid the challenges and so alot of time was invested, but I think I enjoyed coding much more this week!!

Aims for next week

To increase my fluency specific to graphs
To start graphing for experiment 2’s data set and descriptives
Start on the exploratory section of the verification report
Try and be more help specifically coding-wise and trouble shooting for my team mates
Increase my rubber ducking - I realised towards the end and after reading other people’s learning logs, I still tend to comment a bit too much within the lines of coding rather than on top.

Once again, I would like to preface an understanding of a lack of plots for my group verification project but my aim for this week was focused on catching up instead of just copying plotting codes for another week - so the aim for next week is to do all the plots that I need to, thanks for reading (if you bothered to read all of this haha)!

Experiment 2

Loading libraries

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.2     v dplyr   1.0.6
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Reading the data

HaighData2 <- readr::read_csv("Study 7 data.csv")

## Warning: Duplicated column names deduplicated: 'Timer_First Click' =>
## 'Timer_First Click_1' [26], 'Timer_Last Click' => 'Timer_Last Click_1' [27],
## 'Timer_Page Submit' => 'Timer_Page Submit_1' [28], 'Timer_Click Count' =>
## 'Timer_Click Count_1' [29], 'Timer_First Click' => 'Timer_First Click_2' [30],
## 'Timer_Last Click' => 'Timer_Last Click_2' [31], 'Timer_Page Submit' =>
## 'Timer_Page Submit_2' [32], 'Timer_Click Count' => 'Timer_Click Count_2' [33],
## 'Timer_First Click' => 'Timer_First Click_3' [34], 'Timer_Last Click' =>
## 'Timer_Last Click_3' [35], 'Timer_Page Submit' => 'Timer_Page Submit_3' [36],
## 'Timer_Click Count' => 'Timer_Click Count_3' [37], 'Timer_First Click' =>
## 'Timer_First Click_4' [38], 'Timer_Last Click' => 'Timer_Last Click_4' [39],
## 'Timer_Page Submit' => 'Timer_Page Submit_4' [40], 'Timer_Click Count' =>
## 'Timer_Click Count_4' [41], 'Timer_First Click' => 'Timer_First Click_5' [42],
## 'Timer_Last Click' => 'Timer_Last Click_5' [43], 'Timer_Page Submit' =>
## 'Timer_Page Submit_5' [44], 'Timer_Click Count' => 'Timer_Click Count_5' [45],
## 'Timer_First Click' => 'Timer_First Click_6' [46], 'Timer_Last Click' =>
## 'Timer_Last Click_6' [47], 'Timer_Page Submit' => 'Timer_Page Submit_6' [48],
## 'Timer_Click Count' => 'Timer_Click Count_6' [49], 'Timer_First Click' =>
## 'Timer_First Click_7' [50], 'Timer_Last Click' => 'Timer_Last Click_7' [51],
## 'Timer_Page Submit' => 'Timer_Page Submit_7' [52], 'Timer_Click Count' =>
## 'Timer_Click Count_7' [53], 'Timer_First Click' => 'Timer_First Click_8' [54],
## 'Timer_Last Click' => 'Timer_Last Click_8' [55], 'Timer_Page Submit' =>
## 'Timer_Page Submit_8' [56], 'Timer_Click Count' => 'Timer_Click Count_8' [57],
## 'Timer_First Click' => 'Timer_First Click_9' [58], 'Timer_Last Click' =>
## 'Timer_Last Click_9' [59], 'Timer_Page Submit' => 'Timer_Page Submit_9' [60],
## 'Timer_Click Count' => 'Timer_Click Count_9' [61], 'Timer_First Click' =>
## 'Timer_First Click_10' [62], 'Timer_Last Click' => 'Timer_Last Click_10' [63],
## 'Timer_Page Submit' => 'Timer_Page