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Lesson 8: Import data
Run the code in the block below before you start the rest of the code
blocks in this lesson. Firstly, the library of Tidyverse is
loaded to use code to import data in an easy way. Secondly, the library
kableExtra is loaded to transform the tibbles that we use
to a more presentable table. You will only have to pass the data frame
as an argument in the function formatted table later
on.
# RUN THIS CODE
library(tidyverse)
library(kableExtra)
library(knitr)
library(pillar)
formatted_table <- function(df) {
col_types <- sapply(df, pillar::type_sum)
new_col_names <- paste0(names(df), "<br>", "<span style='font-weight: normal;'>", col_types, "</span>")
kbl(df, col.names = new_col_names, escape = F, format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hoover", "responsive"))
}As shown in the previous lesson it is quite easy to create data frames (transform them to tibbles). Just as a reminder to create a tibble use the following vectors:
# Create a data frame (tibble) with the following vectors
protein <- c("AmyE", "AtpE", "BdbD", "SipS", "SunA", "YdgA")
AAs <- c(123, 342, 612, 441, 47, 510)
signal_peptide <- c("Yes", "No", "No", "No", "Yes", "No")
cleavage_site <- c(31, NA, NA, NA, 22, NA)
my_tibble <- tibble(protein, AAs, signal_peptide, cleavage_site)
formatted_table(my_tibble)|
protein chr |
AAs dbl |
signal_peptide chr |
cleavage_site dbl |
|---|---|---|---|
| AmyE | 123 | Yes | 31 |
| AtpE | 342 | No | NA |
| BdbD | 612 | No | NA |
| SipS | 441 | No | NA |
| SunA | 47 | Yes | 22 |
| YdgA | 510 | No | NA |
With str() you can check the properties of your tibble.
This is useful to check (for example) if the data in a specific column
has been imported correctly (e.g. numbers).
## tibble [6 × 4] (S3: tbl_df/tbl/data.frame)
## $ protein : chr [1:6] "AmyE" "AtpE" "BdbD" "SipS" ...
## $ AAs : num [1:6] 123 342 612 441 47 510
## $ signal_peptide: chr [1:6] "Yes" "No" "No" "No" ...
## $ cleavage_site : num [1:6] 31 NA NA NA 22 NAThis looks a bit like a short Excel sheet. You will (probably) not
create data frames yourselves, but use data frames from websites to
extract and analyse data. It is nearly impossible to type all that data
in R (especially when there are millions of rows of data), but you need
to import/read the data into RStudio. There are several functions that
you van use from the readr package. It is advisable to use a text editor
to check which separator is used to separate the data. This can be a
common text editor, such as Notepad, but for this course we will use
Visual Studio Code to look at the files.
Read data from local files stored on your computer
Download the files honey.csv, amylase.tsv and prot_pred.csv and move them to the same folder as this RMarkdown file. Determine what is the separator in these files using Visual Studio Code (or a text editor).
Next, we will try to read the files into RStudio and see what we need
to import the data correctly. If you have saved the files in a different
folder, you will have to adjust the file_path accordingly
(which can be tricky).
# Read the csv file with comma-separated data. Save the data frame in df1.
# Check the properties with `str()`.
df1 <- read_csv("./files_04_data_import_exercises/add_exercises/honey.csv")
formatted_table(head(df1))|
state chr |
numcol dbl |
yieldpercol dbl |
totalprod dbl |
stocks dbl |
priceperlb dbl |
prodvalue dbl |
year dbl |
StateName chr |
Region chr |
FIPS dbl |
nCLOTHIANIDIN dbl |
nIMIDACLOPRID dbl |
nTHIAMETHOXAM dbl |
nACETAMIPRID dbl |
nTHIACLOPRID dbl |
nAllNeonic dbl |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AL | 14000 | 66 | 924000 | 92000 | 0.81 | 748000 | 1997 | Alabama | South | 1 | 0 | 6704.8 | 0 | 0 | 0 | 6704.8 |
| AL | 15000 | 64 | 960000 | 96000 | 0.87 | 835000 | 1996 | Alabama | South | 1 | 0 | 371.6 | 0 | 0 | 0 | 371.6 |
| AL | 16000 | 58 | 928000 | 28000 | 0.69 | 640000 | 1995 | Alabama | South | 1 | 0 | 716.5 | 0 | 0 | 0 | 716.5 |
| AL | 18000 | 50 | 900000 | 99000 | 0.52 | 468000 | 1994 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
| AL | 19000 | 45 | 855000 | 103000 | 0.59 | 504000 | 1993 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
| AL | 23000 | 24 | 552000 | 66000 | 0.63 | 348000 | 1991 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
## spc_tbl_ [1,132 × 17] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ state : chr [1:1132] "AL" "AL" "AL" "AL" ...
## $ numcol : num [1:1132] 14000 15000 16000 18000 19000 23000 25000 11000 11000 12000 ...
## $ yieldpercol : num [1:1132] 66 64 58 50 45 24 41 56 72 86 ...
## $ totalprod : num [1:1132] 924000 960000 928000 900000 855000 ...
## $ stocks : num [1:1132] 92000 96000 28000 99000 103000 66000 113000 209000 230000 103000 ...
## $ priceperlb : num [1:1132] 0.81 0.87 0.69 0.52 0.59 0.63 0.59 1.49 1.21 1.18 ...
## $ prodvalue : num [1:1132] 748000 835000 640000 468000 504000 ...
## $ year : num [1:1132] 1997 1996 1995 1994 1993 ...
## $ StateName : chr [1:1132] "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ Region : chr [1:1132] "South" "South" "South" "South" ...
## $ FIPS : num [1:1132] 1 1 1 1 1 1 1 1 1 1 ...
## $ nCLOTHIANIDIN: num [1:1132] 0 0 0 NA NA ...
## $ nIMIDACLOPRID: num [1:1132] 6705 372 716 NA NA ...
## $ nTHIAMETHOXAM: num [1:1132] 0 0 0 NA NA ...
## $ nACETAMIPRID : num [1:1132] 0 0 0 NA NA NA NA 0 0 0 ...
## $ nTHIACLOPRID : num [1:1132] 0 0 0 NA NA NA NA 0 0 0 ...
## $ nAllNeonic : num [1:1132] 6705 372 716 NA NA ...
## - attr(*, "spec")=
## .. cols(
## .. state = col_character(),
## .. numcol = col_double(),
## .. yieldpercol = col_double(),
## .. totalprod = col_double(),
## .. stocks = col_double(),
## .. priceperlb = col_double(),
## .. prodvalue = col_double(),
## .. year = col_double(),
## .. StateName = col_character(),
## .. Region = col_character(),
## .. FIPS = col_double(),
## .. nCLOTHIANIDIN = col_double(),
## .. nIMIDACLOPRID = col_double(),
## .. nTHIAMETHOXAM = col_double(),
## .. nACETAMIPRID = col_double(),
## .. nTHIACLOPRID = col_double(),
## .. nAllNeonic = col_double()
## .. )
## - attr(*, "problems")=<externalptr>In the console you can already see a couple of properties of the data frame: the delimiter is the comma, it contains 3 columns which are character-type and 14 columns that are numeric.
Try to open the ‘prot_pred.csv’ file with
read_csv().
# Read the csv file with semi-colon-separated data with `read_csv()`. Save the data frame in df2.
df2 <- read_csv("./files_04_data_import_exercises/add_exercises/prot_pred.csv")
formatted_table(head(df2))|
ProteinID;Protein;Sequence;Prediction;SP(Sec/SPI);TAT(Tat/SPI);LIPO(Sec/SPII);OTHER;CS_Position;localisation;score;margin;cleavage;+2_position chr |
|---|
| NP_373239.1_;chromosomal_replication_initiator_protein[Staphylococcus_aureus_subsp._aureus_N315];MSEKEIWEKVLEIAQEKLSAVSYSTFLKDTELYTIKDGEAIVLSSIPFNANWLNQQYAEIIQAILFDVVGYEVKPHFITTEELANYSNNETATPKETTKPSTETTEDNHVLGREQFNAHNTFDTFVIGPGNRFPHAASLAVAEAPAKAYNPLFIYGGVGLGKTHLMHAIGHHVLDNNPDAKVIYTSSEKFTNEFIKSIRDNEGEAFRERYRNIDVLLIDDIQFIQNKVQTQEEFFYTFNELHQNNKQIVISSDRPPKEIAQLEDRLRSRFEWGLIVDITPPDYETRMAILQKKIEEEKLDIPPEALNYIANQIQSNIRELEGALTRLLAYSQLLGKPITTELTAEALKDIIQAPKSKKITIQDIQKIVGQYYNVRIEDFSAKKRTKSIAYPRQIAMYLSRELTDFSLPKIGEEFGGRDHTTVIHAHEKISKDLKEDPIFKQEVENLEKEIRNV;OTHER;0.002197;0.000149;0.00042;0.997234;;CYT;score=-0.200913;;; |
| NP_373240.1_;DNA_polymerase_III._beta_chain[Staphylococcus_aureus_subsp._aureus_N315];MMEFTIKRDYFITQLNDTLKAISPRTTLPILTGIKIDAKEHEVILTGSDSEISIEITIPKTVDGEDIVNISETGSVVLPGRFFVDIIKKLPGKDVKLSTNEQFQTLITSGHSEFNLSGLDPDQYPLLPQVSRDDAIQLSVKVLKNVIAQTNFAVSTSETRPVLTGVNWLIQENELICTATDSHRLAVRKLQLEDVSENKNVIIPGKALAELNKIMSDNEEDIDIFFASNQVLFKVGNVNFISRLLEGHYPDTTRLFPENYEIKLSIDNGEFYHAIDRASLLAREGGNNVIKLSTGDDVVELSSTSPEIGTVKEEVDANDVEGGSLKISFNSKYMMDALKAIDNDEVEVEFFGTMKPFILKPKGDDSVTQLILPIRTY;OTHER;0.011306;0.000452;0.000534;0.987708;;CYT;score=-0.200913;;; |
| NP_373241.1_;conserved_hypothetical_protein[Staphylococcus_aureus_subsp._aureus_N315];MIILVQEVVVEGDINLGQFLKTEGIIESGGQAKWFLQDVEVLINGVRETRRGKKLEHQDRIDIPELPEDAGSFLIIHQGEQ;OTHER;0.002067;0.000157;0.000268;0.997509;;CYT;score=-0.200913;;; |
| NP_373242.1_;DNA_repair_and_genetic_recombination_protein[Staphylococcus_aureus_subsp._aureus_N315];MKLNTLQLENYRNYDEVTLKCHPDVNILIGENAQGKTNLLESIYTLALAKSHRTSNDKELIRFNADYAKIEGELSYRHGTMPLTMFITKKGKQVKVNHLEQSRLTQYIGHLNVVLFAPEDLNIVKGSPQIRRRFIDMELGQISAVYLNDLAQYQRILKQKNNYLKQLQLGQKKDLTMLEVLNQQFAEYAMKVTDKRAHFIQELESLAKPIHAGITNDKEALSLNYLPSLKFDYAQNEAARLEEIMSILSDNMQREKERGISLFGPHRDDISFDVNGMDAQTYGSQGQQRTTALSIKLAEIELMNIEVGEYPILLLDDVLSELDDSRQTHLLSTIQHKVQTFVTTTSVDGIDHEIMNNAKLYRINQGEIIK;OTHER;0.007642;0.000271;0.001318;0.990769;;CYT;score=-0.200913;;; |
| NP_373243.1_;DNA_gyrase_subunit_B[Staphylococcus_aureus_subsp._aureus_N315];MVTALSDVNNTDNYGAGQIQVLEGLEAVRKRPGMYIGSTSERGLHHLVWEIVDNSIDEALAGYANKIEVVIEKDNWIKVTDNGRGIPVDIQEKMGRPAVEVILTVLHAGGKFGGGGYKVSGGLHGVGSSVVNALSQDLEVYVHRNETIYHQAYKKGVPQFDLKEVGTTDKTGTVIRFKADGEIFTETTVYNYETLQQRIRELAFLNKGIQITLRDERDEENVREDSYHYEGGIKSYVELLNENKEPIHDEPIYIHQSKDDIEVEIAIQYNSGYATNLLTYANNIHTYEGGTHEDGFKRALTRVLNSYGLSSKIMKEEKDRLSGEDTREGMTAIISIKHGDPQFEGQTKTKLGNSEVRQVVDKLFSEHFERFLYENPQVARTVVEKGIMAARARVAAKKAREVTRRKSALDVASLPGKLADCSSKSPEECEIFLVEGDSAGGSTKSGRDSRTQAILPLRGKILNVEKARLDRILNNNEIRQMITAFGTGIGGDFDLAKARYHKIVIMTDADVDGAHIRTLLLTFFYRFMRPLIEAGYVYIAQPPLYKLTQGKQKYYVYNDRELDKLKSELNPTPKWSIARYKGLGEMNADQLWETTMNPEHRALLQVKLEDAIEADQTFEMLMGDVVENRRQFIEDNAVYANLDF;OTHER;0.003061;0.000289;0.000423;0.996227;;CYT;score=-0.200913;;; |
| NP_373244.1_;DNA_gyrase_subunit_A[Staphylococcus_aureus_subsp._aureus_N315];MAELPQSRINERNITSEMRESFLDYAMSVIVARALPDVRDGLKPVHRRILYGLNEQGMTPDKSYKKSARIVGDVMGKYHPHGDSSIYEAMVRMAQDFSYRYPLVDGQGNFGSMDGDGAAAMRYTEARMTKITLELLRDINKDTIDFIDNYDGNEREPSVLPARFPNLLANGASGIAVGMATNIPPHNLTELINGVLSLSKNPDISIAELMEDIEGPDFPTAGLILGKSGIRRAYETGRGSIQMRSRAVIEERGGGRQRIVVTEIPFQVNKARMIEKIAELVRDKKIDGITDLRDETSLRTGVRVVIDVRKDANASVILNNLYKQTPLQTSFGVNMIALVNGRPKLINLKEALVHYLEHQKTVVRRRTQYNLRKAKDRAHILEGLRIALDHIDEIISTIRESDTDKVAMESLQQRFKLSEKQAQAILDMRLRRLTGLERDKIEAEYNELLNYISELETILADEEVLLQLVRDELTEIRDRFGDDRRTEIQLGGFEDLEDEDLIPEEQIVITLSHNNYIKRLPVSTYRAQNRGGRGVQGMNTLEEDFVSQLVTLSTHDHVLFFTNKGRVYKLKGYEVPELSRQSKGIPVVNAIELENDEVISTMIAVKDLESEDNFLVFATKRGVVKRSALSNFSRINRNGKIAISFREDDELIAVRLTSGQEDILIGTSHASLIRFPESTLRPLGRTATGVKGITLREGDEVVGLDVAHANSVDEVLVVTENGYGKRTPVNDYRLSNRGGKGIKTATITERNGNVVCITTVTGEEDLMIVTNAGVIIRLDVADISQNGRAAQGVRLIRLGDDQFVSTVAKVKEDAEDETNEDEQSTSTVSEDGTEQQREAVVNDETPGNAIHTEVIDSEENDEDGRIEVRQDFMDRVEEDIQQSSDEDEE;OTHER;0.004017;0.006633;0.000645;0.988705;;CYT;score=-0.200913;;; |
## # A tibble: 384 × 1
## ProteinID;Protein;Sequence;Prediction;SP(Sec/SPI);TAT(Tat/SPI);LIPO(Sec/SPI…¹
## <chr>
## 1 NP_373239.1_;_chromosomal_replication_initiator_protein_[Staphylococcus_aure…
## 2 NP_373240.1_;_DNA_polymerase_III._beta_chain_[Staphylococcus_aureus_subsp._a…
## 3 NP_373241.1_;_conserved_hypothetical_protein_[Staphylococcus_aureus_subsp._a…
## 4 NP_373242.1_;_DNA_repair_and_genetic_recombination_protein_[Staphylococcus_a…
## 5 NP_373243.1_;_DNA_gyrase_subunit_B_[Staphylococcus_aureus_subsp._aureus_N315…
## 6 NP_373244.1_;_DNA_gyrase_subunit_A_[Staphylococcus_aureus_subsp._aureus_N315…
## 7 NP_373246.1_;_histidine_ammonia-lyase_[Staphylococcus_aureus_subsp._aureus_N…
## 8 NP_373247.1_;_seryl-tRNA_synthetase_[Staphylococcus_aureus_subsp._aureus_N31…
## 9 NP_373248.1_;_hypothetical_protein._similar_to_amino_acid_permease_[Staphylo…
## 10 NP_373250.1_;_hypothetical_protein._similar_to_homoserine-o-acetyltransferas…
## # ℹ 374 more rows
## # ℹ abbreviated name:
## # ¹`ProteinID;Protein;Sequence;Prediction;SP(Sec/SPI);TAT(Tat/SPI);LIPO(Sec/SPII);OTHER;CS_Position;localisation;score;margin;cleavage;+2_position`You see that this is not a correct data frame; the data is not
separated correctly, because this files has the semi-colon as the
separator of data. The function to read ‘semi-colon’-separated data is
the read_csv2() function.