Submit_10' [64], 'Timer_Click Count' =>
## 'Timer_Click Count_10' [65], 'Timer_First Click' => 'Timer_First Click_11' [66],
## 'Timer_Last Click' => 'Timer_Last Click_11' [67], 'Timer_Page Submit' =>
## 'Timer_Page Submit_11' [68], 'Timer_Click Count' => 'Timer_Click Count_11' [69],
## 'Timer_First Click' => 'Timer_First Click_12' [70], 'Timer_Last Click' =>
## 'Timer_Last Click_12' [71], 'Timer_Page Submit' => 'Timer_Page Submit_12' [72],
## 'Timer_Click Count' => 'Timer_Click Count_12' [73], 'Timer_First Click' =>
## 'Timer_First Click_13' [74], 'Timer_Last Click' => 'Timer_Last Click_13' [75],
## 'Timer_Page Submit' => 'Timer_Page Submit_13' [76], 'Timer_Click Count' =>
## 'Timer_Click Count_13' [77], 'Timer_First Click' => 'Timer_First Click_14' [78],
## 'Timer_Last Click' => 'Timer_Last Click_14' [79], 'Timer_Page Submit' =>
## 'Timer_Page Submit_14' [80], 'Timer_Click Count' => 'Timer_Click Count_14' [81],
## 'Timer_First Click' => 'Timer_First Click_15' [82], 'Timer_Last Click' =>
## 'Timer_Last Click_15' [83], 'Timer_Page Submit' => 'Timer_Page Submit_15' [84],
## 'Timer_Click Count' => 'Timer_Click Count_15' [85], 'Timer_First Click' =>
## 'Timer_First Click_16' [86], 'Timer_Last Click' => 'Timer_Last Click_16' [87],
## 'Timer_Page Submit' => 'Timer_Page Submit_16' [88], 'Timer_Click Count' =>
## 'Timer_Click Count_16' [89], 'Timer_First Click' => 'Timer_First Click_17' [90],
## 'Timer_Last Click' => 'Timer_Last Click_17' [91], 'Timer_Page Submit' =>
## 'Timer_Page Submit_17' [92], 'Timer_Click Count' => 'Timer_Click Count_17' [93],
## 'Timer_First Click' => 'Timer_First Click_18' [94], 'Timer_Last Click' =>
## 'Timer_Last Click_18' [95], 'Timer_Page Submit' => 'Timer_Page Submit_18' [96],
## 'Timer_Click Count' => 'Timer_Click Count_18' [97], 'Timer_First Click' =>
## 'Timer_First Click_19' [98], 'Timer_Last Click' => 'Timer_Last Click_19' [99],
## 'Timer_Page Submit' => 'Timer_Page Submit_19' [100], 'Timer_Click Count'
## => 'Timer_Click Count_19' [101], 'Timer_First Click' => 'Timer_First
## Click_20' [102], 'Timer_Last Click' => 'Timer_Last Click_20' [103], 'Timer_Page
## Submit' => 'Timer_Page Submit_20' [104], 'Timer_Click Count' => 'Timer_Click
## Count_20' [105], 'Timer_First Click' => 'Timer_First Click_21' [106],
## 'Timer_Last Click' => 'Timer_Last Click_21' [107], 'Timer_Page Submit'
## => 'Timer_Page Submit_21' [108], 'Timer_Click Count' => 'Timer_Click
## Count_21' [109], 'Timer_First Click' => 'Timer_First Click_22' [110],
## 'Timer_Last Click' => 'Timer_Last Click_22' [111], 'Timer_Page Submit'
## => 'Timer_Page Submit_22' [112], 'Timer_Click Count' => 'Timer_Click
## Count_22' [113], 'Timer_First Click' => 'Timer_First Click_23' [114],
## 'Timer_Last Click' => 'Timer_Last Click_23' [115], 'Timer_Page Submit'
## => 'Timer_Page Submit_23' [116], 'Timer_Click Count' => 'Timer_Click
## Count_23' [117], 'Timer_First Click' => 'Timer_First Click_24' [118],
## 'Timer_Last Click' => 'Timer_Last Click_24' [119], 'Timer_Page Submit'
## => 'Timer_Page Submit_24' [120], 'Timer_Click Count' => 'Timer_Click
## Count_24' [121], 'Timer_First Click' => 'Timer_First Click_25' [122],
## 'Timer_Last Click' => 'Timer_Last Click_25' [123], 'Timer_Page Submit'
## => 'Timer_Page Submit_25' [124], 'Timer_Click Count' => 'Timer_Click
## Count_25' [125], 'Timer_First Click' => 'Timer_First Click_26' [126],
## 'Timer_Last Click' => 'Timer_Last Click_26' [127], 'Timer_Page Submit'
## => 'Timer_Page Submit_26' [128], 'Timer_Click Count' => 'Timer_Click
## Count_26' [129], 'Timer_First Click' => 'Timer_First Click_27' [130],
## 'Timer_Last Click' => 'Timer_Last Click_27' [131], 'Timer_Page Submit'
## => 'Timer_Page Submit_27' [132], 'Timer_Click Count' => 'Timer_Click
## Count_27' [133], 'Timer_First Click' => 'Timer_First Click_28' [134],
## 'Timer_Last Click' => 'Timer_Last Click_28' [135], 'Timer_Page Submit'
## => 'Timer_Page Submit_28' [136], 'Timer_Click Count' => 'Timer_Click
## Count_28' [137], 'Timer_First Click' => 'Timer_First Click_29' [138],
## 'Timer_Last Click' => 'Timer_Last Click_29' [139], 'Timer_Page Submit'
## => 'Timer_Page Submit_29' [140], 'Timer_Click Count' => 'Timer_Click
## Count_29' [141], 'Timer_First Click' => 'Timer_First Click_30' [142],
## 'Timer_Last Click' => 'Timer_Last Click_30' [143], 'Timer_Page Submit'
## => 'Timer_Page Submit_30' [144], 'Timer_Click Count' => 'Timer_Click
## Count_30' [145], 'Timer_First Click' => 'Timer_First Click_31' [146],
## 'Timer_Last Click' => 'Timer_Last Click_31' [147], 'Timer_Page Submit'
## => 'Timer_Page Submit_31' [148], 'Timer_Click Count' => 'Timer_Click
## Count_31' [149], 'Timer_First Click' => 'Timer_First Click_32' [150],
## 'Timer_Last Click' => 'Timer_Last Click_32' [151], 'Timer_Page Submit'
## => 'Timer_Page Submit_32' [152], 'Timer_Click Count' => 'Timer_Click
## Count_32' [153], 'Timer_First Click' => 'Timer_First Click_33' [154],
## 'Timer_Last Click' => 'Timer_Last Click_33' [155], 'Timer_Page Submit'
## => 'Timer_Page Submit_33' [156], 'Timer_Click Count' => 'Timer_Click
## Count_33' [157], 'Timer_First Click' => 'Timer_First Click_34' [158],
## 'Timer_Last Click' => 'Timer_Last Click_34' [159], 'Timer_Page Submit'
## => 'Timer_Page Submit_34' [160], 'Timer_Click Count' => 'Timer_Click
## Count_34' [161], 'Timer_First Click' => 'Timer_First Click_35' [162],
## 'Timer_Last Click' => 'Timer_Last Click_35' [163], 'Timer_Page Submit'
## => 'Timer_Page Submit_35' [164], 'Timer_Click Count' => 'Timer_Click
## Count_35' [165], 'Timer_First Click' => 'Timer_First Click_36' [166],
## 'Timer_Last Click' => 'Timer_Last Click_36' [167], 'Timer_Page Submit'
## => 'Timer_Page Submit_36' [168], 'Timer_Click Count' => 'Timer_Click
## Count_36' [169], 'Timer_First Click' => 'Timer_First Click_37' [170],
## 'Timer_Last Click' => 'Timer_Last Click_37' [171], 'Timer_Page Submit'
## => 'Timer_Page Submit_37' [172], 'Timer_Click Count' => 'Timer_Click
## Count_37' [173], 'Timer_First Click' => 'Timer_First Click_38' [174],
## 'Timer_Last Click' => 'Timer_Last Click_38' [175], 'Timer_Page Submit'
## => 'Timer_Page Submit_38' [176], 'Timer_Click Count' => 'Timer_Click
## Count_38' [177], 'Timer_First Click' => 'Timer_First Click_39' [178],
## 'Timer_Last Click' => 'Timer_Last Click_39' [179], 'Timer_Page Submit'
## => 'Timer_Page Submit_39' [180], 'Timer_Click Count' => 'Timer_Click
## Count_39' [181], 'Timer_First Click' => 'Timer_First Click_40' [182],
## 'Timer_Last Click'

## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_character()
## )
## i Use `spec()` for the full column specifications.

Cleaning up the Data

Consent

Consent_participants <- HaighData2 %>% 
  filter(Consent == "1") #Filter for those who gave consent 
count(Consent_participants)

## # A tibble: 1 x 1
##       n
##   <int>
## 1   412

HaighData2 <- as.data.frame(HaighData2) #so the filter function can be used 

HaighData2 <- HaighData2 %>% #renaming two variables with strange names
  rename(recall_score = SC0, condition = FL_12_DO)

For the descriptives in experiment 2, they requested a sample of 400 participants and had 412 consented to take part. After applying the pre-registered criteria, there was 400 participants. Participants were aged 18-73 (mean age = 33.5, SD = 12) with demographics being male = 150, 248 = female and 2 = neither.

Excluding Participants

Once again the rules for excluding the participants is as follows: - Those who finished the study - Declared that they answered seriously - And scored 4 and above on recall

HaighData2Tidied <- HaighData2 %>% 
  filter (Finished == 1, 
          Serious_check == 1, 
          recall_score >= 4
          )

count(HaighData2Tidied)

##     n
## 1 400

Demographics

#Age 
HaighData2Tidied %>% 
  summarise(
    Mean_Age = mean(Age), #still not showing the mean age - usually solved by changing it to as numeric, didn't work here
    SD = sd(Age), 
    Max = max(Age), 
    Min = min(Age)
  )

## Warning in mean.default(Age): argument is not numeric or logical: returning NA

##   Mean_Age       SD Max Min
## 1       NA 12.03415  73  18

#Gender

Male_No.2 <- HaighData2Tidied %>%
  filter(HaighData2Tidied$Gender == 1) #Number of male is 150 since I have filtered for everything except those with gender '1' 
  count(Male_No.2)

##     n
## 1 150

Female_No.2 <-HaighData2Tidied %>% 
  filter(HaighData2Tidied$Gender == 2)  
  count(Female_No.2)

##     n
## 1 248

Neither_No.2 <- HaighData2Tidied %>%  
  filter(HaighData2Tidied$Gender == 3)
  count(Neither_No.2)

##   n
## 1 2

Re-doing Dani’s exercises

Here are just some of the highlights of what I learnt/relearned

So this was just reconstructing the dinoplot but actually took me a bit longer than I expected since I was missing some signs. Once again I would like to preface I know that this may seem very simple to others but redoing this process proved itself very useful I guess to boost my confidence(?) with regards to coding fluency

dino <- readr:::read_csv("data_dino.csv")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   horizontal = col_double(),
##   vertical = col_double()
## )

print(dino)

## # A tibble: 142 x 2
##    horizontal vertical
##         <dbl>    <dbl>
##  1       55.4     97.2
##  2       51.5     96.0
##  3       46.2     94.5
##  4       42.8     91.4
##  5       40.8     88.3
##  6       38.7     84.9
##  7       35.6     79.9
##  8       33.1     77.6
##  9       29.0     74.5
## 10       26.2     71.4
## # ... with 132 more rows

picture <- ggplot(data = dino) +
   geom_point(mapping = aes(x = horizontal, y = vertical))
   
plot(picture)

Exercise 1 - Hello Data

Loading libaries

library(tidyverse)

Reading Data

forensic <- read_csv("data_forensic.csv")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   participant = col_double(),
##   handwriting_expert = col_character(),
##   us = col_character(),
##   condition = col_character(),
##   age = col_double(),
##   forensic_scientist = col_character(),
##   forensic_specialty = col_character(),
##   handwriting_reports = col_double(),
##   confidence = col_double(),
##   familiarity = col_double(),
##   feature = col_character(),
##   est = col_double(),
##   true = col_double(),
##   band = col_character()
## )

glimpse(forensic)

## Rows: 5,700
## Columns: 14
## $ participant         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
## $ handwriting_expert  <chr> "HW Expert", "HW Expert", "HW Expert", "HW Expert"~
## $ us                  <chr> "Non-US", "Non-US", "Non-US", "Non-US", "Non-US", ~
## $ condition           <chr> "Non-US HW Expert", "Non-US HW Expert", "Non-US HW~
## $ age                 <dbl> 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52~
## $ forensic_scientist  <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "~
## $ forensic_specialty  <chr> "Handwriting", "Handwriting", "Handwriting", "Hand~
## $ handwriting_reports <dbl> 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20~
## $ confidence          <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,~
## $ familiarity         <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,~
## $ feature             <chr> "PLCW.6.5", "PLCY.6.A", "PLCZ.3.2", "PUCX.4.b", "P~
## $ est                 <dbl> 1, 60, 1, 2, 5, 5, 1, 20, 3, 4, 10, 2, 90, 50, 20,~
## $ true                <dbl> 1.571, 1.971, 2.100, 1.096, 1.104, 1.132, 1.376, 1~
## $ band                <chr> "Band 01", "Band 01", "Band 01", "Band 01", "Band ~

Exercise 2 - Hello Data

Pipe Function and comparing participant coding layout

#participant 1 
participant1 <- ungroup( 
  summarise(
    group_by(
      filter(forensic, participant == 1),
      band
    ),
    mean = mean(est), 
    sd = sd(est)  
  )
)

#participant 2 
x <- filter(forensic, participant == 2)
y <- group_by(x, band)
z <- summarise(y, mean = mean(est), sd = sd(est))
participant2 <- ungroup(z)

#participant 3 
participant3 <- forensic %>%
  filter(participant == 3) %>%
  group_by(band) %>%
  summarise(mean = mean(est), sd = sd(est)) %>%
  ungroup()

#Printing participant summaries 

print(participant1)