# Read the prot_sep.csv file with the correct function. Overwrite the data frame in df2.
df2 <- read_csv2("./files_04_data_import_exercises/add_exercises/prot_pred.csv")
formatted_table(head(df2))|
ProteinID chr |
Protein chr |
Sequence chr |
Prediction chr |
SP(Sec/SPI) chr |
TAT(Tat/SPI) chr |
LIPO(Sec/SPII) chr |
OTHER chr |
CS_Position chr |
localisation chr |
score chr |
margin chr |
cleavage chr |
+2_position chr |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NP_373239.1_ | chromosomal_replication_initiator_protein[Staphylococcus_aureus_subsp._aureus_N315] | MSEKEIWEKVLEIAQEKLSAVSYSTFLKDTELYTIKDGEAIVLSSIPFNANWLNQQYAEIIQAILFDVVGYEVKPHFITTEELANYSNNETATPKETTKPSTETTEDNHVLGREQFNAHNTFDTFVIGPGNRFPHAASLAVAEAPAKAYNPLFIYGGVGLGKTHLMHAIGHHVLDNNPDAKVIYTSSEKFTNEFIKSIRDNEGEAFRERYRNIDVLLIDDIQFIQNKVQTQEEFFYTFNELHQNNKQIVISSDRPPKEIAQLEDRLRSRFEWGLIVDITPPDYETRMAILQKKIEEEKLDIPPEALNYIANQIQSNIRELEGALTRLLAYSQLLGKPITTELTAEALKDIIQAPKSKKITIQDIQKIVGQYYNVRIEDFSAKKRTKSIAYPRQIAMYLSRELTDFSLPKIGEEFGGRDHTTVIHAHEKISKDLKEDPIFKQEVENLEKEIRNV | OTHER | 0.002197 | 0.000149 | 0.00042 | 0.997234 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373240.1_ | DNA_polymerase_III._beta_chain[Staphylococcus_aureus_subsp._aureus_N315] | MMEFTIKRDYFITQLNDTLKAISPRTTLPILTGIKIDAKEHEVILTGSDSEISIEITIPKTVDGEDIVNISETGSVVLPGRFFVDIIKKLPGKDVKLSTNEQFQTLITSGHSEFNLSGLDPDQYPLLPQVSRDDAIQLSVKVLKNVIAQTNFAVSTSETRPVLTGVNWLIQENELICTATDSHRLAVRKLQLEDVSENKNVIIPGKALAELNKIMSDNEEDIDIFFASNQVLFKVGNVNFISRLLEGHYPDTTRLFPENYEIKLSIDNGEFYHAIDRASLLAREGGNNVIKLSTGDDVVELSSTSPEIGTVKEEVDANDVEGGSLKISFNSKYMMDALKAIDNDEVEVEFFGTMKPFILKPKGDDSVTQLILPIRTY | OTHER | 0.011306 | 0.000452 | 0.000534 | 0.987708 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373241.1_ | conserved_hypothetical_protein[Staphylococcus_aureus_subsp._aureus_N315] | MIILVQEVVVEGDINLGQFLKTEGIIESGGQAKWFLQDVEVLINGVRETRRGKKLEHQDRIDIPELPEDAGSFLIIHQGEQ | OTHER | 0.002067 | 0.000157 | 0.000268 | 0.997509 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373242.1_ | DNA_repair_and_genetic_recombination_protein[Staphylococcus_aureus_subsp._aureus_N315] | MKLNTLQLENYRNYDEVTLKCHPDVNILIGENAQGKTNLLESIYTLALAKSHRTSNDKELIRFNADYAKIEGELSYRHGTMPLTMFITKKGKQVKVNHLEQSRLTQYIGHLNVVLFAPEDLNIVKGSPQIRRRFIDMELGQISAVYLNDLAQYQRILKQKNNYLKQLQLGQKKDLTMLEVLNQQFAEYAMKVTDKRAHFIQELESLAKPIHAGITNDKEALSLNYLPSLKFDYAQNEAARLEEIMSILSDNMQREKERGISLFGPHRDDISFDVNGMDAQTYGSQGQQRTTALSIKLAEIELMNIEVGEYPILLLDDVLSELDDSRQTHLLSTIQHKVQTFVTTTSVDGIDHEIMNNAKLYRINQGEIIK | OTHER | 0.007642 | 0.000271 | 0.001318 | 0.990769 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373243.1_ | DNA_gyrase_subunit_B[Staphylococcus_aureus_subsp._aureus_N315] | MVTALSDVNNTDNYGAGQIQVLEGLEAVRKRPGMYIGSTSERGLHHLVWEIVDNSIDEALAGYANKIEVVIEKDNWIKVTDNGRGIPVDIQEKMGRPAVEVILTVLHAGGKFGGGGYKVSGGLHGVGSSVVNALSQDLEVYVHRNETIYHQAYKKGVPQFDLKEVGTTDKTGTVIRFKADGEIFTETTVYNYETLQQRIRELAFLNKGIQITLRDERDEENVREDSYHYEGGIKSYVELLNENKEPIHDEPIYIHQSKDDIEVEIAIQYNSGYATNLLTYANNIHTYEGGTHEDGFKRALTRVLNSYGLSSKIMKEEKDRLSGEDTREGMTAIISIKHGDPQFEGQTKTKLGNSEVRQVVDKLFSEHFERFLYENPQVARTVVEKGIMAARARVAAKKAREVTRRKSALDVASLPGKLADCSSKSPEECEIFLVEGDSAGGSTKSGRDSRTQAILPLRGKILNVEKARLDRILNNNEIRQMITAFGTGIGGDFDLAKARYHKIVIMTDADVDGAHIRTLLLTFFYRFMRPLIEAGYVYIAQPPLYKLTQGKQKYYVYNDRELDKLKSELNPTPKWSIARYKGLGEMNADQLWETTMNPEHRALLQVKLEDAIEADQTFEMLMGDVVENRRQFIEDNAVYANLDF | OTHER | 0.003061 | 0.000289 | 0.000423 | 0.996227 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373244.1_ | DNA_gyrase_subunit_A[Staphylococcus_aureus_subsp._aureus_N315] | MAELPQSRINERNITSEMRESFLDYAMSVIVARALPDVRDGLKPVHRRILYGLNEQGMTPDKSYKKSARIVGDVMGKYHPHGDSSIYEAMVRMAQDFSYRYPLVDGQGNFGSMDGDGAAAMRYTEARMTKITLELLRDINKDTIDFIDNYDGNEREPSVLPARFPNLLANGASGIAVGMATNIPPHNLTELINGVLSLSKNPDISIAELMEDIEGPDFPTAGLILGKSGIRRAYETGRGSIQMRSRAVIEERGGGRQRIVVTEIPFQVNKARMIEKIAELVRDKKIDGITDLRDETSLRTGVRVVIDVRKDANASVILNNLYKQTPLQTSFGVNMIALVNGRPKLINLKEALVHYLEHQKTVVRRRTQYNLRKAKDRAHILEGLRIALDHIDEIISTIRESDTDKVAMESLQQRFKLSEKQAQAILDMRLRRLTGLERDKIEAEYNELLNYISELETILADEEVLLQLVRDELTEIRDRFGDDRRTEIQLGGFEDLEDEDLIPEEQIVITLSHNNYIKRLPVSTYRAQNRGGRGVQGMNTLEEDFVSQLVTLSTHDHVLFFTNKGRVYKLKGYEVPELSRQSKGIPVVNAIELENDEVISTMIAVKDLESEDNFLVFATKRGVVKRSALSNFSRINRNGKIAISFREDDELIAVRLTSGQEDILIGTSHASLIRFPESTLRPLGRTATGVKGITLREGDEVVGLDVAHANSVDEVLVVTENGYGKRTPVNDYRLSNRGGKGIKTATITERNGNVVCITTVTGEEDLMIVTNAGVIIRLDVADISQNGRAAQGVRLIRLGDDQFVSTVAKVKEDAEDETNEDEQSTSTVSEDGTEQQREAVVNDETPGNAIHTEVIDSEENDEDGRIEVRQDFMDRVEEDIQQSSDEDEE | OTHER | 0.004017 | 0.006633 | 0.000645 | 0.988705 | NA | CYT | score=-0.200913 | NA | NA | NA |
The function to read ‘tab’-separated data is the
read_tsv() function.
# Read the amylase.tsv file with the correct function. Save the data frame in df3.
df3 <- read_tsv("./files_04_data_import_exercises/add_exercises/amylase.tsv")
formatted_table(head(df3))|
ID dbl |
Match_number dbl |
Depression_status_(0=no_depression,_1=depression) dbl |
Time dbl |
Time_squared dbl |
Day dbl |
Date dbl |
Beepnumber dbl |
Evening_(0=no_evening,_1=evening) dbl |
Afternoon_(0=no_afternoon,_1=afternoon) dbl |
Morning_(0=no_morning,_1=morning) dbl |
Beeptime_last_beep chr |
Monday_(0=no_Monday,_1=Monday) dbl |
Tuesday_(0=no_Tuesday,_1=Tuesday) dbl |
Wednesday_(0=no_Wednesday,_1=Wednesday) dbl |
Thursday_(0=no_Thursday,_1=Thursday) dbl |
Friday_(0=no_Friday,_1=Friday) dbl |
Saturday_(0=no_Saturday,_1=Saturday) dbl |
Sunday_(0=no_Sunday,_1=Sunday) dbl |
Filter_(to_exclude_the_invalid_datapoints_of_participant_D12) dbl |
Chronic_antidepressant_use_(0=no_antidepressant_use,_1=antidepressant_use) dbl |
BDI_pre dbl |
BDI_post dbl |
Gender_(0=female,_1=male) dbl |
BMI_(kg/l2) dbl |
Age_(years) dbl |
Smoking_(0=not_a_smoker,_1=_a_smoker) dbl |
Positive_affect_mean chr |
Negative_affect_mean chr |
Cortisol_(nmolL) chr |
Amylase_(UmL) dbl |
Caffeine_(0=not_in_previous_day_part,_1=in_previous_day_part) chr |
Cafeine_recent_(0=not_in_previous_1.5h,_1=in_previous_1.5h) chr |
Alcohol_(0=not_in_previous_day_part,_1=in_previous_day_part) chr |
Alcohol_recent chr |
Caloric_rich_food_(0=not_in_previous_1.5_h,_1=in_previous_1.5_h) chr |
Other_food_(0=not_in_previous_1.5h,_1=_in_previous_1.5_h) chr |
Exercise_(0=no_exercise_in_previous_day_part,_1=exercise_in_previous_day_part) chr |
Wakeup_(0=not_in_previous_1_hour,1=in_previous_1_hour) dbl |
Nicotine_(0=not_in_previous_day_part,_1=in_previous_day_part) chr |
Stim_drugs_(0=not_in_previous_day_part,_1=in_previous_day_part) chr |
Other_drugs_(0=not_in_previous_day_part,_1=in_previous_day_part) chr |
Cannabis_(0=not_in_previous_day_part,_1=in_previous_day_part) chr |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 24 | 1 | 0 | 0 | 0 | 1 | 41040 | 2 | 0 | 0 | 1 | 0,979166667 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 2122 | 24 | 0 | 2,14 | 4,29 | 3,282 | 10138 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 1 | 1 | 1 | 41040 | 4 | 0 | 1 | 0 | 0,979166667 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 2122 | 24 | 0 | 3,57 | 3 | 1,37 | 143732 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 2 | 4 | 1 | 41040 | 6 | 1 | 0 | 0 | 0,979166667 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 2122 | 24 | 0 | 2,71 | 3,43 | 0,86 | 17831 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 3 | 9 | 2 | 41041 | 2 | 0 | 0 | 1 | 0,979166667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 1 | 2122 | 24 | 0 | 4,57 | 1,71 | 6,826 | 74939 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 4 | 16 | 2 | 41041 | 4 | 0 | 1 | 0 | 0,979166667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 1 | 2122 | 24 | 0 | 5 | 2 | 0,645 | 108187 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 5 | 25 | 2 | 41041 | 6 | 1 | 0 | 0 | 0,979166667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 1 | 2122 | 24 | 0 | 4,14 | 2,57 | 0,548 | 1602 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
At a first glance, the tibble looks fine. However, if you move to the 12th column (Beeptime_last_beep), you see that the type of data is ‘character’, while this should be ‘numeric’. Because the decimal separator is a comma in this file, the value is read as a character. The function needs an extra argument to tell the function that the comma is the decimal separator.
# Set the decimal separator to a comma using the `locale = ` argument. Save the data frame in df4.
df4 <- read_tsv("./files_04_data_import_exercises/add_exercises/amylase.tsv", locale = locale(decimal_mark = ","))
formatted_table(head(df4))|
ID dbl |
Match_number dbl |
Depression_status_(0=no_depression,_1=depression) dbl |
Time dbl |
Time_squared dbl |
Day dbl |
Date dbl |
Beepnumber dbl |
Evening_(0=no_evening,_1=evening) dbl |
Afternoon_(0=no_afternoon,_1=afternoon) dbl |
Morning_(0=no_morning,_1=morning) dbl |
Beeptime_last_beep dbl |
Monday_(0=no_Monday,_1=Monday) dbl |
Tuesday_(0=no_Tuesday,_1=Tuesday) dbl |
Wednesday_(0=no_Wednesday,_1=Wednesday) dbl |
Thursday_(0=no_Thursday,_1=Thursday) dbl |
Friday_(0=no_Friday,_1=Friday) dbl |
Saturday_(0=no_Saturday,_1=Saturday) dbl |
Sunday_(0=no_Sunday,_1=Sunday) dbl |
Filter_(to_exclude_the_invalid_datapoints_of_participant_D12) dbl |
Chronic_antidepressant_use_(0=no_antidepressant_use,_1=antidepressant_use) dbl |
BDI_pre dbl |
BDI_post dbl |
Gender_(0=female,_1=male) dbl |
BMI_(kg/l2) dbl |
Age_(years) dbl |
Smoking_(0=not_a_smoker,_1=_a_smoker) dbl |
Positive_affect_mean dbl |
Negative_affect_mean dbl |
Cortisol_(nmolL) dbl |
Amylase_(UmL) dbl |
Caffeine_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
Cafeine_recent_(0=not_in_previous_1.5h,_1=in_previous_1.5h) dbl |
Alcohol_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
Alcohol_recent dbl |
Caloric_rich_food_(0=not_in_previous_1.5_h,_1=in_previous_1.5_h) dbl |
Other_food_(0=not_in_previous_1.5h,_1=_in_previous_1.5_h) dbl |
Exercise_(0=no_exercise_in_previous_day_part,_1=exercise_in_previous_day_part) dbl |
Wakeup_(0=not_in_previous_1_hour,1=in_previous_1_hour) dbl |
Nicotine_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
Stim_drugs_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
Other_drugs_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
Cannabis_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 24 | 1 | 0 | 0 | 0 | 1 | 41040 | 2 | 0 | 0 | 1 | 0.9791667 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 2.14 | 4.29 | 3.282 | 101.380 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 1 | 1 | 1 | 41040 | 4 | 0 | 1 | 0 | 0.9791667 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 3.57 | 3.00 | 1.370 | 143.732 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 2 | 4 | 1 | 41040 | 6 | 1 | 0 | 0 | 0.9791667 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 2.71 | 3.43 | 0.860 | 178.310 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 3 | 9 | 2 | 41041 | 2 | 0 | 0 | 1 | 0.9791667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 4.57 | 1.71 | 6.826 | 74.939 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 4 | 16 | 2 | 41041 | 4 | 0 | 1 | 0 | 0.9791667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 5.00 | 2.00 | 0.645 | 108.187 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 5 | 25 | 2 | 41041 | 6 | 1 | 0 | 0 | 0.9791667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 4.14 | 2.57 | 0.548 | 160.200 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Check again the column ‘Beeptime_last_beep’ and see
that the comma is replaced by a dot and that type of the data in this
column is now numeric (outline is to the right).
Read data from a website
It is also possible to read the data directly from the website if there is an address to the file with the data. Try to read the ‘prot_pred’ data directly from the internet.