## # A tibble: 6 x 3
##   band     mean    sd
##   <chr>   <dbl> <dbl>
## 1 Band 01   9.5  16.8
## 2 Band 25  46.7  31.2
## 3 Band 50  53.5  28.3
## 4 Band 75  50.4  24.1
## 5 Band 99  80.9  29.1
## 6 Band NA  NA    NA

print(participant2)

## # A tibble: 5 x 3
##   band     mean    sd
##   <chr>   <dbl> <dbl>
## 1 Band 01  6.26 14.9 
## 2 Band 25 10.9   9.62
## 3 Band 50 17.6  15.9 
## 4 Band 75 21.9  16.5 
## 5 Band 99 49.5  20.6

print(participant3)

## # A tibble: 5 x 3
##   band     mean    sd
##   <chr>   <dbl> <dbl>
## 1 Band 01  31.2  44.1
## 2 Band 25  46.2  49.4
## 3 Band 50  48.3  47.8
## 4 Band 75  60.7  47.8
## 5 Band 99  85    33.7

To answer the question in the discussion, personally participant summaries 1 and 3 are easier for me to understand. Number 2 with the pipe function does make it visually easier to follow with the function into variable and then the next step

Exercise 3 - Calculating mean, sd and organizing data

forensic <- read_csv("data_forensic.csv")

## 
## -- Column specification --------------------------------------------------------
## cols(
##   participant = col_double(),
##   handwriting_expert = col_character(),
##   us = col_character(),
##   condition = col_character(),
##   age = col_double(),
##   forensic_scientist = col_character(),
##   forensic_specialty = col_character(),
##   handwriting_reports = col_double(),
##   confidence = col_double(),
##   familiarity = col_double(),
##   feature = col_character(),
##   est = col_double(),
##   true = col_double(),
##   band = col_character()
## )

# Now that we have the data, what we'll do is calculate the mean and
# standard deviation of the responses (in the "est" variable), with groups
# defined by subject (in the "participant" variable) and the frequency band
# (i.e., the "band" variable)
forensic_banded <- forensic %>%
  group_by(participant, band) %>%
  summarise(mean_est = mean(est), sd_est = sd(est)) %>%
  ungroup()

## `summarise()` has grouped output by 'participant'. You can override using the `.groups` argument.

# Let's draw a plot...
ggplot(data = forensic_banded) + 
  geom_violin(mapping = aes(x = band, y = mean_est)) + 
  xlab("Stimulus Band") + 
  ylab("Responses") + 
  ggtitle("Distribution of responses") #ggtitle adds a title for the plot

## Warning: Removed 3 rows containing non-finite values (stat_ydensity).

Exercise 4 - Cleaning up the graph and using the filter function

Adding a third grouping variable “handwriting expert” and this is to split between the experts and novices

forensic_banded <- forensic %>%
  group_by(participant, band, handwriting_expert) %>% #added handwriting_expert
  summarise(mean_est = mean(est), sd_est = sd(est)) %>%
  ungroup() %>% 
  filter(band != "Band NA") #instead of a separate line, just piped filter

## `summarise()` has grouped output by 'participant', 'band'. You can override using the `.groups` argument.

pic <- ggplot(data = forensic_banded) + 
  geom_violin(mapping = aes(x = band, y = mean_est)) + 
  xlab("Stimulus Band") + 
  ylab("Responses") + 
  ggtitle("Distribution of responses")

plot(pic)

Exercise 5 - Saving it as an extractable data file

write.csv(df, file = “df.csv”), whereby df = data frame or the set of data you want to change

forensic_banded <- forensic %>%
  group_by(participant, band, handwriting_expert) %>% #added handwriting_expert
  summarise(mean_est = mean(est), sd_est = sd(est)) %>%
  ungroup() %>% 
  filter(band != "Band NA")

## `summarise()` has grouped output by 'participant', 'band'. You can override using the `.groups` argument.

write_csv(forensic_banded, file = "summary_forensic_banded.csv")

Exercise 6 - Saving the plot as a picture

Changing the file extension between .png and jpeg does not do too much I think (?), however changing it to a pdf changes it to an actual pdf file. So like I thought .png and .jpeg files are both designed for images however according to google .png is for higher quality images and .jpeg is for lower quality images.

forensic_banded<- write.csv("summary_forensic_banded.csv")

## "","x"
## "1","summary_forensic_banded.csv"

pic <- ggplot(data = forensic_banded) + 
  geom_violin(mapping = aes(x = band, y = mean_est)) + 
  xlab("Stimulus Band") + 
  ylab("Responses") + 
  ggtitle("Distribution of responses")

# Still unsure how to change width and length of the image file

Learning Log Week 5

Edward Kwag

03/07/2021