# Read the data directly from the web.
# The address of the data is "https://tinyurl.com/bdfc3cnx". Store this address in my_url1.
# You can store the address in a variable and use this variable to import the data. Save the data frame in df5.
my_url1 <- "https://tinyurl.com/bdfc3cnx"
df5 <- read_csv2(my_url1)
formatted_table(head(df5))|
ProteinID chr |
Protein chr |
Sequence chr |
Prediction chr |
SP(Sec/SPI) chr |
TAT(Tat/SPI) chr |
LIPO(Sec/SPII) chr |
OTHER chr |
CS_Position chr |
localisation chr |
score chr |
margin chr |
cleavage chr |
+2_position chr |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NP_373239.1_ | chromosomal_replication_initiator_protein[Staphylococcus_aureus_subsp._aureus_N315] | MSEKEIWEKVLEIAQEKLSAVSYSTFLKDTELYTIKDGEAIVLSSIPFNANWLNQQYAEIIQAILFDVVGYEVKPHFITTEELANYSNNETATPKETTKPSTETTEDNHVLGREQFNAHNTFDTFVIGPGNRFPHAASLAVAEAPAKAYNPLFIYGGVGLGKTHLMHAIGHHVLDNNPDAKVIYTSSEKFTNEFIKSIRDNEGEAFRERYRNIDVLLIDDIQFIQNKVQTQEEFFYTFNELHQNNKQIVISSDRPPKEIAQLEDRLRSRFEWGLIVDITPPDYETRMAILQKKIEEEKLDIPPEALNYIANQIQSNIRELEGALTRLLAYSQLLGKPITTELTAEALKDIIQAPKSKKITIQDIQKIVGQYYNVRIEDFSAKKRTKSIAYPRQIAMYLSRELTDFSLPKIGEEFGGRDHTTVIHAHEKISKDLKEDPIFKQEVENLEKEIRNV | OTHER | 0.002197 | 0.000149 | 0.00042 | 0.997234 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373240.1_ | DNA_polymerase_III._beta_chain[Staphylococcus_aureus_subsp._aureus_N315] | MMEFTIKRDYFITQLNDTLKAISPRTTLPILTGIKIDAKEHEVILTGSDSEISIEITIPKTVDGEDIVNISETGSVVLPGRFFVDIIKKLPGKDVKLSTNEQFQTLITSGHSEFNLSGLDPDQYPLLPQVSRDDAIQLSVKVLKNVIAQTNFAVSTSETRPVLTGVNWLIQENELICTATDSHRLAVRKLQLEDVSENKNVIIPGKALAELNKIMSDNEEDIDIFFASNQVLFKVGNVNFISRLLEGHYPDTTRLFPENYEIKLSIDNGEFYHAIDRASLLAREGGNNVIKLSTGDDVVELSSTSPEIGTVKEEVDANDVEGGSLKISFNSKYMMDALKAIDNDEVEVEFFGTMKPFILKPKGDDSVTQLILPIRTY | OTHER | 0.011306 | 0.000452 | 0.000534 | 0.987708 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373241.1_ | conserved_hypothetical_protein[Staphylococcus_aureus_subsp._aureus_N315] | MIILVQEVVVEGDINLGQFLKTEGIIESGGQAKWFLQDVEVLINGVRETRRGKKLEHQDRIDIPELPEDAGSFLIIHQGEQ | OTHER | 0.002067 | 0.000157 | 0.000268 | 0.997509 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373242.1_ | DNA_repair_and_genetic_recombination_protein[Staphylococcus_aureus_subsp._aureus_N315] | MKLNTLQLENYRNYDEVTLKCHPDVNILIGENAQGKTNLLESIYTLALAKSHRTSNDKELIRFNADYAKIEGELSYRHGTMPLTMFITKKGKQVKVNHLEQSRLTQYIGHLNVVLFAPEDLNIVKGSPQIRRRFIDMELGQISAVYLNDLAQYQRILKQKNNYLKQLQLGQKKDLTMLEVLNQQFAEYAMKVTDKRAHFIQELESLAKPIHAGITNDKEALSLNYLPSLKFDYAQNEAARLEEIMSILSDNMQREKERGISLFGPHRDDISFDVNGMDAQTYGSQGQQRTTALSIKLAEIELMNIEVGEYPILLLDDVLSELDDSRQTHLLSTIQHKVQTFVTTTSVDGIDHEIMNNAKLYRINQGEIIK | OTHER | 0.007642 | 0.000271 | 0.001318 | 0.990769 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373243.1_ | DNA_gyrase_subunit_B[Staphylococcus_aureus_subsp._aureus_N315] | MVTALSDVNNTDNYGAGQIQVLEGLEAVRKRPGMYIGSTSERGLHHLVWEIVDNSIDEALAGYANKIEVVIEKDNWIKVTDNGRGIPVDIQEKMGRPAVEVILTVLHAGGKFGGGGYKVSGGLHGVGSSVVNALSQDLEVYVHRNETIYHQAYKKGVPQFDLKEVGTTDKTGTVIRFKADGEIFTETTVYNYETLQQRIRELAFLNKGIQITLRDERDEENVREDSYHYEGGIKSYVELLNENKEPIHDEPIYIHQSKDDIEVEIAIQYNSGYATNLLTYANNIHTYEGGTHEDGFKRALTRVLNSYGLSSKIMKEEKDRLSGEDTREGMTAIISIKHGDPQFEGQTKTKLGNSEVRQVVDKLFSEHFERFLYENPQVARTVVEKGIMAARARVAAKKAREVTRRKSALDVASLPGKLADCSSKSPEECEIFLVEGDSAGGSTKSGRDSRTQAILPLRGKILNVEKARLDRILNNNEIRQMITAFGTGIGGDFDLAKARYHKIVIMTDADVDGAHIRTLLLTFFYRFMRPLIEAGYVYIAQPPLYKLTQGKQKYYVYNDRELDKLKSELNPTPKWSIARYKGLGEMNADQLWETTMNPEHRALLQVKLEDAIEADQTFEMLMGDVVENRRQFIEDNAVYANLDF | OTHER | 0.003061 | 0.000289 | 0.000423 | 0.996227 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373244.1_ | DNA_gyrase_subunit_A[Staphylococcus_aureus_subsp._aureus_N315] | MAELPQSRINERNITSEMRESFLDYAMSVIVARALPDVRDGLKPVHRRILYGLNEQGMTPDKSYKKSARIVGDVMGKYHPHGDSSIYEAMVRMAQDFSYRYPLVDGQGNFGSMDGDGAAAMRYTEARMTKITLELLRDINKDTIDFIDNYDGNEREPSVLPARFPNLLANGASGIAVGMATNIPPHNLTELINGVLSLSKNPDISIAELMEDIEGPDFPTAGLILGKSGIRRAYETGRGSIQMRSRAVIEERGGGRQRIVVTEIPFQVNKARMIEKIAELVRDKKIDGITDLRDETSLRTGVRVVIDVRKDANASVILNNLYKQTPLQTSFGVNMIALVNGRPKLINLKEALVHYLEHQKTVVRRRTQYNLRKAKDRAHILEGLRIALDHIDEIISTIRESDTDKVAMESLQQRFKLSEKQAQAILDMRLRRLTGLERDKIEAEYNELLNYISELETILADEEVLLQLVRDELTEIRDRFGDDRRTEIQLGGFEDLEDEDLIPEEQIVITLSHNNYIKRLPVSTYRAQNRGGRGVQGMNTLEEDFVSQLVTLSTHDHVLFFTNKGRVYKLKGYEVPELSRQSKGIPVVNAIELENDEVISTMIAVKDLESEDNFLVFATKRGVVKRSALSNFSRINRNGKIAISFREDDELIAVRLTSGQEDILIGTSHASLIRFPESTLRPLGRTATGVKGITLREGDEVVGLDVAHANSVDEVLVVTENGYGKRTPVNDYRLSNRGGKGIKTATITERNGNVVCITTVTGEEDLMIVTNAGVIIRLDVADISQNGRAAQGVRLIRLGDDQFVSTVAKVKEDAEDETNEDEQSTSTVSEDGTEQQREAVVNDETPGNAIHTEVIDSEENDEDGRIEVRQDFMDRVEEDIQQSSDEDEE | OTHER | 0.004017 | 0.006633 | 0.000645 | 0.988705 | NA | CYT | score=-0.200913 | NA | NA | NA |
Load the following dataset. What is wrong with the output?
# Load the dataset that is stored at "https://tinyurl.com/ydbte7fp". Store this address in my_url2.
# Save the data frame in df6.
my_url2 <- "https://tinyurl.com/ydbte7fp"
df6 <- read_csv(my_url2)
formatted_table(head(df6))|
AL chr |
14000 dbl |
66 dbl |
924000 dbl |
92000 dbl |
0.81 dbl |
748000 dbl |
1997 dbl |
Alabama chr |
South chr |
1 dbl |
0.0…12 dbl |
6704.8…13 dbl |
0.0…14 dbl |
0.0…15 dbl |
0.0…16 dbl |
6704.8…17 dbl |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AL | 15000 | 64 | 960000 | 96000 | 0.87 | 835000 | 1996 | Alabama | South | 1 | 0 | 371.6 | 0 | 0 | 0 | 371.6 |
| AL | 16000 | 58 | 928000 | 28000 | 0.69 | 640000 | 1995 | Alabama | South | 1 | 0 | 716.5 | 0 | 0 | 0 | 716.5 |
| AL | 18000 | 50 | 900000 | 99000 | 0.52 | 468000 | 1994 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
| AL | 19000 | 45 | 855000 | 103000 | 0.59 | 504000 | 1993 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
| AL | 23000 | 24 | 552000 | 66000 | 0.63 | 348000 | 1991 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
| AL | 25000 | 41 | 1025000 | 113000 | 0.59 | 605000 | 1992 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
You see that this dataset does not have any headers and automatically
the first row is used as the header. If you check the help function for
read_csv() you will see that the default setting for
headers (col_names) is set to TRUE. Load again the same
dataset and adjust the argument for headers, so that the first row is
not read as a header.
# Load the dataset that is stored at "https://tinyurl.com/ydbte7fp". Save the data frame in df7.
df7 <- read_csv(my_url2, col_names = F)
formatted_table(head(df7))|
X1 chr |
X2 dbl |
X3 dbl |
X4 dbl |
X5 dbl |
X6 dbl |
X7 dbl |
X8 dbl |
X9 chr |
X10 chr |
X11 dbl |
X12 dbl |
X13 dbl |
X14 dbl |
X15 dbl |
X16 dbl |
X17 dbl |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AL | 14000 | 66 | 924000 | 92000 | 0.81 | 748000 | 1997 | Alabama | South | 1 | 0 | 6704.8 | 0 | 0 | 0 | 6704.8 |
| AL | 15000 | 64 | 960000 | 96000 | 0.87 | 835000 | 1996 | Alabama | South | 1 | 0 | 371.6 | 0 | 0 | 0 | 371.6 |
| AL | 16000 | 58 | 928000 | 28000 | 0.69 | 640000 | 1995 | Alabama | South | 1 | 0 | 716.5 | 0 | 0 | 0 | 716.5 |
| AL | 18000 | 50 | 900000 | 99000 | 0.52 | 468000 | 1994 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
| AL | 19000 | 45 | 855000 | 103000 | 0.59 | 504000 | 1993 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
| AL | 23000 | 24 | 552000 | 66000 | 0.63 | 348000 | 1991 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
You see that the tibble now has headers (X1-X17). If you want to
change the names of the headers you can use the function
names() which contains a vector with the names of the
headers. A vector with the headers is given in the next block of code.
Change the names of the headers of the last data frame.
# Give the correct headers to the data frame df7. The names of the headers are stored in `head_names`.
head_names <- c("state", "numcol", "yieldpercol", "totalprod", "stocks", "priceperlb", "prodvalue", "year", "StateName", "Region", "FIPS", "nCLOTHIANIDIN", "nIMIDACLOPRID", "nTHIAMETHOXAM", "nACETAMIPRID", "nTHIACLOPRID", "nAllNeonic")
names(df7) <- head_names
formatted_table(head(df7))|
state chr |
numcol dbl |
yieldpercol dbl |
totalprod dbl |
stocks dbl |
priceperlb dbl |
prodvalue dbl |
year dbl |
StateName chr |
Region chr |
FIPS dbl |
nCLOTHIANIDIN dbl |
nIMIDACLOPRID dbl |
nTHIAMETHOXAM dbl |
nACETAMIPRID dbl |
nTHIACLOPRID dbl |
nAllNeonic dbl |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AL | 14000 | 66 | 924000 | 92000 | 0.81 | 748000 | 1997 | Alabama | South | 1 | 0 | 6704.8 | 0 | 0 | 0 | 6704.8 |
| AL | 15000 | 64 | 960000 | 96000 | 0.87 | 835000 | 1996 | Alabama | South | 1 | 0 | 371.6 | 0 | 0 | 0 | 371.6 |
| AL | 16000 | 58 | 928000 | 28000 | 0.69 | 640000 | 1995 | Alabama | South | 1 | 0 | 716.5 | 0 | 0 | 0 | 716.5 |
| AL | 18000 | 50 | 900000 | 99000 | 0.52 | 468000 | 1994 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
| AL | 19000 | 45 | 855000 | 103000 | 0.59 | 504000 | 1993 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
| AL | 23000 | 24 | 552000 | 66000 | 0.63 | 348000 | 1991 | Alabama | South | 1 | NA | NA | NA | NA | NA | NA |
Now try to read the following file and see what is wrong when you use
read_csv() to read the file.
# Read the data stored at "https://tinyurl.com/mpev7ua6". Save this address in my_url3.
# Save the data frame in df8.
my_url3 <- "https://tinyurl.com/mpev7ua6"
df8 <- read_csv(my_url3)
formatted_table(head(df8))|
@ Sometimes you find comments in the file chr |
|---|
| ProteinIDProteinSequencePredictionSP(Sec/SPI)TAT(Tat/SPI)LIPO(Sec/SPII)OTHERCS_Positionlocalisationscoremargincleavage*+2_position |
| NP_373239.1_chromosomal_replication_initiator_protein[Staphylococcus_aureus_subsp._aureus_N315]MSEKEIWEKVLEIAQEKLSAVSYSTFLKDTELYTIKDGEAIVLSSIPFNANWLNQQYAEIIQAILFDVVGYEVKPHFITTEELANYSNNETATPKETTKPSTETTEDNHVLGREQFNAHNTFDTFVIGPGNRFPHAASLAVAEAPAKAYNPLFIYGGVGLGKTHLMHAIGHHVLDNNPDAKVIYTSSEKFTNEFIKSIRDNEGEAFRERYRNIDVLLIDDIQFIQNKVQTQEEFFYTFNELHQNNKQIVISSDRPPKEIAQLEDRLRSRFEWGLIVDITPPDYETRMAILQKKIEEEKLDIPPEALNYIANQIQSNIRELEGALTRLLAYSQLLGKPITTELTAEALKDIIQAPKSKKITIQDIQKIVGQYYNVRIEDFSAKKRTKSIAYPRQIAMYLSRELTDFSLPKIGEEFGGRDHTTVIHAHEKISKDLKEDPIFKQEVENLEKEIRNVOTHER0.0021970.0001490.00042*0.997234CYTscore=-0.200913 |
| NP_373240.1_DNA_polymerase_III._beta_chain[Staphylococcus_aureus_subsp._aureus_N315]MMEFTIKRDYFITQLNDTLKAISPRTTLPILTGIKIDAKEHEVILTGSDSEISIEITIPKTVDGEDIVNISETGSVVLPGRFFVDIIKKLPGKDVKLSTNEQFQTLITSGHSEFNLSGLDPDQYPLLPQVSRDDAIQLSVKVLKNVIAQTNFAVSTSETRPVLTGVNWLIQENELICTATDSHRLAVRKLQLEDVSENKNVIIPGKALAELNKIMSDNEEDIDIFFASNQVLFKVGNVNFISRLLEGHYPDTTRLFPENYEIKLSIDNGEFYHAIDRASLLAREGGNNVIKLSTGDDVVELSSTSPEIGTVKEEVDANDVEGGSLKISFNSKYMMDALKAIDNDEVEVEFFGTMKPFILKPKGDDSVTQLILPIRTYOTHER0.0113060.0004520.000534*0.987708CYTscore=-0.200913 |
| NP_373241.1_conserved_hypothetical_protein[Staphylococcus_aureus_subsp._aureus_N315]MIILVQEVVVEGDINLGQFLKTEGIIESGGQAKWFLQDVEVLINGVRETRRGKKLEHQDRIDIPELPEDAGSFLIIHQGEQOTHER0.0020670.0001570.000268*0.997509CYTscore=-0.200913 |
| NP_373242.1_DNA_repair_and_genetic_recombination_protein[Staphylococcus_aureus_subsp._aureus_N315]MKLNTLQLENYRNYDEVTLKCHPDVNILIGENAQGKTNLLESIYTLALAKSHRTSNDKELIRFNADYAKIEGELSYRHGTMPLTMFITKKGKQVKVNHLEQSRLTQYIGHLNVVLFAPEDLNIVKGSPQIRRRFIDMELGQISAVYLNDLAQYQRILKQKNNYLKQLQLGQKKDLTMLEVLNQQFAEYAMKVTDKRAHFIQELESLAKPIHAGITNDKEALSLNYLPSLKFDYAQNEAARLEEIMSILSDNMQREKERGISLFGPHRDDISFDVNGMDAQTYGSQGQQRTTALSIKLAEIELMNIEVGEYPILLLDDVLSELDDSRQTHLLSTIQHKVQTFVTTTSVDGIDHEIMNNAKLYRINQGEIIKOTHER0.0076420.0002710.001318*0.990769CYTscore=-0.200913 |
| NP_373243.1_DNA_gyrase_subunit_B[Staphylococcus_aureus_subsp._aureus_N315]MVTALSDVNNTDNYGAGQIQVLEGLEAVRKRPGMYIGSTSERGLHHLVWEIVDNSIDEALAGYANKIEVVIEKDNWIKVTDNGRGIPVDIQEKMGRPAVEVILTVLHAGGKFGGGGYKVSGGLHGVGSSVVNALSQDLEVYVHRNETIYHQAYKKGVPQFDLKEVGTTDKTGTVIRFKADGEIFTETTVYNYETLQQRIRELAFLNKGIQITLRDERDEENVREDSYHYEGGIKSYVELLNENKEPIHDEPIYIHQSKDDIEVEIAIQYNSGYATNLLTYANNIHTYEGGTHEDGFKRALTRVLNSYGLSSKIMKEEKDRLSGEDTREGMTAIISIKHGDPQFEGQTKTKLGNSEVRQVVDKLFSEHFERFLYENPQVARTVVEKGIMAARARVAAKKAREVTRRKSALDVASLPGKLADCSSKSPEECEIFLVEGDSAGGSTKSGRDSRTQAILPLRGKILNVEKARLDRILNNNEIRQMITAFGTGIGGDFDLAKARYHKIVIMTDADVDGAHIRTLLLTFFYRFMRPLIEAGYVYIAQPPLYKLTQGKQKYYVYNDRELDKLKSELNPTPKWSIARYKGLGEMNADQLWETTMNPEHRALLQVKLEDAIEADQTFEMLMGDVVENRRQFIEDNAVYANLDFOTHER0.0030610.0002890.000423*0.996227CYTscore=-0.200913 |
You see that there is a comment in the first line and the data is
separated with an asterisk (*). You cannot use read_csv(),
read_csv2(), or read_tsv() to read this file.
These functions have preset delimiters: comma, semi-colon and tab,
respectively. There is a function that will read files where you have to
indicate the delimiter: read_delim(). Load the previous
data with this function. First, use help on this function and try to
figure out how to set the delimiter and remove the comment.
# Read the file and use the delimiter and comment arguments to read the data properly.
# Save the data frame in df9.
df9 <- read_delim(my_url3, delim = "*", comment = "@")
formatted_table(head(df9))|
ProteinID chr |
Protein chr |
Sequence chr |
Prediction chr |
SP(Sec/SPI) dbl |
TAT(Tat/SPI) dbl |
LIPO(Sec/SPII) dbl |
OTHER dbl |
CS_Position chr |
localisation chr |
score chr |
margin chr |
cleavage chr |
+2_position chr |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NP_373239.1_ | chromosomal_replication_initiator_protein[Staphylococcus_aureus_subsp._aureus_N315] | MSEKEIWEKVLEIAQEKLSAVSYSTFLKDTELYTIKDGEAIVLSSIPFNANWLNQQYAEIIQAILFDVVGYEVKPHFITTEELANYSNNETATPKETTKPSTETTEDNHVLGREQFNAHNTFDTFVIGPGNRFPHAASLAVAEAPAKAYNPLFIYGGVGLGKTHLMHAIGHHVLDNNPDAKVIYTSSEKFTNEFIKSIRDNEGEAFRERYRNIDVLLIDDIQFIQNKVQTQEEFFYTFNELHQNNKQIVISSDRPPKEIAQLEDRLRSRFEWGLIVDITPPDYETRMAILQKKIEEEKLDIPPEALNYIANQIQSNIRELEGALTRLLAYSQLLGKPITTELTAEALKDIIQAPKSKKITIQDIQKIVGQYYNVRIEDFSAKKRTKSIAYPRQIAMYLSRELTDFSLPKIGEEFGGRDHTTVIHAHEKISKDLKEDPIFKQEVENLEKEIRNV | OTHER | 0.002197 | 0.000149 | 0.000420 | 0.997234 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373240.1_ | DNA_polymerase_III._beta_chain[Staphylococcus_aureus_subsp._aureus_N315] | MMEFTIKRDYFITQLNDTLKAISPRTTLPILTGIKIDAKEHEVILTGSDSEISIEITIPKTVDGEDIVNISETGSVVLPGRFFVDIIKKLPGKDVKLSTNEQFQTLITSGHSEFNLSGLDPDQYPLLPQVSRDDAIQLSVKVLKNVIAQTNFAVSTSETRPVLTGVNWLIQENELICTATDSHRLAVRKLQLEDVSENKNVIIPGKALAELNKIMSDNEEDIDIFFASNQVLFKVGNVNFISRLLEGHYPDTTRLFPENYEIKLSIDNGEFYHAIDRASLLAREGGNNVIKLSTGDDVVELSSTSPEIGTVKEEVDANDVEGGSLKISFNSKYMMDALKAIDNDEVEVEFFGTMKPFILKPKGDDSVTQLILPIRTY | OTHER | 0.011306 | 0.000452 | 0.000534 | 0.987708 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373241.1_ | conserved_hypothetical_protein[Staphylococcus_aureus_subsp._aureus_N315] | MIILVQEVVVEGDINLGQFLKTEGIIESGGQAKWFLQDVEVLINGVRETRRGKKLEHQDRIDIPELPEDAGSFLIIHQGEQ | OTHER | 0.002067 | 0.000157 | 0.000268 | 0.997509 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373242.1_ | DNA_repair_and_genetic_recombination_protein[Staphylococcus_aureus_subsp._aureus_N315] | MKLNTLQLENYRNYDEVTLKCHPDVNILIGENAQGKTNLLESIYTLALAKSHRTSNDKELIRFNADYAKIEGELSYRHGTMPLTMFITKKGKQVKVNHLEQSRLTQYIGHLNVVLFAPEDLNIVKGSPQIRRRFIDMELGQISAVYLNDLAQYQRILKQKNNYLKQLQLGQKKDLTMLEVLNQQFAEYAMKVTDKRAHFIQELESLAKPIHAGITNDKEALSLNYLPSLKFDYAQNEAARLEEIMSILSDNMQREKERGISLFGPHRDDISFDVNGMDAQTYGSQGQQRTTALSIKLAEIELMNIEVGEYPILLLDDVLSELDDSRQTHLLSTIQHKVQTFVTTTSVDGIDHEIMNNAKLYRINQGEIIK | OTHER | 0.007642 | 0.000271 | 0.001318 | 0.990769 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373243.1_ | DNA_gyrase_subunit_B[Staphylococcus_aureus_subsp._aureus_N315] | MVTALSDVNNTDNYGAGQIQVLEGLEAVRKRPGMYIGSTSERGLHHLVWEIVDNSIDEALAGYANKIEVVIEKDNWIKVTDNGRGIPVDIQEKMGRPAVEVILTVLHAGGKFGGGGYKVSGGLHGVGSSVVNALSQDLEVYVHRNETIYHQAYKKGVPQFDLKEVGTTDKTGTVIRFKADGEIFTETTVYNYETLQQRIRELAFLNKGIQITLRDERDEENVREDSYHYEGGIKSYVELLNENKEPIHDEPIYIHQSKDDIEVEIAIQYNSGYATNLLTYANNIHTYEGGTHEDGFKRALTRVLNSYGLSSKIMKEEKDRLSGEDTREGMTAIISIKHGDPQFEGQTKTKLGNSEVRQVVDKLFSEHFERFLYENPQVARTVVEKGIMAARARVAAKKAREVTRRKSALDVASLPGKLADCSSKSPEECEIFLVEGDSAGGSTKSGRDSRTQAILPLRGKILNVEKARLDRILNNNEIRQMITAFGTGIGGDFDLAKARYHKIVIMTDADVDGAHIRTLLLTFFYRFMRPLIEAGYVYIAQPPLYKLTQGKQKYYVYNDRELDKLKSELNPTPKWSIARYKGLGEMNADQLWETTMNPEHRALLQVKLEDAIEADQTFEMLMGDVVENRRQFIEDNAVYANLDF | OTHER | 0.003061 | 0.000289 | 0.000423 | 0.996227 | NA | CYT | score=-0.200913 | NA | NA | NA |
| NP_373244.1_ | DNA_gyrase_subunit_A[Staphylococcus_aureus_subsp._aureus_N315] | MAELPQSRINERNITSEMRESFLDYAMSVIVARALPDVRDGLKPVHRRILYGLNEQGMTPDKSYKKSARIVGDVMGKYHPHGDSSIYEAMVRMAQDFSYRYPLVDGQGNFGSMDGDGAAAMRYTEARMTKITLELLRDINKDTIDFIDNYDGNEREPSVLPARFPNLLANGASGIAVGMATNIPPHNLTELINGVLSLSKNPDISIAELMEDIEGPDFPTAGLILGKSGIRRAYETGRGSIQMRSRAVIEERGGGRQRIVVTEIPFQVNKARMIEKIAELVRDKKIDGITDLRDETSLRTGVRVVIDVRKDANASVILNNLYKQTPLQTSFGVNMIALVNGRPKLINLKEALVHYLEHQKTVVRRRTQYNLRKAKDRAHILEGLRIALDHIDEIISTIRESDTDKVAMESLQQRFKLSEKQAQAILDMRLRRLTGLERDKIEAEYNELLNYISELETILADEEVLLQLVRDELTEIRDRFGDDRRTEIQLGGFEDLEDEDLIPEEQIVITLSHNNYIKRLPVSTYRAQNRGGRGVQGMNTLEEDFVSQLVTLSTHDHVLFFTNKGRVYKLKGYEVPELSRQSKGIPVVNAIELENDEVISTMIAVKDLESEDNFLVFATKRGVVKRSALSNFSRINRNGKIAISFREDDELIAVRLTSGQEDILIGTSHASLIRFPESTLRPLGRTATGVKGITLREGDEVVGLDVAHANSVDEVLVVTENGYGKRTPVNDYRLSNRGGKGIKTATITERNGNVVCITTVTGEEDLMIVTNAGVIIRLDVADISQNGRAAQGVRLIRLGDDQFVSTVAKVKEDAEDETNEDEQSTSTVSEDGTEQQREAVVNDETPGNAIHTEVIDSEENDEDGRIEVRQDFMDRVEEDIQQSSDEDEE | OTHER | 0.004017 | 0.006633 | 0.000645 | 0.988705 | NA | CYT | score=-0.200913 | NA | NA | NA |
Write data to a file on your computer
Of course it is also possible to write data to a new file (maybe you made some changes to a dataset and you want to save it to work on later). Check with a text editor if the data is separated with the delimiter that you expect for the function that you have used to write the data.
# Use the `write` functions that are derived from the `read` functions to create a
# - comma-separated file
# - semi-colon-separated file
# - tab-separated file
# - where $ is the delimiter
# from the first tibble that has been created (my_tibble). Make sure they have unique file names.
write_csv(my_tibble, "./files_04_data_import_exercises/add_exercises/my_tibble_comma.csv")
write_csv2(my_tibble, "./files_04_data_import_exercises/add_exercises/my_tibble_semi_colon.csv")
write_tsv(my_tibble, "./files_04_data_import_exercises/add_exercises/my_tibble_tab.tsv")
write_delim(my_tibble, "./files_04_data_import_exercises/add_exercises/my_tibble_delim.csv", delim = "$")
Read and write data from and to an Excel file
It is also possible to read and write Excel files in R. The package
to read/write Excel files comes with the readxl and
openxlsx libraries. You can install the readxl
and openxlsx packages if for some reason it has not been
installed yet.
# RUN THIS CODE before you move on to the next block of code.
# install.packages("readxl")
# install.packages("openxlsx")
library(readxl)
library(openxlsx)Try to read the data in the Excel file amylase.xlsx
in R using the read_excel() function.
# Read the Excel file. Save the data frame in df10.
df10 <- read_excel("./files_04_data_import_exercises/add_exercises/amylase.xlsx")
formatted_table(head(df10))|
ID dbl |
Match_number dbl |
Depression_status_(0=no_depression,_1=depression) dbl |
Time dbl |
Time_squared dbl |
Day dbl |
Date dbl |
Beepnumber dbl |
Evening_(0=no_evening,_1=evening) dbl |
Afternoon_(0=no_afternoon,_1=afternoon) dbl |
Morning_(0=no_morning,_1=morning) dbl |
Beeptime_last_beep dbl |
Monday_(0=no_Monday,_1=Monday) dbl |
Tuesday_(0=no_Tuesday,_1=Tuesday) dbl |
Wednesday_(0=no_Wednesday,_1=Wednesday) dbl |
Thursday_(0=no_Thursday,_1=Thursday) dbl |
Friday_(0=no_Friday,_1=Friday) dbl |
Saturday_(0=no_Saturday,_1=Saturday) dbl |
Sunday_(0=no_Sunday,_1=Sunday) dbl |
Filter_(to_exclude_the_invalid_datapoints_of_participant_D12) dbl |
Chronic_antidepressant_use_(0=no_antidepressant_use,_1=antidepressant_use) dbl |
BDI_pre dbl |
BDI_post dbl |
Gender_(0=female,_1=male) dbl |
BMI_(kg/l2) dbl |
Age_(years) dbl |
Smoking_(0=not_a_smoker,_1=_a_smoker) dbl |
Positive_affect_mean dbl |
Negative_affect_mean dbl |
Cortisol_(nmolL) dbl |
Amylase_(UmL) dbl |
Caffeine_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
Cafeine_recent_(0=not_in_previous_1.5h,_1=in_previous_1.5h) dbl |
Alcohol_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
Alcohol_recent dbl |
Caloric_rich_food_(0=not_in_previous_1.5_h,_1=in_previous_1.5_h) dbl |
Other_food_(0=not_in_previous_1.5h,_1=_in_previous_1.5_h) dbl |
Exercise_(0=no_exercise_in_previous_day_part,_1=exercise_in_previous_day_part) dbl |
Wakeup_(0=not_in_previous_1_hour,1=in_previous_1_hour) dbl |
Nicotine_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
Stim_drugs_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
Other_drugs_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
Cannabis_(0=not_in_previous_day_part,_1=in_previous_day_part) dbl |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 24 | 1 | 0 | 0 | 0 | 1 | 41040 | 2 | 0 | 0 | 1 | 0.9791667 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 2.14 | 4.29 | 3.282 | 101.380 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 1 | 1 | 1 | 41040 | 4 | 0 | 1 | 0 | 0.9791667 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 3.57 | 3.00 | 1.370 | 143.732 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 2 | 4 | 1 | 41040 | 6 | 1 | 0 | 0 | 0.9791667 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 2.71 | 3.43 | 0.860 | 178.310 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 3 | 9 | 2 | 41041 | 2 | 0 | 0 | 1 | 0.9791667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 4.57 | 1.71 | 6.826 | 74.939 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 4 | 16 | 2 | 41041 | 4 | 0 | 1 | 0 | 0.9791667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 5.00 | 2.00 | 0.645 | 108.187 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 1 | 0 | 5 | 25 | 2 | 41041 | 6 | 1 | 0 | 0 | 0.9791667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 | 1 | 21.22 | 24 | 0 | 4.14 | 2.57 | 0.548 | 160.200 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Try to write the data frame my_tibble to an Excel file
using the write.xlsx() function.
# Write `my_tibble` to an Excel file.
write.xlsx(my_tibble, "./files_04_data_import_exercises/add_exercises/my_tibble.xlsx")
Learning outcomes
This lesson you have learned to:
- read and write files that have a comma as the delimiter with the
read_csv() and write_csv()
functions,
- read and write files that have a semi-colon as the delimiter with
the read_csv2() and
write_csv2()functions,
- read and write files that have a tab as the delimiter with the
read_tsv() and write_tsv()
functions,
- read and write files that have a semi-colon as the delimiter with
the read_delim() and write_delim()
functions,
- read and write Excel files with the read_xlsx() and
write.xlsx() functions.
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